Web Coverage 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ACEAndrea de BonoData officerChemin des Anemones 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testing hq sat images for hotspots01252019_483_clip2_optWCSGeoTIFFwesr_swipe__01252019_483_clip2_hotspots_test_australiaRectifiedGridCoverage626040.0 5992236.589859170.0 6187096.589148.37206943562214 -36.20649674079373150.99161622893746 -34.395236062857902042020_483_clip2_hotspots_test_australiahq sat image Australia for swipe test in hotspot project02042020_483_clip2_optWCSGeoTIFFwesr_swipe__02042020_483_clip2_hotspots_test_australiaRectifiedGridCoverage626040.0 5992236.589859170.0 6187096.589148.37206943562214 -36.20649674079373150.99161622893746 -34.3952360628579171208_Zambia_Mantapala_Contours_etc171208_Zambia_Mantapala_Contours_etcWCSGeoTIFFMAPX__171208_Zambia_Mantapala_Contours_etcRectifiedGridCoverage28.834857143 -9.63029432128.907962887 -9.49554130228.834857143 -9.63029432128.907962887 -9.4955413021991-2000 Average year precipitations change since previous decade (1981-1900)Average year precipitations anomalies in mm across the period 1991-2000 calculated over the average precipitations per year in the period 1981-1990. Calculated at UNEP/GRID-Geneva in R based on CRU TS 4.07 data available <a href="https://crudata.uea.ac.uk/cru/data/hrg/cru_ts_4.07/cruts.2304141047.v4.07/pre/"target=_blank>here</a> <br> Read release notes of CRU TS 4.07 at <a href="https://crudata.uea.ac.uk/cru/data/hrg/cru_ts_4.07/Release_Notes_CRU_TS_4.07.txt"target=_blank>this link</a> <br><br> Credits: <br> - <a href="https://sites.uea.ac.uk/cru/"target=_blank>Climatic Research Unit (University of East Anglia) and Met Office</a><br> - <a href="https://doi.org/10.1038/s41597-020-0453-3"target=_blank>Harris, I., Osborn, T.J., Jones, P. et al. Version 4 of the CRU TS monthly high-resolution gridded multivariate climate dataset.Sci Data 7, 109 (2020).</a>cru_pre_91_00_anom_81_90WCSGeoTIFFwesr_climat__cru_pre_91_00_anom_81_90RectifiedGridCoverage-180.0 -90.0180.0 90.0-180.0 -90.0180.0 90.01991-2000 Average year temperature change since previous decade (1981-1990)Average year temperature anomalies in °C across the period 1991-2000calculated over the average temperature per year in the period 1981-1990. Calculated at UNEP/GRID-Geneva in R based on CRU TS 4.07 data available <a href="https://crudata.uea.ac.uk/cru/data/hrg/cru_ts_4.07/cruts.2304141047.v4.07/tmp/"target=_blank>here</a> <br> Read release notes of CRU TS 4.07 at <a href="https://crudata.uea.ac.uk/cru/data/hrg/cru_ts_4.07/Release_Notes_CRU_TS_4.07.txt"target=_blank>this link</a> <br><br> Credits: <br> - <a href="https://sites.uea.ac.uk/cru/"target=_blank>Climatic Research Unit (University of East Anglia) and Met Office</a><br> - <a href="https://doi.org/10.1038/s41597-020-0453-3"target=_blank>Harris, I., Osborn, T.J., Jones, P. et al. Version 4 of the CRU TS monthly high-resolution gridded multivariate climate dataset.Sci Data 7, 109 (2020).</a>cru_tmp_91_00_anom_81_90WCSGeoTIFFwesr_climat__cru_tmp_91_00_anom_81_90RectifiedGridCoverage-180.0 -90.0180.0 90.0-180.0 -90.0180.0 90.02001-2010 Average year precipitations change since previous decade (1991-2000)Average year precipitations anomalies in mm across the period 2001-2010 calculated over the average precipitations per year in the period 1991-2000. Calculated at UNEP/GRID-Geneva in R based on CRU TS 4.07 data available <a href="https://crudata.uea.ac.uk/cru/data/hrg/cru_ts_4.07/cruts.2304141047.v4.07/pre/"target=_blank>here</a> <br> Read release notes of CRU TS 4.07 at <a href="https://crudata.uea.ac.uk/cru/data/hrg/cru_ts_4.07/Release_Notes_CRU_TS_4.07.txt"target=_blank>this link</a> <br><br> Credits: <br> - <a href="https://sites.uea.ac.uk/cru/"target=_blank>Climatic Research Unit (University of East Anglia) and Met Office</a><br> - <a href="https://doi.org/10.1038/s41597-020-0453-3"target=_blank>Harris, I., Osborn, T.J., Jones, P. et al. Version 4 of the CRU TS monthly high-resolution gridded multivariate climate dataset.Sci Data 7, 109 (2020).</a>cru_pre_01_10_anom_91_00WCSGeoTIFFwesr_climat__cru_pre_01_10_anom_91_00RectifiedGridCoverage-180.0 -90.0180.0 90.0-180.0 -90.0180.0 90.02001-2010 Average year temperature change since previous decade (1991-2000)Average year temperature anomalies in °C across the period 2001-2010 calculated over the average temperature per year in the period 1991-2000. Calculated at UNEP/GRID-Geneva in R based on CRU TS 4.07 data available <a href="https://crudata.uea.ac.uk/cru/data/hrg/cru_ts_4.07/cruts.2304141047.v4.07/tmp/"target=_blank>here</a> <br> Read release notes of CRU TS 4.07 at <a href="https://crudata.uea.ac.uk/cru/data/hrg/cru_ts_4.07/Release_Notes_CRU_TS_4.07.txt"target=_blank>this link</a> <br><br> Credits: <br> - <a href="https://sites.uea.ac.uk/cru/"target=_blank>Climatic Research Unit (University of East Anglia) and Met Office</a><br> - <a href="https://doi.org/10.1038/s41597-020-0453-3"target=_blank>Harris, I., Osborn, T.J., Jones, P. et al. Version 4 of the CRU TS monthly high-resolution gridded multivariate climate dataset.Sci Data 7, 109 (2020).</a>cru_tmp_01_10_anom_91_00WCSGeoTIFFwesr_climat__cru_tmp_01_10_anom_91_00RectifiedGridCoverage-180.0 -90.0180.0 90.0-180.0 -90.0180.0 90.02011-2020 Average year precipitations change since previous decade (2001-2010)Average year precipitations anomalies in mm across the period 2011-2020 calculated over the average precipitations per year in the period 2001-2010. Calculated at UNEP/GRID-Geneva in R based on CRU TS 4.07 data available <a href="https://crudata.uea.ac.uk/cru/data/hrg/cru_ts_4.07/cruts.2304141047.v4.07/pre/"target=_blank>here</a> <br> Read release notes of CRU TS 4.07 at <a href="https://crudata.uea.ac.uk/cru/data/hrg/cru_ts_4.07/Release_Notes_CRU_TS_4.07.txt"target=_blank>this link</a> <br><br> Credits: <br> - <a href="https://sites.uea.ac.uk/cru/"target=_blank>Climatic Research Unit (University of East Anglia) and Met Office</a><br> - <a href="https://doi.org/10.1038/s41597-020-0453-3"target=_blank>Harris, I., Osborn, T.J., Jones, P. et al. Version 4 of the CRU TS monthly high-resolution gridded multivariate climate dataset.Sci Data 7, 109 (2020).</a>cru_pre_11_20_anom_01_10WCSGeoTIFFwesr_climat__cru_pre_11_20_anom_01_10RectifiedGridCoverage-180.0 -90.0180.0 90.0-180.0 -90.0180.0 90.02011-2020 Average year temperature change since previous decade (2001-2100)Average year temperature anomalies in °C across the period 2011-2020 calculated over the average temperature per year in the period 2001-2010. Calculated at UNEP/GRID-Geneva in R based on CRU TS 4.07 data available <a href="https://crudata.uea.ac.uk/cru/data/hrg/cru_ts_4.07/cruts.2304141047.v4.07/tmp/"target=_blank>here</a> <br> Read release notes of CRU TS 4.07 at <a href="https://crudata.uea.ac.uk/cru/data/hrg/cru_ts_4.07/Release_Notes_CRU_TS_4.07.txt"target=_blank>this link</a> <br><br> Credits: <br> - <a href="https://sites.uea.ac.uk/cru/"target=_blank>Climatic Research Unit (University of East Anglia) and Met Office</a><br> - <a href="https://doi.org/10.1038/s41597-020-0453-3"target=_blank>Harris, I., Osborn, T.J., Jones, P. et al. Version 4 of the CRU TS monthly high-resolution gridded multivariate climate dataset.Sci Data 7, 109 (2020).</a>cru_tmp_11_20_anom_01_10WCSGeoTIFFwesr_climat__cru_tmp_11_20_anom_01_10RectifiedGridCoverage-180.0 -90.0180.0 90.0-180.0 -90.0180.0 90.02013040120130401WCSGeoTIFFhotspots__20130401RectifiedGridCoverage421965.0 5127945.0439785.0 5146185.07.983671848622689 46.300591030152118.218081323368821 46.4665472941693320130401_originals20130401_originalsWCSGeoTIFFhotspots__20130401_originalsRectifiedGridCoverage356385.0 4984185.0583815.0 5209815.07.1100148908217635 44.99657664623024510.103215000055275 47.0413023329938720160708ClassesClassification of a Sentinel-2 image with post-processing using field knowledge. This research was funded by the Copernicus RawMatCop programme, through funding from EIT RawMaterials and the European Commission DG Internal Market, Industry, Entrepreneurship and SMEs (DG GROW). https://eitrawmaterials.eu/eit-rm-academy/rawmatcop/, in the scope of project CopX: "Geospatial mining transparency through Copernicus and MapX"20160708ClassesWCSGeoTIFFUNILIEGE__20160708ClassesRectifiedGridCoverage515330.0 836290.0524760.0 842800.0-74.86103149540197 7.565680150087606-74.77551708391071 7.62460327462928920170703ClassesClassification of a Sentinel-2 image with post-processing using field knowledge. This research was funded by the Copernicus RawMatCop programme, through funding from EIT RawMaterials and the European Commission DG Internal Market, Industry, Entrepreneurship and SMEs (DG GROW). https://eitrawmaterials.eu/eit-rm-academy/rawmatcop/, in the scope of project CopX: "Geospatial mining transparency through Copernicus and MapX"20170703ClassesWCSGeoTIFFUNILIEGE__20170703ClassesRectifiedGridCoverage515330.0 836290.0524760.0 842800.0-74.86103149540197 7.565680150087606-74.77551708391071 7.62460327462928920180623ClassesClassification of a Sentinel-2 image with post-processing using field knowledge. This research was funded by the Copernicus RawMatCop programme, through funding from EIT RawMaterials and the European Commission DG Internal Market, Industry, Entrepreneurship and SMEs (DG GROW). https://eitrawmaterials.eu/eit-rm-academy/rawmatcop/, in the scope of project CopX: "Geospatial mining transparency through Copernicus and MapX"20180623ClassesWCSGeoTIFFUNILIEGE__20180623ClassesRectifiedGridCoverage515330.0 836290.0524760.0 842800.0-74.86103149540197 7.565680150087606-74.77551708391071 7.62460327462928920181230ClassesClassification of a Sentinel-2 image with post-processing using field knowledge. This research was funded by the Copernicus RawMatCop programme, through funding from EIT RawMaterials and the European Commission DG Internal Market, Industry, Entrepreneurship and SMEs (DG GROW). https://eitrawmaterials.eu/eit-rm-academy/rawmatcop/, in the scope of project CopX: "Geospatial mining transparency through Copernicus and MapX"20181230ClassesWCSGeoTIFFUNILIEGE__20181230ClassesRectifiedGridCoverage515330.0 836290.0524760.0 842800.0-74.86103149540197 7.565680150087606-74.77551708391071 7.6246032746292892019-09_albedo2019-09_albedoWCSGeoTIFFwesr_mountains__2019-09_albedoRectifiedGridCoverage-180.004464286 -59.995535714179.995535715 80.004464286-180.004464286 -59.995535714179.995535715 80.0044642862019-12_albedo2019-12_albedoWCSGeoTIFFwesr_mountains__2019-12_albedoRectifiedGridCoverage-180.004464286 -59.995535714179.995535715 80.004464286-180.004464286 -59.995535714179.995535715 80.00446428620190124ClassesClassification of a Sentinel-2 image with post-processing using field knowledge. This research was funded by the Copernicus RawMatCop programme, through funding from EIT RawMaterials and the European Commission DG Internal Market, Industry, Entrepreneurship and SMEs (DG GROW). https://eitrawmaterials.eu/eit-rm-academy/rawmatcop/, in the scope of project CopX: "Geospatial mining transparency through Copernicus and MapX"20190124ClassesWCSGeoTIFFUNILIEGE__20190124ClassesRectifiedGridCoverage515330.0 836290.0524760.0 842800.0-74.86103149540197 7.565680150087606-74.77551708391071 7.62460327462928920190618ClassesClassification of a Sentinel-2 image with post-processing using field knowledge. This research was funded by the Copernicus RawMatCop programme, through funding from EIT RawMaterials and the European Commission DG Internal Market, Industry, Entrepreneurship and SMEs (DG GROW). https://eitrawmaterials.eu/eit-rm-academy/rawmatcop/, in the scope of project CopX: "Geospatial mining transparency through Copernicus and MapX"20190618ClassesWCSGeoTIFFUNILIEGE__20190618ClassesRectifiedGridCoverage515230.0 836230.0524790.0 842950.0-74.8619381795564 7.565137269898085-74.77524438684844 7.62596041797297820190927_843_QG_copy20190927_843_QG_copyWCSImageMosaicwesr_swipe__20190927_843_QG_copyRectifiedGridCoverage199980.0 5990200.0309780.0 6400000.0149.66386433258654 -36.21435579772867150.97478007537504 -32.4968135319873920190927_843_QG_copy_mos_opt20190927_843_QG_copyWCSImageMosaicwesr_swipe__20190927_843_QG_copy_mos_optRectifiedGridCoverage199980.0 5990200.0309780.0 6400000.0149.66386433258654 -36.21435579772867150.97478007537504 -32.4968135319873920190927_843_QG_copy_opt_part20190927_843_QG_copyWCSImageMosaicwesr_swipe__20190927_843_QG_copy_opt_partRectifiedGridCoverage199980.0 5990200.0309780.0 6400000.0149.66386433258654 -36.21435579772867150.97478007537504 -32.4968135319873920190927_843_QG_mos20190927_843_QGWCSImageMosaicwesr_swipe__20190927_843_QGRectifiedGridCoverage199980.0 5990200.0309780.0 6400000.0149.66386433258654 -36.21435579772867150.97478007537504 -32.4968135319873920190927_843_grass_mos20190927_843WCSImageMosaicwesr_swipe__20190927_843RectifiedGridCoverage199980.0 5990200.0309780.0 6400000.0149.66386433258654 -36.21435579772867150.97478007537504 -32.4968135319873920190927_843_mergedQG20190927_843_mergedQGWCSGeoTIFFwesr_swipe__20190927_843_mergedQGRectifiedGridCoverage199980.0 5990200.0309780.0 6400000.0149.66386433258654 -36.21435579772867150.97478007537504 -32.4968135319873920190927_843_mergedQG_opt20190927_843_mergedQG_optWCSGeoTIFFwesr_swipe__20190927_843_mergedQG_optRectifiedGridCoverage199980.0 5990200.0309780.0 6400000.0149.66386433258654 -36.21435579772867150.97478007537504 -32.4968135319873920190927_843_mergedQG_opt_part20190927_843_mergedQG_optWCSGeoTIFFwesr_swipe__20190927_843_mergedQG_opt_partRectifiedGridCoverage199980.0 5990200.0309780.0 6400000.0149.66386433258654 -36.21435579772867150.97478007537504 -32.496813531987392020-03_albedo2020-03_albedoWCSGeoTIFFwesr_mountains__2020-03_albedoRectifiedGridCoverage-180.004464286 -59.995535714179.995535715 80.004464286-180.004464286 -59.995535714179.995535715 80.0044642862020-06_albedo2020-06_albedoWCSGeoTIFFwesr_mountains__2020-06_albedoRectifiedGridCoverage-180.004464286 -59.995535714179.995535715 80.004464286-180.004464286 -59.995535714179.995535715 80.0044642862021-2022 Average year precipitations change since previous decade (2011-2020)Average year precipitations anomalies in mm across the period 2021-2022 calculated over the average precipitations per year in the period 2011-2020. Calculated at UNEP/GRID-Geneva in R based on CRU TS 4.07 data available <a href="https://crudata.uea.ac.uk/cru/data/hrg/cru_ts_4.07/cruts.2304141047.v4.07/pre/"target=_blank>here</a> <br> Read release notes of CRU TS 4.07 at <a href="https://crudata.uea.ac.uk/cru/data/hrg/cru_ts_4.07/Release_Notes_CRU_TS_4.07.txt"target=_blank>this link</a> <br><br> Credits: <br> - <a href="https://sites.uea.ac.uk/cru/"target=_blank>Climatic Research Unit (University of East Anglia) and Met Office</a><br> - <a href="https://doi.org/10.1038/s41597-020-0453-3"target=_blank>Harris, I., Osborn, T.J., Jones, P. et al. Version 4 of the CRU TS monthly high-resolution gridded multivariate climate dataset.Sci Data 7, 109 (2020).</a>cru_pre_21_22_anom_11_20WCSGeoTIFFwesr_climat__cru_pre_21_22_anom_11_20RectifiedGridCoverage-180.0 -90.0180.0 90.0-180.0 -90.0180.0 90.02021-2022 Average year temperature change since previous decade (2011-2020)Average year temperature anomalies in °C across the period 2021-2022 calculated over the average temperature per year in the period 2011-2020. Calculated at UNEP/GRID-Geneva in R based on CRU TS 4.07 data available <a href="https://crudata.uea.ac.uk/cru/data/hrg/cru_ts_4.07/cruts.2304141047.v4.07/tmp/"target=_blank>here</a> <br> Read release notes of CRU TS 4.07 at <a href="https://crudata.uea.ac.uk/cru/data/hrg/cru_ts_4.07/Release_Notes_CRU_TS_4.07.txt"target=_blank>this link</a> <br><br> Credits: <br> - <a href="https://sites.uea.ac.uk/cru/"target=_blank>Climatic Research Unit (University of East Anglia) and Met Office</a><br> - <a href="https://doi.org/10.1038/s41597-020-0453-3"target=_blank>Harris, I., Osborn, T.J., Jones, P. et al. Version 4 of the CRU TS monthly high-resolution gridded multivariate climate dataset.Sci Data 7, 109 (2020).</a>cru_tmp_21_22_anom_11_20WCSGeoTIFFwesr_climat__cru_tmp_21_22_anom_11_20RectifiedGridCoverage-180.0 -90.0180.0 90.0-180.0 -90.0180.0 90.0Aboveground biomass density of vegetation (Mg/ha)This file provides the pan-tropical biomass map published by Avitabile et al. (2016) "An integrated pan-tropical biomass map using multiple reference datasets". The data shows the aboveground biomass in Mg per ha in the tropic region at approximately 1 km resolution. For a proper use and description of this dataset, please refer to the mentioned article. Source: Avitabile, V., Herold, M., Heuvelink, G. B. M., Lewis, S. L., Phillips, O. L., Asner, G. P., Armston, J., Ashton, P. S., Banin, L., Bayol, N., Berry, N. J., Boeckx, P., de Jong, B. H. J., DeVries, B., Girardin, C. A. J., Kearsley, E., Lindsell, J. A., Lopez-Gonzalez, G., Lucas, R., Malhi, Y., Morel, A., Mitchard, E. T. A., Nagy, L., Qie, L., Quinones, M. J., Ryan, C. M., Ferry, S. J. W., Sunderland, T., Laurin, G. V., Gatti, R. C., Valentini, R., Verbeeck, H., Wijaya, A. and Willcock, S. (2016), An integrated pan-tropical biomass map using multiple reference datasets. Glob Change Biol, 22: 1406–1420. doi:10.1111/gcb.13139Avitabile_AGB_MapWCSGeoTIFFMapX_UNDP__Avitabile_AGB_MapRectifiedGridCoverage-112.85865304959007 -56.259938619115786154.9996695694099 39.99839086388421-112.85865304959007 -56.259938619115786154.9996695694099 39.99839086388421Aboveground live woody carbon density change (2003-2014)Aboveground live woody carbon density change (2003-2014): The data provided here are the result of a time-series analysis of carbon density change between 2003-2014 spanning tropical America, Africa, and Asia (23.45 N lat.-23.45 S lat.). For further information about these results please see the associated journal article (Baccini et al. 2017, Science). Spatial (raster) and tabular data described in the journal article are available for download from the links below. Data can be visualized at www.thecarbonsource.org. The visualization includes the ability to select a given change pixel (loss or gain) and display the trajectory of carbon density during the 2003-2014 study period. Raster Data Information: The carbon density change data are divided into three regions: America, Africa, and Asia. For each region there are two raster (.tif) files representing: 1) carbon density net gain, and 2) carbon density net loss. The value of each pixel (463 x 463 m) represents the total net carbon density change (Mg/ha) over the period 2003-2014. Only pixels exhibiting statistical significance at the 95% level are reported. All raster files are in the original MODIS sinusoidal projection. Baccini, A., W. Walker, L. Carvalho, M. Farina, D. Sulla-Menashe, R.A. Houghton. 2017. Tropical forests are a net carbon source based on aboveground measurements of gain and loss. Science 2017 Vol. 358, Issue 6360, pp. 230-234 DOI:10.1126/science.aam5962. Data available online from www.thecarbonsource.org.baccini_carbon_change_global_optWCSGeoTIFFMapX_UNDP_nd__baccini_carbon_change_global_optRectifiedGridCoverage-119.85892985444444 -23.402497460000003170.00430119527778 23.400000000555554-119.85892985444444 -23.402497460000003170.00430119527778 23.400000000555554Accessibility To Cities 2015This global accessibility map enumerates land-based travel time to the nearest densely-populated area for all areas between 85 degrees north and 60 degrees south for a nominal year 2015. Densely-populated areas are defined as contiguous areas with 1,500 or more inhabitants per square kilometre or a majority of built-up land cover types coincident with a population centre of at least 50,000 inhabitants. This map was produced through a collaboration between MAP (University of Oxford), Google, the European Union Joint Research Centre (JRC), and the University of Twente, Netherlands.The underlying datasets used to produce the map include roads (comprising the first ever global-scale use of Open Street Map and Google roads datasets), railways, rivers, lakes, oceans, topographic conditions (slope and elevation), landcover types, and national borders. These datasets were each allocated a speed or speeds of travel in terms of time to cross each pixel of that type. The datasets were then combined to produce a "friction surface"; a map where every pixel is allocated a nominal overall speed of travel based on the types occurring within that pixel. Least-cost-path algorithms (running in Google Earth Engine and, for high-latitude areas, in R) were used in conjunction with this friction surface to calculate the time of travel from all locations to the nearest (in time) city. The cities dataset used is the high-density-cover product created by the Global Human Settlement Project. Each pixel in the resultant accessibility map thus represents the modelled shortest time from that location to a city. Authors: D.J. Weiss, A. Nelson, H.S. Gibson, W. Temperley, S. Peedell, A. Lieber, M. Hancher, E. Poyart, S. Belchior, N. Fullman, B. Mappin, U. Dalrymple, J. Rozier, T.C.D. Lucas, R.E. Howes, L.S. Tusting, S.Y. Kang, E. Cameron, D. Bisanzio, K.E. Battle, S. Bhatt, and P.W. Gething. A global map of travel time to cities to assess inequalities in accessibility in 2015. (2018). Nature. doi:10.1038/nature25181 Processing notes: Data were processed from numerous sources including OpenStreetMap, Google Maps, Land Cover mapping, and others, to generate a global friction surface of average land-based travel speed. This accessibility surface was then derived from that friction surface via a least-cost-path algorithm finding at each location the closest point from global databases of population centres and densely-populated areas. Please see the associated publication for full details of the processing. Source: https://map.ox.ac.uk/research-project/accessibility_to_cities/accessibility_to_cities_2015WCSGeoTIFFMapX_UNDP__accessibility_to_cities_2015RectifiedGridCoverage-180.0 -60.0179.99985600000002 84.999942-180.0 -60.0179.99985600000002 84.999942Active Fires Frequency (2003 - 2025)Raster of Active Fires frequency per square kilometer, based on MODIS Collection 6 Active Fire Product MCD14ML. Only location points described as "presumed vegetation fire" in the attributes are included in the frequency calculation. <br> This layer was produced by UNEP/GRID-Geneva.Fi2003_2025dtWCSGeoTIFFwesr_risk__fires_density_totalRectifiedGridCoverage-180.0 -90.0180.0 90.0-180.0 -90.0180.0 90.0Active Fires Frequency (2025)Raster of Active Fires frequency per square kilometer, based on MODIS Collection 6 Active Fire Product MCD14ML. Only location points described as "presumed vegetation fire" in the attributes are included in the frequency calculation. <br> This layer was produced by UNEP/GRID-Geneva.Fi2025dyWCSGeoTIFFwesr_risk__fires_density_lastyearRectifiedGridCoverage-180.0 -90.0180.0 90.0-180.0 -90.0180.0 90.0Africa Forest Height (Montana State University)The forest structural condition index is derived from the University of Maryland canopy cover, canopy height, and time since forest loss data sets. The index spans from short, open-canopy, recently disturbed forests to tall, closed canopy forests that have not been disturbed with the last 14 years. Forest stature and canopy cover are products of both the biophysical potential of a local site and of disturbance history. The tallest, most dense forests are found in settings with favorable climate and soils but with low levels if natural or human disturbance. Such forests have been shown to support high levels of biodiversity, store high levels of carbon, and be more resilient to climate variability. Our maps of forest structural condition are the first to identify locations in the humid tropics of tall, dense forests resulting from high biophysical potential and low disturbance rates. Data are provided by the Montana State University for South America, Africa and Asia separately, and have been merged into a single dataset here. License information: CC-4.0 Attribution.africa_forheight_optWCSGeoTIFFMapX_UNDP__africa_forheightRectifiedGridCoverage-26.0005 -32.000561.0005 32.0005-26.0005 -32.000561.0005 32.0005Albedo Autumn 2019 (Copernicus)Unit: ratio of the solar radiation reflected by a surface to the total incoming solar radiation, usually measured between 0 (all incident radiation absorbed) and 1 (all incident radiation refleted). Surface albedo quantifies the fraction of the sunlight reflected by the surface of the Earth. The present layer represents the black-sky albedo (directional albedo), which is considered as an Essential Climate Variable by the Global Climate Observing System (GCOS). Surface albedo is a sensitive indicator of environmental vulnerability making it a key element required by climate, biogeochemical, hydrological, and weather forecast models. It has a spatial resolution of 1km and is available from the Copernicus Global Land Service (https://land.copernicus.eu/global/products/sa) as netCDF files (technical documentation available at https://land.copernicus.eu/global/sites/cgls.vito.be/files/products/CGLOPS1_PUM_SA1km-V1_I1.40.pdf. The present raster is the broadband directional albedo over full spectrum [0.3-4µm]. It is the average of the various albedo files available in autumn 2019 (3 September 2019, 13 September 2019, 23 September 2019, 3 October 2019, 13 October 2019, 24 October 2019, 3 November 2019, 13 November 2019 and 23 November 2019), then transformed into a geotiff in order to display and example of albedo reflectance in autumn.c_gls_ALDH_2019_automne_raster_optWCSGeoTIFFalbedowesr_mountains__c_gls_ALDH_2019_automne_raster_optRectifiedGridCoverage-180.0044642857143 -59.995535714253904179.9955357142039 80.00446428571429-180.0044642857143 -59.995535714253904179.9955357142039 80.00446428571429Albedo Spring 2020 (Copernicus)Unit: ratio of the solar radiation reflected by a surface to the total incoming solar radiation, usually measured between 0 (all incident radiation absorbed) and 1 (all incident radiation refleted). Surface albedo quantifies the fraction of the sunlight reflected by the surface of the Earth. The present layer represents the black-sky albedo (directional albedo), which is considered as an Essential Climate Variable by the Global Climate Observing System (GCOS). Surface albedo is a sensitive indicator of environmental vulnerability making it a key element required by climate, biogeochemical, hydrological, and weather forecast models. It has a spatial resolution of 1km and is available from the Copernicus Global Land Service (https://land.copernicus.eu/global/products/sa) as netCDF files (technical documentation available at https://land.copernicus.eu/global/sites/cgls.vito.be/files/products/CGLOPS1_PUM_SA1km-V1_I1.40.pdf. The present raster is the broadband directional albedo over full spectrum [0.3-4µm]. It is the average of the various albedo files available in spring 2020 (3 March 2020, 13 March 2020, 24 March 2020, 3 April 2020, 13 April 2020, 23 April 2020, 3 May 2020, 13 May 2020 and 24 May 2020), then transformed into a geotiff in order to display and example of albedo reflectance in spring.c_gls_ALDH_2020_printemps_raster_optWCSGeoTIFFalbedowesr_mountains__c_gls_ALDH_2020_printemps_raster_optRectifiedGridCoverage-180.0044642857143 -59.995535714253904179.9955357142039 80.00446428571429-180.0044642857143 -59.995535714253904179.9955357142039 80.00446428571429Albedo Summer 2019 (Copernicus)Unit: ratio of the solar radiation reflected by a surface to the total incoming solar radiation, usually measured between 0 (all incident radiation absorbed) and 1 (all incident radiation refleted). Surface albedo quantifies the fraction of the sunlight reflected by the surface of the Earth. The present layer represents the black-sky albedo (directional albedo), which is considered as an Essential Climate Variable by the Global Climate Observing System (GCOS). Surface albedo is a sensitive indicator of environmental vulnerability making it a key element required by climate, biogeochemical, hydrological, and weather forecast models. It has a spatial resolution of 1km and is available from the Copernicus Global Land Service (https://land.copernicus.eu/global/products/sa) as netCDF files (technical documentation available at https://land.copernicus.eu/global/sites/cgls.vito.be/files/products/CGLOPS1_PUM_SA1km-V1_I1.40.pdf. The present raster is the broadband directional albedo over full spectrum [0.3-4µm]. It is the average of the various albedo files available in summer 2019 (3 June 2019, 13 June 2019, 23 June 2019, 3 July 2019, 13 July 2019, 24 July 2019, 3 August 2019, 13 August 2019 et 24 August 2019), then transformed into a geotiff in order to display and example of albedo reflectance in summer.c_gls_ALDH_2019_ete_raster_optWCSGeoTIFFalbedowesr_mountains__c_gls_ALDH_2019_ete_raster_optRectifiedGridCoverage-180.0044642857143 -59.995535714253904179.9955357142039 80.00446428571429-180.0044642857143 -59.995535714253904179.9955357142039 80.00446428571429Albedo Winter 2019 - 2020 (Copernicus)Unit: ratio of the solar radiation reflected by a surface to the total incoming solar radiation, usually measured between 0 (all incident radiation absorbed) and 1 (all incident radiation refleted). Surface albedo quantifies the fraction of the sunlight reflected by the surface of the Earth. The present layer represents the black-sky albedo (directional albedo), which is considered as an Essential Climate Variable by the Global Climate Observing System (GCOS). Surface albedo is a sensitive indicator of environmental vulnerability making it a key element required by climate, biogeochemical, hydrological, and weather forecast models. It has a spatial resolution of 1km and is available from the Copernicus Global Land Service (https://land.copernicus.eu/global/products/sa) as netCDF files (technical documentation available at https://land.copernicus.eu/global/sites/cgls.vito.be/files/products/CGLOPS1_PUM_SA1km-V1_I1.40.pdf. The present raster is the broadband directional albedo over full spectrum [0.3-4µm]. It is the average of the various albedo files available in winter 2019-2020 (3 December 2019, 13 December 2019, 24 December 2020, 3 January 2020, 13 January 2020, 24 January 2020, 3 February 2020, 13 February 2020 et 22 February 2020), then transformed into a geotiff in order to display and example of albedo reflectance in winter.c_gls_ALDH_2019-20_hiver_raster_optWCSGeoTIFFalbedowesr_mountains__c_gls_ALDH_2019-20_hiver_raster_optRectifiedGridCoverage-180.0044642857143 -59.995535714253904179.9955357142039 80.00446428571429-180.0044642857143 -59.995535714253904179.9955357142039 80.00446428571429Annual Mean Temperature - Anomaly [°C] (2041-2060 vs 1979-2013) RCP4.5 - CIMP5Anomaly [°C] for the period 2041-2060 compared to climatological data (1979-2013) on the mean annual daily mean air temperatures averaged over 1 year for the period 2041-2060 based on RCP4.5. The layer is calculated at UNEP/GRID-Geneva from the layers on annual precipitation [mm/year] provided in the products CHELSA V1.2 and CHELSA-[CMIP5]. CHELSA-[CMIP5] is a delta change climatological dataset for the years 2041-2060 and 2061- 2080 for mean monthly maximum temperatures, mean monthly minimum temperatures, monthly precipitation amounts, and several derived parameters. We use the delta change method by B-spline interpolation of anomalies (deltas) of the respective CMIP5 GCM dataset. Anomalies were interpolated between all CMIP5 grid cells and are then added (for temperature variables) or multiplied (in case of precipitation) to high resolution climate data from CHELSA V1.2. This method has the assumption that climate only varies on the scale of the coarser (CMIP5) dataset, and the spatial pattern (from CHELSA) is consistent over time. CHELSA- [CMIP5] does not take changing wind patterns, or temperature lapse rates into account, but rather expects them to be constant over time, and similar to the long term averages. CHELSA V1.2 (http://chelsa-climate.org/) is a high resolution (30 arc sec, ~1 km) climate data set for the earth land surface areas. It includes monthly and annual mean temperature and precipitation patterns for the time period 1979-2013. Methods are described in http://chelsa-climate.org/wp-admin/download-page/CHELSA_tech_specification.pdf. CHELSA Version 1.2 is licensed under a Creative Commons Attribution 4.0 International License. Specifications: High resolution (30 arcsec, ~1 km) Precipitation & Temperature Climatologies for the years 1979 – 2013 Incorporation of topoclimate (e.g. orographic rainfall & wind fields). All products of CHELSA are in a geographic coordinate system referenced to the WGS 84 horizontal datum, with the horizontal coordinates expressed in decimal degrees. The CHELSA layer extents (minimum and maximum latitude and longitude) are a result of the coordinate system inherited from the 1-arc-second GMTED2010 data which itself inherited the grid extent from the 1-arc-second SRTM data. Note that because of the pixel center referencing of the input GMTED2010 data the full extent of each CHELSA grid as defined by the outside edges of the pixels differs from an integer value of latitude or longitude by 0.000138888888 degree (or 1/2 arc-second). Users of products based on the legacy GTOPO30 product should note that the coordinate referencing of CHELSA (and GMTED2010) and GTOPO30 are not the same. In GTOPO30, the integer lines of latitude and longitude fall directly on the edges of a 30-arc-second pixel. Thus, when overlaying CHELSA with products based on GTOPO30 a slight shift of 1/2 arc-second will be observed between the edges of corresponding 30-arc-second pixels. To redistribute the data, please cite the following peer reviewed articles: <a href="https://www.nature.com/articles/sdata2017122"target=_blank>Karger, D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N.E., Linder, H.P. & Kessler, M. (2017) Climatologies at high resolution for the earth’s land surface areas. Scientific Data 4, 170122.</a> <a href="https://doi.org/10.5061/dryad.kd1d4"target=_blank>Karger, D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N.E., Linder, H.P., Kessler, M. (2017) Data from: Climatologies at high resolution for the earth’s land surface areas. Dryad Digital Repository. </a> CHELSA – Climatologies at high resolution for the Earth land surface areas. Version 1.2Diff_CHELSA_bio_mon_ACCESS1-0_rcp45_r1i1p1_g025.nc_1_2041-2060_V12_optWCSGeoTIFFUN_Sahel__Diff_CHELSA_bio_mon_ACCESS1-0_rcp45_r1i1p1_g025.nc_1_2041-2060_V12_optRectifiedGridCoverage-180.00013888885002 -90.00013888884999179.99985967115003 83.99986041515001-180.00013888885002 -90.00013888884999179.99985967115003 83.99986041515001Annual Mean Temperature - Anomaly [°C] (2041-2060 vs 1979-2013) RCP8.5 - CIMP5Anomaly [°C] for the period 2041-2060 compared to climatological data (1979-2013) on the mean annual daily mean air temperatures averaged over 1 year for the period 2041-2060 based on RCP8.5. The layer is calculated at UNEP/GRID-Geneva from the layers on annual precipitation [mm/year] provided in the products CHELSA V1.2 and CHELSA-[CMIP5]. CHELSA-[CMIP5] is a delta change climatological dataset for the years 2041-2060 and 2061- 2080 for mean monthly maximum temperatures, mean monthly minimum temperatures, monthly precipitation amounts, and several derived parameters. We use the delta change method by B-spline interpolation of anomalies (deltas) of the respective CMIP5 GCM dataset. Anomalies were interpolated between all CMIP5 grid cells and are then added (for temperature variables) or multiplied (in case of precipitation) to high resolution climate data from CHELSA V1.2. This method has the assumption that climate only varies on the scale of the coarser (CMIP5) dataset, and the spatial pattern (from CHELSA) is consistent over time. CHELSA- [CMIP5] does not take changing wind patterns, or temperature lapse rates into account, but rather expects them to be constant over time, and similar to the long term averages. CHELSA V1.2 (http://chelsa-climate.org/) is a high resolution (30 arc sec, ~1 km) climate data set for the earth land surface areas. It includes monthly and annual mean temperature and precipitation patterns for the time period 1979-2013. Methods are described in http://chelsa-climate.org/wp-admin/download-page/CHELSA_tech_specification.pdf. CHELSA Version 1.2 is licensed under a Creative Commons Attribution 4.0 International License. Specifications: High resolution (30 arcsec, ~1 km) Precipitation & Temperature Climatologies for the years 1979 – 2013 Incorporation of topoclimate (e.g. orographic rainfall & wind fields). All products of CHELSA are in a geographic coordinate system referenced to the WGS 84 horizontal datum, with the horizontal coordinates expressed in decimal degrees. The CHELSA layer extents (minimum and maximum latitude and longitude) are a result of the coordinate system inherited from the 1-arc-second GMTED2010 data which itself inherited the grid extent from the 1-arc-second SRTM data. Note that because of the pixel center referencing of the input GMTED2010 data the full extent of each CHELSA grid as defined by the outside edges of the pixels differs from an integer value of latitude or longitude by 0.000138888888 degree (or 1/2 arc-second). Users of products based on the legacy GTOPO30 product should note that the coordinate referencing of CHELSA (and GMTED2010) and GTOPO30 are not the same. In GTOPO30, the integer lines of latitude and longitude fall directly on the edges of a 30-arc-second pixel. Thus, when overlaying CHELSA with products based on GTOPO30 a slight shift of 1/2 arc-second will be observed between the edges of corresponding 30-arc-second pixels. To redistribute the data, please cite the following peer reviewed articles: <a href="https://www.nature.com/articles/sdata2017122"target=_blank>Karger, D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N.E., Linder, H.P. & Kessler, M. (2017) Climatologies at high resolution for the earth’s land surface areas. Scientific Data 4, 170122.</a> <a href="https://doi.org/10.5061/dryad.kd1d4"target=_blank>Karger, D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N.E., Linder, H.P., Kessler, M. (2017) Data from: Climatologies at high resolution for the earth’s land surface areas. Dryad Digital Repository. </a> CHELSA – Climatologies at high resolution for the Earth land surface areas. Version 1.2Diff_CHELSA_bio_mon_ACCESS1-0_rcp85_r1i1p1_g025.nc_1_2041-2060_V12_optWCSGeoTIFFUN_Sahel__Diff_CHELSA_bio_mon_ACCESS1-0_rcp85_r1i1p1_g025.nc_1_2041-2060_V12_optRectifiedGridCoverage-180.00013888885002 -90.00013888884999179.99985967115003 83.99986041515001-180.00013888885002 -90.00013888884999179.99985967115003 83.99986041515001Annual Mean Temperature - Climatology (1979-2013) [°C/10]Mean annual daily mean air temperatures averaged over 1 year [°C/10]. CHELSA V1.2 (http://chelsa-climate.org/) is a high resolution (30 arc sec, ~1 km) climate data set for the earth land surface areas. It includes monthly and annual mean temperature and precipitation patterns for the time period 1979-2013. Methods are described in http://chelsa-climate.org/wp-admin/download-page/CHELSA_tech_specification.pdf. CHELSA Version 1.2 is licensed under a Creative Commons Attribution 4.0 International License. Specifications: High resolution (30 arcsec, ~1 km) Precipitation & Temperature Climatologies for the years 1979 – 2013 Incorporation of topoclimate (e.g. orographic rainfall & wind fields). All products of CHELSA are in a geographic coordinate system referenced to the WGS 84 horizontal datum, with the horizontal coordinates expressed in decimal degrees. The CHELSA layer extents (minimum and maximum latitude and longitude) are a result of the coordinate system inherited from the 1-arc-second GMTED2010 data which itself inherited the grid extent from the 1-arc-second SRTM data. Note that because of the pixel center referencing of the input GMTED2010 data the full extent of each CHELSA grid as defined by the outside edges of the pixels differs from an integer value of latitude or longitude by 0.000138888888 degree (or 1/2 arc-second). Users of products based on the legacy GTOPO30 product should note that the coordinate referencing of CHELSA (and GMTED2010) and GTOPO30 are not the same. In GTOPO30, the integer lines of latitude and longitude fall directly on the edges of a 30-arc-second pixel. Thus, when overlaying CHELSA with products based on GTOPO30 a slight shift of 1/2 arc-second will be observed between the edges of corresponding 30-arc-second pixels. To redistribute the data, please cite the following peer reviewed articles: <a href="https://www.nature.com/articles/sdata2017122"target=_blank>Karger, D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N.E., Linder, H.P. & Kessler, M. (2017) Climatologies at high resolution for the earth’s land surface areas. Scientific Data 4, 170122.</a> <a href="https://doi.org/10.5061/dryad.kd1d4"target=_blank>Karger, D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N.E., Linder, H.P., Kessler, M. (2017) Data from: Climatologies at high resolution for the earth’s land surface areas. Dryad Digital Repository. </a> CHELSA – Climatologies at high resolution for the Earth land surface areas. Version 1.2CHELSA_bio10_01_optWCSGeoTIFFUN_Sahel__CHELSA_bio10_01_optRectifiedGridCoverage-180.00013888885002 -90.00013888884999179.99985967115003 83.99986041515001-180.00013888885002 -90.00013888884999179.99985967115003 83.99986041515001Annual Mean Temperature [°C/10] (2041-2060) RCP4.5 - CIMP5Mean annual daily mean air temperatures averaged over 1 year [°C/10] for the period 2041-2060 based on RCP4.5. CHELSA-[CMIP5] is a delta change climatological dataset for the years 2041-2060 and 2061- 2080 for mean monthly maximum temperatures, mean monthly minimum temperatures, monthly precipitation amounts, and several derived parameters. We use the delta change method by B-spline interpolation of anomalies (deltas) of the respective CMIP5 GCM dataset. Anomalies were interpolated between all CMIP5 grid cells and are then added (for temperature variables) or multiplied (in case of precipitation) to high resolution climate data from CHELSA V1.2. This method has the assumption that climate only varies on the scale of the coarser (CMIP5) dataset, and the spatial pattern (from CHELSA) is consistent over time. CHELSA- [CMIP5] does not take changing wind patterns, or temperature lapse rates into account, but rather expects them to be constant over time, and similar to the long term averages. CHELSA V1.2 (http://chelsa-climate.org/) is a high resolution (30 arc sec, ~1 km) climate data set for the earth land surface areas. It includes monthly and annual mean temperature and precipitation patterns for the time period 1979-2013. Methods are described in http://chelsa-climate.org/wp-admin/download-page/CHELSA_tech_specification.pdf. CHELSA Version 1.2 is licensed under a Creative Commons Attribution 4.0 International License. Specifications: High resolution (30 arcsec, ~1 km) Precipitation & Temperature Climatologies for the years 1979 – 2013 Incorporation of topoclimate (e.g. orographic rainfall & wind fields). All products of CHELSA are in a geographic coordinate system referenced to the WGS 84 horizontal datum, with the horizontal coordinates expressed in decimal degrees. The CHELSA layer extents (minimum and maximum latitude and longitude) are a result of the coordinate system inherited from the 1-arc-second GMTED2010 data which itself inherited the grid extent from the 1-arc-second SRTM data. Note that because of the pixel center referencing of the input GMTED2010 data the full extent of each CHELSA grid as defined by the outside edges of the pixels differs from an integer value of latitude or longitude by 0.000138888888 degree (or 1/2 arc-second). Users of products based on the legacy GTOPO30 product should note that the coordinate referencing of CHELSA (and GMTED2010) and GTOPO30 are not the same. In GTOPO30, the integer lines of latitude and longitude fall directly on the edges of a 30-arc-second pixel. Thus, when overlaying CHELSA with products based on GTOPO30 a slight shift of 1/2 arc-second will be observed between the edges of corresponding 30-arc-second pixels. To redistribute the data, please cite the following peer reviewed articles: <a href="https://www.nature.com/articles/sdata2017122"target=_blank>Karger, D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N.E., Linder, H.P. & Kessler, M. (2017) Climatologies at high resolution for the earth’s land surface areas. Scientific Data 4, 170122.</a> <a href="https://doi.org/10.5061/dryad.kd1d4"target=_blank>Karger, D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N.E., Linder, H.P., Kessler, M. (2017) Data from: Climatologies at high resolution for the earth’s land surface areas. Dryad Digital Repository. </a> CHELSA – Climatologies at high resolution for the Earth land surface areas. Version 1.2CHELSA_bio_mon_ACCESS1-0_rcp45_r1i1p1_g025.nc_1_2041-2060_V1.2_optWCSGeoTIFFUN_Sahel__CHELSA_bio_mon_ACCESS1-0_rcp45_r1i1p1_g025.nc_1_2041-2060_V1.2_optRectifiedGridCoverage-180.00013888885002 -90.00013888884999179.99985967115003 83.99986041515001-180.00013888885002 -90.00013888884999179.99985967115003 83.99986041515001Annual Mean Temperature [°C/10] (2041-2060) RCP8.5 - CIMP5Mean annual daily mean air temperatures averaged over 1 year [°C/10] for the period 2041-2060 based on RCP8.5. CHELSA-[CMIP5] is a delta change climatological dataset for the years 2041-2060 and 2061- 2080 for mean monthly maximum temperatures, mean monthly minimum temperatures, monthly precipitation amounts, and several derived parameters. We use the delta change method by B-spline interpolation of anomalies (deltas) of the respective CMIP5 GCM dataset. Anomalies were interpolated between all CMIP5 grid cells and are then added (for temperature variables) or multiplied (in case of precipitation) to high resolution climate data from CHELSA V1.2. This method has the assumption that climate only varies on the scale of the coarser (CMIP5) dataset, and the spatial pattern (from CHELSA) is consistent over time. CHELSA- [CMIP5] does not take changing wind patterns, or temperature lapse rates into account, but rather expects them to be constant over time, and similar to the long term averages. CHELSA V1.2 (http://chelsa-climate.org/) is a high resolution (30 arc sec, ~1 km) climate data set for the earth land surface areas. It includes monthly and annual mean temperature and precipitation patterns for the time period 1979-2013. Methods are described in http://chelsa-climate.org/wp-admin/download-page/CHELSA_tech_specification.pdf. CHELSA Version 1.2 is licensed under a Creative Commons Attribution 4.0 International License. Specifications: High resolution (30 arcsec, ~1 km) Precipitation & Temperature Climatologies for the years 1979 – 2013 Incorporation of topoclimate (e.g. orographic rainfall & wind fields). All products of CHELSA are in a geographic coordinate system referenced to the WGS 84 horizontal datum, with the horizontal coordinates expressed in decimal degrees. The CHELSA layer extents (minimum and maximum latitude and longitude) are a result of the coordinate system inherited from the 1-arc-second GMTED2010 data which itself inherited the grid extent from the 1-arc-second SRTM data. Note that because of the pixel center referencing of the input GMTED2010 data the full extent of each CHELSA grid as defined by the outside edges of the pixels differs from an integer value of latitude or longitude by 0.000138888888 degree (or 1/2 arc-second). Users of products based on the legacy GTOPO30 product should note that the coordinate referencing of CHELSA (and GMTED2010) and GTOPO30 are not the same. In GTOPO30, the integer lines of latitude and longitude fall directly on the edges of a 30-arc-second pixel. Thus, when overlaying CHELSA with products based on GTOPO30 a slight shift of 1/2 arc-second will be observed between the edges of corresponding 30-arc-second pixels. To redistribute the data, please cite the following peer reviewed articles: <a href="https://www.nature.com/articles/sdata2017122"target=_blank>Karger, D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N.E., Linder, H.P. & Kessler, M. (2017) Climatologies at high resolution for the earth’s land surface areas. Scientific Data 4, 170122.</a> <a href="https://doi.org/10.5061/dryad.kd1d4"target=_blank>Karger, D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N.E., Linder, H.P., Kessler, M. (2017) Data from: Climatologies at high resolution for the earth’s land surface areas. Dryad Digital Repository. </a> CHELSA – Climatologies at high resolution for the Earth land surface areas. Version 1.2CHELSA_bio_mon_ACCESS1-0_rcp85_r1i1p1_g025.nc_1_2041-2060_V1.2_optWCSGeoTIFFUN_Sahel__CHELSA_bio_mon_ACCESS1-0_rcp85_r1i1p1_g025.nc_1_2041-2060_V1.2_optRectifiedGridCoverage-180.00013888885002 -90.00013888884999179.99985967115003 83.99986041515001-180.00013888885002 -90.00013888884999179.99985967115003 83.99986041515001Annual Precipitation - Anomaly [%] (2041-2060 vs 1979-2013) RCP4.5 - CIMP5Anomaly [%] for the period 2041-2060 compared to climatological data (1979-2013) on the accumulated precipitation amount over 1 year based on RCP4.5. The layer is calculated at UNEP/GRID-Geneva from the layers on annual precipitation [mm/year] provided in the products CHELSA V1.2 and CHELSA-[CMIP5]. CHELSA-[CMIP5] is a delta change climatological dataset for the years 2041-2060 and 2061- 2080 for mean monthly maximum temperatures, mean monthly minimum temperatures, monthly precipitation amounts, and several derived parameters. We use the delta change method by B-spline interpolation of anomalies (deltas) of the respective CMIP5 GCM dataset. Anomalies were interpolated between all CMIP5 grid cells and are then added (for temperature variables) or multiplied (in case of precipitation) to high resolution climate data from CHELSA V1.2. This method has the assumption that climate only varies on the scale of the coarser (CMIP5) dataset, and the spatial pattern (from CHELSA) is consistent over time. CHELSA- [CMIP5] does not take changing wind patterns, or temperature lapse rates into account, but rather expects them to be constant over time, and similar to the long term averages. CHELSA V1.2 (http://chelsa-climate.org/) is a high resolution (30 arc sec, ~1 km) climate data set for the earth land surface areas. It includes monthly and annual mean temperature and precipitation patterns for the time period 1979-2013. Methods are described in http://chelsa-climate.org/wp-admin/download-page/CHELSA_tech_specification.pdf. CHELSA Version 1.2 is licensed under a Creative Commons Attribution 4.0 International License. Specifications: High resolution (30 arcsec, ~1 km) Precipitation & Temperature Climatologies for the years 1979 – 2013 Incorporation of topoclimate (e.g. orographic rainfall & wind fields). All products of CHELSA are in a geographic coordinate system referenced to the WGS 84 horizontal datum, with the horizontal coordinates expressed in decimal degrees. The CHELSA layer extents (minimum and maximum latitude and longitude) are a result of the coordinate system inherited from the 1-arc-second GMTED2010 data which itself inherited the grid extent from the 1-arc-second SRTM data. Note that because of the pixel center referencing of the input GMTED2010 data the full extent of each CHELSA grid as defined by the outside edges of the pixels differs from an integer value of latitude or longitude by 0.000138888888 degree (or 1/2 arc-second). Users of products based on the legacy GTOPO30 product should note that the coordinate referencing of CHELSA (and GMTED2010) and GTOPO30 are not the same. In GTOPO30, the integer lines of latitude and longitude fall directly on the edges of a 30-arc-second pixel. Thus, when overlaying CHELSA with products based on GTOPO30 a slight shift of 1/2 arc-second will be observed between the edges of corresponding 30-arc-second pixels. To redistribute the data, please cite the following peer reviewed articles: <a href="https://www.nature.com/articles/sdata2017122"target=_blank>Karger, D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N.E., Linder, H.P. & Kessler, M. (2017) Climatologies at high resolution for the earth’s land surface areas. Scientific Data 4, 170122.</a> <a href="https://doi.org/10.5061/dryad.kd1d4"target=_blank>Karger, D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N.E., Linder, H.P., Kessler, M. (2017) Data from: Climatologies at high resolution for the earth’s land surface areas. Dryad Digital Repository. </a> CHELSA – Climatologies at high resolution for the Earth land surface areas. Version 1.2Perc_Diff_CHELSA_bio_mon_ACCESS1-0_rcp45_r1i1p1_g025.nc_12_2041-2060_V12_optWCSGeoTIFFUN_Sahel__Perc_Diff_CHELSA_bio_mon_ACCESS1-0_rcp45_r1i1p1_g025.nc_12_2041-2060_V12_optRectifiedGridCoverage-180.00013888885002 -90.00013888884999179.99985967115003 83.99986041515001-180.00013888885002 -90.00013888884999179.99985967115003 83.99986041515001Annual Precipitation - Anomaly [%] (2041-2060 vs 1979-2013) RCP8.5 - CIMP5Anomaly [%] for the period 2041-2060 compared to climatological data (1979-2013) on the accumulated precipitation amount over 1 year based on RCP8.5. The layer is calculated at UNEP/GRID-Geneva from the layers on annual precipitation [mm/year] provided in the products CHELSA V1.2 and CHELSA-[CMIP5]. CHELSA-[CMIP5] is a delta change climatological dataset for the years 2041-2060 and 2061- 2080 for mean monthly maximum temperatures, mean monthly minimum temperatures, monthly precipitation amounts, and several derived parameters. We use the delta change method by B-spline interpolation of anomalies (deltas) of the respective CMIP5 GCM dataset. Anomalies were interpolated between all CMIP5 grid cells and are then added (for temperature variables) or multiplied (in case of precipitation) to high resolution climate data from CHELSA V1.2. This method has the assumption that climate only varies on the scale of the coarser (CMIP5) dataset, and the spatial pattern (from CHELSA) is consistent over time. CHELSA- [CMIP5] does not take changing wind patterns, or temperature lapse rates into account, but rather expects them to be constant over time, and similar to the long term averages. CHELSA V1.2 (http://chelsa-climate.org/) is a high resolution (30 arc sec, ~1 km) climate data set for the earth land surface areas. It includes monthly and annual mean temperature and precipitation patterns for the time period 1979-2013. Methods are described in http://chelsa-climate.org/wp-admin/download-page/CHELSA_tech_specification.pdf. CHELSA Version 1.2 is licensed under a Creative Commons Attribution 4.0 International License. Specifications: High resolution (30 arcsec, ~1 km) Precipitation & Temperature Climatologies for the years 1979 – 2013 Incorporation of topoclimate (e.g. orographic rainfall & wind fields). All products of CHELSA are in a geographic coordinate system referenced to the WGS 84 horizontal datum, with the horizontal coordinates expressed in decimal degrees. The CHELSA layer extents (minimum and maximum latitude and longitude) are a result of the coordinate system inherited from the 1-arc-second GMTED2010 data which itself inherited the grid extent from the 1-arc-second SRTM data. Note that because of the pixel center referencing of the input GMTED2010 data the full extent of each CHELSA grid as defined by the outside edges of the pixels differs from an integer value of latitude or longitude by 0.000138888888 degree (or 1/2 arc-second). Users of products based on the legacy GTOPO30 product should note that the coordinate referencing of CHELSA (and GMTED2010) and GTOPO30 are not the same. In GTOPO30, the integer lines of latitude and longitude fall directly on the edges of a 30-arc-second pixel. Thus, when overlaying CHELSA with products based on GTOPO30 a slight shift of 1/2 arc-second will be observed between the edges of corresponding 30-arc-second pixels. To redistribute the data, please cite the following peer reviewed articles: <a href="https://www.nature.com/articles/sdata2017122"target=_blank>Karger, D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N.E., Linder, H.P. & Kessler, M. (2017) Climatologies at high resolution for the earth’s land surface areas. Scientific Data 4, 170122.</a> <a href="https://doi.org/10.5061/dryad.kd1d4"target=_blank>Karger, D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N.E., Linder, H.P., Kessler, M. (2017) Data from: Climatologies at high resolution for the earth’s land surface areas. Dryad Digital Repository. </a> CHELSA – Climatologies at high resolution for the Earth land surface areas. Version 1.2Perc_Diff_CHELSA_bio_mon_ACCESS1-0_rcp85_r1i1p1_g025.nc_12_2041-2060_V12_optWCSGeoTIFFUN_Sahel__Perc_Diff_CHELSA_bio_mon_ACCESS1-0_rcp85_r1i1p1_g025.nc_12_2041-2060_V12_optRectifiedGridCoverage-180.00013888885002 -90.00013888884999179.99985967115003 83.99986041515001-180.00013888885002 -90.00013888884999179.99985967115003 83.99986041515001Annual Precipitation - Climatology (1979-2013) [mm/year]Accumulated precipitation amount over 1 year [mm/year]. CHELSA V1.2 (http://chelsa-climate.org/) is a high resolution (30 arc sec, ~1 km) climate data set for the earth land surface areas. It includes monthly and annual mean temperature and precipitation patterns for the time period 1979-2013. Methods are described in http://chelsa-climate.org/wp-admin/download-page/CHELSA_tech_specification.pdf. CHELSA Version 1.2 is licensed under a Creative Commons Attribution 4.0 International License. Specifications: High resolution (30 arcsec, ~1 km) Precipitation & Temperature Climatologies for the years 1979 – 2013 Incorporation of topoclimate (e.g. orographic rainfall & wind fields). All products of CHELSA are in a geographic coordinate system referenced to the WGS 84 horizontal datum, with the horizontal coordinates expressed in decimal degrees. The CHELSA layer extents (minimum and maximum latitude and longitude) are a result of the coordinate system inherited from the 1-arc-second GMTED2010 data which itself inherited the grid extent from the 1-arc-second SRTM data. Note that because of the pixel center referencing of the input GMTED2010 data the full extent of each CHELSA grid as defined by the outside edges of the pixels differs from an integer value of latitude or longitude by 0.000138888888 degree (or 1/2 arc-second). Users of products based on the legacy GTOPO30 product should note that the coordinate referencing of CHELSA (and GMTED2010) and GTOPO30 are not the same. In GTOPO30, the integer lines of latitude and longitude fall directly on the edges of a 30-arc-second pixel. Thus, when overlaying CHELSA with products based on GTOPO30 a slight shift of 1/2 arc-second will be observed between the edges of corresponding 30-arc-second pixels. To redistribute the data, please cite the following peer reviewed articles: <a href="https://www.nature.com/articles/sdata2017122"target=_blank>Karger, D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N.E., Linder, H.P. & Kessler, M. (2017) Climatologies at high resolution for the earth’s land surface areas. Scientific Data 4, 170122.</a> <a href="https://doi.org/10.5061/dryad.kd1d4"target=_blank>Karger, D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N.E., Linder, H.P., Kessler, M. (2017) Data from: Climatologies at high resolution for the earth’s land surface areas. Dryad Digital Repository. </a> CHELSA – Climatologies at high resolution for the Earth land surface areas. Version 1.2CHELSA_bio10_12_optWCSGeoTIFFUN_Sahel__CHELSA_bio10_12_optRectifiedGridCoverage-180.00013888885002 -90.00013888884999179.99985967115003 83.99986041515001-180.0 -90.0179.99985967115003 83.99986041515001Annual Precipitation [mm/year] (2041-2060) RCP4.5 - CIMP5Accumulated precipitation amount over 1 year [mm/year] for the period 2041-2060 based on RCP4.5. CHELSA-[CMIP5] is a delta change climatological dataset for the years 2041-2060 and 2061- 2080 for mean monthly maximum temperatures, mean monthly minimum temperatures, monthly precipitation amounts, and several derived parameters. We use the delta change method by B-spline interpolation of anomalies (deltas) of the respective CMIP5 GCM dataset. Anomalies were interpolated between all CMIP5 grid cells and are then added (for temperature variables) or multiplied (in case of precipitation) to high resolution climate data from CHELSA V1.2. This method has the assumption that climate only varies on the scale of the coarser (CMIP5) dataset, and the spatial pattern (from CHELSA) is consistent over time. CHELSA- [CMIP5] does not take changing wind patterns, or temperature lapse rates into account, but rather expects them to be constant over time, and similar to the long term averages. CHELSA V1.2 (http://chelsa-climate.org/) is a high resolution (30 arc sec, ~1 km) climate data set for the earth land surface areas. It includes monthly and annual mean temperature and precipitation patterns for the time period 1979-2013. Methods are described in http://chelsa-climate.org/wp-admin/download-page/CHELSA_tech_specification.pdf. CHELSA Version 1.2 is licensed under a Creative Commons Attribution 4.0 International License. Specifications: High resolution (30 arcsec, ~1 km) Precipitation & Temperature Climatologies for the years 1979 – 2013 Incorporation of topoclimate (e.g. orographic rainfall & wind fields). All products of CHELSA are in a geographic coordinate system referenced to the WGS 84 horizontal datum, with the horizontal coordinates expressed in decimal degrees. The CHELSA layer extents (minimum and maximum latitude and longitude) are a result of the coordinate system inherited from the 1-arc-second GMTED2010 data which itself inherited the grid extent from the 1-arc-second SRTM data. Note that because of the pixel center referencing of the input GMTED2010 data the full extent of each CHELSA grid as defined by the outside edges of the pixels differs from an integer value of latitude or longitude by 0.000138888888 degree (or 1/2 arc-second). Users of products based on the legacy GTOPO30 product should note that the coordinate referencing of CHELSA (and GMTED2010) and GTOPO30 are not the same. In GTOPO30, the integer lines of latitude and longitude fall directly on the edges of a 30-arc-second pixel. Thus, when overlaying CHELSA with products based on GTOPO30 a slight shift of 1/2 arc-second will be observed between the edges of corresponding 30-arc-second pixels. To redistribute the data, please cite the following peer reviewed articles: <a href="https://www.nature.com/articles/sdata2017122"target=_blank>Karger, D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N.E., Linder, H.P. & Kessler, M. (2017) Climatologies at high resolution for the earth’s land surface areas. Scientific Data 4, 170122.</a> <a href="https://doi.org/10.5061/dryad.kd1d4"target=_blank>Karger, D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N.E., Linder, H.P., Kessler, M. (2017) Data from: Climatologies at high resolution for the earth’s land surface areas. Dryad Digital Repository. </a> CHELSA – Climatologies at high resolution for the Earth land surface areas. Version 1.2CHELSA_bio_mon_ACCESS1-0_rcp45_r1i1p1_g025.nc_12_2041-2060_V1.2_optWCSGeoTIFFUN_Sahel__CHELSA_bio_mon_ACCESS1-0_rcp45_r1i1p1_g025.nc_12_2041-2060_V1.2_optRectifiedGridCoverage-180.00013888885002 -90.00013888884999179.99985967115003 83.99986041515001-180.00013888885002 -90.00013888884999179.99985967115003 83.99986041515001Annual Precipitation [mm/year] (2041-2060) RCP8.5 - CIMP5Accumulated precipitation amount over 1 year [mm/year] for the period 2041-2060 based on RCP8.5. CHELSA-[CMIP5] is a delta change climatological dataset for the years 2041-2060 and 2061- 2080 for mean monthly maximum temperatures, mean monthly minimum temperatures, monthly precipitation amounts, and several derived parameters. We use the delta change method by B-spline interpolation of anomalies (deltas) of the respective CMIP5 GCM dataset. Anomalies were interpolated between all CMIP5 grid cells and are then added (for temperature variables) or multiplied (in case of precipitation) to high resolution climate data from CHELSA V1.2. This method has the assumption that climate only varies on the scale of the coarser (CMIP5) dataset, and the spatial pattern (from CHELSA) is consistent over time. CHELSA- [CMIP5] does not take changing wind patterns, or temperature lapse rates into account, but rather expects them to be constant over time, and similar to the long term averages. CHELSA V1.2 (http://chelsa-climate.org/) is a high resolution (30 arc sec, ~1 km) climate data set for the earth land surface areas. It includes monthly and annual mean temperature and precipitation patterns for the time period 1979-2013. Methods are described in http://chelsa-climate.org/wp-admin/download-page/CHELSA_tech_specification.pdf. CHELSA Version 1.2 is licensed under a Creative Commons Attribution 4.0 International License. Specifications: High resolution (30 arcsec, ~1 km) Precipitation & Temperature Climatologies for the years 1979 – 2013 Incorporation of topoclimate (e.g. orographic rainfall & wind fields). All products of CHELSA are in a geographic coordinate system referenced to the WGS 84 horizontal datum, with the horizontal coordinates expressed in decimal degrees. The CHELSA layer extents (minimum and maximum latitude and longitude) are a result of the coordinate system inherited from the 1-arc-second GMTED2010 data which itself inherited the grid extent from the 1-arc-second SRTM data. Note that because of the pixel center referencing of the input GMTED2010 data the full extent of each CHELSA grid as defined by the outside edges of the pixels differs from an integer value of latitude or longitude by 0.000138888888 degree (or 1/2 arc-second). Users of products based on the legacy GTOPO30 product should note that the coordinate referencing of CHELSA (and GMTED2010) and GTOPO30 are not the same. In GTOPO30, the integer lines of latitude and longitude fall directly on the edges of a 30-arc-second pixel. Thus, when overlaying CHELSA with products based on GTOPO30 a slight shift of 1/2 arc-second will be observed between the edges of corresponding 30-arc-second pixels. To redistribute the data, please cite the following peer reviewed articles: <a href="https://www.nature.com/articles/sdata2017122"target=_blank>Karger, D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N.E., Linder, H.P. & Kessler, M. (2017) Climatologies at high resolution for the earth’s land surface areas. Scientific Data 4, 170122.</a> <a href="https://doi.org/10.5061/dryad.kd1d4"target=_blank>Karger, D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N.E., Linder, H.P., Kessler, M. (2017) Data from: Climatologies at high resolution for the earth’s land surface areas. Dryad Digital Repository. </a> CHELSA – Climatologies at high resolution for the Earth land surface areas. Version 1.2CHELSA_bio_mon_ACCESS1-0_rcp85_r1i1p1_g025.nc_12_2041-2060_V1.2_optWCSGeoTIFFUN_Sahel__CHELSA_bio_mon_ACCESS1-0_rcp85_r1i1p1_g025.nc_12_2041-2060_V1.2_optRectifiedGridCoverage-180.00013888885002 -90.00013888884999179.99985967115003 83.99986041515001-180.00013888885002 -90.00013888884999179.99985967115003 83.99986041515001Annualy Actual Evapotranspiration 2005-2010 - Nile BasinSource: Bastiaanssen, W., Karimi, P., Rebelo, L.-M., Duan, Z., Senay, G., Muthuwatte, L., & et al. (6). (2014). Earth Observation Based Assessment of the Water Production and Water Consumption of Nile Basin Agro-Ecosystems. Remote Sensing, 6(11), 10306-10334. doi:10.3390/rs61110306 <br><br>The vast wetlands in Southern Sudan are characterised by huge evaporation rates from the Sudd, Bahr el Ghazal and the Sobat sub-basins (Mohamed, Bastiaanssen, & Savenije, 2004). About half of the flow into the White Nile is lost mainly to evaporation and transpiration in the wetlands of South Sudan (FAO, 2011) and approximately half of the Bahr el Jebel flow is lost to evaporation (NBI, 2016). In total, long-term evaporation is thought to account for water losses as high as 85 per cent of the total inflows into the Sudd wetlands (Senay, Velpur, Bohma, Demissie, & Gebremkhael, 2014). Analysis of the rate of water loss to evapotranspiration in the Sudd wetlands found that the central part of the Sudd (not counting the seasonally flooded pastures surrounding the wetlands) has evapotranspiration rates between 1,500 and 2,000 mm/yr.Nile_ET_optWCSGeoTIFFMAPX_nd__Nile_ET_optRectifiedGridCoverage23.550893783569364 -4.033069610595660539.804861615169365 31.6021140202043423.550893783569364 -4.033069610595660539.804861615169365 31.60211402020434Areas exposed to extreme heat in Kedougou between 1995 and 2004Extreme heat is studied by calculating the Land Surface Temperature (LST), defined as the temperature felt when the earth's surface is touched with the hands, or the temperature of the skin on the ground. This temperature estimate is influenced by several parameters, including vegetation and surface emissivity. LST was estimated in relation to Normalized Difference Vegetation Index (NDVI) values determined from red and near-infrared bands. Land surface emissivity (LSE) is recovered directly from thermal infrared bands.heat_1995_2004WCSGeoTIFFrisk_southeast_senegal__heat_1995_2004RectifiedGridCoverage-13.239460488881921 12.307817707721565-11.342218608821492 13.461254532531031-13.239460488881921 12.307817707721565-11.342218608821492 13.461254532531031Areas exposed to extreme heat in Kedougou between 2005 and 2014Extreme heat is studied by calculating the Land Surface Temperature (LST), defined as the temperature felt when the earth's surface is touched with the hands, or the temperature of the skin on the ground. This temperature estimate is influenced by several parameters, including vegetation and surface emissivity. LST was estimated in relation to Normalized Difference Vegetation Index (NDVI) values determined from red and near-infrared bands. Land surface emissivity (LSE) is recovered directly from thermal infrared bands.heat_2005_2014WCSGeoTIFFrisk_southeast_senegal__heat_2005_2014RectifiedGridCoverage-13.239460488881921 12.307817707721565-11.342218608821492 13.461254532531031-13.239460488881921 12.307817707721565-11.342218608821492 13.461254532531031Areas exposed to extreme heat in Kedougou between 2015 and 2024Extreme heat is studied by calculating the Land Surface Temperature (LST), defined as the temperature felt when the earth's surface is touched with the hands, or the temperature of the skin on the ground. This temperature estimate is influenced by several parameters, including vegetation and surface emissivity. LST was estimated in relation to Normalized Difference Vegetation Index (NDVI) values determined from red and near-infrared bands. Land surface emissivity (LSE) is recovered directly from thermal infrared bands.heat_2015_2024WCSGeoTIFFrisk_southeast_senegal__heat_2015_2024RectifiedGridCoverage-13.239460488881921 12.307817707721565-11.342218608821492 13.461254532531031-13.239460488881921 12.307817707721565-11.342218608821492 13.461254532531031Areas of Global Significance for Biodiversity Conservation and Carbon Storage<style> .p1 { overflow: auto; font-style: oblique; font-size: .9em; padding: 10px; margin: 0; text-align: justify; -moz-hyphens: auto; -webkit-hyphens: auto; -ms-hypens: auto; -ms-hyphens: auto; hyphens: auto; max-height: 220px; overflow-x: auto; overflow-y: hidden; columns: 160px; column-rule: 1px solid #ccc; grid-column-gap: 10px; column-gap: 10px; } </style> <div class="p1"> <p> This map is the result of a global joint optimization of valuable biodiversity and carbon storage. This analysis collated distribution data for over 180000 terrestrial vertebrate and plant species globally, removing unsuitable areas using information on species' habitat preferences and species' known altitudinal limits or, where unknown, by removing anthropogenically modified land. </p> <p> Given existing biases in taxonomic coverage for plant species, we calculated 10 representative sets of species, containing approximately 10% of species of each taxonomic group. For carbon we used the combined amount of above-ground and below-ground biomass carbon density and vulnerable soil organic carbon density. We then determined those areas globally that jointly contributed the most to biodiversity and carbon conservation if up to 10%, 20%, or 100% of the Earth were to be protected. <p> <u>References:</u> </p> <dl> <dt> Jung, M., et al. (2020). Areas of global importance for terrestrial biodiversity, carbon, and water. BioRxiv. <a href="https://www.biorxiv.org/content/10.1101/2020.04.16.021444v1" target="_blank">biorxiv.org/content/10.1101/2020.04.16.021444v1</a>. </dt> </dl> </div>conservation_biodiversity_carbon_optWCSGeoTIFFMapX_UNDP__conservation_biodiversity_carbon_optRectifiedGridCoverage-179.995201157628 -84.69466806618583179.8859620772232 85.91771612300938-179.995201157628 -84.69466806618583179.8859620772232 85.91771612300938Areas of Global Significance for Biodiversity Conservation and Water Provision<span style="font-size:1em"> This map is the result of a global joint optimization of valuable biodiversity and water regulation. This analysis collated distribution data for over 180000 terrestrial vertebrate and plant species globally, removing unsuitable areas using information on species' habitat preferences and species' known altitudinal limits or, where unknown, by removing anthropogenically modified land. <br /> <br /> Given existing biases in taxonomic coverage for plant species, we calculated 10 representative sets of species, containing approximately 10% of species of each taxonomic group. For water we took potential water regulation outputs from the WaterWorld model normalized by water basin. We then determined those areas globally that jointly contributed the most to biodiversity and water conservation if up to 10%, 20%, or 100% of the Earth were to be protected. <br> <br> <u>References:</u> <ul> <li> Jung, M., et al. (2020). Areas of global importance for terrestrial biodiversity, carbon, and water. BioRxiv. <a href="https://www.biorxiv.org/content/10.1101/2020.04.16.021444v1" target="_blank">biorxiv.org/content/10.1101/2020.04.16.021444v1</a>. </li> </ul> </span>conservation_biodiversity_water_optWCSGeoTIFFMapX_UNDP__conservation_biodiversity_water_optRectifiedGridCoverage-179.995201157628 -84.69466806618583179.8859620772232 85.91771612300938-179.995201157628 -84.69466806618583179.8859620772232 85.91771612300938Areas of Global Significance for Biodiversity Conservation, Carbon Storage, and Water Provision<span style="font-size:1em"> This map is the result of a global joint optimization of valuable biodiversity, carbon storage and water regulation. This analysis collated distribution data for over 180000 terrestrial vertebrate and plant species globally, removing unsuitable areas using information on species' habitat preferences and species' known altitudinal limits or, where unknown, by removing anthropogenically modified land. <br /> <br /> Given existing biases in taxonomic coverage for plant species, we calculated 10 representative sets of species, containing approximately 10% of species of each taxonomic group. For carbon we used the combined amount of above-ground and below-ground biomass carbon density and vulnerable soil organic carbon density. For water we took potential water regulation outputs from the WaterWorld model normalized by water basin. We then determined those areas globally that jointly contributed the most to biodiversity, carbon and water conservation if up to 10%, 20%, or 100% of the Earth were to be protected. <br> <br> <u>References:</u> <ul> <li> Jung, M., et al. (2020). Areas of global importance for terrestrial biodiversity, carbon, and water. BioRxiv. <a href="https://www.biorxiv.org/content/10.1101/2020.04.16.021444v1" target="_blank">biorxiv.org/content/10.1101/2020.04.16.021444v1</a>. </li> </ul> </span>conservation_biodiversity_carbon_water_optWCSGeoTIFFMapX_UNDP__conservation_biodiversity_carbon_water_optRectifiedGridCoverage-179.995201157628 -84.69466806618583179.8859620772232 85.91771612300938-179.995201157628 -84.69466806618583179.8859620772232 85.91771612300938Areas of Global Significance for Restoration<span style="font-size:1em"> This map shows the results of a global prioritization analysis for the restoration of habitats for terrestrial species and carbon sequestering. Species data used in the analysis consisted in fine-scale distributions maps of more than 20,000 mammals, birds, amphibians, and a geographically-unbiased sample of over 14,000 plant species. The carbon-related benefit was measured by the amount of carbon dioxide sequestered following restoration up to a reference ecosystem structure in each geographical zone, considering above and below-ground biomass and soil carbon. <br> <br> <u>References:</u> <ul> <li> Dinerstein, E., et al. (2017). An ecoregion-based approach to protecting half the terrestrial realm. Bioscience 67:534-545. </li> <li> Erb, K.H., et al.(2018). Unexpectedly large impact of forest management and grazing on global vegetation biomass. Nature 553:73-76. </li> <li> Erb, K.-H., V. Gaube, F. Krausmann, C. Plutzar, A. Bondeau, and H. Haberl. 2007. A comprehensive global 5 min resolution land-use data set for the year 2000 consistent with national census data. Journal of Land Use Science 2:191-224. </li> <li> IUCN (International Union for Conservation of Nature) 2019. The IUCN Red List of Threatened Species. Version 2019-1. <a href="http://www.iucnredlist.org" target="_blank">www.iucnredlist.org</a>. Downloaded on July 2019. </li> <li> Roll U, Feldman A, Novosolov M, Allison A, Bauer A, Bernard R, et al. (2017) The global distribution of tetrapods reveals a need for targeted reptile conservation. Nature Ecology and Evolution; 1, 1677–1682. </li> <li>Sanderman, J., T. Hengl, and G. J. Fiske. 2017. Soil carbon debt of 12,000 years of human land use. Proceedings of the National Academy of Sciences 115. </li> </<ul> </span>areas_global_significance_restoration_optWCSGeoTIFFMapX_UNDP__areas_global_significance_restoration_optRectifiedGridCoverage-179.87896164729798 -72.35059978902385179.85992227851924 85.07288135595564-179.87896164729798 -72.35059978902385179.85992227851924 85.07288135595564Asia Forest Height (Montana State University)The forest structural condition index is derived from the University of Maryland canopy cover, canopy height, and time since forest loss data sets. The index spans from short, open-canopy, recently disturbed forests to tall, closed canopy forests that have not been disturbed with the last 14 years. Forest stature and canopy cover are products of both the biophysical potential of a local site and of disturbance history. The tallest, most dense forests are found in settings with favorable climate and soils but with low levels if natural or human disturbance. Such forests have been shown to support high levels of biodiversity, store high levels of carbon, and be more resilient to climate variability. Our maps of forest structural condition are the first to identify locations in the humid tropics of tall, dense forests resulting from high biophysical potential and low disturbance rates. Data are provided by the Montana State University for South America, Africa and Asia separately, and have been merged into a single dataset here. License information: CC-4.0 Attribution.asia_forheight_optWCSGeoTIFFMapX_UNDP__asia_forheightRectifiedGridCoverage60.9995 -32.0005180.0005 32.000560.9995 -32.0005180.0005 32.0005Atmospheric mercury emissions from anthropogenic sources (2010)Dataset of gridded (0.5 x 0.5 degree) global emissions inventory of mercury emissions to the atmosphere in 2010 from anthropogenic sources. Units: g/km2mercure_emWCSGeoTIFFamapunea_pollution__mercure_emRectifiedGridCoverage-180.0 -55.0180.0 84.5-180.0 -55.0180.0 84.5Automn precipitations change (%) for the period 2020 - 2039 under the RCP 2.6 scenarioProjected change of precipitations for the specified season and reference period, under the specified scenario with respect to the reference period (1980-1999). Data provided by MedECC Atlas.pr_son_NEARREF_rcp26_optWCSGeoTIFFMedicip__pr_son_NEARREF_rcp26_optRectifiedGridCoverage-12.0 27.537.0 49.0-12.0 27.537.0 49.0Automn precipitations change (%) for the period 2020 - 2039 under the RCP 8.5 scenarioProjected change of precipitations for the specified season and reference period, under the specified scenario with respect to the reference period (1980-1999). Data provided by MedECC Atlas.pr_son_NEARREF_rcp85_optWCSGeoTIFFMedicip__pr_son_NEARREF_rcp85_optRectifiedGridCoverage-12.0 27.537.0 49.0-12.0 27.537.0 49.0Automn precipitations change (%) for the period 2040 - 2059 under the RCP 2.6 scenarioProjected change of precipitations for the specified season and reference period, under the specified scenario with respect to the reference period (1980-1999). Data provided by MedECC Atlas.pr_son_MIDREF_rcp26_optWCSGeoTIFFMedicip__pr_son_MIDREF_rcp26_optRectifiedGridCoverage-12.0 27.537.0 49.0-12.0 27.537.0 49.0Automn precipitations change (%) for the period 2040 - 2059 under the RCP 8.5 scenarioProjected change of precipitations for the specified season and reference period, under the specified scenario with respect to the reference period (1980-1999). Data provided by MedECC Atlas.pr_son_MIDREF_rcp85_optWCSGeoTIFFMedicip__pr_son_MIDREF_rcp85_optRectifiedGridCoverage-12.0 27.537.0 49.0-12.0 27.537.0 49.0Automn precipitations change (%) for the period 2080 - 2099 under the RCP 2.6 scenarioProjected change of precipitations for the specified season and reference period, under the specified scenario with respect to the reference period (1980-1999). Data provided by MedECC Atlas.pr_son_ENDREF_rcp26_optWCSGeoTIFFMedicip__pr_son_ENDREF_rcp26_optRectifiedGridCoverage-12.0 27.537.0 49.0-12.0 27.537.0 49.0Automn precipitations change (%) for the period 2080 - 2099 under the RCP 8.5 scenarioProjected change of precipitations for the specified season and reference period, under the specified scenario with respect to the reference period (1980-1999). Data provided by MedECC Atlas.pr_son_ENDREF_rcp85_optWCSGeoTIFFMedicip__pr_son_ENDREF_rcp85_optRectifiedGridCoverage-12.0 27.537.0 49.0-12.0 27.537.0 49.0Automn temperature change (°C) for the period 2020 - 2039 under the RCP 2.6 scenarioProjected change of temperature for the specified season and reference period, under the specified scenario with respect to the reference period (1980-1999). Data provided by MedECC Atlas.tas_son_NEARREF_rcp26_optWCSGeoTIFFMedicip__tas_son_NEARREF_rcp26_optRectifiedGridCoverage-12.0 27.537.0 49.0-12.0 27.537.0 49.0Automn temperature change (°C) for the period 2020 - 2039 under the RCP 8.5 scenarioProjected change of temperature for the specified season and reference period, under the specified scenario with respect to the reference period (1980-1999). Data provided by MedECC Atlas.tas_son_NEARREF_rcp85_optWCSGeoTIFFMedicip__tas_son_NEARREF_rcp85_optRectifiedGridCoverage-12.0 27.537.0 49.0-12.0 27.537.0 49.0Automn temperature change (°C) for the period 2040 - 2059 under the RCP 2.6 scenarioProjected change of temperature for the specified season and reference period, under the specified scenario with respect to the reference period (1980-1999). Data provided by MedECC Atlas.tas_son_MIDREF_rcp26_optWCSGeoTIFFMedicip__tas_son_MIDREF_rcp26_optRectifiedGridCoverage-12.0 27.537.0 49.0-12.0 27.537.0 49.0Automn temperature change (°C) for the period 2040 - 2059 under the RCP 8.5 scenarioProjected change of temperature for the specified season and reference period, under the specified scenario with respect to the reference period (1980-1999). Data provided by MedECC Atlas.tas_son_MIDREF_rcp85_optWCSGeoTIFFMedicip__tas_son_MIDREF_rcp85_optRectifiedGridCoverage-12.0 27.537.0 49.0-12.0 27.537.0 49.0Automn temperature change (°C) for the period 2080 - 2099 under the RCP 2.6 scenarioProjected change of temperature for the specified season and reference period, under the specified scenario with respect to the reference period (1980-1999). Data provided by MedECC Atlas.tas_son_ENDREF_rcp26_optWCSGeoTIFFMedicip__tas_son_ENDREF_rcp26_optRectifiedGridCoverage-12.0 27.537.0 49.0-12.0 27.537.0 49.0Automn temperature change (°C) for the period 2080 - 2099 under the RCP 8.5 scenarioProjected change of temperature for the specified season and reference period, under the specified scenario with respect to the reference period (1980-1999). Data provided by MedECC Atlas.tas_son_ENDREF_rcp85_optWCSGeoTIFFMedicip__tas_son_ENDREF_rcp85_optRectifiedGridCoverage-12.0 27.537.0 49.0-12.0 27.537.0 49.0Average annual temperature across 18 models for future climate using SRES A2 Scenario (2041 - 2060)Future climate projections from the World Climate Research Programme's (WCRP's) CMIP3 multi-model dataset downscaled using the Worldclim 2.5-minute 20th century climate dataset. The CMIP3 multi-model datasets were used for the IPCC 4th Assessment Report. The B1 scenario assumes the most ecologically friendly future. The A1B scenario assumes future energy sources will be balanced between fossil-intensive and non-fossil energy sources. The A2 scenario is characterized by a future world still heavily dependent on fossil fuel consumption. All models are historical and future climate simulations collected from leading modeling centers around the world. The original model simulations are collected and achieved by the Program for Climate Model Diagnosis and Intercomparison (PCMDI) to create the World Climate Research Programme's (WCRP's) phase 3 of the Coupled Model Intercomparison Project (CMIP3) multi-model dataset. The downscaled data were produced by Conservation International through collaboration with the Department of Geography, Center of Climatic Research, and Land Tenure Center at the University of Wisconsin and support from the National Center of Ecological Analysis and Synthesis <br> Downscaled Future Climate Scenarios 2.5-minute resolution, 2009. This is a product of Conservation International based on techniques developed in collaboration with the Department of Geography, Center of Climatic Research, and Land Tenure Center at the University of Wisconsin. Additional support provided by the National Center of Ecological Analysis and Synthesis. http://futureclimates.conservation.org (accessed: 06/03/19)future_Av_annual_temp_Av_18_models_optWCSGeoTIFFLWP__future_Av_annual_temp_Av_18_models_optRectifiedGridCoverage-180.0 -60.0180.00288 90.00120000000001-180.0 -60.0180.00288 90.00120000000001Average annual temperature for current climate (1970-2000) from WorldClim V2Average annual temperature from current monthly temperature rasters. <br> WorldClim version 2 has average monthly climate data for minimum, mean, and maximum temperature and for precipitation for 1970-2000. You can download the variables (minimum temperature (°C), maximum temperature (°C), average temperature (°C), precipitation (mm), solar radiation (kJ m-2 day-1), wind speed (m s-1) and water vapor pressure (kPa)) for different spatial resolutions, from 30 seconds (~1 km2) to 10 minutes (~340 km2). Each download is a "zip" file containing 12 GeoTiff (.tif) files, one for each month of the year (January is 1; December is 12). <br> Fick, S.E. and R.J. Hijmans, 2017. Worldclim 2: New 1-km spatial resolution climate surfaces for global land areas. International Journal of Climatology.current_wc2_0_2_5m_tavg_annual_optWCSGeoTIFFLWP__current_wc2_0_2_5m_tavg_annual_optRectifiedGridCoverage-180.0 -90.0180.0 90.0-180.0 -90.0180.0 90.0Average annual temperature for future climate using SRES A2 Scenario (2041 - 2060) from Bjerknes Centre for Climate Research, Norway, BCM2.0 ModelFuture climate projections from the World Climate Research Programme's (WCRP's) CMIP3 multi-model dataset downscaled using the Worldclim 2.5-minute 20th century climate dataset. The CMIP3 multi-model datasets were used for the IPCC 4th Assessment Report. The B1 scenario assumes the most ecologically friendly future. The A1B scenario assumes future energy sources will be balanced between fossil-intensive and non-fossil energy sources. The A2 scenario is characterized by a future world still heavily dependent on fossil fuel consumption. All models are historical and future climate simulations collected from leading modeling centers around the world. The original model simulations are collected and achieved by the Program for Climate Model Diagnosis and Intercomparison (PCMDI) to create the World Climate Research Programme's (WCRP's) phase 3 of the Coupled Model Intercomparison Project (CMIP3) multi-model dataset. The downscaled data were produced by Conservation International through collaboration with the Department of Geography, Center of Climatic Research, and Land Tenure Center at the University of Wisconsin and support from the National Center of Ecological Analysis and Synthesis. <br> Downscaled Future Climate Scenarios 2.5-minute resolution, 2009. This is a product of Conservation International based on techniques developed in collaboration with the Department of Geography, Center of Climatic Research, and Land Tenure Center at the University of Wisconsin. Additional support provided by the National Center of Ecological Analysis and Synthesis. http://futureclimates.conservation.org (accessed: 06/03/19)sresa2_bccr_bcm2_0_5km_2041-2060_debiasedmean_tas_av_12month_optWCSGeoTIFFLWP__sresa2_bccr_bcm2_0_5km_2041-2060_debiasedmean_tas_av_12month_optRectifiedGridCoverage-180.0 -60.0180.00002016000002 90.00000840000001-180.0 -60.0180.00002016000002 90.00000840000001Average annual temperature for future climate using SRES A2 Scenario (2041 - 2060) from CCSR/NIES/FRCGC, Japan, MIROC3.2, medium resolutionFuture climate projections from the World Climate Research Programme's (WCRP's) CMIP3 multi-model dataset downscaled using the Worldclim 2.5-minute 20th century climate dataset. The CMIP3 multi-model datasets were used for the IPCC 4th Assessment Report. The B1 scenario assumes the most ecologically friendly future. The A1B scenario assumes future energy sources will be balanced between fossil-intensive and non-fossil energy sources. The A2 scenario is characterized by a future world still heavily dependent on fossil fuel consumption. All models are historical and future climate simulations collected from leading modeling centers around the world. The original model simulations are collected and achieved by the Program for Climate Model Diagnosis and Intercomparison (PCMDI) to create the World Climate Research Programme's (WCRP's) phase 3 of the Coupled Model Intercomparison Project (CMIP3) multi-model dataset. The downscaled data were produced by Conservation International through collaboration with the Department of Geography, Center of Climatic Research, and Land Tenure Center at the University of Wisconsin and support from the National Center of Ecological Analysis and Synthesis. <br> Downscaled Future Climate Scenarios 2.5-minute resolution, 2009. This is a product of Conservation International based on techniques developed in collaboration with the Department of Geography, Center of Climatic Research, and Land Tenure Center at the University of Wisconsin. Additional support provided by the National Center of Ecological Analysis and Synthesis. http://futureclimates.conservation.org (accessed: 06/03/19)sresa2_miroc3_2_medres_5km_2041-2060_debiasedmean_tas_av_12m_optWCSGeoTIFFLWP__sresa2_miroc3_2_medres_5km_2041-2060_debiasedmean_tas_av_12m_optRectifiedGridCoverage-180.0 -60.0180.00002016000002 90.00000840000001-180.0 -60.0180.00002016000002 90.00000840000001Average annual temperature for future climate using SRES A2 Scenario (2041 - 2060) from CSIRO Atmospheric Research, Australia, Mk3.0 ModelFuture climate projections from the World Climate Research Programme's (WCRP's) CMIP3 multi-model dataset downscaled using the Worldclim 2.5-minute 20th century climate dataset. The CMIP3 multi-model datasets were used for the IPCC 4th Assessment Report. The B1 scenario assumes the most ecologically friendly future. The A1B scenario assumes future energy sources will be balanced between fossil-intensive and non-fossil energy sources. The A2 scenario is characterized by a future world still heavily dependent on fossil fuel consumption. All models are historical and future climate simulations collected from leading modeling centers around the world. The original model simulations are collected and achieved by the Program for Climate Model Diagnosis and Intercomparison (PCMDI) to create the World Climate Research Programme's (WCRP's) phase 3 of the Coupled Model Intercomparison Project (CMIP3) multi-model dataset. The downscaled data were produced by Conservation International through collaboration with the Department of Geography, Center of Climatic Research, and Land Tenure Center at the University of Wisconsin and support from the National Center of Ecological Analysis and Synthesis. <br> Downscaled Future Climate Scenarios 2.5-minute resolution, 2009. This is a product of Conservation International based on techniques developed in collaboration with the Department of Geography, Center of Climatic Research, and Land Tenure Center at the University of Wisconsin. Additional support provided by the National Center of Ecological Analysis and Synthesis. http://futureclimates.conservation.org (accessed: 06/03/19)sresa2_csiro_mk3_0_5km_2041-2060_debiasedmean_tas_av_12month_optWCSGeoTIFFLWP__sresa2_csiro_mk3_0_5km_2041-2060_debiasedmean_tas_av_12month_optRectifiedGridCoverage-180.0 -60.0180.00002016000002 90.00000840000001-180.0 -60.0180.00002016000002 90.00000840000001Average annual temperature for future climate using SRES A2 Scenario (2041 - 2060) from CSIRO Atmospheric Research, Australia, Mk3.5 ModelFuture climate projections from the World Climate Research Programme's (WCRP's) CMIP3 multi-model dataset downscaled using the Worldclim 2.5-minute 20th century climate dataset. The CMIP3 multi-model datasets were used for the IPCC 4th Assessment Report. The B1 scenario assumes the most ecologically friendly future. The A1B scenario assumes future energy sources will be balanced between fossil-intensive and non-fossil energy sources. The A2 scenario is characterized by a future world still heavily dependent on fossil fuel consumption. All models are historical and future climate simulations collected from leading modeling centers around the world. The original model simulations are collected and achieved by the Program for Climate Model Diagnosis and Intercomparison (PCMDI) to create the World Climate Research Programme's (WCRP's) phase 3 of the Coupled Model Intercomparison Project (CMIP3) multi-model dataset. The downscaled data were produced by Conservation International through collaboration with the Department of Geography, Center of Climatic Research, and Land Tenure Center at the University of Wisconsin and support from the National Center of Ecological Analysis and Synthesis. <br> Downscaled Future Climate Scenarios 2.5-minute resolution, 2009. This is a product of Conservation International based on techniques developed in collaboration with the Department of Geography, Center of Climatic Research, and Land Tenure Center at the University of Wisconsin. Additional support provided by the National Center of Ecological Analysis and Synthesis. http://futureclimates.conservation.org (accessed: 06/03/19)sresa2_csiro_mk3_5_5km_2041-2060_debiasedmean_tas_av_12month_optWCSGeoTIFFLWP__sresa2_csiro_mk3_5_5km_2041-2060_debiasedmean_tas_av_12month_optRectifiedGridCoverage-180.0 -60.0180.00002016000002 90.00000840000001-180.0 -60.0180.00002016000002 90.00000840000001Average annual temperature for future climate using SRES A2 Scenario (2041 - 2060) from Canadian Centre for Climate Modelling and Analysis, CGCM3.1 Model, T47 resolutionFuture climate projections from the World Climate Research Programme's (WCRP's) CMIP3 multi-model dataset downscaled using the Worldclim 2.5-minute 20th century climate dataset. The CMIP3 multi-model datasets were used for the IPCC 4th Assessment Report. The B1 scenario assumes the most ecologically friendly future. The A1B scenario assumes future energy sources will be balanced between fossil-intensive and non-fossil energy sources. The A2 scenario is characterized by a future world still heavily dependent on fossil fuel consumption. All models are historical and future climate simulations collected from leading modeling centers around the world. The original model simulations are collected and achieved by the Program for Climate Model Diagnosis and Intercomparison (PCMDI) to create the World Climate Research Programme's (WCRP's) phase 3 of the Coupled Model Intercomparison Project (CMIP3) multi-model dataset. The downscaled data were produced by Conservation International through collaboration with the Department of Geography, Center of Climatic Research, and Land Tenure Center at the University of Wisconsin and support from the National Center of Ecological Analysis and Synthesis. <br> Downscaled Future Climate Scenarios 2.5-minute resolution, 2009. This is a product of Conservation International based on techniques developed in collaboration with the Department of Geography, Center of Climatic Research, and Land Tenure Center at the University of Wisconsin. Additional support provided by the National Center of Ecological Analysis and Synthesis. http://futureclimates.conservation.org (accessed: 06/03/19)sresa2_cccma_cgcm3_1_5km_2041-2060_debiasedmean_tas_av_12month_optWCSGeoTIFFLWP__sresa2_cccma_cgcm3_1_5km_2041-2060_debiasedmean_tas_av_12month_optRectifiedGridCoverage-180.0 -60.0180.00002016000002 90.00000840000001-180.0 -60.0180.00002016000002 90.00000840000001Average annual temperature for future climate using SRES A2 Scenario (2041 - 2060) from Hadley Centre for Climate Prediction, Met Office, UK, HadCM3 Model"Future climate projections from the World Climate Research Programme's (WCRP's) CMIP3 multi-model dataset downscaled using the Worldclim 2.5-minute 20th century climate dataset. The CMIP3 multi-model datasets were used for the IPCC 4th Assessment Report. The B1 scenario assumes the most ecologically friendly future. The A1B scenario assumes future energy sources will be balanced between fossil-intensive and non-fossil energy sources. The A2 scenario is characterized by a future world still heavily dependent on fossil fuel consumption. All models are historical and future climate simulations collected from leading modeling centers around the world. The original model simulations are collected and achieved by the Program for Climate Model Diagnosis and Intercomparison (PCMDI) to create the World Climate Research Programme's (WCRP's) phase 3 of the Coupled Model Intercomparison Project (CMIP3) multi-model dataset. The downscaled data were produced by Conservation International through collaboration with the Department of Geography, Center of Climatic Research, and Land Tenure Center at the University of Wisconsin and support from the National Center of Ecological Analysis and Synthesis. <br> Downscaled Future Climate Scenarios 2.5-minute resolution, 2009. This is a product of Conservation International based on techniques developed in collaboration with the Department of Geography, Center of Climatic Research, and Land Tenure Center at the University of Wisconsin. Additional support provided by the National Center of Ecological Analysis and Synthesis. http://futureclimates.conservation.org (accessed: 06/03/19)"sresa2_ukmo_hadcm3_5km_2041-2060_debiasedmean_tas_av12_optWCSGeoTIFFLWP__sresa2_ukmo_hadcm3_5km_2041-2060_debiasedmean_tas_av12_optRectifiedGridCoverage-180.0 -60.0180.00002016000002 90.00000840000001-180.0 -60.0180.00002016000002 90.00000840000001Average annual temperature for future climate using SRES A2 Scenario (2041 - 2060) from INGV, National Institute of Geophysics and Volcanology, Italy, ECHAM 4.6 ModelFuture climate projections from the World Climate Research Programme's (WCRP's) CMIP3 multi-model dataset downscaled using the Worldclim 2.5-minute 20th century climate dataset. The CMIP3 multi-model datasets were used for the IPCC 4th Assessment Report. The B1 scenario assumes the most ecologically friendly future. The A1B scenario assumes future energy sources will be balanced between fossil-intensive and non-fossil energy sources. The A2 scenario is characterized by a future world still heavily dependent on fossil fuel consumption. All models are historical and future climate simulations collected from leading modeling centers around the world. The original model simulations are collected and achieved by the Program for Climate Model Diagnosis and Intercomparison (PCMDI) to create the World Climate Research Programme's (WCRP's) phase 3 of the Coupled Model Intercomparison Project (CMIP3) multi-model dataset. The downscaled data were produced by Conservation International through collaboration with the Department of Geography, Center of Climatic Research, and Land Tenure Center at the University of Wisconsin and support from the National Center of Ecological Analysis and Synthesis. <br> Downscaled Future Climate Scenarios 2.5-minute resolution, 2009. This is a product of Conservation International based on techniques developed in collaboration with the Department of Geography, Center of Climatic Research, and Land Tenure Center at the University of Wisconsin. Additional support provided by the National Center of Ecological Analysis and Synthesis. http://futureclimates.conservation.org (accessed: 06/03/19)sresa2_ingv_echam4_5km_2041-2060_debiasedmean_tas_av_12m_optWCSGeoTIFFLWP__sresa2_ingv_echam4_5km_2041-2060_debiasedmean_tas_av_12m_optRectifiedGridCoverage-180.0 -60.0180.00002016000002 90.00000840000001-180.0 -60.0180.00002016000002 90.00000840000001Average annual temperature for future climate using SRES A2 Scenario (2041 - 2060) from IPSL/LMD/LSCE, France, CM4 V1 ModelFuture climate projections from the World Climate Research Programme's (WCRP's) CMIP3 multi-model dataset downscaled using the Worldclim 2.5-minute 20th century climate dataset. The CMIP3 multi-model datasets were used for the IPCC 4th Assessment Report. The B1 scenario assumes the most ecologically friendly future. The A1B scenario assumes future energy sources will be balanced between fossil-intensive and non-fossil energy sources. The A2 scenario is characterized by a future world still heavily dependent on fossil fuel consumption. All models are historical and future climate simulations collected from leading modeling centers around the world. The original model simulations are collected and achieved by the Program for Climate Model Diagnosis and Intercomparison (PCMDI) to create the World Climate Research Programme's (WCRP's) phase 3 of the Coupled Model Intercomparison Project (CMIP3) multi-model dataset. The downscaled data were produced by Conservation International through collaboration with the Department of Geography, Center of Climatic Research, and Land Tenure Center at the University of Wisconsin and support from the National Center of Ecological Analysis and Synthesis. <br> Downscaled Future Climate Scenarios 2.5-minute resolution, 2009. This is a product of Conservation International based on techniques developed in collaboration with the Department of Geography, Center of Climatic Research, and Land Tenure Center at the University of Wisconsin. Additional support provided by the National Center of Ecological Analysis and Synthesis. http://futureclimates.conservation.org (accessed: 06/03/19)sresa2_ipsl_cm4_5km_2041-2060_debiasedmean_tas_av_12m_optWCSGeoTIFFLWP__sresa2_ipsl_cm4_5km_2041-2060_debiasedmean_tas_av_12m_optRectifiedGridCoverage-180.0 -60.0180.00002016000002 90.00000840000001-180.0 -60.0180.00002016000002 90.00000840000001Average annual temperature for future climate using SRES A2 Scenario (2041 - 2060) from Institute for Numerical Mathematics, Russia, INMCM3.0 ModelFuture climate projections from the World Climate Research Programme's (WCRP's) CMIP3 multi-model dataset downscaled using the Worldclim 2.5-minute 20th century climate dataset. The CMIP3 multi-model datasets were used for the IPCC 4th Assessment Report. The B1 scenario assumes the most ecologically friendly future. The A1B scenario assumes future energy sources will be balanced between fossil-intensive and non-fossil energy sources. The A2 scenario is characterized by a future world still heavily dependent on fossil fuel consumption. All models are historical and future climate simulations collected from leading modeling centers around the world. The original model simulations are collected and achieved by the Program for Climate Model Diagnosis and Intercomparison (PCMDI) to create the World Climate Research Programme's (WCRP's) phase 3 of the Coupled Model Intercomparison Project (CMIP3) multi-model dataset. The downscaled data were produced by Conservation International through collaboration with the Department of Geography, Center of Climatic Research, and Land Tenure Center at the University of Wisconsin and support from the National Center of Ecological Analysis and Synthesis. <br> Downscaled Future Climate Scenarios 2.5-minute resolution, 2009. This is a product of Conservation International based on techniques developed in collaboration with the Department of Geography, Center of Climatic Research, and Land Tenure Center at the University of Wisconsin. Additional support provided by the National Center of Ecological Analysis and Synthesis. http://futureclimates.conservation.org (accessed: 06/03/19)sresa2_inmcm3_0_5km_2041-2060_debiasedmean_tas_av_12m_optWCSGeoTIFFLWP__sresa2_inmcm3_0_5km_2041-2060_debiasedmean_tas_av_12m_optRectifiedGridCoverage-180.0 -60.0180.00002016000002 90.00000840000001-180.0 -60.0180.00002016000002 90.00000840000001Average annual temperature for future climate using SRES A2 Scenario (2041 - 2060) from Max Planck Institute for Meteorology, Germany, ECHAM5 / MPI OMFuture climate projections from the World Climate Research Programme's (WCRP's) CMIP3 multi-model dataset downscaled using the Worldclim 2.5-minute 20th century climate dataset. The CMIP3 multi-model datasets were used for the IPCC 4th Assessment Report. The B1 scenario assumes the most ecologically friendly future. The A1B scenario assumes future energy sources will be balanced between fossil-intensive and non-fossil energy sources. The A2 scenario is characterized by a future world still heavily dependent on fossil fuel consumption. All models are historical and future climate simulations collected from leading modeling centers around the world. The original model simulations are collected and achieved by the Program for Climate Model Diagnosis and Intercomparison (PCMDI) to create the World Climate Research Programme's (WCRP's) phase 3 of the Coupled Model Intercomparison Project (CMIP3) multi-model dataset. The downscaled data were produced by Conservation International through collaboration with the Department of Geography, Center of Climatic Research, and Land Tenure Center at the University of Wisconsin and support from the National Center of Ecological Analysis and Synthesis. <br> Downscaled Future Climate Scenarios 2.5-minute resolution, 2009. This is a product of Conservation International based on techniques developed in collaboration with the Department of Geography, Center of Climatic Research, and Land Tenure Center at the University of Wisconsin. Additional support provided by the National Center of Ecological Analysis and Synthesis. http://futureclimates.conservation.org (accessed: 06/03/19)sresa2_mpi_echam5_5km_2041-2060_debiasedmean_tas_av12mon_optWCSGeoTIFFLWP__sresa2_mpi_echam5_5km_2041-2060_debiasedmean_tas_av12mon_optRectifiedGridCoverage-180.0 -60.0180.00002016000002 90.00000840000001-180.0 -60.0180.00002016000002 90.00000840000001Average annual temperature for future climate using SRES A2 Scenario (2041 - 2060) from Meteo-France, Centre National de Recherches Meteorologiques, CM3 ModelFuture climate projections from the World Climate Research Programme's (WCRP's) CMIP3 multi-model dataset downscaled using the Worldclim 2.5-minute 20th century climate dataset. The CMIP3 multi-model datasets were used for the IPCC 4th Assessment Report. The B1 scenario assumes the most ecologically friendly future. The A1B scenario assumes future energy sources will be balanced between fossil-intensive and non-fossil energy sources. The A2 scenario is characterized by a future world still heavily dependent on fossil fuel consumption. All models are historical and future climate simulations collected from leading modeling centers around the world. The original model simulations are collected and achieved by the Program for Climate Model Diagnosis and Intercomparison (PCMDI) to create the World Climate Research Programme's (WCRP's) phase 3 of the Coupled Model Intercomparison Project (CMIP3) multi-model dataset. The downscaled data were produced by Conservation International through collaboration with the Department of Geography, Center of Climatic Research, and Land Tenure Center at the University of Wisconsin and support from the National Center of Ecological Analysis and Synthesis. <br> Downscaled Future Climate Scenarios 2.5-minute resolution, 2009. This is a product of Conservation International based on techniques developed in collaboration with the Department of Geography, Center of Climatic Research, and Land Tenure Center at the University of Wisconsin. Additional support provided by the National Center of Ecological Analysis and Synthesis. http://futureclimates.conservation.org (accessed: 06/03/19)sresa2_cnrm_cm3_5km_2041-2060_debiasedmean_tas_av_12month_optWCSGeoTIFFLWP__sresa2_cnrm_cm3_5km_2041-2060_debiasedmean_tas_av_12month_optRectifiedGridCoverage-180.0 -60.0180.00002016000002 90.00000840000001-180.0 -60.0180.00002016000002 90.00000840000001Average annual temperature for future climate using SRES A2 Scenario (2041 - 2060) from Meteorological Institute of the University of Bonn, ECHO-G ModelFuture climate projections from the World Climate Research Programme's (WCRP's) CMIP3 multi-model dataset downscaled using the Worldclim 2.5-minute 20th century climate dataset. The CMIP3 multi-model datasets were used for the IPCC 4th Assessment Report. The B1 scenario assumes the most ecologically friendly future. The A1B scenario assumes future energy sources will be balanced between fossil-intensive and non-fossil energy sources. The A2 scenario is characterized by a future world still heavily dependent on fossil fuel consumption. All models are historical and future climate simulations collected from leading modeling centers around the world. The original model simulations are collected and achieved by the Program for Climate Model Diagnosis and Intercomparison (PCMDI) to create the World Climate Research Programme's (WCRP's) phase 3 of the Coupled Model Intercomparison Project (CMIP3) multi-model dataset. The downscaled data were produced by Conservation International through collaboration with the Department of Geography, Center of Climatic Research, and Land Tenure Center at the University of Wisconsin and support from the National Center of Ecological Analysis and Synthesis. <br> Downscaled Future Climate Scenarios 2.5-minute resolution, 2009. This is a product of Conservation International based on techniques developed in collaboration with the Department of Geography, Center of Climatic Research, and Land Tenure Center at the University of Wisconsin. Additional support provided by the National Center of Ecological Analysis and Synthesis. http://futureclimates.conservation.org (accessed: 06/03/19)sresa2_miub_echo_g_5km_2041-2060_debiasedmean_tas_av12mon_optWCSGeoTIFFLWP__sresa2_miub_echo_g_5km_2041-2060_debiasedmean_tas_av12mon_optRectifiedGridCoverage-180.0 -60.0180.00002016000002 90.00000840000001-180.0 -60.0180.00002016000002 90.00000840000001Average annual temperature for future climate using SRES A2 Scenario (2041 - 2060) from Meteorological Research Institute, Japan, CGCM2.3.2aFuture climate projections from the World Climate Research Programme's (WCRP's) CMIP3 multi-model dataset downscaled using the Worldclim 2.5-minute 20th century climate dataset. The CMIP3 multi-model datasets were used for the IPCC 4th Assessment Report. The B1 scenario assumes the most ecologically friendly future. The A1B scenario assumes future energy sources will be balanced between fossil-intensive and non-fossil energy sources. The A2 scenario is characterized by a future world still heavily dependent on fossil fuel consumption. All models are historical and future climate simulations collected from leading modeling centers around the world. The original model simulations are collected and achieved by the Program for Climate Model Diagnosis and Intercomparison (PCMDI) to create the World Climate Research Programme's (WCRP's) phase 3 of the Coupled Model Intercomparison Project (CMIP3) multi-model dataset. The downscaled data were produced by Conservation International through collaboration with the Department of Geography, Center of Climatic Research, and Land Tenure Center at the University of Wisconsin and support from the National Center of Ecological Analysis and Synthesis. <br> Downscaled Future Climate Scenarios 2.5-minute resolution, 2009. This is a product of Conservation International based on techniques developed in collaboration with the Department of Geography, Center of Climatic Research, and Land Tenure Center at the University of Wisconsin. Additional support provided by the National Center of Ecological Analysis and Synthesis. http://futureclimates.conservation.org (accessed: 06/03/19)sresa2_mri_cgcm2_3_2a_5km_2041-2060_debiasedmean_tas_12mav_optWCSGeoTIFFLWP__sresa2_mri_cgcm2_3_2a_5km_2041-2060_debiasedmean_tas_12mav_optRectifiedGridCoverage-180.0 -60.0180.00002016000002 90.00000840000001-180.0 -60.0180.00002016000002 90.00000840000001Average annual temperature for future climate using SRES A2 Scenario (2041 - 2060) from NASA Goddard Institute for Space Studies, ModelE20/RussellFuture climate projections from the World Climate Research Programme's (WCRP's) CMIP3 multi-model dataset downscaled using the Worldclim 2.5-minute 20th century climate dataset. The CMIP3 multi-model datasets were used for the IPCC 4th Assessment Report. The B1 scenario assumes the most ecologically friendly future. The A1B scenario assumes future energy sources will be balanced between fossil-intensive and non-fossil energy sources. The A2 scenario is characterized by a future world still heavily dependent on fossil fuel consumption. All models are historical and future climate simulations collected from leading modeling centers around the world. The original model simulations are collected and achieved by the Program for Climate Model Diagnosis and Intercomparison (PCMDI) to create the World Climate Research Programme's (WCRP's) phase 3 of the Coupled Model Intercomparison Project (CMIP3) multi-model dataset. The downscaled data were produced by Conservation International through collaboration with the Department of Geography, Center of Climatic Research, and Land Tenure Center at the University of Wisconsin and support from the National Center of Ecological Analysis and Synthesis. <br> Downscaled Future Climate Scenarios 2.5-minute resolution, 2009. This is a product of Conservation International based on techniques developed in collaboration with the Department of Geography, Center of Climatic Research, and Land Tenure Center at the University of Wisconsin. Additional support provided by the National Center of Ecological Analysis and Synthesis. http://futureclimates.conservation.org (accessed: 06/03/19)sresa2_giss_model_e_r_5km_2041-2060_debiasedmean_tas_av_12m_optWCSGeoTIFFLWP__sresa2_giss_model_e_r_5km_2041-2060_debiasedmean_tas_av_12m_optRectifiedGridCoverage-180.0 -60.0180.00288 90.00120000000001-180.0 -60.0180.00288 90.00120000000001Average annual temperature for future climate using SRES A2 Scenario (2041 - 2060) from NOAA Geophysical Fluid Dynamics Laboratory, CM2.0 ModelFuture climate projections from the World Climate Research Programme's (WCRP's) CMIP3 multi-model dataset downscaled using the Worldclim 2.5-minute 20th century climate dataset. The CMIP3 multi-model datasets were used for the IPCC 4th Assessment Report. The B1 scenario assumes the most ecologically friendly future. The A1B scenario assumes future energy sources will be balanced between fossil-intensive and non-fossil energy sources. The A2 scenario is characterized by a future world still heavily dependent on fossil fuel consumption. All models are historical and future climate simulations collected from leading modeling centers around the world. The original model simulations are collected and achieved by the Program for Climate Model Diagnosis and Intercomparison (PCMDI) to create the World Climate Research Programme's (WCRP's) phase 3 of the Coupled Model Intercomparison Project (CMIP3) multi-model dataset. The downscaled data were produced by Conservation International through collaboration with the Department of Geography, Center of Climatic Research, and Land Tenure Center at the University of Wisconsin and support from the National Center of Ecological Analysis and Synthesis. <br> Downscaled Future Climate Scenarios 2.5-minute resolution, 2009. This is a product of Conservation International based on techniques developed in collaboration with the Department of Geography, Center of Climatic Research, and Land Tenure Center at the University of Wisconsin. Additional support provided by the National Center of Ecological Analysis and Synthesis. http://futureclimates.conservation.org (accessed: 06/03/19)sresa2_gfdl_cm2_0_5km_2041-2060_debiasedmean_tas_av_12mon_optWCSGeoTIFFLWP__sresa2_gfdl_cm2_0_5km_2041-2060_debiasedmean_tas_av_12mon_optRectifiedGridCoverage-180.0 -60.0180.00002016000002 90.00000840000001-180.0 -60.0180.00002016000002 90.00000840000001Average annual temperature for future climate using SRES A2 Scenario (2041 - 2060) from NOAA Geophysical Fluid Dynamics Laboratory, CM2.1 ModelFuture climate projections from the World Climate Research Programme's (WCRP's) CMIP3 multi-model dataset downscaled using the Worldclim 2.5-minute 20th century climate dataset. The CMIP3 multi-model datasets were used for the IPCC 4th Assessment Report. The B1 scenario assumes the most ecologically friendly future. The A1B scenario assumes future energy sources will be balanced between fossil-intensive and non-fossil energy sources. The A2 scenario is characterized by a future world still heavily dependent on fossil fuel consumption. All models are historical and future climate simulations collected from leading modeling centers around the world. The original model simulations are collected and achieved by the Program for Climate Model Diagnosis and Intercomparison (PCMDI) to create the World Climate Research Programme's (WCRP's) phase 3 of the Coupled Model Intercomparison Project (CMIP3) multi-model dataset. The downscaled data were produced by Conservation International through collaboration with the Department of Geography, Center of Climatic Research, and Land Tenure Center at the University of Wisconsin and support from the National Center of Ecological Analysis and Synthesis. <br> Downscaled Future Climate Scenarios 2.5-minute resolution, 2009. This is a product of Conservation International based on techniques developed in collaboration with the Department of Geography, Center of Climatic Research, and Land Tenure Center at the University of Wisconsin. Additional support provided by the National Center of Ecological Analysis and Synthesis. http://futureclimates.conservation.org (accessed: 06/03/19)sresa2_gfdl_cm2_1_5km_2041-2060_debiasedmean_tas_av_12m_optWCSGeoTIFFLWP__sresa2_gfdl_cm2_1_5km_2041-2060_debiasedmean_tas_av_12m_optRectifiedGridCoverage-180.0 -60.0180.00002016000002 90.00000840000001-180.0 -60.0180.00002016000002 90.00000840000001Average annual temperature for future climate using SRES A2 Scenario (2041 - 2060) from National Center for Atmospheric Research, CCSM3.0Future climate projections from the World Climate Research Programme's (WCRP's) CMIP3 multi-model dataset downscaled using the Worldclim 2.5-minute 20th century climate dataset. The CMIP3 multi-model datasets were used for the IPCC 4th Assessment Report. The B1 scenario assumes the most ecologically friendly future. The A1B scenario assumes future energy sources will be balanced between fossil-intensive and non-fossil energy sources. The A2 scenario is characterized by a future world still heavily dependent on fossil fuel consumption. All models are historical and future climate simulations collected from leading modeling centers around the world. The original model simulations are collected and achieved by the Program for Climate Model Diagnosis and Intercomparison (PCMDI) to create the World Climate Research Programme's (WCRP's) phase 3 of the Coupled Model Intercomparison Project (CMIP3) multi-model dataset. The downscaled data were produced by Conservation International through collaboration with the Department of Geography, Center of Climatic Research, and Land Tenure Center at the University of Wisconsin and support from the National Center of Ecological Analysis and Synthesis. <br> Downscaled Future Climate Scenarios 2.5-minute resolution, 2009. This is a product of Conservation International based on techniques developed in collaboration with the Department of Geography, Center of Climatic Research, and Land Tenure Center at the University of Wisconsin. Additional support provided by the National Center of Ecological Analysis and Synthesis. http://futureclimates.conservation.org (accessed: 06/03/19)sresa2_ncar_ccsm3_0_5km_2041-2060_debiasedmean_tas_av12_optWCSGeoTIFFLWP__sresa2_ncar_ccsm3_0_5km_2041-2060_debiasedmean_tas_av12_optRectifiedGridCoverage-180.0 -60.0180.00002016000002 90.00000840000001-180.0 -60.0180.00002016000002 90.00000840000001Average annual temperature for future climate using SRES A2 Scenario (2041 - 2060) from National Center for Atmospheric Research, PCM1Future climate projections from the World Climate Research Programme's (WCRP's) CMIP3 multi-model dataset downscaled using the Worldclim 2.5-minute 20th century climate dataset. The CMIP3 multi-model datasets were used for the IPCC 4th Assessment Report. The B1 scenario assumes the most ecologically friendly future. The A1B scenario assumes future energy sources will be balanced between fossil-intensive and non-fossil energy sources. The A2 scenario is characterized by a future world still heavily dependent on fossil fuel consumption. All models are historical and future climate simulations collected from leading modeling centers around the world. The original model simulations are collected and achieved by the Program for Climate Model Diagnosis and Intercomparison (PCMDI) to create the World Climate Research Programme's (WCRP's) phase 3 of the Coupled Model Intercomparison Project (CMIP3) multi-model dataset. The downscaled data were produced by Conservation International through collaboration with the Department of Geography, Center of Climatic Research, and Land Tenure Center at the University of Wisconsin and support from the National Center of Ecological Analysis and Synthesis. <br> Downscaled Future Climate Scenarios 2.5-minute resolution, 2009. This is a product of Conservation International based on techniques developed in collaboration with the Department of Geography, Center of Climatic Research, and Land Tenure Center at the University of Wisconsin. Additional support provided by the National Center of Ecological Analysis and Synthesis. http://futureclimates.conservation.org (accessed: 06/03/19)sresa2_ncar_pcm1_5km_2041-2060_debiasedmean_tas_av12_optWCSGeoTIFFLWP__sresa2_ncar_pcm1_5km_2041-2060_debiasedmean_tas_av12_optRectifiedGridCoverage-180.0 -60.0180.00002016000002 90.00000840000001-180.0 -60.0180.00002016000002 90.00000840000001Average temperature of the 3 coldest months (1991-2020)Average temperature of the 3 coldest months per pixel in °Celsius across the period 1991-2020. Calculated at UNEP/GRID-Geneva in R based on CRU TS 4.07 data available <a href="https://crudata.uea.ac.uk/cru/data/hrg/cru_ts_4.07/cruts.2304141047.v4.07/tmp/"target=_blank>here</a> <br> Read release notes of CRU TS 4.07 at <a href="https://crudata.uea.ac.uk/cru/data/hrg/cru_ts_4.07/Release_Notes_CRU_TS_4.07.txt"target=_blank>this link</a> <br><br> Credits: <br> - <a href="https://sites.uea.ac.uk/cru/"target=_blank>Climatic Research Unit (University of East Anglia) and Met Office</a><br> - <a href="https://doi.org/10.1038/s41597-020-0453-3"target=_blank>Harris, I., Osborn, T.J., Jones, P. et al. Version 4 of the CRU TS monthly high-resolution gridded multivariate climate dataset.Sci Data 7, 109 (2020).</a>cru_tmp_91_20_3coldest_meanWCSGeoTIFFwesr_climat__cru_tmp_91_20_3coldest_meanRectifiedGridCoverage-180.0 -90.0180.0 90.0-180.0 -90.0180.0 90.0Average temperature of the 3 hottest months (1991-2020)Average temperature of the 3 hottest months per pixel in °Celsius across the period 1991-2020. Calculated at UNEP/GRID-Geneva in R based on CRU TS 4.07 data available <a href="https://crudata.uea.ac.uk/cru/data/hrg/cru_ts_4.07/cruts.2304141047.v4.07/tmp/"target=_blank>here</a> <br> Read release notes of CRU TS 4.07 at <a href="https://crudata.uea.ac.uk/cru/data/hrg/cru_ts_4.07/Release_Notes_CRU_TS_4.07.txt"target=_blank>this link</a> <br><br> Credits: <br> - <a href="https://sites.uea.ac.uk/cru/"target=_blank>Climatic Research Unit (University of East Anglia) and Met Office</a><br> - <a href="https://doi.org/10.1038/s41597-020-0453-3"target=_blank>Harris, I., Osborn, T.J., Jones, P. et al. Version 4 of the CRU TS monthly high-resolution gridded multivariate climate dataset.Sci Data 7, 109 (2020).</a>cru_tmp_91_20_3hottest_meanWCSGeoTIFFwesr_climat__cru_tmp_91_20_3hottest_meanRectifiedGridCoverage-180.0 -90.0180.0 90.0-180.0 -90.0180.0 90.0Average year precipitations (1991-2000)Average year precipitations in mm across the period 1991-2000. Calculated at UNEP/GRID-Geneva in R based on CRU TS 4.07 data available <a href="https://crudata.uea.ac.uk/cru/data/hrg/cru_ts_4.07/cruts.2304141047.v4.07/pre/"target=_blank>here</a> <br> Read release notes of CRU TS 4.07 at <a href="https://crudata.uea.ac.uk/cru/data/hrg/cru_ts_4.07/Release_Notes_CRU_TS_4.07.txt"target=_blank>this link</a> <br><br> Credits: <br> - <a href="https://sites.uea.ac.uk/cru/"target=_blank>Climatic Research Unit (University of East Anglia) and Met Office</a><br> - <a href="https://doi.org/10.1038/s41597-020-0453-3"target=_blank>Harris, I., Osborn, T.J., Jones, P. et al. Version 4 of the CRU TS monthly high-resolution gridded multivariate climate dataset.Sci Data 7, 109 (2020).</a>cru_pre_91_00_meanWCSGeoTIFFwesr_climat__cru_pre_91_00_meanRectifiedGridCoverage-180.0 -90.0180.0 90.0-180.0 -90.0180.0 90.0Average year precipitations (1991-2020)Average year precipitations in mm across the period 1991-2020. Calculated at UNEP/GRID-Geneva in R based on CRU TS 4.07 data available <a href="https://crudata.uea.ac.uk/cru/data/hrg/cru_ts_4.07/cruts.2304141047.v4.07/pre/"target=_blank>here</a> <br> Read release notes of CRU TS 4.07 at <a href="https://crudata.uea.ac.uk/cru/data/hrg/cru_ts_4.07/Release_Notes_CRU_TS_4.07.txt"target=_blank>this link</a> <br><br> Credits: <br> - <a href="https://sites.uea.ac.uk/cru/"target=_blank>Climatic Research Unit (University of East Anglia) and Met Office</a><br> - <a href="https://doi.org/10.1038/s41597-020-0453-3"target=_blank>Harris, I., Osborn, T.J., Jones, P. et al. Version 4 of the CRU TS monthly high-resolution gridded multivariate climate dataset.Sci Data 7, 109 (2020).</a>cru_pre_91_20_meanWCSGeoTIFFwesr_climat__cru_pre_91_20_meanRectifiedGridCoverage-180.0 -90.0180.0 90.0-180.0 -90.0180.0 90.0Average year precipitations (1991-2022)Average year precipitations in mm across the period 1991-2022. Calculated at UNEP/GRID-Geneva in R based on CRU TS 4.07 data available <a href="https://crudata.uea.ac.uk/cru/data/hrg/cru_ts_4.07/cruts.2304141047.v4.07/pre/"target=_blank>here</a> <br> Read release notes of CRU TS 4.07 at <a href="https://crudata.uea.ac.uk/cru/data/hrg/cru_ts_4.07/Release_Notes_CRU_TS_4.07.txt"target=_blank>this link</a> <br><br> Credits: <br> - <a href="https://sites.uea.ac.uk/cru/"target=_blank>Climatic Research Unit (University of East Anglia) and Met Office</a><br> - <a href="https://doi.org/10.1038/s41597-020-0453-3"target=_blank>Harris, I., Osborn, T.J., Jones, P. et al. Version 4 of the CRU TS monthly high-resolution gridded multivariate climate dataset.Sci Data 7, 109 (2020).</a>cru_pre_91_22_meanWCSGeoTIFFwesr_climat__cru_pre_91_22_meanRectifiedGridCoverage-180.0 -90.0180.0 90.0-180.0 -90.0180.0 90.0Average year precipitations (2001-2010)Average year precipitations in mm across the period 2001-2010. Calculated at UNEP/GRID-Geneva in R based on CRU TS 4.07 data available <a href="https://crudata.uea.ac.uk/cru/data/hrg/cru_ts_4.07/cruts.2304141047.v4.07/pre/"target=_blank>here</a> <br> Read release notes of CRU TS 4.07 at <a href="https://crudata.uea.ac.uk/cru/data/hrg/cru_ts_4.07/Release_Notes_CRU_TS_4.07.txt"target=_blank>this link</a> <br><br> Credits: <br> - <a href="https://sites.uea.ac.uk/cru/"target=_blank>Climatic Research Unit (University of East Anglia) and Met Office</a><br> - <a href="https://doi.org/10.1038/s41597-020-0453-3"target=_blank>Harris, I., Osborn, T.J., Jones, P. et al. Version 4 of the CRU TS monthly high-resolution gridded multivariate climate dataset.Sci Data 7, 109 (2020).</a>cru_pre_01_10_meanWCSGeoTIFFwesr_climat__cru_pre_01_10_meanRectifiedGridCoverage-180.0 -90.0180.0 90.0-180.0 -90.0180.0 90.0Average year precipitations (2011-2020)Average year precipitations in mm across the period 2011-2020. Calculated at UNEP/GRID-Geneva in R based on CRU TS 4.07 data available <a href="https://crudata.uea.ac.uk/cru/data/hrg/cru_ts_4.07/cruts.2304141047.v4.07/pre/"target=_blank>here</a> <br> Read release notes of CRU TS 4.07 at <a href="https://crudata.uea.ac.uk/cru/data/hrg/cru_ts_4.07/Release_Notes_CRU_TS_4.07.txt"target=_blank>this link</a> <br><br> Credits: <br> - <a href="https://sites.uea.ac.uk/cru/"target=_blank>Climatic Research Unit (University of East Anglia) and Met Office</a><br> - <a href="https://doi.org/10.1038/s41597-020-0453-3"target=_blank>Harris, I., Osborn, T.J., Jones, P. et al. Version 4 of the CRU TS monthly high-resolution gridded multivariate climate dataset.Sci Data 7, 109 (2020).</a>cru_pre_11_20_meanWCSGeoTIFFwesr_climat__cru_pre_11_20_meanRectifiedGridCoverage-180.0 -90.0180.0 90.0-180.0 -90.0180.0 90.0Average year precipitations (2021-2022)Average year precipitations in mm across the period 2021-2022. Calculated at UNEP/GRID-Geneva in R based on CRU TS 4.07 data available <a href="https://crudata.uea.ac.uk/cru/data/hrg/cru_ts_4.07/cruts.2304141047.v4.07/pre/"target=_blank>here</a> <br> Read release notes of CRU TS 4.07 at <a href="https://crudata.uea.ac.uk/cru/data/hrg/cru_ts_4.07/Release_Notes_CRU_TS_4.07.txt"target=_blank>this link</a> <br><br> Credits: <br> - <a href="https://sites.uea.ac.uk/cru/"target=_blank>Climatic Research Unit (University of East Anglia) and Met Office</a><br> - <a href="https://doi.org/10.1038/s41597-020-0453-3"target=_blank>Harris, I., Osborn, T.J., Jones, P. et al. Version 4 of the CRU TS monthly high-resolution gridded multivariate climate dataset.Sci Data 7, 109 (2020).</a>cru_pre_21_22_meanWCSGeoTIFFwesr_climat__cru_pre_21_22_meanRectifiedGridCoverage-180.0 -90.0180.0 90.0-180.0 -90.0180.0 90.0Average year precipitations anomalies (1991-2000) against 1901-1920Average year precipitations anomalies in mm across the period 1991-2000 calculated over the average precipitations per year in the period 1901-1920 (as close as possible to the pre-industrial era). Calculated at UNEP/GRID-Geneva in R based on CRU TS 4.07 data available <a href="https://crudata.uea.ac.uk/cru/data/hrg/cru_ts_4.07/cruts.2304141047.v4.07/pre/"target=_blank>here</a> <br> Read release notes of CRU TS 4.07 at <a href="https://crudata.uea.ac.uk/cru/data/hrg/cru_ts_4.07/Release_Notes_CRU_TS_4.07.txt"target=_blank>this link</a> <br><br> Credits: <br> - <a href="https://sites.uea.ac.uk/cru/"target=_blank>Climatic Research Unit (University of East Anglia) and Met Office</a><br> - <a href="https://doi.org/10.1038/s41597-020-0453-3"target=_blank>Harris, I., Osborn, T.J., Jones, P. et al. Version 4 of the CRU TS monthly high-resolution gridded multivariate climate dataset.Sci Data 7, 109 (2020).</a>cru_pre_91_00_anom_1901_1920WCSGeoTIFFwesr_climat__cru_pre_91_00_anom_1901_1920RectifiedGridCoverage-180.0 -90.0180.0 90.0-180.0 -90.0180.0 90.0Average year precipitations anomalies (1991-2000) over 1991-2020 climatologyAverage year precipitations anomalies in mm across the period 1991-2000 calculated over the average precipitations per year in the period 1991-2020 (climatology). Calculated at UNEP/GRID-Geneva in R based on CRU TS 4.07 data available <a href="https://crudata.uea.ac.uk/cru/data/hrg/cru_ts_4.07/cruts.2304141047.v4.07/pre/"target=_blank>here</a> <br> Read release notes of CRU TS 4.07 at <a href="https://crudata.uea.ac.uk/cru/data/hrg/cru_ts_4.07/Release_Notes_CRU_TS_4.07.txt"target=_blank>this link</a> <br><br> Credits: <br> - <a href="https://sites.uea.ac.uk/cru/"target=_blank>Climatic Research Unit (University of East Anglia) and Met Office</a><br> - <a href="https://doi.org/10.1038/s41597-020-0453-3"target=_blank>Harris, I., Osborn, T.J., Jones, P. et al. Version 4 of the CRU TS monthly high-resolution gridded multivariate climate dataset.Sci Data 7, 109 (2020).</a>cru_pre_91_00_anom9120WCSGeoTIFFwesr_climat__cru_pre_91_00_anom9120RectifiedGridCoverage-180.0 -90.0180.0 90.0-180.0 -90.0180.0 90.0Average year precipitations anomalies (2001-2010) against 1901-1920Average year precipitations anomalies in mm across the period 2001-2010 calculated over the average precipitations per year in the period 1901-1920 (as close as possible to the pre-industrial era). Calculated at UNEP/GRID-Geneva in R based on CRU TS 4.07 data available <a href="https://crudata.uea.ac.uk/cru/data/hrg/cru_ts_4.07/cruts.2304141047.v4.07/pre/"target=_blank>here</a> <br> Read release notes of CRU TS 4.07 at <a href="https://crudata.uea.ac.uk/cru/data/hrg/cru_ts_4.07/Release_Notes_CRU_TS_4.07.txt"target=_blank>this link</a> <br><br> Credits: <br> - <a href="https://sites.uea.ac.uk/cru/"target=_blank>Climatic Research Unit (University of East Anglia) and Met Office</a><br> - <a href="https://doi.org/10.1038/s41597-020-0453-3"target=_blank>Harris, I., Osborn, T.J., Jones, P. et al. Version 4 of the CRU TS monthly high-resolution gridded multivariate climate dataset.Sci Data 7, 109 (2020).</a>cru_pre_01_10_anom_1901_1920WCSGeoTIFFwesr_climat__cru_pre_01_10_anom_1901_1920RectifiedGridCoverage-180.0 -90.0180.0 90.0-180.0 -90.0180.0 90.0Average year precipitations anomalies (2001-2010) over 1991-2020 climatologyAverage year precipitations anomalies in mm across the period 2001-2010 calculated over the average precipitations per year in the period 1991-2020 (climatology). Calculated at UNEP/GRID-Geneva in R based on CRU TS 4.07 data available <a href="https://crudata.uea.ac.uk/cru/data/hrg/cru_ts_4.07/cruts.2304141047.v4.07/pre/"target=_blank>here</a> <br> Read release notes of CRU TS 4.07 at <a href="https://crudata.uea.ac.uk/cru/data/hrg/cru_ts_4.07/Release_Notes_CRU_TS_4.07.txt"target=_blank>this link</a> <br><br> Credits: <br> - <a href="https://sites.uea.ac.uk/cru/"target=_blank>Climatic Research Unit (University of East Anglia) and Met Office</a><br> - <a href="https://doi.org/10.1038/s41597-020-0453-3"target=_blank>Harris, I., Osborn, T.J., Jones, P. et al. Version 4 of the CRU TS monthly high-resolution gridded multivariate climate dataset.Sci Data 7, 109 (2020).</a>cru_pre_01_10_anom9120WCSGeoTIFFwesr_climat__cru_pre_01_10_anom9120RectifiedGridCoverage-180.0 -90.0180.0 90.0-180.0 -90.0180.0 90.0Average year precipitations anomalies (2011-2020) against 1901-1920Average year precipitations anomalies in mm across the period 2011-2020 calculated over the average precipitations per year in the period 1901-1920 (as close as possible to the pre-industrial era). Calculated at UNEP/GRID-Geneva in R based on CRU TS 4.07 data available <a href="https://crudata.uea.ac.uk/cru/data/hrg/cru_ts_4.07/cruts.2304141047.v4.07/pre/"target=_blank>here</a> <br> Read release notes of CRU TS 4.07 at <a href="https://crudata.uea.ac.uk/cru/data/hrg/cru_ts_4.07/Release_Notes_CRU_TS_4.07.txt"target=_blank>this link</a> <br><br> Credits: <br> - <a href="https://sites.uea.ac.uk/cru/"target=_blank>Climatic Research Unit (University of East Anglia) and Met Office</a><br> - <a href="https://doi.org/10.1038/s41597-020-0453-3"target=_blank>Harris, I., Osborn, T.J., Jones, P. et al. Version 4 of the CRU TS monthly high-resolution gridded multivariate climate dataset.Sci Data 7, 109 (2020).</a>cru_pre_11_20_anom_1901_1920WCSGeoTIFFwesr_climat__cru_pre_11_20_anom_1901_1920RectifiedGridCoverage-180.0 -90.0180.0 90.0-180.0 -90.0180.0 90.0Average year precipitations anomalies (2011-2020) over 1991-2020 climatologyAverage year precipitations anomalies in mm across the period 2011-2020 calculated over the average precipitations per year in the period 1991-2020 (climatology). Calculated at UNEP/GRID-Geneva in R based on CRU TS 4.07 data available <a href="https://crudata.uea.ac.uk/cru/data/hrg/cru_ts_4.07/cruts.2304141047.v4.07/pre/"target=_blank>here</a> <br> Read release notes of CRU TS 4.07 at <a href="https://crudata.uea.ac.uk/cru/data/hrg/cru_ts_4.07/Release_Notes_CRU_TS_4.07.txt"target=_blank>this link</a> <br><br> Credits: <br> - <a href="https://sites.uea.ac.uk/cru/"target=_blank>Climatic Research Unit (University of East Anglia) and Met Office</a><br> - <a href="https://doi.org/10.1038/s41597-020-0453-3"target=_blank>Harris, I., Osborn, T.J., Jones, P. et al. Version 4 of the CRU TS monthly high-resolution gridded multivariate climate dataset.Sci Data 7, 109 (2020).</a>cru_pre_11_20_anom9120WCSGeoTIFFwesr_climat__cru_pre_11_20_anom9120RectifiedGridCoverage-180.0 -90.0180.0 90.0-180.0 -90.0180.0 90.0Average year precipitations anomalies (2021-2022) against 1901-1920Average year precipitations anomalies in mm across the period 2021-2022 calculated over the average precipitations per year in the period 1901-1920 (as close as possible to the pre-industrial era). Calculated at UNEP/GRID-Geneva in R based on CRU TS 4.07 data available <a href="https://crudata.uea.ac.uk/cru/data/hrg/cru_ts_4.07/cruts.2304141047.v4.07/pre/"target=_blank>here</a> <br> Read release notes of CRU TS 4.07 at <a href="https://crudata.uea.ac.uk/cru/data/hrg/cru_ts_4.07/Release_Notes_CRU_TS_4.07.txt"target=_blank>this link</a> <br><br> Credits: <br> - <a href="https://sites.uea.ac.uk/cru/"target=_blank>Climatic Research Unit (University of East Anglia) and Met Office</a><br> - <a href="https://doi.org/10.1038/s41597-020-0453-3"target=_blank>Harris, I., Osborn, T.J., Jones, P. et al. Version 4 of the CRU TS monthly high-resolution gridded multivariate climate dataset.Sci Data 7, 109 (2020).</a>cru_pre_21_22_anom_1901_1920WCSGeoTIFFwesr_climat__cru_pre_21_22_anom_1901_1920RectifiedGridCoverage-180.0 -90.0180.0 90.0-180.0 -90.0180.0 90.0Average year precipitations anomalies (2021-2022) over 1991-2020 climatologyAverage year precipitations anomalies in mm across the period 2021-2022 calculated over the average precipitations per year in the period 1991-2020 (climatology). Calculated at UNEP/GRID-Geneva in R based on CRU TS 4.07 data available <a href="https://crudata.uea.ac.uk/cru/data/hrg/cru_ts_4.07/cruts.2304141047.v4.07/pre/"target=_blank>here</a> <br> Read release notes of CRU TS 4.07 at <a href="https://crudata.uea.ac.uk/cru/data/hrg/cru_ts_4.07/Release_Notes_CRU_TS_4.07.txt"target=_blank>this link</a> <br><br> Credits: <br> - <a href="https://sites.uea.ac.uk/cru/"target=_blank>Climatic Research Unit (University of East Anglia) and Met Office</a><br> - <a href="https://doi.org/10.1038/s41597-020-0453-3"target=_blank>Harris, I., Osborn, T.J., Jones, P. et al. Version 4 of the CRU TS monthly high-resolution gridded multivariate climate dataset.Sci Data 7, 109 (2020).</a>cru_pre_21_22_anom9120WCSGeoTIFFwesr_climat__cru_pre_21_22_anom9120RectifiedGridCoverage-180.0 -90.0180.0 90.0-180.0 -90.0180.0 90.0Average year temperature (1991-2000)Average year temperature in °Celsius across the period 1991-2000. Calculated at UNEP/GRID-Geneva in R based on CRU TS 4.07 data available <a href="https://crudata.uea.ac.uk/cru/data/hrg/cru_ts_4.07/cruts.2304141047.v4.07/tmp/"target=_blank>here</a> <br> Read release notes of CRU TS 4.07 at <a href="https://crudata.uea.ac.uk/cru/data/hrg/cru_ts_4.07/Release_Notes_CRU_TS_4.07.txt"target=_blank>this link</a> <br><br> Credits: <br> - <a href="https://sites.uea.ac.uk/cru/"target=_blank>Climatic Research Unit (University of East Anglia) and Met Office</a><br> - <a href="https://doi.org/10.1038/s41597-020-0453-3"target=_blank>Harris, I., Osborn, T.J., Jones, P. et al. Version 4 of the CRU TS monthly high-resolution gridded multivariate climate dataset.Sci Data 7, 109 (2020).</a>cru_tmp_91_00_meanWCSGeoTIFFwesr_climat__cru_tmp_91_00_meanRectifiedGridCoverage-180.0 -90.0180.0 90.0-180.0 -90.0180.0 90.0Average year temperature (1991-2020)Average year temperature in °Celsius across the period 1991-2020. Calculated at UNEP/GRID-Geneva in R based on CRU TS 4.07 data available <a href="https://crudata.uea.ac.uk/cru/data/hrg/cru_ts_4.07/cruts.2304141047.v4.07/tmp/"target=_blank>here</a> <br> Read release notes of CRU TS 4.07 at <a href="https://crudata.uea.ac.uk/cru/data/hrg/cru_ts_4.07/Release_Notes_CRU_TS_4.07.txt"target=_blank>this link</a> <br><br> Credits: <br> - <a href="https://sites.uea.ac.uk/cru/"target=_blank>Climatic Research Unit (University of East Anglia) and Met Office</a><br> - <a href="https://doi.org/10.1038/s41597-020-0453-3"target=_blank>Harris, I., Osborn, T.J., Jones, P. et al. Version 4 of the CRU TS monthly high-resolution gridded multivariate climate dataset.Sci Data 7, 109 (2020).</a>cru_tmp_91_20_meanWCSGeoTIFFwesr_climat__cru_tmp_91_20_meanRectifiedGridCoverage-180.0 -90.0180.0 90.0-180.0 -90.0180.0 90.0Average year temperature (1991-2022)Average year temperature in °Celsius across the period 1991-2022. Calculated at UNEP/GRID-Geneva in R based on CRU TS 4.07 data available <a href="https://crudata.uea.ac.uk/cru/data/hrg/cru_ts_4.07/cruts.2304141047.v4.07/tmp/"target=_blank>here</a> <br> Read release notes of CRU TS 4.07 at <a href="https://crudata.uea.ac.uk/cru/data/hrg/cru_ts_4.07/Release_Notes_CRU_TS_4.07.txt"target=_blank>this link</a> <br><br> Credits: <br> - <a href="https://sites.uea.ac.uk/cru/"target=_blank>Climatic Research Unit (University of East Anglia) and Met Office</a><br> - <a href="https://doi.org/10.1038/s41597-020-0453-3"target=_blank>Harris, I., Osborn, T.J., Jones, P. et al. Version 4 of the CRU TS monthly high-resolution gridded multivariate climate dataset.Sci Data 7, 109 (2020).</a>cru_tmp_91_22_meanWCSGeoTIFFwesr_climat__cru_tmp_91_22_meanRectifiedGridCoverage-180.0 -90.0180.0 90.0-180.0 -90.0180.0 90.0Average year temperature (2001-2010)Average year temperature in °Celsius across the period 2001-2010. Calculated at UNEP/GRID-Geneva in R based on CRU TS 4.07 data available <a href="https://crudata.uea.ac.uk/cru/data/hrg/cru_ts_4.07/cruts.2304141047.v4.07/tmp/"target=_blank>here</a> <br> Read release notes of CRU TS 4.07 at <a href="https://crudata.uea.ac.uk/cru/data/hrg/cru_ts_4.07/Release_Notes_CRU_TS_4.07.txt"target=_blank>this link</a> <br><br> Credits: <br> - <a href="https://sites.uea.ac.uk/cru/"target=_blank>Climatic Research Unit (University of East Anglia) and Met Office</a><br> - <a href="https://doi.org/10.1038/s41597-020-0453-3"target=_blank>Harris, I., Osborn, T.J., Jones, P. et al. Version 4 of the CRU TS monthly high-resolution gridded multivariate climate dataset.Sci Data 7, 109 (2020).</a>cru_tmp_01_10_meanWCSGeoTIFFwesr_climat__cru_tmp_01_10_meanRectifiedGridCoverage-180.0 -90.0180.0 90.0-180.0 -90.0180.0 90.0Average year temperature (2011-2020)Average year temperature in °Celsius across the period 2011-2020. Calculated at UNEP/GRID-Geneva in R based on CRU TS 4.07 data available <a href="https://crudata.uea.ac.uk/cru/data/hrg/cru_ts_4.07/cruts.2304141047.v4.07/tmp/"target=_blank>here</a> <br> Read release notes of CRU TS 4.07 at <a href="https://crudata.uea.ac.uk/cru/data/hrg/cru_ts_4.07/Release_Notes_CRU_TS_4.07.txt"target=_blank>this link</a> <br><br> Credits: <br> - <a href="https://sites.uea.ac.uk/cru/"target=_blank>Climatic Research Unit (University of East Anglia) and Met Office</a><br> - <a href="https://doi.org/10.1038/s41597-020-0453-3"target=_blank>Harris, I., Osborn, T.J., Jones, P. et al. Version 4 of the CRU TS monthly high-resolution gridded multivariate climate dataset.Sci Data 7, 109 (2020).</a>cru_tmp_11_20_meanWCSGeoTIFFwesr_climat__cru_tmp_11_20_meanRectifiedGridCoverage-180.0 -90.0180.0 90.0-180.0 -90.0180.0 90.0Average year temperature (2021-2022)Average year temperature in °Celsius across the period 2021-2022. Calculated at UNEP/GRID-Geneva in R based on CRU TS 4.07 data available <a href="https://crudata.uea.ac.uk/cru/data/hrg/cru_ts_4.07/cruts.2304141047.v4.07/tmp/"target=_blank>here</a> <br> Read release notes of CRU TS 4.07 at <a href="https://crudata.uea.ac.uk/cru/data/hrg/cru_ts_4.07/Release_Notes_CRU_TS_4.07.txt"target=_blank>this link</a> <br><br> Credits: <br> - <a href="https://sites.uea.ac.uk/cru/"target=_blank>Climatic Research Unit (University of East Anglia) and Met Office</a><br> - <a href="https://doi.org/10.1038/s41597-020-0453-3"target=_blank>Harris, I., Osborn, T.J., Jones, P. et al. Version 4 of the CRU TS monthly high-resolution gridded multivariate climate dataset.Sci Data 7, 109 (2020).</a>cru_tmp_21_22_meanWCSGeoTIFFwesr_climat__cru_tmp_21_22_meanRectifiedGridCoverage-180.0 -90.0180.0 90.0-180.0 -90.0180.0 90.0Average year temperature anomalies (1991-2000) against 1901-1920Average year temperature anomalies in °Celsius across the period 1991-2000 calculated over the average temperatures per year in the period 1901-1920 (as close as possible to the pre-industrial era). Calculated at UNEP/GRID-Geneva in R based on CRU TS 4.07 data available <a href="https://crudata.uea.ac.uk/cru/data/hrg/cru_ts_4.07/cruts.2304141047.v4.07/pre/"target=_blank>here</a> <br> Read release notes of CRU TS 4.07 at <a href="https://crudata.uea.ac.uk/cru/data/hrg/cru_ts_4.07/Release_Notes_CRU_TS_4.07.txt"target=_blank>this link</a> <br><br> Credits: <br> - <a href="https://sites.uea.ac.uk/cru/"target=_blank>Climatic Research Unit (University of East Anglia) and Met Office</a><br> - <a href="https://doi.org/10.1038/s41597-020-0453-3"target=_blank>Harris, I., Osborn, T.J., Jones, P. et al. Version 4 of the CRU TS monthly high-resolution gridded multivariate climate dataset.Sci Data 7, 109 (2020).</a>cru_tmp_91_00_anom_1901_1920WCSGeoTIFFwesr_climat__cru_tmp_91_00_anom_1901_1920RectifiedGridCoverage-180.0 -90.0180.0 90.0-180.0 -90.0180.0 90.0Average year temperature anomalies (1991-2000) over 1991-2020 climatologyAverage year temperature anomalies in °Celsius across the period 1991-2000 calculated over the average temperatures per year in the period 1991-2020 (climatology). Calculated at UNEP/GRID-Geneva in R based on CRU TS 4.07 data available <a href="https://crudata.uea.ac.uk/cru/data/hrg/cru_ts_4.07/cruts.2304141047.v4.07/tmp/"target=_blank>here</a> <br> Read release notes of CRU TS 4.07 at <a href="https://crudata.uea.ac.uk/cru/data/hrg/cru_ts_4.07/Release_Notes_CRU_TS_4.07.txt"target=_blank>this link</a> <br><br> Credits: <br> - <a href="https://sites.uea.ac.uk/cru/"target=_blank>Climatic Research Unit (University of East Anglia) and Met Office</a><br> - <a href="https://doi.org/10.1038/s41597-020-0453-3"target=_blank>Harris, I., Osborn, T.J., Jones, P. et al. Version 4 of the CRU TS monthly high-resolution gridded multivariate climate dataset.Sci Data 7, 109 (2020).</a>cru_tmp_91_00_anom9120WCSGeoTIFFwesr_climat__cru_tmp_91_00_anom9120RectifiedGridCoverage-180.0 -90.0180.0 90.0-180.0 -90.0180.0 90.0Average year temperature anomalies (2001-2010) against 1901-1920Average year temperature anomalies in °Celsius across the period 2001-2010 calculated over the average temperatures per year in the period 1901-1920 (as close as possible to the pre-industrial era). Calculated at UNEP/GRID-Geneva in R based on CRU TS 4.07 data available <a href="https://crudata.uea.ac.uk/cru/data/hrg/cru_ts_4.07/cruts.2304141047.v4.07/pre/"target=_blank>here</a> <br> Read release notes of CRU TS 4.07 at <a href="https://crudata.uea.ac.uk/cru/data/hrg/cru_ts_4.07/Release_Notes_CRU_TS_4.07.txt"target=_blank>this link</a> <br><br> Credits: <br> - <a href="https://sites.uea.ac.uk/cru/"target=_blank>Climatic Research Unit (University of East Anglia) and Met Office</a><br> - <a href="https://doi.org/10.1038/s41597-020-0453-3"target=_blank>Harris, I., Osborn, T.J., Jones, P. et al. Version 4 of the CRU TS monthly high-resolution gridded multivariate climate dataset.Sci Data 7, 109 (2020).</a>cru_tmp_01_10_anom_1901_1920WCSGeoTIFFwesr_climat__cru_tmp_01_10_anom_1901_1920RectifiedGridCoverage-180.0 -90.0180.0 90.0-180.0 -90.0180.0 90.0Average year temperature anomalies (2001-2010) over 1991-2020 climatologyAverage year temperature anomalies in °Celsius across the period 2001-2010 calculated over the average temperatures per year in the period 1991-2020 (climatology). Calculated at UNEP/GRID-Geneva in R based on CRU TS 4.07 data available <a href="https://crudata.uea.ac.uk/cru/data/hrg/cru_ts_4.07/cruts.2304141047.v4.07/tmp/"target=_blank>here</a> <br> Read release notes of CRU TS 4.07 at <a href="https://crudata.uea.ac.uk/cru/data/hrg/cru_ts_4.07/Release_Notes_CRU_TS_4.07.txt"target=_blank>this link</a> <br><br> Credits: <br> - <a href="https://sites.uea.ac.uk/cru/"target=_blank>Climatic Research Unit (University of East Anglia) and Met Office</a><br> - <a href="https://doi.org/10.1038/s41597-020-0453-3"target=_blank>Harris, I., Osborn, T.J., Jones, P. et al. Version 4 of the CRU TS monthly high-resolution gridded multivariate climate dataset.Sci Data 7, 109 (2020).</a>cru_tmp_01_10_anom9120WCSGeoTIFFwesr_climat__cru_tmp_01_10_anom9120RectifiedGridCoverage-180.0 -90.0180.0 90.0-180.0 -90.0180.0 90.0Average year temperature anomalies (2011-2020) against 1901-1920Average year temperature anomalies in °Celsius across the period 2011-2020 calculated over the average temperatures per year in the period 1901-1920 (as close as possible to the pre-industrial era). Calculated at UNEP/GRID-Geneva in R based on CRU TS 4.07 data available <a href="https://crudata.uea.ac.uk/cru/data/hrg/cru_ts_4.07/cruts.2304141047.v4.07/pre/"target=_blank>here</a> <br> Read release notes of CRU TS 4.07 at <a href="https://crudata.uea.ac.uk/cru/data/hrg/cru_ts_4.07/Release_Notes_CRU_TS_4.07.txt"target=_blank>this link</a> <br><br> Credits: <br> - <a href="https://sites.uea.ac.uk/cru/"target=_blank>Climatic Research Unit (University of East Anglia) and Met Office</a><br> - <a href="https://doi.org/10.1038/s41597-020-0453-3"target=_blank>Harris, I., Osborn, T.J., Jones, P. et al. Version 4 of the CRU TS monthly high-resolution gridded multivariate climate dataset.Sci Data 7, 109 (2020).</a>cru_tmp_11_20_anom_1901_1920WCSGeoTIFFwesr_climat__cru_tmp_11_20_anom_1901_1920RectifiedGridCoverage-180.0 -90.0180.0 90.0-180.0 -90.0180.0 90.0Average year temperature anomalies (2011-2020) over 1991-2020 climatologyAverage year temperature anomalies in °Celsius across the period 2011-2020 calculated over the average temperatures per year in the period 1991-2020 (climatology). Calculated at UNEP/GRID-Geneva in R based on CRU TS 4.07 data available <a href="https://crudata.uea.ac.uk/cru/data/hrg/cru_ts_4.07/cruts.2304141047.v4.07/tmp/"target=_blank>here</a> <br> Read release notes of CRU TS 4.07 at <a href="https://crudata.uea.ac.uk/cru/data/hrg/cru_ts_4.07/Release_Notes_CRU_TS_4.07.txt"target=_blank>this link</a> <br><br> Credits: <br> - <a href="https://sites.uea.ac.uk/cru/"target=_blank>Climatic Research Unit (University of East Anglia) and Met Office</a><br> - <a href="https://doi.org/10.1038/s41597-020-0453-3"target=_blank>Harris, I., Osborn, T.J., Jones, P. et al. Version 4 of the CRU TS monthly high-resolution gridded multivariate climate dataset.Sci Data 7, 109 (2020).</a>cru_tmp_11_20_anom9120WCSGeoTIFFwesr_climat__cru_tmp_11_20_anom9120RectifiedGridCoverage-180.0 -90.0180.0 90.0-180.0 -90.0180.0 90.0Average year temperature anomalies (2021-2022) against 1901-1920Average year temperature anomalies in °Celsius across the period 2021-2022 calculated over the average temperatures per year in the period 1901-1920 (as close as possible to the pre-industrial era). Calculated at UNEP/GRID-Geneva in R based on CRU TS 4.07 data available <a href="https://crudata.uea.ac.uk/cru/data/hrg/cru_ts_4.07/cruts.2304141047.v4.07/pre/"target=_blank>here</a> <br> Read release notes of CRU TS 4.07 at <a href="https://crudata.uea.ac.uk/cru/data/hrg/cru_ts_4.07/Release_Notes_CRU_TS_4.07.txt"target=_blank>this link</a> <br><br> Credits: <br> - <a href="https://sites.uea.ac.uk/cru/"target=_blank>Climatic Research Unit (University of East Anglia) and Met Office</a><br> - <a href="https://doi.org/10.1038/s41597-020-0453-3"target=_blank>Harris, I., Osborn, T.J., Jones, P. et al. Version 4 of the CRU TS monthly high-resolution gridded multivariate climate dataset.Sci Data 7, 109 (2020).</a>cru_tmp_21_22_anom_1901_1920WCSGeoTIFFwesr_climat__cru_tmp_21_22_anom_1901_1920RectifiedGridCoverage-180.0 -90.0180.0 90.0-180.0 -90.0180.0 90.0Average year temperature anomalies (2021-2022) over 1991-2020 climatologyAverage year temperature anomalies in °Celsius across the period 2021-2022 calculated over the average temperatures per year in the period 1991-2020 (climatology). Calculated at UNEP/GRID-Geneva in R based on CRU TS 4.07 data available <a href="https://crudata.uea.ac.uk/cru/data/hrg/cru_ts_4.07/cruts.2304141047.v4.07/tmp/"target=_blank>here</a> <br> Read release notes of CRU TS 4.07 at <a href="https://crudata.uea.ac.uk/cru/data/hrg/cru_ts_4.07/Release_Notes_CRU_TS_4.07.txt"target=_blank>this link</a> <br><br> Credits: <br> - <a href="https://sites.uea.ac.uk/cru/"target=_blank>Climatic Research Unit (University of East Anglia) and Met Office</a><br> - <a href="https://doi.org/10.1038/s41597-020-0453-3"target=_blank>Harris, I., Osborn, T.J., Jones, P. et al. Version 4 of the CRU TS monthly high-resolution gridded multivariate climate dataset.Sci Data 7, 109 (2020).</a>cru_tmp_21_22_anom9120WCSGeoTIFFwesr_climat__cru_tmp_21_22_anom9120RectifiedGridCoverage-180.0 -90.0180.0 90.0-180.0 -90.0180.0 90.0Ba200702Ba200702WCSGeoTIFFMAPX__Ba200702RectifiedGridCoverage0.166015625 11.695312515.995117188 23.5253906250.166015625 11.695312515.995117188 23.525390625Ba200804Ba200804WCSGeoTIFFMAPX__Ba200804RectifiedGridCoverage0.166015625 11.695312515.995117188 23.5253906250.166015625 11.695312515.995117188 23.525390625Bare Ground Change 1982 - 2016This analysis of 35 years’ worth of satellite data (at approximately 25 square kilometer resolution at the equator) provides a comprehensive record of global land-change dynamics during the period 1982–2016. Contrary to the prevailing view that forest area has declined globally — tree cover has increased by 2.24 million km2 (+7.1% relative to the 1982 level), largely the result of a net loss in the tropics being outweighed by a net gain in the extratropics. Global bare ground cover has decreased by 1.16 million km2 (−3.1%), most notably in agricultural regions in Asia. Of all land changes, 60% are associated with direct human activities and 40% with indirect drivers such as climate change. Land-use change exhibits regional dominance, including tropical deforestation and agricultural expansion, temperate reforestation or afforestation, cropland intensification and urbanization. Consistently across all climate domains, montane systems have gained tree cover and many arid and semi-arid ecosystems have lost vegetation cover.<br><br>For full details see: <a href="https://doi.org/10.1038/s41586-018-0411-9">Song, X.-P., Hansen, M.C., Stehman, S.V., Potapov, P.V., Tyukavina, A., Vermote, E.F., Townshend, J.R., 2018. Global land change from 1982 to 2016. Nature 1</a><br/>.Bare_ground_change_1982-2016WCSGeoTIFFMapX_UNDP__Bare_ground_change_1982-2016RectifiedGridCoverage-180.0 -90.0180.0 90.0-180.0 -90.0180.0 90.0Bare Soil Index in Keur Massar Senegal (2025)This layer shows the distribution of bare soil in Keur Massar (Dakar, Senegal) derived from Sentinel-2 imagery at 10 m spatial resolution. The satellite images were collected from Copernicus Browser (open access) on the 16th of April 2025. The Bare Soil Index (BSI) was calculated using the blue, red, near-infrared, and shortwave-infrared bands of Sentinel-2. Bare soil appears on the map starting from BSI values of 0.14, representing low bare soil presence, and gradually increases up to 0.3, representing high bare soil presence. This layer serves as an input for the project, providing baseline information on bare soil distribution to support the susceptibility assessment of illegal dumping sites.Solid Waste ManagementRemote SensingGISCitizen ScienceMulti-Criteria AnalysisIllegal Dumpingdakar_waste__BSIRectifiedGridCoverage-17.382029187007333 14.733719739640827-17.2719124771499 14.84106039798925-17.382029187007333 14.733719739640827-17.2719124771499 14.84106039798925Bhutan - Carbon Sequestration (upper) 2010Carbon sequestration map for Bhutan simulated for 2010 following a Business As Usual (BAU) scenario using a potential carbon sequestration value of 394.74 ton/ha for forests including carbon in biomass and soil.tot_C_cur_3857_optWCSGeoTIFFTEEB__BHU_carbonSeq_upper_2010RectifiedGridCoverage9877771.0709 3084170.70771.02577605154E7 3280281.563388.73352726023123 26.68499377382725592.1470305182156 28.248035723321824Bhutan - Carbon Sequestration (upper) 2030Carbon sequestration map for Bhutan simulated for 2030 following a Business As Usual (BAU) scenario using a potential carbon sequestration value of 394.74 ton/ha for forests including carbon in biomass and soil.tot_C_fut_3857_optWCSGeoTIFFTEEB__BHU_carbonSeq_upper_2030RectifiedGridCoverage9877771.0709 3084170.70771.02577605154E7 3280281.563388.73352726023123 26.68499377382725592.1470305182156 28.248035723321824Bhutan - Water Yield 2010Layer received from TEEB partner in Bhutan in Q2 2020.wateryeild_2010_wgs_optWCSGeoTIFFTEEB__BHU_Water_yield_2010RectifiedGridCoverage88.737534326 26.68511015992.146924212 28.24531426888.737534326 26.68511015992.146924212 28.245314268Bhutan - Water Yield 2030Layer received from TEEB partner in Bhutan Q2 2020wateryeild_2030_wgs_optWCSGeoTIFFTEEB__BHU_Water_yield_2030RectifiedGridCoverage88.737534326 26.68511015992.146924212 28.24531426888.737534326 26.68511015992.146924212 28.245314268Biodiversity Intactness IndexThe Biodiversity Intactness Index is the modeled average abundance of originally-present species, relative to their abundance in an intact ecosystem. Please refer to Newbold et al. (2016) for all details. Citations: Article: Newbold T, Hudson LN, Arnell AP, Contu S, De Palma A, Ferrier S, Hill SL, Hoskins AJ, Lysenko I, Phillips HR, Burton VJ, Chng CW, Emerson S, Gao D, Pask-Hale G, Hutton J, Jung M, Sanchez-Ortiz K, Simmons BI, Whitmee S, Zhang H, Scharlemann JP, Purvis A. Has land use pushed terrestrial biodiversity beyond the planetary boundary? A global assessment. Science. 2016 Jul 15;353(6296):288-91. doi: 10.1126/science.aaf2201. PMID: 27418509. Dataset Tim Newbold; Lawrence Hudson; Andy Arnell; Sara Contu et al. (2016). Global map of the Biodiversity Intactness Index, from Newbold et al. (2016) Science [Data set]. Natural History Museum. https://doi.org/10.5519/0009936biodiv_intact_indWCSGeoTIFFwesr_biodiversity__biodiv_intact_indRectifiedGridCoverage-180.0 -60.0180.0 90.0-180.0 -60.0180.0 90.0Burned area 2003This layer was produced by UNEP/GRID-Geneva in December 2023.burned areafireunccd__barea2003RectifiedGridCoverage-180.0 -90.0180.0 90.0-180.0 -90.0180.0 90.0Burned area 2004This layer was produced by UNEP/GRID-Geneva in December 2023.burned areafireunccd__barea2004RectifiedGridCoverage-180.0 -90.0180.0 90.0-180.0 -90.0180.0 90.0Burned area 2005This layer was produced by UNEP/GRID-Geneva in December 2023.burned areafireunccd__barea2005RectifiedGridCoverage-180.0 -90.0180.0 90.0-180.0 -90.0180.0 90.0Burned area 2006This layer was produced by UNEP/GRID-Geneva in December 2023.burned areafireunccd__barea2006RectifiedGridCoverage-180.0 -90.0180.0 90.0-180.0 -90.0180.0 90.0Burned area 2007This layer was produced by UNEP/GRID-Geneva in December 2023.burned areafireunccd__barea2007RectifiedGridCoverage-180.0 -90.0180.0 90.0-180.0 -90.0180.0 90.0Burned area 2008This layer was produced by UNEP/GRID-Geneva in December 2023.burned areafireunccd__barea2008RectifiedGridCoverage-180.0 -90.0180.0 90.0-180.0 -90.0180.0 90.0Burned area 2009This layer was produced by UNEP/GRID-Geneva in December 2023.burned areafireunccd__barea2009RectifiedGridCoverage-180.0 -90.0180.0 90.0-180.0 -90.0180.0 90.0Burned area 2010This layer was produced by UNEP/GRID-Geneva in December 2023.burned areafireunccd__barea2010RectifiedGridCoverage-180.0 -90.0180.0 90.0-180.0 -90.0180.0 90.0Burned area 2011This layer was produced by UNEP/GRID-Geneva in December 2023.burned areafireunccd__barea2011RectifiedGridCoverage-180.0 -90.0180.0 90.0-180.0 -90.0180.0 90.0Burned area 2012This layer was produced by UNEP/GRID-Geneva in December 2023.burned areafireunccd__barea2012RectifiedGridCoverage-180.0 -90.0180.0 90.0-180.0 -90.0180.0 90.0Burned area 2013This layer was produced by UNEP/GRID-Geneva in December 2023.burned areafireunccd__barea2013RectifiedGridCoverage-180.0 -90.0180.0 90.0-180.0 -90.0180.0 90.0Burned area 2014This layer was produced by UNEP/GRID-Geneva in December 2023.burned areafireunccd__barea2014RectifiedGridCoverage-180.0 -90.0180.0 90.0-180.0 -90.0180.0 90.0Burned area 2015This layer was produced by UNEP/GRID-Geneva in December 2023.burned areafireunccd__barea2015RectifiedGridCoverage-180.0 -90.0180.0 90.0-180.0 -90.0180.0 90.0Burned area 2016This layer was produced by UNEP/GRID-Geneva in December 2023.burned areafireunccd__barea2016RectifiedGridCoverage-180.0 -90.0180.0 90.0-180.0 -90.0180.0 90.0Burned area 2017This layer was produced by UNEP/GRID-Geneva in December 2023.burned areafireunccd__barea2017RectifiedGridCoverage-180.0 -90.0180.0 90.0-180.0 -90.0180.0 90.0Burned area 2018This layer was produced by UNEP/GRID-Geneva in December 2023.burned areafireunccd__barea2018RectifiedGridCoverage-180.0 -90.0180.0 90.0-180.0 -90.0180.0 90.0Burned area 2019This layer was produced by UNEP/GRID-Geneva in December 2023.burned areafireunccd__barea2019RectifiedGridCoverage-180.0 -90.0180.0 90.0-180.0 -90.0180.0 90.0Burned area 2020This layer was produced by UNEP/GRID-Geneva in December 2023.burned areafireunccd__barea2020RectifiedGridCoverage-180.0 -90.0180.0 90.0-180.0 -90.0180.0 90.0Burned area 2021This layer was produced by UNEP/GRID-Geneva in December 2023.burned areafireunccd__barea2021RectifiedGridCoverage-180.0 -90.0180.0 90.0-180.0 -90.0180.0 90.0Burned area 2022This layer was produced by UNEP/GRID-Geneva in December 2023.burned areafireunccd__barea2022RectifiedGridCoverage-180.0 -90.0180.0 90.0-180.0 -90.0180.0 90.0Burnt areas in Kedougou between 1995 and 2004Forest fire hazard assessment is based on a retrospective analysis of burnt areas, using satellite data from different time periods. The methodology uses the Normalized Burn Ratio (NBR) index, calculated from the near-infrared (NIR) and short-wave infrared (SWIR) spectral bands of the Landsat.burn_1995_2004WCSGeoTIFFrisk_southeast_senegal__burn_1995_2004RectifiedGridCoverage-13.238921499711449 12.308356696892037-11.345452543844322 13.463410489212919-13.238921499711449 12.308356696892037-11.345452543844322 13.463410489212919Burnt areas in Kedougou between 2005 and 2014Forest fire hazard assessment is based on a retrospective analysis of burnt areas, using satellite data from different time periods. The methodology uses the Normalized Burn Ratio (NBR) index, calculated from the near-infrared (NIR) and short-wave infrared (SWIR) spectral bands of the Landsat.burn_2005_2014WCSGeoTIFFrisk_southeast_senegal__burn_2005_2014RectifiedGridCoverage-13.238921499711449 12.308356696892037-11.345452543844322 13.463410489212919-13.238921499711449 12.308356696892037-11.345452543844322 13.463410489212919Burnt areas in Kedougou between 2015 and 2024Forest fire hazard assessment is based on a retrospective analysis of burnt areas, using satellite data from different time periods. The methodology uses the Normalized Burn Ratio (NBR) index, calculated from the near-infrared (NIR) and short-wave infrared (SWIR) spectral bands of the Landsat.burn_2015_2024WCSGeoTIFFrisk_southeast_senegal__burn_2015_2024RectifiedGridCoverage-13.238921499711449 12.308356696892037-11.345452543844322 13.463410489212919-13.238921499711449 12.308356696892037-11.345452543844322 13.463410489212919CAFI_FNF_2015_sinu_wgs84_optThe layer shows the extent of forests in the Congo Basin. Data produced in the frame of the project "Assessing deforestation and forest degradation trends and associated current and historical direct drivers in the six CAFI partner countries". Credits: CAFI. Central African Forest InitiativeCAFI_FNF_2015_sinu_wgs84_optWCSGeoTIFFpeatlands__CAFI_FNF_2015_sinuRectifiedGridCoverage5.354407954878081 -13.45910566948061631.7723896460075 13.0835563559515995.354407954878081 -13.45910566948061631.7723896460075 13.083556355951599CGMFC-21, MFW, Global Mangrove Levels by Pixel (~30m), 2000 - Hamilton, Stuart, 2015Please visit bit.ly/1lMJ9zj for full information before downloading. These data is one version of the GIS global mangrove database (MFW). The database is 30 m pixel with global coverage annually for 2000 to 2014. This layer shows the information concerning the year 2000. Data are mangrove loss in the mangrove biome since 2000 as defined by MFW. CGMFC-21 provides high resolution local, regional, national, and global estimates of annual mangrove forest levels using continuous data from 2000 through to 2012 with the goal of driving mangrove research questions pertaining to biodiversity, climate change, food security, livelihoods, fisheries support, and conservation that have been hindered until now by a lack of suitable data. CGMFC-21 provides the required spatiotemporal resolutions to not only set REDD baseline measures globally in a systematic manner, but also to account for forest degradation as well as deforestation on an annual basis. Countries showing relatively high levels of 21st Century mangrove loss include Myanmar, Guatemala, Malaysia, Cambodia, and Indonesia. Many nations that have reported mangrove deforestation in earlier periods such as Ecuador, Bangladesh and Nigeria, have stabilized their mangrove levels during this period. Indonesia remains by far the largest mangrove holding nation containing between 26.16% and 28.50% of the global mangrove area with a deforestation rate of between 0.26% and 0.63% annually. Global mangrove deforestation continues but at a much reduced rate of between 0.16% and 0.39% annually. Annual mangrove deforestation is now close to zero in the Americas, Africa, and Australia as well as in selected Ramsar sites and protected areas. The global mangrove deforestation pattern during the 21st Century is one of decreasing rates of deforestation, with many nations essentially stable, with the exception of the largest mangrove holding region of Southeast Asia. (2015-09-01) Hamilton, S. E., & Casey, D. (2016). Creation of a high spatio-temporal resolution global database of continuous mangrove forest cover for the 21st century (CGMFC-21). Global Ecology and Biogeography, 25(6), 729-738. doi:10.1111/geb.1244. doi: 10.1111/geb.1244a2000mfw_optWCSGeoTIFFMapX_UNDP__CGMFC-21_mangroves_forest_cover_a2000mfw_optRectifiedGridCoverage-179.9506858152323 -38.76215863757685179.98793546539514 32.35012047466632-179.9506858152323 -38.76215863757685179.98793546539514 32.35012047466632CGMFC-21, MFW, Global Mangrove Levels by Pixel (~30m), 2014 - Hamilton, Stuart, 2015A2014_optWCSGeoTIFFMapX_UNDP__CGMFC-21_mangroves_forest_cover_a2014RectifiedGridCoverage-179.950685815232 -38.762158638242894179.98793546876746 32.350120473757-179.950685815232 -38.762158638242894179.98793546876746 32.350120473757CPSCCF - Coastal Exposure IndexExposure-related variables: Land Cover, Population density Area: Morocco Child Project: SCCF Source: GEF-MedProgrammeMOR_cei_optWCSGeoTIFFmedprogramme__MOR_cei_optRectifiedGridCoverage-6.243055555556 34.6680215-3.8036111091600007 35.922465945448-6.243055555556 34.6680215-3.8036111091600007 35.922465945448CRI_pesato_02_03CRI_pesato_02_03_optWCSGeoTIFFMedicip__CRI_pesato_02_03RectifiedGridCoverage-5.3554163761242854 30.26458369651888335.977083623875714 45.50708369651888-5.3554163761242854 30.26458369651888335.977083623875714 45.50708369651888Central Congo Basin Savanna probability (2022)Metadata for files in the zip archive: Crezee_2022_Central_Congo_Peat_Probability_Layers.zip This folder contains 5 probability layers, the output of 1000 classification runs using a random 2/3 subset of training data, that was used to produce the peatland extent estimates and probability map (Figure 1b) in: Crezee et al. (2022). Nature Geoscience. Mapping peat thickness and carbon stocks of the central Congo Basin using field data. ------------------------------------------------------------------------------------- This folder contains 5 layers: - Probability_2022_HardwoodDominatedPeatSwamp.tif - Probability_2022_PalmDominatedPeatSwamp.tif - Probability_2022_NonPeatFormingForest.tif - Probability_2022_Savanna.tif - Probability_2022_Water.tif Each layer has pixel values from 0-1000, representing the number of times that class was given as the most likely class for that pixel in 1000 Maximum Likelihood classifications using a random 2/3 subset of the training data. For example, a value of 995 in the Probability_2022_Water.tif file means that in 995 of the 1000 runs this pixel was mapped as 'water'. Conversely, a value of 4 in the Probability_2022_Savanna.tif would mean that that pixel was only mapped as savanna 4 times in the 1000 runs. As an approximation, dividing these values by 1000 gives the probability that that pixel contains a majority of vegetation of that class. Total peat swamp forest probability is obtained by summing the probabilities of the hardwood and palm-dominated peat swamp forest files. ---------------------------------------------------------------------------------------- Projection and file details: The file is a GeoTIFF in a lat/long WGS-84 projection and a pixel size of 0.00044444 degrees (c. 50 m). --------------------------------------------------------------------------------------- You are free to use these data for any purpose provided you cite the original paper. The file was created by Bart Crezee and Edward Mitchard. For questions about these data layers please contact Edward Mitchard at edward.mitchard@ed.ac.ukProbability_2022_Savanna_optWCSGeoTIFFpeatlands__Crezee_2022_Probability_SavannaRectifiedGridCoverage15.0 -4.99991694999999925.72744828 3.3053333315.0 -4.99991694999999925.72744828 3.30533333Central Congo Basin hardwood-dominated peat swamp forest probability (2022)Metadata for files in the zip archive: Crezee_2022_Central_Congo_Peat_Probability_Layers.zip This folder contains 5 probability layers, the output of 1000 classification runs using a random 2/3 subset of training data, that was used to produce the peatland extent estimates and probability map (Figure 1b) in: Crezee et al. (2022). Nature Geoscience. Mapping peat thickness and carbon stocks of the central Congo Basin using field data. ------------------------------------------------------------------------------------- This folder contains 5 layers: - Probability_2022_HardwoodDominatedPeatSwamp.tif - Probability_2022_PalmDominatedPeatSwamp.tif - Probability_2022_NonPeatFormingForest.tif - Probability_2022_Savanna.tif - Probability_2022_Water.tif Each layer has pixel values from 0-1000, representing the number of times that class was given as the most likely class for that pixel in 1000 Maximum Likelihood classifications using a random 2/3 subset of the training data. For example, a value of 995 in the Probability_2022_Water.tif file means that in 995 of the 1000 runs this pixel was mapped as 'water'. Conversely, a value of 4 in the Probability_2022_Savanna.tif would mean that that pixel was only mapped as savanna 4 times in the 1000 runs. As an approximation, dividing these values by 1000 gives the probability that that pixel contains a majority of vegetation of that class. Total peat swamp forest probability is obtained by summing the probabilities of the hardwood and palm-dominated peat swamp forest files. ---------------------------------------------------------------------------------------- Projection and file details: The file is a GeoTIFF in a lat/long WGS-84 projection and a pixel size of 0.00044444 degrees (c. 50 m). --------------------------------------------------------------------------------------- You are free to use these data for any purpose provided you cite the original paper. The file was created by Bart Crezee and Edward Mitchard. For questions about these data layers please contact Edward Mitchard at edward.mitchard@ed.ac.ukProbability_2022_HardwoodDominatedPeatSwamp_optWCSGeoTIFFpeatlands__Crezee_2022_Probability_HardwoodDominatedPeatSwamp_optRectifiedGridCoverage15.0 -4.99991694999999925.72744828 3.3053333315.0 -4.99991694999999925.72744828 3.30533333Central Congo Basin non-peat-forming forest probability (2022)Metadata for files in the zip archive: Crezee_2022_Central_Congo_Peat_Probability_Layers.zip This folder contains 5 probability layers, the output of 1000 classification runs using a random 2/3 subset of training data, that was used to produce the peatland extent estimates and probability map (Figure 1b) in: Crezee et al. (2022). Nature Geoscience. Mapping peat thickness and carbon stocks of the central Congo Basin using field data. ------------------------------------------------------------------------------------- This folder contains 5 layers: - Probability_2022_HardwoodDominatedPeatSwamp.tif - Probability_2022_PalmDominatedPeatSwamp.tif - Probability_2022_NonPeatFormingForest.tif - Probability_2022_Savanna.tif - Probability_2022_Water.tif Each layer has pixel values from 0-1000, representing the number of times that class was given as the most likely class for that pixel in 1000 Maximum Likelihood classifications using a random 2/3 subset of the training data. For example, a value of 995 in the Probability_2022_Water.tif file means that in 995 of the 1000 runs this pixel was mapped as 'water'. Conversely, a value of 4 in the Probability_2022_Savanna.tif would mean that that pixel was only mapped as savanna 4 times in the 1000 runs. As an approximation, dividing these values by 1000 gives the probability that that pixel contains a majority of vegetation of that class. Total peat swamp forest probability is obtained by summing the probabilities of the hardwood and palm-dominated peat swamp forest files. ---------------------------------------------------------------------------------------- Projection and file details: The file is a GeoTIFF in a lat/long WGS-84 projection and a pixel size of 0.00044444 degrees (c. 50 m). --------------------------------------------------------------------------------------- You are free to use these data for any purpose provided you cite the original paper. The file was created by Bart Crezee and Edward Mitchard. For questions about these data layers please contact Edward Mitchard at edward.mitchard@ed.ac.ukProbability_2022_NonPeatFormingForest_optWCSGeoTIFFpeatlands__Crezee_2022_Probability_NonPeatFormingForestRectifiedGridCoverage15.0 -4.99991694999999925.72744828 3.3053333315.0 -4.99991694999999925.72744828 3.30533333Central Congo Basin palm-dominated peat swamp forest probability (2022)Metadata for files in the zip archive: Crezee_2022_Central_Congo_Peat_Probability_Layers.zip This folder contains 5 probability layers, the output of 1000 classification runs using a random 2/3 subset of training data, that was used to produce the peatland extent estimates and probability map (Figure 1b) in: Crezee et al. (2022). Nature Geoscience. Mapping peat thickness and carbon stocks of the central Congo Basin using field data. ------------------------------------------------------------------------------------- This folder contains 5 layers: - Probability_2022_HardwoodDominatedPeatSwamp.tif - Probability_2022_PalmDominatedPeatSwamp.tif - Probability_2022_NonPeatFormingForest.tif - Probability_2022_Savanna.tif - Probability_2022_Water.tif Each layer has pixel values from 0-1000, representing the number of times that class was given as the most likely class for that pixel in 1000 Maximum Likelihood classifications using a random 2/3 subset of the training data. For example, a value of 995 in the Probability_2022_Water.tif file means that in 995 of the 1000 runs this pixel was mapped as 'water'. Conversely, a value of 4 in the Probability_2022_Savanna.tif would mean that that pixel was only mapped as savanna 4 times in the 1000 runs. As an approximation, dividing these values by 1000 gives the probability that that pixel contains a majority of vegetation of that class. Total peat swamp forest probability is obtained by summing the probabilities of the hardwood and palm-dominated peat swamp forest files. ---------------------------------------------------------------------------------------- Projection and file details: The file is a GeoTIFF in a lat/long WGS-84 projection and a pixel size of 0.00044444 degrees (c. 50 m). --------------------------------------------------------------------------------------- You are free to use these data for any purpose provided you cite the original paper. The file was created by Bart Crezee and Edward Mitchard. For questions about these data layers please contact Edward Mitchard at edward.mitchard@ed.ac.ukProbability_2022_PalmDominatedPeatSwamp_optWCSGeoTIFFpeatlands__Crezee_2022_Probability_PalmDominatedPeatSwampRectifiedGridCoverage15.0 -4.99991694999999925.72744828 3.3053333315.0 -4.99991694999999925.72744828 3.30533333Central Congo Basin peat carbon density (2022)Metadata for the file: Crezee_2022_Median_Carbon_density_2000runs.tif This is the file used to create Figure 4a of the paper: Crezee et al. (2022). Nature Geoscience. Mapping peat thickness and carbon stocks of the central Congo Basin using field data. ------------------------------------------------------------------------------------- This is a raster file representing modelled peat carbon density (in Mg C per ha) across the central Congo Basin peatland complex. For each pixel, the median value of 2,000 carbon density estimates is provided. The 2,000 carbon density estimates were obtained by multiplying 100 Random Forest peat thickness estimates with 20 bootstrapped peat thickness-carbon density regressions (ED Fig. 7), which were normally distributed arond the best-fitting peat thickness-carbon density regression. The peat thickness-carbon density regression is based on 80 peat cores sampled in the field. ---------------------------------------------------------------------------------------- Projection and file details: The file is a GeoTIFF in a lat/long WGS-84 projection and a pixel size of 0.00044444 degrees (c. 50 m). --------------------------------------------------------------------------------------- You are free to use these data for any purpose provided you cite the original paper. The file was created by Bart Crezee and Edward Mitchard. For questions about these data layers please contact Edward Mitchard at edward.mitchard@ed.ac.ukCrezee_2022_Median_Carbon_density_2000runs_optWCSGeoTIFFpeatlands__Crezee_2022_Median_Carbon_density_2000runs_optRectifiedGridCoverage14.999619457 -5.01574338924.000289446 3.51106528814.999619457 -5.01574338924.000289446 3.511065288Central Congo Basin peat carbon density uncertainty (2022)Metadata for the file: Crezee_2022_Uncertainty_Carbon_density_2000runs.tif This is the file used to create Figure 4b of the paper: Crezee et al. (2022). Nature Geoscience. Mapping peat thickness and carbon stocks of the central Congo Basin using field data. ------------------------------------------------------------------------------------- This is a raster file representing relative uncertainty (in %) around the median carbon density estimate (Fig. 4a of the paper). For each pixel, the width of the 95% confidence interval was calculated from 2,000 carbon density estimates by substracting the 2.5th from the 97.5th percentile. Relative uncertainty was then calculated as ± half the width of the 95% confidence interval, expressed as percentage of the median carbon density estimate. ---------------------------------------------------------------------------------------- Projection and file details: The file is a GeoTIFF in a lat/long WGS-84 projection and a pixel size of 0.00044444 degrees (c. 50 m). --------------------------------------------------------------------------------------- You are free to use these data for any purpose provided you cite the original paper. The file was created by Bart Crezee and Edward Mitchard. For questions about these data layers please contact Edward Mitchard at edward.mitchard@ed.ac.ukCrezee_2022_Uncertainty_Carbon_density_2000runs_optWCSGeoTIFFpeatlands__Crezee_2022_Uncertainty_Carbon_density_2000runs_optRectifiedGridCoverage14.999619457 -5.01574338924.000289446 3.51106528814.999619457 -5.01574338924.000289446 3.511065288Central Congo Basin peat thickness (2022)Metadata for the file: Crezee_2022_Median_Peat_thickness_RF_100runs.tif This is the file used to create Figure 3a of the paper: Crezee et al. (2022). Nature Geoscience. Mapping peat thickness and carbon stocks of the central Congo Basin using field data. ------------------------------------------------------------------------------------- This is a raster file representing modelled peat thickness (in m below the surface) across the central Congo Basin peatland complex. For each pixel, the median value of 100 Random Forest (RF) regressions is provided. Each RF regression was trained on 463 peat thickness measurements in the field, and using 4 predictor variables: - distance from the peatland margin, - precipitation seasonality, - climatic water balance (precipitation minus potential evapotranspiration), - distance from the nearest drainage point. For each of the 100 regressions, a different input map of distance from the peatland margin was constructed, by randomly selecting (with replacement) a minimum peat probability threshold > 0 and < 100 % and generating a binary peat/non-peat map from the peat probability map (combined hardwood + palm dominated peat swamp probabilities). This binary map was then used to calculate the shortest distance to a non-peat pixel. ---------------------------------------------------------------------------------------- Projection and file details: The file is a GeoTIFF in a lat/long WGS-84 projection and a pixel size of 0.00044444 degrees (c. 50 m). --------------------------------------------------------------------------------------- You are free to use these data for any purpose provided you cite the original paper. The file was created by Bart Crezee and Edward Mitchard. For questions about these data layers please contact Edward Mitchard at edward.mitchard@ed.ac.ukCrezee_2022_Median_Peat_thickness_RF_100runs_optWCSGeoTIFFpeatlands__Crezee_2022_Median_Peat_thickness_RF_100runs_optRectifiedGridCoverage14.999619457 -5.01484507399999923.800414295 3.5106161314.999619457 -5.01484507399999923.800414295 3.51061613Central Congo Basin peat thickness uncertainty (2022)Metadata for the file: Crezee_2022_Uncertainty_Peat_thickness_RF_100runs.tif This is the file used to create Figure 3b of the paper: Crezee et al. (2022). Nature Geoscience. Mapping peat thickness and carbon stocks of the central Congo Basin using field data. ------------------------------------------------------------------------------------- This is a raster file representing relative uncertainty (in %) around the median peat thickness estimate (Fig. 3a of the paper). For each pixel, the width of the 95% confidence interval was calculated from 100 peat thickness estimates by substracting the 2.5th from the 97.5th percentile. Relative uncertainty was then calculated as ± half the width of the 95% confidence interval, expressed as percentage of the median peat thickness estimate. ---------------------------------------------------------------------------------------- Projection and file details: The file is a GeoTIFF in a lat/long WGS-84 projection and a pixel size of 0.00044444 degrees (c. 50 m). --------------------------------------------------------------------------------------- You are free to use these data for any purpose provided you cite the original paper. The file was created by Bart Crezee and Edward Mitchard. For questions about these data layers please contact Edward Mitchard at edward.mitchard@ed.ac.ukCrezee_2022_Uncertainty_Peat_thickness_RF_100runs_optWCSGeoTIFFpeatlands__Crezee_2022_Uncertainty_Peat_thickness_RF_100runs_optRectifiedGridCoverage14.999619457 -5.01484507399999923.800414295 3.5106161314.999619457 -5.01484507399999923.800414295 3.51061613Central Congo Basin peatland map (2022, smoothed)Metadata for the file: Crezee_2022_Smoothed_7x7_Classification_Most_likely_class.tif This file is a simplified version of the map presented in Figure 1b of the paper: Crezee et al. (2022). Nature Geoscience. Mapping peat thickness and carbon stocks of the central Congo Basin using field data. ------------------------------------------------------------------------------------- This raster file is a smoothed version of the landcover classification map. For each pixel, the most likely class is displayed, based on the highest probability obtained from the 5 landcover classes presented in the Crezee_2022_Central_Congo_Peat_Probability_Layers.zip archive. The class values are: 0. No data 1. Open water 2. Savanna 3. Non-peat forming forest (Terra firme and seasonally inundated forest) 4. Palm-dominated peat swamp forest 5. Hardwood-dominated peat swamp forest Only class 4 and 5 are associated with the presence of peat. Total peatland area extent is obtained by summing class 4 and 5 areas. The speckle inherent in the classification was reduced through the use of a 7x7 majority filter, whereby the central pixel in a moving 7x7 window was replaced by the most common pixel type found in that window. ---------------------------------------------------------------------------------------- Projection and file details: The file is a GeoTIFF in a lat/long WGS-84 projection and a pixel size of 0.00044444 degrees (c. 50 m). --------------------------------------------------------------------------------------- You are free to use these data for any purpose provided you cite the original paper. The file was created by Bart Crezee and Edward Mitchard. For questions about these data layers please contact Edward Mitchard at edward.mitchard@ed.ac.ukCrezee_2022_Smoothed_7x7_Classification_Most_likely_class_optWCSGeoTIFFpeatlands__Crezee_2022_Smoothed_7x7_Classification_Most_likely_class_optRectifiedGridCoverage15.0 -4.99991694999999925.72744828 3.3053333315.0 -4.99991694999999925.72744828 3.30533333Central Congo Basin peatland map (2022, unsmoothed)Metadata for the file: Crezee_2022_Unsmoothed_Classification_Most_likely_class.tif This file is a simplified version of the map presented in Figure 1b of the paper: Crezee et al. (2022). Nature Geoscience. Mapping peat thickness and carbon stocks of the central Congo Basin using field data. ------------------------------------------------------------------------------------- This raster file is an unsmoothed version of the landcover classification map. For each pixel, the most likely class is displayed, based on the highest probability obtained from the 5 landcover classes presented in the Crezee_2022_Central_Congo_Peat_Probability_Layers.zip archive. The class values are: 0. No data 1. Open water 2. Savanna 3. Non-peat forming forest (Terra firme and seasonally inundated forest) 4. Palm-dominated peat swamp forest 5. Hardwood-dominated peat swamp forest Only class 4 and 5 are associated with the presence of peat. Total peatland area extent is obtained by summing class 4 and 5 areas. ---------------------------------------------------------------------------------------- Projection and file details: The file is a GeoTIFF in a lat/long WGS-84 projection and a pixel size of 0.00044444 degrees (c. 50 m). --------------------------------------------------------------------------------------- You are free to use these data for any purpose provided you cite the original paper. The file was created by Bart Crezee and Edward Mitchard. For questions about these data layers please contact Edward Mitchard at edward.mitchard@ed.ac.ukCrezee_2022_Unsmoothed_Classification_Most_likely_class_optWCSGeoTIFFpeatlands__Crezee_2022_Unsmoothed_Classification_Most_likely_class_optRectifiedGridCoverage15.0 -4.99991694999999925.72744828 3.3053333315.0 -4.99991694999999925.72744828 3.30533333Central Congo Basin water probability (2022)Metadata for files in the zip archive: Crezee_2022_Central_Congo_Peat_Probability_Layers.zip This folder contains 5 probability layers, the output of 1000 classification runs using a random 2/3 subset of training data, that was used to produce the peatland extent estimates and probability map (Figure 1b) in: Crezee et al. (2022). Nature Geoscience. Mapping peat thickness and carbon stocks of the central Congo Basin using field data. ------------------------------------------------------------------------------------- This folder contains 5 layers: - Probability_2022_HardwoodDominatedPeatSwamp.tif - Probability_2022_PalmDominatedPeatSwamp.tif - Probability_2022_NonPeatFormingForest.tif - Probability_2022_Savanna.tif - Probability_2022_Water.tif Each layer has pixel values from 0-1000, representing the number of times that class was given as the most likely class for that pixel in 1000 Maximum Likelihood classifications using a random 2/3 subset of the training data. For example, a value of 995 in the Probability_2022_Water.tif file means that in 995 of the 1000 runs this pixel was mapped as 'water'. Conversely, a value of 4 in the Probability_2022_Savanna.tif would mean that that pixel was only mapped as savanna 4 times in the 1000 runs. As an approximation, dividing these values by 1000 gives the probability that that pixel contains a majority of vegetation of that class. Total peat swamp forest probability is obtained by summing the probabilities of the hardwood and palm-dominated peat swamp forest files. ---------------------------------------------------------------------------------------- Projection and file details: The file is a GeoTIFF in a lat/long WGS-84 projection and a pixel size of 0.00044444 degrees (c. 50 m). --------------------------------------------------------------------------------------- You are free to use these data for any purpose provided you cite the original paper. The file was created by Bart Crezee and Edward Mitchard. For questions about these data layers please contact Edward Mitchard at edward.mitchard@ed.ac.ukProbability_2022_Water_optWCSGeoTIFFpeatlands__Crezee_2022_Probability_WaterRectifiedGridCoverage15.0 -4.99991694999999925.72744828 3.3053333315.0 -4.99991694999999925.72744828 3.30533333Change in cumulative human impact to marine ecosystemsHuman pressures on the ocean are thought to be increasing globally, yet we know little about their patterns of cumulative change, which pressures are most responsible for change, and which places are experiencing the greatest increases. Managers and policymakers require such information to make strategic decisions and monitor progress towards management objectives. Here we calculate and map recent change over 5 years in cumulative impacts to marine ecosystems globally from fishing, climate change, and ocean- and land-based stressors. Nearly 66% of the ocean and 77% of national jurisdictions show increased human impact, driven mostly by climate change pressures. Five percent of the ocean is heavily impacted with increasing pressures, requiring management attention. Ten percent has very low impact with decreasing pressures. Our results provide large-scale guidance about where to prioritize management efforts and affirm the importance of addressing climate change to maintain and improve the condition of marine ecosystems. Authors: Halpern, B. S. et al. Spatial and temporal changes in cumulative human impacts on the world’s ocean. Nat. Commun. 6:7615 doi: 10.1038/ncomms8615 (2015). Source: https://www.nature.com/articles/ncomms8615#s1global_cumul_impact_2013_minus_2008_wgsWCSGeoTIFFMapX_UNDP__global_cumul_impact_2013_minus_2008_wgsRectifiedGridCoverage-180.0 -79.0180.00000000000006 84.0-180.0 -79.0180.00000000000006 84.0Climate risk based on Novel and Disappearing Climates 2050 SRES A2Climate risk data are used to identify climate stability or scope for climate-adaptation focused interventions. The recent datasets from Tabor et al. 2018 which assess climate change exposure using downscaled climate projections with the SRES A2 emissions scenario was selected for ranking climate risk, combining two measures of radically changing climates. Tabor highlight (a) the threat status of the climate, assigning high values where the late 20th century climates will cease to exist anywhere in the world and therefore this climate space is very threatened as it may be disappearing from the world; and (b) the distance from current climates, with high values indicating areas with novel climates not currently experienced anywhere in the world, and where there is high uncertainty on future species communities. Climate risk values are grouped by deciles whereby landscapes with moderate climate risk are deemed most appropriate for selection, in that there are some adaptation challenges that the LWP intervention may be able to tackle. John W. Williams, Karyn Tabor. (2018). Novel and Disappearing Climates 2050 SRES A2 (Version 1) [Data set]. Zenodo. http://doi.org/10.5281/zenodo.1559299 Support for this project was provided by Conservation International, the Land Tenure Center at the University of Wisconsin, the Center for Climatic Research at the University of Wisconsin, and the Environment Program at the University of Wisconsin–Madison. This research has been funded in part by the Walton Family Foundation, the Gordon and Betty Moore Foundation, and a gift from Betty and Gordon Moore.novel_disp_0to1_normalisedq_optWCSGeoTIFFLWP__novel_disp_0to1_normalisedq_optRectifiedGridCoverage-180.0003335 -59.08371450000001179.9996665 83.66628549999999-180.0003335 -59.08371450000001179.9996665 83.66628549999999Closed forest total area in km2forestcfkm2aWCSGeoTIFFECO-DRR__forestcfkm2aRectifiedGridCoverage-180.0 -90.0180.0 90.0-180.0 -90.0180.0 90.0Coastal Forcing IndexThe Coastal Forcing Index shows the coastal areas that are facing significant pressures from multiple forces driven by climate change, such as rising sea levels and increasing impacts of waves and storms coming from the sea, as well as extreme precipitation and desertification that are affecting both coastal areas and the inland. Population growth and tourists’ presence represent an added pressure to coastal and inland areas. Combination of: - Sea Level Rise; - Significant wave height; - Soil Aridity; - Extreme precipitations; - Population Growth; - Tourism trend Area: Morocco Child Project: SCCF Source: GEF-MedProgrammeMOR_cfi_optWCSGeoTIFFmedprogramme__MOR_cfi_optRectifiedGridCoverage-6.243194443839 34.6680215-3.804305552999 35.920521501002-6.243194443839 34.6680215-3.804305552999 35.920521501002Coastal Risk IndexMultiplication of 3 sub-indexes: Coastal Vulnerability Index Coastal Exposure Index Coastal Forcing Index Area: Morocco Child Project: SCCF Source: GEF-MedProgrammeMOR_cri_optWCSGeoTIFFmedprogramme__MOR_cri_optRectifiedGridCoverage-6.243055555556 34.6680215-3.8041666647160004 35.920521501002-6.243055555556 34.6680215-3.8041666647160004 35.920521501002Coastal Vulnerability IndexThe Coastal Vulnerability Index shows the susceptibility of coastal areas to extreme climate impacts and their capacity to recover, survive, and adapt depending on their geo-morphological, ecological, and socioeconomic characteristics. Combination of climate related / environmental variables: - Elevation; - Coastal slope; - Land roughness; - Distance from the shoreline; - Ecosystem health and gender-related variables: - Education; - Labour market; - Health status and quality of life; - Socio-economic status. Area: Morocco Child Project: SCCF Source: GEF-MedProgrammeMOR_cvi_optWCSGeoTIFFmedprogramme__MOR_cvi_optRectifiedGridCoverage-6.243055555556 34.6680215-3.8041666647160004 35.922465945448-6.243055555556 34.6680215-3.8041666647160004 35.922465945448Coastal Vulnerability Index (only environmental variables)The Coastal Vulnerability Index (only environmental variables) shows the susceptibility of coastal areas to extreme climate impacts and their capacity to recover, survive, and adapt depending on their geo-morphological and ecological conditions. Combination of climate related / environmental variables: - Elevation; - Coastal slope; - Land roughness; - Distance from the shoreline; - Ecosystem health. Area: Morocco Child Project: SCCF Source: GEF-MedProgrammeMOR_cvi_only_env_optWCSGeoTIFFmedprogramme__MOR_cvi_only_env_optRectifiedGridCoverage-6.243055555556 34.513854833210004-3.8041666647160004 35.922465945448-6.243055555556 34.513854833210004-3.8041666647160004 35.922465945448Combination Flood/Forest at global level - ProtectionGLO_flo_for6_46WCSGeoTIFFECO-DRR__GLO_flo_for6_46RectifiedGridCoverage-180.0 -90.0180.0 90.0-180.0 -90.0180.0 90.0Combination Flood/Forest at global level - RestorationGLO_flo_for6_13WCSGeoTIFFECO-DRR__GLO_flo_for6_13RectifiedGridCoverage-180.0 -90.0180.0 90.0-180.0 -90.0180.0 90.0Combination Flood/Forest at global level - othersGLO_flo_for9_1112WCSGeoTIFFECO-DRR__GLO_flo_for9_1112RectifiedGridCoverage-180.0 -90.0180.0 90.0-180.0 -90.0180.0 90.0Combination Flood/Forest at national level - OthersNAT_flo_for9_1112WCSGeoTIFFECO-DRR__NAT_flo_for9_1112RectifiedGridCoverage-180.0 -90.0180.0 90.0-180.0 -90.0180.0 90.0Combination Flood/Forest at national level - ProtectionNAT_flo_for6_46WCSGeoTIFFECO-DRR__NAT_flo_for6_46RectifiedGridCoverage-180.0 -90.0180.0 90.0-180.0 -90.0180.0 90.0Combination Flood/Forest at national level - RestorationNAT_flo_for6_13WCSGeoTIFFECO-DRR__NAT_flo_for6_13RectifiedGridCoverage-180.0 -90.0180.0 90.0-180.0 -90.0180.0 90.0Combination Landslide/Forest at global level - OthersGLO_lds_for9_1112WCSGeoTIFFECO-DRR__GLO_lds_for9_1112RectifiedGridCoverage-180.0 -90.0180.0 90.0-180.0 -90.0180.0 90.0Combination Landslide/Forest at global level - ProtectionGLO_lds_for6_46WCSGeoTIFFECO-DRR__GLO_lds_for6_46RectifiedGridCoverage-180.0 -90.0180.0 90.0-180.0 -90.0180.0 90.0Combination Landslide/Forest at global level - RestorationGLO_lds_for6_13WCSGeoTIFFECO-DRR__GLO_lds_for6_13RectifiedGridCoverage-180.0 -90.0180.0 90.0-180.0 -90.0180.0 90.0Combination Landslide/Forest at national level - ProtectionNAT_lds_for6_46WCSGeoTIFFECO-DRR__NAT_lds_for6_46RectifiedGridCoverage-180.0 -90.0180.0 90.0-180.0 -90.0180.0 90.0Combination Landslide/Forest at national level - RestorationNAT_lds_for6_13WCSGeoTIFFECO-DRR__NAT_lds_for6_13RectifiedGridCoverage-180.0 -90.0180.0 90.0-180.0 -90.0180.0 90.0Combination Landslides/Forest at national level - OthersNAT_lds_for9_1112WCSGeoTIFFECO-DRR__NAT_lds_for9_1112RectifiedGridCoverage-180.0 -90.0180.0 90.0-180.0 -90.0180.0 90.0Combination Tropical cyclone / Forest at national level - OthersNAT_tcw_for9_1112WCSGeoTIFFECO-DRR__NAT_tcw_for9_1112RectifiedGridCoverage-180.0 -90.0180.0 90.0-180.0 -90.0180.0 90.0Combination Tropical cyclone / Forest at national level - ProtectionNAT_tcw_for6_46WCSGeoTIFFECO-DRR__NAT_tcw_for6_46RectifiedGridCoverage-180.0 -90.0180.0 90.0-180.0 -90.0180.0 90.0Combination Tropical cyclone / Forest at national level - RestorationNAT_tcw_for6_13WCSGeoTIFFECO-DRR__NAT_tcw_for6_13RectifiedGridCoverage-180.0 -90.0180.0 90.0-180.0 -90.0180.0 90.0Combination Tropical cyclone surge/ Coral reefs at global level - OthersGLO_tcs_cor9_1112WCSGeoTIFFECO-DRR__GLO_tcs_cor9_1112RectifiedGridCoverage-180.0 -90.0180.0 90.0-180.0 -90.0180.0 90.0Combination Tropical cyclone surge/ Coral reefs at global level - ProtectionGLO_tcs_cor6_46WCSGeoTIFFECO-DRR__GLO_tcs_cor6_46RectifiedGridCoverage-180.0 -90.0180.0 90.0-180.0 -90.0180.0 90.0Combination Tropical cyclone surge/ Coral reefs at global level - RestorationGLO_tcs_cor6_13WCSGeoTIFFECO-DRR__GLO_tcs_cor6_13RectifiedGridCoverage-180.0 -90.0180.0 90.0-180.0 -90.0180.0 90.0Combination Tropical cyclone surge/ Coral reefs at national level - OthersNAT_tcs_cor9_1112WCSGeoTIFFECO-DRR__NAT_tcs_cor9_1112RectifiedGridCoverage-180.0 -90.0180.0 90.0-180.0 -90.0180.0 90.0Combination Tropical cyclone surge/ Coral reefs at national level - ProtectionNAT_tcs_cor6_46WCSGeoTIFFECO-DRR__NAT_tcs_cor6_46RectifiedGridCoverage-180.0 -90.0180.0 90.0-180.0 -90.0180.0 90.0Combination Tropical cyclone surge/ Coral reefs at national level - RestorationNAT_tcs_cor6_13WCSGeoTIFFECO-DRR__NAT_tcs_cor6_13RectifiedGridCoverage-180.0 -90.0180.0 90.0-180.0 -90.0180.0 90.0Combination Tropical cyclone surge/ Forest at national level - OthersNAT_tcs_for9_1112WCSGeoTIFFECO-DRR__NAT_tcs_for9_1112RectifiedGridCoverage-180.0 -90.0180.0 90.0-180.0 -90.0180.0 90.0Combination Tropical cyclone surge/ Forest at national level - ProtectionNAT_tcs_for6_46WCSGeoTIFFECO-DRR__NAT_tcs_for6_46RectifiedGridCoverage-180.0 -90.0180.0 90.0-180.0 -90.0180.0 90.0Combination Tropical cyclone surge/ Forest at national level - RestorationNAT_tcs_for6_13WCSGeoTIFFECO-DRR__NAT_tcs_for6_13RectifiedGridCoverage-180.0 -90.0180.0 90.0-180.0 -90.0180.0 90.0Combination Tropical cyclone surge/ Mangroves at global level - OthersGLO_tcs_man9_1112WCSGeoTIFFECO-DRR__GLO_tcs_man9_1112RectifiedGridCoverage-180.0 -90.0180.0 90.0-180.0 -90.0180.0 90.0Combination Tropical cyclone surge/ Mangroves at global level - ProtectionGLO_tcs_man6_46WCSGeoTIFFECO-DRR__GLO_tcs_man6_46RectifiedGridCoverage-180.0 -90.0180.0 90.0-180.0 -90.0180.0 90.0Combination Tropical cyclone surge/ Mangroves at global level - RestorationGLO_tcs_man6_13WCSGeoTIFFECO-DRR__GLO_tcs_man6_13RectifiedGridCoverage-180.0 -90.0180.0 90.0-180.0 -90.0180.0 90.0Combination Tropical cyclone surge/ Mangroves at national level - OthersNAT_tcs_man9_1112WCSGeoTIFFECO-DRR__NAT_tcs_man9_1112RectifiedGridCoverage-180.0 -90.0180.0 90.0-180.0 -90.0180.0 90.0Combination Tropical cyclone surge/ Mangroves at national level - ProtectionNAT_tcs_man6_46WCSGeoTIFFECO-DRR__NAT_tcs_man6_46RectifiedGridCoverage-180.0 -90.0180.0 90.0-180.0 -90.0180.0 90.0Combination Tropical cyclone surge/ Mangroves at national level - RestorationNAT_tcs_man6_13WCSGeoTIFFECO-DRR__NAT_tcs_man6_13RectifiedGridCoverage-180.0 -90.0180.0 90.0-180.0 -90.0180.0 90.0Combination Tropical cyclone surge/ Sea grasses at global level - OthersGLO_tcs_sea9_1112WCSGeoTIFFECO-DRR__GLO_tcs_sea9_1112RectifiedGridCoverage-180.0 -90.0180.0 90.0-180.0 -90.0180.0 90.0Combination Tropical cyclone surge/ Sea grasses at global level - ProtectionGLO_tcs_sea6_46WCSGeoTIFFECO-DRR__GLO_tcs_sea6_46RectifiedGridCoverage-180.0 -90.0180.0 90.0-180.0 -90.0180.0 90.0Combination Tropical cyclone surge/ Sea grasses at global level - RestorationGLO_tcs_sea6_13WCSGeoTIFFECO-DRR__GLO_tcs_sea6_13RectifiedGridCoverage-180.0 -90.0180.0 90.0-180.0 -90.0180.0 90.0Combination Tropical cyclone surge/ Sea grasses at national level - OthersNAT_tcs_sea9_1112WCSGeoTIFFECO-DRR__NAT_tcs_sea9_1112RectifiedGridCoverage-180.0 -90.0180.0 90.0-180.0 -90.0180.0 90.0Combination Tropical cyclone surge/ Sea grasses at national level - ProtectionNAT_tcs_sea6_46WCSGeoTIFFECO-DRR__NAT_tcs_sea6_46RectifiedGridCoverage-180.0 -90.0180.0 90.0-180.0 -90.0180.0 90.0Combination Tropical cyclone surge/ Sea grasses at national level - RestorationNAT_tcs_sea6_13WCSGeoTIFFECO-DRR__NAT_tcs_sea6_13RectifiedGridCoverage-180.0 -90.0180.0 90.0-180.0 -90.0180.0 90.0Combination Tropical cyclone surge/Forest at global level - OthersGLO_tcs_for9_1112WCSGeoTIFFECO-DRR__GLO_tcs_for9_1112RectifiedGridCoverage-180.0 -90.0180.0 90.0-180.0 -90.0180.0 90.0Combination Tropical cyclone surge/Forest at global level - ProtectionGLO_tcs_for6_46WCSGeoTIFFECO-DRR__GLO_tcs_for6_46RectifiedGridCoverage-180.0 -90.0180.0 90.0-180.0 -90.0180.0 90.0Combination Tropical cyclone surge/Forest at global level - RestorationGLO_tcs_for6_13WCSGeoTIFFECO-DRR__GLO_tcs_for6_13RectifiedGridCoverage-180.0 -90.0180.0 90.0-180.0 -90.0180.0 90.0Combination Tropical cyclone/Forest at global level - OthersGLO_tcw_for9_1112WCSGeoTIFFECO-DRR__GLO_tcw_for9_1112RectifiedGridCoverage-180.0 -90.0180.0 90.0-180.0 -90.0180.0 90.0Combination Tropical cyclone/Forest at global level - ProtectionGLO_tcw_for6_46WCSGeoTIFFECO-DRR__GLO_tcw_for6_46RectifiedGridCoverage-180.0 -90.0180.0 90.0-180.0 -90.0180.0 90.0Combination Tropical cyclone/Forest at global level - RestorationGLO_tcw_for6_13WCSGeoTIFFECO-DRR__GLO_tcw_for6_13RectifiedGridCoverage-180.0 -90.0180.0 90.0-180.0 -90.0180.0 90.0Combination Tsunami/Coral reefs at global level - OthersGLO_tsu_cor9_1112WCSGeoTIFFECO-DRR__GLO_tsu_cor9_1112RectifiedGridCoverage-180.0 -90.0180.0 90.0-180.0 -90.0180.0 90.0Combination Tsunami/Coral reefs at global level - ProtectionGLO_tsu_cor6_46WCSGeoTIFFECO-DRR__GLO_tsu_cor6_46RectifiedGridCoverage-180.0 -90.0180.0 90.0-180.0 -90.0180.0 90.0Combination Tsunami/Coral reefs at global level - RestorationGLO_tsu_cor6_13WCSGeoTIFFECO-DRR__GLO_tsu_cor6_13RectifiedGridCoverage-180.0 -90.0180.0 90.0-180.0 -90.0180.0 90.0Combination Tsunami/Coral reefs at national level - OthersNAT_tsu_cor9_1112WCSGeoTIFFECO-DRR__NAT_tsu_cor9_1112RectifiedGridCoverage-180.0 -90.0180.0 90.0-180.0 -90.0180.0 90.0Combination Tsunami/Coral reefs at national level - ProtectionNAT_tsu_cor6_46WCSGeoTIFFECO-DRR__NAT_tsu_cor6_46RectifiedGridCoverage-180.0 -90.0180.0 90.0-180.0 -90.0180.0 90.0Combination Tsunami/Coral reefs at national level - RestorationNAT_tsu_cor6_13WCSGeoTIFFECO-DRR__NAT_tsu_cor6_13RectifiedGridCoverage-180.0 -90.0180.0 90.0-180.0 -90.0180.0 90.0Combination Tsunami/Forest at global level - OthersGLO_tsu_for9_1112WCSGeoTIFFECO-DRR__GLO_tsu_for9_1112RectifiedGridCoverage-180.0 -90.0180.0 90.0-180.0 -90.0180.0 90.0Combination Tsunami/Forest at global level - ProtectionGLO_tsu_for6_46WCSGeoTIFFECO-DRR__GLO_tsu_for6_46RectifiedGridCoverage-180.0 -90.0180.0 90.0-180.0 -90.0180.0 90.0Combination Tsunami/Forest at global level - RestorationGLO_tsu_for6_13WCSGeoTIFFECO-DRR__GLO_tsu_for6_13RectifiedGridCoverage-180.0 -90.0180.0 90.0-180.0 -90.0180.0 90.0Combination Tsunami/Forest at national level - OthersNAT_tsu_for9_1112WCSGeoTIFFECO-DRR__NAT_tsu_for9_1112RectifiedGridCoverage-180.0 -90.0180.0 90.0-180.0 -90.0180.0 90.0Combination Tsunami/Forest at national level - ProtectionNAT_tsu_for6_46WCSGeoTIFFECO-DRR__NAT_tsu_for6_46RectifiedGridCoverage-180.0 -90.0180.0 90.0-180.0 -90.0180.0 90.0Combination Tsunami/Forest at national level - RestorationNAT_tsu_for6_13WCSGeoTIFFECO-DRR__NAT_tsu_for6_13RectifiedGridCoverage-180.0 -90.0180.0 90.0-180.0 -90.0180.0 90.0Combination Tsunami/Mangroves at global level - OthersGLO_tsu_man9_1112WCSGeoTIFFECO-DRR__GLO_tsu_man9_1112RectifiedGridCoverage-180.0 -90.0180.0 90.0-180.0 -90.0180.0 90.0Combination Tsunami/Mangroves at global level - ProtectionGLO_tsu_man6_46WCSGeoTIFFECO-DRR__GLO_tsu_man6_46RectifiedGridCoverage-180.0 -90.0180.0 90.0-180.0 -90.0180.0 90.0Combination Tsunami/Mangroves at global level - RestorationGLO_tsu_man6_13WCSGeoTIFFECO-DRR__GLO_tsu_man6_13RectifiedGridCoverage-180.0 -90.0180.0 90.0-180.0 -90.0180.0 90.0Combination Tsunami/Mangroves at national level - OthersNAT_tsu_man9_1112WCSGeoTIFFECO-DRR__NAT_tsu_man9_1112RectifiedGridCoverage-180.0 -90.0180.0 90.0-180.0 -90.0180.0 90.0Combination Tsunami/Mangroves at national level - ProtectionNAT_tsu_man6_46WCSGeoTIFFECO-DRR__NAT_tsu_man6_46RectifiedGridCoverage-180.0 -90.0180.0 90.0-180.0 -90.0180.0 90.0Combination Tsunami/Mangroves at national level - RestorationNAT_tsu_man6_13WCSGeoTIFFECO-DRR__NAT_tsu_man6_13RectifiedGridCoverage-180.0 -90.0180.0 90.0-180.0 -90.0180.0 90.0Combination Tsunami/Sea grasses at global level - OthersGLO_tsu_sea9_1112WCSGeoTIFFECO-DRR__GLO_tsu_sea9_1112RectifiedGridCoverage-180.0 -90.0180.0 90.0-180.0 -90.0180.0 90.0Combination Tsunami/Sea grasses at global level - ProtectionGLO_tsu_sea6_46WCSGeoTIFFECO-DRR__GLO_tsu_sea6_46RectifiedGridCoverage-180.0 -90.0180.0 90.0-180.0 -90.0180.0 90.0Combination Tsunami/Sea grasses at global level - RestorationGLO_tsu_sea6_13WCSGeoTIFFECO-DRR__GLO_tsu_sea6_13RectifiedGridCoverage-180.0 -90.0180.0 90.0-180.0 -90.0180.0 90.0Combination Tsunami/Sea grasses at national level - OthersNAT_tsu_sea9_1112WCSGeoTIFFECO-DRR__NAT_tsu_sea9_1112RectifiedGridCoverage-180.0 -90.0180.0 90.0-180.0 -90.0180.0 90.0Combination Tsunami/Sea grasses at national level - ProtectionNAT_tsu_sea6_46WCSGeoTIFFECO-DRR__NAT_tsu_sea6_46RectifiedGridCoverage-180.0 -90.0180.0 90.0-180.0 -90.0180.0 90.0Combination Tsunami/Sea grasses at national level - RestorationNAT_tsu_sea6_13WCSGeoTIFFECO-DRR__NAT_tsu_sea6_13RectifiedGridCoverage-180.0 -90.0180.0 90.0-180.0 -90.0180.0 90.0Combined map of natural hazardsThe multi-risk assessment integrates risks related to drought, flooding, wildfires, and extreme heat through a multi-criteria analysis by AHP, weighted by participatory surveys with local institutions such as the CSE, the DPGI, the ANAT, and the DGPRE. This approach makes it possible to prioritize risks according to their perceived impact and frequency of occurrence. Densely populated areas and areas with rich ecosystems have medium to high multi-risk levels. These areas are systematically characterized by medium to high levels of risk related to flooding, wildfires, and extreme heat.multiriskrisk_southeast_senegal__multiriskRectifiedGridCoverage-13.239460488881921 12.307817707721565-11.342398271878315 13.461074869474206-13.239460488881921 12.307817707721565-11.342398271878315 13.461074869474206Commercial Activity (Shipping Lanes) 2008 - KNBVessel identity and location information was obtained using two approaches. (1) Over the past 20 years, 10-20% of the vessel fleet has voluntarily participated in collecting meteorological data for the open ocean, which includes location at the time of measurement, as part of the Volunteer Observing System (VOS). (2) In order to improve maritime safety, in 2002 the International Maritime Organization SOLAS agreement required all vessels over 300 gross tonnage (GT) and vessels carrying passengers to equip Automatic Identification System (AIS) transceivers, which use the Global Positioning System (GPS) to precisely locate vessels. A single year sample of the VOS data was used for analysis. These data ignores vessel type, and included observations from only 12% of the vessel fleet. The ships included are a spatially- and statistically-biased sample of the population, making the modelled results somewhat misleading. Data have limited observation frequency, leading to gaps that when directly interpolated with geodesic paths, create invalid routes which cross land masses. Routing model was used to create a visibility graph of the oceans, creating valid potential movement paths. These movement paths are based on the assumption that mariners will prefer great circle distances when possible. Raw stressor data from "Benjamin Halpern, Melanie Frazier, John Potapenko, Kenneth Casey, Kellee Koenig, et al. 2015. Cumulative human impacts: raw stressor data (2008 and 2013). Knowledge Network for Biocomplexity. doi:10.5063/F1S180FS."08_shipping_halpern_KNB_WGS84WCSGeoTIFFunea_pollution__08_shipping_halpern_KNB_WGS84RectifiedGridCoverage-179.99999978909742 -89.99765318080641179.99802237886044 85.2846797894314-179.99999978909742 -89.99765318080641179.99802237886044 85.2846797894314Commercial Activity (Shipping Lanes) 2013 - KNBVessel identity and location information was obtained using two approaches. (1) Over the past 20 years, 10-20% of the vessel fleet has voluntarily participated in collecting meteorological data for the open ocean, which includes location at the time of measurement, as part of the Volunteer Observing System (VOS). (2) In order to improve maritime safety, in 2002 the International Maritime Organization SOLAS agreement required all vessels over 300 gross tonnage (GT) and vessels carrying passengers to equip Automatic Identification System (AIS) transceivers, which use the Global Positioning System (GPS) to precisely locate vessels. Eight broad classes of vessels were taken into account separately: authority, cargo, fishing, high-speed, passenger, pleasure, support, tanker and an ‘other’ class. The vessel classes which move globally (cargo, tanker, and passenger) are required to carry AIS transceivers, and in these three classes 60-70% of the total vessel fleet was observed using AIS. The resulting data layer is primarily composed of these vessel classes in both the AIS and VOS data sources, and is almost exclusively these ship types in the open ocean. We used a simple linear average of the two data sources, producing a final model resolved for the whole ocean at a resolution of 0.1 decimal degrees (~11km). Data have limited observation frequency, leading to gaps that when directly interpolated with geodesic paths, create invalid routes which cross land masses. Routing model was used to create a visibility graph of the oceans, creating valid potential movement paths. These movement paths are based on the assumption that mariners will prefer great circle distances when possible. Raw stressor data from "Benjamin Halpern, Melanie Frazier, John Potapenko, Kenneth Casey, Kellee Koenig, et al. 2015. Cumulative human impacts: raw stressor data (2008 and 2013). Knowledge Network for Biocomplexity. doi:10.5063/F1S180FS."13_shipping_halpern_KNB_WGS84WCSGeoTIFFunea_pollution__13_shipping_halpern_KNB_WGS84RectifiedGridCoverage-179.99999978909761 -85.28265369039616179.99802237886036 89.9996792798417-179.99999978909761 -85.28265369039616179.99802237886036 89.9996792798417Comparison of central Congo Basin peatland maps (2022 and 2017)Metadata for the file: Crezee_2022_Map_comparison.tif This is the file used to create Figure 2 of the paper: Crezee et al. (2022). Nature Geoscience. Mapping peat thickness and carbon stocks of the central Congo Basin using field data. ------------------------------------------------------------------------------------- This is a raster file comparing peatland predictions from Crezee et al. (2022) and Dargie et al. (2017). For each study, a binary peat/non-peat map was created, based on a 50% peat probability threshold. In both maps, each pixel was classified as peat if it had >500 hardwood- or palm-dominated peat swamp forests predictions out of 1,000 model runs. A comparison was then made by overlaying the resulting maps. The class values are: 0. Neither study predicted peat swamp forest 1. Only Dargie et al. (2017) predicted peat swamp forest 2. Only Crezee et al. (2022) predicted peat swamp forest 3. Both studies predited peat swamp forest ---------------------------------------------------------------------------------------- Projection and file details: The file is a GeoTIFF in a lat/long WGS-84 projection and a pixel size of 0.00044444 degrees (c. 50 m). --------------------------------------------------------------------------------------- You are free to use these data for any purpose provided you cite the original paper. The file was created by Bart Crezee and Edward Mitchard. For questions about these data layers please contact Edward Mitchard at edward.mitchard@ed.ac.ukCrezee_2022_Map_comparison_optWCSGeoTIFFpeatlands__Crezee_2022_Map_comparison_optRectifiedGridCoverage14.99985 -5.00039444000000125.72774272 3.3057447214.99985 -5.00039444000000125.72774272 3.30574472Coral reefs total area in km2coralkm2aWCSGeoTIFFECO-DRR__coralkm2aRectifiedGridCoverage-180.0 -90.0180.0 90.0-180.0 -90.0180.0 90.0Corine LandCover - WarsawCorine_LCovWCSGeoTIFFunep_storymap__Corine_LCov_WarsawRectifiedGridCoverage-2693291.68 -3087431.71.003670832E7 5440568.3-81.23107933735224 -29.12934253840480693.48635817033637 71.33111662209056Crop Suitability 1981-2010This dataset show general agricultural suitability at a spatial resolution of 30 arc-second (~1km), considering rainfed conditions and irrigation on currently irrigated areas. The agricultural suitability represents for each pixel the maximum suitability value of considered 16 plants, including: We show a subset of the data that covers three time periods (1981-2010, 2011-2040, 2071-2100), as well as changes in agricultural suitability over the same periods.<br><br>For futher details see: <a href="http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0107522">Zabel F., Putzenlechner B., Mauser W. (2014): Global agricultural land resources – a high resolution suitability evaluation and its perspectives until 2100 under climate change conditions</a><br/><br>Data can also be downloaded from <a href="http://geoportal-glues.ufz.de/stories/globalsuitability.html">here</a>.<br/>overall_cropsuit_i_1981-2010WCSGeoTIFFMapX_UNDP__overall_cropsuit_i_1981-2010RectifiedGridCoverage-180.0 -60.0180.0 90.0-180.0 -60.0180.0 90.0Crop Suitability 2011-2040This dataset show general agricultural suitability at a spatial resolution of 30 arc-second (~1km), considering rainfed conditions and irrigation on currently irrigated areas. The agricultural suitability represents for each pixel the maximum suitability value of considered 16 plants, including: We show a subset of the data that covers three time periods (1981-2010, 2011-2040, 2071-2100), as well as changes in agricultural suitability over the same periods.<br><br>For futher details see: <a href="http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0107522">Zabel F., Putzenlechner B., Mauser W. (2014): Global agricultural land resources – a high resolution suitability evaluation and its perspectives until 2100 under climate change conditions</a><br/><br>Data can also be downloaded from <a href="http://geoportal-glues.ufz.de/stories/globalsuitability.html">here</a>.<br/>overall_cropsuit_i_2011-2040WCSGeoTIFFMapX_UNDP__overall_cropsuit_i_2011-2040RectifiedGridCoverage-180.0 -60.0180.0 90.0-180.0 -60.0180.0 90.0Crop Suitability 2071-2100This dataset show general agricultural suitability at a spatial resolution of 30 arc-second (~1km), considering rainfed conditions and irrigation on currently irrigated areas. The agricultural suitability represents for each pixel the maximum suitability value of considered 16 plants, including: We show a subset of the data that covers three time periods (1981-2010, 2011-2040, 2071-2100), as well as changes in agricultural suitability over the same periods.<br><br>For futher details see: <a href="http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0107522">Zabel F., Putzenlechner B., Mauser W. (2014): Global agricultural land resources – a high resolution suitability evaluation and its perspectives until 2100 under climate change conditions</a><br/><br>Data can also be downloaded from <a href="http://geoportal-glues.ufz.de/stories/globalsuitability.html">here</a>.<br/>overall_cropsuit_i_2071-2100WCSGeoTIFFMapX_UNDP__overall_cropsuit_i_2071-2100RectifiedGridCoverage-180.0 -60.0180.0 90.0-180.0 -60.0180.0 90.0Crop Suitability Change 1981-2040This dataset show general agricultural suitability at a spatial resolution of 30 arc-second (~1km), considering rainfed conditions and irrigation on currently irrigated areas. The agricultural suitability represents for each pixel the maximum suitability value of considered 16 plants, including: We show a subset of the data that covers three time periods (1981-2010, 2011-2040, 2071-2100), as well as changes in agricultural suitability over the same periods.<br><br>For futher details see: <a href="http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0107522">Zabel F., Putzenlechner B., Mauser W. (2014): Global agricultural land resources – a high resolution suitability evaluation and its perspectives until 2100 under climate change conditions</a><br/><br>Data can also be downloaded from <a href="http://geoportal-glues.ufz.de/stories/globalsuitability.html">here</a>.<br/>overall_cropsuit_i_diff_19812010_20112040WCSGeoTIFFMapX_UNDP__overall_cropsuit_i_diff_19812010_20112040RectifiedGridCoverage-180.0 -60.0180.0 90.0-180.0 -60.0180.0 90.0Crop Suitability Change 1981-2100This dataset show general agricultural suitability at a spatial resolution of 30 arc-second (~1km), considering rainfed conditions and irrigation on currently irrigated areas. The agricultural suitability represents for each pixel the maximum suitability value of considered 16 plants, including: We show a subset of the data that covers three time periods (1981-2010, 2011-2040, 2071-2100), as well as changes in agricultural suitability over the same periods.<br><br>For futher details see: <a href="http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0107522">Zabel F., Putzenlechner B., Mauser W. (2014): Global agricultural land resources – a high resolution suitability evaluation and its perspectives until 2100 under climate change conditions</a><br/><br>Data can also be downloaded from <a href="http://geoportal-glues.ufz.de/stories/globalsuitability.html">here</a>.<br/>overall_cropsuit_i_diff_19812010_20712100WCSGeoTIFFMapX_UNDP__overall_cropsuit_i_diff_19812010_20712100RectifiedGridCoverage-180.0 -60.0180.0 90.0-180.0 -60.0180.0 90.0Crop Suitability Change 2011-2100This dataset show general agricultural suitability at a spatial resolution of 30 arc-second (~1km), considering rainfed conditions and irrigation on currently irrigated areas. The agricultural suitability represents for each pixel the maximum suitability value of considered 16 plants, including: We show a subset of the data that covers three time periods (1981-2010, 2011-2040, 2071-2100), as well as changes in agricultural suitability over the same periods.<br><br>For futher details see: <a href="http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0107522">Zabel F., Putzenlechner B., Mauser W. (2014): Global agricultural land resources – a high resolution suitability evaluation and its perspectives until 2100 under climate change conditions</a><br/><br>Data can also be downloaded from <a href="http://geoportal-glues.ufz.de/stories/globalsuitability.html">here</a>.<br/>overall_cropsuit_i_diff_20112040_20712100WCSGeoTIFFMapX_UNDP__overall_cropsuit_i_diff_20112040_20712100RectifiedGridCoverage-180.0 -60.0180.0 90.0-180.0 -60.0180.0 90.0Crops yield 2010 (FAO)The production yield for the main crops for the year 2010 is derived from the sub-theme 'Area, Yield and Production' of the theme 5: 'Actual Yields and Production' of GAEZ v4 of the FAO. The sub-theme presents maps of the spatial distribution of harvested areas (1000 ha), production (1000 tons) and yield (t/ha) for 26 major crops/crop groups for 3-year averages of 1999-2001 (representing year 2000) and 2009–2011 (representing 2010). Together, these 26 crop commodities account for all recorded crop production of each delineated spatial allocation unit (i.e., a country) for which crop statistics were assembled. Yield and production are provided in fresh/harvested weight.all_2010_yld_optWCSGeoTIFFunccd__all_2010_yld_optRectifiedGridCoverage-180.0 -90.0180.0 90.0-180.0 -90.0180.0 90.0Cumulative Impact - Sum of Pressure Data 2013 - KNBThis is the cumulative human impact based on raw stressors for the year 2013 (Halpern et al. 2015. Spatial and temporal changes in cumulative human impacts on the world's ocean.). The cumulative human impact for the year 2013 is the sum of all normalized stressor data adjusted for habitat/pressure vulnerability. List of stressor data: artisanal_fishing, demersal_destructive_fishing, demersal_nondest_high_bycatch, demersal_nondest_low_bycatch, inorganic, invasives, night_lights, ocean_acidification, ocean_pollution, oil_rigs, pelagic_high_bycatch, pelagic_low_bycatch, plumes_fert, plumes_pest, population, shipping, slr, sst, uv. Cumulative human impact from "Benjamin Halpern, Melanie Frazier, John Potapenko, Kenneth Casey, Kellee Koenig, et al. 2015. Cumulative human impacts: pressure and cumulative impacts data (2013, all pressures). Knowledge Network for Biocomplexity. doi:10.5063/F15718ZN."13_global_cumul_impact_all_pressures_KNB_WGS84WCSGeoTIFFcumulative impactall pressuresPollutionOceanunea_pollution__13_global_cumul_impact_all_pressures_KNB_WGS84RectifiedGridCoverage-179.99999978928923 -89.99765318081656179.99802237904845 85.28467978960619-179.99999978928923 -89.99765318081656179.99802237904845 85.28467978960619Deforestation and Degradation - 2015-2020 (CAFI)The layer shows the degraded forest areas and the deforested areas in the Congo Basin. Data produced in the frame of the project "Assessing deforestation and forest degradation trends and associated current and historical direct drivers in the six CAFI partner countries". Credits: CAFI. Central African Forest InitiativeCAFI_change_map_2015_2020_sinu_wgs84_optWCSGeoTIFFpeatlands__CAFI_change_map_2015_2020_sinu_wgs84_optRectifiedGridCoverage5.354407954878081 -13.45910566948061631.7723896460075 13.0835563559515995.354407954878081 -13.45910566948061631.7723896460075 13.083556355951599Degraded Areas - PERUNational map of degraded areas of Peru. Source: http://geoservidor.minam.gob.pe/informacion-institucional/intercambio-de-datos/ Methodology: http://geoservidor.minam.gob.pe/wp-content/uploads/2017/06/Estudio-Identificaci%C3%B3n-de-%C3%81reas-Degradadas.pdf Wallmap: http://geoservidor.minam.gob.pe/wp-content/uploads/2017/06/Mapa-Nacional-de-Areas-Degradadas.pdfdegrad_nac_wgsWCSGeoTIFFMapX_UNDP_nd__degraded_areas_PERRectifiedGridCoverage-82.8117704208033 -19.05097182878838-67.7006922413798 0.5435622953186765-82.8117704208033 -19.05097182878838-67.7006922413798 0.5435622953186765Degraded lands of PeruLayer provided by Ministry of Environment of Peru. Land degradation is the long-term loss of ecosystem function and services, caused by disturbances from which the system cannot recover unaided. he following link has information on the methodology used to produce the national map pf degradation. https://geoservidor.minam.gob.pe/wp-content/uploads/2017/06/Estudio-Identificaci%C3%B3n-de-%C3%81reas-Degradadas.pdf SDG Goal 15. Protect, restore and promote sustainable use of terrestrial ecosystems, sustainably manage forests, combat desertification, and halt and reverse land degradation and halt biodiversity loss.ad_2019WCSGeoTIFFwesr_cca__Degraded landsRectifiedGridCoverage-324782.0 7893540.01270348.0 1.006008E7-82.811770420087 -19.050723822685583-67.70058473717306 0.5435574567185314Difference in projected timing of annual severe bleaching between SSP5-8.5 and SSP2-4.5The data and complementary report have two purposes, and are intended for use by the global coral reef scientific and management community, which had these 2 objectives. Objective 1. To present projections of coral bleaching conditions (i.e., exposure to the primary climate threat to coral reefs) and potential adaptation at a spatial scale that enables use of the data in management and conservation planning and in support of other decisions influencing coral reefs and reef use. Objective 2. To evaluate the implications of the Paris Agreement as well as failure to achieve its goal, by comparing the projected timing of annual severe bleaching between Shared Socioeconomic Pathways SSP5-8.5 and SSP2-4.5. Data source used: Intergovernmental Panel on Climate Change (IPCC) Coupled Model Interpolation Project Phase 6 (CMIP6) model outputs for sea surface temperature (https://www.ipcc-data.org/), combined with United States National Oceanic and Atmospheric Administration (NOAA) CoralTemp data from NOAA Coral Reef Watch (https://www.coralreefwatch.noaa.gov/product/5km/index_5km_sst.php).diff_ssp245_ssp585WCSGeoTIFFwesr_climat__diff_ssp245_ssp585RectifiedGridCoverage-180.0 -90.0180.0 90.0-180.0 -90.0180.0 90.0Digital elevation model at 90m resolutionThis digitial elevation model (DEM) is derived from CGIAR-CSI SRTM v4.1 and ASTER GDEM v2 data products, and has been processed and mergedm inculding void-filling and smoothing of irregularities, to provide continuous coverage of ~91% of the globe.<br><br>For full processing and merging methodology see: <a href="https://www.sciencedirect.com/science/article/abs/pii/S0924271613002360"target="_blank">Robinson et al. (2014)</a>.<br/><br>Data can also be downloaded from <a href="http://www.earthenv.org/DEM"target="_blank">EarthEnv</a>.<br/><br>This data available under a <a href="https://creativecommons.org/licenses/by/4.0/"target="_blank">Creative Commons Attribution 4.0 International License</a> and is provided without any warranty of any kind whatsoever, either express or implied, including warranties of merchantability and fitness for a particular purpose. The creators of the product shall not be liable for incidental, consequential, or special damages arising out of the use of any data.<br/>dem90WCSGeoTIFFMapX_UNDP__dem90RectifiedGridCoverage-179.99999999999966 -59.999999999948244179.99999999986994 82.99999999999997-179.99999999999966 -59.999999999948244179.99999999986994 82.99999999999997Digital elevation model at 90m resolution - South SudanThis digitial elevation model (DEM) is derived from CGIAR-CSI SRTM v4.1 and ASTER GDEM v2 data products, and has been processed and mergedm inculding void-filling and smoothing of irregularities, to provide continuous coverage of ~91% of the globe.<br><br>For full processing and merging methodology see: <a href="https://www.sciencedirect.com/science/article/abs/pii/S0924271613002360"target="_blank">Robinson et al. (2014)</a>.<br/><br>Data can also be downloaded from <a href="http://www.earthenv.org/DEM"target="_blank">EarthEnv</a>. This specific layer is the cropped version by the South Sudan administrative boundaries of the global layer that is available at <a href="https://app.mapx.org?project=MX-XNI-RMZ-KKL-FMS-DVH&views=MX-AYR3I-HH9EC-LDLGC" target="_blank"> DEM90 - UnBiodiversityLab</a>. <br/><br> <br/><br>This data available under a <a href="https://creativecommons.org/licenses/by/4.0/"target="_blank">Creative Commons Attribution 4.0 International License</a> and is provided without any warranty of any kind whatsoever, either express or implied, including warranties of merchantability and fitness for a particular purpose. The creators of the product shall not be liable for incidental, consequential, or special damages arising out of the use of any data.<br/>SSD_dem90_optWCSGeoTIFFMAPX__SSD_dem90_optRectifiedGridCoverage24.150833333259754 3.4875000000287635.94749999992215 12.23750000002559124.150833333259754 3.4875000000287635.94749999992215 12.237500000025591Disappearing Climates 2050 SRES A2This is the disappearing climate risk dataset - measure of dissimilarity between a pixel’s late 20th century climate and its closest matching pixel in the global set of 21st-century climatesThis dataset contains two metrics for climate change exposure using downscaled climate projections with the SRES A2 emissions scenario (Tabor and Williams, 2007). <br> This dataset contains two metrics for climate change exposure using downscaled climate projections with the SRES A2 emissions scenario (Tabor and Williams, 2007).The metrics represent dissimilarity measurements of the squared Euclidean distance between seasonal (June–August and December–February) temperature and precipitation variables in the 20th century climate and mid-21st century climate. (1) disappearing climate risk - measure of dissimilarity between a pixel’s late 20th century climate and its closest matching pixel in the global set of 21st-century climates (2) novel climate risk - measure of dissimilarity between a pixel’s future climate and its closest matching pixel in the global set of late 20th-century climates. <br> John W. Williams, Karyn Tabor. (2018). Novel and Disappearing Climates 2050 SRES A2 (Version 1) [Data set]. Zenodo. http://doi.org/10.5281/zenodo.1559299 John W. Williams, Karyn Tabor. (2018). Novel and Disappearing Climates 2050 SRES A2 (Version 1) [Data set]. Zenodo. http://doi.org/10.5281/zenodo.1559299 <br> Support for this project was provided by Conservation International, the Land Tenure Center at the University of Wisconsin, the Center for Climatic Research at the University of Wisconsin, and the Environment Program at the University of Wisconsin–Madison. This research has been funded in part by the Walton Family Foundation, the Gordon and Betty Moore Foundation, and a gift from Betty and Gordon Moore.disp_sresa2_2050_10min_int1000_optWCSGeoTIFFLWP__disp_sresa2_2050_10min_int1000_optRectifiedGridCoverage-180.0003335 -59.16733350000001180.0003865 83.66628549999999-180.0003335 -59.16733350000001180.0003865 83.66628549999999Dogondoutchi_Duration_78_T100Dogondoutchi_Duration_78_T100_opt_deflateWCSGeoTIFFrisk_niger_undp__Dogondoutchi_Duration_78_T100RectifiedGridCoverage2.431818181818182 11.931818181818186.068181818181818 15.5681818181818182.431818181818182 11.931818181818186.068181818181818 15.568181818181818Dogondoutchi_Duration_78_T25Dogondoutchi_Duration_78_T25_opt_deflateWCSGeoTIFFrisk_niger_undp__Dogondoutchi_Duration_78_T25RectifiedGridCoverage2.431818181818182 11.931818181818186.068181818181818 15.5681818181818182.431818181818182 11.931818181818186.068181818181818 15.568181818181818Dogondoutchi_Duration_78_T250Dogondoutchi_Duration_78_T250_opt_deflateWCSGeoTIFFrisk_niger_undp__Dogondoutchi_Duration_78_T250RectifiedGridCoverage2.431818181818182 11.931818181818186.068181818181818 15.5681818181818182.431818181818182 11.931818181818186.068181818181818 15.568181818181818Dogondoutchi_Duration_78_T50Dogondoutchi_Duration_78_T50_opt_deflateWCSGeoTIFFrisk_niger_undp__Dogondoutchi_Duration_78_T50RectifiedGridCoverage2.431818181818182 11.931818181818186.068181818181818 15.5681818181818182.431818181818182 11.931818181818186.068181818181818 15.568181818181818Dogondoutchi_Intensity_78_T100Dogondoutchi_Intensity_78_T100_opt_deflateWCSGeoTIFFrisk_niger_undp__Dogondoutchi_Intensity_78_T100RectifiedGridCoverage2.431818181818182 11.931818181818186.068181818181818 15.5681818181818182.431818181818182 11.931818181818186.068181818181818 15.568181818181818Dogondoutchi_Intensity_78_T25Dogondoutchi_Intensity_78_T25_opt_deflateWCSGeoTIFFrisk_niger_undp__Dogondoutchi_Intensity_78_T25RectifiedGridCoverage2.431818181818182 11.931818181818186.068181818181818 15.5681818181818182.431818181818182 11.931818181818186.068181818181818 15.568181818181818Dogondoutchi_Intensity_78_T250Dogondoutchi_Intensity_78_T250_opt_deflateWCSGeoTIFFrisk_niger_undp__Dogondoutchi_Intensity_78_T250RectifiedGridCoverage2.431818181818182 11.931818181818186.068181818181818 15.5681818181818182.431818181818182 11.931818181818186.068181818181818 15.568181818181818Dogondoutchi_Intensity_78_T50Dogondoutchi_Intensity_78_T50_opt_deflateWCSGeoTIFFrisk_niger_undp__Dogondoutchi_Intensity_78_T50RectifiedGridCoverage2.431818181818182 11.931818181818186.068181818181818 15.5681818181818182.431818181818182 11.931818181818186.068181818181818 15.568181818181818Dogondoutchi_Severity_78_T100Dogondoutchi_Severity_78_T100_opt_deflateWCSGeoTIFFrisk_niger_undp__Dogondoutchi_Severity_78_T100RectifiedGridCoverage2.431818181818182 11.931818181818186.068181818181818 15.5681818181818182.431818181818182 11.931818181818186.068181818181818 15.568181818181818Dogondoutchi_Severity_78_T25Dogondoutchi_Severity_78_T25_opt_deflateWCSGeoTIFFrisk_niger_undp__Dogondoutchi_Severity_78_T25RectifiedGridCoverage2.431818181818182 11.931818181818186.068181818181818 15.5681818181818182.431818181818182 11.931818181818186.068181818181818 15.568181818181818Dogondoutchi_Severity_78_T250Dogondoutchi_Severity_78_T250_opt_deflateWCSGeoTIFFrisk_niger_undp__Dogondoutchi_Severity_78_T250RectifiedGridCoverage2.431818181818182 11.931818181818186.068181818181818 15.5681818181818182.431818181818182 11.931818181818186.068181818181818 15.568181818181818Dogondoutchi_Severity_78_T50Dogondoutchi_Severity_78_T50_opt_deflateWCSGeoTIFFrisk_niger_undp__Dogondoutchi_Severity_78_T50RectifiedGridCoverage2.431818181818182 11.931818181818186.068181818181818 15.5681818181818182.431818181818182 11.931818181818186.068181818181818 15.568181818181818Drought recurrence map between 1995 and 2024In the context of global warming, drought is becoming more frequent and intense, particularly in West Africa, where erratic rainfall and rising temperatures are exacerbating water deficits. Senegal, located in the Sahel region, is an emblematic case: since the 1970s, the country has been undergoing progressive aridification, with major impacts on rain-fed agriculture, water resources, and ecosystems. And the Kédougou region is no exception.drought hazardrisk_southeast_senegal__droughtRectifiedGridCoverage-13.239460488881921 12.307817707721565-11.342218608821492 13.461254532531031-13.239460488881921 12.307817707721565-11.342218608821492 13.461254532531031Drought vulnerability recurrence map between 1995 and 2024Cultivated and densely populated areas in the Kédougou region are particularly vulnerable to drought due to the intense pressure on water resources and soil quality. In this area, intensive agriculture, often practiced without appropriate rotations or soil conservation measures, leads to a degradation of soil structure and a reduction in its moisture retention capacity. At the same time, high population density increases water demand for domestic and agricultural uses, which, combined with increasingly erratic rainfall patterns due to climate change, exacerbates water stress. These combined socioeconomic and environmental factors make these areas particularly susceptible to drought.drought vulnerabilityrisk_southeast_senegal__drought_vulnerabilityRectifiedGridCoverage691002.071985387 1362051.4207955943896820.5992062466 1490045.1911373287-13.243602482435989 12.296668462171974-11.336189526734906 13.472239258295431Drought-affected areas in Kedougou between 1995 and 2004The TCI, VCI and VHI indices are used to identify areas affected by drought. They are characterized as vegetation indices because they describe the state of vegetation in a specific area. The VCI is widely used to detect changes in vegetation from poor to favorable conditions. TCI detects vegetative stress induced by high temperatures and high humidity. VHI, on the other hand, is the combination of TCI (temperature) and VCI (vegetation condition) that describes vegetation health.vhi_1995_2004WCSGeoTIFFrisk_southeast_senegal__vhi_1995_2004RectifiedGridCoverage-13.239460488881921 12.307817707721565-11.342218608821492 13.461254532531031-13.239460488881921 12.307817707721565-11.342218608821492 13.461254532531031Drought-affected areas in Kedougou between 2005 and 2014The TCI, VCI and VHI indices are used to identify areas affected by drought. They are characterized as vegetation indices because they describe the state of vegetation in a specific area. The VCI is widely used to detect changes in vegetation from poor to favorable conditions. TCI detects vegetative stress induced by high temperatures and high humidity. VHI, on the other hand, is the combination of TCI (temperature) and VCI (vegetation condition) that describes vegetation health.vhi_2005_2014WCSGeoTIFFrisk_southeast_senegal__vhi_2005_2014RectifiedGridCoverage-13.239460488881921 12.307817707721565-11.342218608821492 13.461254532531031-13.239460488881921 12.307817707721565-11.342218608821492 13.461254532531031Drought-affected areas in Kedougou between 2015 and 2024The TCI, VCI and VHI indices are used to identify areas affected by drought. They are characterized as vegetation indices because they describe the state of vegetation in a specific area. The VCI is widely used to detect changes in vegetation from poor to favorable conditions. TCI detects vegetative stress induced by high temperatures and high humidity. VHI, on the other hand, is the combination of TCI (temperature) and VCI (vegetation condition) that describes vegetation health.vhi_2015_2024WCSGeoTIFFrisk_southeast_senegal__vhi_2015_2024RectifiedGridCoverage-13.239460488881921 12.307817707721565-11.342218608821492 13.461254532531031-13.239460488881921 12.307817707721565-11.342218608821492 13.461254532531031Dust Concentration Extreme Events 2017-2022dust_extreme_events_2017_2022_outputWCSGeoTIFFunccd__dust_extreme_events_2017_2022_outputRectifiedGridCoverage-17.499999999999943 -1.499999999999943275.00000000000006 45.00000000000006-17.499999999999943 -1.499999999999943275.00000000000006 45.00000000000006Dust Seasonal Strong Events - Summer 1980 - 2022total_strong_events_summer_output_optWCSGeoTIFFgrid_data_core__total_strong_events_summer_output_optRectifiedGridCoverage-17.999999999999943 -1.499999999999943275.00000000000006 45.00000000000006-17.999999999999943 -1.499999999999943275.00000000000006 45.00000000000006Earthquakes PGA GSHAP (m/s2)gshapWCSGeoTIFFwesr_risk__gshapRectifiedGridCoverage-180.00000000000006 -65.0180.09999999999997 90.10000000000002-180.00000000000006 -65.0180.09999999999997 90.10000000000002Economic StockThe Global Exposure Database (GED) was developed for GAR15 by UNEP/GRID-Geneva with close collaboration and inputs from WAPMERR (World Agency of Planetary Monitoring and Earthquake Risk Reduction), EU Joint Research Center (JRC), and Kokusai Kogyo. The GED provides estimation on the economic value of the exposed assets (public and private buildings located in urban and rural areas), as well as their physical characteristics and occupancy levels. This information is key to assess the potential damages from different hazards to each of the exposed elements and to assign the appropriate vulnerability models. The global exposure database is developed at 1x1 km spatial resolution at coastal areas and at 5x5 km spatial resolution everywhere else on the globe. The GED is based on a top-down approach where statistical information including socio-economic, building type, and capital stock at a national level are transposed onto the grids of 5x5 or 1x1 using geographic distribution of population data and gross domestic product (GDP) as proxies. For more details see: De Bono and Chatenoux (2014).gar_eco_expWCSGeoTIFFwesr_risk__gar_eco_expRectifiedGridCoverage-179.99954 -55.71600000000001179.95786999999999 81.82433-179.99954 -55.71600000000001179.95786999999999 81.82433Economical Exposition To Landslides Triggered By EarthquakesThis dataset includes an estimate of the annual economical exposition of landslide triggered by earthquakes. It depends on the combination of trigger and susceptibility defined by six parameters: slope factor, lithological (or geological) conditions, soil moisture condition, vegetation cover, precipitation and seismic conditions. A Global Domestic Product grid for the year 2010, provided by the World Bank, has also been used. Unit is expected average annual GDP (2010 as the year of reference) exposed in (US $, year 2000 equivalent). This product was designed by International Centre for Geohazards (NGI) for the Global Assessment Report on Risk Reduction (GAR). It was modeled using global data. Credit: GIS processing International Centre for Geohazards (NGI).le1010ieeyWCSGeoTIFFwesr_risk__le1010ieeyRectifiedGridCoverage-180.0 -90.0179.99999999999983 89.99166666666659-180.0 -90.0179.99999999999983 89.99166666666659Economical Exposition To Landslides Triggered By PrecipitationsThis dataset includes an estimate of the annual frequency of landslide triggered by precipitations. It depends on the combination of trigger and susceptibility defined by six parameters: slope factor, lithological (or geological) conditions, soil moisture condition, vegetation cover, precipitation and seismic conditions. Unit is expected annual probability and percentage of pixel of occurrence of a potentially destructive landslide event x 1000000. This product was designed by International Centre for Geohazards (NGI) for the Global Assessment Report on Risk Reduction (GAR). It was modeled using global data. Credit: GIS processing International Centre for Geohazards (NGI).ls_ecoexp2WCSGeoTIFFwesr_risk__ls_ecoexp2RectifiedGridCoverage-180.0 -90.0179.99999999999983 89.99166666666659-180.0 -90.0179.99999999999983 89.99166666666659Economical Exposition To TsunamisThis dataset includes an estimation of the annual economical exposition to tsunamis. It is based on three sources: 1) A comprehensive list of reports and scientific papers compiled and utilized in producing tsunami hazard maps as well as finding return periods of future events. 2) Applying numerical tsunami models and zooming on selected areas. 3) A Global Domestic Product grid for the year 2010, provided by the World Bank. Unit is expected average annual GDP (2010 as the year of reference) exposed in (US $, year 2000 equivalent). This product was designed by International Centre for Geohazards (NGI) for the Global Assessment Report on Risk Reduction (GAR). It was modeled using global data. Credit: GIS processing International Centre for Geohazards (NGI)ts_ecoexpWCSGeoTIFFwesr_risk__ts_ecoexpRectifiedGridCoverage-180.0 -90.0179.99999999985602 83.99999999993042-180.0 -90.0179.99999999985602 83.99999999993042Economical Exposition to Drought Events in GHA RegionThis dataset includes an estimation of the annual economical exposition to drought based on Standardized Precipitation Index. It is based on three sources: 1) A global monthly gridded precipitation dataset obtained from the Climatic Research Unit (University of East Anglia). 2) A GIS modeling of global Standardized Precipitation Index based on Brad Lyon (IRI, Columbia University) methodology. Data provided by ICPAC Geoportal at http://geoportal.icpac.net/.geonode_drought_econ_exposureWCSGeoTIFFwesr_cca__geonode_drought_econ_exposure_icpacRectifiedGridCoverage21.838947 -11.74570000029600451.440099935812 23.1428621.838947 -11.74570000029600451.440099935812 23.14286EmodNet_HA_Vessel_density_2019avg_Part1_st5_Medcut_optEmodNet_HA_Vessel_density_2019avg_Part1_st5_Medcut_optWCSGeoTIFFMedicip__EmodNet_HA_Vessel_density_2019avgRectifiedGridCoverage2905000.0 748000.06776000.0 2653000.0-8.270934566444502 26.19942651646425740.671063797404635 46.83058065129969Eq_phexEq_phexWCSGeoTIFFwesr_risk__Eq_physexpRectifiedGridCoverage-180.0 -90.0180.0 90.0-180.0 -90.0180.0 90.0Evapotranspiration Anomaly 2017 (1-10) USGSCalculated through raster calculator as average of 10 specific rasters, each per month from jan to oct 2017.ET2017_1-10WCSGeoTIFFMAPX__ET2017_1-10_usgsRectifiedGridCoverage-20.008449316024837 -38.0030921809375455.00689406692973 40.004371177405076-20.008449316024837 -38.0030921809375455.00689406692973 40.004371177405076Evapotranspiration Autumn 2020 (USGS)The original data is from the USGS FEWS Data Downloads (https://earlywarning.usgs.gov/fews/datadownloads/Global/PET). The present raster in the average monthly evapotranspiration for autumn 2020. It in the average of the various evapotranspiration files available in autumn 2020 (September 2020, October 2020 and November 2020). On vegetated landscapes, Evapotranspiration (ET) can be simplified as the combination of evaporation from soil and transpiration from vegetation. Actual ET (ETa) is produced using the Operational Simplified Surface Energy Balance (SSEBop) model (Senay et al., 2013) for 2003 to present using data stream from the Aqua satellite. The SSEBop setup is based on the original Simplified Surface Energy Balance (SSEB) approach (Senay et al., 2007, 2011) with unique and improved parameterizations for operational applications. It combines ET fractions generated from remotely sensed MODIS thermal imagery, summarized every 10 days (dekadal), with reference ET, generated from weather data fields using the Penman-Monteith Equation. The unique feature of the SSEBop parameterization is that it uses a pre-defined, seasonally dynamic surface psychrometer constant parameter that helps define the hot/dry and cold/wet reference limits, unique to each pixel. Furthermore, the cold/wet limit is determined using the daily maximum air temperature (Ta) using the c factor parameter which relates Ta to land surface temperature at well-vegetated/well-watered surfaces. The original SSEB model was formulated based on an adaptation of the hot and cold pixel principles of SEBAL (Bastiaanssen et al., 1998) and METRIC (Allen et al., 2007) models. NoData pixels are indicated as NoDATA in ArcGIS. However, other software (e.g., ERDAS imagine) show nodata as -32768.00. ETa data and anomaly products (current vs. 2003 &#8211; 2015 median), produced at 1 km resolution, are available at: https://earlywarning.usgs.gov/fews.m202009-11_automne_optWCSGeoTIFFwesr_mountains__m202009-11_automne_optRectifiedGridCoverage-180.0 -60.00000000570601180.000293053458 80.002258848399-180.0 -60.00000000570601180.000293053458 80.002258848399Evapotranspiration Spring 2020 (USGS)The original data is from the USGS FEWS Data Downloads (https://earlywarning.usgs.gov/fews/datadownloads/Global/PET). The present raster in the average monthly evapotranspiration for spring 2020. It in the average of the various evapotranspiration files available in spring 2020 (March 2020, April 2020 and May 2020). On vegetated landscapes, Evapotranspiration (ET) can be simplified as the combination of evaporation from soil and transpiration from vegetation. Actual ET (ETa) is produced using the Operational Simplified Surface Energy Balance (SSEBop) model (Senay et al., 2013) for 2003 to present using data stream from the Aqua satellite. The SSEBop setup is based on the original Simplified Surface Energy Balance (SSEB) approach (Senay et al., 2007, 2011) with unique and improved parameterizations for operational applications. It combines ET fractions generated from remotely sensed MODIS thermal imagery, summarized every 10 days (dekadal), with reference ET, generated from weather data fields using the Penman-Monteith Equation. The unique feature of the SSEBop parameterization is that it uses a pre-defined, seasonally dynamic surface psychrometer constant parameter that helps define the hot/dry and cold/wet reference limits, unique to each pixel. Furthermore, the cold/wet limit is determined using the daily maximum air temperature (Ta) using the c factor parameter which relates Ta to land surface temperature at well-vegetated/well-watered surfaces. The original SSEB model was formulated based on an adaptation of the hot and cold pixel principles of SEBAL (Bastiaanssen et al., 1998) and METRIC (Allen et al., 2007) models. NoData pixels are indicated as NoDATA in ArcGIS. However, other software (e.g., ERDAS imagine) show nodata as -32768.00. ETa data and anomaly products (current vs. 2003 &#8211; 2015 median), produced at 1 km resolution, are available at: https://earlywarning.usgs.gov/fews.m202003-05_printemps_optWCSGeoTIFFevapotranspirationwesr_mountains__m202003-05_printemps_optRectifiedGridCoverage-180.0 -60.00000000570601180.000293053458 80.002258848399-180.0 -60.00000000570601180.000293053458 80.002258848399Evapotranspiration Summer 2020 (USGS)The original data is from the USGS FEWS Data Downloads (https://earlywarning.usgs.gov/fews/datadownloads/Global/PET). The present raster in the average monthly evapotranspiration for summer 2020. It in the average of the various evapotranspiration files available in summer 2020 (June 2020, July 2020 and August 2020). On vegetated landscapes, Evapotranspiration (ET) can be simplified as the combination of evaporation from soil and transpiration from vegetation. Actual ET (ETa) is produced using the Operational Simplified Surface Energy Balance (SSEBop) model (Senay et al., 2013) for 2003 to present using data stream from the Aqua satellite. The SSEBop setup is based on the original Simplified Surface Energy Balance (SSEB) approach (Senay et al., 2007, 2011) with unique and improved parameterizations for operational applications. It combines ET fractions generated from remotely sensed MODIS thermal imagery, summarized every 10 days (dekadal), with reference ET, generated from weather data fields using the Penman-Monteith Equation. The unique feature of the SSEBop parameterization is that it uses a pre-defined, seasonally dynamic surface psychrometer constant parameter that helps define the hot/dry and cold/wet reference limits, unique to each pixel. Furthermore, the cold/wet limit is determined using the daily maximum air temperature (Ta) using the c factor parameter which relates Ta to land surface temperature at well-vegetated/well-watered surfaces. The original SSEB model was formulated based on an adaptation of the hot and cold pixel principles of SEBAL (Bastiaanssen et al., 1998) and METRIC (Allen et al., 2007) models. NoData pixels are indicated as NoDATA in ArcGIS. However, other software (e.g., ERDAS imagine) show nodata as -32768.00. ETa data and anomaly products (current vs. 2003 &#8211; 2015 median), produced at 1 km resolution, are available at: https://earlywarning.usgs.gov/fews.m202006-08_ete_optWCSGeoTIFFevapotranspirationwesr_mountains__m202006-08_ete_optRectifiedGridCoverage-180.0 -60.00000000570601180.000293053458 80.002258848399-180.0 -60.00000000570601180.000293053458 80.002258848399Evapotranspiration Winter 2019 - 2020 (USGS)The original data is from the USGS FEWS Data Downloads (https://earlywarning.usgs.gov/fews/datadownloads/Global/PET). The present raster in the average monthly evapotranspiration for winter 2019-2020. It in the average of the various evapotranspiration files available in winter 2019-2020 (December 2019, January 2020 and February 2020). On vegetated landscapes, Evapotranspiration (ET) can be simplified as the combination of evaporation from soil and transpiration from vegetation. Actual ET (ETa) is produced using the Operational Simplified Surface Energy Balance (SSEBop) model (Senay et al., 2013) for 2003 to present using data stream from the Aqua satellite. The SSEBop setup is based on the original Simplified Surface Energy Balance (SSEB) approach (Senay et al., 2007, 2011) with unique and improved parameterizations for operational applications. It combines ET fractions generated from remotely sensed MODIS thermal imagery, summarized every 10 days (dekadal), with reference ET, generated from weather data fields using the Penman-Monteith Equation. The unique feature of the SSEBop parameterization is that it uses a pre-defined, seasonally dynamic surface psychrometer constant parameter that helps define the hot/dry and cold/wet reference limits, unique to each pixel. Furthermore, the cold/wet limit is determined using the daily maximum air temperature (Ta) using the c factor parameter which relates Ta to land surface temperature at well-vegetated/well-watered surfaces. The original SSEB model was formulated based on an adaptation of the hot and cold pixel principles of SEBAL (Bastiaanssen et al., 1998) and METRIC (Allen et al., 2007) models. NoData pixels are indicated as NoDATA in ArcGIS. However, other software (e.g., ERDAS imagine) show nodata as -32768.00. ETa data and anomaly products (current vs. 2003 &#8211; 2015 median), produced at 1 km resolution, are available at: https://earlywarning.usgs.gov/fews.m201912-202001-02_hiver_optWCSGeoTIFFwesr_mountains__m201912-202001-02_hiver_optRectifiedGridCoverage-180.0 -60.00000000570601180.000293053458 80.002258848399-180.0 -60.00000000570601180.000293053458 80.002258848399Evapotranspiration anomaly 2016_USGSData downloaded directly from USGS.y2016_modisSSEBopETv4_anomaly_pctWCSGeoTIFFMAPX__y2016_modisSSEBopETv4_anomaly_pctRectifiedGridCoverage-20.00844965760001 -38.00535077740005455.006893565199974 40.002112414399946-20.00844965760001 -38.00535077740005455.006893565199974 40.002112414399946Extreme Heat recurrence map between 1995 and 2024The study of extreme heat in the Kédougou region, based on a three-decadal analysis (1995–2004, 2005–2014, 2015–2024), highlights an alarming change in surface temperatures, corroborated by the observations of Sy et al. (2021) in northern Senegal. The combined mapping of the three periods, synthesizing the occurrences of exceedances of the critical threshold of 42°C (corresponding to the 90th percentile of local maxima), makes it possible to visualize the areas most exposed over the long term.heatrisk_southeast_senegal__heatRectifiedGridCoverage-13.239460488881921 12.307817707721565-11.342218608821492 13.461254532531031-13.239460488881921 12.307817707721565-11.342218608821492 13.461254532531031Extreme Heat vulnerability recurrence map between 1995 and 2024Vulnerability to extreme heat was assessed through the degree of urbanization and population density, highlighting socio-spatial inequalities in the face of heat waves. Urbanization, with a rate of 7.16% in 2024, exacerbates the phenomenon, and population density intensifies exposure to health risks.heat vulnerabilityrisk_southeast_senegal__heat_vulnerabilityRectifiedGridCoverage691016.3363796656 1362065.7892065302896833.3834748773 1490057.9779641528-13.243470490606473 12.296796567458285-11.336069905109735 13.472353901206636Fire vulnerability recurrence map between 1995 and 2024Vulnerability to forest fires in the Kédougou region was analyzed through three key indicators: cultivated areas, population density and wooded savannahs. These elements were selected for their ability to reflect the interactions between human activities, natural ecosystems and fire propagation mechanisms.burnrisk_southeast_senegal__burn_vulnerabilityRectifiedGridCoverage-13.239460488881921 12.307817707721565-11.342218608821492 13.461254532531031-13.239460488881921 12.307817707721565-11.342218608821492 13.461254532531031Flood Hazard 10 Years - SSP5 RCP8.5 - CAFThis layer shows the height (in cm) reached by the water in consequence of a flooding event with 10 years return period based on a Upper Boundary climate scenario (RCP8.5). The hydrological model used is the Continuum model (Silvestro et al. 2013 and 2015). It is a continuous, distributed and physically based hydrological model able to reproduce the spatial-temporal evolution of soil moisture, energy fluxes, surface soil temperature, evapotranspiration and discharge. Climate dataset used for the simulation of basin response are the W5E5 Global Meteorological dataset for present climate, and the ISIMIP3b Global Meteorological reanalysis dataset for the futurE climate. In order to generate flood hazard maps, resulting discharge estimates are input to an hydraulic model based on the Manning equation that compute channel uniform flow depth. This simplified approach fits to determine flood maps on large areas.global_rcp85_h10glob_CentreAfrique_optWCSGeoTIFFcc_and_health__global_rcp85_h10glob_CentreAfrique_optRectifiedGridCoverage14.420833333 2.224166667000000427.463333333 11.007514.420833333 2.224166667000000427.463333333 11.0075Flood Hazard 100 Years (cm)The GAR Atlas global flood hazard assessment uses a probabilistic approach for modelling riverine flood major river basins around the globe. This has been possible after compiling a global database of stream-flow data, merging different sources and gathering more than 8000 stations over the globe in order to calculate the range of possible discharges from very low to the maximum possible scales at different locations along the rivers. The calculated discharges were introduced in the river sections to model water levels downstream. This procedure allowed for the determination of stochastic event-sets of riverine floods from which hazard maps for several return periods (25, 50, 100, 200, 500, 1000 years) were obtained. The hazard maps are developed at 1kmx1km resolution and have been validated against satellite flood footprints from different sources (DFO archive, UNOSAT flood portal) performing well especially for big events For smaller events (lower return periods), the GAR Atlas flood hazard maps tend to overestimate with respect to similar maps produced locally (hazard maps where available for some countries and were used as benchmark). The main issue being that, due to the resolution, the GAR Atlas flood hazard maps do not take into account flood defenses that are normally present to preserve the value exposed to floods. More information about the flood hazard assessment can be found in the background paper (Rudari et al., 2015).fl_hazard_100_yrpWCSGeoTIFFwesr_risk__fl_hazard_100_yrpRectifiedGridCoverage-141.09900045782885 -56.085762831133565180.08131581575455 60.09233546603441-141.09900045782885 -56.085762831133565180.08131581575455 60.09233546603441Flood Hazard 1000 Years (cm)The GAR Atlas global flood hazard assessment uses a probabilistic approach for modelling riverine flood major river basins around the globe. This has been possible after compiling a global database of stream-flow data, merging different sources and gathering more than 8000 stations over the globe in order to calculate the range of possible discharges from very low to the maximum possible scales at different locations along the rivers. The calculated discharges were introduced in the river sections to model water levels downstream. This procedure allowed for the determination of stochastic event-sets of riverine floods from which hazard maps for several return periods (25, 50, 100, 200, 500, 1000 years) were obtained. The hazard maps are developed at 1kmx1km resolution and have been validated against satellite flood footprints from different sources (DFO archive, UNOSAT flood portal) performing well especially for big events For smaller events (lower return periods), the GAR Atlas flood hazard maps tend to overestimate with respect to similar maps produced locally (hazard maps where available for some countries and were used as benchmark). The main issue being that, due to the resolution, the GAR Atlas flood hazard maps do not take into account flood defenses that are normally present to preserve the value exposed to floods. More information about the flood hazard assessment can be found in the background paper (Rudari et al., 2015).fl_hazard_1000_yrpWCSGeoTIFFwesr_risk__fl_hazard_1000_yrpRectifiedGridCoverage-141.09900045782885 -56.085762831133565180.08131581575455 60.09233546603441-141.09900045782885 -56.085762831133565180.08131581575455 60.09233546603441Flood Hazard 200 Years (cm)The GAR Atlas global flood hazard assessment uses a probabilistic approach for modelling riverine flood major river basins around the globe. This has been possible after compiling a global database of stream-flow data, merging different sources and gathering more than 8000 stations over the globe in order to calculate the range of possible discharges from very low to the maximum possible scales at different locations along the rivers. The calculated discharges were introduced in the river sections to model water levels downstream. This procedure allowed for the determination of stochastic event-sets of riverine floods from which hazard maps for several return periods (25, 50, 100, 200, 500, 1000 years) were obtained. The hazard maps are developed at 1kmx1km resolution and have been validated against satellite flood footprints from different sources (DFO archive, UNOSAT flood portal) performing well especially for big events For smaller events (lower return periods), the GAR Atlas flood hazard maps tend to overestimate with respect to similar maps produced locally (hazard maps where available for some countries and were used as benchmark). The main issue being that, due to the resolution, the GAR Atlas flood hazard maps do not take into account flood defenses that are normally present to preserve the value exposed to floods. More information about the flood hazard assessment can be found in the background paper (Rudari et al., 2015).fl_hazard_200_yrpWCSGeoTIFFwesr_risk__fl_hazard_200_yrpRectifiedGridCoverage-141.09900045782885 -56.085762831133565180.08131581575455 60.09233546603441-141.09900045782885 -56.085762831133565180.08131581575455 60.09233546603441Flood Hazard 25 Years (cm)The GAR Atlas global flood hazard assessment uses a probabilistic approach for modelling riverine flood major river basins around the globe. This has been possible after compiling a global database of stream-flow data, merging different sources and gathering more than 8000 stations over the globe in order to calculate the range of possible discharges from very low to the maximum possible scales at different locations along the rivers. The calculated discharges were introduced in the river sections to model water levels downstream. This procedure allowed for the determination of stochastic event-sets of riverine floods from which hazard maps for several return periods (25, 50, 100, 200, 500, 1000 years) were obtained. The hazard maps are developed at 1kmx1km resolution and have been validated against satellite flood footprints from different sources (DFO archive, UNOSAT flood portal) performing well especially for big events For smaller events (lower return periods), the GAR Atlas flood hazard maps tend to overestimate with respect to similar maps produced locally (hazard maps where available for some countries and were used as benchmark). The main issue being that, due to the resolution, the GAR Atlas flood hazard maps do not take into account flood defenses that are normally present to preserve the value exposed to floods. More information about the flood hazard assessment can be found in the background paper (Rudari et al., 2015).fl_hazard_25_yrpWCSGeoTIFFwesr_risk__fl_hazard_25_yrpRectifiedGridCoverage-141.09900045782885 -56.085762831133565180.08131581575455 60.09233546603441-141.09900045782885 -56.085762831133565180.08131581575455 60.09233546603441Flood Hazard 50 Years (cm)The GAR Atlas global flood hazard assessment uses a probabilistic approach for modelling riverine flood major river basins around the globe. This has been possible after compiling a global database of stream-flow data, merging different sources and gathering more than 8000 stations over the globe in order to calculate the range of possible discharges from very low to the maximum possible scales at different locations along the rivers. The calculated discharges were introduced in the river sections to model water levels downstream. This procedure allowed for the determination of stochastic event-sets of riverine floods from which hazard maps for several return periods (25, 50, 100, 200, 500, 1000 years) were obtained. The hazard maps are developed at 1kmx1km resolution and have been validated against satellite flood footprints from different sources (DFO archive, UNOSAT flood portal) performing well especially for big events For smaller events (lower return periods), the GAR Atlas flood hazard maps tend to overestimate with respect to similar maps produced locally (hazard maps where available for some countries and were used as benchmark). The main issue being that, due to the resolution, the GAR Atlas flood hazard maps do not take into account flood defenses that are normally present to preserve the value exposed to floods. More information about the flood hazard assessment can be found in the background paper (Rudari et al., 2015).fl_hazard_50_yrpWCSGeoTIFFwesr_risk__fl_hazard_50_yrpRectifiedGridCoverage-141.09900045782885 -56.085762831133565180.08131581575455 60.09233546603441-141.09900045782885 -56.085762831133565180.08131581575455 60.09233546603441Flood Hazard 500 Years (cm)The GAR Atlas global flood hazard assessment uses a probabilistic approach for modelling riverine flood major river basins around the globe. This has been possible after compiling a global database of stream-flow data, merging different sources and gathering more than 8000 stations over the globe in order to calculate the range of possible discharges from very low to the maximum possible scales at different locations along the rivers. The calculated discharges were introduced in the river sections to model water levels downstream. This procedure allowed for the determination of stochastic event-sets of riverine floods from which hazard maps for several return periods (25, 50, 100, 200, 500, 1000 years) were obtained. The hazard maps are developed at 1kmx1km resolution and have been validated against satellite flood footprints from different sources (DFO archive, UNOSAT flood portal) performing well especially for big events For smaller events (lower return periods), the GAR Atlas flood hazard maps tend to overestimate with respect to similar maps produced locally (hazard maps where available for some countries and were used as benchmark). The main issue being that, due to the resolution, the GAR Atlas flood hazard maps do not take into account flood defenses that are normally present to preserve the value exposed to floods. More information about the flood hazard assessment can be found in the background paper (Rudari et al., 2015).fl_hazard_500_yrpWCSGeoTIFFwesr_risk__fl_hazard_500_yrpRectifiedGridCoverage-141.09900045782885 -56.085762831133565180.08131581575455 60.09233546603441-141.09900045782885 -56.085762831133565180.08131581575455 60.09233546603441Flood Mortality RiskThis dataset includes an estimate of the global risk induced by flood hazard. Unit is estimated risk index from 1 (low) to 5 (extreme). This product was designed by UNEP/GRID-Europe for the Global Assessment Report on Risk Reduction (GAR). It was modeled using global data. Credit: UNEP/GRID-Europe.fl_riskWCSGeoTIFFwesr_risk__fl_riskRectifiedGridCoverage-180.0 -90.0179.99999999999858 83.99999999999932-180.0 -90.0179.99999999999858 83.99999999999932Flood Population Exposure (People/Year)fl1010ipeyWCSGeoTIFFwesr_risk__fl1010ipeyRectifiedGridCoverage-180.0 -90.0179.99999999985602 83.99999999993042-180.0 -90.0179.99999999985602 83.99999999993042Flood hazard exposurefl_phexWCSGeoTIFFECO-DRR__fl_phexRectifiedGridCoverage-180.0 -90.0180.0 90.0-180.0 -90.0180.0 90.0Flood hazard frequencyfl_freqWCSGeoTIFFECO-DRR__fl_freqRectifiedGridCoverage-180.0 -90.0180.0 90.0-180.0 -90.0180.0 90.0Flood recurrence map between 1995 and 2024The northeastern part of the Kédougou region is the most affected, particularly the Saraya department. This situation is largely due to the overflowing of the Falémé River and several backwaters. These rivers, the main tributaries of the Senegal River, leave their banks after intense rains, causing flooding, as evidenced by the events of 2024. Flooding in Saraya is also aggravated by water erosion, as demonstrated by the mechanisms described in the study by Boissy et al. (2022). Intense and prolonged rainfall leads to erosion of soils already weakened by a low-permeability geological substrate and rugged terrain. In addition, human activities (agriculture, gold mining) disrupt soil stability. These factors, coupled with steep slopes (up to 61.57%), concentrate the flow towards low-lying areas, creating conditions conducive to flooding.flood hazardrisk_southeast_senegal__floodRectifiedGridCoverage-13.239460488881921 12.307817707721565-11.342218608821492 13.461254532531031-13.239460488881921 12.307817707721565-11.342218608821492 13.461254532531031Flood vulnerability recurrence map between 1995 and 2024In the Kédougou region, cultivated, urbanized, populated, and low-lying areas are more vulnerable to flooding for several interrelated reasons. On the one hand, soil sealing in urbanized areas and intensive agriculture significantly reduce water infiltration capacity, resulting in significant surface runoff during extreme rainfall events. On the other hand, high population density increases pressure on water resources and drainage infrastructure, which is often insufficient to channel the volume of water during heavy rains.flood vulnerabilityrisk_southeast_senegal__flood_vulnerabilityRectifiedGridCoverage690972.8897606854 1362021.6503080174896851.0096287506 1490075.0902433824-13.24387253466399 12.296396064761913-11.335905049138686 13.472511381334042Flooded areas in Kedougou between 1995 and 2004Flood zone mapping is based on the analysis of Landsat satellite images (series 5, 7 and 8) covering the three decadal periods of the study (1995-2024). These data, available on GEE, offer spectral bands suitable for water detection. The green and short-wave infrared spectral bands were selected to calculate the Modified Normalized Difference Water Index (MNDWI), while the red and near-infrared (NIR) bands were used in the case of another hazard to calculate the NDVI to eliminate false detections linked to vegetation.flood_1995_2004WCSGeoTIFFrisk_southeast_senegal__flood_1995_2004RectifiedGridCoverage690989.0000999998 1362037.5249000005896846.0000999998 1490068.2728000004-13.243723483393213 12.296539942972935-11.335952171241116 13.472448719878967Flooded areas in Kedougou between 2005 and 2014Flood zone mapping is based on the analysis of Landsat satellite images (series 5, 7 and 8) covering the three decadal periods of the study (1995-2024). These data, available on GEE, offer spectral bands suitable for water detection. The green and short-wave infrared spectral bands were selected to calculate the Modified Normalized Difference Water Index (MNDWI), while the red and near-infrared (NIR) bands were used in the case of another hazard to calculate the NDVI to eliminate false detections linked to vegetation.flood_2005_2014WCSGeoTIFFrisk_southeast_senegal__flood_2005_2014RectifiedGridCoverage690989.0000999998 1362037.5249000005896846.0000999998 1490068.2728000004-13.243723483393213 12.296539942972935-11.335952171241116 13.472448719878967Flooded areas in Kedougou between 2015 and 2024Flood zone mapping is based on the analysis of Landsat satellite images (series 5, 7 and 8) covering the three decadal periods of the study (1995-2024). These data, available on GEE, offer spectral bands suitable for water detection. The green and short-wave infrared spectral bands were selected to calculate the Modified Normalized Difference Water Index (MNDWI), while the red and near-infrared (NIR) bands were used in the case of another hazard to calculate the NDVI to eliminate false detections linked to vegetation.flood_2015_2024WCSGeoTIFFrisk_southeast_senegal__flood_2015_2024RectifiedGridCoverage690989.0000999998 1362037.5249000005896846.0000999998 1490068.2728000004-13.243723483393213 12.296539942972935-11.335952171241116 13.472448719878967Forest Biodiversity Intactness<span style="font-size:1em"> This newly produced layer assesses the impacts of forest management on biodiversity intactness across the globe. This layer focuses on the forested regions of the planet (natural and non-natural) and estimates biodiversity intactness by using information on forest management based on the layer that has been produced under the umbrella of the Nature Map project (Lesiv et al., 2020) and biodiversity intactness from models derived from the PREDICTS database (Hudson et al., 2017). The Biodiversity Intactness Index (BII) describes the average abundance of a taxonomically and ecologically broad set of species in an area, relative to their abundances in an intact reference ecosystem (Scholes & Biggs, 2005). Demonstrating the relevance of the index, BII has recently been included as a core indicator by IPBES and as an Essential Biodiversity Variable by GEO BON. <br> <br> <u>References:</u> <ul> <li> Hill, S.L.L. et al. (2018) Worldwide Impacts of Past and Projected Future Land-Use Change on Local Species Richness and the Biodiversity Intactness Index. BioRxiv, May 1, 2018, 311787. <a href="https://doi.org/10.1101/311787" target="_blank">doi.org/10.1101/311787</a>. </li> <li>Hudson, L.N., et al. (2017) The Database of the PREDICTS (Projecting Responses of Ecological Diversity In Changing Terrestrial Systems) Project. Ecology and Evolution 7, no. 1: 145–88. <a href="https://doi.org/10.1002/ece3.2579" target="_blank">doi.org/10.1002/ece3.2579</a>. </li> <li>Lesiv, M., et al. (2020) Methodology for Generating a Global Forest Management Layer. Zenodo, July 7, 2020. <a href="https://doi.org/10.5281/zenodo.3933966" target="_blank">doi.org/10.5281/zenodo.3933966</a>. </li> <li>Newbold, T. et al. (2016) Has Land Use Pushed Terrestrial Biodiversity beyond the Planetary Boundary? A Global Assessment. Science 353, no. 6296: 288–91.<a href="https://doi.org/10.1126/science.aaf2201" target="_blank">doi.org/10.1126/science.aaf2201.</a> </li> <li>Scholes, R. J. et al. (2005) A Biodiversity Intactness Index. Nature 434, no. 7029: 45–49.<a href=" https://doi.org/10.1038/nature03289." target="_blank">doi.org/10.1038/nature03289.</a> </li> <li>Steffen, W. et al. (2015) Planetary Boundaries: Guiding Human Development on a Changing Planet. Science 347, no. 6223.<a href="https://doi.org/10.1126/science.1259855."> doi.org/10.1126/science.1259855.</a> </li> </ul> </span>forest_biodiversity_intactness_index_naturemap_2020_optWCSGeoTIFFMapX_UNDP__forest_biodiversity_intactness_index_naturemap_2020_optRectifiedGridCoverage-179.991848215593 -81.8206539215438179.9875281634017 81.8207424633558-179.991848215593 -81.8206539215438179.9875281634017 81.8207424633558Forest Condition - 2015 (CAFI)The layer shows the status of forested areas in the Congo Basin. Data produced in the frame of the project "Assessing deforestation and forest degradation trends and associated current and historical direct drivers in the six CAFI partner countries". Credits: CAFI. Central African Forest InitiativeCongo_Basin_Condition_wgs84_optWCSGeoTIFFpeatlands__CAFI_Congo_Basin_ConditionRectifiedGridCoverage5.602366924286013 -13.45540156281399631.305988384304207 13.0781214592313835.602366924286013 -13.45540156281399631.305988384304207 13.078121459231383Forest Connectivity (2000) (NASA)Forest connectivity identifies key areas between Intact Forest Landscapes (IFLs) in 2000. IFLs are large areas (greater than or equal to 500 square kilometres) of forest and other natural vegetation that show no remotely detected signs of human disturbance. Data Sources: Hansen, M.C., et al. 2013. High-Resolution Global Maps of 21st-Century Forest Cover Change. Science 342, 850–853. DOI: <a href="https://doi.org/10.1126/science.1244693" target="_blank">10.1126/science.1244693</a> Potapov, P., et al., 2017. The last frontiers of wilderness: Tracking loss of intact forest landscapes from 2000 to 2013. Science Advances 3, e1600821. <a href="https://doi.org/10.1126/sciadv.1600821" target="_blank">10.1126/sciadv.1600821</a>c2000WCSGeoTIFFMapX_UNDP_nd__c2000RectifiedGridCoverage-95.26972112443687 -26.368039400717038167.81411628372408 25.126944105146272-95.26972112443687 -26.368039400717038167.81411628372408 25.126944105146272Forest Connectivity (2013) (NASA)Forest connectivity identifies key areas between Intact Forest Landscapes (IFLs) in 2013. IFLs are large areas (greater than or equal to 500 square kilometres) of forest and other natural vegetation that show no remotely detected signs of human disturbance. Data Sources: Hansen, M.C., et al. 2013. High-Resolution Global Maps of 21st-Century Forest Cover Change. Science 342, 850–853. DOI: <a href="https://doi.org/10.1126/science.1244693" target="_blank">10.1126/science.1244693</a> Potapov, P., et al., 2017. The last frontiers of wilderness: Tracking loss of intact forest landscapes from 2000 to 2013. Science Advances 3, e1600821. <a href="https://doi.org/10.1126/sciadv.1600821" target="_blank">10.1126/sciadv.1600821</a>c2013WCSGeoTIFFMapX_UNDP_nd__c2013RectifiedGridCoverage-95.26972112443687 -26.368039400717038167.81411628372408 25.126944105146272-95.26972112443687 -26.368039400717038167.81411628372408 25.126944105146272Forest Fragmentation (2000) 1000mThe fragmentation dataset classifies forested areas into several fragmentation classes using spatial pattern analysis, and includes effects of both deforestation and regrowth on forest fragmentation. Multiple variations of this dataset are available for the years 2000 and 2012, each making different assumptions about: the distance that fragmentation effects extend into forests (500 metres or 1000 metres); the canopy cover (%) that defines forest area (25 or 50%); and the minimum area (in hectares) of a fragment to be be considered a forest fragment (0, 1 or 5 ha). The map presented here shows effects extending 500 metres into forest interiors. This dataset has a 30-metre spatial resolution. Data derived from: Hansen, M.C., et al. 2013. High-Resolution Global Maps of 21st-Century Forest Cover Change. Science 342, 850–853. 10.1126/science.1244693 Created as part of the GEF-funded Global Support to Sixth National Report Project in collaboration with the NASA-funded Forest Integrity Project.frag_2000_1000_25_0WCSGeoTIFFMapX_UNDP_nd__frag_2000_1000_25_0RectifiedGridCoverage-119.36170706306382 -34.213866389533806179.99995545035347 30.001378-119.36170706306382 -34.213866389533806179.99995545035347 30.001378Forest Fragmentation (2000) 500mThe fragmentation dataset classifies forested areas into several fragmentation classes using spatial pattern analysis, and includes effects of both deforestation and regrowth on forest fragmentation. Multiple variations of this dataset are available for the years 2000 and 2012, each making different assumptions about: the distance that fragmentation effects extend into forests (500 metres or 1000 metres); the canopy cover (%) that defines forest area (25 or 50%); and the minimum area (in hectares) of a fragment to be be considered a forest fragment (0, 1 or 5 ha). The map presented here shows effects extending 500 metres into forest interiors. This dataset has a 30-metre spatial resolution. Data derived from: Hansen, M.C., et al. 2013. High-Resolution Global Maps of 21st-Century Forest Cover Change. Science 342, 850–853. 10.1126/science.1244693 Created as part of the GEF-funded Global Support to Sixth National Report Project in collaboration with the NASA-funded Forest Integrity Project.frag_2000_500_25_0WCSGeoTIFFMapX_UNDP_nd__frag_2000_500_25_0RectifiedGridCoverage-119.36170706306382 -34.213866389533806179.99995545035347 30.001378-119.36170706306382 -34.213866389533806179.99995545035347 30.001378Forest Fragmentation (2012) 1000mThe fragmentation dataset classifies forested areas into several fragmentation classes using spatial pattern analysis, and includes effects of both deforestation and regrowth on forest fragmentation. Multiple variations of this dataset are available for the years 2000 and 2012, each making different assumptions about: the distance that fragmentation effects extend into forests (500 metres or 1000 metres); the canopy cover (%) that defines forest area (25 or 50%); and the minimum area (in hectares) of a fragment to be be considered a forest fragment (0, 1 or 5 ha). The map presented here shows effects extending 500 metres into forest interiors. This dataset has a 30-metre spatial resolution. Data derived from: Hansen, M.C., et al. 2013. High-Resolution Global Maps of 21st-Century Forest Cover Change. Science 342, 850–853. 10.1126/science.1244693 Created as part of the GEF-funded Global Support to Sixth National Report Project in collaboration with the NASA-funded Forest Integrity Project.frag_2012_1000_25_0WCSGeoTIFFMapX_UNDP_nd__frag_2012_1000_25_0RectifiedGridCoverage-119.36170706306382 -34.213866389533806179.99995545035347 30.001378-119.36170706306382 -34.213866389533806179.99995545035347 30.001378Forest Fragmentation (2012) 500mThe fragmentation dataset classifies forested areas into several fragmentation classes using spatial pattern analysis, and includes effects of both deforestation and regrowth on forest fragmentation. Multiple variations of this dataset are available for the years 2000 and 2012, each making different assumptions about: the distance that fragmentation effects extend into forests (500 metres or 1000 metres); the canopy cover (%) that defines forest area (25 or 50%); and the minimum area (in hectares) of a fragment to be be considered a forest fragment (0, 1 or 5 ha). The map presented here shows effects extending 500 metres into forest interiors. This dataset has a 30-metre spatial resolution. Data derived from: Hansen, M.C., et al. 2013. High-Resolution Global Maps of 21st-Century Forest Cover Change. Science 342, 850–853. 10.1126/science.1244693 Created as part of the GEF-funded Global Support to Sixth National Report Project in collaboration with the NASA-funded Forest Integrity Project.frag_2012_500_25_0WCSGeoTIFFMapX_UNDP_nd__frag_2012_500_25_0RectifiedGridCoverage-119.36170706306382 -34.213866389533806179.99995545035347 30.001378-119.36170706306382 -34.213866389533806179.99995545035347 30.001378Forest Integrity ColombiaForest Integrity (100m res.) in Colombia (Montana State University).col_forint100WCSGeoTIFFMapX_UNDP__col_forint100RectifiedGridCoverage-79.758379943848 -4.470114545847-66.21837994384799 11.205885454153-79.758379943848 -4.470114545847-66.21837994384799 11.205885454153Forest Integrity EcuadorForest Integrity (100m res.) in Ecuador (Montana State University) The forest integrrity index is derived by overlaying the human footprint (Venter et al. 2016) on the forest structural condition. The name is consistent with the concept of ecological integrity. Ecological integrity has been defined as, “the system’s capacity to maintain structure and ecosystem functions using processes and elements characteristic for its ecoregion.” (Parks Canada 2008).  This capacity is a result of the climate, soil, topography, biota and other biophysical properties of the ecoregion and the extent to which these properties are not altered by modern human pressures. Consistent with this definition, the forest integrity index is based on on the structural complexity of a stand relative to the natural potential of the ecoregion and level of human pressure. Thus, forest of high integrity are relatively tall, high in canopy cover, older, and with relatively low human pressure.  An increasing number of studies have shown that human pressure in various forms can have negative effects on native species.  Thus, high integrity forests may be uniquely important for conservation because they support species and processes that are require well-developed forests and are sensitive to human activities.  Such forests often also have high economic value and have likely been preferentially converted to more intense human land uses.  Thus, identifying remaining areas of high forest integrity is important for conservation planning.<br><br>Data is provided by Montana State University.<br/><br>License information: <a href "https://creativecommons.org/licenses/by/4.0/">CC-4.0 Attribution</a>.<br/>ecu_forint100WCSGeoTIFFMapX_UNDP__ecu_forint100RectifiedGridCoverage-81.007003784197 -5.015087128068-75.189781557321 1.4260239881959995-81.007003784197 -5.015087128068-75.189781557321 1.4260239881959995Forest Integrity Index (2019) (Montana State University)The distribution of forest biomass vertically and horizontally is an important predictor of biodiversity, disturbance risk, carbon storage, and hydrological flows. Human activities may alter the influence of forest structure on biodiversity through hunting, introducing non-native species, and altering disturbance regimes. The authors introduce two new remotely sensed indices describing forest structure and human pressure in tropical forests. The Forest Structural Condition Index (SCI) uses best existing global forest data sets to represent a gradient from low to high forest structure development. Remotely sensed estimates of canopy height, tree cover, and time since disturbance comprise inputs of the index. The index distinguishes short, open-canopy, or recently disturbed stands such as those recently deforested from tall, closed-canopy, older stands typical of primary of late secondary forest. The SCI was validated against estimates of foliage height diversity derived from airborne lidar and estimates of aboveground biomass derived from forest inventory plots. The Forest Integrity Index overlays an index of human pressure, the Human Footprint, on SCI to identify structurally complex forests with low human pressure that are likely to be most valuable for biodiversity and ecosystem services. The SCI and Forest Integrity Index are being used to assess progress for countries in reaching the 2020 forest fragmentation and connectivity targets under the Convention on Biodiversity. Broader potential applications include using the SCI and Forest Integrity as predictors of habitat quality, community richness, carbon storage, hydrological yield, and restoration of secondary forest.<br><br>This dataset is provided from the University of Montana through a partnerhsip with the NASA Biodiversity and Ecological Forecasting Program.<br/><br>License information: <a href "https://creativecommons.org/licenses/by/4.0/" target="_blank">CC-4.0 Attribution</a>.<br/><br>To download a subset of the FII data clipped to your country please click <a href="https://app.box.com/s/380mxwzhuqece2v9fy8ovhcv3s5cb82g/folder/71324555438"target="_blank">here</a>.<br/>FSII_mergedWCSGeoTIFFMapX_UNDP__FSII_mergedRectifiedGridCoverage-81.70887178141503 -20.582648947388527153.4281182773073 25.91783325130279-81.70887178141503 -20.582648947388527153.4281182773073 25.91783325130279Forest Integrity Index (FI) - AfricaThe distribution of forest biomass vertically and horizontally is an important predictor of biodiversity, disturbance risk, carbon storage, and hydrological flows. Human activities may alter the influence of forest structure on biodiversity through hunting, introducing non-native species, and altering disturbance regimes. The authors introduce two new remotely sensed indices describing forest structure and human pressure in tropical forests. The Forest Structural Condition Index (SCI) uses best existing global forest data sets to represent a gradient from low to high forest structure development. Remotely sensed estimates of canopy height, tree cover, and time since disturbance comprise inputs of the index. The index distinguishes short, open-canopy, or recently disturbed stands such as those recently deforested from tall, closed-canopy, older stands typical of primary of late secondary forest. The SCI was validated against estimates of foliage height diversity derived from airborne lidar and estimates of aboveground biomass derived from forest inventory plots. The Forest Integrity Index overlays an index of human pressure, the Human Footprint, on SCI to identify structurally complex forests with low human pressure that are likely to be most valuable for biodiversity and ecosystem services. The SCI and Forest Integrity Index are being used to assess progress for countries in reaching the 2020 forest fragmentation and connectivity targets under the Convention on Biodiversity. Broader potential applications include using the SCI and Forest Integrity as predictors of habitat quality, community richness, carbon storage, hydrological yield, and restoration of secondary forest.<br><br>This dataset is provided from the University of Montana through a partnerhsip with the NASA Biodiversity and Ecological Forecasting Program.<br/><br>License information: <a href "https://creativecommons.org/licenses/by/4.0/" target="_blank">CC-4.0 Attribution</a>.<br/>africaFIWCSGeoTIFFMapX_UNDP__africaFIRectifiedGridCoverage4.291832979587211 -7.90307101884661935.40579740803462 6.83430087006617454.291832979587211 -7.90307101884661935.40579740803462 6.8343008700661745Forest Integrity Index (FI) - AsiaThe distribution of forest biomass vertically and horizontally is an important predictor of biodiversity, disturbance risk, carbon storage, and hydrological flows. Human activities may alter the influence of forest structure on biodiversity through hunting, introducing non-native species, and altering disturbance regimes. The authors introduce two new remotely sensed indices describing forest structure and human pressure in tropical forests. The Forest Structural Condition Index (SCI) uses best existing global forest data sets to represent a gradient from low to high forest structure development. Remotely sensed estimates of canopy height, tree cover, and time since disturbance comprise inputs of the index. The index distinguishes short, open-canopy, or recently disturbed stands such as those recently deforested from tall, closed-canopy, older stands typical of primary of late secondary forest. The SCI was validated against estimates of foliage height diversity derived from airborne lidar and estimates of aboveground biomass derived from forest inventory plots. The Forest Integrity Index overlays an index of human pressure, the Human Footprint, on SCI to identify structurally complex forests with low human pressure that are likely to be most valuable for biodiversity and ecosystem services. The SCI and Forest Integrity Index are being used to assess progress for countries in reaching the 2020 forest fragmentation and connectivity targets under the Convention on Biodiversity. Broader potential applications include using the SCI and Forest Integrity as predictors of habitat quality, community richness, carbon storage, hydrological yield, and restoration of secondary forest.<br><br>This dataset is provided from the University of Montana through a partnerhsip with the NASA Biodiversity and Ecological Forecasting Program.<br/><br>License information: <a href "https://creativecommons.org/licenses/by/4.0/" target="_blank">CC-4.0 Attribution</a>.<br/>asiaFIWCSGeoTIFFMapX_UNDP__asiaFIRectifiedGridCoverage90.32202306186224 -11.768033998374744153.42801388207823 25.9176021116239990.32202306186224 -11.768033998374744153.42801388207823 25.91760211162399Forest Integrity Index (FI) - South AmericaThe distribution of forest biomass vertically and horizontally is an important predictor of biodiversity, disturbance risk, carbon storage, and hydrological flows. Human activities may alter the influence of forest structure on biodiversity through hunting, introducing non-native species, and altering disturbance regimes. The authors introduce two new remotely sensed indices describing forest structure and human pressure in tropical forests. The Forest Structural Condition Index (SCI) uses best existing global forest data sets to represent a gradient from low to high forest structure development. Remotely sensed estimates of canopy height, tree cover, and time since disturbance comprise inputs of the index. The index distinguishes short, open-canopy, or recently disturbed stands such as those recently deforested from tall, closed-canopy, older stands typical of primary of late secondary forest. The SCI was validated against estimates of foliage height diversity derived from airborne lidar and estimates of aboveground biomass derived from forest inventory plots. The Forest Integrity Index overlays an index of human pressure, the Human Footprint, on SCI to identify structurally complex forests with low human pressure that are likely to be most valuable for biodiversity and ecosystem services. The SCI and Forest Integrity Index are being used to assess progress for countries in reaching the 2020 forest fragmentation and connectivity targets under the Convention on Biodiversity. Broader potential applications include using the SCI and Forest Integrity as predictors of habitat quality, community richness, carbon storage, hydrological yield, and restoration of secondary forest.<br><br>This dataset is provided from the University of Montana through a partnerhsip with the NASA Biodiversity and Ecological Forecasting Program.<br/><br>License information: <a href "https://creativecommons.org/licenses/by/4.0/" target="_blank">CC-4.0 Attribution</a>.<br/>samericaFI2WCSGeoTIFFMapX_UNDP__samericaFI2RectifiedGridCoverage-81.70875583396088 -20.582447168229265-34.24721833519705 13.047396176983916-81.70875583396088 -20.582447168229265-34.24721833519705 13.047396176983916Forest Integrity Indonesia VietnamForest Integrity (100m res.) in Indonesia and Vietnam (Montana State University) The forest integrrity index is derived by overlaying the human footprint (Venter et al. 2016) on the forest structural condition. The name is consistent with the concept of ecological integrity. Ecological integrity has been defined as, “the system’s capacity to maintain structure and ecosystem functions using processes and elements characteristic for its ecoregion.” (Parks Canada 2008).  This capacity is a result of the climate, soil, topography, biota and other biophysical properties of the ecoregion and the extent to which these properties are not altered by modern human pressures. Consistent with this definition, the forest integrity index is based on on the structural complexity of a stand relative to the natural potential of the ecoregion and level of human pressure. Thus, forest of high integrity are relatively tall, high in canopy cover, older, and with relatively low human pressure.  An increasing number of studies have shown that human pressure in various forms can have negative effects on native species.  Thus, high integrity forests may be uniquely important for conservation because they support species and processes that are require well-developed forests and are sensitive to human activities.  Such forests often also have high economic value and have likely been preferentially converted to more intense human land uses.  Thus, identifying remaining areas of high forest integrity is important for conservation planning.<br><br>Data is provided by Montana State University.<br/><br>License information: <a href "https://creativecommons.org/licenses/by/4.0/">CC-4.0 Attribution</a>.<br/>indo_forint100WCSGeoTIFFMapX_UNDP__indo_forint100RectifiedGridCoverage100.0 -9.999861112471002141.016129418236 23.384477504501100.0 -9.999861112471002141.016129418236 23.384477504501Forest Structual Condition Index (2019) (Montana State University)The distribution of forest biomass vertically and horizontally is an important predictor of biodiversity, disturbance risk, carbon storage, and hydrological flows. Human activities may alter the influence of forest structure on biodiversity through hunting, introducing non-native species, and altering disturbance regimes. The authors introduce two new remotely sensed indices describing forest structure and human pressure in tropical forests. The Forest Structural Condition Index (SCI) uses best existing global forest data sets to represent a gradient from low to high forest structure development. Remotely sensed estimates of canopy height, tree cover, and time since disturbance comprise inputs of the index. The index distinguishes short, open-canopy, or recently disturbed stands such as those recently deforested from tall, closed-canopy, older stands typical of primary of late secondary forest. The SCI was validated against estimates of foliage height diversity derived from airborne lidar and estimates of aboveground biomass derived from forest inventory plots. The Forest Integrity Index overlays an index of human pressure, the Human Footprint, on SCI to identify structurally complex forests with low human pressure that are likely to be most valuable for biodiversity and ecosystem services. The SCI and Forest Integrity Index are being used to assess progress for countries in reaching the 2020 forest fragmentation and connectivity targets under the Convention on Biodiversity. Broader potential applications include using the SCI and Forest Integrity as predictors of habitat quality, community richness, carbon storage, hydrological yield, and restoration of secondary forest.<br><br>This dataset is provided from the University of Montana through a partnerhsip with the NASA Biodiversity and Ecological Forecasting Program.<br/><br>License information: <a href "https://creativecommons.org/licenses/by/4.0/" target="_blank">CC-4.0 Attribution</a>.<br/>To download a subset of the FII data clipped to your country please click <a href="https://app.box.com/s/380mxwzhuqece2v9fy8ovhcv3s5cb82g/folder/71324508781"target="_blank">here</a>.<br/>SCI_mergedWCSGeoTIFFMapX_UNDP__SCI_mergedRectifiedGridCoverage-81.70887178141503 -20.58264894738855153.42811827730736 25.91783325130279-81.70887178141503 -20.58264894738855153.42811827730736 25.91783325130279Forest Structual Condition Index (SCI) - AfricaThe distribution of forest biomass vertically and horizontally is an important predictor of biodiversity, disturbance risk, carbon storage, and hydrological flows. Human activities may alter the influence of forest structure on biodiversity through hunting, introducing non-native species, and altering disturbance regimes. The authors introduce two new remotely sensed indices describing forest structure and human pressure in tropical forests. The Forest Structural Condition Index (SCI) uses best existing global forest data sets to represent a gradient from low to high forest structure development. Remotely sensed estimates of canopy height, tree cover, and time since disturbance comprise inputs of the index. The index distinguishes short, open-canopy, or recently disturbed stands such as those recently deforested from tall, closed-canopy, older stands typical of primary of late secondary forest. The SCI was validated against estimates of foliage height diversity derived from airborne lidar and estimates of aboveground biomass derived from forest inventory plots. The Forest Integrity Index overlays an index of human pressure, the Human Footprint, on SCI to identify structurally complex forests with low human pressure that are likely to be most valuable for biodiversity and ecosystem services. The SCI and Forest Integrity Index are being used to assess progress for countries in reaching the 2020 forest fragmentation and connectivity targets under the Convention on Biodiversity. Broader potential applications include using the SCI and Forest Integrity as predictors of habitat quality, community richness, carbon storage, hydrological yield, and restoration of secondary forest.<br><br>This dataset is provided from the University of Montana through a partnerhsip with the NASA Biodiversity and Ecological Forecasting Program.<br/><br>License information: <a href "https://creativecommons.org/licenses/by/4.0/" target="_blank">CC-4.0 Attribution</a>.<br/>africaSCIWCSGeoTIFFMapX_UNDP__africaSCIRectifiedGridCoverage4.291832979587211 -7.90307101884662335.40579740803463 6.83430087006617454.291832979587211 -7.90307101884662335.40579740803463 6.8343008700661745Forest Structual Condition Index (SCI) - AsiaThe distribution of forest biomass vertically and horizontally is an important predictor of biodiversity, disturbance risk, carbon storage, and hydrological flows. Human activities may alter the influence of forest structure on biodiversity through hunting, introducing non-native species, and altering disturbance regimes. The authors introduce two new remotely sensed indices describing forest structure and human pressure in tropical forests. The Forest Structural Condition Index (SCI) uses best existing global forest data sets to represent a gradient from low to high forest structure development. Remotely sensed estimates of canopy height, tree cover, and time since disturbance comprise inputs of the index. The index distinguishes short, open-canopy, or recently disturbed stands such as those recently deforested from tall, closed-canopy, older stands typical of primary of late secondary forest. The SCI was validated against estimates of foliage height diversity derived from airborne lidar and estimates of aboveground biomass derived from forest inventory plots. The Forest Integrity Index overlays an index of human pressure, the Human Footprint, on SCI to identify structurally complex forests with low human pressure that are likely to be most valuable for biodiversity and ecosystem services. The SCI and Forest Integrity Index are being used to assess progress for countries in reaching the 2020 forest fragmentation and connectivity targets under the Convention on Biodiversity. Broader potential applications include using the SCI and Forest Integrity as predictors of habitat quality, community richness, carbon storage, hydrological yield, and restoration of secondary forest.<br><br>This dataset is provided from the University of Montana through a partnerhsip with the NASA Biodiversity and Ecological Forecasting Program.<br/><br>License information: <a href "https://creativecommons.org/licenses/by/4.0/" target="_blank">CC-4.0 Attribution</a>.<br/>asiaSCIWCSGeoTIFFMapX_UNDP__asiaSCIRectifiedGridCoverage90.32202306186224 -11.768033998374701153.4280138820783 25.9176021116239990.32202306186224 -11.768033998374701153.4280138820783 25.91760211162399Forest Structual Condition Index (SCI) - South AmericaThe distribution of forest biomass vertically and horizontally is an important predictor of biodiversity, disturbance risk, carbon storage, and hydrological flows. Human activities may alter the influence of forest structure on biodiversity through hunting, introducing non-native species, and altering disturbance regimes. The authors introduce two new remotely sensed indices describing forest structure and human pressure in tropical forests. The Forest Structural Condition Index (SCI) uses best existing global forest data sets to represent a gradient from low to high forest structure development. Remotely sensed estimates of canopy height, tree cover, and time since disturbance comprise inputs of the index. The index distinguishes short, open-canopy, or recently disturbed stands such as those recently deforested from tall, closed-canopy, older stands typical of primary of late secondary forest. The SCI was validated against estimates of foliage height diversity derived from airborne lidar and estimates of aboveground biomass derived from forest inventory plots. The Forest Integrity Index overlays an index of human pressure, the Human Footprint, on SCI to identify structurally complex forests with low human pressure that are likely to be most valuable for biodiversity and ecosystem services. The SCI and Forest Integrity Index are being used to assess progress for countries in reaching the 2020 forest fragmentation and connectivity targets under the Convention on Biodiversity. Broader potential applications include using the SCI and Forest Integrity as predictors of habitat quality, community richness, carbon storage, hydrological yield, and restoration of secondary forest.<br><br>This dataset is provided from the University of Montana through a partnerhsip with the NASA Biodiversity and Ecological Forecasting Program.<br/><br>License information: <a href "https://creativecommons.org/licenses/by/4.0/" target="_blank">CC-4.0 Attribution</a>.<br/>samericaSCI2WCSGeoTIFFMapX_UNDP__samericaSCI2RectifiedGridCoverage-81.70875583396088 -20.582447168229265-34.24721833519705 13.047396176983916-81.70875583396088 -20.582447168229265-34.24721833519705 13.047396176983916Forest cover (Hansen) in mining concession areas in Congo - 2013The data was generated on Google Earth Engine starting from the layer Forest_cover_2000 (Hansen) in DRC and removing the data where there was a forest loss from 2001 to 2012 (loss_year values from 1 to 12). The data was then clipped to the mining concession areas (CAMI) in DRC.clip2013tot_WCSGeoTIFFMAPX_nd__drc_hansen_cover_clipped_mines_2013RectifiedGridCoverage12.383333300292 -13.45567600004700130.841583300292 5.16657399995312.383333300292 -13.45567600004700130.841583300292 5.166573999953Forest cover (Hansen) in mining concession areas in Congo - 2014The data was generated on Google Earth Engine starting from the layer Forest_cover_2000 (Hansen) in DRC and removing the data where there was a forest loss from 2001 to 2013 (loss_year values from 1 to 13). The data was then clipped to the mining concession areas (CAMI) in DRC.clip2014tot_newWCSGeoTIFFMAPX_nd__drc_hansen_cover_clipped_mines_2014RectifiedGridCoverage12.383333300292 -13.45567600004700130.808333300292 5.03332399995312.383333300292 -13.45567600004700130.808333300292 5.033323999953Forest cover (Hansen) in mining concession areas in Congo - 2015The data was generated on Google Earth Engine starting from the layer Forest_cover_2000 (Hansen) in DRC and removing the data where there was a forest loss from 2001 to 2014 (loss_year values from 1 to 14). The data was then clipped to the mining concession areas (CAMI) in DRC.clip2015tot_WCSGeoTIFFMAPX_nd__drc_hansen_cover_clipped_mines_2015RectifiedGridCoverage12.383333300292 -13.45567600004700130.808333300292 5.03332399995312.383333300292 -13.45567600004700130.808333300292 5.033323999953Forest cover (Hansen) in mining concession areas in Congo - 2016The data was generated on Google Earth Engine starting from the layer Forest_cover_2000 (Hansen) in DRC and removing the data where there was a forest loss from 2001 to 2015 (loss_year values from 1 to 15). The data was then clipped to the mining concession areas (CAMI) in DRC.clip2016tot_WCSGeoTIFFMAPX_nd__drc_hansen_cover_clipped_mines_2016RectifiedGridCoverage10.0 -20.030.80825 5.03324999999999910.0 -20.030.80825 5.033249999999999Forest total area in km2forestkm2aWCSGeoTIFFECO-DRR__forestkm2a_P3RectifiedGridCoverage-180.0 -90.0180.0 90.0-180.0 -90.0180.0 90.0Frequency of Landslides Triggered By EarthquakesThis dataset includes an estimate of the annual frequency of landslide triggered by earthquakes. It depends on the combination of trigger and susceptibility defined by six parameters: slope factor, lithological (or geological) conditions, soil moisture condition, vegetation cover, precipitation and seismic conditions. Unit is expected annual probability and percentage of pixel of occurrence of a potentially destructive landslide event x 1000000. This product was designed by International Centre for Geohazards (NGI) for the Global Assessment Report on Risk Reduction (GAR). It was modeled using global data. Credit: GIS processing International Centre for Geohazards (NGI).ls_eqWCSGeoTIFFwesr_risk__ls_eqRectifiedGridCoverage-180.0 -56.01339243789985179.9916666666665 89.99494089543342-180.0 -56.01339243789985179.9916666666665 89.99494089543342Frequency of Landslides Triggered By PrecipitationsThis dataset includes an estimate of the annual frequency of landslide triggered by precipitations. It depends on the combination of trigger and susceptibility defined by six parameters: slope factor, lithological (or geological) conditions, soil moisture condition, vegetation cover, precipitation and seismic conditions. Unit is expected annual probability and percentage of pixel of occurrence of a potentially destructive landslide event x 1000000. This product was designed by International Centre for Geohazards (NGI) for the Global Assessment Report on Risk Reduction (GAR). It was modeled using global data. Credit: GIS processing International Centre for Geohazards (NGI).ls_prWCSGeoTIFFwesr_risk__ls_prRectifiedGridCoverage-180.0 -56.01339243789985179.9916666666665 89.99494089543342-180.0 -56.01339243789985179.9916666666665 89.99494089543342Functional Diversity MapLayer provided by Ministry of Environment of Peru. Data are a suite of forest canopy functional trait maps from laser-guided imaging spectroscopy and used them to define distinct forest functional classes. These were then compared with government deforestation and land allocation data, which enabled an analysis of conservation threats and opportunities across the region.PeruFFDiversity2017WCSGeoTIFFwesr_cca__PeruFFDiversity2017RectifiedGridCoverage-315902.71 7927904.5581515197.29 1.0102904558E7-82.71417274508761 -18.737938819947015-65.41909110049332 0.9308945156352922GDP - 2019The GDP data includes 175 countries (or regions). It also provides electricity consumption data for 134 countries (or regions). As such, the research scope covers over 70% of the global land area, and over 90% of the GDP and electricity consumption. It is provided as millions of 2017 US dollars. "We employed nighttime light data to revise the real GDP growth rate. In particular, the real GDP growth rate was estimated using Eq: y∗i,t=ρyi,t+(1−ρ)y′i,t where y∗i,t is the ith country’s real GDP growth in period t; yi,t is the official GDP growth of the ith country in period t; y′i,t presents the ith country’s predicted GDP growth based on the night-time light data in period t; and (1−ρ) is the optimal weight of predicted growth based on the night-time light data." "Next, based on the gridded real GDP growth rate during 1993–2019, the gridded GDP data in 1992 or 2019 were estimated as basic values to obtain the gridded real GDP data in other years. Since the DN values in newly built-up areas were zero in 1992, these areas’ basic GDP values in 1992 were also zero, thereby leading to values of zero in subsequent years. Thus, the gridded GDP data in 2019 was selected as the basic value, which was calculated based on the top-down method. Finally, the gridded real GDP based on the real growth rate can be calculated." Credits: Global 1km×1km gridded revised real gross domestic product and electricity consumption during 1992–2019 based on calibrated nighttime light data (Chen et al., 2022) https://www.nature.com/articles/s41597-022-01322-5 Download from: https://figshare.com/articles/dataset/Global_1_km_1_km_gridded_revised_real_gross_domestic_product_and_electricity_consumption_during_1992-2019_based_on_calibrated_nighttime_light_data/17004523/1gdp2019_wgs84_optWCSGeoTIFFunccd__gdp2019_wgs84_optRectifiedGridCoverage-179.99999957737955 -65.03459342294055179.99807001075922 75.00630943315629-179.99999957737955 -65.03459342294055179.99807001075922 75.00630943315629GEOCARBON Global Forest Aboveground Biomass (Mg/ha)This file provides the global biomass map produced with the EU FP7 GEOCARBON project (www.geocarbon.net) and presented by Avitabile et al. (2014) at the Global Vegetation Monitoring and Modeling, 3-7 February 2014, Avignon (France). The map is obtained by combining and harmonizing the pan-tropical biomass map by Avitabile et al. (2016) with the boreal forest biomass map by Santoro et al. (2015). The map covers only forest areas, where forest are defined as areas with dominance of tree cover in the GLC2000 map (Bartholomé and Belward, 2005). For a proper use and description of this dataset, please refer to the mentioned articles. Source: Avitabile, V., Herold, M., Lewis, S.L., Phillips, O.L., Aguilar-Amuchastegui, N., Asner, G. P., Brienen, R.J.W., DeVries, B., Cazzolla Gatti, R., Feldpausch, T.R., Girardin, C., de Jong, B., Kearsley, E., Klop, E., Lin, X., Lindsell, J., Lopez-Gonzalez, G., Lucas, R., Malhi, Y., Morel, A.,  Mitchard, E., Pandey, D., Piao, S., Ryan, C., Sales, M., Santoro, M., Vaglio Laurin, G., Valentini, R., Verbeeck, H., Wijaya, A., Willcock, S., 2014. Comparative analysis and fusion for improved global biomass mapping.  Global Vegetation Monitoring and Modeling, 3 – 7 February 2014, Avignon (France) (https://colloque.inra.fr/gv2m) Based on data from: Avitabile, V., Herold, M., Heuvelink, G. B. M., Lewis, S. L., Phillips, O. L., Asner, G. P., Armston, J., Ashton, P. S., Banin, L., Bayol, N., Berry, N. J., Boeckx, P., de Jong, B. H. J., DeVries, B., Girardin, C. A. J., Kearsley, E., Lindsell, J. A., Lopez-Gonzalez, G., Lucas, R., Malhi, Y., Morel, A., Mitchard, E. T. A., Nagy, L., Qie, L., Quinones, M. J., Ryan, C. M., Ferry, S. J. W., Sunderland, T., Laurin, G. V., Gatti, R. C., Valentini, R., Verbeeck, H., Wijaya, A. and Willcock, S. (2016), An integrated pan-tropical biomass map using multiple reference datasets. Glob Change Biol, 22: 1406–1420. doi:10.1111/gcb.13139 Santoro, M., Beaudoin, A., Beer, C., Cartus, O., Fransson, J.E.S., Hall, R.J., Pathe, C., Schmullius, C., Schepaschenko, D., Shvidenko, A., Thurner, M. and Wegmüller, U. (2015). Forest growing stock volume of the northern hemisphere: Spatially explicit estimates for 2010 derived from Envisat ASAR. Remote Sensing of Environment, Vol. 168, pag. 316-334 Source: Avitabile V, Herold M, Heuvelink G, Lewis SL, Phillips OL, Asner GP et al. (2016). An integrated pan-tropical biomass maps using multiple reference datasets. Global Change Biology, 22: 1406–1420. doi:10.1111/gcb.13139. Data Source: https://www.wur.nl/en/Expertise-Services/Chair-groups/Environmental-Sciences/Laboratory-of-Geo-information-Science-and-Remote-Sensing/Research/Integrated-land-monitoring/Forest_Biomass/Forest-Biomass-downloads.htmGEOCARBON_Global_Forest_AGB_10072015WCSGeoTIFFMapX_UNDP__GEOCARBON_Global_Forest_AGB_10072015RectifiedGridCoverage-180.0 -56.02000000000001180.0 84.0-180.0 -56.02000000000001180.0 84.0GHA_Stream_OrderStream_Order_optWCSGeoTIFFMAPX__GHA_Stream_OrderRectifiedGridCoverage-2.942754639 9.62256316-1.420219031 11.034631553-2.942754639 9.62256316-1.420219031 11.034631553GHA_Stream_ProfileStream_Profile_optWCSGeoTIFFMAPX__GHA_Stream_ProfileRectifiedGridCoverage-2.942754639 9.62256316-1.420219031 11.034631553-2.942754639 9.62256316-1.420219031 11.034631553GHS built-up volume grid (2020)The GHS-BUILT-V dataset depicts the distribution of the built-up (BU) volume estimates between 1975 and 2030 in 5 years intervals and two functional use components a) the total BU volume and b) the non-residential (NRES) BU volume. The data is made by projection of the assessment of the average building height in the year 2018 (GHS_BUILT_H_ANBH_E2018_GLOBE_R2023A_54009_100_V1_0) assumed as constant in the 100m resolution cell, to the multitemporal built-up surface estimates (GHS-BUILT-S_GLOBE_R2023A, GHS-BUILT-S_NRES_GLOBE_R2023A). The complete information about the GHSL main products can be found in the GHSL Data Package 2023 report. Publication: Schiavina, Marcello; Freire, Sergio; Alessandra Carioli; MacManus, Kytt (2023): GHS-POP R2023A - GHS population grid multitemporal (1975-2030). European Commission, Joint Research Centre (JRC) [Dataset] doi: 10.2905/2FF68A52-5B5B-4A22-8F40-C41DA8332CFE PID: http://data.europa.eu/89h/2ff68a52-5b5b-4a22-8f40-c41da8332cfe Concept & Methodology: Freire S., MacManus K., Pesaresi M., Doxsey-Whitfield E., Mills J. (2016) Development of new open and free multi-temporal global population grids at 250 m resolution. Geospatial Data in a Changing World; Association of Geographic Information Laboratories in Europe (AGILE), AGILE 2016GHS_BUILT_V_E2020_GLOBE_R2023A_4326_3ss_V1_0_optWCSGeoTIFFMAPX__GHS_BUILT_V_E2020_GLOBE_R2023A_4326_3ss_V1_0_optRectifiedGridCoverage-180.00041593133002 -89.09291610516487180.00124930938549 89.09208317767352-180.00041593133002 -89.09291610516487180.00124930938549 89.09208317767352GHS population grid (2020)The GHS-POP depicts the distribution of residential population, expressed as the number of people per cell. Residential population estimates between 1975 and 2020 in 5 years intervals and projections to 2025 and 2030 derived from CIESIN GPWv4.11 were disaggregated from census or administrative units to grid cells, informed by the distribution, density, and classification of built-up as mapped in the Global Human Settlement Layer (GHSL) global layer per corresponding epoch. This dataset is an update of the product released in 2022. Major improvements are the following: use of built-up volume maps (GHS-BUILT-V R2022A); use of more recent and detailed population estimates derived from GPWv4.11 integrating both UN World Population Prospects 2022 country population data and World Urbanisation Prospects 2018 data on Cities; revision of GPWv4.11 population growthrates by convergence to upper administrative level growthrates; systematic improvement of census coastlines; systematic revision of census units declared as unpopulated; integration of non-residential built-up volume information (GHS-BUILT-V_NRES R2023A); spatial resolution of 100m Mollweide (and 3 arcseconds in WGS84); projections to 2030. The complete information about the GHSL main products can be found in the GHSL Data Package 2023 report. Publication: Schiavina, Marcello; Freire, Sergio; Alessandra Carioli; MacManus, Kytt (2023): GHS-POP R2023A - GHS population grid multitemporal (1975-2030). European Commission, Joint Research Centre (JRC) [Dataset] doi: 10.2905/2FF68A52-5B5B-4A22-8F40-C41DA8332CFE PID: http://data.europa.eu/89h/2ff68a52-5b5b-4a22-8f40-c41da8332cfe Concept & Methodology: Freire S., MacManus K., Pesaresi M., Doxsey-Whitfield E., Mills J. (2016) Development of new open and free multi-temporal global population grids at 250 m resolution. Geospatial Data in a Changing World; Association of Geographic Information Laboratories in Europe (AGILE), AGILE 2016GHS_POP_E2020_GLOBE_R2023A_4326_3ss_V1_0_optWCSGeoTIFFMAPX__GHS_POP_E2020_GLOBE_R2023A_4326_3ss_V1_0_optRectifiedGridCoverage-180.00041593133002 -89.09291610520242180.0012493094487 89.09208317767579-180.00041593133002 -89.09291610520242180.0012493094487 89.09208317767579GHS_POP_GPW41975GHS_POP_GPW41975_GLOBE_R2015A_54009_1k_v1_0_wgs84_optWCSGeoTIFFwesr_climat__GHS_POP_GPW41975RectifiedGridCoverage-179.99999680823143 -55.98302144283261179.99218561802442 83.63964763391371-179.99999680823143 -55.98302144283261179.99218561802442 83.63964763391371GHS_POP_GPW41990GHS_POP_GPW41990_GLOBE_R2015A_54009_1k_v1_0_wgs84_optWCSGeoTIFFwesr_climat__GHS_POP_GPW41990RectifiedGridCoverage-179.99999680823143 -55.98302144283261179.99218561802442 83.63964763391371-179.99999680823143 -55.98302144283261179.99218561802442 83.63964763391371GHS_POP_GPW42000GHS_POP_GPW42000_GLOBE_R2015A_54009_1k_v1_0_wgs84_optWCSGeoTIFFwesr_climat__GHS_POP_GPW42000RectifiedGridCoverage-179.99999680823143 -55.98302144283261179.99218561802442 83.63964763391371-179.99999680823143 -55.98302144283261179.99218561802442 83.63964763391371GHS_POP_GPW42015GHS_POP_GPW42015_GLOBE_R2015A_54009_1k_v1_0_wgs84_optWCSGeoTIFFwesr_climat__GHS_POP_GPW42015RectifiedGridCoverage-179.99999680823143 -55.98302144283261179.99218561802442 83.63964763391371-179.99999680823143 -55.98302144283261179.99218561802442 83.63964763391371GIRI_Pop_count_2022ghrsl22aWCSGeoTIFFECO-DRR__HRSL_wGHSL_pop_countRectifiedGridCoverage-180.0 -59.999999994239985180.00833333318934 84.00833333903576-180.0 -59.999999994239985180.00833333318934 84.00833333903576GSOCmapV150Soil organic carbon (SOC) is the carbon that remains in the soil after partial decomposition of any material produced by living organisms. It constitutes a key element of the global carbon cycle through atmosphere, vegetation, soil, rivers and the ocean. The Global Soil Organic Carbon Map (GSOCmap) allows the estimation of SOC stock from 0 to 30 cm. It represents a key contribution to SDG indicator 15.3.1 which defines the area of degraded land. The GSOCmap represents the first ever global soil organic carbon assessment produced through a participatory approach in which countries developed their capacities and stepped up efforts to compile all the available soil information at national level. Values are in tonnes per hectare. Source: Food and Agricultural Organization of The United Nations (FAO) Data Source: http://www.fao.org/global-soil-partnership/pillars-action/4-information-and-data/global-soil-organic-carbon-gsoc-map/en/GSOCv150weboptWCSGeoTIFFMapX_UNDP__GSOCmapV150RectifiedGridCoverage-180.0 -70.91725146799998179.99999856000005 83.616081247-180.0 -70.91725146799998179.99999856000005 83.616081247Global Aridity Index_ET0_CIGAR(1970-2000)Aridity is usually expressed as a generalized function of precipitation, temperature and reference evapo-transpiration (ET0). An Aridity Index (UNEP, 1997) can be used to quantify precipitation availability over atmospheric water demand. The Global-Aridity_ET0 surface shows moisture availability for potential growth of reference vegetation excluding the impact of soil mediating water runoff events. Aridity Index values increase for more humid conditions, and decrease with more arid conditions. Global-Aridity_ET0 is available as one grid layer representing the annual average over the 1970-2000 period. The Aridity Index values reported within the Global Aridity Index_ET0 geodataset have been multiplied by a factor of 10,000 to derive and distribute the data as integers (with 4 decimal accuracy). Generalized climate classification scheme for Aridity Index values (UNEP 1997). Aridity Index Value | Climate Class < 0.03 | Hyper Arid 0.03 – 0.2 | Arid 0.2 – 0.5 | Semi-Arid 0.5 – 0.65 | Dry sub-humid > 0.65 | Humid The Global Aridity Index (Global-Aridity_ET0) dataset provide high-resolution (30 arc-seconds) global raster climate data for the 1970-2000 period, related to evapo-transpiration processes and rainfall deficit for potential vegetative growth, based upon implementation of a Penman Montieth Reference Evapotranspiration (ET0) equation. The geo-spatial datasets has been produced by Antonio Trabucco and Robert Zomer, with the support of the CGIAR-CSI (funding from IFPRI Project No. 203008.000.002 515-01-01). These datasets have been downloaded and are available from the CGIAR-CSI GeoPortal (https://cgiarcsi.community). The Consortium for Spatial Information (CGIAR-CSI) is an initiative of the Consultative Group for International Agriculture Research (CGIAR), linking the international science, research and development communities, with CGIAR scientists, national and international partners, and others working to apply and advance geospatial science for sustainable development, conservation, and poverty alleviation in developing countries. The Global-Aridity_ET0 and Global-ET0 datasets are provided for non-commercial use in standard GeoTiff format, at 30 arc seconds or ~ 1km at the equator, to support studies contributing to sustainable development, biodiversity and environmental conservation, poverty alleviation, and adaption to climate change globally, and in particular in developing countries. Comments, feedback, suggestions or bug reports regarding downloading of the Global-ET0 and Global-Aridity datasets should be sent to: csi@cgiar.org. Technical questions regarding the Global-ET0 and Global-Aridity_ET0 datasets can be directed to Antonio Trabucco: antoniotrabucco@cmcc.it . Correct Citation: Trabucco, A., and Zomer, R.J. 2018. Global Aridity Index and Potential Evapo-Transpiration (ET0) Climate Database v2. CGIAR Consortium for Spatial Information (CGIAR-CSI). Published online, available from the CGIAR-CSI GeoPortal at https://cgiarcsi.communityai_et0_optWCSGeoTIFFUN_Sahel__ai_et0_optRectifiedGridCoverage-180.0 -59.99999999999994179.99999999999983 90.0-180.0 -59.99999999999994179.99999999999983 90.0Global Built-up Reference Layer (BUREF 2010)WCSGeoTIFFburef09grid_data_core_pro__buref09RectifiedGridCoverage-180.0 -59.999999994239985180.00818933 84.00827573576002-180.0 -59.999999994239985180.00818933 84.00827573576002Global Emissions CO 1970 - EDGARDGlobal Emissions EDGAR v4.3.1 (January 2016) In EDGARv4.3.1 emissions are calculated for gaseous and particulate air pollutants per sector and country. For the energy related sectors the activity data are mainly based on the energy balance statistics of IEA (2014) Emission gridmaps are expressed in ton substance / 0.1degree x 0.1degree / yearedg70_coWCSGeoTIFFCarbon MonoxideAir polluantsunea_pollution__edg70_coRectifiedGridCoverage-180.05 -77.95179.95 88.15000000000002-180.05 -77.95179.95 88.15000000000002Global Emissions CO 1990 - EDGARDGlobal Emissions CO 1990 - EDGARD Global Emissions EDGAR v4.3.1 (January 2016) In EDGARv4.3.1 emissions are calculated for gaseous and particulate air pollutants per sector and country. For the energy related sectors the activity data are mainly based on the energy balance statistics of IEA (2014) Emission gridmaps are expressed in ton substance / 0.1degree x 0.1degree / yearedg90_coWCSGeoTIFFCarbon Monoxidegrid_data_core__edg90_coRectifiedGridCoverage-180.05 -77.95179.95 88.15000000000002-180.05 -77.95179.95 88.15000000000002Global Emissions CO 2010 - EDGARDedg10_coWCSGeoTIFFCarbon Monoxideunea_pollution__edg10_coRectifiedGridCoverage-180.05 -77.95179.95 88.15000000000002-180.05 -77.95179.95 88.15000000000002Global Emissions NOx 1970 - EDGARDGlobal Emissions EDGAR v4.3.1 (January 2016) In EDGARv4.3.1 emissions are calculated for gaseous and particulate air pollutants per sector and country. For the energy related sectors the activity data are mainly based on the energy balance statistics of IEA (2014) Emission gridmaps are expressed in ton substance / 0.1degree x 0.1degree / yearedg70_nxWCSGeoTIFFnitrogen oxidesnitric oxide (NO)nitrogen dioxide (NO2)unea_pollution__edg70_nxRectifiedGridCoverage-180.05 -77.95179.95 88.15000000000002-180.05 -77.95179.95 88.15000000000002Global Emissions NOx 1990 - EDGARDGlobal Emissions EDGAR v4.3.1 (January 2016) In EDGARv4.3.1 emissions are calculated for gaseous and particulate air pollutants per sector and country. For the energy related sectors the activity data are mainly based on the energy balance statistics of IEA (2014) Emission gridmaps are expressed in ton substance / 0.1degree x 0.1degree / yearedg90_nxWCSGeoTIFFnitrogen oxidesnitrogen dioxide (NO2)nitric oxide (NO)unea_pollution__edg90_nxRectifiedGridCoverage-180.05 -77.95179.95 88.15000000000002-180.05 -77.95179.95 88.15000000000002Global Emissions NOx 2010 - EDGARDGlobal Emissions EDGAR v4.3.1 (January 2016) In EDGARv4.3.1 emissions are calculated for gaseous and particulate air pollutants per sector and country. For the energy related sectors the activity data are mainly based on the energy balance statistics of IEA (2014) Emission gridmaps are expressed in ton substance / 0.1degree x 0.1degree / yearedg10_nxWCSGeoTIFFnitrogen oxidesnitric oxide (NO)nitrogen dioxide (NO2)unea_pollution__edg10_nxRectifiedGridCoverage-180.05 -77.95179.95 88.15000000000002-180.05 -77.95179.95 88.15000000000002Global Estimated Risk Index For Landslide Hazard Triggered By PrecipitationsThis dataset includes an estimate of the global risk induced by landslide hazard triggered by precipitations. Unit is estimated risk index from 1 (low) to 5 (extreme). This product was designed by UNEP/GRID-Europe for the Global Assessment Report on Risk Reduction (GAR). It was modeled using global data. Credit: UNEP/GRID-Europe.ls_riskWCSGeoTIFFwesr_risk__ls_riskRectifiedGridCoverage-181.0 -90.0180.9999999999999 89.99999999999994-181.0 -90.0180.9999999999999 89.99999999999994Global High Resolution Estimates of the Human Development Index, Version 2.0These are the predictions generated from the machine learning model combining MOSAIKS daytime image and nightlight (NL) features These predictions are clipped to have values that fall within 0 and 1. These estimates are centered such that the population-weighted average of the 0.01 x 0.01 degree tile HDI estimates matches the ADM1 data from Smits and Permanyer (https://globaldatalab.org/shdi). Population weights come from the Global Human Settlement Data (GHS-POP) (https://ghsl.jrc.ec.europa.eu/download.php?ds=pop). Note that we only release estimates where human settlements are known to exist.hdi_raster_predictions_optWCSGeoTIFFunccd__hdi_predictionsRectifiedGridCoverage-180.0 -56.0180.0 74.0-180.0 -56.0180.0 74.0Global Human Modification Index 1990Detailed global datasets for 1990, 2015, and 2017 for land use modification of terrestrial lands (excluding Antarctica) are provided here. These data were calculated using the degree of human modification approach that combines the proportion of a pixel of a given stressor (i.e. footprint) times the intensity of that stressor (ranging from 0 to 1.0). Our novel datasets are detailed (~300m resolution), temporally consistent (for 1990-2015), comprehensive (11 change stressors, 14 current), robust (using an established framework and incorporating classification errors and parameter uncertainty), and strongly validated. A dataset for ~2017 is also provide, that is derived from 14 stressors, for an even more comprehensive dataset, but it should not be used to calculate change with the other datasets (1990-2015). Theobald, D. M., Kennedy, C., Chen, B., Oakleaf, J., Baruch-Mordo, S., and Kiesecker, J.: Earth transformed: detailed mapping of global human modification from 1990 to 2017, Earth Syst. Sci. Data., https://doi.org/10.5194/essd-2019-252, 2020 Source: https://zenodo.org/record/3963013#.X5ChM0JKjUIgHMv1_1990_wgsWCSGeoTIFFMapX_UNDP__gHMv1_1990_wgsRectifiedGridCoverage-180.0 -75.00027807853427179.99932064640564 85.00013997386212-180.0 -75.00027807853427179.99932064640564 85.00013997386212Global Human Modification Index 2015Detailed global datasets for 1990, 2015, and 2017 for land use modification of terrestrial lands (excluding Antarctica) are provided here. These data were calculated using the degree of human modification approach that combines the proportion of a pixel of a given stressor (i.e. footprint) times the intensity of that stressor (ranging from 0 to 1.0). Our novel datasets are detailed (~300m resolution), temporally consistent (for 1990-2015), comprehensive (11 change stressors, 14 current), robust (using an established framework and incorporating classification errors and parameter uncertainty), and strongly validated. A dataset for ~2017 is also provide, that is derived from 14 stressors, for an even more comprehensive dataset, but it should not be used to calculate change with the other datasets (1990-2015). Theobald, D. M., Kennedy, C., Chen, B., Oakleaf, J., Baruch-Mordo, S., and Kiesecker, J.: Earth transformed: detailed mapping of global human modification from 1990 to 2017, Earth Syst. Sci. Data., https://doi.org/10.5194/essd-2019-252, 2020 Source: https://zenodo.org/record/3963013#.X5ChM0JKjUIgHMv1_2015_wgsWCSGeoTIFFMapX_UNDP__gHMv1_2015_wgsRectifiedGridCoverage-180.0 -75.00027807853427179.99932064640564 85.00013997386212-180.0 -75.00027807853427179.99932064640564 85.00013997386212Global Human Modification Index 2017Detailed global datasets for 1990, 2015, and 2017 for land use modification of terrestrial lands (excluding Antarctica) are provided here. These data were calculated using the degree of human modification approach that combines the proportion of a pixel of a given stressor (i.e. footprint) times the intensity of that stressor (ranging from 0 to 1.0). Our novel datasets are detailed (~300m resolution), temporally consistent (for 1990-2015), comprehensive (11 change stressors, 14 current), robust (using an established framework and incorporating classification errors and parameter uncertainty), and strongly validated. A dataset for ~2017 is also provide, that is derived from 14 stressors, for an even more comprehensive dataset, but it should not be used to calculate change with the other datasets (1990-2015). Theobald, D. M., Kennedy, C., Chen, B., Oakleaf, J., Baruch-Mordo, S., and Kiesecker, J.: Earth transformed: detailed mapping of global human modification from 1990 to 2017, Earth Syst. Sci. Data., https://doi.org/10.5194/essd-2019-252, 2020 Source: https://zenodo.org/record/3963013#.X5ChM0JKjUIgHMv1_2017_wgsWCSGeoTIFFMapX_UNDP__gHMv1_2017_wgsRectifiedGridCoverage-180.0 -75.00027807853427179.99932064640564 85.00013997386212-180.0 -75.00027807853427179.99932064640564 85.00013997386212Global Intertidal Change (1984-2016)The intertidal environment is one of the last remaining unmapped coastal ecosystems on Earth. Here we present an analysis of over 700,000 satellite images that maps the global extent of and change in tidal flats over the course of 33 years (1984–2016). About 70% of the global extent of tidal flats is found in three continents (Asia (44% of total), North America (15.5% of total) and South America (11% of total)), with 49.2% being concentrated in just eight countries (Indonesia, China, Australia, the United States, Canada, India, Brazil and Myanmar). For regions with sufficient data to develop a consistent multi-decadal time series—which included East Asia, the Middle East and North America—we estimate that 16.02% (15.62–16.47%, 95% confidence interval) of tidal flats were lost between 1984 and 2016. Extensive degradation from coastal development, reduced sediment delivery from major rivers, sinking of riverine deltas, increased coastal erosion and sea-level rise signal a continuing negative trajectory for tidal flat ecosystems around the world.<br><br> For complete information please see: <a href="http://dx.doi.org/10.1038/s41586-018-0805-8"target="_blank">Murray N. J., Phinn S. R., DeWitt M., Ferrari R., Johnston R., Lyons M. B., Clinton N., Thau D. & Fuller R. A. (2019) The global distribution and trajectory of tidal flats. Nature. 565:222-225</a><br/><br>or visit the<br/><br><a href="https://www.intertidal.app/home"target="_blank">Global Intertidal Change</a> website>.<br/>murray_intertidal_globalWCSGeoTIFFMapX_UNDP__murray_intertidal_globalRectifiedGridCoverage-179.9997453923923 -60.00000496232559179.9997453923915 60.000004962325846-179.9997453923923 -60.00000496232559179.9997453923915 60.000004962325846Global Land Cover -2015 v2.0.7The Climate Change Initiative (CCI) Land Cover (LC) project of the European Space Agency (ESA) delivers consistent global LC maps at 300 m spatial resolution on an annual basis from 1992 to 2015. Each pixel value corresponds to the label of a land cover class defined based on the UN Land Cover Classification System (LCCS). UN Biodiversity Lab shows only the data for year 2015.<br><br>For further information or to download the full dataset please visit the: <a href="http://maps.elie.ucl.ac.be/CCI/viewer/">European Space Agency Climate Change Innitiative</a>.<br><br>Citation: European Space Agency Climate Change Initiative, Land Cover project. 2017. 300m Annual Global Land Cover Time Series from 1992 to 2015. Retrieved from http://maps.elie.ucl.ac.be/CCI/viewer/. Accessed through UN Biodiversity Lab, (date). www.unbiodiversitylab.org.ESACCI-LC-L4-LCCS-Map-300m-P1Y-1992_2015-v2.0.7_2015WCSGeoTIFFMapX_UNDP__ESACCI-LC-L4-LCCS-Map-300m-P1Y-1992_2015-v2.0.7_2015RectifiedGridCoverage-180.0 -90.00000000001432180.00000000002865 90.0-180.0 -90.00000000001432180.00000000002865 90.0Global Peatlands DistributionThe Global Peatland Map, launched by the Global Peatlands Initiative and its partners, enhances our core knowledge of the location and extent of peatlands worldwide, and serves as the foundational map for the Global Peatlands Assessment, which consolidates the best available science to provide a comprehensive global overview of the State of the World’s Peatlands. The map’s uncertainty levels vary depending on the region. The data primarily originates from scientific publications on soil and peatland research, as well as from other ‘external’ sources such as national agencies and online repositories. For more information see Global Peatland Map This dataset was used for map production, statistics and analysis in the Global Peatland Assessment 2022. The dataset was compiled by the Greifswald Mire Centre with data of the Global Peatland Database. UNEP (2022). Global Peatlands Assessment – The State of the World’s Peatlands: Evidence for action toward the conservation, restoration, and sustainable management of peatlands. Main Report. Global Peatlands Initiative. United Nations Environment Programme, Nairobi. Greifswald Mire Centre (2022) Global Peatland Map 2.0. Underlying dataset of the UNEP Global Peatland Assessment - The State of the World’s Peatlands: Evidence for action toward the conservation, restoration, and sustainable management of peatlands, Global Peatlands Initiative, United Nations Environment Programme, Nairobi.peatGPA22WGS_2cl_optWCSGeoTIFFpeatlands__peatGPA22WGS_2cl_optRectifiedGridCoverage-179.766242816 -74.342809401179.733393112 78.32251340541809-179.766242816 -74.342809401179.733393112 78.32251340541809Global Rainfall Erosivity (R factor) - ESDAC - JRCThe new global erosivity map is a critical input to global and continental assessments of soil erosion by water, flood risk and natural hazard prevention. Current global estimates of soil erosion by water are very uncertain, ranging over one order of magnitude (from around 20 to over 200 Pg per year). More accurate global predictions of rill and interrill soil erosion rates can only be achieved when the rainfall erosivity factor is thoroughly computed. The global erosivity map is publicly available and can be used by other research groups to perform national, continental and global soil erosion modelling. This map provides a complete rainfall erosivity dataset for the whole World based on 3,625 precipitation stations and around 60,000 years of rainfall records at high temporal resolution (1 to 60 minutes). Gaussian Process Regression(GPR) model was used to interpolate the rainfall erosivity values of single stations and to generate the R-factor map. Spatial coverage: World Pixel size: 30 arc-seconds (~1 km at the Equator). Measurement Unit: MJ mm ha-1 h-1 yr-1 Temporal coverage: 30-40 years - Predominant in the last decade: 2000 - 2010 References: A complete description of the methodology and the application in World is described in the paper: Panagos P., Borrelli P., Meusburger K., Yu B., Klik A., Lim K.J., Yang J.E, Ni J., Miao C., Chattopadhyay N., Sadeghi S.H., Hazbavi Z., Zabihi M., Larionov G.A., Krasnov S.F., Garobets A., Levi Y., Erpul G., Birkel C., Hoyos N., Naipal V., Oliveira P.T.S., Bonilla C.A., Meddi M., Nel W., Dashti H., Boni M., Diodato N., Van Oost K., Nearing M.A., Ballabio C., 2017. Global rainfall erosivity assessment based on high-temporal resolution rainfall records. Scientific Reports 7: 4175. DOI: 10.1038/s41598-017-04282-8.GlobalR_NoPol_optWCSGeoTIFFUN_Sahel__GlobalR_NoPol_optRectifiedGridCoverage-179.99999999999997 -58.49228515076997179.99999999999986 72.25771484922997-179.99999999999997 -58.49228515076997179.99999999999986 72.25771484922997Global Reference Evapo-Transpiration (Global-ET0) CGIARPotential Evapo-Transpiration (PET) is a measure of the ability of the atmosphere to remove water through Evapo-Transpiration (ET) processes. Among several equations to estimate PET, a FAO application of the Penman-Monteith equation (Allen et al. 1998), here referred as FAOPM, is currently widely considered as a standard method (Walter et al. 2000). The FAO introduced the definition of PET as the ET of a reference crop (ET0) under optimal conditions, having the characteristics of well-watered grass with an assumed height of 12 centimeters, a fixed surface resistance of 70 seconds per meter and an albedo of 0.23 (Allen et al. 1998). The FAO-PM is a predominately physically based approach, which can be used globally because it does not require estimations of additional site-specific parameters. However, a major drawback of the FAO-PM method is its relatively high need for specific data for a variety of parameters (i.e. windspeed, relative humidity, solar radiation, etc.). The Global Reference Evapo-Transpiration (Global-ET0) dataset provide high-resolution (30 arc-seconds) global raster climate data for the 1970-2000 period, related to evapo-transpiration processes and rainfall deficit for potential vegetative growth, based upon implementation of a Penman Montieth Reference Evapotranspiration (ET0) equation. The geo-spatial datasets has been produced by Antonio Trabucco and Robert Zomer, with the support of the CGIAR-CSI (funding from IFPRI Project No. 203008.000.002 515-01-01). These datasets have been downloaded and are available from the CGIAR-CSI GeoPortal (https://cgiarcsi.community). The Consortium for Spatial Information (CGIAR-CSI) is an initiative of the Consultative Group for International Agriculture Research (CGIAR), linking the international science, research and development communities, with CGIAR scientists, national and international partners, and others working to apply and advance geospatial science for sustainable development, conservation, and poverty alleviation in developing countries. The Global-Aridity_ET0 and Global-ET0 datasets are provided for non-commercial use in standard GeoTiff format, at 30 arc seconds or ~ 1km at the equator, to support studies contributing to sustainable development, biodiversity and environmental conservation, poverty alleviation, and adaption to climate change globally, and in particular in developing countries. Comments, feedback, suggestions or bug reports regarding downloading of the Global-ET0 and Global-Aridity datasets should be sent to: csi@cgiar.org. Technical questions regarding the Global-ET0 and Global-Aridity_ET0 datasets can be directed to Antonio Trabucco: antoniotrabucco@cmcc.it . Trabucco, A., and Zomer, R.J. 2018. Global Aridity Index and Potential Evapo-Transpiration (ET0) Climate Database v2. CGIAR Consortium for Spatial Information (CGIAR-CSI). Published online, available from the CGIAR-CSI GeoPortal at <a href="https://cgiarcsi.community"target="_blank">https://cgiarcsi.community</a>et0_yr_optWCSGeoTIFFUN_Sahel__et0_yr_optRectifiedGridCoverage-180.0 -59.99999997599997179.99999998616659 90.00000001798031-180.0 -59.99999997599997179.99999998616659 90.00000001798031Global Soil Organic Carbon Map V1.0Soil organic carbon (SOC) is the carbon that remains in the soil after partial decomposition of any material produced by living organisms. It constitutes a key element of the global carbon cycle through atmosphere, vegetation, soil, rivers and the ocean. The Global Soil Organic Carbon Map (GSOCmap) allows the estimation of SOC stock from 0 to 30 cm. It represents a key contribution to SDG indicator 15.3.1 which defines the area of degraded land. The GSOCmap represents the first ever global soil organic carbon assessment produced through a participatory approach in which countries developed their capacities and stepped up efforts to compile all the available soil information at national level. Values are in tonnes per hectare. Source: Food and Agricultural Organization of The United Nations (FAO) Data Source: http://www.fao.org/global-soil-partnership/pillars-action/4-information-and-data/global-soil-organic-carbon-gsoc-map/en/GSOCmapV1WCSGeoTIFFMapX_UNDP__GSOCmapV1RectifiedGridCoverage-180.0 -70.91725146799998180.00833189299993 83.616081247-180.0 -70.91725146799998180.00833189299993 83.616081247Global Terrestrial Human Footprint (HFP) - 2000 (subset)This update to the Human Footprint (HFP) provides a measure of the direct and indirect human pressures on the environment globally in years 2000, 2005, 2010, and 2013. Per the orinal Human Footprint, this dataset is derived from remotely-sensed and bottom-up survey information compiled on eight measured variables. This represents not only the most current information of its type, but also the first temporally-consistent set of Human Footprint maps. Data on human pressures were acquired or developed for: 1) built environments, 2) population density, 3) electric infrastructure, 4) crop lands, 5) pasture lands, 6) roads, 7) railways, and 8) navigable waterways. This update incorporates updated and higher resolution population, nightlights, pasture, road, and railway input datasets. The Human Footprint maps find a range of uses as proxies for human disturbance of natural systems and can provide an increased understanding of the human pressures that drive macro-ecological patterns, as well as for tracking environmental change and informing conservation science and application. HFP values range from 0 (no human impact) to 50 (heavily human impacted).<br><br>See: <a href="https://www.nature.com/articles/ncomms12558" target="_blank">Venter, O. et al., 2016. Sixteen years of change in the global terrestrial human footprint and implications for biodiversity conservation. Nature Communications, 7, pp.1–11</a>.<br/><br>This dataset can be downloaded uniquly from UN Biodiversity Lab. <br> Updated data is made available only to FIP pilot countires at present - rasters are clipped to other FIP data extents.hfp2000_merisINT_subsetsGlobalWCSGeoTIFFMapX_UNDP__hfp2000_merisINT_subsetsGlobalRectifiedGridCoverage-81.71613729134685 -20.618092964606667153.42326553667291 26.043388408099844-81.71613729134685 -20.618092964606667153.42326553667291 26.043388408099844Global Terrestrial Human Footprint (HFP) - 2005 (subset)This update to the Human Footprint (HFP) provides a measure of the direct and indirect human pressures on the environment globally in years 2000, 2005, 2010, and 2013. Per the orinal Human Footprint, this dataset is derived from remotely-sensed and bottom-up survey information compiled on eight measured variables. This represents not only the most current information of its type, but also the first temporally-consistent set of Human Footprint maps. Data on human pressures were acquired or developed for: 1) built environments, 2) population density, 3) electric infrastructure, 4) crop lands, 5) pasture lands, 6) roads, 7) railways, and 8) navigable waterways. This update incorporates updated and higher resolution population, nightlights, pasture, road, and railway input datasets. The Human Footprint maps find a range of uses as proxies for human disturbance of natural systems and can provide an increased understanding of the human pressures that drive macro-ecological patterns, as well as for tracking environmental change and informing conservation science and application. HFP values range from 0 (no human impact) to 50 (heavily human impacted).<br><br>See: <a href="https://www.nature.com/articles/ncomms12558" target="_blank">Venter, O. et al., 2016. Sixteen years of change in the global terrestrial human footprint and implications for biodiversity conservation. Nature Communications, 7, pp.1–11</a>.<br/><br>This dataset can be downloaded uniquly from UN Biodiversity Lab. <br> Updated data is made available only to FIP pilot countires at present - rasters are clipped to other FIP data extents.hfp2005_merisINT_subsetsGlobalWCSGeoTIFFMapX_UNDP__hfp2005_merisINT_subsetsGlobalRectifiedGridCoverage-81.71613729134685 -20.618092964606667153.42326553667291 26.043388408099844-81.71613729134685 -20.618092964606667153.42326553667291 26.043388408099844Global Terrestrial Human Footprint (HFP) - 2010 (subset)This update to the Human Footprint (HFP) provides a measure of the direct and indirect human pressures on the environment globally in years 2000, 2005, 2010, and 2013. Per the orinal Human Footprint, this dataset is derived from remotely-sensed and bottom-up survey information compiled on eight measured variables. This represents not only the most current information of its type, but also the first temporally-consistent set of Human Footprint maps. Data on human pressures were acquired or developed for: 1) built environments, 2) population density, 3) electric infrastructure, 4) crop lands, 5) pasture lands, 6) roads, 7) railways, and 8) navigable waterways. This update incorporates updated and higher resolution population, nightlights, pasture, road, and railway input datasets. The Human Footprint maps find a range of uses as proxies for human disturbance of natural systems and can provide an increased understanding of the human pressures that drive macro-ecological patterns, as well as for tracking environmental change and informing conservation science and application. HFP values range from 0 (no human impact) to 50 (heavily human impacted).<br><br>See: <a href="https://www.nature.com/articles/ncomms12558" target="_blank">Venter, O. et al., 2016. Sixteen years of change in the global terrestrial human footprint and implications for biodiversity conservation. Nature Communications, 7, pp.1–11</a>.<br/><br>This dataset can be downloaded uniquly from UN Biodiversity Lab. <br> Updated data is made available only to FIP pilot countires at present - rasters are clipped to other FIP data extents.hfp2010_merisINT_subsetsGlobalWCSGeoTIFFMapX_UNDP__hfp2010_merisINT_subsetsGlobalRectifiedGridCoverage-81.71613729134685 -20.618092964606667153.42326553667291 26.043388408099844-81.71613729134685 -20.618092964606667153.42326553667291 26.043388408099844Global Terrestrial Human Footprint (HFP) - 2013 (subset)This update to the Human Footprint (HFP) provides a measure of the direct and indirect human pressures on the environment globally in years 2000, 2005, 2010, and 2013. Per the orinal Human Footprint, this dataset is derived from remotely-sensed and bottom-up survey information compiled on eight measured variables. This represents not only the most current information of its type, but also the first temporally-consistent set of Human Footprint maps. Data on human pressures were acquired or developed for: 1) built environments, 2) population density, 3) electric infrastructure, 4) crop lands, 5) pasture lands, 6) roads, 7) railways, and 8) navigable waterways. This update incorporates updated and higher resolution population, nightlights, pasture, road, and railway input datasets. The Human Footprint maps find a range of uses as proxies for human disturbance of natural systems and can provide an increased understanding of the human pressures that drive macro-ecological patterns, as well as for tracking environmental change and informing conservation science and application. HFP values range from 0 (no human impact) to 50 (heavily human impacted).<br><br>See: <a href="https://www.nature.com/articles/ncomms12558" target="_blank">Venter, O. et al., 2016. Sixteen years of change in the global terrestrial human footprint and implications for biodiversity conservation. Nature Communications, 7, pp.1–11</a>.<br/><br>This dataset can be downloaded uniquly from UN Biodiversity Lab. <br> Updated data is made available only to FIP pilot countires at present - rasters are clipped to other FIP data extents.hfp2013_merisINT_subsetsGlobalWCSGeoTIFFMapX_UNDP__hfp2013_merisINT_subsetsGlobalRectifiedGridCoverage-81.71613729134685 -20.618092964606667153.42326553667291 26.043388408099844-81.71613729134685 -20.618092964606667153.42326553667291 26.043388408099844Global Wetlands (v2) - CIFORThis dataset shows a distribution of wetland that covers the tropics and sub tropics (40° N to 60° S; 180° E to -180° W), excluding small islands. It was mapped in 231 meters spatial resolution by combining a hydrological model and annual time series of satellite-derived estimates of soil moisture to represent water flow and surface wetness that are then combined with geomorphological data.<br><br>The Global Wetlands Map is produced by the Sustainable Wetlands Adaptation and Mitigation Program (SWAMP), a collaborative effort between the Center for International Forestry Research (<a href="https://www.cifor.org">CIFOR</a>) and the <a href="http://www.fs.fed.us">United States Forest Service</a>, supported by the United States Agency for International Development (<a href="https://www.usaid.gov">USAID</a>) and the CGIAR Research Program on Forests, Trees and Agroforestry (<a href="http://foreststreesagroforestry.org">FTA</a>).<br/><br>Term of Use: The information contained in this dataset is the exclusive property of Center for International Forestry Research (CIFOR) and any respective copyright owners. This work is protected under international copyright treaties and convention (<a href="https://creativecommons.org/licenses/by/4.0/">CC BY 4.0</a>). All uses for commercial purposes require the written permission of the <a href="https://www.cifor.org/">Center for International Forestry Research (CIFOR)</a><br/>.trop-subtrop_wetlandv2_2016_cifor_optWCSGeoTIFFMapX_UNDP__trop-subtrop_wetlandv2_2016_cifor_optRectifiedGridCoverage-179.99999777114783 -60.001279841670446179.99926251758217 40.00080455854803-179.99999777114783 -60.001279841670446179.99926251758217 40.00080455854803Global estimated risk index for tropical cyclone hazardwld__cy1010irmt_cl_wgs84WCSGeoTIFFMAPX__wld_cy1010irmt_cl_wgs84RectifiedGridCoverage-180.0 -90.0179.99999999985602 83.99999999993042-180.0 -90.0179.99999999985602 83.99999999993042Global habitats<style> .p1 { overflow: auto; font-style: italic; font-size: .9em; padding: 10px; margin: 0; text-align: justify; -moz-hyphens: auto; -webkit-hyphens: auto; -ms-hypens: auto; -ms-hyphens: auto; hyphens: auto; max-height: 230px; overflow-x: auto; overflow-y: hidden; columns: 180px; column-rule: 1px solid #ccc; grid-column-gap: 10px; column-gap: 10px; } ul { list-style-type: none; padding: 0; } ul li { margin-bottom: 5px; } </style> <div class="p1"><p>This map provides area-based estimates of at least 75 habitat types at ~100m resolution, placing it among some of the most comprehensive global maps of habitats that can be used for biodiversity assessments.The map follows the thematic legend of habitat classes as defined by the IUCN habitat classification system and maps as many terrestrial, freshwater and marine habitats as possible at global scale.</p><p>To create the habitat type map we used land-cover data from the Copernicus land-cover product as well as the most recent data for the world's climatic zones based on the global Köppen-geiger climate classification system and the distribution of biomes. We relied on ancillary datasets describing the global distribution of mountains, wetlands, intertidal zones or mangroves as well as currently best available global datasets on human pressures.</p><p><u>See:</u><ul><li>Jung, M., Dahal, P.R., Butchart, S.H.M., Donald, P.F., De Lamo, X., Lesiv, M., Kapos, V., Rondinini, C., Visconti, P., (2020). A global map of terrestrial habitat types. Sci. Data 7, 256. <a href="https://www.nature.com/articles/s41597-020-00599-8" target="_blank">www.nature.com/articles/s41597-020-00599-8.</a></li></ul></p></div>global_habitat_v004_optWCSGeoTIFFMapX_UNDP__global_habitat_v004_optRectifiedGridCoverage-179.99917662946004 -81.81826968418555179.9994217980732 81.81855607761675-179.99917662946004 -81.81826968418555179.9994217980732 81.81855607761675Global mercury emissions to air from anthropogenic sources in 2015 (g/km2)Geospatially distributed (gridded) global mercury emissions to air from anthropogenic sources in 2015. Emissions are derived from the AMAP/UNEP global inventory (AMAP/UN-Environment, 2019), geospatially distributed according to methods described in Steenhuisen and Wilson (2019). The dataset is the version dated 5 January 2018.HgT_2015_allsectors_025d__g_km2_optWCSGeoTIFFwesr_pollution__HgT_2015_allsectors_025d__g_km2_optRectifiedGridCoverage-180.0 -90.0180.0 90.0-180.0 -90.0180.0 90.0Global mercury emissions to air from anthropogenic sources in 2015 (kg/cell)Geospatially distributed (gridded) global mercury emissions to air from anthropogenic sources in 2015. Emissions are derived from the AMAP/UNEP global inventory (AMAP/UN-Environment, 2019), geospatially distributed according to methods described in Steenhuisen and Wilson (2019). The dataset is the version dated 5 January 2018.HgT_2015_allsectors_025d__kg_cell_optWCSGeoTIFFwesr_pollution__HgT_2015_allsectors_025d__kg_cellRectifiedGridCoverage-180.0 -90.0180.0 90.0-180.0 -90.0180.0 90.0Global protected marine areas: IUCN categoriesIUCN protected area management categories classify protected areas according to their management objectives. The categories are recognised by international bodies such as the United Nations and by many national governments as the global standard for defining and recording protected areas and as such are increasingly being incorporated into government legislationprot_marine_iucnWCSGeoTIFFWCMCreservenational parkRamsarBiosphereHeritageunea_pollution_nd__prot_marine_iucnRectifiedGridCoverage-179.99998853999992 -63.99999999999994179.99999706000008 83.73332742400007-179.99998853999992 -63.99999999999994179.99999706000008 83.73332742400007Global urban extent - MODIS 500mWCSGeoTIFFu4_modis500grid_data_core_pro__u4_modis500RectifiedGridCoverage-180.0020835 -89.99148149999999180.0267165 90.0020835-180.0020835 -89.99148149999999180.0267165 90.0020835Gridded Livestock of the World – CattleThis updated layer of The Gridded Livestock of the World (GLW)database provided modelled livestock densities of the world, adjusted to match official (FAOSTAT)national estimates for the reference year 2005, at a spatial resolution of 3 minutes of arc (about 565 km at the equator).Recent methodological improvements have significantly enhanced these distributions: more up-to date and detailed sub-national livestock statistics have been collected; a new, higher resolution set of predictor variables is used; and the analyticalprocedure has been revised and extended to include a more systematic assessment of model accuracy and therepresentation of uncertainties associated with the predictions.<br><br>For further details on mapping methods see: Robinson, T.P., Wint, G.R.W., Conchedda, G., Van Boeckel, T.P., Ercoli, V., Palamara, E., Cinardi, G., D’Aietti, L., Hay, S.I., Gilbert, M., 2014. Mapping the Global Distribution of Livestock. PLoS ONE 9, e96084. <a href=\"https://doi.org/10.1371/journal.pone.0096084\"target=_blank>https://doi.org/10.1371/journal.pone.0096084</a><br/><br>These digital layers are made publically available via the Livestock Geo-Wiki (<a href=\"http://www.livestock.geo-wiki.org\"target=_blank>livestock.geo-wiki.org</a><br/>Glb_Cattle_CC2006_ADWCSGeoTIFFMapX_UNDP__Glb_Cattle_CC2006_ADRectifiedGridCoverage-180.0 -90.0180.0 90.0-180.0 -90.0180.0 90.0Gridded Livestock of the World – GoatsThis updated layer of The Gridded Livestock of the World (GLW)database provided modelled livestock densities of the world, adjusted to match official (FAOSTAT)national estimates for the reference year 2005, at a spatial resolution of 3 minutes of arc (about 565 km at the equator).Recent methodological improvements have significantly enhanced these distributions: more up-to date and detailed sub-national livestock statistics have been collected; a new, higher resolution set of predictor variables is used; and the analyticalprocedure has been revised and extended to include a more systematic assessment of model accuracy and therepresentation of uncertainties associated with the predictions.<br><br>For further details on mapping methods see: Robinson, T.P., Wint, G.R.W., Conchedda, G., Van Boeckel, T.P., Ercoli, V., Palamara, E., Cinardi, G., D’Aietti, L., Hay, S.I., Gilbert, M., 2014. Mapping the Global Distribution of Livestock. PLoS ONE 9, e96084. <a href=\"https://doi.org/10.1371/journal.pone.0096084\"target=_blank>https://doi.org/10.1371/journal.pone.0096084</a><br/><br>These digital layers are made publically available via the Livestock Geo-Wiki (<a href=\"http://www.livestock.geo-wiki.org\"target=_blank>livestock.geo-wiki.org</a><br/>Glb_GTAD_2006WCSGeoTIFFMapX_UNDP__Glb_GTAD_2006RectifiedGridCoverage-180.00000000000003 -90.00000000000001180.00000000000003 90.00000000000001-180.00000000000003 -90.00000000000001180.00000000000003 90.00000000000001Gridded Livestock of the World – SheepThis updated layer of The Gridded Livestock of the World (GLW)database provided modelled livestock densities of the world, adjusted to match official (FAOSTAT)national estimates for the reference year 2005, at a spatial resolution of 3 minutes of arc (about 565 km at the equator).Recent methodological improvements have significantly enhanced these distributions: more up-to date and detailed sub-national livestock statistics have been collected; a new, higher resolution set of predictor variables is used; and the analyticalprocedure has been revised and extended to include a more systematic assessment of model accuracy and therepresentation of uncertainties associated with the predictions.<br><br>For further details on mapping methods see: Robinson, T.P., Wint, G.R.W., Conchedda, G., Van Boeckel, T.P., Ercoli, V., Palamara, E., Cinardi, G., D’Aietti, L., Hay, S.I., Gilbert, M., 2014. Mapping the Global Distribution of Livestock. PLoS ONE 9, e96084. <a href=\"https://doi.org/10.1371/journal.pone.0096084\"target=_blank>https://doi.org/10.1371/journal.pone.0096084</a><br/><br>These digital layers are made publically available via the Livestock Geo-Wiki (<a href=\"http://www.livestock.geo-wiki.org\"target=_blank>livestock.geo-wiki.org</a><br/>Glb_SHAD_2006WCSGeoTIFFMapX_UNDP__Glb_SHAD_2006RectifiedGridCoverage-180.0 -90.0180.0 90.0-180.0 -90.0180.0 90.0Habitat Quality difference 2010 - 2030 (Mitigation scenario)New map generated at GRIDHQ_30-10WCSGeoTIFFTEEB__BHU_HQ_30-10RectifiedGridCoverage88.733529924 26.68479659592.146928137 28.24781153488.733529924 26.68479659592.146928137 28.247811534Historical Generalized Difference Vegetation Index of emerging Northwest Mining Region of Ghana (1990)The Generalized Normalised Difference Vegetation Index (GNDVI) is a modification of the Normalised Difference Vegetation Index (NDVI), which has been proposed by Weicheng Wu (2014) for the assessment of dryland environments due to the inadequacy of the NDVI in these areas. Indeed, this has been very appropriate and effective in mapping the vegetative cover of the Northwest region of Ghana, which is a semi-arid zone. The GNDVI of 1990 seeks to validate the land cover patterns that existed in the area with regard to biodiversity and land use. This data sets the baseline analysis of the emerging Northwest mining region of Ghana, which would enhance the monitoring of sustainable miningGNDVI1990_optWCSGeoTIFFMAPX__GHA_GNDVI1990RectifiedGridCoverage-2.941804951 9.626453977-1.42736896 11.030613419-2.941804951 9.626453977-1.42736896 11.030613419Historical Generalized Difference Vegetation Index of emerging Northwest Mining Region of Ghana (2010)The Generalized Normalised Difference Vegetation Index (GNDVI) is a modification of the Normalised Difference Vegetation Index (NDVI), which has been proposed by Weicheng Wu (2014) for the assessment of dryland environments due to the inadequacy of the NDVI in these areas. The Generalised Normalised Difference Vegetation Index (GNDVI) 2010 presents a wider outlook of the environmental effects of illegal artisanal small-scale gold mining (galamsey) in the Northwest region of Ghana. However, vegetation displacement in the area cannot entirely be attributed to galamsey. There has been a fast urban growth that is leading to vegetation clearance. The question is, can the trends of urban growth and vegetation depletion be associated to illegal mining? These are some of the scientific questions that these data will help to address. This notwithstanding, how could the looming large-scale gold mining excerbate the observed trends of environmental and land cover change in the Northwest region of Ghana? These data help to address all these relevant questions.GNDVI2010_optWCSGeoTIFFMAPX__GHA_GNDVI2010RectifiedGridCoverage-2.941804951 9.626453977-1.42736896 11.030613419-2.941804951 9.626453977-1.42736896 11.030613419Historical Generalized Difference Vegetation Index of emerging Northwest Mining Region of Ghana (2019)The Generalized Normalised Difference Vegetation Index (GNDVI) is a modification of the Normalised Difference Vegetation Index (NDVI), which has been proposed by Weicheng Wu (2014) for the assessment of dryland environments due to the inadequacy of the NDVI in these areas. The Generalised Normalised Difference Vegetation Index (GNDVI) 2019 highlights the effects of the Government of Ghana's fight against illegal mining on vegetation and 'blue mining'. These data can be used to monitor the health of vegetation before and after the fight against galamsey. Further, the data can be used to assess the success or failure of the combat against dessertation initiative of the government of Ghana in the Northwest region as against its approval for large-scale industrial mining in the same areaGNDVI2019_optWCSGeoTIFFMAPX__GHA_GNDVI2019RectifiedGridCoverage-2.941804951 9.626453977-1.42736896 11.030613419-2.941804951 9.626453977-1.42736896 11.030613419Historical Land Cover of emerging Northwest Mining Region of Ghana (1990)This shows the existing land use and land cover of the Northwest region of Ghana prior to exploration and illegal mining activities in the area. The 1990 land cover data would instigate keen observations about the trends of land cover and land use change and the dynamics of the change, which could be associated to either mining or unsustainable local exploitation of naturals resources in the area. Thus, the data gives a three decade landscape outlook of the area, which could be used as baseline data for monitoring and validating the environmental responsiveness of both small-scale and large-scale mining activities in the Northwest Mining Region of Ghana.LULC1990_optWCSGeoTIFFMAPX__GHA_LULC1990RectifiedGridCoverage-2.941804951 9.626453977-1.42736896 11.030613419-2.941804951 9.626453977-1.42736896 11.030613419Historical Land Cover of emerging Northwest Mining Region of Ghana (2000)This shows a decade landscape perspective of the emerging Northwest mining region after the Rio De Jeneiro Conference on Sustainable Development with a focus on environmental resources. This data would highlight the appreciable contributions of illegal mining and the begining of large-scale exploration activities in the area. The trends and patterns of these actitivities' interactions with biodiversity can be observed and monitored for impact remediation in the area. The environmental responsiveness and mining generating carbon stock concentrations can be monitored as well. The activities of illegal mining in relation to the environment, water and vegetation can be tracked and regulated.LULC2000_optWCSGeoTIFFMAPX__GHA_LULC2000RectifiedGridCoverage-2.941942292 9.62626436-1.427242947 11.030913256-2.941942292 9.62626436-1.427242947 11.030913256Historical Land Cover of emerging Northwest Mining Region of Ghana (2010)The 2010 land use and land cover data of the emerging Northwest mining region shows the current state of environmental conditions in the area, two decades after the Rio Conference, and how this has been impacted by illegal mining or quarrying or large-scale exploration activities in the area. This data could also be used to identify the gaps that need to be filled before the commencement of large-scale mining in the very nearest future. It is important to ask how this data could be used to inform relevant decision making on illegal mining in the area. The simple answer to the question is that environmental and land use impacts of the activities can be identified and mitigated, the presence and movement patterns of both illegal small-scale gold mining (galamsey) and large-scale mining can be tracked and estimate their environmental footprint using this data. Potential conflicts with regards to land use and concession encroachment could also be averted with these data.LULC2010_optWCSGeoTIFFMAPX__GHA_LULC2010RectifiedGridCoverage-2.941952153 9.62648323-1.4272597149999997 11.03077215-2.941952153 9.62648323-1.4272597149999997 11.03077215Historical Land Cover of emerging Northwest Mining Region of Ghana (2019)The 2019 land cover data mark the biophysical state of the area, three decades after the Rio conference: (1) after the government of Ghana fight against galamsey (illegal artisanal small-scale gold mining), and (2) the initial environmental parameters for validating the environmental and social impact assessment reports prior to the finalisation of construction of large-scale mining sites waiting for commissioning. This data makes it possible to map the mining activities onto the Agenda 2030 SDGs, making it easy to take critically relevant decisions, track the activities of the large-scale operations as well as small-scale illegal mining operations, and mitigate the environmental hazardsLULC2019_optWCSGeoTIFFMAPX__GHA_LULC2019RectifiedGridCoverage-2.941804951 9.626453977-1.42736896 11.030613419-2.941804951 9.626453977-1.42736896 11.030613419Histtorical Generalized Difference Vegetation Index of emerging Northwest Mining Region of Ghana (2000)The Generalized Normalised Difference Vegetation Index (GNDVI) is a modification of the Normalised Difference Vegetation Index (NDVI), which has been proposed by Weicheng Wu (2014) for the assessment of dryland environments due to the inadequacy of the NDVI in these areas. The Generalised Normalised Difference Vegetation Index (GNDVI) of 2000 shows the trends and patterns of vegetation health and degradation in the area. The data further shows the elements contributing to the fast approaching dessertification of the area. The data further sets the baseline analysis and the amount of cost-benefit analysis that must be triggered in view of the fast approaching dessertification and looming large-scale mining in the Northwest Region of GhanaGNDVI2000_optWCSGeoTIFFMAPX__GHA_GNDVI2000RectifiedGridCoverage-2.941804951 9.626453977-1.42736896 11.030613419-2.941804951 9.626453977-1.42736896 11.030613419Human Footprint 1993 (Terrestrial)Venter, O. et al., 2016. Sixteen years of change in the global terrestrial human footprint and implications for biodiversity conservation. Nature Communications, 7, pp.1–11. Available at: http://www.nature.com/doifinder/10.1038/ncomms12558 [Accessed October 18, 2016].hfp1993WCSGeoTIFFMapX_UNDP__hfp1993RectifiedGridCoverage-179.99999995447251 -65.25013301721049179.99661098079287 89.89762321520959-179.99999995447251 -65.25013301721049179.99661098079287 89.89762321520959Human Footprint 2009 (Terrestrial)Venter, O. et al., 2016. Sixteen years of change in the global terrestrial human footprint and implications for biodiversity conservation. Nature Communications, 7, pp.1–11. Available at: http://www.nature.com/doifinder/10.1038/ncomms12558 [Accessed October 18, 2016].hfp2009WCSGeoTIFFMapX_UNDP__hfp2009RectifiedGridCoverage-179.99999995447251 -65.25013301721049179.99661098079287 89.89762321520959-179.99999995447251 -65.25013301721049179.99661098079287 89.89762321520959Human Footprint difference 1993-2009 (Terrestrial)hfp_differenceWCSGeoTIFFMapX_UNDP__hfp_difference_93-09RectifiedGridCoverage-179.99999995447251 -65.25013301721049179.99661098079287 89.89762321520959-179.99999995447251 -65.25013301721049179.99661098079287 89.89762321520959Human Pressures in Protected Areas (2018)In an era of massive biodiversity loss, the greatest conservation success story has been the growth of protected land globally. Protected areas are the primary defense against biodiversity loss, but extensive human activity within their boundaries can undermine this. Using the most comprehensive global map of human pressure, we show that 6 million square kilometers (32.8%) of protected land is under intense human pressure. For protected areas designated before the Convention on Biological Diversity was ratified in 1992, 55% have since experienced human pressure increases. These increases were lowest in large, strict protected areas, showing that they are potentially effective, at least in some nations. Transparent reporting on human pressure within protected areas is now critical, as are global targets aimed at efforts required to halt biodiversity loss. One-third of global protected land is under intense human pressure Kendall R. Jones1,2,*, Oscar Venter3, Richard A. Fuller2,4, James R. Allan1,2, Sean L. Maxwell1,2, Pablo Jose Negret1,2, James E. M. Watson1,2,5 See all authors and affiliations Science 18 May 2018: Vol. 360, Issue 6390, pp. 788-791 DOI: 10.1126/science.aap9565int_pct_conv_in_pas_wgsWCSGeoTIFFMapX_UNDP__int_pct_conv_in_pas_wgsRectifiedGridCoverage-179.99998854073996 -69.47709354376937179.99795085448952 83.70375566498902-179.99998854073996 -69.47709354376937179.99795085448952 83.70375566498902Human footprint 2000Brooke A. Williams, Oscar Venter, James R. Allan, Scott C. Atkinson, Jose A. Rehbein, Michelle S. Ward, Moreno Di Marco, Hedley S. Grantham, Jamison Ervin, Scott Goetz, Andrew J. Hansen, Patrick Jantz, Rajeev Pillay, Susana Rodríguez-Buriticá, Christina Supples, Anne L. S. Virnig, James E.M. Watson. (2020). Change in terrestrial human footprint drives continued loss of intact ecosystems. OneEarth. http://dx.doi.org/10.2139/ssrn.3600547hfp2000_merisINT_wgs84_optWCSGeoTIFFwesr_biodiversity__hfp2000_merisINTRectifiedGridCoverage-179.99999995447251 -65.25013301721049179.99661098079287 89.89762321520959-179.99999995447251 -65.25013301721049179.99661098079287 89.89762321520959Human footprint 2005Brooke A. Williams, Oscar Venter, James R. Allan, Scott C. Atkinson, Jose A. Rehbein, Michelle S. Ward, Moreno Di Marco, Hedley S. Grantham, Jamison Ervin, Scott Goetz, Andrew J. Hansen, Patrick Jantz, Rajeev Pillay, Susana Rodríguez-Buriticá, Christina Supples, Anne L. S. Virnig, James E.M. Watson. (2020). Change in terrestrial human footprint drives continued loss of intact ecosystems. OneEarth. http://dx.doi.org/10.2139/ssrn.3600547hfp2005_merisINT_wgs84_optWCSGeoTIFFwesr_biodiversity__hfp2005_merisINTRectifiedGridCoverage-179.99999995447251 -65.25013301721049179.99661098079287 89.89762321520959-179.99999995447251 -65.25013301721049179.99661098079287 89.89762321520959Human footprint 2010Brooke A. Williams, Oscar Venter, James R. Allan, Scott C. Atkinson, Jose A. Rehbein, Michelle S. Ward, Moreno Di Marco, Hedley S. Grantham, Jamison Ervin, Scott Goetz, Andrew J. Hansen, Patrick Jantz, Rajeev Pillay, Susana Rodríguez-Buriticá, Christina Supples, Anne L. S. Virnig, James E.M. Watson. (2020). Change in terrestrial human footprint drives continued loss of intact ecosystems. OneEarth. http://dx.doi.org/10.2139/ssrn.3600547hfp2010_merisINT_wgs84_optWCSGeoTIFFwesr_biodiversity__hfp2010_merisINTRectifiedGridCoverage-179.99999995447251 -65.25013301721049179.99661098079287 89.89762321520959-179.99999995447251 -65.25013301721049179.99661098079287 89.89762321520959Human footprint 2013Brooke A. Williams, Oscar Venter, James R. Allan, Scott C. Atkinson, Jose A. Rehbein, Michelle S. Ward, Moreno Di Marco, Hedley S. Grantham, Jamison Ervin, Scott Goetz, Andrew J. Hansen, Patrick Jantz, Rajeev Pillay, Susana Rodríguez-Buriticá, Christina Supples, Anne L. S. Virnig, James E.M. Watson. (2020). Change in terrestrial human footprint drives continued loss of intact ecosystems. OneEarth. http://dx.doi.org/10.2139/ssrn.3600547hfp2013_merisINT_wgs84_optWCSGeoTIFFwesr_biodiversity__hfp2013_merisINTRectifiedGridCoverage-179.99999995447251 -65.25013301721049179.99661098079287 89.89762321520959-179.99999995447251 -65.25013301721049179.99661098079287 89.89762321520959Human impact on forests<span style="font-size:0.95em"><span style="font-size:1.1em"><b>Description</b></span> A global map containing information on forest management, using the following categories: 1) forest without any signs of human impact, 2) forest with signs of human impact, including clear cuts, logging, and built-up roads; 3) replanted forest and forest - rotation period longer than 20 years; 4) woody plantations - rotation period of maximum 15 years; 5) oil palm plantations; and 6) agroforestry, including fruit tree plantations, tree shelter-belts, and individual trees on pastures. This information has been produced based on more than 100,000 observations collected through a citizen science campaign using the <a href="https://www.geo-wiki.org" target="_blank">Geo-Wiki Platform</a>. Machine learning techniques were applied in combination with Proba-V 100m images to generate the final map.<br><br>NatureMap is currently soliciting feedback on the validity of this map at the national and sub-national levels. Please submit any comments via the <a href="https://explorer.naturemap.earth/" target="_blank">Nature Map Explorer</a>.</span>forest_management_layer_v3_2_optWCSGeoTIFFMapX_UNDP__forest_management_layer_v3_2_optRectifiedGridCoverage-179.9999885408441 -60.00069958862515179.99982972004844 80.00049603174595-179.9999885408441 -60.00069958862515179.99982972004844 80.00049603174595Human pressures<span style="font-size:1em"> This maps shows the spatial distribution of combined pressures on biodiversity, water provisioning services, and carbon (in biomass and soils) at a global scale. We selected a number of existing datasets which best represented pressures on these 3 components, including: <ul> <li> The Human Footprint (HFP)(Venter et al. 2009); </li> <li> Data derived from the top five weighted water security risk factors obtained from Vörösmarty et al. (2010), aggregated within HydroBASINS (level 5) catchments (Lehner and Grill, 2013); </li> <li> Data on the number and intensity of active fires (MODIS), and climate change risk, based on the estimated difference between average annual future temperature for the reference year 2050 and mean annual historic temperature (1970 – present)(Karger et al. 2017). </li> </ul> These three layers are combined to illustrate broad scale patterns of cumulative human pressures and ascertain where the intrinsic values of nature are most at risk. <br /> <br /> <u>See:</u> <ul> <li> UNEP-WCMC (2020). Human pressures on biodiversity, water and carbon. Cambridge, UK. </li> </ul> <u>Other References:</u> <ul> <li> Karger, D.N. et al. (2017) Climatologies at high resolution for the earth's land surface areas. Scientific Data 4, 170122. <a href="https://doi.org/10.1038/sdata.2017.122." target="_blank">doi.org/10.1038/sdata.2017.122.</a> </li> <li> Lehner, B. and Grill G. (2013): Global river hydrography and network routing: baseline data and new approaches to study the world's large river systems. Hydrological Processes, 27(15): 2171–2186. Data is available at <a href="www.hydrosheds.org">www.hydrosheds.org.</a> </li> <li> Venter, O., Sanderson, E. W., Magrach, A., et al. (2016). Global terrestrial Human Footprint maps for 1993 and 2009. Scientific Data, 3, 160067. <a href="https://doi.org/10.1038/sdata.2016.67." target="_blank">doi.org/10.1038/sdata.2016.67.</a> </li> <li> Vörösmarty, C., McIntyre, P., Gessner, M. et al. (2010). Global threats to human water security and river biodiversity. Nature, 467(7315), 555–561. <a href="https://doi.org/10.1038/nature09440." target="_blank">doi.org/10.1038/nature09440.</a> </li> </ul> </span>Human_pressures_wgs_opt_optWCSGeoTIFFMapX_UNDP__Human_pressures_wgs_opt_optRectifiedGridCoverage-179.99051999985159 -81.82870752472896179.99257988732398 81.82264272729027-179.99051999985159 -81.82870752472896179.99257988732398 81.82264272729027Illegal Dumping Susceptibility in Keur Massar Senegal in 2025 (unweighted)This map shows the non-weighted illegal dumping susceptibility across the three communes of Keur Massar (Malika, Keur Massar, and Yeumbeul Nord) in Dakar, Senegal. The map was produced in 2025 as part of a research project using multi-criteria analysis at UNEP/GRID Geneva. The map represents the mean of several positively correlated criteria with illegal dumping: proximity to water edges, vegetation areas, bare soil areas, population density (2025), land surface temperature, market areas, and areas around PRNs, as well as a social perception criterion derived from a survey of students living in the three communes. Waste collection routes, which are negatively correlated with illegal dumping, are also included. All criteria were rasterized when necessary and normalized before averaging to produce the final map. The Focal Statistics tool was applied with a radius of 7 pixels to smooth the results and reduce local noise. The susceptibility levels are visualized using a color scale: dark green (very low, ≤ 0.19), light green (low, ≤ 0.24), beige (medium, ≤ 0.27), orange (high, ≤ 0.31), and red (very high, ≤ 0.42). This output provides an integrated overview of spatial susceptibility patterns for illegal dumping, supporting targeted interventions and planning.Solid Waste ManagementRemote SensingGISCitizen ScienceMulti-Criteria AnalysisIllegal Dumpingdakar_waste__carte_non_pondereeRectifiedGridCoverage245238.84590000007 1633496.9376999997253849.32088053942 1639987.295728304-17.36716999545483 14.763385550148119-17.286595649628648 14.822825777160343Illegal Dumping Susceptibility in Keur Massar Senegal in 2025 (weighted)This map shows the weighted illegal dumping susceptibility across the three communes of Keur Massar (Malika, Keur Massar, and Yeumbeul Nord) in Dakar, Senegal. The map was produced in 2025 as part of a research project using multi-criteria analysis at UNEP/GRID Geneva. The map represents a weighted combination of several positively correlated criteria with illegal dumping: proximity to water edges, vegetation areas, bare soil areas, population density (2025), land surface temperature, market areas, areas around PRNs, and a social perception criterion derived from a survey of students living in the three communes. Waste collection routes, which are negatively correlated with illegal dumping, are also included. Weights for each criterion were determined using the Analytic Hierarchy Process (AHP). The relative importance of the criteria was established based on the student surveys and scientific literature. A pairwise comparison matrix was then created to calculate the weights. Each criterion raster was multiplied by its corresponding weight, and the weighted rasters were summed to produce the final map. All criteria were rasterized when necessary and normalized before weighting. The Focal Statistics tool was applied with a radius of 7 pixels to smooth the results and reduce local noise. The susceptibility levels are visualized using a color scale: dark green (very low, ≤ 0.19), light green (low, ≤ 0.24), beige (medium, ≤ 0.27), orange (high, ≤ 0.31), and red (very high, ≤ 0.42). This output provides a comprehensive overview of spatial susceptibility patterns for illegal dumping, supporting targeted interventions and planning.Solid Waste ManagementRemote SensingGISCitizen ScienceMulti-Criteria AnalysisIllegal Dumpingdakar_waste__carte_pondereeRectifiedGridCoverage245238.84590000007 1633496.9376999997253849.32088053942 1639987.295728304-17.36716999545483 14.763385550148119-17.286595649628648 14.822825777160343Increase in SOC on Croplands After 20 Yr - High ScenarioThis dataset provides estimate of the potential increase in soil organic carbon within the top 30 cm of soil in croplands after 20 years, following implementation of better land managment practices under a high sequestration scenario. The per pixel values here take in to consideration the percent of each pixel which is classified as cropland (from the GLC-Share/GLC-02 dataset), and values have been converted to total tonnes of carbon (x 100) per pixel.<br/><br>See: <a href="https://doi.org/10.1038/s41598-017-15794-8">Zomer, R.J., Bossio, D.A., Sommer, R., Verchot, L.V., 2017. Global Sequestration Potential of Increased Organic Carbon in Cropland Soils. Scientific Reports 7, 15554</a>.<br/>For descriptions of sequrestion scenarions see: <a href="https://doi.org/10.1016/j.jenvman.2014.05.017">Sommer, R., Bossio, D., 2014. Dynamics and climate change mitigation potential of soil organic carbon sequestration. Journal of Environmental Management 144, 83–87</a>.<br/>soc_dif_hiWCSGeoTIFFMapX_UNDP__soc_dif_hiRectifiedGridCoverage-180.0 -62.002087813179.9999424 87.37080495400002-180.0 -62.002087813179.9999424 87.37080495400002Increase in SOC on Croplands After 20 Yr - Medium ScenarioThis dataset provides estimate of the potential increase in soil organic carbon within the top 30 cm of soil in croplands after 20 years, following implementation of better land managment practices under a medium sequestration scenario. The per pixel values here take in to consideration the percent of each pixel which is classified as cropland (from the GLC-Share/GLC-02 dataset), and values have been converted to total tonnes of carbon (x 100) per pixel.<br/><br>See: <a href="https://doi.org/10.1038/s41598-017-15794-8">Zomer, R.J., Bossio, D.A., Sommer, R., Verchot, L.V., 2017. Global Sequestration Potential of Increased Organic Carbon in Cropland Soils. Scientific Reports 7, 15554</a>.<br/>For descriptions of sequrestion scenarions see: <a href="https://doi.org/10.1016/j.jenvman.2014.05.017">Sommer, R., Bossio, D., 2014. Dynamics and climate change mitigation potential of soil organic carbon sequestration. Journal of Environmental Management 144, 83–87</a>.<br/>soc_dif_meWCSGeoTIFFMapX_UNDP__soc_dif_meRectifiedGridCoverage-180.0 -62.002087813179.9999424 87.37080495400002-180.0 -62.002087813179.9999424 87.37080495400002Inorganic Pollution 2008 - KNBNon-point source inorganic pollution was modelled with global 1 km2 impervious surface area data (http://www.ngdc.noaa.gov/dmsp/) under the assumption that most of this pollution comes from urban runoff. These data will not capture point-sources of pollution or nonpoint sources where paved roads do not exist (e.g., select places in developing countries). These values were then aggregated to the watershed and distributed to the pour point (i.e., stream and river mouths) for the watershed with raster statistics (i.e., aggregation by watershed). Finally, spread of the driver values into coastal waters at each pour point was modelled with a cost-path surface on the basis of a decay function that assigns a fixed amount of the driver (0.5% of the value in the previous cell) in the initial cell and then evenly distributes the remaining amount of driver in all adjacent and ‘unvisited’ cells, repeated until a minimum threshold (0.05% of global maximum) is reached. This approach to modelling river plumes is diffusive and so allows drivers to wrap around headlands and islands. Raw stressor data from "Benjamin Halpern, Melanie Frazier, John Potapenko, Kenneth Casey, Kellee Koenig, et al. 2015. Cumulative human impacts: raw stressor data (2008 and 2013). Knowledge Network for Biocomplexity. doi:10.5063/F1S180FS."08_inorganic_poll_halpern_KNB_WGS84WCSGeoTIFFunea_pollution__08_inorganic_poll_halpern_KNB_WGS84RectifiedGridCoverage-179.9999998015721 -89.90838300321447180.00137028902103 83.6214908114224-179.99998890825066 -89.90931632117805180.0014259209304 83.62149081142242K1-KAPOSK1-KAPOSWCSGeoTIFFwesr_mountains__K1-KAPOSRectifiedGridCoverage-179.99998890825066 -55.90638888888891180.00001109174872 83.32902777777751-179.99998890825066 -55.90638888888891180.00001109174872 83.32902777777751K2_new_YGK2_new_YGWCSGeoTIFFgrid_data_core__K2_new_YGRectifiedGridCoverage-180.02083 -59.47894000000001179.97859 83.64583-180.02083 -59.47894000000001179.97859 83.64583K3-ELU-LFK3-ELU-LFWCSGeoTIFFwesr_mountains__K3-ELU-LFRectifiedGridCoverage-180.0000000000001 -56.00013888888891179.99999999999926 83.62486111111086-180.0000000000001 -56.00013888888891179.99999999999926 83.62486111111086Kerala DEMcodemWCSGeoTIFFECO-DRR__Kerala_MERIT_DEMRectifiedGridCoverage74.85875 8.2537577.49291666666667 12.84291666666667374.85875 8.2537577.49291666666667 12.842916666666673Kerala Flood 10 Years RP - HistoricalKerala State flood hazard map - Historical. Maximum expected flood event water height in a 10 years return period, expressed in cm.h10iWCSGeoTIFFECO-DRR__Kerala_Flood_10_Yrs_HistoricalRectifiedGridCoverage74.85875 8.2537577.49291666666667 12.84291666666667374.85875 8.2537577.49291666666667 12.842916666666673Kerala Flood 10 Years RP - RCP85Kerala State flood hazard map - RCP 8.5 scenario. Maximum expected flood event water height in a 10 years return period, expressed in cm.h10iWCSGeoTIFFECO-DRR__Kerala_Flood_10_Yrs_RCP85RectifiedGridCoverage74.85958333333333 8.2537577.49208333333334 12.84291666666667374.85958333333333 8.2537577.49208333333334 12.842916666666673Kerala Flood 100 Years RP - HistoricalKerala State flood hazard map - Historical. Maximum expected flood event water height in a 100 years return period, expressed in cm.h100iWCSGeoTIFFECO-DRR__Kerala_Flood_100_Yrs_HistoricalRectifiedGridCoverage74.85875 8.2537577.49291666666667 12.84291666666667374.85875 8.2537577.49291666666667 12.842916666666673Kerala Flood 100 Years RP - RCP85Kerala State flood hazard map - RCP 8.5 scenario. Maximum expected flood event water height in a 100 years return period, expressed in cm.h100iWCSGeoTIFFECO-DRR__Kerala_Flood_100_Yrs_RCP85RectifiedGridCoverage74.85958333333333 8.2537577.49208333333334 12.84291666666667374.85958333333333 8.2537577.49208333333334 12.842916666666673Kerala Flood 200 Years RP - HistoricalKerala State flood hazard map - Historical. Maximum expected flood event water height in a 200 years return period, expressed in cm.h200iWCSGeoTIFFECO-DRR__Kerala_Flood_200_Yrs_HistoricalRectifiedGridCoverage74.85875 8.2537577.49291666666667 12.84291666666667374.85875 8.2537577.49291666666667 12.842916666666673Kerala Flood 200 Years RP - RCP85Kerala State flood hazard map - RCP 8.5 scenario. Maximum expected flood event water height in a 200 years return period, expressed in cm.h200iWCSGeoTIFFECO-DRR__Kerala_Flood_200_Yrs_RCP85RectifiedGridCoverage74.85958333333333 8.2537577.49208333333334 12.84291666666667374.85958333333333 8.2537577.49208333333334 12.842916666666673Kerala Flood 25 Years RP - HistoricalKerala State flood hazard map - Historical. Maximum expected flood event water height in a 25 years return period, expressed in cm.h25iWCSGeoTIFFECO-DRR__Kerala_Flood_25_Yrs_HistoricalRectifiedGridCoverage74.85875 8.2537577.49291666666667 12.84291666666667374.85875 8.2537577.49291666666667 12.842916666666673Kerala Flood 25 Years RP - RCP85Kerala State flood hazard map - RCP 8.5 scenario. Maximum expected flood event water height in a 25 years return period, expressed in cm.h25iWCSGeoTIFFECO-DRR__Kerala_Flood_25_Yrs_RCP85RectifiedGridCoverage74.85958333333333 8.2537577.49208333333334 12.84291666666667374.85958333333333 8.2537577.49208333333334 12.842916666666673Kerala Flood 50 Years RP - HistoricalKerala State flood hazard map - Historical. Maximum expected flood event water height in a 50 years return period, expressed in cm.h50iWCSGeoTIFFECO-DRR__Kerala_Flood_50_Yrs_HistoricalRectifiedGridCoverage74.85875 8.2537577.49291666666667 12.84291666666667374.85875 8.2537577.49291666666667 12.842916666666673Kerala Flood 50 Years RP - RCP85Kerala State flood hazard map - RCP 8.5 scenario. Maximum expected flood event water height in a 50 years return period, expressed in cm.h50iWCSGeoTIFFECO-DRR__Kerala_Flood_50_Yrs_RCP85RectifiedGridCoverage74.85958333333333 8.2537577.49208333333334 12.84291666666667374.85958333333333 8.2537577.49208333333334 12.842916666666673Kerala Flood 500 Years RP - HistoricalKerala State flood hazard map - Historical. Maximum expected flood event water height in a 500 years return period, expressed in cm.h500iWCSGeoTIFFECO-DRR__Kerala_Flood_500_Yrs_HistoricalRectifiedGridCoverage74.85875 8.2537577.49291666666667 12.84291666666667374.85875 8.2537577.49291666666667 12.842916666666673Kerala Flood 500 Years RP - RCP85Kerala State flood hazard map - RCP 8.5 scenario. Maximum expected flood event water height in a 500 years return period, expressed in cm.h500iWCSGeoTIFFECO-DRR__Kerala_Flood_500_Yrs_RCP85RectifiedGridCoverage74.85958333333333 8.2537577.49208333333334 12.84291666666667374.85958333333333 8.2537577.49208333333334 12.842916666666673Kerala Global Land Cover 2009ESA Global Land Cover 2009 for Kerala State.glc2009WCSGeoTIFFECO-DRR__Kerala_Global_Land_Cover_2009RectifiedGridCoverage74.85972222222227 8.25416666666667777.49305555555561 12.84305555555556674.85972222222227 8.25416666666667777.49305555555561 12.843055555555566Kerala HillshadeHillshade on Kerala MERIT DEM 240-30codemhWCSGeoTIFFECO-DRR__Kerala_HillshadeRectifiedGridCoverage74.85875 8.2537577.49291666666667 12.84291666666667374.85875 8.2537577.49291666666667 12.842916666666673Kerala Hillshade MulticodemhmWCSGeoTIFFECO-DRR__Kerala_Hillshade_MultiRectifiedGridCoverage74.85875 8.2537577.49291666666667 12.84291666666667374.85875 8.2537577.49291666666667 12.842916666666673Kerala SWBDSRTM Water Body Data for Kerala StateswbdkalkWCSGeoTIFFECO-DRR__Kerala_SWBDRectifiedGridCoverage74.85874450133333 8.25374837333333477.49289430933334 12.8428856693333374.85874450133333 8.25374837333333477.49289430933334 12.84288566933333Ksat-based Soil ErodibilityThe modified global soil erodibility map (Kksat factor) where the measured Ksat values were incorporated into the Wischmeier and Smith (1978) soil texture-based K factor equation. This dataset encompasses global soil erodibility (K) factor maps, with the K factor being estimated through the Wischmeier and Smith (1978) method. The equation incorporates permeability information crudely and indirectly, mainly relying on soil texture details, potentially overlooking factors like vegetation, biopores, and clay minerals. To address this limitation, we integrated measured Ksat values, representing soil hydraulic properties, into the Wischmeier and Smith (1978) soil texture-based K factor equation (referred to as KWischmeier factor) to formulate the Ksat-based soil erodibility (Kksat factor). Credits: Gupta, S., Borrelli, P., Panagos, P., Alewell, C., 2024. An advanced global soil erodibility (K) assessment including the effects of saturated hydraulic conductivity. Science of The Total Environment 908, 168249. https://doi.org/10.1016/j.scitotenv.2023.168249K_factor_with_Ksat_optWCSGeoTIFFunccd__K_factor_with_KsatRectifiedGridCoverage-180.0 -62.0180.0 87.37-180.0 -62.0180.0 87.37L8_mosaicL8_mosaicWCSGeoTIFFsdc_test__L8_mosaicRectifiedGridCoverage5.7348574 45.67551085610.667580872 47.9162766645.7348574 45.67551085610.667580872 47.916276664Land CoverSource: ESA (European Space Agency), retrieved <a href="http://due.esrin.esa.int/page_globcover.php" target="_blank"> here </a> <br><br> Most of the country is covered with natural and semi-natural vegetation with variable tree density. Vegetation cover is mostly high in the southwest, with thick tropical forests in the Greater Equatoria region, and low in the southeast and north, where semi-arid savannah dominates. Grasslands, aquatic vegetation and open water occupy the wetter regions. A large part of South Sudan is covered by the Sudd swamp, a conglomeration of smaller wetlands.LandCover_optWCSGeoTIFFMAPX_nd__LandCover_optRectifiedGridCoverage24.15069586900006 3.487470934000043635.94791809216606 12.23747093470004424.15069586900006 3.487470934000043635.94791809216606 12.237470934700044Land Surface Temperature in Dakar Senegal (2025)This layer shows the estimated temperature at a 100m resolution derived from Landsat 8 imagery. The temporal statistics from January 2025 to June 2025 have been aggregated and invalide values were filtered. This layer serves as as a base for the project, identifying areas that are warmer and susceptible to indicate waste decomposition. This layer serves as a base for the broader project, identifying areas that are warmer and potentially indicate the susceptibility of illegal waste.Solid Waste ManagementRemote SensingGISCitizen ScienceMulti-Criteria AnalysisIllegal Dumpingdakar_waste__LST_meanRectifiedGridCoverage-17.55611637429027 14.634522359756563-17.19121319151092 14.86324689854362-17.55611637429027 14.634522359756563-17.19121319151092 14.86324689854362Landslide hazard exposurels_phexWCSGeoTIFFECO-DRR__ls_phexRectifiedGridCoverage-180.0 -90.0180.0 90.0-180.0 -90.0180.0 90.0Landslide hazard frequencyls_freq2afWCSGeoTIFFECO-DRR__ls_freq2afRectifiedGridCoverage-180.0 -90.0180.0 90.0-180.0 -90.0180.0 90.0Live Biomass Carbon Density<style> .p1 { overflow: auto; font-style: italic; font-size: .9em; padding: 10px; margin: 0; text-align: justify; -moz-hyphens: auto; -webkit-hyphens: auto; -ms-hypens: auto; -ms-hyphens: auto; hyphens: auto; max-height: 230px; overflow-x: auto; overflow-y: hidden; columns: 180px; column-rule: 1px solid #ccc; grid-column-gap: 10px; column-gap: 10px; } ul { list-style-type: none; padding: 0; } ul li { margin-bottom: 5px; } </style> <div class="p1"><p>This dataset provides a spatially explicit estimation of above- and below-ground terrestrial live biomass carbon density. The map was produced by combining the most reliable publicly-available datasets on biomass carbon. These datasets were selected based on a literature review of existing datasets on biomass carbon in terrestrial ecosystems, and evaluated against a criteria based on resolution, accuracy, biomass definition and reference date. The selected datasets were then overlaid with the Copernicus land cover dataset (Buchhorn et al. 2019) assigning to each grid cell the corresponding above-ground biomass value from the biomass map that was most appropriate for the grid cell's land cover type. Below-ground biomass was added by using root-to-shoot ratios from the 2006 IPCC guidelines for National Greenhouse Gas Inventories. The values of the resulting map (in tonnes of dry organic matter per hectare) were multiplied by 0.5 to convert to carbon, following the guidelines established in the IPCC Good Practice Guidance for Land Use, Land-Use Change and Forestry (Penman et al. 2003).</p><p><u>See:</u><ul><li>García-Rangel, S. et al. (In prep.) Global distribution of natural carbon stocks potentially vulnerable to land use changes References.</li></ul></p><p><u>Other References:</u><ul><li>Buchhorn, M. ; Smets, B. ; Bertels, L. ; De Roo, B. ; Lesiv, M. ; Tsendbazar, N. - E. ; Herold, M. ; Fritz, S. Copernicus Global Land Service: Land Cover 100m: collection 3: epoch 2015: Globe 2020. DOI 10.5281/zenodo.3939038.</li><li>Penman J et al. (2003) Good practice guidance for land use, land-use change and forestry. Kanagawa Prefecture: Institute for Global Environmental Strategies.</li></ul></p></div>biomass_carbon_density_optWCSGeoTIFFMapX_UNDP__biomass_carbon_density_optRectifiedGridCoverage-179.99994506847912 -59.59552039034499179.99993096842002 83.66748265638508-179.99994506847912 -59.59552039034499179.99993096842002 83.66748265638508Low Elevation Coastal Zone (LECZ) of the Adriatic sub-regionLECZ, i.e. areas at less then 5 m above sea level with highest risk to be impacted by flooding.Below_5m_ASLWCSGeoTIFFSeafloor topographyBathymetrymedqsr__below_5m_aslRectifiedGridCoverage12.114305555555555 39.6448611111111120.265138888888888 45.8948611111111112.114305555555555 39.6448611111111120.265138888888888 45.89486111111111Mammals of the World (Jenkins, Pimm & Joppa - 2013)Maps of mammal diversity include the total species richness, major taxonomic Orders (Cetartiodactyla, Carnivora, Primates, Eulipotyphla, Chiroptera, Rodentia), marsupials, threatened species, and small-ranged species. Species considered small-ranged are those with a geographic range size smaller than the global median (i.e., the 50% of species with the smallest ranges). Maps are based on data from the IUCN, July 2013 update. All maps were generated at a spatial resolution of 10×10 km and use the Eckert IV equal-area projection. Maps represent native, extant species only. Tables of the mammal species included are available for download here: http://biodiversitymapping.org/wordpress/index.php/mammals/ Direct Link to download:http://biodiversitymapping.org/wordpress/index.php/download/wld_richness_mammals_10kmWCSGeoTIFFMAPX_nd__wld_richness_mammals_10kmRectifiedGridCoverage-179.50889388818507 -85.69633751266441179.53549311992452 83.78813167493983-179.50889388818507 -85.69633751266441179.53549311992452 83.78813167493983Mangroves total area in km2mangrokm2aWCSGeoTIFFECO-DRR__mangrokm2aRectifiedGridCoverage-180.0 -90.0180.0 90.0-180.0 -90.0180.0 90.0Marine Proportional Range Rarity (Aquamaps)See: <a href="www.aquamaps.org">Kaschner, K., K. Kesner-Reyes, C. Garilao, J. Rius-Barile, T. Rees, and R. Froese. 2016. AquaMaps: Predicted range maps for aquatic species. World wide web electronic publication, www.aquamaps.org, Version 08/2016.</a><br/><br>Under terms of the licence <a href="https://creativecommons.org/licenses/by-nc/3.0/">Aquamaps cannot be used for commercial purposes</a>prrarityWCSGeoTIFFMapX_UNDP__prrarityRectifiedGridCoverage-179.99999501081106 -89.99967927984171180.00000498918894 90.00032072015829-179.99999501081106 -89.99967927984171180.00000498918894 90.00032072015829Marine Range Rarity (Aquamaps)See: <a href="www.aquamaps.org">Kaschner, K., K. Kesner-Reyes, C. Garilao, J. Rius-Barile, T. Rees, and R. Froese. 2016. AquaMaps: Predicted range maps for aquatic species. World wide web electronic publication, www.aquamaps.org, Version 08/2016.</a><br/><br>Under terms of the licence <a href="https://creativecommons.org/licenses/by-nc/3.0/">Aquamaps cannot be used for commercial purposes</a>rrarityWCSGeoTIFFMapX_UNDP__rrarityRectifiedGridCoverage-179.99999501081106 -89.99967927984171180.00000498918894 90.00032072015829-179.99999501081106 -89.99967927984171180.00000498918894 90.00032072015829Marine Species Richness (Aquamaps)See: <a href="www.aquamaps.org">Kaschner, K., K. Kesner-Reyes, C. Garilao, J. Rius-Barile, T. Rees, and R. Froese. 2016. AquaMaps: Predicted range maps for aquatic species. World wide web electronic publication, www.aquamaps.org, Version 08/2016.</a><br/><br>Under terms of the licence <a href="https://creativecommons.org/licenses/by-nc/3.0/">Aquamaps cannot be used for commercial purposes</a>richnessWCSGeoTIFFMapX_UNDP__richnessRectifiedGridCoverage-179.99999501081106 -89.99967927984171180.00000498918894 90.00032072015829-179.99999501081106 -89.99967927984171180.00000498918894 90.00032072015829Mean predicted temperatures 2020 - Central African RepublicMean predicted temperatures in 2020 in the Central African Republic.TemperatureMean temperaturecc_and_health__mean_temp_predict_2020_cafRectifiedGridCoverage14.373044545912343 2.155956681886850727.488447694057356 11.04927799467011314.373044545912343 2.155956681886850727.488447694057356 11.049277994670113Mean predicted temperatures 2021 - Central African RepublicMean predicted temperatures in 2021 in the Central African Republic.TemperatureMean temperaturecc_and_health__mean_temp_predict_2021_cafRectifiedGridCoverage14.373044545912343 2.155956681886850727.488447694057356 11.04927799467011314.373044545912343 2.155956681886850727.488447694057356 11.049277994670113Mean predicted temperatures 2022 - Central African RepublicMean predicted temperatures in 2022 in the Central African Republic.TemperatureMean temperaturecc_and_health__mean_temp_predict_2022_cafRectifiedGridCoverage14.373044545912343 2.155956681886850727.488447694057356 11.04927799467011314.373044545912343 2.155956681886850727.488447694057356 11.049277994670113Mean predicted temperatures 2023 - Reference - Central African RepublicMean predicted temperatures in 2023 in the Central African Republic. Reference layer.TemperatureMean temperaturecc_and_health__mean_temp_predict_2023_ref_cafRectifiedGridCoverage14.373044546 2.155956681999999327.488447694 11.04927799514.373044546 2.155956681999999327.488447694 11.049277995Mean temperatures 2000 - Central African RepublicMean observed temperatures in 2000 in the Central African Republic.TemperatureMean temperaturecc_and_health__mean_temp_2000_cafRectifiedGridCoverage14.373044545912343 2.155956681886850727.488447694057356 11.04927799467011314.373044545912343 2.155956681886850727.488447694057356 11.049277994670113Mean temperatures 2001 - Central African RepublicMean observed temperatures in 2001 in the Central African Republic.TemperatureMean temperaturecc_and_health__mean_temp_2001_cafRectifiedGridCoverage14.373044545912343 2.155956681886850727.488447694057356 11.04927799467011314.373044545912343 2.155956681886850727.488447694057356 11.049277994670113Mean temperatures 2002 - Central African RepublicMean observed temperatures in 2002 in the Central African Republic.TemperatureMean temperaturecc_and_health__mean_temp_2002_cafRectifiedGridCoverage14.373044545912343 2.155956681886850727.488447694057356 11.04927799467011314.373044545912343 2.155956681886850727.488447694057356 11.049277994670113Mean temperatures 2003 - Central African RepublicMean observed temperatures in 2003 in the Central African Republic.TemperatureMean temperaturecc_and_health__mean_temp_2003_cafRectifiedGridCoverage14.373044545912343 2.155956681886850727.488447694057356 11.04927799467011314.373044545912343 2.155956681886850727.488447694057356 11.049277994670113Mean temperatures 2004 - Central African RepublicMean observed temperatures in 2004 in the Central African Republic.TemperatureMean temperaturecc_and_health__mean_temp_2004_cafRectifiedGridCoverage14.373044545912343 2.155956681886850727.488447694057356 11.04927799467011314.373044545912343 2.155956681886850727.488447694057356 11.049277994670113Mean temperatures 2005 - Central African RepublicMean observed temperatures in 2005 in the Central African Republic.TemperatureMean temperaturecc_and_health__mean_temp_2005_cafRectifiedGridCoverage14.373044545912343 2.155956681886850727.488447694057356 11.04927799467011314.373044545912343 2.155956681886850727.488447694057356 11.049277994670113Mean temperatures 2006 - Central African RepublicMean observed temperatures in 2006 in the Central African Republic.TemperatureMean temperaturecc_and_health__mean_temp_2006_cafRectifiedGridCoverage14.373044545912343 2.155956681886850727.488447694057356 11.04927799467011314.373044545912343 2.155956681886850727.488447694057356 11.049277994670113Mean temperatures 2007 - Central African RepublicMean observed temperatures in 2007 in the Central African Republic.TemperatureMean temperaturecc_and_health__mean_temp_2007_cafRectifiedGridCoverage14.373044545912343 2.155956681886850727.488447694057356 11.04927799467011314.373044545912343 2.155956681886850727.488447694057356 11.049277994670113Mean temperatures 2008 - Central African RepublicMean observed temperatures in 2008 in the Central African Republic.TemperatureMean temperaturecc_and_health__mean_temp_2008_cafRectifiedGridCoverage14.373044545912343 2.155956681886850727.488447694057356 11.04927799467011314.373044545912343 2.155956681886850727.488447694057356 11.049277994670113Mean temperatures 2009 - Central African RepublicMean observed temperatures in 2009 in the Central African Republic.TemperatureMean temperaturecc_and_health__mean_temp_2009_cafRectifiedGridCoverage14.373044545912343 2.155956681886850727.488447694057356 11.04927799467011314.373044545912343 2.155956681886850727.488447694057356 11.049277994670113Mean temperatures 2010 - Central African RepublicMean observed temperatures in 2010 in the Central African Republic.TemperatureMean temperaturecc_and_health__mean_temp_2010_cafRectifiedGridCoverage14.373044545912343 2.155956681886850727.488447694057356 11.04927799467011314.373044545912343 2.155956681886850727.488447694057356 11.049277994670113Mean temperatures 2011 - Central African RepublicMean observed temperatures in 2011 in the Central African Republic.TemperatureMean temperaturecc_and_health__mean_temp_2011_cafRectifiedGridCoverage14.373044545912343 2.155956681886850727.488447694057356 11.04927799467011314.373044545912343 2.155956681886850727.488447694057356 11.049277994670113Mean temperatures 2012 - Central African RepublicMean observed temperatures in 2012 in the Central African Republic.TemperatureMean temperaturecc_and_health__mean_temp_2012_cafRectifiedGridCoverage14.373044545912343 2.155956681886850727.488447694057356 11.04927799467011314.373044545912343 2.155956681886850727.488447694057356 11.049277994670113Mean temperatures 2013 - Central African RepublicMean observed temperatures in 2013 in the Central African Republic.TemperatureMean temperaturecc_and_health__mean_temp_2013_cafRectifiedGridCoverage14.373044545912343 2.155956681886850727.488447694057356 11.04927799467011314.373044545912343 2.155956681886850727.488447694057356 11.049277994670113Mean temperatures 2014 - Central African RepublicMean observed temperatures in 2014 in the Central African Republic.TemperatureMean temperaturecc_and_health__mean_temp_2014_cafRectifiedGridCoverage14.373044545912343 2.155956681886850727.488447694057356 11.04927799467011314.373044545912343 2.155956681886850727.488447694057356 11.049277994670113Mean temperatures 2015 - Central African RepublicMean observed temperatures in 2015 in the Central African Republic.TemperatureMean temperaturecc_and_health__mean_temp_2015_cafRectifiedGridCoverage14.373044545912343 2.155956681886850727.488447694057356 11.04927799467011314.373044545912343 2.155956681886850727.488447694057356 11.049277994670113Mean temperatures 2016 - Central African RepublicMean observed temperatures in 2016 in the Central African Republic.TemperatureMean temperaturecc_and_health__mean_temp_2016_cafRectifiedGridCoverage14.373044545912343 2.155956681886850727.488447694057356 11.04927799467011314.373044545912343 2.155956681886850727.488447694057356 11.049277994670113Mean temperatures 2017 - Central African RepublicMean observed temperatures in 2017 in the Central African Republic.TemperatureMean temperaturecc_and_health__mean_temp_2017_cafRectifiedGridCoverage14.373044545912343 2.155956681886850727.488447694057356 11.04927799467011314.373044545912343 2.155956681886850727.488447694057356 11.049277994670113Mean temperatures 2018 - Central African RepublicMean observed temperatures in 2018 in the Central African Republic.TemperatureMean temperaturecc_and_health__mean_temp_2018_cafRectifiedGridCoverage14.373044545912343 2.155956681886850727.488447694057356 11.04927799467011314.373044545912343 2.155956681886850727.488447694057356 11.049277994670113Mean temperatures 2019 - Central African RepublicMean observed temperatures in 2019 in the Central African Republic.TemperatureMean temperaturecc_and_health__mean_temp_2019_cafRectifiedGridCoverage14.373044545912343 2.155956681886850727.488447694057356 11.04927799467011314.373044545912343 2.155956681886850727.488447694057356 11.049277994670113Mean temperatures 2020 - Central African RepublicMean observed temperatures in 2020 in the Central African Republic.TemperatureMean temperaturecc_and_health__mean_temp_2020_cafRectifiedGridCoverage14.373044545912343 2.155956681886850727.488447694057356 11.04927799467011314.373044545912343 2.155956681886850727.488447694057356 11.049277994670113Mean temperatures in 2023 time total precipitations in 2021 - Central African RepublicThis layer shows the product between the mean temperatures observed in 2023 and the total year precipitations occurred in 2021 per each pixel in the Central African Republic.PrecipitationsTemperaturecc_and_health__temp_2023_x_precip_2021_cafRectifiedGridCoverage14.41796031 2.21883875227.470481388 11.01334538314.41796031 2.21883875227.470481388 11.013345383Modeled Exposures of PM2.5 - WHOThe recently developed Data Integration Model for Air Quality (DIMAQ) has produced estimates based on data from ground measurements together with information from other sources including data from satellite retrievals of aerosol optical depth and chemical transport models. It provides estimates of annual exposures of PM2.5 levels at high spatial resolution (0.1° × 0.1°, which equates to approximately 11x11km at the equator) globally. The sources of data include: Ground measurements from 6 003 monitoring locations around the world, satellite remote sensing; population estimates; topography; and information on local monitoring networks and measures of specific contributors of air pollution from chemical transport models. The DIMAQ model calibrates data from these sources with ground measurements. This model has provided produced estimates of air quality, expressed in terms of median concentrations of PM2.5, for all regions of the world, including areas in which PM2.5 monitoring is not available.who25WCSGeoTIFFParticulate MattersParticulate Mattersunea_pollution__who25RectifiedGridCoverage-180.03333333333333 -55.93333333333335179.96666666673863 70.06666666669184-180.03333333333333 -55.93333333333335179.96666666673863 70.06666666669184MovMasa_COSTA_SIERRALayer provided by Ministry of Environment of Peru. Areas prone to landslides. Methods and info related to the susceptibility analysis. https://geoservidor.minam.gob.pe/wp-content/uploads/2017/06/Texto-MAPA-DE-SUSCEPTIBILIDAD.pdfMovMasa_COSTA_SIERRAWCSGeoTIFFwesr_cca__MovMasa_COSTA_SIERRARectifiedGridCoverage-203260.78749999963 7964768.9114999991162013.8154244935 9624754.079073677-81.64209767609421 -18.407041570165664-68.74570664904627 -3.3742169907173394MudugLoss Georefenced Somaliasom__MudugLoss_georefWCSGeoTIFFMAPX_nd__som__MudugLoss_georefRectifiedGridCoverage46.10560211084111 3.898447724666512650.10348179017403 7.95606093175894146.10560211084111 3.898447724666512650.10348179017403 7.956060931758941Multi Hazards Mortality RiskThis dataset includes an estimate of the global risk induced by multiple hazards (tropical cyclone, flood and landslide induced by precipitations). Unit is estimated risk index from 1 (low) to 5 (extreme). This product was designed by UNEP/GRID-Europe for the Global Assessment Report on Risk Reduction (GAR). It was modeled using global data. Credit: UNEP/GRID-Europe.ml_riskWCSGeoTIFFwesr_risk__ml_riskRectifiedGridCoverage-180.0 -90.0179.99999999985602 83.99999999993042-180.0 -90.0179.99999999985602 83.99999999993042NVG_2000NVG_2000_optWCSGeoTIFFmapx_chemobs__NVG_2000RectifiedGridCoverage-180.0 -90.0180.0 90.0-180.0 -90.0180.0 90.0Normalized Difference Water Index in Keur Massar SenegalThis layer shows the distribution of water surfaces in Keur Massar (Dakar, Senegal) derived from Sentinel-2 imagery at 10 m spatial resolution. The satellite images were collected from Copernicus Browser (open access) on the 16th of April 2025. The Normalized Difference Water Index (NDWI) was calculated using the green and near-infrared bands of Sentinel-2. Water appears on the map starting from NDWI values of 0.01 (light blue), gradually increasing up to 0.38 (dark blue), representing areas with higher water presence. This layer serves as an input for a broader project, providing baseline information on water distribution to support the susceptibility assessment of illegal dumping sites.Solid Waste ManagementRemote SensingGISCitizen ScienceMulti-Criteria AnalysisIllegal Dumpingdakar_waste__NDWIRectifiedGridCoverage-17.382029187007333 14.733719739640827-17.2719124771499 14.84106039798925-17.382029187007333 14.733719739640827-17.2719124771499 14.84106039798925Novel Climate RiskThis is the novel climate risk dataset - measure of dissimilarity between a pixel’s future climate and its closest matching pixel in the global set of late 20th-century climates. <br> This dataset contains two metrics for climate change exposure using downscaled climate projections with the SRES A2 emissions scenario (Tabor and Williams, 2007).The metrics represent dissimilarity measurements of the squared Euclidean distance between seasonal (June–August and December–February) temperature and precipitation variables in the 20th century climate and mid-21st century climate. (1) disappearing climate risk - measure of dissimilarity between a pixel’s late 20th century climate and its closest matching pixel in the global set of 21st-century climates (2) novel climate risk - measure of dissimilarity between a pixel’s future climate and its closest matching pixel in the global set of late 20th-century climates. <br> John W. Williams, Karyn Tabor. (2018). Novel and Disappearing Climates 2050 SRES A2 (Version 1) [Data set]. Zenodo. http://doi.org/10.5281/zenodo.1559299 John W. Williams, Karyn Tabor. (2018). Novel and Disappearing Climates 2050 SRES A2 (Version 1) [Data set]. Zenodo. http://doi.org/10.5281/zenodo.1559299novel_sresa2_2050_ENSEMB_10min_int1000_optWCSGeoTIFFLWP__novel_sresa2_2050_ENSEMB_10min_int1000_optRectifiedGridCoverage-180.0003335 -59.16733350000001180.0003865 83.66628549999999-180.0003335 -59.16733350000001180.0003865 83.66628549999999Nutrient Pollution (fertilizers) 2008 - KNBInput data are from FAO statistics published in the time period 2003-2006. FAO data on annual country-level fertilizer were used were available, averaged over the time periods. Missing values were filled using a linear regression model of fertilizers as a function of pesticides (gaps: N=4; regression: R2 = 0.72) when pesticide data were available or agricultural GDP (gaps: N=22; regression: R2 = 0.62) when not. These country-level average fertilizer values were then dasymetrically distributed over a country’s landscape using global land cover data from 2005. Finally, spread of the driver values into coastal waters at each pour point was modelled with a cost-path surface on the basis of a decay function that assigns a fixed amount of the driver (0.5% of the value in the previous cell) in the initial cell and then evenly distributes the remaining amount of driver in all adjacent and ‘unvisited’ cells, repeated until a minimum threshold (0.05% of global maximum) is reached. This approach to modelling river plumes is diffusive and so allows drivers to wrap around headlands and islands. Raw stressor data from "Benjamin Halpern, Melanie Frazier, John Potapenko, Kenneth Casey, Kellee Koenig, et al. 2015. Cumulative human impacts: raw stressor data (2008 and 2013). Knowledge Network for Biocomplexity. doi:10.5063/F1S180FS."08_plumes_fert_halpern_KNB_WGS84WCSGeoTIFFunea_pollution__08_plumes_fert_halpern_KNB_WGS84RectifiedGridCoverage-179.99999948447567 -57.923325435334434179.99443435918158 84.90643464931345-179.99999948447567 -57.923325435334434179.99443435918158 84.90643464931345Nutrient Pollution (fertilizers) 2013 - KNBInput data are from 2007-2010 FAO statistics, the most recent available. FAO data on annual country-level fertilizer were used were available, averaged over the time periods. Missing values were filled using a linear regression model of fertilizers as a function of pesticides (gaps: N=4; regression: R2 = 0.72) when pesticide data were available or agricultural GDP (gaps: N=22; regression: R2 = 0.62) when not. These country-level average fertilizer values were then dasymetrically distributed over a country’s landscape using global land cover data from 2009, derived from the Moderate Resolution imaging Spectroradiometer (MODIS) instrument at ~500m resolution. Finally, spread of the driver values into coastal waters at each pour point was modelled with a cost-path surface on the basis of a decay function that assigns a fixed amount of the driver (0.5% of the value in the previous cell) in the initial cell and then evenly distributes the remaining amount of driver in all adjacent and ‘unvisited’ cells, repeated until a minimum threshold (0.05% of global maximum) is reached. This approach to modelling river plumes is diffusive and so allows drivers to wrap around headlands and islands. Raw stressor data from "Benjamin Halpern, Melanie Frazier, John Potapenko, Kenneth Casey, Kellee Koenig, et al. 2015. Cumulative human impacts: raw stressor data (2008 and 2013). Knowledge Network for Biocomplexity. doi:10.5063/F1S180FS."13_plumes_fert_halpern_KNB_WGS84WCSGeoTIFFunea_pollution__13_plumes_fert_halpern_KNB_WGS84RectifiedGridCoverage-179.99999869409874 -57.96420981377538179.9994455075789 84.9958245860871-179.99999869409874 -57.96420981377538179.9994455075789 84.9958245860871Ocean Pollution (Shipping Lanes, Ports) 2008 - KNBThis data layer combines estimates of pollution coming from commercial shipping and from ports. As such, it is a combination of the shipping and port volume data layers, with the port volume data plumed to estimate pollution from commercial ports (with exponential decline in intensity from the port). Ocean-based pollution is assumed to derive from commercial and recreational ship activity. No data on global recreational ship activity currently exist, and therefore we modelled this driver to oceans using a combination of the commercial shipping traffic data and port data. The shipping data provide an estimate of the occurrence of ships at a particular location, and therefore an estimate of the amount of pollution they produce (via fuel leaks, oil discharge, waste disposal, etc.) that is unique from their contribution to ship strikes, etc. described above. We recognize that ocean currents can disperse this pollution into untraveled regions, but small-scale oceanography is known for only a few select locations around the world, and pollutants are likely to be most concentrated in high traffic areas. The dispersal of port-derived pollution was modelled as a diffusive plume with a maximum distance of 100 km. These plumes were not clipped to shallow regions as was done for Invasive Species. Raw stressor data from "Benjamin Halpern, Melanie Frazier, John Potapenko, Kenneth Casey, Kellee Koenig, et al. 2015. Cumulative human impacts: raw stressor data (2008 and 2013). Knowledge Network for Biocomplexity. doi:10.5063/F1S180FS."08_ocean_pollution_halpern_KNB_WGS84WCSGeoTIFFecosystemunea_pollution__08_ocean_pollution_halpern_KNB_WGS84RectifiedGridCoverage-179.9999997890976 -89.99765318080647179.99802237886038 85.2846797894314-179.9999997890976 -89.99765318080647179.99802237886038 85.2846797894314Ocean Pollution (Shipping Lanes, Ports) 2013 - KNBThis data layer combines estimates of pollution coming from commercial shipping and from ports. As such, it is a combination of the shipping and port volume data layers, with the port volume data plumed to estimate pollution from commercial ports (with exponential decline in intensity from the port). Ocean-based pollution is assumed to derive from commercial and recreational ship activity. No data on global recreational ship activity currently exist, and therefore we modelled this driver to oceans using a combination of the commercial shipping traffic data and port data. The shipping data provide an estimate of the occurrence of ships at a particular location, and therefore an estimate of the amount of pollution they produce (via fuel leaks, oil discharge, waste disposal, etc.) that is unique from their contribution to ship strikes, etc. described above. We recognize that ocean currents can disperse this pollution into untraveled regions, but small-scale oceanography is known for only a few select locations around the world, and pollutants are likely to be most concentrated in high traffic areas. The dispersal of port-derived pollution was modelled as a diffusive plume with a maximum distance of 100 km. These plumes were not clipped to shallow regions as was done for Invasive Species. Raw stressor data from "Benjamin Halpern, Melanie Frazier, John Potapenko, Kenneth Casey, Kellee Koenig, et al. 2015. Cumulative human impacts: raw stressor data (2008 and 2013). Knowledge Network for Biocomplexity. doi:10.5063/F1S180FS."13_ocean_pollution_halpern_KNB_WGS84WCSGeoTIFFunea_pollution__13_ocean_pollution_halpern_KNB_WGS84RectifiedGridCoverage-179.9999992159167 -89.94351550779915179.99646810995122 89.94750143086456-179.9999992159167 -89.94351550779915179.99646810995122 89.94750143086456Ocean Pollution (Shipping Lanes, Ports) Pressures 2013 - KNBThis data layer shows the ocean-based pollution from stressor data after adjusting for habitat/pressure vulnerability. This data layer combines estimates of pollution coming from commercial shipping and from ports. As such, it is a combination of the shipping and port volume data layers, with the port volume data plumed to estimate pollution from commercial ports (with exponential decline in intensity from the port). Ocean-based pollution is assumed to derive from commercial and recreational ship activity. No data on global recreational ship activity currently exist, and therefore we modelled this driver to oceans using a combination of the commercial shipping traffic data and port data. The shipping data provide an estimate of the occurrence of ships at a particular location, and therefore an estimate of the amount of pollution they produce (via fuel leaks, oil discharge, waste disposal, etc.) that is unique from their contribution to ship strikes, etc. described above. We recognize that ocean currents can disperse this pollution into untraveled regions, but small-scale oceanography is known for only a few select locations around the world, and pollutants are likely to be most concentrated in high traffic areas. The dispersal of port-derived pollution was modelled as a diffusive plume with a maximum distance of 100 km. These plumes were not clipped to shallow regions as was done for Invasive Species. Pressure data was calculated for each stressor by: (1) multiplying the rescaled stressor (rescaled using only the 2013 data) by each habitat layer and the corresponding stressor/habitat vulnerability score (for each stressor this generates: 20 rasters); (2) summing the resulting stressor/habitat/vulnerability rasters (generates 1 raster for each stressor); (3) dividing by the number of habitats found in each raster cell layer. Benjamin Halpern, Melanie Frazier, John Potapenko, Kenneth Casey, Kellee Koenig, et al. 2015. Cumulative human impacts: pressure and cumulative impacts data (2013, all pressures). Knowledge Network for Biocomplexity. doi:10.5063/F15718ZN.ocean_pollution_combo_pressures_2013WCSGeoTIFFMapX_UNDP__13_ocean_pollution_pressures_halpern_KNB_WGS84RectifiedGridCoverage-179.9999997892864 -89.9976531808165179.99802237904566 85.28467978960349-179.9999997892864 -89.9976531808165179.99802237904566 85.28467978960349Ocean acidificationChanges in CO2 concentration alter the aragonite saturation state (ASS) of the ocean, among other chemical properties of seawater, and as ASS levels drop the ability of calcifying species such as corals and shelled invertebrates to create calcium carbonate structures declines (S22). The global distribution of ASS values has been modeled at 1- degree resolution for pre-industrial (circa 1870) and modern times (2000-2009) (S23). KNB used the difference between these values as an estimate of the human-derived driver of changes in ocean acidification. Raw stressor data from "Benjamin Halpern, Melanie Frazier, John Potapenko, Kenneth Casey, Kellee Koenig, et al. 2015. Cumulative human impacts: raw stressor data (2008 and 2013). Knowledge Network for Biocomplexity. doi:10.5063/F1S180FS."13_ocean_acidificationWCSGeoTIFFunea_pollution__13_ocean_acidificationRectifiedGridCoverage-180.0 -90.0180.0 90.0-180.0 -90.0180.0 90.0Oil Spill DeepwaterOn April 20, 2010, an explosion on the Deepwater Horizon Macondo oil well drilling platform tragically killed 11 workers, and started the largest marine oil spill in U.S. history, releasing millions of barrels of oil into the Gulf of Mexico. NOAA was on the scene from the earliest moments of the crisis, bringing more than 25 years of experience protecting and restoring our coasts from oil spills.oil20100325WCSGeoTIFFgulf of mexicodeepwaterunea_pollution_nd__oil20100325RectifiedGridCoverage-96.4439 23.45089999999986-81.18219999999984 36.9407-96.4439 23.45089999999986-81.18219999999984 36.9407Opened forest total area in km2forestofkm2aWCSGeoTIFFECO-DRR__forestofkm2aRectifiedGridCoverage-180.0 -90.0180.0 90.0-180.0 -90.0180.0 90.0Organic Pollution (pesticides) 2008 - KNBPesticides were used as a proxy measure for organic pollution. Input data are from FAO statistics published in the time period 2003-2006. Missing pesticide values were filled using a linear regression model of pesticides as a function of fertilizers (gaps: N=69; regression: R2 = 0.72) when fertilizer data were available or agricultural GDP (gaps: N=22; regression: R2 = 0.82) when not. These country-level average pesticides values were then dasymetrically distributed over a country’s landscape using global land cover data from 2005. Finally, spread of the driver values into coastal waters at each pour point was modelled with a cost-path surface on the basis of a decay function that assigns a fixed amount of the driver (0.5% of the value in the previous cell) in the initial cell and then evenly distributes the remaining amount of driver in all adjacent and ‘unvisited’ cells, repeated until a minimum threshold (0.05% of global maximum) is reached. This approach to modelling river plumes is diffusive and so allows drivers to wrap around headlands and islands. Raw stressor data from "Benjamin Halpern, Melanie Frazier, John Potapenko, Kenneth Casey, Kellee Koenig, et al. 2015. Cumulative human impacts: raw stressor data (2008 and 2013). Knowledge Network for Biocomplexity. doi:10.5063/F1S180FS."08_plumes_pest_halpern_KNB_WGS84WCSGeoTIFFunea_pollution__08_plumes_pest_halpern_KNB_WGS84RectifiedGridCoverage-179.99999948447567 -57.923325435334434179.99443435918158 84.90643464931345-179.99999948447567 -57.923325435334434179.99443435918158 84.90643464931345Organic Pollution (pesticides) 2013 - KNBPesticides were used as a proxy measure for organic pollution. Input data are from 2007-2010 FAO statistics, the most recent available. Missing pesticide values were filled using a linear regression model of pesticides as a function of fertilizers (gaps: N=69; regression: R2 = 0.72) when fertilizer data were available or agricultural GDP (gaps: N=22; regression: R2 = 0.82) when not. These country-level average pesticides values were then dasymetrically distributed over a country’s landscape using global land cover data from 2009, derived from the Moderate Resolution imaging Spectroradiometer (MODIS) instrument at ~500m resolution. Finally, spread of the driver values into coastal waters at each pour point was modelled with a cost-path surface on the basis of a decay function that assigns a fixed amount of the driver (0.5% of the value in the previous cell) in the initial cell and then evenly distributes the remaining amount of driver in all adjacent and ‘unvisited’ cells, repeated until a minimum threshold (0.05% of global maximum) is reached. This approach to modelling river plumes is diffusive and so allows drivers to wrap around headlands and islands. Raw stressor data from "Benjamin Halpern, Melanie Frazier, John Potapenko, Kenneth Casey, Kellee Koenig, et al. 2015. Cumulative human impacts: raw stressor data (2008 and 2013). Knowledge Network for Biocomplexity. doi:10.5063/F1S180FS."13_plumes_pest_halpern_KNB_WGS84WCSGeoTIFFunea_pollution__13_plumes_pest_halpern_KNB_WGS84RectifiedGridCoverage-179.99999869409874 -57.96420981377538179.9994455075789 84.9958245860871-179.99999869409874 -57.96420981377538179.9994455075789 84.9958245860871POP_FACEBOOK_Lekoumou_2019_3km_UTM_optPOP_FACEBOOK_Lekoumou_2019_3km_UTM_optWCSGeoTIFFAccessMod__POP_FACEBOOK_Lekoumou_2019_3km_UTM_optRectifiedGridCoverage255304.7702 -447998.186427808.6623 -230959.277312.79605904597841 -4.05285031457979314.350819887086997 -2.0880026703818597POP_FACEBOOK_Lekoumou_2019_92m_UTM_optPOP_FACEBOOK_Lekoumou_2019_92m_UTM_optWCSGeoTIFFAccessMod__POP_FACEBOOK_Lekoumou_2019_92m_UTM_optRectifiedGridCoverage255304.7702 -447998.186427808.6623 -230959.277312.79605904597841 -4.05285031457979314.350819887086997 -2.0880026703818597POP_FACEBOOK_Sangha_2019_92m_UTM_optPOP_FACEBOOK_Sangha_2019_92m_UTM_optWCSGeoTIFFAccessMod__POP_FACEBOOK_Sangha_2019_92m_UTM_optRectifiedGridCoverage291431.72354 3912.117329999979722275.67044 300966.2218313.123883469958477 0.035372508422871216.999368548454783 2.722913660432471POP_FACEBOOK_Sangha_2019_Agg3km_UTM_optPOP_FACEBOOK_Sangha_2019_Agg3km_UTM_optWCSGeoTIFFAccessMod__POP_FACEBOOK_Sangha_2019_Agg3km_UTM_optRectifiedGridCoverage291431.7235 3912.1172999999835722275.6704 300966.221813.123883469599217 0.035372508151625616.999368548094683 2.722913660161077POP_LEKOUMOU_2019_finale_Worlpop_optPOP_LEKOUMOU_2019_finale_Worlpop_optWCSGeoTIFFAccessMod__POP_LEKOUMOU_2019_finale_Worlpop_optRectifiedGridCoverage12.796059046335326 -4.05279724996322214.35138242981778 -2.08800267007868512.796059046335326 -4.05279724996322214.35138242981778 -2.088002670078685POP_SANGHA_2019_finale_Worlpop_optPOP_SANGHA_2019_finale_Worlpop_optWCSGeoTIFFAccessMod__POP_SANGHA_2019_finale_Worlpop_optRectifiedGridCoverage13.12388346995083 0.03574461520971139416.999434297770676 2.72291524502080913.12388346995083 0.035744615209711416.999434297770676 2.722915245020809Peak Ground Acceleration PGA 1500 Years (cm/s2)For the GAR Atlas, a fully probabilistic seismic hazard analysis at global level was developed by CIMNE and INGENIAR Ltda. This hazard model is a continuation and improvement of the one developed in the framework of GAR13 where a set of tectonic provinces were identified and characterized by means of a set of parameters that describe the future seismic activity on each of them based on historical records together with relationships to obtain hazard intensities as a function of magnitude and distance. The hazard analysis was performed using the program CRISIS2014, a state-of-the-art tool for these kinds of tasks and widely known and acknowledged by experts in the field across the world. The chosen hazard intensity measure for this case was spectral acceleration and to guarantee a good connection between the hazard and the exposure characteristics, results for several ordinates beteween 0.0 and 2.3 seconds were computed. More details about the probabilistic seismic hazard analysis can be found in Cardona et al. (2015).pga_1500_yrpWCSGeoTIFFwesr_risk__pga_1500_yrpRectifiedGridCoverage-180.25 -90.25180.25 90.25-180.25 -90.25180.25 90.25Peak Ground Acceleration PGA 2475 Years (cm/s2)For the GAR Atlas, a fully probabilistic seismic hazard analysis at global level was developed by CIMNE and INGENIAR Ltda. This hazard model is a continuation and improvement of the one developed in the framework of GAR13 where a set of tectonic provinces were identified and characterized by means of a set of parameters that describe the future seismic activity on each of them based on historical records together with relationships to obtain hazard intensities as a function of magnitude and distance. The hazard analysis was performed using the program CRISIS2014, a state-of-the-art tool for these kinds of tasks and widely known and acknowledged by experts in the field across the world. The chosen hazard intensity measure for this case was spectral acceleration and to guarantee a good connection between the hazard and the exposure characteristics, results for several ordinates beteween 0.0 and 2.3 seconds were computed. More details about the probabilistic seismic hazard analysis can be found in Cardona et al. (2015).pga_2475_yrpWCSGeoTIFFwesr_risk__pga_2475_yrpRectifiedGridCoverage-180.25 -90.25180.25 90.25-180.25 -90.25180.25 90.25Peak Ground Acceleration PGA 250 Years (cm/s2)For the GAR Atlas, a fully probabilistic seismic hazard analysis at global level was developed by CIMNE and INGENIAR Ltda. This hazard model is a continuation and improvement of the one developed in the framework of GAR13 where a set of tectonic provinces were identified and characterized by means of a set of parameters that describe the future seismic activity on each of them based on historical records together with relationships to obtain hazard intensities as a function of magnitude and distance. The hazard analysis was performed using the program CRISIS2014, a state-of-the-art tool for these kinds of tasks and widely known and acknowledged by experts in the field across the world. The chosen hazard intensity measure for this case was spectral acceleration and to guarantee a good connection between the hazard and the exposure characteristics, results for several ordinates beteween 0.0 and 2.3 seconds were computed. More details about the probabilistic seismic hazard analysis can be found in Cardona et al. (2015).pga_250_yrpWCSGeoTIFFwesr_risk__pga_250_yrpRectifiedGridCoverage-180.25 -90.25180.25 90.25-180.25 -90.25180.25 90.25Peak Ground Acceleration PGA 475 Years (cm/s2)For the GAR Atlas, a fully probabilistic seismic hazard analysis at global level was developed by CIMNE and INGENIAR Ltda. This hazard model is a continuation and improvement of the one developed in the framework of GAR13 where a set of tectonic provinces were identified and characterized by means of a set of parameters that describe the future seismic activity on each of them based on historical records together with relationships to obtain hazard intensities as a function of magnitude and distance. The hazard analysis was performed using the program CRISIS2014, a state-of-the-art tool for these kinds of tasks and widely known and acknowledged by experts in the field across the world. The chosen hazard intensity measure for this case was spectral acceleration and to guarantee a good connection between the hazard and the exposure characteristics, results for several ordinates beteween 0.0 and 2.3 seconds were computed. More details about the probabilistic seismic hazard analysis can be found in Cardona et al. (2015).pga_475_yrpWCSGeoTIFFwesr_risk__pga_475_yrpRectifiedGridCoverage-180.25 -90.25180.25 90.25-180.25 -90.25180.25 90.25Peak Ground Acceleration PGA 975 Years (cm/s2)For the GAR Atlas, a fully probabilistic seismic hazard analysis at global level was developed by CIMNE and INGENIAR Ltda. This hazard model is a continuation and improvement of the one developed in the framework of GAR13 where a set of tectonic provinces were identified and characterized by means of a set of parameters that describe the future seismic activity on each of them based on historical records together with relationships to obtain hazard intensities as a function of magnitude and distance. The hazard analysis was performed using the program CRISIS2014, a state-of-the-art tool for these kinds of tasks and widely known and acknowledged by experts in the field across the world. The chosen hazard intensity measure for this case was spectral acceleration and to guarantee a good connection between the hazard and the exposure characteristics, results for several ordinates beteween 0.0 and 2.3 seconds were computed. More details about the probabilistic seismic hazard analysis can be found in Cardona et al. (2015).pga_975_yrpWCSGeoTIFFwesr_risk__pga_975_yrpRectifiedGridCoverage-180.25 -90.25180.25 90.25-180.25 -90.25180.25 90.25Peru_ACDLayer provided by Ministry of Environment of Peru. To create a map of aboveground carbon density (ACD) at one-hectare resolution, we integrated airborne Light Detection and Ranging (LiDAR) technology, a new field plot inventory network, freely available satellite imagery, and geostatistical scaling techniques. Following initial establishment and operationalization of the described methodology, the approach can be readily updated over time to provide a long-term monitoring capability with reported uncertainties for every hectare within Perú.Peru_ACDWCSGeoTIFFwesr_cca__Peru_ACDRectifiedGridCoverage-315902.71 7911404.56000000051515197.29 1.010290456E7-82.72089353764397 -18.887043202719365-65.41079891321573 0.930894533727649Phospate (autumn) - WOA13World Ocean Atlas 2013 (WOA13) is a long-term set of objectively analyzed climatologies of temperature, salinity, oxygen, phosphate, silicate, and nitrate for annual, seasonal, and monthly periods for the World Ocean. Data (at surface) are expressed in µmol/lph_autWCSGeoTIFFunea_pollution__ph_autRectifiedGridCoverage-180.0 -78.0180.0 90.0-180.0 -78.0180.0 90.0Phospate (spring) - WOA13World Ocean Atlas 2013 (WOA13) is a long-term set of objectively analyzed climatologies of temperature, salinity, oxygen, phosphate, silicate, and nitrate for annual, seasonal, and monthly periods for the World Ocean. Data (at surface) are expressed in µmol/lph_sprWCSGeoTIFFunea_pollution__ph_sprRectifiedGridCoverage-180.0 -78.0180.0 90.0-180.0 -78.0180.0 90.0Phospate (summer) - WOA13World Ocean Atlas 2013 (WOA13) is a long-term set of objectively analyzed climatologies of temperature, salinity, oxygen, phosphate, silicate, and nitrate for annual, seasonal, and monthly periods for the World Ocean. Data (at surface) are expressed in µmol/lph_summWCSGeoTIFFunea_pollution__ph_summRectifiedGridCoverage-180.0 -78.0180.0 90.0-180.0 -78.0180.0 90.0Phospate (winter) - WOA13World Ocean Atlas 2013 (WOA13) is a long-term set of objectively analyzed climatologies of temperature, salinity, oxygen, phosphate, silicate, and nitrate for annual, seasonal, and monthly periods for the World Ocean. Data (at surface) are expressed in µmol/lph_winWCSGeoTIFFunea_pollution__ph_winRectifiedGridCoverage-180.0 -78.0180.0 90.0-180.0 -78.0180.0 90.0Physical Exposition To Landslides Triggered By EarthquakesThis dataset includes an estimate of the annual physical exposition of landslide triggered by earthquakes. It depends on the combination of trigger and susceptibility defined by six parameters: slope factor, lithological (or geological) conditions, soil moisture condition, vegetation cover, precipitation and seismic conditions. A population grid for the year 2010, provided by LandScanTM Global Population Database (Oak Ridge, TN: Oak Ridge National Laboratory) has also been used. Unit is expected average annual population (2010 as the year of reference) exposed (inhabitants) . This product was designed by International Centre for Geohazards (NGI) for the Global Assessment Report on Risk Reduction (GAR). It was modeled using global data. Credit: GIS processing International Centre for Geohazards (NGI).le1010ipeyWCSGeoTIFFwesr_risk__le1010ipeyRectifiedGridCoverage-180.0 -90.0179.99999999999983 89.99166666666659-180.0 -90.0179.99999999999983 89.99166666666659Physical Exposition To Landslides Triggered By PrecipitationsThis dataset includes an estimate of the annual frequency of landslide triggered by precipitations. It depends on the combination of trigger and susceptibility defined by six parameters: slope factor, lithological (or geological) conditions, soil moisture condition, vegetation cover, precipitation and seismic conditions. Unit is expected annual probability and percentage of pixel of occurrence of a potentially destructive landslide event x 1000000. This product was designed by International Centre for Geohazards (NGI) for the Global Assessment Report on Risk Reduction (GAR). It was modeled using global data. Credit: GIS processing International Centre for Geohazards (NGI).ls_physexp2WCSGeoTIFFwesr_risk__ls_physexp2RectifiedGridCoverage-180.0 -90.0179.99999999999983 89.99166666666659-180.0 -90.0179.99999999999983 89.99166666666659Physical Exposition to Drought Events in the GHA RegionThis dataset includes an estimation of the annual physical exposition to drought based on Standardized Precipitation Index. It is based on three sources: 1) A global monthly gridded precipitation dataset obtained from the Climatic Research Unit (University of East Anglia). 2) A GIS modelling of global Standardized Precipitation Index based on Brad Lyon (IRI, Columbia University) methodology. 3) A population grid for the year 2010, provided by LandScanTM Global Population Database (Oak Ridge, TN: Oak Ridge National Laboratorygeonode_physical_exposition_to_drought_eventsWCSGeoTIFFwesr_cca__geonode_physical_exposition_to_drought_eventsRectifiedGridCoverage21.838947 -11.74570000029600451.440099935812 23.1428621.838947 -11.74570000029600451.440099935812 23.14286Physical Exposure To TsunamisThis dataset includes an estimation of the annual physical exposure to tsunamis. It is based on three sources: 1) a comprehensive list of reports and scientific papers compiled and utilized in producing tsunami hazard maps as well as finding return periods of future events; 2) applying numerical tsunami models and zooming on selected areas; and 3) a population grid for the year 2010 (provided by LandScanTM Global Population Database, Oak Ridge National Laboratory, Oak Ridge, USA). The unit of reference is the expected average annual population (2010 as the year of reference) exposed (inhabitants). This product was designed by the International Centre for Geohazards (NGI) for the Global Assessment Report on Risk Reduction (GAR). The dataset was was modelled using global data. Credit: GIS processing International Centre for Geohazards (NGI)ts_physexpWCSGeoTIFFwesr_risk__ts_physexpRectifiedGridCoverage-180.0 -90.0179.99999999985602 83.99999999993042-180.0 -90.0179.99999999985602 83.99999999993042Pop_wgs84_optWCSGeoTIFFAccessMod__Pop_Togo_wgs84_optRectifiedGridCoverage-0.14628583774401568 5.6398389709460341.8324070818850766 11.150878574337117-0.1462858377440157 5.6398389709460341.8324070818850766 11.150878574337117Population Based Global Carbon Emissions Dataset in 0.1°Resolution (2014)Since mid of 20th century, anthropogenic greenhouse gas emissions have increased, it is very possible of being driven largely by economic and population growth, and causing the global warming. Based on the global carbon emissions data of 2014 in each country from CDIAC (Carbon Dioxide Information Analysis Center) and population density data in 2015 from SEDAC (Socioeconomic Data and Applications Center), the population based global carbon emissions dataset in 0.1° resolution (2014) was developed by the model of integrating population density as an economic-population composite indicator to weighted carbon emissions. The result shows the main carbon emission areas are located in the eastern United States, eastern China, Japan, Korea, India, Southeast Asia and Europe, and there are spatial differences in each region. The result can reflect spatial distribution of the current global carbon emissions and provide basic data for global change research. The dataset was archived in .tif data format with the data size of 22.7 MB (3.92MB in compressed file). Foundation Item： Ministry of Science and Technology of P. R. China (2016YFA0602704) Data Citation： FAN Zhixin,SU Yun*,FANG Xiuqi.2017.Population Based Global Carbon Emissions Dataset in 0.1°Resolution (2014) ( GlobalPopCarbonEmis2014 ) ,Global Change Research Data Publishing & Repository,DOI:10.3974/geodb.2017.03.12.V1GlobalPopCarbonEmis2014WCSGeoTIFFglobalcarbon emissionspopulation densityspatial distribution0.1°resolutionunea_pollution__GlobalPopCarbonEmis2014RectifiedGridCoverage-180.0 -59.99999999999177180.0 83.60000000000822-180.0 -59.99999999999177180.0 83.60000000000822Population Density (EPI - 2018)Source: Map created by EPI (Elephant Protection Initiative) with data from CIESIN, Columbia University, USA. The map is published on UNEP's South Sudan: First State of Environment and Outlook Report 2018, using data from WCS. The UNEP's report could be found <a href="https://www.unenvironment.org/resources/report/south-sudan-first-state-environment-and-outlook-report-2018" target=_blank> here </a> <br><br> The map shows the population distribution in South Sudan. Jonglei is the most populous area, with 16 per cent of the total population, and Western Bahr el Ghazal is the least populous area with only 4 per cent of the total. The highest population densities are along the Nile River and their tributaries.Population_Density_optWCSGeoTIFFMAPX__Population_Density_optRectifiedGridCoverage24.15069586900006 3.4874709343535.95069586900014 12.2374709343500624.15069586900006 3.4874709343535.95069586900014 12.23747093435006Population Estimates for 2019 GHA Regionthe data indicates to the estimated total number of people per grid-cell at a resolution of 1 Km.icpac_worldpop_2019_population_ghaWCSGeoTIFFwesr_cca__icpac_worldpop_2019_population_ghaRectifiedGridCoverage21.838947 -11.74569999919800351.4196463067 23.1428621.838947 -11.74569999919800351.4196463067 23.14286Population density in Dakar Senegal (2025)This layer shows the estimated population distribution for the Dakar region in 2025, based on the Global Human Settlement Layer (GHSL, open access). The dataset is provided as a raster at 100 m spatial resolution. Each pixel contains the estimated number of inhabitants living within a 100 m × 100 m cell. This gridded representation allows the analysis of population patterns and supports the derivation of population density values at different spatial scales. This layer serves as an input for the project, supporting the identification of areas where higher population concentrations may increase the susceptibility of illegal dumping sites.Solid Waste ManagementRemote SensingGISCitizen ScienceMulti-Criteria AnalysisIllegal Dumpingdakar_waste__popu_2025_DakarRectifiedGridCoverage-17.574657994490252 14.62972698943294-17.17229869382342 14.86844154301869-17.574657994490252 14.62972698943294-17.17229869382342 14.86844154301869Population density in Dakar Senegal (2030)This layer shows the estimated population distribution for the Dakar region in 2030, based on the Global Human Settlement Layer (GHSL, open access). The dataset is provided as a raster at 100 m spatial resolution. Each pixel contains the estimated number of inhabitants living within a 100 m × 100 m cell. This gridded representation allows the analysis of population patterns and supports the derivation of population density values at different spatial scales. This layer serves as an input for the project, supporting the identification of areas where higher population concentrations may increase the susceptibility of illegal dumping sites.Solid Waste ManagementRemote SensingGISCitizen ScienceMulti-Criteria AnalysisIllegal Dumpingdakar_waste__popu_2030_DakarRectifiedGridCoverage-17.574657994490252 14.62972698943294-17.17229869382342 14.86844154301869-17.574657994490252 14.62972698943294-17.17229869382342 14.86844154301869Population density map 2018 based on GSHL and the World Population Prospect 2019Population density map for 2018 at 30arcsec resolution. <br><br> The layer uses the Global Human Settlement Layer (GHSL) - JRC and the national population count for 2018 reported on the World Population Prospects 2019. <br><br> The original GHSL layer was recalculated at 30arcsec resolution (~1km) through an aggregation process (sum) in Google Earth Engine. Pixel counts were then recalculated for 2018 based on the country population data reported for 2018 by the World Population Prospects 2019. <br><br> Source of data: <br> GHSL: GHS_POP_E2015_GLOBE_R2019A_4326_30SS_V1_0 (downloaded from <a href="https://ghsl.jrc.ec.europa.eu/download.php?ds=pop at 30arcsec resolution"target=_blank>JRC</a>)<br> Dataset:<br> Schiavina, Marcello; Freire, Sergio; MacManus, Kytt (2019): GHS population grid multitemporal (1975, 1990, 2000, 2015) R2019A. European Commission, Joint Research Centre (JRC) DOI: <a href="http://data.europa.eu/89h/0c6b9751-a71f-4062-830b-43c9f432370f"target=_blank>10.2905/42E8BE89-54FF-464E-BE7B-BF9E64DA5218</a> <br> Concept & Methodology: Freire, Sergio; MacManus, Kytt; Pesaresi, Martino; Doxsey-Whitfield, Erin; Mills, Jane (2016): Development of new open and free multi-temporal global population grids at 250 m resolution. Geospatial Data in a Changing World; Association of Geographic Information Laboratories in Europe (AGILE). AGILE 2016. <br><br><a href="https://population.un.org/wpp/"target=_blank>World Population Prospects 2019 (UN)</a> <br><br>GHSL_pop_count_2018_wnodata_optWCSGeoTIFFgrid_data_core_pro__GHSL_pop_count_2018_wnodata_optRectifiedGridCoverage-179.999999999928 -59.99999999424179.999999999928 84.00833333903574-179.999999999928 -59.99999999424179.999999999928 84.00833333903574Population density map 2018 based on HRSL, GSHL and the World Population Prospect 2019Population density map for 2018 at 30arcsec resolution. <br><br> The layer integrates data from the High Resolution Settlement Layer (HRSL) - META (originally Facebook), the Global Human Settlement Layer (GHSL) - JRC, and the national population count for 2018 reported on the World Population Prospects 2019. <br><br> Using the country borders (level 0) of the UN Cartographic Section 2020 (BNDA_CTY), countries with full data coverage from HRSL use it. Countries with missing or partial coverage from HRSL use GHSL. <br> Both the original layers of HRSL and GHSL were, first, recalculated at 30arcsec resolution (~1km) through an aggregation process (sum) and, then, integrated in Google Earth Engine. Pixel counts were then recalculated for 2018 based on the country population data reported for 2018 by the World Population Prospects 2019. <br><br> List of GIDs of polygons of the BNDA_CTY layer of the UNMap2020 at level 0 that uses population data from GSHL: [6, 203, 228, 111, 250, 203, 65, 22, 234, 146, 208, 77, 223, 157, 273, 81, 221, 116, 2, 51, 156, 191, 20, 101, 47, 266, 61, 62, 26, 286, 119, 197, 277, 189, 195, 213, 215, 214, 212, 35, 145, 232, 84, 56, 175, 239, 38, 222, 19, 27, 28, 196, 207, 206, 265, 274, 275, 276, 278, 279, 280, 281, 282, 283, 284, 285] <br><br> Source of data: <br> GHSL: GHS_POP_E2015_GLOBE_R2019A_4326_30SS_V1_0 (downloaded from <a href="https://ghsl.jrc.ec.europa.eu/download.php?ds=pop at 30arcsec resolution"target=_blank>JRC</a>)<br> Dataset:<br> Schiavina, Marcello; Freire, Sergio; MacManus, Kytt (2019): GHS population grid multitemporal (1975, 1990, 2000, 2015) R2019A. European Commission, Joint Research Centre (JRC) DOI: <a href="http://data.europa.eu/89h/0c6b9751-a71f-4062-830b-43c9f432370f"target=_blank>10.2905/42E8BE89-54FF-464E-BE7B-BF9E64DA5218</a> <br> Concept & Methodology: Freire, Sergio; MacManus, Kytt; Pesaresi, Martino; Doxsey-Whitfield, Erin; Mills, Jane (2016): Development of new open and free multi-temporal global population grids at 250 m resolution. Geospatial Data in a Changing World; Association of Geographic Information Laboratories in Europe (AGILE). AGILE 2016. <br><br>HRSL: ee.ImageCollection("projects/sat-io/open-datasets/hrsl/hrslpop") on Google Earth Engine at 30m resolution. <br> Facebook Connectivity Lab and Center for International Earth Science Information Network - CIESIN - Columbia University. 2016. High Resolution Settlement Layer (HRSL). Source imagery for HRSL © 2016 DigitalGlobe. <br><br><a href="https://population.un.org/wpp/"target=_blank>World Population Prospects 2019 (UN)</a> <br><br>HRSL_wGHSL_pop_count_2018_wnodata_optWCSGeoTIFFgrid_data_core_pro__HRSL_wGHSL_pop_count_2018_wnodata_optRectifiedGridCoverage-180.0 -59.999999994239985180.00833333318934 84.00833333903576-180.0 -59.999999994239985180.00833333318934 84.00833333903576Population distribution (GHSL) 2020The GHS-POP depicts the distribution of human population, expressed as the number of people per cell. Residential population are derived from the raw global census data harmonized by CIESIN for the Gridded Population of the World, version 4.11 (GPWv4.11) at polygon level, and disaggregated from census or administrative units to grid cells, informed by the distribution, classification and density of built-up as mapped in the GHSL global layers. More information at JRC: Global Human Settlement Layer (GHSL) – original source: GHS_POP_E2020_GLOBE_R2022A_54009_1000_V1_0. Dataset: Schiavina M., Freire S., MacManus K. (2022): GHS-POP R2022A - GHS population grid multitemporal (1975-2030).European Commission, Joint Research Centre (JRC) PID: http://data.europa.eu/89h/d6d86a90-4351-4508-99c1-cb074b022c4a, doi:10.2905/D6D86A90-4351-4508-99C1-CB074B022C4A Concept & Methodology: Freire S., MacManus K., Pesaresi M., Doxsey-Whitfield E., Mills J. (2016) Development of new open and free multi-temporal global population grids at 250 m resolution. Geospatial Data in a Changing World; Association of Geographic Information Laboratories in Europe (AGILE), AGILE 2016GHS_POP_E2020_GLOBE_R2022A_54009_1000_V1_0_wgs84_optWCSGeoTIFFgrid_data_core_pro__GHS_POP_E2020_GLOBE_R2022A_54009_1000_V1_0_wgs84_optRectifiedGridCoverage-180.0 -60.01666666090665180.00833333318934 84.01666667236907-180.0 -60.01666666090665180.00833333318934 84.01666667236907Potential Clean Water Provision<span style="font-size:1em"> This dataset shows the total potential supply of clean water available to users in m³. Water quantity in each pixel is calculated as the water balance (rainfall minus actual evapotranspiration) cumulated downstream. See Mulligan et al. (2013) for a description of the global water balance dataset. Potential water provisioning services for each cell are first calculated as the volume of clean water available from upstream. The volume of water is calculated as the downstream cumulated water balance based on (rainfall+fog+snowmelt)-actual evapotranspiration. <br> <br> <u>References:</u> <ul> <li> Mulligan, M. (2013) WaterWorld: a self-parameterising, physically based model for application in data-poor but problem-rich environments globally. Hydrology research 44, 5; 748-769. </li> </ul> </span>potential_clean_water_prov_optWCSGeoTIFFMapX_UNDP__potential_clean_water_prov_optRectifiedGridCoverage-179.99999999990345 -56.016220913563274179.97221821435372 83.99999999995521-179.99999999990345 -56.016220913563274179.97221821435372 83.99999999995521Precipitation Anomalies Apr-May-Jun_2016_(mm) - CHIRPSCHIRPS_Anomaly_3-Monthly_Apr-May-Jun_2016_(mm)WCSGeoTIFFMAPX__CHIRPS_Anomaly_3-Monthly_Apr-May-Jun_2016_(mm)RectifiedGridCoverage-19.125063083055668 -35.3723825785817252.6296893492118 38.256795423617206-19.125063083055668 -35.3723825785817252.6296893492118 38.256795423617206Precipitation Anomalies Apr-May-Jun_2017_(mm) - CHIRPSCHIRPS_Anomaly_3-Monthly_Apr-May-Jun_2017_(mm)WCSGeoTIFFMAPX__CHIRPS_Anomaly_3-Monthly_Apr-May-Jun_2017_(mm)RectifiedGridCoverage-19.125063083055668 -35.3723825785817252.6296893492118 38.256795423617206-19.125063083055668 -35.3723825785817252.6296893492118 38.256795423617206Precipitation Anomalies Oct_2017_(mm) - CHIRPShttps://earlywarning.usgs.gov/fews/ewx/index.html?region=afCHIRPS_Anomaly_Monthly_Oct_2017_(mm)WCSGeoTIFFMAPX__CHIRPS_Anomaly_Monthly_Oct_2017_(mm)RectifiedGridCoverage-19.125063083055668 -35.3723825785817252.6296893492118 38.256795423617206-19.125063083055668 -35.3723825785817252.6296893492118 38.256795423617206Precipitation Anomalies Oct_Nov_2016_(mm) - CHIRPShttps://earlywarning.usgs.gov/fews/ewx/index.html?region=afCHIRPS_Anomaly_2-Monthly_Oct-Nov_2016_(mm)WCSGeoTIFFMAPX__CHIRPS_Anomaly_2-Monthly_Oct-Nov_2016_(mm)RectifiedGridCoverage-19.125063083055668 -35.3723825785817252.6296893492118 38.256795423617206-19.125063083055668 -35.3723825785817252.6296893492118 38.256795423617206Precipitation Anomaly 2020 (1991-2020)This variable is the accumulated liquid and frozen water, including rain and snow, that falls to the Earth's surface. It is the sum of large-scale precipitation (that precipitation which is generated by large-scale weather patterns, such as troughs and cold fronts) and convective precipitation (generated by convection which occurs when air at lower levels in the atmosphere is warmer and less dense than the air above, so it rises). Precipitation variables do not include fog, dew or the precipitation that evaporates in the atmosphere before it lands at the surface of the Earth. Monthly mean precipitation data is in units of “m”, effectively accumulated over a day, thereby giving “m day-1”. For consistency with the ECMWF parameter database, the units in the GRIB files are “m”. Credits: https://cds.climate.copernicus.eu/cdsapp#!/dataset/ecv-for-climate-change?tab=overview12month_anomaly_Global_ea_tp_202001-202012_1991-2020_v02.gribWCSGeoTIFFwesr_climat__12month_anomaly_Global_ea_tp_202001-202012_1991-2020_v02RectifiedGridCoverage-179.875 -90.125180.125 90.125-179.875 -90.125180.125 90.125Precipitation anomalies (mm) 2045-2065 (RCP4.5)Yearly precipitation anomalies (mm) for the period 2045-2065 based on the climate scenario RCP4.5 (reference 1986-2005).precipitationsMedicip__pr_45_46_65RectifiedGridCoverage-26.619999885559082 -6.81999993324279875.4600133895874 45.10000681877136-26.619999885559082 -6.81999993324279875.4600133895874 45.10000681877136Precipitation anomalies (mm) 2045-2065 (RCP8.5)Yearly precipitation anomalies (mm) for the period 2045-2065 based on the climate scenario RCP8.5 (reference 1986-2005).precipitationsMedicip__pr_85_46_65RectifiedGridCoverage-26.619999885559082 -6.81999993324279875.4600133895874 45.10000681877136-26.619999885559082 -6.81999993324279875.4600133895874 45.10000681877136Precipitation anomalies (mm) 2081-2100 (RCP4.5)Yearly precipitation anomalies (mm) for the period 2081-2100 based on the climate scenario RCP4.5 (reference 1986-2005).precipitationsMedicip__pr_45_81_00RectifiedGridCoverage-26.619999885559082 -6.81999993324279875.4600133895874 45.10000681877136-26.619999885559082 -6.81999993324279875.4600133895874 45.10000681877136Precipitation anomalies (mm) 2081-2100 (RCP8.5)Yearly precipitation anomalies (mm) for the period 2081-2100 based on the climate scenario RCP8.5 (reference 1986-2005).precipitationsMedicip__pr_85_81_00RectifiedGridCoverage-26.619999885559082 -6.81999993324279875.4600133895874 45.10000681877136-26.619999885559082 -6.81999993324279875.4600133895874 45.10000681877136Precipitation averages (mm) 1986-2005 (RCP4.5)Yearly precipitation averages in the reference period 1986-2005 based on the climate scenario RCP4.5.precipitationsMedicip__pr_45_86_05RectifiedGridCoverage-26.619999885559082 -6.81999993324279875.4600133895874 45.10000681877136-26.619999885559082 -6.81999993324279875.4600133895874 45.10000681877136Precipitation averages (mm) 1986-2005 (RCP8.5)Yearly precipitation averages in the reference period 1986-2005 based on the climate scenario RCP8.5.precipitationsMedicip__pr_85_86_05RectifiedGridCoverage-26.619999885559082 -6.81999993324279875.4600133895874 45.10000681877136-26.619999885559082 -6.81999993324279875.4600133895874 45.10000681877136Precipitation change trend (1991-2022)Trend of year precipitations in 'mm per year' across the period 1991-2022. Calculated at UNEP/GRID-Geneva in R using linear regression model's slope based on CRU TS 4.07 data available <a href="https://crudata.uea.ac.uk/cru/data/hrg/cru_ts_4.07/cruts.2304141047.v4.07/pre/"target=_blank>here</a> <br> Read release notes of CRU TS 4.07 at <a href="https://crudata.uea.ac.uk/cru/data/hrg/cru_ts_4.07/Release_Notes_CRU_TS_4.07.txt"target=_blank>this link</a> <br><br> Credits: <br> - <a href="https://sites.uea.ac.uk/cru/"target=_blank>Climatic Research Unit (University of East Anglia) and Met Office</a><br> - <a href="https://doi.org/10.1038/s41597-020-0453-3"target=_blank>Harris, I., Osborn, T.J., Jones, P. et al. Version 4 of the CRU TS monthly high-resolution gridded multivariate climate dataset.Sci Data 7, 109 (2020).</a>cru_pre_91_22_lmWCSGeoTIFFwesr_climat__cru_pre_91_22_lmRectifiedGridCoverage-180.0 -90.0180.0 90.0-180.0 -90.0180.0 90.0Precipitation seasonality - Climatology (1973-2013) [coefficient of variation]The Coefficient of Variation is the standard deviation of the monthly precipitation estimates expressed as a percentage of the mean of those estimates (i.e. the annual mean). CHELSA V1.2 (http://chelsa-climate.org/) is a high resolution (30 arc sec, ~1 km) climate data set for the earth land surface areas. It includes monthly and annual mean temperature and precipitation patterns for the time period 1979-2013. Methods are described in http://chelsa-climate.org/wp-admin/download-page/CHELSA_tech_specification.pdf. CHELSA Version 1.2 is licensed under a Creative Commons Attribution 4.0 International License. Specifications: High resolution (30 arcsec, ~1 km) Precipitation & Temperature Climatologies for the years 1979 – 2013 Incorporation of topoclimate (e.g. orographic rainfall & wind fields). All products of CHELSA are in a geographic coordinate system referenced to the WGS 84 horizontal datum, with the horizontal coordinates expressed in decimal degrees. The CHELSA layer extents (minimum and maximum latitude and longitude) are a result of the coordinate system inherited from the 1-arc-second GMTED2010 data which itself inherited the grid extent from the 1-arc-second SRTM data. Note that because of the pixel center referencing of the input GMTED2010 data the full extent of each CHELSA grid as defined by the outside edges of the pixels differs from an integer value of latitude or longitude by 0.000138888888 degree (or 1/2 arc-second). Users of products based on the legacy GTOPO30 product should note that the coordinate referencing of CHELSA (and GMTED2010) and GTOPO30 are not the same. In GTOPO30, the integer lines of latitude and longitude fall directly on the edges of a 30-arc-second pixel. Thus, when overlaying CHELSA with products based on GTOPO30 a slight shift of 1/2 arc-second will be observed between the edges of corresponding 30-arc-second pixels. To redistribute the data, please cite the following peer reviewed articles: <a href="https://www.nature.com/articles/sdata2017122"target=_blank>Karger, D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N.E., Linder, H.P. & Kessler, M. (2017) Climatologies at high resolution for the earth’s land surface areas. Scientific Data 4, 170122.</a> <a href="https://doi.org/10.5061/dryad.kd1d4"target=_blank>Karger, D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N.E., Linder, H.P., Kessler, M. (2017) Data from: Climatologies at high resolution for the earth’s land surface areas. Dryad Digital Repository. </a> CHELSA – Climatologies at high resolution for the Earth land surface areas. Version 1.2CHELSA_bio10_15_optWCSGeoTIFFUN_Sahel__CHELSA_bio10_15_optRectifiedGridCoverage-180.00013888885002 -90.00013888884999179.99985967115003 83.99986041515001-180.00013888885002 -90.00013888884999179.99985967115003 83.99986041515001Precipitation seasonality [coefficient of variation] (2041-2060) RCP4.5 - CIMP5The Coefficient of Variation is the standard deviation of the monthly precipitation estimates expressed as a percentage of the mean of those estimates (i.e. the annual mean) for the period 2041-2060 based on RCP4.5. CHELSA-[CMIP5] is a delta change climatological dataset for the years 2041-2060 and 2061- 2080 for mean monthly maximum temperatures, mean monthly minimum temperatures, monthly precipitation amounts, and several derived parameters. We use the delta change method by B-spline interpolation of anomalies (deltas) of the respective CMIP5 GCM dataset. Anomalies were interpolated between all CMIP5 grid cells and are then added (for temperature variables) or multiplied (in case of precipitation) to high resolution climate data from CHELSA V1.2. This method has the assumption that climate only varies on the scale of the coarser (CMIP5) dataset, and the spatial pattern (from CHELSA) is consistent over time. CHELSA- [CMIP5] does not take changing wind patterns, or temperature lapse rates into account, but rather expects them to be constant over time, and similar to the long term averages. CHELSA V1.2 (http://chelsa-climate.org/) is a high resolution (30 arc sec, ~1 km) climate data set for the earth land surface areas. It includes monthly and annual mean temperature and precipitation patterns for the time period 1979-2013. Methods are described in http://chelsa-climate.org/wp-admin/download-page/CHELSA_tech_specification.pdf. CHELSA Version 1.2 is licensed under a Creative Commons Attribution 4.0 International License. Specifications: High resolution (30 arcsec, ~1 km) Precipitation & Temperature Climatologies for the years 1979 – 2013 Incorporation of topoclimate (e.g. orographic rainfall & wind fields). All products of CHELSA are in a geographic coordinate system referenced to the WGS 84 horizontal datum, with the horizontal coordinates expressed in decimal degrees. The CHELSA layer extents (minimum and maximum latitude and longitude) are a result of the coordinate system inherited from the 1-arc-second GMTED2010 data which itself inherited the grid extent from the 1-arc-second SRTM data. Note that because of the pixel center referencing of the input GMTED2010 data the full extent of each CHELSA grid as defined by the outside edges of the pixels differs from an integer value of latitude or longitude by 0.000138888888 degree (or 1/2 arc-second). Users of products based on the legacy GTOPO30 product should note that the coordinate referencing of CHELSA (and GMTED2010) and GTOPO30 are not the same. In GTOPO30, the integer lines of latitude and longitude fall directly on the edges of a 30-arc-second pixel. Thus, when overlaying CHELSA with products based on GTOPO30 a slight shift of 1/2 arc-second will be observed between the edges of corresponding 30-arc-second pixels. To redistribute the data, please cite the following peer reviewed articles: <a href="https://www.nature.com/articles/sdata2017122"target=_blank>Karger, D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N.E., Linder, H.P. & Kessler, M. (2017) Climatologies at high resolution for the earth’s land surface areas. Scientific Data 4, 170122.</a> <a href="https://doi.org/10.5061/dryad.kd1d4"target=_blank>Karger, D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N.E., Linder, H.P., Kessler, M. (2017) Data from: Climatologies at high resolution for the earth’s land surface areas. Dryad Digital Repository. </a> CHELSA – Climatologies at high resolution for the Earth land surface areas. Version 1.2CHELSA_bio_mon_ACCESS1-0_rcp45_r1i1p1_g025.nc_15_2041-2060_V1.2_optWCSGeoTIFFUN_Sahel__CHELSA_bio_mon_ACCESS1-0_rcp45_r1i1p1_g025.nc_15_2041-2060_V1.2_optRectifiedGridCoverage-180.00013888885002 -90.00013888884999179.99985967115003 83.99986041515001-180.00013888885002 -90.00013888884999179.99985967115003 83.99986041515001Precipitation seasonality [coefficient of variation] (2041-2060) RCP8.5 - CIMP5The Coefficient of Variation is the standard deviation of the monthly precipitation estimates expressed as a percentage of the mean of those estimates (i.e. the annual mean) for the period 2041-2060 based on RCP8.5. CHELSA-[CMIP5] is a delta change climatological dataset for the years 2041-2060 and 2061- 2080 for mean monthly maximum temperatures, mean monthly minimum temperatures, monthly precipitation amounts, and several derived parameters. We use the delta change method by B-spline interpolation of anomalies (deltas) of the respective CMIP5 GCM dataset. Anomalies were interpolated between all CMIP5 grid cells and are then added (for temperature variables) or multiplied (in case of precipitation) to high resolution climate data from CHELSA V1.2. This method has the assumption that climate only varies on the scale of the coarser (CMIP5) dataset, and the spatial pattern (from CHELSA) is consistent over time. CHELSA- [CMIP5] does not take changing wind patterns, or temperature lapse rates into account, but rather expects them to be constant over time, and similar to the long term averages. CHELSA V1.2 (http://chelsa-climate.org/) is a high resolution (30 arc sec, ~1 km) climate data set for the earth land surface areas. It includes monthly and annual mean temperature and precipitation patterns for the time period 1979-2013. Methods are described in http://chelsa-climate.org/wp-admin/download-page/CHELSA_tech_specification.pdf. CHELSA Version 1.2 is licensed under a Creative Commons Attribution 4.0 International License. Specifications: High resolution (30 arcsec, ~1 km) Precipitation & Temperature Climatologies for the years 1979 – 2013 Incorporation of topoclimate (e.g. orographic rainfall & wind fields). All products of CHELSA are in a geographic coordinate system referenced to the WGS 84 horizontal datum, with the horizontal coordinates expressed in decimal degrees. The CHELSA layer extents (minimum and maximum latitude and longitude) are a result of the coordinate system inherited from the 1-arc-second GMTED2010 data which itself inherited the grid extent from the 1-arc-second SRTM data. Note that because of the pixel center referencing of the input GMTED2010 data the full extent of each CHELSA grid as defined by the outside edges of the pixels differs from an integer value of latitude or longitude by 0.000138888888 degree (or 1/2 arc-second). Users of products based on the legacy GTOPO30 product should note that the coordinate referencing of CHELSA (and GMTED2010) and GTOPO30 are not the same. In GTOPO30, the integer lines of latitude and longitude fall directly on the edges of a 30-arc-second pixel. Thus, when overlaying CHELSA with products based on GTOPO30 a slight shift of 1/2 arc-second will be observed between the edges of corresponding 30-arc-second pixels. To redistribute the data, please cite the following peer reviewed articles: <a href="https://www.nature.com/articles/sdata2017122"target=_blank>Karger, D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N.E., Linder, H.P. & Kessler, M. (2017) Climatologies at high resolution for the earth’s land surface areas. Scientific Data 4, 170122.</a> <a href="https://doi.org/10.5061/dryad.kd1d4"target=_blank>Karger, D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N.E., Linder, H.P., Kessler, M. (2017) Data from: Climatologies at high resolution for the earth’s land surface areas. Dryad Digital Repository. </a> CHELSA – Climatologies at high resolution for the Earth land surface areas. Version 1.2CHELSA_bio_mon_ACCESS1-0_rcp85_r1i1p1_g025.nc_15_2041-2060_V1.2_optWCSGeoTIFFUN_Sahel__CHELSA_bio_mon_ACCESS1-0_rcp85_r1i1p1_g025.nc_15_2041-2060_V1.2_optRectifiedGridCoverage-180.00013888885002 -90.00013888884999179.99985967115003 83.99986041515001-180.00013888885002 -90.00013888884999179.99985967115003 83.99986041515001Precipitation trend (% of change) 2008-2018 in Somalia (ICPAC)Seasonal trends expressed as percentage of change of rainfall time series (2008-2018) reconstructed from station observations, remote sensing and other proxies. The rainfall data were created by combining quality-controlled station observations with satellite rainfall estimates and were reconstructed over land areas on a 0.05 x 0.05 lat/lon grid. Data downloaded from http://digilib.icpac.net/maproom/Climatology/Climate_Analysis/Seasonal_Trend.html.enSOM_ICPAC_prec_trend_perc_Jan_Dec_2008_2018_optWCSGeoTIFFwesr_cca__SOM_ICPAC_prec_trend_perc_Jan_Dec_2008_2018_optRectifiedGridCoverage40.93000030517578 -1.700001157820224851.43000124394894 11.99999904632568440.93000030517578 -1.700001157820224851.43000124394894 11.999999046325684Precipitation trend (% of change) April to Jun (Gu wet Season) 2008-2018 in Somalia (ICPAC)Seasonal trends expressed as percentage of change of rainfall time series (Gu season April to June 2008-2018) reconstructed from station observations, remote sensing and other proxies. The rainfall data were created by combining quality-controlled station observations with satellite rainfall estimates and were reconstructed over land areas on a 0.05 x 0.05 lat/lon grid. Data downloaded from http://digilib.icpac.net/maproom/Climatology/Climate_Analysis/Seasonal_Trend.html.enSOM_ICPAC_prec_trend_perc_Apr_Jun_2008_2018_optWCSGeoTIFFwesr_cca__SOM_ICPAC_prec_trend_perc_Apr_Jun_2008_2018_optRectifiedGridCoverage40.93000030517578 -1.700001157820224851.43000124394894 11.99999904632568440.93000030517578 -1.700001157820224851.43000124394894 11.999999046325684Precipitation trend (% of change) Oct to Dec (Deyr wet Season) 2008-2018 in Somalia (ICPAC) (ICPAC)Seasonal trends expressed as percentage of change of rainfall time series (Deyr season October to December 2008-2018) reconstructed from station observations, remote sensing and other proxies. The rainfall data were created by combining quality-controlled station observations with satellite rainfall estimates and were reconstructed over land areas on a 0.05 x 0.05 lat/lon grid. Data downloaded from http://digilib.icpac.net/maproom/Climatology/Climate_Analysis/Seasonal_Trend.html.enSOM_ICPAC_prec_trend_perc_Oct_Dec_2008_2018_optWCSGeoTIFFwesr_cca__SOM_ICPAC_prec_trend_perc_Oct_Dec_2008_2018_optRectifiedGridCoverage40.93000030517578 -1.700001157820224851.43000124394894 11.99999904632568440.93000030517578 -1.700001157820224851.43000124394894 11.999999046325684Projected timing of annual severe bleaching (annual exceedance of 8 Degree Heating Weeks of thermal stress in reef areas) under SSP5-8.5 using CMIP6 modelsThe data and complementary report have two purposes, and are intended for use by the global coral reef scientific and management community, which had these 2 objectives. Objective 1. To present projections of coral bleaching conditions (i.e., exposure to the primary climate threat to coral reefs) and potential adaptation at a spatial scale that enables use of the data in management and conservation planning and in support of other decisions influencing coral reefs and reef use. Objective 2. To evaluate the implications of the Paris Agreement as well as failure to achieve its goal, by comparing the projected timing of annual severe bleaching between Shared Socioeconomic Pathways SSP5-8.5 and SSP2-4.5. Data source used: Intergovernmental Panel on Climate Change (IPCC) Coupled Model Interpolation Project Phase 6 (CMIP6) model outputs for sea surface temperature (https://www.ipcc-data.org/), combined with United States National Oceanic and Atmospheric Administration (NOAA) CoralTemp data from NOAA Coral Reef Watch (https://www.coralreefwatch.noaa.gov/product/5km/index_5km_sst.php).asb_year_0_ssp585WCSGeoTIFFwesr_climat__asb_year_0_ssp585RectifiedGridCoverage-180.0 -90.0180.0 90.0-180.0 -90.0180.0 90.0Projected timing of annual severe bleaching (annual exceedance of 8 Degree Heating Weeks of thermal stress in reef areas) under SSPP2-4.5 using CMIP6 models.The data and complementary report have two purposes, and are intended for use by the global coral reef scientific and management community, which had these 2 objectives. Objective 1. To present projections of coral bleaching conditions (i.e., exposure to the primary climate threat to coral reefs) and potential adaptation at a spatial scale that enables use of the data in management and conservation planning and in support of other decisions influencing coral reefs and reef use. Objective 2. To evaluate the implications of the Paris Agreement as well as failure to achieve its goal, by comparing the projected timing of annual severe bleaching between Shared Socioeconomic Pathways SSP5-8.5 and SSP2-4.5. Data source used: Intergovernmental Panel on Climate Change (IPCC) Coupled Model Interpolation Project Phase 6 (CMIP6) model outputs for sea surface temperature (https://www.ipcc-data.org/), combined with United States National Oceanic and Atmospheric Administration (NOAA) CoralTemp data from NOAA Coral Reef Watch (https://www.coralreefwatch.noaa.gov/product/5km/index_5km_sst.php).asb_year_0_ssp245WCSGeoTIFFwesr_climat__asb_year_0_ssp245RectifiedGridCoverage-180.0 -90.0180.0 90.0-180.0 -90.0180.0 90.0Rarity-weighted richness<span style="font-size:0.95em"><span style="font-size:1.1em"><b>Description</b></span> Rarity weighted richness is a measure that combines endemism and species richness of amphibians, birds, mammals, reptiles and a representative set of plant taxa in each 10 km cell. This index lowers the contribution of wide ranging species to overall species richness and highlights the areas that have a relatively high proportion of narrow‐range species. Species ranges were rasterised at 1 km resolution from polygon maps from the IUCN Red List (IUCN Red List of Threatened Species (2019) Version 2019.2. http://www.iucnredlist.org), the Global Assessment of Reptile Distributions (GARD) (Roll et al. (2017), Version 1.5, https://datadryad.org/stash/dataset/doi:10.5061/dryad.83s7k) and the Botanical Information and Ecology Network (BIEN) database (Enquist et al. 2019 and Maitner et al. 2017, version 4.1. http://bien.nceas.ucsb.edu/bien/biendata/). Additional vascular plant species ranges were created from point data from the IUCN Red List (IUCN Red List of Threatened Species (2019) Version 2019.2. http://www.iucnredlist.org), Botanic Gardens Conservation International (BGCI) (https://www.bgci.org/) and the Global Biodiversity Information Facility (GBIF) (https://www.gbif.org/). Species range maps were refined, when possible, by removing unsuitable areas using information on species’ habitat preferences and species' known altitudinal limits. Habitats distributions were obtained from the global map of terrestrial habitat types (Jung et al. in prep), while altitudinal data was obtained from the Global Multi-resolution Terrain Elevation Data (GMTED2010) (USGS). This refinement process produced Areas of Habitat (AOH) maps for each species (Brooks et al. 2019).<br><br>Each grid cell of the species’ AOH was then scored for range-size rarity (the proportion of the species’ AOH the cell represents; i.e. size of grid cell/AOH). The total score for each cell was calculated by summing scores across all the species whose AOH intersected with it. Higher values occur in cells with more species that have smaller ranges (i.e. both the number of species and the degree to which their ranges are restricted contribute to the range rarity score).<br><br>NatureMap is currently soliciting feedback on the validity of this map at the national and sub-national levels. Please submit any comments via the <a href="https://explorer.naturemap.earth/" target="_blank">Nature Map Explorer</a>.<br><br><b>References</b><br></<span>Brooks, T. M. et al. (2019). Measuring Terrestrial Area of Habitat (AOH) and Its Utility for the IUCN Red List. Trends in Ecology & Evolution 34:977–986. <a href="https://doi.org/10.1016/j.tree.2019.06.009" target="_blank">doi.org/10.1016/j.tree.2019.06.009</a>.<br><br>BGCI (2019). ThreatSearch online database. <a href="www.bgci.org/threat_search.php" target="_blank">www.bgci.org/threat_search.php</a>.<br><br>Enquist, B.J. et al. (in prep.). Botanical big data shows that plant diversity in the New World is driven by climatic-linked differences in evolutionary rates and biotic exclusion.<br><br>Jung, M. et al. (in prep). A global map of species terrestrial habitat types.<br><br>Maitner, B.S. et al. (2017). The BIEN R package: A tool to access the Botanical Information and Ecology Network (BIEN) database. Methods in Ecology and Evolution; 9:373–379. <a href="https://besjournals.onlinelibrary.wiley.com/doi/10.1111/2041-210X.12861" target="_blank">doi/10.1111/2041-210X.12861</a>.<br><br>Roll, U. et al. (2017), The global distribution of tetrapods reveals a need for targeted reptile conservation, Nature Ecology & Evolution, 1: 1677–1682, <a href="https://doi.org/10.1038/s41559-017-0332-2" target="_blank">doi.org/10.1038/s41559-017-0332-2</a>.</span>rarity_weighted_richness_optWCSGeoTIFFMapX_UNDP__rarity_weighted_richness_optRectifiedGridCoverage-179.995201157628 -84.69466806618583179.8859620772232 85.91771612300938-179.995201157628 -84.69466806618583179.8859620772232 85.91771612300938Realised Clean Water Provision<span style="font-size:1em"> This dataset consists of a globally normalized map (from 0 to 1) of pixels ranked for their relative importance in delivering clean water to downstream beneficiaries. Water pollution is estimated based on the Human Footprint on Water Quality (HFWQ) index. The HFWQ is a measure of the extent to which water runoff is drawn from contaminating human land uses, both point (urban, roads, mining, oil and gas) and non-point (unprotected cropland, unprotected pasture) sources. The HFWQ is calculated relative to all runoff by cumulating the downstream runoff from polluting and non-polluting land uses and expressing the former runoff as a proportion of the total runoff. <br> <br> Realised provisioning is high where the prevailing climate and land use generate high volumes of clean water which can be used (and reused) by large numbers of downstream users. The greater the downstream population, number of dams and actual water available, the greater the service provided. If there is plenty of water, but no people or dams, then there is no realised service. In this way, not all water provides a direct service, only that water that is accessed and used. <br> <br> All analyses were carried out using the WaterWorld (Mulligan 2013) and Co$ting Nature (Mulligan et al. 2010) ecosystem services assessment tools (<a href="https://www.policysupport.org" target="_blank">www.policysupport.org</a>). <br> <br> <u>References:</u> <ul> <li> Mulligan, M. (2013). WaterWorld: a self-parameterising, physically based model for application in data-poor but problem-rich environments globally. Hydrology research 44, 5; 748-769. </li> <li> Mulligan, M.A. Guerry, K. Arkema, K. Bagstad and F. Villa (2010) Capturing and quantifying the flow of ecosystem services in Silvestri S., Kershaw F., (eds.). Framing the flow: Innovative Approaches to Understand, Protect and Value Ecosystem Services Across Linked Habitats. UN Environment Programme World Conservation Monitoring Centre, Cambridge, UK. ISBN 978-92-807-3065-4. </li> </ul> </span>realized_clean_water_prov_optWCSGeoTIFFgrid_data_core__realized_clean_water_prov_optRectifiedGridCoverage-179.58826144943154 -55.983070494175905179.5477094321325 83.99999999995521-179.58826144943154 -55.983070494175905179.5477094321325 83.99999999995521Recurrence map of burned areas between 1995 and 2024The combined burned area map shows the recurrence of wildfires throughout the study period. It allows for the identification of fire "hotspots, " i.e., areas where fires recur chronically. This map identifies three levels of recurrence: areas burned at least once during one of the periods, areas burned at least once during two separate periods, and areas burned at least once during each study period.burnrisk_southeast_senegal__burnRectifiedGridCoverage-13.239460488881921 12.307817707721565-11.342218608821492 13.461254532531031-13.239460488881921 12.307817707721565-11.342218608821492 13.461254532531031Ruminant livestock density (2010) - FAOThe layer shows the density of livestock in LSU/km2 for the year 2010. It is derived from the Land and Water Resources theme of the GAEZv4 database of FAO. Livestock distribution data contained in GAEZ v4 were provided by the 30 arc-second resolution raster maps of the Gridded Livestock of the World database (GLW 2.01; released in May 2014). The GLW includes separate layers with the number of cattle, sheep, goats, pigs and poultry. The example included in the portal refers to ruminant (cattle, sheep, goats) livestock density and can, for instance, be used to compute land requirements for grazing livestock and to estimate the amount of grass/shrub land that may be available for other uses.TLU30s_AD_2010_v2_1_optWCSGeoTIFFunccd__TLU30s_AD_2010_v2_1_optRectifiedGridCoverage-180.0 -90.0180.0 90.0-180.0 -90.0180.0 90.0SDG 15.3.1 Land Degradation - SahelThis dataset is clipped for the Sahel region from the original SDG15.3.1 Land Degradation global dataset.<br> The original dataset has been generated using the Trends.Earth model to asses Land Degradation according to the UN Metadata Guideline for SDG. <br><br> More information on <a href= "http://trends.earth/docs/en/" target="_blank">Trends.Earth</a>. <br> More information on <a href="https://knowledge.unccd.int/topics/sustainable-development-goals-sdgs/sdg-indicator-1531" target= "_blank">Land Degradation</a>. <br> More information on <a href="https://unstats.un.org/sdgs/metadata/files/Metadata-15-03-01.pdf" target="_blank">SDG15.3.1</a>. <br> <br> Generated using the GEOEssential VLabsdg1531_sahel_optWCSGeoTIFFUN_Sahel__sdg1531_sahel_optRectifiedGridCoverage-25.357194800661915 -0.0336866878891015651.4128293216946 37.54284158619938-25.357194800661915 -0.033686687889101651.4128293216946 37.54284158619938Sand Demand Distribution in Western Area and Lungi-Mahera corridorsandsand__new_road_aggregate_change__2030RectifiedGridCoverage-1480502.8360725055 903934.8657441306-1436562.2346710372 974052.8467039203-13.2995832582623 8.093137521714812-12.904858119938874 8.716250167822336Satellite Mogadisho Urban Growth 2001Landsat tm7 sensor. RGB raster map using bands 5-3-1. Date 2001-06-28. Edited in Grass GIS 7.2.2. Editings: - gdal_fillnodata for the satellite images from 2003 to 2018. - i.landsat.toar for DN to kind of reflectance transformation in order to later perform supervised classification of the imageries. - RGB raster map from bands 5-3-1. Image downloaded from www.earthexplorer.usgs.gov.Mogadisho_Landsat7_20010628-531WCSGeoTIFFMAPX__Mogadisho_Landsat7_20010628-531RectifiedGridCoverage45.195394649846214 1.95522902955153845.47923060483453 2.1414766133531345.195394649846214 1.95522902955153845.47923060483453 2.14147661335313Satellite Mogadisho Urban Growth 2003Landsat tm7 sensor. RGB raster map using bands 5-3-1. Date 2003-12-27. Edited in Grass GIS 7.2.2. Editings: - gdal_fillnodata for the satellite images from 2003 to 2018. - i.landsat.toar for DN to kind of reflectance transformation in order to later perform supervised classification of the imageries. - RGB raster map from bands 5-3-1. Image downloaded from www.earthexplorer.usgs.gov.Mogadisho_Landsat7_20031227-531WCSGeoTIFFMAPX__Mogadisho_Landsat7_20031227-531RectifiedGridCoverage521730.0 216120.0553290.0 236700.045.195394649846214 1.95522902955153845.47923060483454 2.141476613353127Satellite Mogadisho Urban Growth 2004Landsat tm7 sensor. RGB raster map using bands 5-3-1. Date 2004-09-24. Edited in Grass GIS 7.2.2. Editings: - gdal_fillnodata for the satellite images from 2003 to 2018. - i.landsat.toar for DN to kind of reflectance transformation in order to later perform supervised classification of the imageries. - RGB raster map from bands 5-3-1. Image downloaded from www.earthexplorer.usgs.gov.Mogadisho_Landsat7_20040924-531WCSGeoTIFFMAPX__Mogadisho_Landsat7_20040924-531RectifiedGridCoverage521730.0 216120.0553290.0 236700.045.195394649846214 1.95522902955153845.47923060483454 2.141476613353127Satellite Mogadisho Urban Growth 2007Landsat tm7 sensor. RGB raster map using bands 5-3-1. Date 2007-03-09. Edited in Grass GIS 7.2.2. Editings: - gdal_fillnodata for the satellite images from 2003 to 2018. - i.landsat.toar for DN to kind of reflectance transformation in order to later perform supervised classification of the imageries. - RGB raster map from bands 5-3-1. Image downloaded from www.earthexplorer.usgs.gov.Mogadisho_Landsat7_20070309-531WCSGeoTIFFMAPX__Mogadisho_Landsat7_20070309-531RectifiedGridCoverage521730.0 216120.0553290.0 236700.045.195394649846214 1.95522902955153845.47923060483454 2.141476613353127Satellite Mogadisho Urban Growth 2009Landsat tm7 sensor. RGB raster map using bands 5-3-1. Date 2009-03-14. Edited in Grass GIS 7.2.2. Editings: - gdal_fillnodata for the satellite images from 2003 to 2018. - i.landsat.toar for DN to kind of reflectance transformation in order to later perform supervised classification of the imageries. - RGB raster map from bands 5-3-1. Image downloaded from www.earthexplorer.usgs.gov.Mogadisho_Landsat7_20090314-531WCSGeoTIFFMAPX__Mogadisho_Landsat7_20090314-531RectifiedGridCoverage521730.0 216120.0553290.0 236700.045.195394649846214 1.95522902955153845.47923060483454 2.141476613353127Satellite Mogadisho Urban Growth 2012Landsat tm7 sensor. RGB raster map using bands 5-3-1. Date 2012-01-18. Edited in Grass GIS 7.2.2. Editings: - gdal_fillnodata for the satellite images from 2003 to 2018. - i.landsat.toar for DN to kind of reflectance transformation in order to later perform supervised classification of the imageries. - RGB raster map from bands 5-3-1. Image downloaded from www.earthexplorer.usgs.gov.Mogadisho_Landsat7_20120118-531WCSGeoTIFFMAPX__Mogadisho_Landsat7_20120118-531RectifiedGridCoverage521730.0 216120.0553290.0 236700.045.195394649846214 1.95522902955153845.47923060483454 2.141476613353127Satellite Mogadisho Urban Growth 2014Landsat tm7 sensor. RGB raster map using bands 5-3-1. Date 2014-06-16. Edited in Grass GIS 7.2.2. Editings: - gdal_fillnodata for the satellite images from 2003 to 2018. - i.landsat.toar for DN to kind of reflectance transformation in order to later perform supervised classification of the imageries. - RGB raster map from bands 5-3-1. Image downloaded from www.earthexplorer.usgs.gov.Mogadisho_Landsat7_20140616-531WCSGeoTIFFMAPX__Mogadisho_Landsat7_20140616-531RectifiedGridCoverage521727.491393018 216113.661172091553296.1526237235 236706.943172503845.19537208608338 1.955171666992917145.47928595286078 2.1415394320566414Satellite Mogadisho Urban Growth 2016Landsat tm7 sensor. RGB raster map using bands 5-3-1. Date 2016-02-14. Edited in Grass GIS 7.2.2. Editings: - gdal_fillnodata for the satellite images from 2003 to 2018. - i.landsat.toar for DN to kind of reflectance transformation in order to later perform supervised classification of the imageries. - RGB raster map from bands 5-3-1. Image downloaded from www.earthexplorer.usgs.gov.Mogadisho_Landsat7_20160214-531WCSGeoTIFFMAPX__Mogadisho_Landsat7_20160214-531RectifiedGridCoverage521730.0 216120.0553290.0 236700.045.195394649846214 1.95522902955153845.47923060483454 2.141476613353127Satellite Mogadisho Urban Growth 2018Landsat tm7 sensor. RGB raster map using bands 5-3-1. Date 2018-01-02. Edited in Grass GIS 7.2.2. Editings: - gdal_fillnodata for the satellite images from 2003 to 2018. - i.landsat.toar for DN to kind of reflectance transformation in order to later perform supervised classification of the imageries. - RGB raster map from bands 5-3-1. Image downloaded from www.earthexplorer.usgs.gov.Mogadisho_Landsat7_20180102-531WCSGeoTIFFMAPX__Mogadisho_Landsat7_20180102-531RectifiedGridCoverage521730.0 216120.0553290.0 236700.045.195394649846214 1.95522902955153845.47923060483454 2.141476613353127Sea grasses total area in km2seagrakm2aWCSGeoTIFFECO-DRR__seagrakm2aRectifiedGridCoverage-180.0 -90.0180.0 90.0-180.0 -90.0180.0 90.0Self-calibrating Palmer Drought Severity Index (scPDSI) 1960-1969 (CRU)This layer shows the average values over the 1960 to 1969 decade of the Self-calibrating Palmer Drought Severity Index (scPDSI), based on the scPDSI dataset provided by the Climatic Research Unit (CRU) that cover the time interval from 1901 to 2018. Original data were subset and processed using QGIS at UNEP/GRID-Geneva.<br><br> The scPSDI indicates the degree of drought severity (negative values means higher severity) based on climatic and environmental parameters. The scPDSI metric was introduced by Wells et al. (2004), who give detailed information about its calculation. The scPDSI is a variant on the original PDSI of Palmer (1965), with the aim to make results from different climate regimes more comparable. As with the PDSI, the scPDSI is calculated from time series of precipitation and temperature, together with fixed parameters related to the soil/surface characteristics at each location. <br><br> The dataset has been updated each year using newer versions of CRU TS input data, currently to the end of 2018 using a preliminary version of CRU TS 4.03 (0.5° resolution). <br><br> (Dataset method) van der Schrier G, Barichivich J, Briffa KR and Jones PD (2013) A scPDSI-based global data set of dry and wet spells for 1901-2009. J. Geophys. Res. Atmos. 118, 4025-4048 (10.1002/jgrd.50355).<br> (1901-2018 update) Barichivich J, Osborn TJ, Harris I, van der Schrier G and Jones PD (2018) Drought [in "State of the Climate in 2018"]. Bulletin of the American Meteorological Society under review.<br> (1901-2017 update) Osborn TJ, Barichivich J, Harris I, van der Schrier G and Jones PD (2018) Drought [in "State of the Climate in 2017"]. Bulletin of the American Meteorological Society 99, S36-S37. (doi:10.1175/2018BAMSStateoftheClimate.1)<br> (1901-2016 update) Osborn TJ, Barichivich J, Harris I, van der Schrier G and Jones PD (2017) Monitoring global drought using the self-calibrating Palmer Drought Severity Index [in "State of the Climate in 2016"]. Bulletin of the American Meteorological Society 98, S32-S33 (doi:10.1175/2017BAMSStateoftheClimate.1) (available here).<br> (1901-2015 update) Osborn TJ, Barichivich J, Harris I, van der Schrier G and Jones PD (2016) Monitoring global drought using the self-calibrating Palmer Drought Severity Index [in "State of the Climate in 2015"]. Bulletin of the American Meteorological Society 97, S32-S36 (available here). <br><br> Data were downloaded from <a href="https://crudata.uea.ac.uk/cru/data/drought/"target=_blank>CRUDATA/Drought</a>scPDSI.cru_ts403_Mean60sWCSGeoTIFFUN_Sahel__scPDSI.cru_ts403_1960-1969RectifiedGridCoverage-180.0 -90.0180.0 90.0-180.0 -90.0180.0 90.0Self-calibrating Palmer Drought Severity Index (scPDSI) 1961-1970 (CRU)This layer shows the average values over the 1961 to 1970 decade of the Self-calibrating Palmer Drought Severity Index (scPDSI), based on the scPDSI dataset provided by the Climatic Research Unit (CRU) that cover the time interval from 1901. Original data were subset and processed using R at UNEP/GRID-Geneva.<br><br> The scPSDI indicates the degree of drought severity (negative values means higher severity) based on climatic and environmental parameters. The scPDSI metric was introduced by Wells et al. (2004), who give detailed information about its calculation. The scPDSI is a variant on the original PDSI of Palmer (1965), with the aim to make results from different climate regimes more comparable. As with the PDSI, the scPDSI is calculated from time series of precipitation and temperature, together with fixed parameters related to the soil/surface characteristics at each location. <br><br> Version: preliminary CRU TS 4.07, 0.5° lat-lon resolution <br><br> (Dataset method) van der Schrier G, Barichivich J, Briffa KR and Jones PD (2013) A scPDSI-based global data set of dry and wet spells for 1901-2009. J. Geophys. Res. Atmos. 118, 4025-4048 (10.1002/jgrd.50355).<br> (1901-2021 update) Barichivich J, Osborn TJ, Harris I, van der Schrier G and Jones PD (2022) Monitoring global drought using the self-calibrating Palmer Drought Severity Index [in “State of the Climate in 2021“]. Bull. Amer. Meteor. Soc., 103 (8), S31–S33, (https://doi.org/10.1175/BAMS-D-22-0092.1).<br> (1901-2020 update) Barichivich J, Osborn TJ, Harris I, van der Schrier G and Jones PD (2021) Monitoring global drought using the self-calibrating Palmer Drought Severity Index [in "State of the Climate in 2020" eds. Dunn RJH, Aldred F, Gobron N, Miller JB & Willett KM]. Bulletin of the American Meteorological Society 102, S68-S70. (https://doi.org/10.1175/BAMS-D-21-0098.1)<br> (1901-2019 update) Barichivich J, Osborn TJ, Harris I, van der Schrier G and Jones PD (2020) Drought [in "State of the Climate in 2019"]. Bulletin of the American Meteorological Society 101, S1-S429. (doi:10.1175/2020BAMSStateoftheClimate.1)<br> (1901-2018 update) Barichivich J, Osborn TJ, Harris I, van der Schrier G and Jones PD (2018) Drought [in "State of the Climate in 2018"]. Bulletin of the American Meteorological Society under review.<br> (1901-2017 update) Osborn TJ, Barichivich J, Harris I, van der Schrier G and Jones PD (2018) Drought [in "State of the Climate in 2017"]. Bulletin of the American Meteorological Society 99, S36-S37. (doi:10.1175/2018BAMSStateoftheClimate.1)<br> (1901-2016 update) Osborn TJ, Barichivich J, Harris I, van der Schrier G and Jones PD (2017) Monitoring global drought using the self-calibrating Palmer Drought Severity Index [in "State of the Climate in 2016"]. Bulletin of the American Meteorological Society 98, S32-S33 (doi:10.1175/2017BAMSStateoftheClimate.1) (available here).<br> (1901-2015 update) Osborn TJ, Barichivich J, Harris I, van der Schrier G and Jones PD (2016) Monitoring global drought using the self-calibrating Palmer Drought Severity Index [in "State of the Climate in 2015"]. Bulletin of the American Meteorological Society 97, S32-S36 (available here). <br><br> Data were downloaded from <a href="https://crudata.uea.ac.uk/cru/data/drought/"target=_blank>CRUDATA/Drought</a>cru_scpdsi_61_70_meanWCSGeoTIFFwesr_climat__cru_scpdsi_61_70_meanRectifiedGridCoverage-180.0 -90.0180.0 90.0-180.0 -90.0180.0 90.0Self-calibrating Palmer Drought Severity Index (scPDSI) 1970-1979 (CRU)This layer shows the average values over the 1970 to 1979 decade of the Self-calibrating Palmer Drought Severity Index (scPDSI), based on the scPDSI dataset provided by the Climatic Research Unit (CRU) that cover the time interval from 1901 to 2018. Original data were subset and processed using QGIS at UNEP/GRID-Geneva.<br><br> The scPSDI indicates the degree of drought severity (negative values means higher severity) based on climatic and environmental parameters. The scPDSI metric was introduced by Wells et al. (2004), who give detailed information about its calculation. The scPDSI is a variant on the original PDSI of Palmer (1965), with the aim to make results from different climate regimes more comparable. As with the PDSI, the scPDSI is calculated from time series of precipitation and temperature, together with fixed parameters related to the soil/surface characteristics at each location. <br><br> The dataset has been updated each year using newer versions of CRU TS input data, currently to the end of 2018 using a preliminary version of CRU TS 4.03 (0.5° resolution). <br><br> (Dataset method) van der Schrier G, Barichivich J, Briffa KR and Jones PD (2013) A scPDSI-based global data set of dry and wet spells for 1901-2009. J. Geophys. Res. Atmos. 118, 4025-4048 (10.1002/jgrd.50355).<br> (1901-2018 update) Barichivich J, Osborn TJ, Harris I, van der Schrier G and Jones PD (2018) Drought [in "State of the Climate in 2018"]. Bulletin of the American Meteorological Society under review.<br> (1901-2017 update) Osborn TJ, Barichivich J, Harris I, van der Schrier G and Jones PD (2018) Drought [in "State of the Climate in 2017"]. Bulletin of the American Meteorological Society 99, S36-S37. (doi:10.1175/2018BAMSStateoftheClimate.1)<br> (1901-2016 update) Osborn TJ, Barichivich J, Harris I, van der Schrier G and Jones PD (2017) Monitoring global drought using the self-calibrating Palmer Drought Severity Index [in "State of the Climate in 2016"]. Bulletin of the American Meteorological Society 98, S32-S33 (doi:10.1175/2017BAMSStateoftheClimate.1) (available here).<br> (1901-2015 update) Osborn TJ, Barichivich J, Harris I, van der Schrier G and Jones PD (2016) Monitoring global drought using the self-calibrating Palmer Drought Severity Index [in "State of the Climate in 2015"]. Bulletin of the American Meteorological Society 97, S32-S36 (available here). <br><br> Data were downloaded from <a href="https://crudata.uea.ac.uk/cru/data/drought/"target=_blank>CRUDATA/Drought</a>scPDSI.cru_ts403_Mean70sWCSGeoTIFFUN_Sahel__scPDSI.cru_ts403_1970-1979RectifiedGridCoverage-180.0 -90.0180.0 90.0-180.0 -90.0180.0 90.0Self-calibrating Palmer Drought Severity Index (scPDSI) 1971-1980 (CRU)This layer shows the average values over the 1971 to 1980 decade of the Self-calibrating Palmer Drought Severity Index (scPDSI), based on the scPDSI dataset provided by the Climatic Research Unit (CRU) that cover the time interval from 1901. Original data were subset and processed using R at UNEP/GRID-Geneva.<br><br> The scPSDI indicates the degree of drought severity (negative values means higher severity) based on climatic and environmental parameters. The scPDSI metric was introduced by Wells et al. (2004), who give detailed information about its calculation. The scPDSI is a variant on the original PDSI of Palmer (1965), with the aim to make results from different climate regimes more comparable. As with the PDSI, the scPDSI is calculated from time series of precipitation and temperature, together with fixed parameters related to the soil/surface characteristics at each location. <br><br> Version: preliminary CRU TS 4.07, 0.5° lat-lon resolution <br><br> (Dataset method) van der Schrier G, Barichivich J, Briffa KR and Jones PD (2013) A scPDSI-based global data set of dry and wet spells for 1901-2009. J. Geophys. Res. Atmos. 118, 4025-4048 (10.1002/jgrd.50355).<br> (1901-2021 update) Barichivich J, Osborn TJ, Harris I, van der Schrier G and Jones PD (2022) Monitoring global drought using the self-calibrating Palmer Drought Severity Index [in “State of the Climate in 2021“]. Bull. Amer. Meteor. Soc., 103 (8), S31–S33, (https://doi.org/10.1175/BAMS-D-22-0092.1).<br> (1901-2020 update) Barichivich J, Osborn TJ, Harris I, van der Schrier G and Jones PD (2021) Monitoring global drought using the self-calibrating Palmer Drought Severity Index [in "State of the Climate in 2020" eds. Dunn RJH, Aldred F, Gobron N, Miller JB & Willett KM]. Bulletin of the American Meteorological Society 102, S68-S70. (https://doi.org/10.1175/BAMS-D-21-0098.1)<br> (1901-2019 update) Barichivich J, Osborn TJ, Harris I, van der Schrier G and Jones PD (2020) Drought [in "State of the Climate in 2019"]. Bulletin of the American Meteorological Society 101, S1-S429. (doi:10.1175/2020BAMSStateoftheClimate.1)<br> (1901-2018 update) Barichivich J, Osborn TJ, Harris I, van der Schrier G and Jones PD (2018) Drought [in "State of the Climate in 2018"]. Bulletin of the American Meteorological Society under review.<br> (1901-2017 update) Osborn TJ, Barichivich J, Harris I, van der Schrier G and Jones PD (2018) Drought [in "State of the Climate in 2017"]. Bulletin of the American Meteorological Society 99, S36-S37. (doi:10.1175/2018BAMSStateoftheClimate.1)<br> (1901-2016 update) Osborn TJ, Barichivich J, Harris I, van der Schrier G and Jones PD (2017) Monitoring global drought using the self-calibrating Palmer Drought Severity Index [in "State of the Climate in 2016"]. Bulletin of the American Meteorological Society 98, S32-S33 (doi:10.1175/2017BAMSStateoftheClimate.1) (available here).<br> (1901-2015 update) Osborn TJ, Barichivich J, Harris I, van der Schrier G and Jones PD (2016) Monitoring global drought using the self-calibrating Palmer Drought Severity Index [in "State of the Climate in 2015"]. Bulletin of the American Meteorological Society 97, S32-S36 (available here). <br><br> Data were downloaded from <a href="https://crudata.uea.ac.uk/cru/data/drought/"target=_blank>CRUDATA/Drought</a>cru_scpdsi_71_80_meanWCSGeoTIFFwesr_climat__cru_scpdsi_71_80_meanRectifiedGridCoverage-180.0 -90.0180.0 90.0-180.0 -90.0180.0 90.0Self-calibrating Palmer Drought Severity Index (scPDSI) 1980-1989 (CRU)This layer shows the average values over the 1980 to 1989 decade of the Self-calibrating Palmer Drought Severity Index (scPDSI), based on the scPDSI dataset provided by the Climatic Research Unit (CRU) that cover the time interval from 1901 to 2018. Original data were subset and processed using QGIS at UNEP/GRID-Geneva.<br><br> The scPSDI indicates the degree of drought severity (negative values means higher severity) based on climatic and environmental parameters. The scPDSI metric was introduced by Wells et al. (2004), who give detailed information about its calculation. The scPDSI is a variant on the original PDSI of Palmer (1965), with the aim to make results from different climate regimes more comparable. As with the PDSI, the scPDSI is calculated from time series of precipitation and temperature, together with fixed parameters related to the soil/surface characteristics at each location. <br><br> The dataset has been updated each year using newer versions of CRU TS input data, currently to the end of 2018 using a preliminary version of CRU TS 4.03 (0.5° resolution). <br><br> (Dataset method) van der Schrier G, Barichivich J, Briffa KR and Jones PD (2013) A scPDSI-based global data set of dry and wet spells for 1901-2009. J. Geophys. Res. Atmos. 118, 4025-4048 (10.1002/jgrd.50355).<br> (1901-2018 update) Barichivich J, Osborn TJ, Harris I, van der Schrier G and Jones PD (2018) Drought [in "State of the Climate in 2018"]. Bulletin of the American Meteorological Society under review.<br> (1901-2017 update) Osborn TJ, Barichivich J, Harris I, van der Schrier G and Jones PD (2018) Drought [in "State of the Climate in 2017"]. Bulletin of the American Meteorological Society 99, S36-S37. (doi:10.1175/2018BAMSStateoftheClimate.1)<br> (1901-2016 update) Osborn TJ, Barichivich J, Harris I, van der Schrier G and Jones PD (2017) Monitoring global drought using the self-calibrating Palmer Drought Severity Index [in "State of the Climate in 2016"]. Bulletin of the American Meteorological Society 98, S32-S33 (doi:10.1175/2017BAMSStateoftheClimate.1) (available here).<br> (1901-2015 update) Osborn TJ, Barichivich J, Harris I, van der Schrier G and Jones PD (2016) Monitoring global drought using the self-calibrating Palmer Drought Severity Index [in "State of the Climate in 2015"]. Bulletin of the American Meteorological Society 97, S32-S36 (available here). <br><br> Data were downloaded from <a href="https://crudata.uea.ac.uk/cru/data/drought/"target=_blank>CRUDATA/Drought</a>scPDSI.cru_ts403_Mean80sWCSGeoTIFFUN_Sahel__scPDSI.cru_ts403_1980-1989RectifiedGridCoverage-180.0 -90.0180.0 90.0-180.0 -90.0180.0 90.0Self-calibrating Palmer Drought Severity Index (scPDSI) 1981-1990 (CRU)This layer shows the average values over the 1981 to 1990 decade of the Self-calibrating Palmer Drought Severity Index (scPDSI), based on the scPDSI dataset provided by the Climatic Research Unit (CRU) that cover the time interval from 1901. Original data were subset and processed using R at UNEP/GRID-Geneva.<br><br> The scPSDI indicates the degree of drought severity (negative values means higher severity) based on climatic and environmental parameters. The scPDSI metric was introduced by Wells et al. (2004), who give detailed information about its calculation. The scPDSI is a variant on the original PDSI of Palmer (1965), with the aim to make results from different climate regimes more comparable. As with the PDSI, the scPDSI is calculated from time series of precipitation and temperature, together with fixed parameters related to the soil/surface characteristics at each location. <br><br> Version: preliminary CRU TS 4.07, 0.5° lat-lon resolution <br><br> (Dataset method) van der Schrier G, Barichivich J, Briffa KR and Jones PD (2013) A scPDSI-based global data set of dry and wet spells for 1901-2009. J. Geophys. Res. Atmos. 118, 4025-4048 (10.1002/jgrd.50355).<br> (1901-2021 update) Barichivich J, Osborn TJ, Harris I, van der Schrier G and Jones PD (2022) Monitoring global drought using the self-calibrating Palmer Drought Severity Index [in “State of the Climate in 2021“]. Bull. Amer. Meteor. Soc., 103 (8), S31–S33, (https://doi.org/10.1175/BAMS-D-22-0092.1).<br> (1901-2020 update) Barichivich J, Osborn TJ, Harris I, van der Schrier G and Jones PD (2021) Monitoring global drought using the self-calibrating Palmer Drought Severity Index [in "State of the Climate in 2020" eds. Dunn RJH, Aldred F, Gobron N, Miller JB & Willett KM]. Bulletin of the American Meteorological Society 102, S68-S70. (https://doi.org/10.1175/BAMS-D-21-0098.1)<br> (1901-2019 update) Barichivich J, Osborn TJ, Harris I, van der Schrier G and Jones PD (2020) Drought [in "State of the Climate in 2019"]. Bulletin of the American Meteorological Society 101, S1-S429. (doi:10.1175/2020BAMSStateoftheClimate.1)<br> (1901-2018 update) Barichivich J, Osborn TJ, Harris I, van der Schrier G and Jones PD (2018) Drought [in "State of the Climate in 2018"]. Bulletin of the American Meteorological Society under review.<br> (1901-2017 update) Osborn TJ, Barichivich J, Harris I, van der Schrier G and Jones PD (2018) Drought [in "State of the Climate in 2017"]. Bulletin of the American Meteorological Society 99, S36-S37. (doi:10.1175/2018BAMSStateoftheClimate.1)<br> (1901-2016 update) Osborn TJ, Barichivich J, Harris I, van der Schrier G and Jones PD (2017) Monitoring global drought using the self-calibrating Palmer Drought Severity Index [in "State of the Climate in 2016"]. Bulletin of the American Meteorological Society 98, S32-S33 (doi:10.1175/2017BAMSStateoftheClimate.1) (available here).<br> (1901-2015 update) Osborn TJ, Barichivich J, Harris I, van der Schrier G and Jones PD (2016) Monitoring global drought using the self-calibrating Palmer Drought Severity Index [in "State of the Climate in 2015"]. Bulletin of the American Meteorological Society 97, S32-S36 (available here). <br><br> Data were downloaded from <a href="https://crudata.uea.ac.uk/cru/data/drought/"target=_blank>CRUDATA/Drought</a>cru_scpdsi_81_90_meanWCSGeoTIFFwesr_climat__cru_scpdsi_81_90_meanRectifiedGridCoverage-180.0 -90.0180.0 90.0-180.0 -90.0180.0 90.0Self-calibrating Palmer Drought Severity Index (scPDSI) 1990-1999 (CRU)This layer shows the average values over the 1990 to 1999 decade of the Self-calibrating Palmer Drought Severity Index (scPDSI), based on the scPDSI dataset provided by the Climatic Research Unit (CRU) that covers the time interval from 1901 to 2018. Original data were subset and processed using QGIS at UNEP/GRID-Geneva. <br><br> The scPSDI indicates the degree of drought severity (negative values means higher severity) based on climatic and environmental parameters. The scPDSI metric was introduced by Wells et al. (2004), who give detailed information about its calculation. The scPDSI is a variant on the original PDSI of Palmer (1965), with the aim to make results from different climate regimes more comparable. As with the PDSI, the scPDSI is calculated from time series of precipitation and temperature, together with fixed parameters related to the soil/surface characteristics at each location. <br><br> The dataset has been updated each year using newer versions of CRU TS input data, currently to the end of 2018 using a preliminary version of CRU TS 4.03 (0.5° resolution). <br><br> (Dataset method) van der Schrier G, Barichivich J, Briffa KR and Jones PD (2013) A scPDSI-based global data set of dry and wet spells for 1901-2009. J. Geophys. Res. Atmos. 118, 4025-4048 (10.1002/jgrd.50355).<br> (1901-2018 update) Barichivich J, Osborn TJ, Harris I, van der Schrier G and Jones PD (2018) Drought [in "State of the Climate in 2018"]. Bulletin of the American Meteorological Society under review.<br> (1901-2017 update) Osborn TJ, Barichivich J, Harris I, van der Schrier G and Jones PD (2018) Drought [in "State of the Climate in 2017"]. Bulletin of the American Meteorological Society 99, S36-S37. (doi:10.1175/2018BAMSStateoftheClimate.1)<br> (1901-2016 update) Osborn TJ, Barichivich J, Harris I, van der Schrier G and Jones PD (2017) Monitoring global drought using the self-calibrating Palmer Drought Severity Index [in "State of the Climate in 2016"]. Bulletin of the American Meteorological Society 98, S32-S33 (doi:10.1175/2017BAMSStateoftheClimate.1) (available here).<br> (1901-2015 update) Osborn TJ, Barichivich J, Harris I, van der Schrier G and Jones PD (2016) Monitoring global drought using the self-calibrating Palmer Drought Severity Index [in "State of the Climate in 2015"]. Bulletin of the American Meteorological Society 97, S32-S36 (available here). <br><br> Data were downloaded from <a href="https://crudata.uea.ac.uk/cru/data/drought/"target=_blank>CRUDATA/Drought</a>scPDSI.cru_ts403_Mean90sWCSGeoTIFFUN_Sahel__scPDSI.cru_ts403_1990-1999RectifiedGridCoverage-180.0 -90.0180.0 90.0-180.0 -90.0180.0 90.0Self-calibrating Palmer Drought Severity Index (scPDSI) 1991-2000 (CRU)This layer shows the average values over the 1991 to 2000 decade of the Self-calibrating Palmer Drought Severity Index (scPDSI), based on the scPDSI dataset provided by the Climatic Research Unit (CRU) that cover the time interval from 1901. Original data were subset and processed using R at UNEP/GRID-Geneva.<br><br> The scPSDI indicates the degree of drought severity (negative values means higher severity) based on climatic and environmental parameters. The scPDSI metric was introduced by Wells et al. (2004), who give detailed information about its calculation. The scPDSI is a variant on the original PDSI of Palmer (1965), with the aim to make results from different climate regimes more comparable. As with the PDSI, the scPDSI is calculated from time series of precipitation and temperature, together with fixed parameters related to the soil/surface characteristics at each location. <br><br> Version: preliminary CRU TS 4.07, 0.5° lat-lon resolution <br><br> (Dataset method) van der Schrier G, Barichivich J, Briffa KR and Jones PD (2013) A scPDSI-based global data set of dry and wet spells for 1901-2009. J. Geophys. Res. Atmos. 118, 4025-4048 (10.1002/jgrd.50355).<br> (1901-2021 update) Barichivich J, Osborn TJ, Harris I, van der Schrier G and Jones PD (2022) Monitoring global drought using the self-calibrating Palmer Drought Severity Index [in “State of the Climate in 2021“]. Bull. Amer. Meteor. Soc., 103 (8), S31–S33, (https://doi.org/10.1175/BAMS-D-22-0092.1).<br> (1901-2020 update) Barichivich J, Osborn TJ, Harris I, van der Schrier G and Jones PD (2021) Monitoring global drought using the self-calibrating Palmer Drought Severity Index [in "State of the Climate in 2020" eds. Dunn RJH, Aldred F, Gobron N, Miller JB & Willett KM]. Bulletin of the American Meteorological Society 102, S68-S70. (https://doi.org/10.1175/BAMS-D-21-0098.1)<br> (1901-2019 update) Barichivich J, Osborn TJ, Harris I, van der Schrier G and Jones PD (2020) Drought [in "State of the Climate in 2019"]. Bulletin of the American Meteorological Society 101, S1-S429. (doi:10.1175/2020BAMSStateoftheClimate.1)<br> (1901-2018 update) Barichivich J, Osborn TJ, Harris I, van der Schrier G and Jones PD (2018) Drought [in "State of the Climate in 2018"]. Bulletin of the American Meteorological Society under review.<br> (1901-2017 update) Osborn TJ, Barichivich J, Harris I, van der Schrier G and Jones PD (2018) Drought [in "State of the Climate in 2017"]. Bulletin of the American Meteorological Society 99, S36-S37. (doi:10.1175/2018BAMSStateoftheClimate.1)<br> (1901-2016 update) Osborn TJ, Barichivich J, Harris I, van der Schrier G and Jones PD (2017) Monitoring global drought using the self-calibrating Palmer Drought Severity Index [in "State of the Climate in 2016"]. Bulletin of the American Meteorological Society 98, S32-S33 (doi:10.1175/2017BAMSStateoftheClimate.1) (available here).<br> (1901-2015 update) Osborn TJ, Barichivich J, Harris I, van der Schrier G and Jones PD (2016) Monitoring global drought using the self-calibrating Palmer Drought Severity Index [in "State of the Climate in 2015"]. Bulletin of the American Meteorological Society 97, S32-S36 (available here). <br><br> Data were downloaded from <a href="https://crudata.uea.ac.uk/cru/data/drought/"target=_blank>CRUDATA/Drought</a>cru_scpdsi_91_00_meanWCSGeoTIFFwesr_climat__cru_scpdsi_91_00_meanRectifiedGridCoverage-180.0 -90.0180.0 90.0-180.0 -90.0180.0 90.0Self-calibrating Palmer Drought Severity Index (scPDSI) 2000-2009 (CRU)This layer shows the average values over the 2000 to 2010 decade of the Self-calibrating Palmer Drought Severity Index (scPDSI), based on the scPDSI dataset provided by the Climatic Research Unit (CRU) that cover the time interval from 1901 to 2018. Original data were subset and processed using QGIS at UNEP/GRID-Geneva.<br><br> The scPSDI indicates the degree of drought severity (negative values means higher severity) based on climatic and environmental parameters. The scPDSI metric was introduced by Wells et al. (2004), who give detailed information about its calculation. The scPDSI is a variant on the original PDSI of Palmer (1965), with the aim to make results from different climate regimes more comparable. As with the PDSI, the scPDSI is calculated from time series of precipitation and temperature, together with fixed parameters related to the soil/surface characteristics at each location. <br><br> The dataset has been updated each year using newer versions of CRU TS input data, currently to the end of 2018 using a preliminary version of CRU TS 4.03 (0.5° resolution). <br><br> (Dataset method) van der Schrier G, Barichivich J, Briffa KR and Jones PD (2013) A scPDSI-based global data set of dry and wet spells for 1901-2009. J. Geophys. Res. Atmos. 118, 4025-4048 (10.1002/jgrd.50355).<br> (1901-2018 update) Barichivich J, Osborn TJ, Harris I, van der Schrier G and Jones PD (2018) Drought [in "State of the Climate in 2018"]. Bulletin of the American Meteorological Society under review.<br> (1901-2017 update) Osborn TJ, Barichivich J, Harris I, van der Schrier G and Jones PD (2018) Drought [in "State of the Climate in 2017"]. Bulletin of the American Meteorological Society 99, S36-S37. (doi:10.1175/2018BAMSStateoftheClimate.1)<br> (1901-2016 update) Osborn TJ, Barichivich J, Harris I, van der Schrier G and Jones PD (2017) Monitoring global drought using the self-calibrating Palmer Drought Severity Index [in "State of the Climate in 2016"]. Bulletin of the American Meteorological Society 98, S32-S33 (doi:10.1175/2017BAMSStateoftheClimate.1) (available here).<br> (1901-2015 update) Osborn TJ, Barichivich J, Harris I, van der Schrier G and Jones PD (2016) Monitoring global drought using the self-calibrating Palmer Drought Severity Index [in "State of the Climate in 2015"]. Bulletin of the American Meteorological Society 97, S32-S36 (available here). <br><br> Data were downloaded from <a href="https://crudata.uea.ac.uk/cru/data/drought/"target=_blank>CRUDATA/Drought</a>scPDSI.cru_ts403_Mean00sWCSGeoTIFFUN_Sahel__scPDSI.cru_ts403_2000-2009RectifiedGridCoverage-180.0 -90.0180.0 90.0-180.0 -90.0180.0 90.0Self-calibrating Palmer Drought Severity Index (scPDSI) 2001-2010 (CRU)This layer shows the average values over the 2001 to 2010 decade of the Self-calibrating Palmer Drought Severity Index (scPDSI), based on the scPDSI dataset provided by the Climatic Research Unit (CRU) that cover the time interval from 1901. Original data were subset and processed using R at UNEP/GRID-Geneva.<br><br> The scPSDI indicates the degree of drought severity (negative values means higher severity) based on climatic and environmental parameters. The scPDSI metric was introduced by Wells et al. (2004), who give detailed information about its calculation. The scPDSI is a variant on the original PDSI of Palmer (1965), with the aim to make results from different climate regimes more comparable. As with the PDSI, the scPDSI is calculated from time series of precipitation and temperature, together with fixed parameters related to the soil/surface characteristics at each location. <br><br> Version: preliminary CRU TS 4.07, 0.5° lat-lon resolution <br><br> (Dataset method) van der Schrier G, Barichivich J, Briffa KR and Jones PD (2013) A scPDSI-based global data set of dry and wet spells for 1901-2009. J. Geophys. Res. Atmos. 118, 4025-4048 (10.1002/jgrd.50355).<br> (1901-2021 update) Barichivich J, Osborn TJ, Harris I, van der Schrier G and Jones PD (2022) Monitoring global drought using the self-calibrating Palmer Drought Severity Index [in “State of the Climate in 2021“]. Bull. Amer. Meteor. Soc., 103 (8), S31–S33, (https://doi.org/10.1175/BAMS-D-22-0092.1).<br> (1901-2020 update) Barichivich J, Osborn TJ, Harris I, van der Schrier G and Jones PD (2021) Monitoring global drought using the self-calibrating Palmer Drought Severity Index [in "State of the Climate in 2020" eds. Dunn RJH, Aldred F, Gobron N, Miller JB & Willett KM]. Bulletin of the American Meteorological Society 102, S68-S70. (https://doi.org/10.1175/BAMS-D-21-0098.1)<br> (1901-2019 update) Barichivich J, Osborn TJ, Harris I, van der Schrier G and Jones PD (2020) Drought [in "State of the Climate in 2019"]. Bulletin of the American Meteorological Society 101, S1-S429. (doi:10.1175/2020BAMSStateoftheClimate.1)<br> (1901-2018 update) Barichivich J, Osborn TJ, Harris I, van der Schrier G and Jones PD (2018) Drought [in "State of the Climate in 2018"]. Bulletin of the American Meteorological Society under review.<br> (1901-2017 update) Osborn TJ, Barichivich J, Harris I, van der Schrier G and Jones PD (2018) Drought [in "State of the Climate in 2017"]. Bulletin of the American Meteorological Society 99, S36-S37. (doi:10.1175/2018BAMSStateoftheClimate.1)<br> (1901-2016 update) Osborn TJ, Barichivich J, Harris I, van der Schrier G and Jones PD (2017) Monitoring global drought using the self-calibrating Palmer Drought Severity Index [in "State of the Climate in 2016"]. Bulletin of the American Meteorological Society 98, S32-S33 (doi:10.1175/2017BAMSStateoftheClimate.1) (available here).<br> (1901-2015 update) Osborn TJ, Barichivich J, Harris I, van der Schrier G and Jones PD (2016) Monitoring global drought using the self-calibrating Palmer Drought Severity Index [in "State of the Climate in 2015"]. Bulletin of the American Meteorological Society 97, S32-S36 (available here). <br><br> Data were downloaded from <a href="https://crudata.uea.ac.uk/cru/data/drought/"target=_blank>CRUDATA/Drought</a>cru_scpdsi_01_10_meanWCSGeoTIFFwesr_climat__cru_scpdsi_01_10_meanRectifiedGridCoverage-180.0 -90.0180.0 90.0-180.0 -90.0180.0 90.0Self-calibrating Palmer Drought Severity Index (scPDSI) 2010-2018 (CRU)This layer shows the average values over the 2010 to 2018 decade of the Self-calibrating Palmer Drought Severity Index (scPDSI), based on the scPDSI dataset provided by the Climatic Research Unit (CRU) that cover the time interval from 1901 to 2018. Original data were subset and processed using QGIS at UNEP/GRID-Geneva.<br><br> The scPSDI indicates the degree of drought severity (negative values means higher severity) based on climatic and environmental parameters. The scPDSI metric was introduced by Wells et al. (2004), who give detailed information about its calculation. The scPDSI is a variant on the original PDSI of Palmer (1965), with the aim to make results from different climate regimes more comparable. As with the PDSI, the scPDSI is calculated from time series of precipitation and temperature, together with fixed parameters related to the soil/surface characteristics at each location. <br><br> The dataset has been updated each year using newer versions of CRU TS input data, currently to the end of 2018 using a preliminary version of CRU TS 4.03 (0.5° resolution). <br> Please read <a href="https://crudata.uea.ac.uk/cru/data/drought/scpdsi.global2018.readme.txt"target=_blank>this document</a> for more information. <br><br> (Dataset method) van der Schrier G, Barichivich J, Briffa KR and Jones PD (2013) A scPDSI-based global data set of dry and wet spells for 1901-2009. J. Geophys. Res. Atmos. 118, 4025-4048 (10.1002/jgrd.50355).<br> (1901-2018 update) Barichivich J, Osborn TJ, Harris I, van der Schrier G and Jones PD (2018) Drought [in "State of the Climate in 2018"]. Bulletin of the American Meteorological Society under review.<br> (1901-2017 update) Osborn TJ, Barichivich J, Harris I, van der Schrier G and Jones PD (2018) Drought [in "State of the Climate in 2017"]. Bulletin of the American Meteorological Society 99, S36-S37. (doi:10.1175/2018BAMSStateoftheClimate.1)<br> (1901-2016 update) Osborn TJ, Barichivich J, Harris I, van der Schrier G and Jones PD (2017) Monitoring global drought using the self-calibrating Palmer Drought Severity Index [in "State of the Climate in 2016"]. Bulletin of the American Meteorological Society 98, S32-S33 (doi:10.1175/2017BAMSStateoftheClimate.1) (available here).<br> (1901-2015 update) Osborn TJ, Barichivich J, Harris I, van der Schrier G and Jones PD (2016) Monitoring global drought using the self-calibrating Palmer Drought Severity Index [in "State of the Climate in 2015"]. Bulletin of the American Meteorological Society 97, S32-S36 (available here). <br><br> Data were downloaded from <a href="https://crudata.uea.ac.uk/cru/data/drought/"target=_blank>CRUDATA/Drought</a>scPDSI.cru_ts403_Mean10sWCSGeoTIFFUN_Sahel__scPDSI.cru_ts403_2010-2018RectifiedGridCoverage-180.0 -90.0180.0 90.0-180.0 -90.0180.0 90.0Self-calibrating Palmer Drought Severity Index (scPDSI) 2011 (CRU)This layer shows the average values for 2011 of the Self-calibrating Palmer Drought Severity Index (scPDSI), based on the scPDSI dataset provided by the Climatic Research Unit (CRU) that cover the time interval from 1901. Original data were subset and processed using R at UNEP/GRID-Geneva.<br><br> The scPSDI indicates the degree of drought severity (negative values means higher severity) based on climatic and environmental parameters. The scPDSI metric was introduced by Wells et al. (2004), who give detailed information about its calculation. The scPDSI is a variant on the original PDSI of Palmer (1965), with the aim to make results from different climate regimes more comparable. As with the PDSI, the scPDSI is calculated from time series of precipitation and temperature, together with fixed parameters related to the soil/surface characteristics at each location. <br><br> Version: preliminary CRU TS 4.07, 0.5° lat-lon resolution <br><br> (Dataset method) van der Schrier G, Barichivich J, Briffa KR and Jones PD (2013) A scPDSI-based global data set of dry and wet spells for 1901-2009. J. Geophys. Res. Atmos. 118, 4025-4048 (10.1002/jgrd.50355).<br> (1901-2021 update) Barichivich J, Osborn TJ, Harris I, van der Schrier G and Jones PD (2022) Monitoring global drought using the self-calibrating Palmer Drought Severity Index [in “State of the Climate in 2021“]. Bull. Amer. Meteor. Soc., 103 (8), S31–S33, (https://doi.org/10.1175/BAMS-D-22-0092.1).<br> (1901-2020 update) Barichivich J, Osborn TJ, Harris I, van der Schrier G and Jones PD (2021) Monitoring global drought using the self-calibrating Palmer Drought Severity Index [in "State of the Climate in 2020" eds. Dunn RJH, Aldred F, Gobron N, Miller JB & Willett KM]. Bulletin of the American Meteorological Society 102, S68-S70. (https://doi.org/10.1175/BAMS-D-21-0098.1)<br> (1901-2019 update) Barichivich J, Osborn TJ, Harris I, van der Schrier G and Jones PD (2020) Drought [in "State of the Climate in 2019"]. Bulletin of the American Meteorological Society 101, S1-S429. (doi:10.1175/2020BAMSStateoftheClimate.1)<br> (1901-2018 update) Barichivich J, Osborn TJ, Harris I, van der Schrier G and Jones PD (2018) Drought [in "State of the Climate in 2018"]. Bulletin of the American Meteorological Society under review.<br> (1901-2017 update) Osborn TJ, Barichivich J, Harris I, van der Schrier G and Jones PD (2018) Drought [in "State of the Climate in 2017"]. Bulletin of the American Meteorological Society 99, S36-S37. (doi:10.1175/2018BAMSStateoftheClimate.1)<br> (1901-2016 update) Osborn TJ, Barichivich J, Harris I, van der Schrier G and Jones PD (2017) Monitoring global drought using the self-calibrating Palmer Drought Severity Index [in "State of the Climate in 2016"]. Bulletin of the American Meteorological Society 98, S32-S33 (doi:10.1175/2017BAMSStateoftheClimate.1) (available here).<br> (1901-2015 update) Osborn TJ, Barichivich J, Harris I, van der Schrier G and Jones PD (2016) Monitoring global drought using the self-calibrating Palmer Drought Severity Index [in "State of the Climate in 2015"]. Bulletin of the American Meteorological Society 97, S32-S36 (available here). <br><br> Data were downloaded from <a href="https://crudata.uea.ac.uk/cru/data/drought/"target=_blank>CRUDATA/Drought</a>cru_scpdsi_11_meanWCSGeoTIFFwesr_climat__cru_scpdsi_11_meanRectifiedGridCoverage-180.0 -90.0180.0 90.0-180.0 -90.0180.0 90.0Self-calibrating Palmer Drought Severity Index (scPDSI) 2011-2020 (CRU)This layer shows the average values over the 2011 to 2020 decade of the Self-calibrating Palmer Drought Severity Index (scPDSI), based on the scPDSI dataset provided by the Climatic Research Unit (CRU) that cover the time interval from 1901. Original data were subset and processed using R at UNEP/GRID-Geneva.<br><br> The scPSDI indicates the degree of drought severity (negative values means higher severity) based on climatic and environmental parameters. The scPDSI metric was introduced by Wells et al. (2004), who give detailed information about its calculation. The scPDSI is a variant on the original PDSI of Palmer (1965), with the aim to make results from different climate regimes more comparable. As with the PDSI, the scPDSI is calculated from time series of precipitation and temperature, together with fixed parameters related to the soil/surface characteristics at each location. <br><br> Version: preliminary CRU TS 4.07, 0.5° lat-lon resolution <br><br> (Dataset method) van der Schrier G, Barichivich J, Briffa KR and Jones PD (2013) A scPDSI-based global data set of dry and wet spells for 1901-2009. J. Geophys. Res. Atmos. 118, 4025-4048 (10.1002/jgrd.50355).<br> (1901-2021 update) Barichivich J, Osborn TJ, Harris I, van der Schrier G and Jones PD (2022) Monitoring global drought using the self-calibrating Palmer Drought Severity Index [in “State of the Climate in 2021“]. Bull. Amer. Meteor. Soc., 103 (8), S31–S33, (https://doi.org/10.1175/BAMS-D-22-0092.1).<br> (1901-2020 update) Barichivich J, Osborn TJ, Harris I, van der Schrier G and Jones PD (2021) Monitoring global drought using the self-calibrating Palmer Drought Severity Index [in "State of the Climate in 2020" eds. Dunn RJH, Aldred F, Gobron N, Miller JB & Willett KM]. Bulletin of the American Meteorological Society 102, S68-S70. (https://doi.org/10.1175/BAMS-D-21-0098.1)<br> (1901-2019 update) Barichivich J, Osborn TJ, Harris I, van der Schrier G and Jones PD (2020) Drought [in "State of the Climate in 2019"]. Bulletin of the American Meteorological Society 101, S1-S429. (doi:10.1175/2020BAMSStateoftheClimate.1)<br> (1901-2018 update) Barichivich J, Osborn TJ, Harris I, van der Schrier G and Jones PD (2018) Drought [in "State of the Climate in 2018"]. Bulletin of the American Meteorological Society under review.<br> (1901-2017 update) Osborn TJ, Barichivich J, Harris I, van der Schrier G and Jones PD (2018) Drought [in "State of the Climate in 2017"]. Bulletin of the American Meteorological Society 99, S36-S37. (doi:10.1175/2018BAMSStateoftheClimate.1)<br> (1901-2016 update) Osborn TJ, Barichivich J, Harris I, van der Schrier G and Jones PD (2017) Monitoring global drought using the self-calibrating Palmer Drought Severity Index [in "State of the Climate in 2016"]. Bulletin of the American Meteorological Society 98, S32-S33 (doi:10.1175/2017BAMSStateoftheClimate.1) (available here).<br> (1901-2015 update) Osborn TJ, Barichivich J, Harris I, van der Schrier G and Jones PD (2016) Monitoring global drought using the self-calibrating Palmer Drought Severity Index [in "State of the Climate in 2015"]. Bulletin of the American Meteorological Society 97, S32-S36 (available here). <br><br> Data were downloaded from <a href="https://crudata.uea.ac.uk/cru/data/drought/"target=_blank>CRUDATA/Drought</a>cru_scpdsi_11_20_meanWCSGeoTIFFwesr_climat__cru_scpdsi_11_20_meanRectifiedGridCoverage-180.0 -90.0180.0 90.0-180.0 -90.0180.0 90.0Self-calibrating Palmer Drought Severity Index (scPDSI) 2012 (CRU)This layer shows the average values for 2012 of the Self-calibrating Palmer Drought Severity Index (scPDSI), based on the scPDSI dataset provided by the Climatic Research Unit (CRU) that cover the time interval from 1901. Original data were subset and processed using R at UNEP/GRID-Geneva.<br><br> The scPSDI indicates the degree of drought severity (negative values means higher severity) based on climatic and environmental parameters. The scPDSI metric was introduced by Wells et al. (2004), who give detailed information about its calculation. The scPDSI is a variant on the original PDSI of Palmer (1965), with the aim to make results from different climate regimes more comparable. As with the PDSI, the scPDSI is calculated from time series of precipitation and temperature, together with fixed parameters related to the soil/surface characteristics at each location. <br><br> Version: preliminary CRU TS 4.07, 0.5° lat-lon resolution <br><br> (Dataset method) van der Schrier G, Barichivich J, Briffa KR and Jones PD (2013) A scPDSI-based global data set of dry and wet spells for 1901-2009. J. Geophys. Res. Atmos. 118, 4025-4048 (10.1002/jgrd.50355).<br> (1901-2021 update) Barichivich J, Osborn TJ, Harris I, van der Schrier G and Jones PD (2022) Monitoring global drought using the self-calibrating Palmer Drought Severity Index [in “State of the Climate in 2021“]. Bull. Amer. Meteor. Soc., 103 (8), S31–S33, (https://doi.org/10.1175/BAMS-D-22-0092.1).<br> (1901-2020 update) Barichivich J, Osborn TJ, Harris I, van der Schrier G and Jones PD (2021) Monitoring global drought using the self-calibrating Palmer Drought Severity Index [in "State of the Climate in 2020" eds. Dunn RJH, Aldred F, Gobron N, Miller JB & Willett KM]. Bulletin of the American Meteorological Society 102, S68-S70. (https://doi.org/10.1175/BAMS-D-21-0098.1)<br> (1901-2019 update) Barichivich J, Osborn TJ, Harris I, van der Schrier G and Jones PD (2020) Drought [in "State of the Climate in 2019"]. Bulletin of the American Meteorological Society 101, S1-S429. (doi:10.1175/2020BAMSStateoftheClimate.1)<br> (1901-2018 update) Barichivich J, Osborn TJ, Harris I, van der Schrier G and Jones PD (2018) Drought [in "State of the Climate in 2018"]. Bulletin of the American Meteorological Society under review.<br> (1901-2017 update) Osborn TJ, Barichivich J, Harris I, van der Schrier G and Jones PD (2018) Drought [in "State of the Climate in 2017"]. Bulletin of the American Meteorological Society 99, S36-S37. (doi:10.1175/2018BAMSStateoftheClimate.1)<br> (1901-2016 update) Osborn TJ, Barichivich J, Harris I, van der Schrier G and Jones PD (2017) Monitoring global drought using the self-calibrating Palmer Drought Severity Index [in "State of the Climate in 2016"]. Bulletin of the American Meteorological Society 98, S32-S33 (doi:10.1175/2017BAMSStateoftheClimate.1) (available here).<br> (1901-2015 update) Osborn TJ, Barichivich J, Harris I, van der Schrier G and Jones PD (2016) Monitoring global drought using the self-calibrating Palmer Drought Severity Index [in "State of the Climate in 2015"]. Bulletin of the American Meteorological Society 97, S32-S36 (available here). <br><br> Data were downloaded from <a href="https://crudata.uea.ac.uk/cru/data/drought/"target=_blank>CRUDATA/Drought</a>cru_scpdsi_12_meanWCSGeoTIFFwesr_climat__cru_scpdsi_12_meanRectifiedGridCoverage-180.0 -90.0180.0 90.0-180.0 -90.0180.0 90.0Self-calibrating Palmer Drought Severity Index (scPDSI) 2013 (CRU)This layer shows the average values for 2013 of the Self-calibrating Palmer Drought Severity Index (scPDSI), based on the scPDSI dataset provided by the Climatic Research Unit (CRU) that cover the time interval from 1901. Original data were subset and processed using R at UNEP/GRID-Geneva.<br><br> The scPSDI indicates the degree of drought severity (negative values means higher severity) based on climatic and environmental parameters. The scPDSI metric was introduced by Wells et al. (2004), who give detailed information about its calculation. The scPDSI is a variant on the original PDSI of Palmer (1965), with the aim to make results from different climate regimes more comparable. As with the PDSI, the scPDSI is calculated from time series of precipitation and temperature, together with fixed parameters related to the soil/surface characteristics at each location. <br><br> Version: preliminary CRU TS 4.07, 0.5° lat-lon resolution <br><br> (Dataset method) van der Schrier G, Barichivich J, Briffa KR and Jones PD (2013) A scPDSI-based global data set of dry and wet spells for 1901-2009. J. Geophys. Res. Atmos. 118, 4025-4048 (10.1002/jgrd.50355).<br> (1901-2021 update) Barichivich J, Osborn TJ, Harris I, van der Schrier G and Jones PD (2022) Monitoring global drought using the self-calibrating Palmer Drought Severity Index [in “State of the Climate in 2021“]. Bull. Amer. Meteor. Soc., 103 (8), S31–S33, (https://doi.org/10.1175/BAMS-D-22-0092.1).<br> (1901-2020 update) Barichivich J, Osborn TJ, Harris I, van der Schrier G and Jones PD (2021) Monitoring global drought using the self-calibrating Palmer Drought Severity Index [in "State of the Climate in 2020" eds. Dunn RJH, Aldred F, Gobron N, Miller JB & Willett KM]. Bulletin of the American Meteorological Society 102, S68-S70. (https://doi.org/10.1175/BAMS-D-21-0098.1)<br> (1901-2019 update) Barichivich J, Osborn TJ, Harris I, van der Schrier G and Jones PD (2020) Drought [in "State of the Climate in 2019"]. Bulletin of the American Meteorological Society 101, S1-S429. (doi:10.1175/2020BAMSStateoftheClimate.1)<br> (1901-2018 update) Barichivich J, Osborn TJ, Harris I, van der Schrier G and Jones PD (2018) Drought [in "State of the Climate in 2018"]. Bulletin of the American Meteorological Society under review.<br> (1901-2017 update) Osborn TJ, Barichivich J, Harris I, van der Schrier G and Jones PD (2018) Drought [in "State of the Climate in 2017"]. Bulletin of the American Meteorological Society 99, S36-S37. (doi:10.1175/2018BAMSStateoftheClimate.1)<br> (1901-2016 update) Osborn TJ, Barichivich J, Harris I, van der Schrier G and Jones PD (2017) Monitoring global drought using the self-calibrating Palmer Drought Severity Index [in "State of the Climate in 2016"]. Bulletin of the American Meteorological Society 98, S32-S33 (doi:10.1175/2017BAMSStateoftheClimate.1) (available here).<br> (1901-2015 update) Osborn TJ, Barichivich J, Harris I, van der Schrier G and Jones PD (2016) Monitoring global drought using the self-calibrating Palmer Drought Severity Index [in "State of the Climate in 2015"]. Bulletin of the American Meteorological Society 97, S32-S36 (available here). <br><br> Data were downloaded from <a href="https://crudata.uea.ac.uk/cru/data/drought/"target=_blank>CRUDATA/Drought</a>cru_scpdsi_13_meanWCSGeoTIFFwesr_climat__cru_scpdsi_13_meanRectifiedGridCoverage-180.0 -90.0180.0 90.0-180.0 -90.0180.0 90.0Self-calibrating Palmer Drought Severity Index (scPDSI) 2014 (CRU)This layer shows the average values for 2014 of the Self-calibrating Palmer Drought Severity Index (scPDSI), based on the scPDSI dataset provided by the Climatic Research Unit (CRU) that cover the time interval from 1901. Original data were subset and processed using R at UNEP/GRID-Geneva.<br><br> The scPSDI indicates the degree of drought severity (negative values means higher severity) based on climatic and environmental parameters. The scPDSI metric was introduced by Wells et al. (2004), who give detailed information about its calculation. The scPDSI is a variant on the original PDSI of Palmer (1965), with the aim to make results from different climate regimes more comparable. As with the PDSI, the scPDSI is calculated from time series of precipitation and temperature, together with fixed parameters related to the soil/surface characteristics at each location. <br><br> Version: preliminary CRU TS 4.07, 0.5° lat-lon resolution <br><br> (Dataset method) van der Schrier G, Barichivich J, Briffa KR and Jones PD (2013) A scPDSI-based global data set of dry and wet spells for 1901-2009. J. Geophys. Res. Atmos. 118, 4025-4048 (10.1002/jgrd.50355).<br> (1901-2021 update) Barichivich J, Osborn TJ, Harris I, van der Schrier G and Jones PD (2022) Monitoring global drought using the self-calibrating Palmer Drought Severity Index [in “State of the Climate in 2021“]. Bull. Amer. Meteor. Soc., 103 (8), S31–S33, (https://doi.org/10.1175/BAMS-D-22-0092.1).<br> (1901-2020 update) Barichivich J, Osborn TJ, Harris I, van der Schrier G and Jones PD (2021) Monitoring global drought using the self-calibrating Palmer Drought Severity Index [in "State of the Climate in 2020" eds. Dunn RJH, Aldred F, Gobron N, Miller JB & Willett KM]. Bulletin of the American Meteorological Society 102, S68-S70. (https://doi.org/10.1175/BAMS-D-21-0098.1)<br> (1901-2019 update) Barichivich J, Osborn TJ, Harris I, van der Schrier G and Jones PD (2020) Drought [in "State of the Climate in 2019"]. Bulletin of the American Meteorological Society 101, S1-S429. (doi:10.1175/2020BAMSStateoftheClimate.1)<br> (1901-2018 update) Barichivich J, Osborn TJ, Harris I, van der Schrier G and Jones PD (2018) Drought [in "State of the Climate in 2018"]. Bulletin of the American Meteorological Society under review.<br> (1901-2017 update) Osborn TJ, Barichivich J, Harris I, van der Schrier G and Jones PD (2018) Drought [in "State of the Climate in 2017"]. Bulletin of the American Meteorological Society 99, S36-S37. (doi:10.1175/2018BAMSStateoftheClimate.1)<br> (1901-2016 update) Osborn TJ, Barichivich J, Harris I, van der Schrier G and Jones PD (2017) Monitoring global drought using the self-calibrating Palmer Drought Severity Index [in "State of the Climate in 2016"]. Bulletin of the American Meteorological Society 98, S32-S33 (doi:10.1175/2017BAMSStateoftheClimate.1) (available here).<br> (1901-2015 update) Osborn TJ, Barichivich J, Harris I, van der Schrier G and Jones PD (2016) Monitoring global drought using the self-calibrating Palmer Drought Severity Index [in "State of the Climate in 2015"]. Bulletin of the American Meteorological Society 97, S32-S36 (available here). <br><br> Data were downloaded from <a href="https://crudata.uea.ac.uk/cru/data/drought/"target=_blank>CRUDATA/Drought</a>cru_scpdsi_14_meanWCSGeoTIFFwesr_climat__cru_scpdsi_14_meanRectifiedGridCoverage-180.0 -90.0180.0 90.0-180.0 -90.0180.0 90.0Self-calibrating Palmer Drought Severity Index (scPDSI) 2015 (CRU)This layer shows the average values for 2015 of the Self-calibrating Palmer Drought Severity Index (scPDSI), based on the scPDSI dataset provided by the Climatic Research Unit (CRU) that cover the time interval from 1901. Original data were subset and processed using R at UNEP/GRID-Geneva.<br><br> The scPSDI indicates the degree of drought severity (negative values means higher severity) based on climatic and environmental parameters. The scPDSI metric was introduced by Wells et al. (2004), who give detailed information about its calculation. The scPDSI is a variant on the original PDSI of Palmer (1965), with the aim to make results from different climate regimes more comparable. As with the PDSI, the scPDSI is calculated from time series of precipitation and temperature, together with fixed parameters related to the soil/surface characteristics at each location. <br><br> Version: preliminary CRU TS 4.07, 0.5° lat-lon resolution <br><br> (Dataset method) van der Schrier G, Barichivich J, Briffa KR and Jones PD (2013) A scPDSI-based global data set of dry and wet spells for 1901-2009. J. Geophys. Res. Atmos. 118, 4025-4048 (10.1002/jgrd.50355).<br> (1901-2021 update) Barichivich J, Osborn TJ, Harris I, van der Schrier G and Jones PD (2022) Monitoring global drought using the self-calibrating Palmer Drought Severity Index [in “State of the Climate in 2021“]. Bull. Amer. Meteor. Soc., 103 (8), S31–S33, (https://doi.org/10.1175/BAMS-D-22-0092.1).<br> (1901-2020 update) Barichivich J, Osborn TJ, Harris I, van der Schrier G and Jones PD (2021) Monitoring global drought using the self-calibrating Palmer Drought Severity Index [in "State of the Climate in 2020" eds. Dunn RJH, Aldred F, Gobron N, Miller JB & Willett KM]. Bulletin of the American Meteorological Society 102, S68-S70. (https://doi.org/10.1175/BAMS-D-21-0098.1)<br> (1901-2019 update) Barichivich J, Osborn TJ, Harris I, van der Schrier G and Jones PD (2020) Drought [in "State of the Climate in 2019"]. Bulletin of the American Meteorological Society 101, S1-S429. (doi:10.1175/2020BAMSStateoftheClimate.1)<br> (1901-2018 update) Barichivich J, Osborn TJ, Harris I, van der Schrier G and Jones PD (2018) Drought [in "State of the Climate in 2018"]. Bulletin of the American Meteorological Society under review.<br> (1901-2017 update) Osborn TJ, Barichivich J, Harris I, van der Schrier G and Jones PD (2018) Drought [in "State of the Climate in 2017"]. Bulletin of the American Meteorological Society 99, S36-S37. (doi:10.1175/2018BAMSStateoftheClimate.1)<br> (1901-2016 update) Osborn TJ, Barichivich J, Harris I, van der Schrier G and Jones PD (2017) Monitoring global drought using the self-calibrating Palmer Drought Severity Index [in "State of the Climate in 2016"]. Bulletin of the American Meteorological Society 98, S32-S33 (doi:10.1175/2017BAMSStateoftheClimate.1) (available here).<br> (1901-2015 update) Osborn TJ, Barichivich J, Harris I, van der Schrier G and Jones PD (2016) Monitoring global drought using the self-calibrating Palmer Drought Severity Index [in "State of the Climate in 2015"]. Bulletin of the American Meteorological Society 97, S32-S36 (available here). <br><br> Data were downloaded from <a href="https://crudata.uea.ac.uk/cru/data/drought/"target=_blank>CRUDATA/Drought</a>cru_scpdsi_15_meanWCSGeoTIFFwesr_climat__cru_scpdsi_15_meanRectifiedGridCoverage-180.0 -90.0180.0 90.0-180.0 -90.0180.0 90.0Self-calibrating Palmer Drought Severity Index (scPDSI) 2016 (CRU)This layer shows the average values for 2016 of the Self-calibrating Palmer Drought Severity Index (scPDSI), based on the scPDSI dataset provided by the Climatic Research Unit (CRU) that cover the time interval from 1901. Original data were subset and processed using R at UNEP/GRID-Geneva.<br><br> The scPSDI indicates the degree of drought severity (negative values means higher severity) based on climatic and environmental parameters. The scPDSI metric was introduced by Wells et al. (2004), who give detailed information about its calculation. The scPDSI is a variant on the original PDSI of Palmer (1965), with the aim to make results from different climate regimes more comparable. As with the PDSI, the scPDSI is calculated from time series of precipitation and temperature, together with fixed parameters related to the soil/surface characteristics at each location. <br><br> Version: preliminary CRU TS 4.07, 0.5° lat-lon resolution <br><br> (Dataset method) van der Schrier G, Barichivich J, Briffa KR and Jones PD (2013) A scPDSI-based global data set of dry and wet spells for 1901-2009. J. Geophys. Res. Atmos. 118, 4025-4048 (10.1002/jgrd.50355).<br> (1901-2021 update) Barichivich J, Osborn TJ, Harris I, van der Schrier G and Jones PD (2022) Monitoring global drought using the self-calibrating Palmer Drought Severity Index [in “State of the Climate in 2021“]. Bull. Amer. Meteor. Soc., 103 (8), S31–S33, (https://doi.org/10.1175/BAMS-D-22-0092.1).<br> (1901-2020 update) Barichivich J, Osborn TJ, Harris I, van der Schrier G and Jones PD (2021) Monitoring global drought using the self-calibrating Palmer Drought Severity Index [in "State of the Climate in 2020" eds. Dunn RJH, Aldred F, Gobron N, Miller JB & Willett KM]. Bulletin of the American Meteorological Society 102, S68-S70. (https://doi.org/10.1175/BAMS-D-21-0098.1)<br> (1901-2019 update) Barichivich J, Osborn TJ, Harris I, van der Schrier G and Jones PD (2020) Drought [in "State of the Climate in 2019"]. Bulletin of the American Meteorological Society 101, S1-S429. (doi:10.1175/2020BAMSStateoftheClimate.1)<br> (1901-2018 update) Barichivich J, Osborn TJ, Harris I, van der Schrier G and Jones PD (2018) Drought [in "State of the Climate in 2018"]. Bulletin of the American Meteorological Society under review.<br> (1901-2017 update) Osborn TJ, Barichivich J, Harris I, van der Schrier G and Jones PD (2018) Drought [in "State of the Climate in 2017"]. Bulletin of the American Meteorological Society 99, S36-S37. (doi:10.1175/2018BAMSStateoftheClimate.1)<br> (1901-2016 update) Osborn TJ, Barichivich J, Harris I, van der Schrier G and Jones PD (2017) Monitoring global drought using the self-calibrating Palmer Drought Severity Index [in "State of the Climate in 2016"]. Bulletin of the American Meteorological Society 98, S32-S33 (doi:10.1175/2017BAMSStateoftheClimate.1) (available here).<br> (1901-2015 update) Osborn TJ, Barichivich J, Harris I, van der Schrier G and Jones PD (2016) Monitoring global drought using the self-calibrating Palmer Drought Severity Index [in "State of the Climate in 2015"]. Bulletin of the American Meteorological Society 97, S32-S36 (available here). <br><br> Data were downloaded from <a href="https://crudata.uea.ac.uk/cru/data/drought/"target=_blank>CRUDATA/Drought</a>cru_scpdsi_16_meanWCSGeoTIFFwesr_climat__cru_scpdsi_16_meanRectifiedGridCoverage-180.0 -90.0180.0 90.0-180.0 -90.0180.0 90.0Self-calibrating Palmer Drought Severity Index (scPDSI) 2017 (CRU)This layer shows the average values for 2017 of the Self-calibrating Palmer Drought Severity Index (scPDSI), based on the scPDSI dataset provided by the Climatic Research Unit (CRU) that cover the time interval from 1901. Original data were subset and processed using R at UNEP/GRID-Geneva.<br><br> The scPSDI indicates the degree of drought severity (negative values means higher severity) based on climatic and environmental parameters. The scPDSI metric was introduced by Wells et al. (2004), who give detailed information about its calculation. The scPDSI is a variant on the original PDSI of Palmer (1965), with the aim to make results from different climate regimes more comparable. As with the PDSI, the scPDSI is calculated from time series of precipitation and temperature, together with fixed parameters related to the soil/surface characteristics at each location. <br><br> Version: preliminary CRU TS 4.07, 0.5° lat-lon resolution <br><br> (Dataset method) van der Schrier G, Barichivich J, Briffa KR and Jones PD (2013) A scPDSI-based global data set of dry and wet spells for 1901-2009. J. Geophys. Res. Atmos. 118, 4025-4048 (10.1002/jgrd.50355).<br> (1901-2021 update) Barichivich J, Osborn TJ, Harris I, van der Schrier G and Jones PD (2022) Monitoring global drought using the self-calibrating Palmer Drought Severity Index [in “State of the Climate in 2021“]. Bull. Amer. Meteor. Soc., 103 (8), S31–S33, (https://doi.org/10.1175/BAMS-D-22-0092.1).<br> (1901-2020 update) Barichivich J, Osborn TJ, Harris I, van der Schrier G and Jones PD (2021) Monitoring global drought using the self-calibrating Palmer Drought Severity Index [in "State of the Climate in 2020" eds. Dunn RJH, Aldred F, Gobron N, Miller JB & Willett KM]. Bulletin of the American Meteorological Society 102, S68-S70. (https://doi.org/10.1175/BAMS-D-21-0098.1)<br> (1901-2019 update) Barichivich J, Osborn TJ, Harris I, van der Schrier G and Jones PD (2020) Drought [in "State of the Climate in 2019"]. Bulletin of the American Meteorological Society 101, S1-S429. (doi:10.1175/2020BAMSStateoftheClimate.1)<br> (1901-2018 update) Barichivich J, Osborn TJ, Harris I, van der Schrier G and Jones PD (2018) Drought [in "State of the Climate in 2018"]. Bulletin of the American Meteorological Society under review.<br> (1901-2017 update) Osborn TJ, Barichivich J, Harris I, van der Schrier G and Jones PD (2018) Drought [in "State of the Climate in 2017"]. Bulletin of the American Meteorological Society 99, S36-S37. (doi:10.1175/2018BAMSStateoftheClimate.1)<br> (1901-2016 update) Osborn TJ, Barichivich J, Harris I, van der Schrier G and Jones PD (2017) Monitoring global drought using the self-calibrating Palmer Drought Severity Index [in "State of the Climate in 2016"]. Bulletin of the American Meteorological Society 98, S32-S33 (doi:10.1175/2017BAMSStateoftheClimate.1) (available here).<br> (1901-2015 update) Osborn TJ, Barichivich J, Harris I, van der Schrier G and Jones PD (2016) Monitoring global drought using the self-calibrating Palmer Drought Severity Index [in "State of the Climate in 2015"]. Bulletin of the American Meteorological Society 97, S32-S36 (available here). <br><br> Data were downloaded from <a href="https://crudata.uea.ac.uk/cru/data/drought/"target=_blank>CRUDATA/Drought</a>cru_scpdsi_17_meanWCSGeoTIFFwesr_climat__cru_scpdsi_17_meanRectifiedGridCoverage-180.0 -90.0180.0 90.0-180.0 -90.0180.0 90.0Self-calibrating Palmer Drought Severity Index (scPDSI) 2018 (CRU)This layer shows the average values for 2018 of the Self-calibrating Palmer Drought Severity Index (scPDSI), based on the scPDSI dataset provided by the Climatic Research Unit (CRU) that cover the time interval from 1901. Original data were subset and processed using R at UNEP/GRID-Geneva.<br><br> The scPSDI indicates the degree of drought severity (negative values means higher severity) based on climatic and environmental parameters. The scPDSI metric was introduced by Wells et al. (2004), who give detailed information about its calculation. The scPDSI is a variant on the original PDSI of Palmer (1965), with the aim to make results from different climate regimes more comparable. As with the PDSI, the scPDSI is calculated from time series of precipitation and temperature, together with fixed parameters related to the soil/surface characteristics at each location. <br><br> Version: preliminary CRU TS 4.07, 0.5° lat-lon resolution <br><br> (Dataset method) van der Schrier G, Barichivich J, Briffa KR and Jones PD (2013) A scPDSI-based global data set of dry and wet spells for 1901-2009. J. Geophys. Res. Atmos. 118, 4025-4048 (10.1002/jgrd.50355).<br> (1901-2021 update) Barichivich J, Osborn TJ, Harris I, van der Schrier G and Jones PD (2022) Monitoring global drought using the self-calibrating Palmer Drought Severity Index [in “State of the Climate in 2021“]. Bull. Amer. Meteor. Soc., 103 (8), S31–S33, (https://doi.org/10.1175/BAMS-D-22-0092.1).<br> (1901-2020 update) Barichivich J, Osborn TJ, Harris I, van der Schrier G and Jones PD (2021) Monitoring global drought using the self-calibrating Palmer Drought Severity Index [in "State of the Climate in 2020" eds. Dunn RJH, Aldred F, Gobron N, Miller JB & Willett KM]. Bulletin of the American Meteorological Society 102, S68-S70. (https://doi.org/10.1175/BAMS-D-21-0098.1)<br> (1901-2019 update) Barichivich J, Osborn TJ, Harris I, van der Schrier G and Jones PD (2020) Drought [in "State of the Climate in 2019"]. Bulletin of the American Meteorological Society 101, S1-S429. (doi:10.1175/2020BAMSStateoftheClimate.1)<br> (1901-2018 update) Barichivich J, Osborn TJ, Harris I, van der Schrier G and Jones PD (2018) Drought [in "State of the Climate in 2018"]. Bulletin of the American Meteorological Society under review.<br> (1901-2017 update) Osborn TJ, Barichivich J, Harris I, van der Schrier G and Jones PD (2018) Drought [in "State of the Climate in 2017"]. Bulletin of the American Meteorological Society 99, S36-S37. (doi:10.1175/2018BAMSStateoftheClimate.1)<br> (1901-2016 update) Osborn TJ, Barichivich J, Harris I, van der Schrier G and Jones PD (2017) Monitoring global drought using the self-calibrating Palmer Drought Severity Index [in "State of the Climate in 2016"]. Bulletin of the American Meteorological Society 98, S32-S33 (doi:10.1175/2017BAMSStateoftheClimate.1) (available here).<br> (1901-2015 update) Osborn TJ, Barichivich J, Harris I, van der Schrier G and Jones PD (2016) Monitoring global drought using the self-calibrating Palmer Drought Severity Index [in "State of the Climate in 2015"]. Bulletin of the American Meteorological Society 97, S32-S36 (available here). <br><br> Data were downloaded from <a href="https://crudata.uea.ac.uk/cru/data/drought/"target=_blank>CRUDATA/Drought</a>cru_scpdsi_18_meanWCSGeoTIFFwesr_climat__cru_scpdsi_18_meanRectifiedGridCoverage-180.0 -90.0180.0 90.0-180.0 -90.0180.0 90.0Self-calibrating Palmer Drought Severity Index (scPDSI) 2019 (CRU)This layer shows the average values for 2019 of the Self-calibrating Palmer Drought Severity Index (scPDSI), based on the scPDSI dataset provided by the Climatic Research Unit (CRU) that cover the time interval from 1901. Original data were subset and processed using R at UNEP/GRID-Geneva.<br><br> The scPSDI indicates the degree of drought severity (negative values means higher severity) based on climatic and environmental parameters. The scPDSI metric was introduced by Wells et al. (2004), who give detailed information about its calculation. The scPDSI is a variant on the original PDSI of Palmer (1965), with the aim to make results from different climate regimes more comparable. As with the PDSI, the scPDSI is calculated from time series of precipitation and temperature, together with fixed parameters related to the soil/surface characteristics at each location. <br><br> Version: preliminary CRU TS 4.07, 0.5° lat-lon resolution <br><br> (Dataset method) van der Schrier G, Barichivich J, Briffa KR and Jones PD (2013) A scPDSI-based global data set of dry and wet spells for 1901-2009. J. Geophys. Res. Atmos. 118, 4025-4048 (10.1002/jgrd.50355).<br> (1901-2021 update) Barichivich J, Osborn TJ, Harris I, van der Schrier G and Jones PD (2022) Monitoring global drought using the self-calibrating Palmer Drought Severity Index [in “State of the Climate in 2021“]. Bull. Amer. Meteor. Soc., 103 (8), S31–S33, (https://doi.org/10.1175/BAMS-D-22-0092.1).<br> (1901-2020 update) Barichivich J, Osborn TJ, Harris I, van der Schrier G and Jones PD (2021) Monitoring global drought using the self-calibrating Palmer Drought Severity Index [in "State of the Climate in 2020" eds. Dunn RJH, Aldred F, Gobron N, Miller JB & Willett KM]. Bulletin of the American Meteorological Society 102, S68-S70. (https://doi.org/10.1175/BAMS-D-21-0098.1)<br> (1901-2019 update) Barichivich J, Osborn TJ, Harris I, van der Schrier G and Jones PD (2020) Drought [in "State of the Climate in 2019"]. Bulletin of the American Meteorological Society 101, S1-S429. (doi:10.1175/2020BAMSStateoftheClimate.1)<br> (1901-2018 update) Barichivich J, Osborn TJ, Harris I, van der Schrier G and Jones PD (2018) Drought [in "State of the Climate in 2018"]. Bulletin of the American Meteorological Society under review.<br> (1901-2017 update) Osborn TJ, Barichivich J, Harris I, van der Schrier G and Jones PD (2018) Drought [in "State of the Climate in 2017"]. Bulletin of the American Meteorological Society 99, S36-S37. (doi:10.1175/2018BAMSStateoftheClimate.1)<br> (1901-2016 update) Osborn TJ, Barichivich J, Harris I, van der Schrier G and Jones PD (2017) Monitoring global drought using the self-calibrating Palmer Drought Severity Index [in "State of the Climate in 2016"]. Bulletin of the American Meteorological Society 98, S32-S33 (doi:10.1175/2017BAMSStateoftheClimate.1) (available here).<br> (1901-2015 update) Osborn TJ, Barichivich J, Harris I, van der Schrier G and Jones PD (2016) Monitoring global drought using the self-calibrating Palmer Drought Severity Index [in "State of the Climate in 2015"]. Bulletin of the American Meteorological Society 97, S32-S36 (available here). <br><br> Data were downloaded from <a href="https://crudata.uea.ac.uk/cru/data/drought/"target=_blank>CRUDATA/Drought</a>cru_scpdsi_19_meanWCSGeoTIFFwesr_climat__cru_scpdsi_19_meanRectifiedGridCoverage-180.0 -90.0180.0 90.0-180.0 -90.0180.0 90.0Self-calibrating Palmer Drought Severity Index (scPDSI) 2020 (CRU)This layer shows the average values for 2020 of the Self-calibrating Palmer Drought Severity Index (scPDSI), based on the scPDSI dataset provided by the Climatic Research Unit (CRU) that cover the time interval from 1901. Original data were subset and processed using R at UNEP/GRID-Geneva.<br><br> The scPSDI indicates the degree of drought severity (negative values means higher severity) based on climatic and environmental parameters. The scPDSI metric was introduced by Wells et al. (2004), who give detailed information about its calculation. The scPDSI is a variant on the original PDSI of Palmer (1965), with the aim to make results from different climate regimes more comparable. As with the PDSI, the scPDSI is calculated from time series of precipitation and temperature, together with fixed parameters related to the soil/surface characteristics at each location. <br><br> Version: preliminary CRU TS 4.07, 0.5° lat-lon resolution <br><br> (Dataset method) van der Schrier G, Barichivich J, Briffa KR and Jones PD (2013) A scPDSI-based global data set of dry and wet spells for 1901-2009. J. Geophys. Res. Atmos. 118, 4025-4048 (10.1002/jgrd.50355).<br> (1901-2021 update) Barichivich J, Osborn TJ, Harris I, van der Schrier G and Jones PD (2022) Monitoring global drought using the self-calibrating Palmer Drought Severity Index [in “State of the Climate in 2021“]. Bull. Amer. Meteor. Soc., 103 (8), S31–S33, (https://doi.org/10.1175/BAMS-D-22-0092.1).<br> (1901-2020 update) Barichivich J, Osborn TJ, Harris I, van der Schrier G and Jones PD (2021) Monitoring global drought using the self-calibrating Palmer Drought Severity Index [in "State of the Climate in 2020" eds. Dunn RJH, Aldred F, Gobron N, Miller JB & Willett KM]. Bulletin of the American Meteorological Society 102, S68-S70. (https://doi.org/10.1175/BAMS-D-21-0098.1)<br> (1901-2019 update) Barichivich J, Osborn TJ, Harris I, van der Schrier G and Jones PD (2020) Drought [in "State of the Climate in 2019"]. Bulletin of the American Meteorological Society 101, S1-S429. (doi:10.1175/2020BAMSStateoftheClimate.1)<br> (1901-2018 update) Barichivich J, Osborn TJ, Harris I, van der Schrier G and Jones PD (2018) Drought [in "State of the Climate in 2018"]. Bulletin of the American Meteorological Society under review.<br> (1901-2017 update) Osborn TJ, Barichivich J, Harris I, van der Schrier G and Jones PD (2018) Drought [in "State of the Climate in 2017"]. Bulletin of the American Meteorological Society 99, S36-S37. (doi:10.1175/2018BAMSStateoftheClimate.1)<br> (1901-2016 update) Osborn TJ, Barichivich J, Harris I, van der Schrier G and Jones PD (2017) Monitoring global drought using the self-calibrating Palmer Drought Severity Index [in "State of the Climate in 2016"]. Bulletin of the American Meteorological Society 98, S32-S33 (doi:10.1175/2017BAMSStateoftheClimate.1) (available here).<br> (1901-2015 update) Osborn TJ, Barichivich J, Harris I, van der Schrier G and Jones PD (2016) Monitoring global drought using the self-calibrating Palmer Drought Severity Index [in "State of the Climate in 2015"]. Bulletin of the American Meteorological Society 97, S32-S36 (available here). <br><br> Data were downloaded from <a href="https://crudata.uea.ac.uk/cru/data/drought/"target=_blank>CRUDATA/Drought</a>cru_scpdsi_20_meanWCSGeoTIFFwesr_climat__cru_scpdsi_20_meanRectifiedGridCoverage-180.0 -90.0180.0 90.0-180.0 -90.0180.0 90.0Self-calibrating Palmer Drought Severity Index (scPDSI) 2021 (CRU)This layer shows the average values for 2021 of the Self-calibrating Palmer Drought Severity Index (scPDSI), based on the scPDSI dataset provided by the Climatic Research Unit (CRU) that cover the time interval from 1901. Original data were subset and processed using R at UNEP/GRID-Geneva.<br><br> The scPSDI indicates the degree of drought severity (negative values means higher severity) based on climatic and environmental parameters. The scPDSI metric was introduced by Wells et al. (2004), who give detailed information about its calculation. The scPDSI is a variant on the original PDSI of Palmer (1965), with the aim to make results from different climate regimes more comparable. As with the PDSI, the scPDSI is calculated from time series of precipitation and temperature, together with fixed parameters related to the soil/surface characteristics at each location. <br><br> Version: preliminary CRU TS 4.07, 0.5° lat-lon resolution <br><br> (Dataset method) van der Schrier G, Barichivich J, Briffa KR and Jones PD (2013) A scPDSI-based global data set of dry and wet spells for 1901-2009. J. Geophys. Res. Atmos. 118, 4025-4048 (10.1002/jgrd.50355).<br> (1901-2021 update) Barichivich J, Osborn TJ, Harris I, van der Schrier G and Jones PD (2022) Monitoring global drought using the self-calibrating Palmer Drought Severity Index [in “State of the Climate in 2021“]. Bull. Amer. Meteor. Soc., 103 (8), S31–S33, (https://doi.org/10.1175/BAMS-D-22-0092.1).<br> (1901-2020 update) Barichivich J, Osborn TJ, Harris I, van der Schrier G and Jones PD (2021) Monitoring global drought using the self-calibrating Palmer Drought Severity Index [in "State of the Climate in 2020" eds. Dunn RJH, Aldred F, Gobron N, Miller JB & Willett KM]. Bulletin of the American Meteorological Society 102, S68-S70. (https://doi.org/10.1175/BAMS-D-21-0098.1)<br> (1901-2019 update) Barichivich J, Osborn TJ, Harris I, van der Schrier G and Jones PD (2020) Drought [in "State of the Climate in 2019"]. Bulletin of the American Meteorological Society 101, S1-S429. (doi:10.1175/2020BAMSStateoftheClimate.1)<br> (1901-2018 update) Barichivich J, Osborn TJ, Harris I, van der Schrier G and Jones PD (2018) Drought [in "State of the Climate in 2018"]. Bulletin of the American Meteorological Society under review.<br> (1901-2017 update) Osborn TJ, Barichivich J, Harris I, van der Schrier G and Jones PD (2018) Drought [in "State of the Climate in 2017"]. Bulletin of the American Meteorological Society 99, S36-S37. (doi:10.1175/2018BAMSStateoftheClimate.1)<br> (1901-2016 update) Osborn TJ, Barichivich J, Harris I, van der Schrier G and Jones PD (2017) Monitoring global drought using the self-calibrating Palmer Drought Severity Index [in "State of the Climate in 2016"]. Bulletin of the American Meteorological Society 98, S32-S33 (doi:10.1175/2017BAMSStateoftheClimate.1) (available here).<br> (1901-2015 update) Osborn TJ, Barichivich J, Harris I, van der Schrier G and Jones PD (2016) Monitoring global drought using the self-calibrating Palmer Drought Severity Index [in "State of the Climate in 2015"]. Bulletin of the American Meteorological Society 97, S32-S36 (available here). <br><br> Data were downloaded from <a href="https://crudata.uea.ac.uk/cru/data/drought/"target=_blank>CRUDATA/Drought</a>cru_scpdsi_21_meanWCSGeoTIFFwesr_climat__cru_scpdsi_21_meanRectifiedGridCoverage-180.0 -90.0180.0 90.0-180.0 -90.0180.0 90.0Self-calibrating Palmer Drought Severity Index (scPDSI) 2021-2022 (CRU)This layer shows the average values over the 2021 to 2022 decade of the Self-calibrating Palmer Drought Severity Index (scPDSI), based on the scPDSI dataset provided by the Climatic Research Unit (CRU) that cover the time interval from 1901. Original data were subset and processed using R at UNEP/GRID-Geneva.<br><br> The scPSDI indicates the degree of drought severity (negative values means higher severity) based on climatic and environmental parameters. The scPDSI metric was introduced by Wells et al. (2004), who give detailed information about its calculation. The scPDSI is a variant on the original PDSI of Palmer (1965), with the aim to make results from different climate regimes more comparable. As with the PDSI, the scPDSI is calculated from time series of precipitation and temperature, together with fixed parameters related to the soil/surface characteristics at each location. <br><br> Version: preliminary CRU TS 4.07, 0.5° lat-lon resolution <br><br> (Dataset method) van der Schrier G, Barichivich J, Briffa KR and Jones PD (2013) A scPDSI-based global data set of dry and wet spells for 1901-2009. J. Geophys. Res. Atmos. 118, 4025-4048 (10.1002/jgrd.50355).<br> (1901-2021 update) Barichivich J, Osborn TJ, Harris I, van der Schrier G and Jones PD (2022) Monitoring global drought using the self-calibrating Palmer Drought Severity Index [in “State of the Climate in 2021“]. Bull. Amer. Meteor. Soc., 103 (8), S31–S33, (https://doi.org/10.1175/BAMS-D-22-0092.1).<br> (1901-2020 update) Barichivich J, Osborn TJ, Harris I, van der Schrier G and Jones PD (2021) Monitoring global drought using the self-calibrating Palmer Drought Severity Index [in "State of the Climate in 2020" eds. Dunn RJH, Aldred F, Gobron N, Miller JB & Willett KM]. Bulletin of the American Meteorological Society 102, S68-S70. (https://doi.org/10.1175/BAMS-D-21-0098.1)<br> (1901-2019 update) Barichivich J, Osborn TJ, Harris I, van der Schrier G and Jones PD (2020) Drought [in "State of the Climate in 2019"]. Bulletin of the American Meteorological Society 101, S1-S429. (doi:10.1175/2020BAMSStateoftheClimate.1)<br> (1901-2018 update) Barichivich J, Osborn TJ, Harris I, van der Schrier G and Jones PD (2018) Drought [in "State of the Climate in 2018"]. Bulletin of the American Meteorological Society under review.<br> (1901-2017 update) Osborn TJ, Barichivich J, Harris I, van der Schrier G and Jones PD (2018) Drought [in "State of the Climate in 2017"]. Bulletin of the American Meteorological Society 99, S36-S37. (doi:10.1175/2018BAMSStateoftheClimate.1)<br> (1901-2016 update) Osborn TJ, Barichivich J, Harris I, van der Schrier G and Jones PD (2017) Monitoring global drought using the self-calibrating Palmer Drought Severity Index [in "State of the Climate in 2016"]. Bulletin of the American Meteorological Society 98, S32-S33 (doi:10.1175/2017BAMSStateoftheClimate.1) (available here).<br> (1901-2015 update) Osborn TJ, Barichivich J, Harris I, van der Schrier G and Jones PD (2016) Monitoring global drought using the self-calibrating Palmer Drought Severity Index [in "State of the Climate in 2015"]. Bulletin of the American Meteorological Society 97, S32-S36 (available here). <br><br> Data were downloaded from <a href="https://crudata.uea.ac.uk/cru/data/drought/"target=_blank>CRUDATA/Drought</a>cru_scpdsi_21_22_meanWCSGeoTIFFwesr_climat__cru_scpdsi_21_22_meanRectifiedGridCoverage-180.0 -90.0180.0 90.0-180.0 -90.0180.0 90.0Self-calibrating Palmer Drought Severity Index (scPDSI) 2022 (CRU)This layer shows the average values for 2022 of the Self-calibrating Palmer Drought Severity Index (scPDSI), based on the scPDSI dataset provided by the Climatic Research Unit (CRU) that cover the time interval from 1901. Original data were subset and processed using R at UNEP/GRID-Geneva.<br><br> The scPSDI indicates the degree of drought severity (negative values means higher severity) based on climatic and environmental parameters. The scPDSI metric was introduced by Wells et al. (2004), who give detailed information about its calculation. The scPDSI is a variant on the original PDSI of Palmer (1965), with the aim to make results from different climate regimes more comparable. As with the PDSI, the scPDSI is calculated from time series of precipitation and temperature, together with fixed parameters related to the soil/surface characteristics at each location. <br><br> Version: preliminary CRU TS 4.07, 0.5° lat-lon resolution <br><br> (Dataset method) van der Schrier G, Barichivich J, Briffa KR and Jones PD (2013) A scPDSI-based global data set of dry and wet spells for 1901-2009. J. Geophys. Res. Atmos. 118, 4025-4048 (10.1002/jgrd.50355).<br> (1901-2021 update) Barichivich J, Osborn TJ, Harris I, van der Schrier G and Jones PD (2022) Monitoring global drought using the self-calibrating Palmer Drought Severity Index [in “State of the Climate in 2021“]. Bull. Amer. Meteor. Soc., 103 (8), S31–S33, (https://doi.org/10.1175/BAMS-D-22-0092.1).<br> (1901-2020 update) Barichivich J, Osborn TJ, Harris I, van der Schrier G and Jones PD (2021) Monitoring global drought using the self-calibrating Palmer Drought Severity Index [in "State of the Climate in 2020" eds. Dunn RJH, Aldred F, Gobron N, Miller JB & Willett KM]. Bulletin of the American Meteorological Society 102, S68-S70. (https://doi.org/10.1175/BAMS-D-21-0098.1)<br> (1901-2019 update) Barichivich J, Osborn TJ, Harris I, van der Schrier G and Jones PD (2020) Drought [in "State of the Climate in 2019"]. Bulletin of the American Meteorological Society 101, S1-S429. (doi:10.1175/2020BAMSStateoftheClimate.1)<br> (1901-2018 update) Barichivich J, Osborn TJ, Harris I, van der Schrier G and Jones PD (2018) Drought [in "State of the Climate in 2018"]. Bulletin of the American Meteorological Society under review.<br> (1901-2017 update) Osborn TJ, Barichivich J, Harris I, van der Schrier G and Jones PD (2018) Drought [in "State of the Climate in 2017"]. Bulletin of the American Meteorological Society 99, S36-S37. (doi:10.1175/2018BAMSStateoftheClimate.1)<br> (1901-2016 update) Osborn TJ, Barichivich J, Harris I, van der Schrier G and Jones PD (2017) Monitoring global drought using the self-calibrating Palmer Drought Severity Index [in "State of the Climate in 2016"]. Bulletin of the American Meteorological Society 98, S32-S33 (doi:10.1175/2017BAMSStateoftheClimate.1) (available here).<br> (1901-2015 update) Osborn TJ, Barichivich J, Harris I, van der Schrier G and Jones PD (2016) Monitoring global drought using the self-calibrating Palmer Drought Severity Index [in "State of the Climate in 2015"]. Bulletin of the American Meteorological Society 97, S32-S36 (available here). <br><br> Data were downloaded from <a href="https://crudata.uea.ac.uk/cru/data/drought/"target=_blank>CRUDATA/Drought</a>cru_scpdsi_22_meanWCSGeoTIFFwesr_climat__cru_scpdsi_22_meanRectifiedGridCoverage-180.0 -90.0180.0 90.0-180.0 -90.0180.0 90.0Selyaninov's Hydrothermic Coefficient (1979-2013) - CHELSAUnit=(kg/m^2/10)/Celsius The Selyaninov's Hydrothermic Coefficient (shc) describes moistening conditions. Unlike precipitation measurements, shc takes into account temperature-related vaporability. shc is calculated using the following formula: the amount of precipitation during the period in which air temperatures are above + 5°C is divided by the sum of average daily temperatures for the same period reduced to its tenth part (Selyaninov, 1937). On the whole, shc values over 1 are typical of normal or excessive moistening, while those less than 1 are typical of moistening insufficient for basic cultivated crops. This dataset is provided by CHELSA. CHELSA V1.2 (http://chelsa-climate.org/) is a high resolution (30 arc sec, ~1 km) climate data set for the earth land surface areas. It includes monthly and annual mean temperature and precipitation patterns for the time period 1979-2013. Methods are described in http://chelsa-climate.org/wp-admin/download-page/CHELSA_tech_specification.pdf. CHELSA Version 1.2 is licensed under a Creative Commons Attribution 4.0 International License. Specifications: High resolution (30 arcsec, ~1 km) Precipitation & Temperature Climatologies for the years 1979 – 2013 Incorporation of topoclimate (e.g. orographic rainfall & wind fields). All products of CHELSA are in a geographic coordinate system referenced to the WGS 84 horizontal datum, with the horizontal coordinates expressed in decimal degrees. The CHELSA layer extents (minimum and maximum latitude and longitude) are a result of the coordinate system inherited from the 1-arc-second GMTED2010 data which itself inherited the grid extent from the 1-arc-second SRTM data. Note that because of the pixel center referencing of the input GMTED2010 data the full extent of each CHELSA grid as defined by the outside edges of the pixels differs from an integer value of latitude or longitude by 0.000138888888 degree (or 1/2 arc-second). Users of products based on the legacy GTOPO30 product should note that the coordinate referencing of CHELSA (and GMTED2010) and GTOPO30 are not the same. In GTOPO30, the integer lines of latitude and longitude fall directly on the edges of a 30-arc-second pixel. Thus, when overlaying CHELSA with products based on GTOPO30 a slight shift of 1/2 arc-second will be observed between the edges of corresponding 30-arc-second pixels. To redistribute the data, please cite the following peer reviewed articles: <a href="https://www.nature.com/articles/sdata2017122"target=_blank>Karger, D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N.E., Linder, H.P. & Kessler, M. (2017) Climatologies at high resolution for the earth’s land surface areas. Scientific Data 4, 170122.</a> <a href="https://doi.org/10.5061/dryad.kd1d4"target=_blank>Karger, D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N.E., Linder, H.P., Kessler, M. (2017) Data from: Climatologies at high resolution for the earth’s land surface areas. Dryad Digital Repository. </a> CHELSA – Climatologies at high resolution for the Earth land surface areas. Version 1.2CHELSA_shc_5_1979-2013_optWCSGeoTIFFUN_Sahel__CHELSA_shc_5_1979-2013_optRectifiedGridCoverage-180.00013888885002 -90.00013888884999179.99985967115003 83.99986041515001-180.00013888885002 -90.00013888884999179.99985967115003 83.99986041515001Short Vegetation Change 1982 - 2016This analysis of 35 years’ worth of satellite data (at approximately 25 square kilometer resolution at the equator) provides a comprehensive record of global land-change dynamics during the period 1982–2016. Contrary to the prevailing view that forest area has declined globally — tree cover has increased by 2.24 million km2 (+7.1% relative to the 1982 level), largely the result of a net loss in the tropics being outweighed by a net gain in the extratropics. Global bare ground cover has decreased by 1.16 million km2 (−3.1%), most notably in agricultural regions in Asia. Of all land changes, 60% are associated with direct human activities and 40% with indirect drivers such as climate change. Land-use change exhibits regional dominance, including tropical deforestation and agricultural expansion, temperate reforestation or afforestation, cropland intensification and urbanization. Consistently across all climate domains, montane systems have gained tree cover and many arid and semi-arid ecosystems have lost vegetation cover.<br><br>For full details see: <a href="https://doi.org/10.1038/s41586-018-0411-9">Song, X.-P., Hansen, M.C., Stehman, S.V., Potapov, P.V., Tyukavina, A., Vermote, E.F., Townshend, J.R., 2018. Global land change from 1982 to 2016. Nature 1</a><br/>.Short_vegetation_change_1982-2016WCSGeoTIFFMapX_UNDP__Short_vegetation_change_1982-2016RectifiedGridCoverage-180.0 -90.0180.0 90.0-180.0 -90.0180.0 90.0Soil Adjusted Vegetation Index in Keur Massar Senegal (2025)This layer shows the distribution of vegetation in Keur Massar (Dakar, Senegal) derived from Sentinel-2 imagery at 10 m spatial resolution. The satellite images were collected from Copernicus Browser (open access) on the 16th of April 2025. The Soil Adjusted Vegetation Index (SAVI) was calculated using the red and near-infrared bands of Sentinel-2, applying a soil brightness correction factor (L = 0.5). Vegetation appears on the map starting from SAVI values of 0.26, representing low vegetation cover, and gradually increases up to 0.95, representing dense vegetation (dark green). This layer serves as an input for the project, providing baseline information on vegetation cover to support the susceptibility assessment of illegal dumping sites.Solid Waste ManagementRemote SensingGISCitizen ScienceMulti-Criteria AnalysisIllegal Dumpingdakar_waste__SAVIRectifiedGridCoverage-17.382029187007333 14.733719739640827-17.2719124771499 14.84106039798925-17.382029187007333 14.733719739640827-17.2719124771499 14.84106039798925Soil Erodibility (K- Factor) - GloSEM_25km - ESDAC - JRCThe soil Erodibility (K-Factor) is one of the input layers used in the Global Soil Erosion map (GeoTIFF format) at 25km resolution is available for free download in the European Soil Data Centre (ESDAC). This is based on the Verion 1.1 of the JRC/ UniBasel "RUSLE-based Global Soil Erosion Modelling platform (GloSEM)". In addition to the K-factor, GloSEM provides the other 25km resolution resampled input datasets: * R-factor (the original one at 1km can be downloaded from https://esdac.jrc.ec.europa.eu/content/global-rainfall-erosivity * C-factor * LS-factor and the RUSLE outputs: - RUSLE Soil loss in 2012 (25km resolution) - RUSLE Soil loss in 2001 (25km resolution) Note: In case you use the data for applications in European Union, we recommend the European Soil Erosion dataset and the relevant K, LS, C, R, P factors. References Borrelli P., Robinson D.A., Fleischer L.R., Lugato E., Ballabio C., Alewell C., Meusburger K., Modugno, S., Schutt, B. Ferro, V. Bagarello, V. Van Oost, K., Montanarella, L., Panagos P. 2017. An assessment of the global impact of 21st century land use change on soil erosion. Nature Communications, 8 (1): art. no. 2013RUSLE_KFactor_v1.1_25kmWCSGeoTIFFUN_Sahel__RUSLE_KFactor_v1.1_25kmRectifiedGridCoverage-180.0 -56.6229166666667179.99999999999943 83.58541666666642-180.0 -56.6229166666667179.99999999999943 83.58541666666642Soil Loss (RUSLE - v1.1) - 2012 - GloSEM_25km - ESDAC - JRCAssessment of global soil erosion for 2012 based on Global Soil Erosion Modelling platform (GloSEM). We used the 250m original data to re-sample at 25km. In this study 202 countries are included with more than 125 millionKm2. The total soil loss has been estimated to 35 Pg yr-1 of soil eroded in 2001. The estimates are lower compared to past studies in 2012, 35.9 Pg yr-1 - Increase of 2.5% in soil erosion globally (due to land use change). Spatial coverage: World (125 million Km2 - c.a 84% of the earht surface) Pixel size: 25km. Measurement Unit: t ha-1 yr-1 Projection: GCS_WGS_1984 Temporal coverage: 2012 a More information: Global Soil Erosion https://esdac.jrc.ec.europa.eu/taxonomy/term/4756RUSLE_SoilLoss_v1.1_yr2012_25kmWCSGeoTIFFUN_Sahel__RUSLE_SoilLoss_v1.1_yr2012_25kmRectifiedGridCoverage-180.0 -56.6229166666667179.99999999999943 83.58541666666642-180.0 -56.6229166666667179.99999999999943 83.58541666666642South America Forest Height (Montana State University)samerica_forheight_optWCSGeoTIFFMapX_UNDP__samerica_forheightRectifiedGridCoverage-117.0005 -32.0005-33.9995 32.0005-117.0005 -32.0005-33.9995 32.0005South Sudan SRTM 30 MetersThis data represents the 30 meters Digital Elevation Model (DEM) from Shuttle Radar Topography Mission (SRTM). This data-set was derived through mosaicking of individual SRTM tiles for a particular country and clipping the mosaicked tiles using the country boundary extent.South_Sudan_SRTM30meters_optWCSGeoTIFFMAPX__South_Sudan_SRTM30meters_optRectifiedGridCoverage24.139861111111113 3.49291666666666635.941250000000004 12.21902777777777724.139861111111113 3.49291666666666635.941250000000004 12.219027777777777Spatial distribution of Drought riskDrought risk in Kédougou is manifested by the combination of a climatic phenomenon and local vulnerability that amplify its impacts on agriculture and livelihoods. Indeed, data from Think Hazard indicate that a water shortage, a direct consequence of marked variability in rainfall and rising temperatures, could occur with a probability of around 20% over the next ten years. Furthermore, the study by (Boissy et al., 2024) highlights that the degradation of vegetation cover, due in particular to intensive agricultural practices and mining expansion, weakens soils and reduces their water retention capacity, thus increasing the vulnerability of local systems.drought riskrisk_southeast_senegal__drought_riskRectifiedGridCoverage-13.239445601904379 12.307811091287101-11.342383384900774 13.461068253039743-13.239445601904379 12.307811091287101-11.342383384900774 13.461068253039743Spatial distribution of Extreme Heat riskIn Kédougou, the risk of extreme heat is exacerbated by the interaction between increasing climate hazards and marked socioeconomic vulnerability. On the one hand, meteorological records (ANACIM, 2021) show that during the dry season, temperature peaks regularly exceeding 40°C occur, in line with regional warming trends (IPCC, 2021). On the other hand, local vulnerability is amplified by precarious socioeconomic and physical conditions. Furthermore, the study by Faye et al. (2021) conducted in Bandafassi, a commune of Kédougou, demonstrates that exposure to heat waves, defined here by the 90th percentile of minimum and maximum temperatures over three consecutive days, leads to a significant increase in daily mortality, particularly among women and people over 55 years of age. These results clearly illustrate that the overall risk linked to extreme heat in Kédougou does not only lie in the increase in temperatures, but also in the high vulnerability of local populations, thus justifying the urgent implementation of integrated adaptation strategies.heat risk aurisk_southeast_senegal__heat_riskRectifiedGridCoverage-13.254663620065084 12.30783537839622-11.324971709823716 13.461101944475129-13.254663620065084 12.30783537839622-11.324971709823716 13.461101944475129Spatial distribution of flood riskAnalysis of flood risk results reveals a complex and variable spatial distribution of the most exposed areas in the Kédougou region. By combining data from satellite imagery, notably the (MNDWI) and topographic information extracted from the SRTM model, the study highlights the sectors where hydrological hazard is combined with increased vulnerability due to urbanization and land use.flood riskrisk_southeast_senegal__flood_riskRectifiedGridCoverage-13.239494659264894 12.307771971227796-11.34243244226129 13.461029132980437-13.239494659264894 12.307771971227796-11.34243244226129 13.461029132980437Spatial distribution of forest fire riskThis risk related to forest fires is defined by the interaction between the inherent danger posed by fires and the vulnerability of ecosystems and local populations. On the one hand, global warming, characterized by rising temperatures and decreasing precipitation, promotes the drying out of vegetation and the accumulation of dry biomass, thus creating an ideal fuel for the outbreak and spread of fires (Mbow, 2003). On the other hand, human practices, such as burning land for field preparation and charcoal production, reinforced by increasing population pressure, increase the probability of uncontrolled fire outbreaks (Benoit, 1998).burnrisk_southeast_senegal__burn_riskRectifiedGridCoverage-13.239460488881921 12.307817707721565-11.342398271878317 13.461074869474206-13.239460488881921 12.307817707721565-11.342398271878317 13.461074869474206Spatial distribution of global sea floor biodiversity (GRID-Arendal)Focal varity. Based on 18 key geomorphic features.Focal_varity_WGS84WCSGeoTIFFfocal varityunep_storymap__Focal_varity_WGS84RectifiedGridCoverage-179.99999952227077 -88.20698944113573179.99994967041263 89.99999999999997-179.99999952227077 -88.20698944113573179.99994967041263 89.99999999999997Species RichnessThis raster layer represents the number of species of mammals, amphibians and birds potentially occurring in each ~300m grid cell.<br><br>Species range data were rasterised at 10 arc-seconds (approximately 300m at the equator) from IUCN Red List polygons (IUCN 2017) and raster layers for each species were summed together into a single layer with equal weighting. The species are used as a proxy to represent terrestrial biodiversity and are based on taxonomic groups that have been comprehensively assessed by IUCN Red List. Thus users should be aware of the taxonomic bias and and that marine values are based only on marine species of mammals and birds.<br/><br>Source: IUCN 2017. The IUCN Red List of Threatened Species. Version 2017-3 <a href="http://www.iucnredlist.org" target="_blank">IUCNRedList</a>.<br>Terms of Use: <a href="http://www.iucnredlist.org/info/terms-of-use" target="_blank">Commercial use is not permitted</a><br/>species_richness_optWCSGeoTIFFMapX_UNDP__species_richness_optRectifiedGridCoverage-179.995201157628 -84.69466806618583179.8859620772232 85.91771612300938-179.995201157628 -84.69466806618583179.8859620772232 85.91771612300938Spectral Acceleration 0.2 Sec 1500 Yearsspectral_acceleration_02_s_1500_yrpWCSGeoTIFFwesr_risk__spectral_acceleration_02_s_1500_yrpRectifiedGridCoverage-180.25 -90.25180.25 90.25-180.25 -90.25180.25 90.25Spectral Acceleration 0.2 Sec 2475 Yearsspectral_acceleration_02_s_2475_yrpWCSGeoTIFFwesr_risk__spectral_acceleration_02_s_2475_yrpRectifiedGridCoverage-180.25 -90.25180.25 90.25-180.25 -90.25180.25 90.25Spectral Acceleration 0.2 Sec 250 Yearsspectral_acceleration_02_s_250_yrpWCSGeoTIFFwesr_risk__spectral_acceleration_02_s_250_yrpRectifiedGridCoverage-180.25 -90.25180.25 90.25-180.25 -90.25180.25 90.25Spectral Acceleration 0.2 Sec 475 Yearsspectral_acceleration_02_s_475_yrpWCSGeoTIFFwesr_risk__spectral_acceleration_02_s_475_yrpRectifiedGridCoverage-180.25 -90.25180.25 90.25-180.25 -90.25180.25 90.25Spectral Acceleration 0.2 Sec 975 Yearsspectral_acceleration_02_s_975_yrpWCSGeoTIFFwesr_risk__spectral_acceleration_02_s_975_yrpRectifiedGridCoverage-180.25 -90.25180.25 90.25-180.25 -90.25180.25 90.25Spectral Acceleration 0.5 Sec 1500 Yearsspectral_acceleration_05_s_1500_yrpWCSGeoTIFFwesr_risk__spectral_acceleration_05_s_1500_yrpRectifiedGridCoverage-180.25 -90.25180.25 90.25-180.25 -90.25180.25 90.25Spectral Acceleration 0.5 Sec 2475 Yearsspectral_acceleration_05_s_2475_yrpWCSGeoTIFFwesr_risk__spectral_acceleration_05_s_2475_yrpRectifiedGridCoverage-180.25 -90.25180.25 90.25-180.25 -90.25180.25 90.25Spectral Acceleration 0.5 Sec 250 Yearsspectral_acceleration_05_s_250_yrpWCSGeoTIFFwesr_risk__spectral_acceleration_05_s_250_yrpRectifiedGridCoverage-180.25 -90.25180.25 90.25-180.25 -90.25180.25 90.25Spectral Acceleration 0.5 Sec 475 Yearsspectral_acceleration_05_s_475_yrpWCSGeoTIFFwesr_risk__spectral_acceleration_05_s_475_yrpRectifiedGridCoverage-180.25 -90.25180.25 90.25-180.25 -90.25180.25 90.25Spectral Acceleration 0.5 Sec 975 Yearsspectral_acceleration_05_s_975_yrpWCSGeoTIFFwesr_risk__spectral_acceleration_05_s_975_yrpRectifiedGridCoverage-180.25 -90.25180.25 90.25-180.25 -90.25180.25 90.25Spectral Acceleration 1.0 Sec 1500 Yearsspectral_acceleration_10_s_1500_yrpWCSGeoTIFFwesr_risk__spectral_acceleration_10_s_1500_yrpRectifiedGridCoverage-180.25 -90.25180.25 90.25-180.25 -90.25180.25 90.25Spectral Acceleration 1.0 Sec 2475 Yearsspectral_acceleration_10_s_2475_yrpWCSGeoTIFFwesr_risk__spectral_acceleration_10_s_2475_yrpRectifiedGridCoverage-180.25 -90.25180.25 90.25-180.25 -90.25180.25 90.25Spectral Acceleration 1.0 Sec 250 Yearsspectral_acceleration_10_s_250_yrpWCSGeoTIFFwesr_risk__spectral_acceleration_10_s_250_yrpRectifiedGridCoverage-180.25 -90.25180.25 90.25-180.25 -90.25180.25 90.25Spectral Acceleration 1.0 Sec 475 Yearsspectral_acceleration_10_s_475_yrpWCSGeoTIFFwesr_risk__spectral_acceleration_10_s_475_yrpRectifiedGridCoverage-180.25 -90.25180.25 90.25-180.25 -90.25180.25 90.25Spectral Acceleration 1.0 Sec 975 Yearsspectral_acceleration_10_s_975_yrpWCSGeoTIFFwesr_risk__spectral_acceleration_10_s_975_yrpRectifiedGridCoverage-180.25 -90.25180.25 90.25-180.25 -90.25180.25 90.25Spring precipitations change (%) for the period 2020 - 2039 under the RCP 2.6 scenarioProjected change of precipitations for the specified season and reference period, under the specified scenario with respect to the reference period (1980-1999). Data provided by MedECC Atlas.pr_mam_NEARREF_rcp26_optWCSGeoTIFFMedicip__pr_mam_NEARREF_rcp26_optRectifiedGridCoverage-12.0 27.537.0 49.0-12.0 27.537.0 49.0Spring precipitations change (%) for the period 2020 - 2039 under the RCP 8.5 scenarioProjected change of precipitations for the specified season and reference period, under the specified scenario with respect to the reference period (1980-1999). Data provided by MedECC Atlas.pr_mam_NEARREF_rcp85_optWCSGeoTIFFMedicip__pr_mam_NEARREF_rcp85_optRectifiedGridCoverage-12.0 27.537.0 49.0-12.0 27.537.0 49.0Spring precipitations change (%) for the period 2040 - 2059 under the RCP 2.6 scenarioProjected change of precipitations for the specified season and reference period, under the specified scenario with respect to the reference period (1980-1999). Data provided by MedECC Atlas.pr_mam_MIDREF_rcp26_optWCSGeoTIFFMedicip__pr_mam_MIDREF_rcp26_optRectifiedGridCoverage-12.0 27.537.0 49.0-12.0 27.537.0 49.0Spring precipitations change (%) for the period 2040 - 2059 under the RCP 8.5 scenarioProjected change of precipitations for the specified season and reference period, under the specified scenario with respect to the reference period (1980-1999). Data provided by MedECC Atlas.pr_mam_MIDREF_rcp85_optWCSGeoTIFFMedicip__pr_mam_MIDREF_rcp85_optRectifiedGridCoverage-12.0 27.537.0 49.0-12.0 27.537.0 49.0Spring precipitations change (%) for the period 2080 - 2099 under the RCP 2.6 scenarioProjected change of precipitations for the specified season and reference period, under the specified scenario with respect to the reference period (1980-1999). Data provided by MedECC Atlas.pr_mam_ENDREF_rcp26_optWCSGeoTIFFMedicip__pr_mam_ENDREF_rcp26_optRectifiedGridCoverage-12.0 27.537.0 49.0-12.0 27.537.0 49.0Spring precipitations change (%) for the period 2080 - 2099 under the RCP 8.5 scenarioProjected change of precipitations for the specified season and reference period, under the specified scenario with respect to the reference period (1980-1999). Data provided by MedECC Atlas.pr_mam_ENDREF_rcp85_optWCSGeoTIFFMedicip__pr_mam_ENDREF_rcp85_optRectifiedGridCoverage-12.0 27.537.0 49.0-12.0 27.537.0 49.0Spring temperature change (°C) for the period 2020 - 2039 under the RCP 2.6 scenarioProjected change of temperature for the specified season and reference period, under the specified scenario with respect to the reference period (1980-1999). Data provided by MedECC Atlas.tas_mam_NEARREF_rcp26_optWCSGeoTIFFMedicip__tas_mam_NEARREF_rcp26_optRectifiedGridCoverage-12.0 27.537.0 49.0-12.0 27.537.0 49.0Spring temperature change (°C) for the period 2020 - 2039 under the RCP 8.5 scenarioProjected change of temperature for the specified season and reference period, under the specified scenario with respect to the reference period (1980-1999). Data provided by MedECC Atlas.tas_mam_NEARREF_rcp85_optWCSGeoTIFFMedicip__tas_mam_NEARREF_rcp85_optRectifiedGridCoverage-12.0 27.537.0 49.0-12.0 27.537.0 49.0Spring temperature change (°C) for the period 2040 - 2059 under the RCP 2.6 scenarioProjected change of temperature for the specified season and reference period, under the specified scenario with respect to the reference period (1980-1999). Data provided by MedECC Atlas.tas_mam_MIDREF_rcp26_optWCSGeoTIFFMedicip__tas_mam_MIDREF_rcp26_optRectifiedGridCoverage-12.0 27.537.0 49.0-12.0 27.537.0 49.0Spring temperature change (°C) for the period 2040 - 2059 under the RCP 8.5 scenarioProjected change of temperature for the specified season and reference period, under the specified scenario with respect to the reference period (1980-1999). Data provided by MedECC Atlas.tas_mam_MIDREF_rcp85_optWCSGeoTIFFMedicip__tas_mam_MIDREF_rcp85_optRectifiedGridCoverage-12.0 27.537.0 49.0-12.0 27.537.0 49.0Spring temperature change (°C) for the period 2080 - 2099 under the RCP 2.6 scenarioProjected change of temperature for the specified season and reference period, under the specified scenario with respect to the reference period (1980-1999). Data provided by MedECC Atlas.tas_mam_ENDREF_rcp26_optWCSGeoTIFFMedicip__tas_mam_ENDREF_rcp26_optRectifiedGridCoverage-12.0 27.537.0 49.0-12.0 27.537.0 49.0Spring temperature change (°C) for the period 2080 - 2099 under the RCP 8.5 scenarioProjected change of temperature for the specified season and reference period, under the specified scenario with respect to the reference period (1980-1999). Data provided by MedECC Atlas.tas_mam_ENDREF_rcp85_optWCSGeoTIFFMedicip__tas_mam_ENDREF_rcp85_optRectifiedGridCoverage-12.0 27.537.0 49.0-12.0 27.537.0 49.0Summer precipitations change (%) for the period 2020 - 2039 under the RCP 2.6 scenarioProjected change of precipitations for the specified season and reference period, under the specified scenario with respect to the reference period (1980-1999). Data provided by MedECC Atlas.pr_jja_NEARREF_rcp26_optWCSGeoTIFFMedicip__pr_jja_NEARREF_rcp26_optRectifiedGridCoverage-12.0 27.537.0 49.0-12.0 27.537.0 49.0Summer precipitations change (%) for the period 2020 - 2039 under the RCP 8.5 scenarioProjected change of precipitations for the specified season and reference period, under the specified scenario with respect to the reference period (1980-1999). Data provided by MedECC Atlas.pr_jja_NEARREF_rcp85_optWCSGeoTIFFMedicip__pr_jja_NEARREF_rcp85_optRectifiedGridCoverage-12.0 27.537.0 49.0-12.0 27.537.0 49.0Summer precipitations change (%) for the period 2040 - 2059 under the RCP 2.6 scenarioProjected change of precipitations for the specified season and reference period, under the specified scenario with respect to the reference period (1980-1999). Data provided by MedECC Atlas.pr_jja_MIDREF_rcp26_optWCSGeoTIFFMedicip__pr_jja_MIDREF_rcp26_optRectifiedGridCoverage-12.0 27.537.0 49.0-12.0 27.537.0 49.0Summer precipitations change (%) for the period 2040 - 2059 under the RCP 8.5 scenarioProjected change of precipitations for the specified season and reference period, under the specified scenario with respect to the reference period (1980-1999). Data provided by MedECC Atlas.pr_jja_MIDREF_rcp85_optWCSGeoTIFFMedicip__pr_jja_MIDREF_rcp85_optRectifiedGridCoverage-12.0 27.537.0 49.0-12.0 27.537.0 49.0Summer precipitations change (%) for the period 2080 - 2099 under the RCP 2.6 scenarioProjected change of precipitations for the specified season and reference period, under the specified scenario with respect to the reference period (1980-1999). Data provided by MedECC Atlas.pr_jja_ENDREF_rcp26_optWCSGeoTIFFMedicip__pr_jja_ENDREF_rcp26_optRectifiedGridCoverage-12.0 27.537.0 49.0-12.0 27.537.0 49.0Summer precipitations change (%) for the period 2080 - 2099 under the RCP 8.5 scenarioProjected change of precipitations for the specified season and reference period, under the specified scenario with respect to the reference period (1980-1999). Data provided by MedECC Atlas.pr_jja_ENDREF_rcp85_optWCSGeoTIFFMedicip__pr_jja_ENDREF_rcp85_optRectifiedGridCoverage-12.0 27.537.0 49.0-12.0 27.537.0 49.0Summer temperature change (°C) for the period 2020 - 2039 under the RCP 2.6 scenarioProjected change of temperature for the specified season and reference period, under the specified scenario with respect to the reference period (1980-1999). Data provided by MedECC Atlas.tas_jja_NEARREF_rcp26_optWCSGeoTIFFMedicip__tas_jja_NEARREF_rcp26_optRectifiedGridCoverage-12.0 27.537.0 49.0-12.0 27.537.0 49.0Summer temperature change (°C) for the period 2020 - 2039 under the RCP 8.5 scenarioProjected change of temperature for the specified season and reference period, under the specified scenario with respect to the reference period (1980-1999). Data provided by MedECC Atlas.tas_jja_NEARREF_rcp85_optWCSGeoTIFFMedicip__tas_jja_NEARREF_rcp85_optRectifiedGridCoverage-12.0 27.537.0 49.0-12.0 27.537.0 49.0Summer temperature change (°C) for the period 2040 - 2059 under the RCP 2.6 scenarioProjected change of temperature for the specified season and reference period, under the specified scenario with respect to the reference period (1980-1999). Data provided by MedECC Atlas.tas_jja_MIDREF_rcp26_optWCSGeoTIFFMedicip__tas_jja_MIDREF_rcp26_optRectifiedGridCoverage-12.0 27.537.0 49.0-12.0 27.537.0 49.0Summer temperature change (°C) for the period 2040 - 2059 under the RCP 8.5 scenarioProjected change of temperature for the specified season and reference period, under the specified scenario with respect to the reference period (1980-1999). Data provided by MedECC Atlas.tas_jja_MIDREF_rcp85_optWCSGeoTIFFMedicip__tas_jja_MIDREF_rcp85_optRectifiedGridCoverage-12.0 27.537.0 49.0-12.0 27.537.0 49.0Summer temperature change (°C) for the period 2080 - 2099 under the RCP 2.6 scenarioProjected change of temperature for the specified season and reference period, under the specified scenario with respect to the reference period (1980-1999). Data provided by MedECC Atlas.tas_jja_ENDREF_rcp26_optWCSGeoTIFFMedicip__tas_jja_ENDREF_rcp26_optRectifiedGridCoverage-12.0 27.537.0 49.0-12.0 27.537.0 49.0Summer temperature change (°C) for the period 2080 - 2099 under the RCP 8.5 scenarioProjected change of temperature for the specified season and reference period, under the specified scenario with respect to the reference period (1980-1999). Data provided by MedECC Atlas.tas_jja_ENDREF_rcp85_optWCSGeoTIFFMedicip__tas_jja_ENDREF_rcp85_optRectifiedGridCoverage-12.0 27.537.0 49.0-12.0 27.537.0 49.0T56HKF_20190927_843_QGT56HKF_20190927_843_QGWCSGeoTIFFwesr_swipe__T56HKF_20190927_843_QGRectifiedGridCoverage199980.0 5990200.0309780.0 6100000.0149.66386433258654 -36.21435579772867150.90985282793 -35.19820772224967T56HKG_20190927_843_QGT56HKG_20190927_843_QGWCSGeoTIFFwesr_swipe__T56HKG_20190927_843_QGRectifiedGridCoverage199980.0 6090220.0309780.0 6200020.0149.701249152867 -35.31315544181972150.93246853305004 -34.29772543003008T56HKH_20190927_843_QGT56HKH_20190927_843_QGWCSGeoTIFFwesr_swipe__T56HKH_20190927_843_QGRectifiedGridCoverage199980.0 6190240.0309780.0 6300040.0149.73701209968527 -34.411807185854414150.95410135717455 -33.39707945018877T56HKJ_20190927_843_QGT56HKJ_20190927_843_QGWCSGeoTIFFwesr_swipe__T56HKJ_20190927_843_QGRectifiedGridCoverage199980.0 6290200.0309780.0 6400000.0149.771202334192 -33.510854102652665150.97478007537504 -32.49681353198739TZA_Kilombero_LULC2014Kilombero_LULC2014_wgs84_optWCSGeoTIFFTEEB__Kilombero_LULC2014_wgs84_optRectifiedGridCoverage34.553167266769464 -10.03804845049055137.239835341004294 -7.66399436803009734.553167266769464 -10.03804845049055137.239835341004294 -7.663994368030097TZA_kilombero_BAU_LULC2030BAU_LULC2030_wgs84_optWCSGeoTIFFTEEB__BAU_LULC2030_wgs84_optRectifiedGridCoverage34.553167266769464 -10.03804845049055137.239835341004294 -7.66399436803009734.553167266769464 -10.03804845049055137.239835341004294 -7.663994368030097TZA_kilombero_SAGCOT_2030INTSAGCOT_2030INT_wgs84_optWCSGeoTIFFTEEB__SAGCOT_2030INT_wgs84_optRectifiedGridCoverage34.553167266769464 -10.03804845049055137.239835341004294 -7.66399436803009734.553167266769464 -10.03804845049055137.239835341004294 -7.663994368030097TZA_kilombero_sequest_fut_BAUsequest_fut_BAU_wgs84_optWCSGeoTIFFTEEB__sequest_fut_BAU_wgs84_optRectifiedGridCoverage34.553167266769464 -10.03804845049055137.239835341004294 -7.66399436803009734.553167266769464 -10.03804845049055137.239835341004294 -7.663994368030097TZA_kilombero_sequest_fut_BAUwcsequest_fut_BAUwc_wgs84_optWCSGeoTIFFTEEB__sequest_fut_BAUwc_wgs84_optRectifiedGridCoverage34.553167266769464 -10.03804845049055137.239835341004294 -7.66399436803009734.553167266769464 -10.03804845049055137.239835341004294 -7.663994368030097TZA_kilombero_sequest_fut_SAGCOT_wgs84_optsequest_fut_SAGCOT_wgs84_optWCSGeoTIFFTEEB__sequest_fut_SAGCOT_wgs84_optRectifiedGridCoverage34.553167266769464 -10.03804845049055137.239835341004294 -7.66399436803009734.553167266769464 -10.03804845049055137.239835341004294 -7.663994368030097TZA_kilombero_sequest_fut_SAGCOTintsequest_fut_SAGCOTint_wgs84_optWCSGeoTIFFTEEB__sequest_fut_SAGCOTint_wgs84_optRectifiedGridCoverage34.553167266769464 -10.03804845049055137.239835341004294 -7.66399436803009734.553167266769464 -10.03804845049055137.239835341004294 -7.663994368030097TZA_kilombero_sequest_fut_SAGCOTwcsequest_fut_SAGCOTwc_wgs84_optWCSGeoTIFFTEEB__sequest_fut_SAGCOTwc_wgs84_optRectifiedGridCoverage34.553167266769464 -10.03804845049055137.239835341004294 -7.66399436803009734.553167266769464 -10.03804845049055137.239835341004294 -7.663994368030097Temperature (2meters) Anomaly 2020 (1991-2020)This variable is the temperature of air at 2m above the surface of land, sea or in-land waters. 2m temperature is calculated by interpolating between the lowest model level and the Earth's surface, taking account of the atmospheric conditions. Credits: https://cds.climate.copernicus.eu/cdsapp#!/dataset/ecv-for-climate-change?tab=overview12month_anomaly_Global_ea_2t_202001-202012_1991-2020_v02.gribWCSGeoTIFFwesr_climat__12month_anomaly_Global_ea_2t_202001-202012_1991-2020_v02RectifiedGridCoverage-179.875 -90.125180.125 90.125-179.875 -90.125180.125 90.125Temperature Seasonality - Climatology (1979-2013) [standard deviation]Standard deviation of the monthly mean temperatures [standard deviation]. CHELSA V1.2 (http://chelsa-climate.org/) is a high resolution (30 arc sec, ~1 km) climate data set for the earth land surface areas. It includes monthly and annual mean temperature and precipitation patterns for the time period 1979-2013. Methods are described in http://chelsa-climate.org/wp-admin/download-page/CHELSA_tech_specification.pdf. CHELSA Version 1.2 is licensed under a Creative Commons Attribution 4.0 International License. Specifications: High resolution (30 arcsec, ~1 km) Precipitation & Temperature Climatologies for the years 1979 – 2013 Incorporation of topoclimate (e.g. orographic rainfall & wind fields). All products of CHELSA are in a geographic coordinate system referenced to the WGS 84 horizontal datum, with the horizontal coordinates expressed in decimal degrees. The CHELSA layer extents (minimum and maximum latitude and longitude) are a result of the coordinate system inherited from the 1-arc-second GMTED2010 data which itself inherited the grid extent from the 1-arc-second SRTM data. Note that because of the pixel center referencing of the input GMTED2010 data the full extent of each CHELSA grid as defined by the outside edges of the pixels differs from an integer value of latitude or longitude by 0.000138888888 degree (or 1/2 arc-second). Users of products based on the legacy GTOPO30 product should note that the coordinate referencing of CHELSA (and GMTED2010) and GTOPO30 are not the same. In GTOPO30, the integer lines of latitude and longitude fall directly on the edges of a 30-arc-second pixel. Thus, when overlaying CHELSA with products based on GTOPO30 a slight shift of 1/2 arc-second will be observed between the edges of corresponding 30-arc-second pixels. To redistribute the data, please cite the following peer reviewed articles: <a href="https://www.nature.com/articles/sdata2017122"target=_blank>Karger, D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N.E., Linder, H.P. & Kessler, M. (2017) Climatologies at high resolution for the earth’s land surface areas. Scientific Data 4, 170122.</a> <a href="https://doi.org/10.5061/dryad.kd1d4"target=_blank>Karger, D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N.E., Linder, H.P., Kessler, M. (2017) Data from: Climatologies at high resolution for the earth’s land surface areas. Dryad Digital Repository. </a> CHELSA – Climatologies at high resolution for the Earth land surface areas. Version 1.2CHELSA_bio10_04_optWCSGeoTIFFUN_Sahel__CHELSA_bio10_04_optRectifiedGridCoverage-180.00013888885002 -90.00013888884999179.99985967115003 83.99986041515001-180.00013888885002 -90.00013888884999179.99985967115003 83.99986041515001Temperature Seasonality [standard deviation] (2041-2060) RCP4.5 - CIMP5Standard deviation of the monthly mean temperatures [standard deviation] for the period 2041-2060 based on RCP4.5. CHELSA-[CMIP5] is a delta change climatological dataset for the years 2041-2060 and 2061- 2080 for mean monthly maximum temperatures, mean monthly minimum temperatures, monthly precipitation amounts, and several derived parameters. We use the delta change method by B-spline interpolation of anomalies (deltas) of the respective CMIP5 GCM dataset. Anomalies were interpolated between all CMIP5 grid cells and are then added (for temperature variables) or multiplied (in case of precipitation) to high resolution climate data from CHELSA V1.2. This method has the assumption that climate only varies on the scale of the coarser (CMIP5) dataset, and the spatial pattern (from CHELSA) is consistent over time. CHELSA- [CMIP5] does not take changing wind patterns, or temperature lapse rates into account, but rather expects them to be constant over time, and similar to the long term averages. CHELSA V1.2 (http://chelsa-climate.org/) is a high resolution (30 arc sec, ~1 km) climate data set for the earth land surface areas. It includes monthly and annual mean temperature and precipitation patterns for the time period 1979-2013. Methods are described in http://chelsa-climate.org/wp-admin/download-page/CHELSA_tech_specification.pdf. CHELSA Version 1.2 is licensed under a Creative Commons Attribution 4.0 International License. Specifications: High resolution (30 arcsec, ~1 km) Precipitation & Temperature Climatologies for the years 1979 – 2013 Incorporation of topoclimate (e.g. orographic rainfall & wind fields). All products of CHELSA are in a geographic coordinate system referenced to the WGS 84 horizontal datum, with the horizontal coordinates expressed in decimal degrees. The CHELSA layer extents (minimum and maximum latitude and longitude) are a result of the coordinate system inherited from the 1-arc-second GMTED2010 data which itself inherited the grid extent from the 1-arc-second SRTM data. Note that because of the pixel center referencing of the input GMTED2010 data the full extent of each CHELSA grid as defined by the outside edges of the pixels differs from an integer value of latitude or longitude by 0.000138888888 degree (or 1/2 arc-second). Users of products based on the legacy GTOPO30 product should note that the coordinate referencing of CHELSA (and GMTED2010) and GTOPO30 are not the same. In GTOPO30, the integer lines of latitude and longitude fall directly on the edges of a 30-arc-second pixel. Thus, when overlaying CHELSA with products based on GTOPO30 a slight shift of 1/2 arc-second will be observed between the edges of corresponding 30-arc-second pixels. To redistribute the data, please cite the following peer reviewed articles: <a href="https://www.nature.com/articles/sdata2017122"target=_blank>Karger, D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N.E., Linder, H.P. & Kessler, M. (2017) Climatologies at high resolution for the earth’s land surface areas. Scientific Data 4, 170122.</a> <a href="https://doi.org/10.5061/dryad.kd1d4"target=_blank>Karger, D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N.E., Linder, H.P., Kessler, M. (2017) Data from: Climatologies at high resolution for the earth’s land surface areas. Dryad Digital Repository. </a> CHELSA – Climatologies at high resolution for the Earth land surface areas. Version 1.2CHELSA_bio_mon_ACCESS1-0_rcp45_r1i1p1_g025.nc_4_2041-2060_V1.2_optWCSGeoTIFFUN_Sahel__CHELSA_bio_mon_ACCESS1-0_rcp45_r1i1p1_g025.nc_4_2041-2060_V1.2_optRectifiedGridCoverage-180.00013888885002 -90.00013888884999179.99985967115003 83.99986041515001-180.00013888885002 -90.00013888884999179.99985967115003 83.99986041515001Temperature Seasonality [standard deviation] (2041-2060) RCP8.5 - CIMP5Standard deviation of the monthly mean temperatures [standard deviation] for the period 2041-2060 based on RCP8.5. CHELSA-[CMIP5] is a delta change climatological dataset for the years 2041-2060 and 2061- 2080 for mean monthly maximum temperatures, mean monthly minimum temperatures, monthly precipitation amounts, and several derived parameters. We use the delta change method by B-spline interpolation of anomalies (deltas) of the respective CMIP5 GCM dataset. Anomalies were interpolated between all CMIP5 grid cells and are then added (for temperature variables) or multiplied (in case of precipitation) to high resolution climate data from CHELSA V1.2. This method has the assumption that climate only varies on the scale of the coarser (CMIP5) dataset, and the spatial pattern (from CHELSA) is consistent over time. CHELSA- [CMIP5] does not take changing wind patterns, or temperature lapse rates into account, but rather expects them to be constant over time, and similar to the long term averages. CHELSA V1.2 (http://chelsa-climate.org/) is a high resolution (30 arc sec, ~1 km) climate data set for the earth land surface areas. It includes monthly and annual mean temperature and precipitation patterns for the time period 1979-2013. Methods are described in http://chelsa-climate.org/wp-admin/download-page/CHELSA_tech_specification.pdf. CHELSA Version 1.2 is licensed under a Creative Commons Attribution 4.0 International License. Specifications: High resolution (30 arcsec, ~1 km) Precipitation & Temperature Climatologies for the years 1979 – 2013 Incorporation of topoclimate (e.g. orographic rainfall & wind fields). All products of CHELSA are in a geographic coordinate system referenced to the WGS 84 horizontal datum, with the horizontal coordinates expressed in decimal degrees. The CHELSA layer extents (minimum and maximum latitude and longitude) are a result of the coordinate system inherited from the 1-arc-second GMTED2010 data which itself inherited the grid extent from the 1-arc-second SRTM data. Note that because of the pixel center referencing of the input GMTED2010 data the full extent of each CHELSA grid as defined by the outside edges of the pixels differs from an integer value of latitude or longitude by 0.000138888888 degree (or 1/2 arc-second). Users of products based on the legacy GTOPO30 product should note that the coordinate referencing of CHELSA (and GMTED2010) and GTOPO30 are not the same. In GTOPO30, the integer lines of latitude and longitude fall directly on the edges of a 30-arc-second pixel. Thus, when overlaying CHELSA with products based on GTOPO30 a slight shift of 1/2 arc-second will be observed between the edges of corresponding 30-arc-second pixels. To redistribute the data, please cite the following peer reviewed articles: <a href="https://www.nature.com/articles/sdata2017122"target=_blank>Karger, D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N.E., Linder, H.P. & Kessler, M. (2017) Climatologies at high resolution for the earth’s land surface areas. Scientific Data 4, 170122.</a> <a href="https://doi.org/10.5061/dryad.kd1d4"target=_blank>Karger, D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N.E., Linder, H.P., Kessler, M. (2017) Data from: Climatologies at high resolution for the earth’s land surface areas. Dryad Digital Repository. </a> CHELSA – Climatologies at high resolution for the Earth land surface areas. Version 1.2CHELSA_bio_mon_ACCESS1-0_rcp85_r1i1p1_g025.nc_4_2041-2060_V1.2_optWCSGeoTIFFUN_Sahel__CHELSA_bio_mon_ACCESS1-0_rcp85_r1i1p1_g025.nc_4_2041-2060_V1.2_optRectifiedGridCoverage-180.00013888885002 -90.00013888884999179.99985967115003 83.99986041515001-180.00013888885002 -90.00013888884999179.99985967115003 83.99986041515001Temperature anomalies (°C) 2045-2065 (RCP4.5)Temperature anomalies (°C) for the period 2045-2065 based on the climate scenario RCP4.5 (reference 1986-2005).temperaturesMedicip__tas_45_46_65RectifiedGridCoverage-26.619999885559082 -6.81999993324279875.4600133895874 45.10000681877136-26.619999885559082 -6.81999993324279875.4600133895874 45.10000681877136Temperature anomalies (°C) 2045-2065 (RCP8.5)Temperature anomalies (°C) for the period 2045-2065 based on the climate scenario RCP8.5 (reference 1986-2005).temperaturesMedicip__tas_85_46_65RectifiedGridCoverage-26.619999885559082 -6.81999993324279875.4600133895874 45.10000681877136-26.619999885559082 -6.81999993324279875.4600133895874 45.10000681877136Temperature anomalies (°C) 2081-2100 (RCP4.5)Temperature anomalies (°C) for the period 2081-2100 based on the climate scenario RCP4.5 (reference 1986-2005).temperaturesMedicip__tas_45_81_00RectifiedGridCoverage-26.619999885559082 -6.81999993324279875.4600133895874 45.10000681877136-26.619999885559082 -6.81999993324279875.4600133895874 45.10000681877136Temperature anomalies (°C) 2081-2100 (RCP8.5)Temperature anomalies (°C) for the period 2081-2100 based on the climate scenario RCP8.5 (reference 1986-2005).temperaturesMedicip__tas_85_81_00RectifiedGridCoverage-26.619999885559082 -6.81999993324279875.4600133895874 45.10000681877136-26.619999885559082 -6.81999993324279875.4600133895874 45.10000681877136Temperature averages (°C) 1986-2005 (RCP4.5)Temperatures averages in the reference period 1986-2005 based on the climate scenario RCP4.5.temperaturesMedicip__tas_45_86_05RectifiedGridCoverage-26.619999885559082 -6.81999993324279875.4600133895874 45.10000681877136-26.619999885559082 -6.81999993324279875.4600133895874 45.10000681877136Temperature averages (°C) 1986-2005 (RCP8.5)Temperatures averages in the reference period 1986-2005 based on the climate scenario RCP8.5.temperaturesMedicip__tas_85_86_05RectifiedGridCoverage-26.619999885559082 -6.81999993324279875.4600133895874 45.10000681877136-26.619999885559082 -6.81999993324279875.4600133895874 45.10000681877136Temperature change trend (1991-2000)Trend of year temperatures change in '°C per year' across the period 1991-2000. Calculated at UNEP/GRID-Geneva in R based on CRU TS 4.07 data available <a href="https://crudata.uea.ac.uk/cru/data/hrg/cru_ts_4.07/cruts.2304141047.v4.07/tmp/"target=_blank>here</a> <br> Read release notes of CRU TS 4.07 at <a href="https://crudata.uea.ac.uk/cru/data/hrg/cru_ts_4.07/Release_Notes_CRU_TS_4.07.txt"target=_blank>this link</a> <br><br> Credits: <br> - <a href="https://sites.uea.ac.uk/cru/"target=_blank>Climatic Research Unit (University of East Anglia) and Met Office</a><br> - <a href="https://doi.org/10.1038/s41597-020-0453-3"target=_blank>Harris, I., Osborn, T.J., Jones, P. et al. Version 4 of the CRU TS monthly high-resolution gridded multivariate climate dataset.Sci Data 7, 109 (2020).</a>cru_tmp_91_00_lmWCSGeoTIFFwesr_climat__cru_tmp_91_00_lmRectifiedGridCoverage-180.0 -90.0180.0 90.0-180.0 -90.0180.0 90.0Temperature change trend (1991-2022)Trend of year temperatures change in '°C per year' across the period 1991-2022. Calculated at UNEP/GRID-Geneva in R based on CRU TS 4.07 data available <a href="https://crudata.uea.ac.uk/cru/data/hrg/cru_ts_4.07/cruts.2304141047.v4.07/tmp/"target=_blank>here</a> <br> Read release notes of CRU TS 4.07 at <a href="https://crudata.uea.ac.uk/cru/data/hrg/cru_ts_4.07/Release_Notes_CRU_TS_4.07.txt"target=_blank>this link</a> <br><br> Credits: <br> - <a href="https://sites.uea.ac.uk/cru/"target=_blank>Climatic Research Unit (University of East Anglia) and Met Office</a><br> - <a href="https://doi.org/10.1038/s41597-020-0453-3"target=_blank>Harris, I., Osborn, T.J., Jones, P. et al. Version 4 of the CRU TS monthly high-resolution gridded multivariate climate dataset.Sci Data 7, 109 (2020).</a>cru_tmp_91_22_lmWCSGeoTIFFwesr_climat__cru_tmp_91_22_lmRectifiedGridCoverage-180.0 -90.0180.0 90.0-180.0 -90.0180.0 90.0Temperature change trend (2001-2010)Trend of year temperatures change in '°C per year' across the period 2001-2010. Calculated at UNEP/GRID-Geneva in R based on CRU TS 4.07 data available <a href="https://crudata.uea.ac.uk/cru/data/hrg/cru_ts_4.07/cruts.2304141047.v4.07/tmp/"target=_blank>here</a> <br> Read release notes of CRU TS 4.07 at <a href="https://crudata.uea.ac.uk/cru/data/hrg/cru_ts_4.07/Release_Notes_CRU_TS_4.07.txt"target=_blank>this link</a> <br><br> Credits: <br> - <a href="https://sites.uea.ac.uk/cru/"target=_blank>Climatic Research Unit (University of East Anglia) and Met Office</a><br> - <a href="https://doi.org/10.1038/s41597-020-0453-3"target=_blank>Harris, I., Osborn, T.J., Jones, P. et al. Version 4 of the CRU TS monthly high-resolution gridded multivariate climate dataset.Sci Data 7, 109 (2020).</a>cru_tmp_01_10_lmWCSGeoTIFFwesr_climat__cru_tmp_01_10_lmRectifiedGridCoverage-180.0 -90.0180.0 90.0-180.0 -90.0180.0 90.0Temperature change trend (2011-2020)Trend of year temperatures change in '°C per year' across the period 2011-2020. Calculated at UNEP/GRID-Geneva in R based on CRU TS 4.07 data available <a href="https://crudata.uea.ac.uk/cru/data/hrg/cru_ts_4.07/cruts.2304141047.v4.07/tmp/"target=_blank>here</a> <br> Read release notes of CRU TS 4.07 at <a href="https://crudata.uea.ac.uk/cru/data/hrg/cru_ts_4.07/Release_Notes_CRU_TS_4.07.txt"target=_blank>this link</a> <br><br> Credits: <br> - <a href="https://sites.uea.ac.uk/cru/"target=_blank>Climatic Research Unit (University of East Anglia) and Met Office</a><br> - <a href="https://doi.org/10.1038/s41597-020-0453-3"target=_blank>Harris, I., Osborn, T.J., Jones, P. et al. Version 4 of the CRU TS monthly high-resolution gridded multivariate climate dataset.Sci Data 7, 109 (2020).</a>cru_tmp_11_20_lmWCSGeoTIFFwesr_climat__cru_tmp_11_20_lmRectifiedGridCoverage-180.0 -90.0180.0 90.0-180.0 -90.0180.0 90.0Testing a layer for training20190927_843_mergedQGWCSGeoTIFFwesr_swipe__20190927_843_mergedQG_to_deleteRectifiedGridCoverage199980.0 5990200.0309780.0 6400000.0149.66386433258654 -36.21435579772867150.97478007537504 -32.49681353198739Threatened Mammals of the World (Jenkins, Pimm & Joppa - 2013)Maps of mammal diversity include the total species richness, major taxonomic Orders (Cetartiodactyla, Carnivora, Primates, Eulipotyphla, Chiroptera, Rodentia), marsupials, threatened species, and small-ranged species. Species considered small-ranged are those with a geographic range size smaller than the global median (i.e., the 50% of species with the smallest ranges). Maps are based on data from the IUCN, July 2013 update. All maps were generated at a spatial resolution of 10×10 km and use the Eckert IV equal-area projection. Maps represent native, extant species only. Tables of the mammal species included are available for download here: http://biodiversitymapping.org/wordpress/index.php/mammals/ Direct Link to download:http://biodiversitymapping.org/wordpress/index.php/download/wld_richness_10km_mammals_threatenedWCSGeoTIFFMAPX_nd__wld_richness_10km_mammals_threatenedRectifiedGridCoverage-179.49349086582234 -56.61353702601424179.52408225466056 83.78813167493983-179.49349086582234 -56.61353702601424179.52408225466056 83.78813167493983Threatened species richnessThis raster layer represents the number of threatened species of mammals, amphibians and birds potentially occurring in each ~1km grid cell. Species range data were rasterised at 30 arc-seconds (approximately 1km at the equator) from IUCN Red List polygons (IUCN 2020) for those with a Red List status of Critically Endangered, Endangered or Vulnerable. Raster layers for each species were summed together with equal weighting into a single richness layer.<br><br>The species included are used as a proxy to represent threatened terrestrial biodiversity and are based on taxonomic groups that have been comprehensively assessed by IUCN Red List. Thus users should be aware of the taxonomic bias and that marine values are based only on marine species of mammals and birds.<br/><br>Source: IUCN 2020. The IUCN Red List of Threatened Species. Version 2020-4 <a href="http://www.iucnredlist.org" target="_blank">IUCNRedList</a>.<br><br>Terms of Use: <a href="http://www.iucnredlist.org/info/terms-of-use" target="_blank">Commercial use is not permitted</a><br/>threatened_species_richness_optWCSGeoTIFFMapX_UNDP__threatened_species_richness_optRectifiedGridCoverage-179.995201157628 -84.69466806618583179.8859620772232 85.91771612300938-179.995201157628 -84.69466806618583179.8859620772232 85.91771612300938Total built-up surface in 1975The spatial raster dataset depicts the distribution of built-up surfaces, expressed as number of square metres. Credits: Pesaresi M., Politis P. (2023): GHS-BUILT-S R2023A - GHS built-up surface grid, derived from Sentinel2 composite and Landsat, multitemporal (1975-2030)European Commission, Joint Research Centre (JRC) PID: http://data.europa.eu/89h/9f06f36f-4b11-47ec-abb0-4f8b7b1d72ea, doi:10.2905/9F06F36F-4B11-47EC-ABB0-4F8B7B1D72EAbuilt-uppaprac__ghs_built_surf_100m_1975RectifiedGridCoverage-23.264767173073253 16.25907770419581852.90404528839234 51.21395264452012-23.264767173073253 16.25907770419581852.90404528839234 51.21395264452012Total built-up surface in 198The spatial raster dataset depicts the distribution of built-up surfaces, expressed as number of square metres. Credits: Pesaresi M., Politis P. (2023): GHS-BUILT-S R2023A - GHS built-up surface grid, derived from Sentinel2 composite and Landsat, multitemporal (1975-2030)European Commission, Joint Research Centre (JRC) PID: http://data.europa.eu/89h/9f06f36f-4b11-47ec-abb0-4f8b7b1d72ea, doi:10.2905/9F06F36F-4B11-47EC-ABB0-4F8B7B1D72EAbuilt-uppaprac__ghs_built_surf_100m_1980RectifiedGridCoverage-23.264767173073253 16.25907770419581852.90404528839234 51.21395264452012-23.264767173073253 16.25907770419581852.90404528839234 51.21395264452012Total built-up surface in 1985The spatial raster dataset depicts the distribution of built-up surfaces, expressed as number of square metres. Credits: Pesaresi M., Politis P. (2023): GHS-BUILT-S R2023A - GHS built-up surface grid, derived from Sentinel2 composite and Landsat, multitemporal (1975-2030)European Commission, Joint Research Centre (JRC) PID: http://data.europa.eu/89h/9f06f36f-4b11-47ec-abb0-4f8b7b1d72ea, doi:10.2905/9F06F36F-4B11-47EC-ABB0-4F8B7B1D72EAbuilt-uppaprac__ghs_built_surf_100m_1985RectifiedGridCoverage-23.264767173073253 16.25907770419581852.90404528839234 51.21395264452012-23.264767173073253 16.25907770419581852.90404528839234 51.21395264452012Total built-up surface in 1990The spatial raster dataset depicts the distribution of built-up surfaces, expressed as number of square metres. Credits: Pesaresi M., Politis P. (2023): GHS-BUILT-S R2023A - GHS built-up surface grid, derived from Sentinel2 composite and Landsat, multitemporal (1975-2030)European Commission, Joint Research Centre (JRC) PID: http://data.europa.eu/89h/9f06f36f-4b11-47ec-abb0-4f8b7b1d72ea, doi:10.2905/9F06F36F-4B11-47EC-ABB0-4F8B7B1D72EAbuilt-uppaprac__ghs_built_surf_100m_1990RectifiedGridCoverage-23.264767173073253 16.25907770419581852.90404528839234 51.21395264452012-23.264767173073253 16.25907770419581852.90404528839234 51.21395264452012Total built-up surface in 1995The spatial raster dataset depicts the distribution of built-up surfaces, expressed as number of square metres. Credits: Pesaresi M., Politis P. (2023): GHS-BUILT-S R2023A - GHS built-up surface grid, derived from Sentinel2 composite and Landsat, multitemporal (1975-2030)European Commission, Joint Research Centre (JRC) PID: http://data.europa.eu/89h/9f06f36f-4b11-47ec-abb0-4f8b7b1d72ea, doi:10.2905/9F06F36F-4B11-47EC-ABB0-4F8B7B1D72EAbuilt-uppaprac__ghs_built_surf_100m_1995RectifiedGridCoverage-23.264767173073253 16.25907770419581852.90404528839234 51.21395264452012-23.264767173073253 16.25907770419581852.90404528839234 51.21395264452012Total built-up surface in 2000The spatial raster dataset depicts the distribution of built-up surfaces, expressed as number of square metres. Credits: Pesaresi M., Politis P. (2023): GHS-BUILT-S R2023A - GHS built-up surface grid, derived from Sentinel2 composite and Landsat, multitemporal (1975-2030)European Commission, Joint Research Centre (JRC) PID: http://data.europa.eu/89h/9f06f36f-4b11-47ec-abb0-4f8b7b1d72ea, doi:10.2905/9F06F36F-4B11-47EC-ABB0-4F8B7B1D72EAbuilt-uppaprac__ghs_built_surf_100m_2000RectifiedGridCoverage-23.264767173073253 16.25907770419581852.90404528839234 51.21395264452012-23.264767173073253 16.25907770419581852.90404528839234 51.21395264452012Total built-up surface in 2005The spatial raster dataset depicts the distribution of built-up surfaces, expressed as number of square metres. Credits: Pesaresi M., Politis P. (2023): GHS-BUILT-S R2023A - GHS built-up surface grid, derived from Sentinel2 composite and Landsat, multitemporal (1975-2030)European Commission, Joint Research Centre (JRC) PID: http://data.europa.eu/89h/9f06f36f-4b11-47ec-abb0-4f8b7b1d72ea, doi:10.2905/9F06F36F-4B11-47EC-ABB0-4F8B7B1D72EAbuilt-uppaprac__ghs_built_surf_100m_2005RectifiedGridCoverage-23.264767173073253 16.25907770419581852.90404528839234 51.21395264452012-23.264767173073253 16.25907770419581852.90404528839234 51.21395264452012Total built-up surface in 2010The spatial raster dataset depicts the distribution of built-up surfaces, expressed as number of square metres. Credits: Pesaresi M., Politis P. (2023): GHS-BUILT-S R2023A - GHS built-up surface grid, derived from Sentinel2 composite and Landsat, multitemporal (1975-2030)European Commission, Joint Research Centre (JRC) PID: http://data.europa.eu/89h/9f06f36f-4b11-47ec-abb0-4f8b7b1d72ea, doi:10.2905/9F06F36F-4B11-47EC-ABB0-4F8B7B1D72EAbuilt-uppaprac__ghs_built_surf_100m_2010RectifiedGridCoverage-23.264767173073253 16.25907770419581852.90404528839234 51.21395264452012-23.264767173073253 16.25907770419581852.90404528839234 51.21395264452012Total built-up surface in 2015The spatial raster dataset depicts the distribution of built-up surfaces, expressed as number of square metres. Credits: Pesaresi M., Politis P. (2023): GHS-BUILT-S R2023A - GHS built-up surface grid, derived from Sentinel2 composite and Landsat, multitemporal (1975-2030)European Commission, Joint Research Centre (JRC) PID: http://data.europa.eu/89h/9f06f36f-4b11-47ec-abb0-4f8b7b1d72ea, doi:10.2905/9F06F36F-4B11-47EC-ABB0-4F8B7B1D72EAbuilt-uppaprac__ghs_built_surf_100m_2015RectifiedGridCoverage-23.264767173073253 16.25907770419581852.90404528839234 51.21395264452012-23.264767173073253 16.25907770419581852.90404528839234 51.21395264452012Total built-up surface in 2020The spatial raster dataset depicts the distribution of built-up surfaces, expressed as number of square metres. Credits: Pesaresi M., Politis P. (2023): GHS-BUILT-S R2023A - GHS built-up surface grid, derived from Sentinel2 composite and Landsat, multitemporal (1975-2030)European Commission, Joint Research Centre (JRC) PID: http://data.europa.eu/89h/9f06f36f-4b11-47ec-abb0-4f8b7b1d72ea, doi:10.2905/9F06F36F-4B11-47EC-ABB0-4F8B7B1D72EAbuilt-uppaprac__ghs_built_surf_100m_2020RectifiedGridCoverage-23.264767173073253 16.25907770419581852.90404528839234 51.21395264452012-23.264767173073253 16.25907770419581852.90404528839234 51.21395264452012Total built-up surface in 2025The spatial raster dataset depicts the distribution of built-up surfaces, expressed as number of square metres. Credits: Pesaresi M., Politis P. (2023): GHS-BUILT-S R2023A - GHS built-up surface grid, derived from Sentinel2 composite and Landsat, multitemporal (1975-2030)European Commission, Joint Research Centre (JRC) PID: http://data.europa.eu/89h/9f06f36f-4b11-47ec-abb0-4f8b7b1d72ea, doi:10.2905/9F06F36F-4B11-47EC-ABB0-4F8B7B1D72EAbuilt-uppaprac__ghs_built_surf_100m_2025RectifiedGridCoverage-23.264767173073253 16.25907770419581852.90404528839234 51.21395264452012-23.264767173073253 16.25907770419581852.90404528839234 51.21395264452012Total built-up surface in 2030The spatial raster dataset depicts the distribution of built-up surfaces, expressed as number of square metres. Credits: Pesaresi M., Politis P. (2023): GHS-BUILT-S R2023A - GHS built-up surface grid, derived from Sentinel2 composite and Landsat, multitemporal (1975-2030)European Commission, Joint Research Centre (JRC) PID: http://data.europa.eu/89h/9f06f36f-4b11-47ec-abb0-4f8b7b1d72ea, doi:10.2905/9F06F36F-4B11-47EC-ABB0-4F8B7B1D72EAbuilt-uppaprac__ghs_built_surf_100m_2030RectifiedGridCoverage-23.264767173073253 16.25907770419581852.90404528839234 51.21395264452012-23.264767173073253 16.25907770419581852.90404528839234 51.21395264452012Travel_time_final_Benin_optTravel_time_final_Benin_optWCSGeoTIFFAccessMod__Travel_time_final_Benin_optRectifiedGridCoverage0.7572938644426767 6.2324830459155073.847729210168526 12.4141867388081020.7572938644426767 6.2324830459155073.847729210168526 12.414186738808102Travel_time_final_Burundi_optTravel_time_final_Burundi_optWCSGeoTIFFAccessMod__Travel_time_final_Burundi_optRectifiedGridCoverage28.995910775304033 -4.47761748803115230.8592085848395 -2.30599222342943928.995910775304033 -4.47761748803115230.8592085848395 -2.305992223429439Travel_time_final_CIV_optTravel_time_final_CIV_optWCSGeoTIFFAccessMod__Travel_time_final_CIV_optRectifiedGridCoverage-8.58395416756133 4.361274354409544-2.4925843424414804 10.745411290664995-8.58395416756133 4.361274354409544-2.4925843424414804 10.745411290664995Travel_time_final_Guinee2_optTravel_time_final_Guinee2_optWCSGeoTIFFAccessMod__Travel_time_final_Guinee2_optRectifiedGridCoverage-15.12552847903117 7.153551977476363-7.622479365710555 12.71750787814737-15.12552847903117 7.153551977476363-7.622479365710555 12.71750787814737Travel_time_final_Lekoumou_optTravel_time_final_Lekoumou_optWCSGeoTIFFAccessMod__Travel_time_final_Lekoumou_optRectifiedGridCoverage12.796059046335326 -4.05279724996322214.35138242981778 -2.08800267007868512.796059046335326 -4.05279724996322214.35138242981778 -2.088002670078685Travel_time_final_Madagascar_1_optTravel_time_final_Madagascar_1_optWCSGeoTIFFAccessMod__Travel_time_final_Madagascar_1_optRectifiedGridCoverage423301.07155636325 7382080.5939300211006792.0040066247 8675262.9493514544.24783271042902 -23.6705714485262349.9629004390912 -11.945018130107131Travel_time_final_Sangha_optTravel_time_final_Sangha_optWCSGeoTIFFAccessMod__Travel_time_final_Sangha_optRectifiedGridCoverage13.12388346995083 0.03574461520971139416.999434297770676 2.72291524502080913.12388346995083 0.035744615209711416.999434297770676 2.722915245020809Travel_time_final_Senegal2_optTravel_time_final_Senegal2_optWCSGeoTIFFAccessMod__Travel_time_final_Senegal2_optRectifiedGridCoverage-17.55393284087355 12.29686893595293-11.27780942134996 16.693373854449966-17.55393284087355 12.29686893595293-11.27780942134996 16.693373854449966Travel_time_final_Sudan_optTravel_time_final_Sudan_optWCSGeoTIFFAccessMod__Travel_time_final_Sudan_optRectifiedGridCoverage21.168981361587942 8.58260690211387238.777586812876365 23.162565295663621.168981361587942 8.58260690211387238.777586812876365 23.1625652956636Travel_time_final_Tchad_optTravel_time_final_Tchad_optWCSGeoTIFFAccessMod__Travel_time_final_Tchad_optRectifiedGridCoverage13.057871509865597 7.413273566429996524.14820348041431 23.5308214680455613.057871509865597 7.413273566429996524.14820348041431 23.53082146804556Travel_time_final_Togo_optTravel_time_final_Togo_optWCSGeoTIFFAccessMod__Travel_time_final_Togo_optRectifiedGridCoverage-0.14628583773611478 5.6398389709594961.832407081892993 11.150878574350621-0.1462858377361148 5.6398389709594961.832407081892993 11.150878574350621Tree Canopy Change 1982 - 2016This analysis of 35 years’ worth of satellite data (at approximately 25 square kilometer resolution at the equator) provides a comprehensive record of global land-change dynamics during the period 1982–2016. Contrary to the prevailing view that forest area has declined globally — tree cover has increased by 2.24 million km2 (+7.1% relative to the 1982 level), largely the result of a net loss in the tropics being outweighed by a net gain in the extratropics. Global bare ground cover has decreased by 1.16 million km2 (−3.1%), most notably in agricultural regions in Asia. Of all land changes, 60% are associated with direct human activities and 40% with indirect drivers such as climate change. Land-use change exhibits regional dominance, including tropical deforestation and agricultural expansion, temperate reforestation or afforestation, cropland intensification and urbanization. Consistently across all climate domains, montane systems have gained tree cover and many arid and semi-arid ecosystems have lost vegetation cover.<br><br>For full details see: <a href="https://doi.org/10.1038/s41586-018-0411-9">Song, X.-P., Hansen, M.C., Stehman, S.V., Potapov, P.V., Tyukavina, A., Vermote, E.F., Townshend, J.R., 2018. Global land change from 1982 to 2016. Nature 1</a><br/>.Tree_canopy_change_1982-2016WCSGeoTIFFMapX_UNDP__Tree_canopy_change_1982-2016RectifiedGridCoverage-180.0 -90.0180.0 90.0-180.0 -90.0180.0 90.0Tree Cover Based on Satellite Data (University of Maryland - 2018)Source: The map is published on UNEP's South Sudan: First State of Environment and Outlook Report 2018 with a source identified as University of Maryland, 2018, no date indicated. The UNEP's report could be found <a href="https://www.unenvironment.org/resources/report/south-sudan-first-state-environment-and-outlook-report-2018" target="_blank"> here </a> <br><br>There are no reliable data on the extent of forests in South Sudan, since a detailed forest survey and inventory has never been carried out. Analyses based on remote sensing exist, which provide different estimates, but they have not been verified on the ground, so the accuracy of such products is unknown. The map is a satellite image that suggests the total area of tree cover in South Sudan is almost 20,000,000 ha (19,166,700 ha or 191,667 km2), which represents about 30 per cent of the country’s total land area (<a href="https://www.cbd.int/doc/world/ss/ss-nr-05-en.pdf" target="_blank"> MOE, 2015 </a>). This includes natural forests and woodlands, tropical moist forests on the hills, in the mountains and in the Nile-Congo watershed, and forests in National Parks and game reserves.SSD_treecover_optWCSGeoTIFFMAPX_nd__SSD_treecover_optRectifiedGridCoverage24.147119842068182 3.456204904839905736.000094869180025 12.2494584714647424.147119842068182 3.456204904839905736.000094869180025 12.24945847146474Tree Cover Loss (2001-2024)Forest loss during the period 2001-2024, defined as a stand-replacement disturbance, or a change from a forest to non-forest state. Encoded as either 0 (no loss) or else a value in the range 1-24, representing loss detected primarily in the year 2001-2024, respectively.tree_cover_loss_2000_2024_optWCSGeoTIFFpeatlands__tree_cover_loss_2001_2024RectifiedGridCoverage10.960797274810812 -13.50001991307506431.421883855574613 5.4645449338009310.960797274810812 -13.50001991307506431.421883855574613 5.46454493380093Tree Cover Loss YearForest loss during the period 2000-2022, defined as a stand-replacement disturbance, or a change from a forest to non-forest state. Encoded as either 0 (no loss) or else a value in the range 1-22, representing loss detected primarily in the year 2001-2022, respectively.tree_mosaic_mask_wgs84_optWCSGeoTIFFpeatlands__tree_mosaic_mask_wgs84_optRectifiedGridCoverage11.089197577536725 -13.46346204335632831.362924324738536 5.42804471101684711.089197577536725 -13.46346204335632831.362924324738536 5.428044711016847Tree cover fraction - 2019 (Copernicus)The layer shows the tree cover expressed as a percentage of ground cover per pixel. It is sourced from the Copernicus Global Land Service: Land Cover 100m: collection 3: epoch 2019: Globe (https://zenodo.org/record/3939050#.X33YymgzaUk). Please visit https://land.copernicus.eu/global/products/lc for more information. Credits: Buchhorn, M.; Smets, B.; Bertels, L.; Lesiv, M.; Tsendbazar, N.-E.; Masiliunas, D.; Linlin, L.; Herold, M.; Fritz, S. (2020). Copernicus Global Land Service: Land Cover 100m: Collection 3: epoch <2019>: Globe (Version V3.0.1) [Data set]. Zenodo. DOI: 10.5281/zenodo.3939050Copernicus_PROBAV_LC100_global_v3.0.1_2019-nrt_Tree-CoverFraction-layer_EPSG-4326_optWCSGeoTIFFwesr_biodiversity__Copernicus_PROBAV_LC100_global_v3.0.1_2019-nrt_Tree-CoverFraction-layer_EPSG-4326_optRectifiedGridCoverage-180.0 -59.999999999999545179.9999999999988 80.0-180.0 -59.999999999999545179.9999999999988 80.0Tropical Cyclone Intensitycy_intensityWCSGeoTIFFwesr_risk__cy_intensityRectifiedGridCoverage-180.0 -90.00000000000003179.99599307699998 83.99199665399996-180.0 -90.00000000000003179.99599307699998 83.99199665399996Tropical Cyclone Sum of Windcy_sumwindsWCSGeoTIFFwesr_risk__cy_sumwindsRectifiedGridCoverage-180.0 -90.00000000000003179.99599307699998 83.99199665399996-180.0 -90.00000000000003179.99599307699998 83.99199665399996Tropical Cyclone Wind 100 Years Return Period (km/h)The tropical cyclonic strong wind and storm surge hazard model uses information from 2594 historical tropical cyclones besides data on topography, terrain roughness, and bathymetry. The historical tropical cyclones used in the cyclone wind and storm surge hazard model for the GAR Atlas cover the five different oceanic basins: Northeast Pacific, Northwest Pacific, South Pacific, North Indian, South Indian and North Atlantic. In all cases, the data associated to each track were obtained from the IBTrACS database (Knapp et al., 2010). Topography data from the Shuttle Radar Topography Mission (SRTM) of NASA, which provides terrain elevation grids at a 90 meters resolution, delivered by quadrants over the world was used and, additionally, to account for surface roughness, polygons of urban areas worldwide were obtained from the Socioeconomic Data and Applications Centre, SEDAC (CIESIN et al., 2011). A digital bathymetry model, with a spatial resolution of 30 arc-seconds, from the GEBCO_08 (General Bathymetric Chart of the Oceans) Grid Database of the British Oceanographic Data Centre (2009) was used. More information about the cyclone wind and storm surge hazard model can be found in Cardona et al., 2015. Hazard analysis was performed using the software CAPRA Team Tropical Cyclones Hazard Modeler (Bernal, 2014).cy_hazard_100_yrpWCSGeoTIFFwesr_risk__cy_hazard_100_yrpRectifiedGridCoverage-180.13566066745048 -55.13566066745048179.9077507461337 59.904585330559826-180.13566066745048 -55.13566066745048179.9077507461337 59.904585330559826Tropical Cyclone Wind 1000 Years Return Period (km/h)The tropical cyclonic strong wind and storm surge hazard model uses information from 2594 historical tropical cyclones besides data on topography, terrain roughness, and bathymetry. The historical tropical cyclones used in the cyclone wind and storm surge hazard model for the GAR Atlas cover the five different oceanic basins: Northeast Pacific, Northwest Pacific, South Pacific, North Indian, South Indian and North Atlantic. In all cases, the data associated to each track were obtained from the IBTrACS database (Knapp et al., 2010). Topography data from the Shuttle Radar Topography Mission (SRTM) of NASA, which provides terrain elevation grids at a 90 meters resolution, delivered by quadrants over the world was used and, additionally, to account for surface roughness, polygons of urban areas worldwide were obtained from the Socioeconomic Data and Applications Centre, SEDAC (CIESIN et al., 2011). A digital bathymetry model, with a spatial resolution of 30 arc-seconds, from the GEBCO_08 (General Bathymetric Chart of the Oceans) Grid Database of the British Oceanographic Data Centre (2009) was used. More information about the cyclone wind and storm surge hazard model can be found in Cardona et al., 2015. Hazard analysis was performed using the software CAPRA Team Tropical Cyclones Hazard Modeler (Bernal, 2014).cy_hazard_1000_yrpWCSGeoTIFFwesr_risk__cy_hazard_1000_yrpRectifiedGridCoverage-180.13566066745048 -55.13566066745048179.9077507461337 59.904585330559826-180.13566066745048 -55.13566066745048179.9077507461337 59.904585330559826Tropical Cyclone Wind 250 Years Return Period (km/h)The tropical cyclonic strong wind and storm surge hazard model uses information from 2594 historical tropical cyclones besides data on topography, terrain roughness, and bathymetry. The historical tropical cyclones used in the cyclone wind and storm surge hazard model for the GAR Atlas cover the five different oceanic basins: Northeast Pacific, Northwest Pacific, South Pacific, North Indian, South Indian and North Atlantic. In all cases, the data associated to each track were obtained from the IBTrACS database (Knapp et al., 2010). Topography data from the Shuttle Radar Topography Mission (SRTM) of NASA, which provides terrain elevation grids at a 90 meters resolution, delivered by quadrants over the world was used and, additionally, to account for surface roughness, polygons of urban areas worldwide were obtained from the Socioeconomic Data and Applications Centre, SEDAC (CIESIN et al., 2011). A digital bathymetry model, with a spatial resolution of 30 arc-seconds, from the GEBCO_08 (General Bathymetric Chart of the Oceans) Grid Database of the British Oceanographic Data Centre (2009) was used. More information about the cyclone wind and storm surge hazard model can be found in Cardona et al., 2015. Hazard analysis was performed using the software CAPRA Team Tropical Cyclones Hazard Modeler (Bernal, 2014).cy_hazard_250_yrpWCSGeoTIFFwesr_risk__cy_hazard_250_yrpRectifiedGridCoverage-180.13566066745048 -55.13566066745048179.9077507461337 59.904585330559826-180.13566066745048 -55.13566066745048179.9077507461337 59.904585330559826Tropical Cyclone Wind 50 Years Return Period (km/h)The tropical cyclonic strong wind and storm surge hazard model uses information from 2594 historical tropical cyclones besides data on topography, terrain roughness, and bathymetry. The historical tropical cyclones used in the cyclone wind and storm surge hazard model for the GAR Atlas cover the five different oceanic basins: Northeast Pacific, Northwest Pacific, South Pacific, North Indian, South Indian and North Atlantic. In all cases, the data associated to each track were obtained from the IBTrACS database (Knapp et al., 2010). Topography data from the Shuttle Radar Topography Mission (SRTM) of NASA, which provides terrain elevation grids at a 90 meters resolution, delivered by quadrants over the world was used and, additionally, to account for surface roughness, polygons of urban areas worldwide were obtained from the Socioeconomic Data and Applications Centre, SEDAC (CIESIN et al., 2011). A digital bathymetry model, with a spatial resolution of 30 arc-seconds, from the GEBCO_08 (General Bathymetric Chart of the Oceans) Grid Database of the British Oceanographic Data Centre (2009) was used. More information about the cyclone wind and storm surge hazard model can be found in Cardona et al., 2015. Hazard analysis was performed using the software CAPRA Team Tropical Cyclones Hazard Modeler (Bernal, 2014).cy_hazard_50_yrpWCSGeoTIFFwesr_risk__cy_hazard_50_yrpRectifiedGridCoverage-180.13566066745048 -55.13566066745048179.9077507461337 59.904585330559826-180.13566066745048 -55.13566066745048179.9077507461337 59.904585330559826Tropical Cyclone Wind 500 Years Return Period (km/h)The tropical cyclonic strong wind and storm surge hazard model uses information from 2594 historical tropical cyclones besides data on topography, terrain roughness, and bathymetry. The historical tropical cyclones used in the cyclone wind and storm surge hazard model for the GAR Atlas cover the five different oceanic basins: Northeast Pacific, Northwest Pacific, South Pacific, North Indian, South Indian and North Atlantic. In all cases, the data associated to each track were obtained from the IBTrACS database (Knapp et al., 2010). Topography data from the Shuttle Radar Topography Mission (SRTM) of NASA, which provides terrain elevation grids at a 90 meters resolution, delivered by quadrants over the world was used and, additionally, to account for surface roughness, polygons of urban areas worldwide were obtained from the Socioeconomic Data and Applications Centre, SEDAC (CIESIN et al., 2011). A digital bathymetry model, with a spatial resolution of 30 arc-seconds, from the GEBCO_08 (General Bathymetric Chart of the Oceans) Grid Database of the British Oceanographic Data Centre (2009) was used. More information about the cyclone wind and storm surge hazard model can be found in Cardona et al., 2015. Hazard analysis was performed using the software CAPRA Team Tropical Cyclones Hazard Modeler (Bernal, 2014).cy_hazard_500_yrpWCSGeoTIFFwesr_risk__cy_hazard_500_yrpRectifiedGridCoverage-180.13566066745048 -55.13566066745048179.9077507461337 59.904585330559826-180.13566066745048 -55.13566066745048179.9077507461337 59.904585330559826Tropical Cyclones FrequencyThis dataset includes an estimate of tropical cyclone frequency of Saffir-Simpson category 5. It is based on two sources: 1) IBTrACS v02r01 (1969 - 2008, http://www.ncdc.noaa.gov/oa/ibtracs/), year 2009 completed by online data from JMA, JTWC, UNISYS, Meteo France and data sent by Alan Sharp from the Australian Bureau of Meteorology. 2) A GIS modeling based on an initial equation from Greg Holland, which was further modified to take into consideration the movement of the cyclones through time. Unit is expected average number of event per 100 years multiplied by 100. This product was designed by UNEP/GRID-Europe for the Global Assessment Report on Risk Reduction (GAR). It was modeled using global data. Credit: Raw data: IBTrACS, compilation and GIS processing UNEP/GRID-Geneva.cy_frequencyWCSGeoTIFFwesr_risk__cy_frequencyRectifiedGridCoverage-180.0 -90.00000000000003179.99599307699998 83.99199665399996-180.0 -90.00000000000003179.99599307699998 83.99199665399996Tropical Cyclones Mortality RiskThis dataset includes an estimate of the global risk induced by tropical cyclone hazard. Unit is estimated risk index from 1 (low) to 5 (extreme). This product was designed by UNEP/GRID-Geneva for the Global Assessment Report on Risk Reduction (GAR). It was modeled using global data. Credit: UNEP/GRID-Geneva.cy_riskWCSGeoTIFFwesr_risk__cy_riskRectifiedGridCoverage-180.0 -90.0179.99999999985602 83.99999999993042-180.0 -90.0179.99999999985602 83.99999999993042Tropical Cyclones Population ExposureThis dataset includes an estimation of the annual physical exposition to tropical cyclones of Saffir-Simpson category 5. It is based on three sources: 1) IBTrACS v02r01 (1969 - 2008, http://www.ncdc.noaa.gov/oa/ibtracs/), year 2009 completed by online data from JMA, JTWC, UNISYS, Meteo France and data sent by Alan Sharp from the Australian Bureau of Meteorology. 2) A GIS modeling based on an initial equation from Greg Holland, which was further modified to take into consideration the movement of the cyclones through time. 3) A population grid for the year 2010, provided by LandScanTM Global Population Database (Oak Ridge, TN: Oak Ridge National Laboratory). Unit is expected average annual population (2010 as the year of reference) exposed (inhabitants). This product was designed by UNEP/GRID-Europe for the Global Assessment Report on Risk Reduction (GAR). It was modeled using global data. Credit: Raw data: IBTrACS, compilation and GIS processing UNEP/GRID-Geneva.cy_physexpWCSGeoTIFFwesr_risk__cy_physexpRectifiedGridCoverage-180.0 -90.0179.99999999985602 83.99999999993042-180.0 -90.0179.99999999985602 83.99999999993042Tropical cyclone hazard exposurecy_phexWCSGeoTIFFECO-DRR__cy_phexRectifiedGridCoverage-180.0 -90.0180.0 90.0-180.0 -90.0180.0 90.0Tropical cyclone hazard frequencycy_freqaWCSGeoTIFFECO-DRR__cy_freqa_P3RectifiedGridCoverage-180.0 -90.0180.0 90.0-180.0 -90.0180.0 90.0Tropical cyclone surge hazard exposurecs_phexWCSGeoTIFFECO-DRR__cs_phexRectifiedGridCoverage-180.0 -90.0180.0 90.0-180.0 -90.0180.0 90.0Tropical cyclone surge hazard frequencycs_freqaWCSGeoTIFFECO-DRR__cs_freqa_P3RectifiedGridCoverage-180.0 -90.0180.0 90.0-180.0 -90.0180.0 90.0Tsunami hazard exposurets_phexWCSGeoTIFFECO-DRR__ts_phexRectifiedGridCoverage-180.0 -90.0180.0 90.0-180.0 -90.0180.0 90.0Tsunami hazard frequencyts_freqafWCSGeoTIFFECO-DRR__ts_freqafRectifiedGridCoverage-180.0 -90.0180.0 90.0-180.0 -90.0180.0 90.0Wetland Complexes (UNEP - 2018)Source: The map is published on UNEP's South Sudan: First State of Environment and Outlook Report 2018, using data from WCS. The UNEP's report could be found <a href="https://www.unenvironment.org/resources/report/south-sudan-first-state-environment-and-outlook-report-2018" target="_blank"> here </a> <br><br>The map shows the location and distribution of South Sudan’s principal wetlands, the most important of which are the Sudd and Machar swamps.SSD_Wetlands_optWCSGeoTIFFMAPX_nd__SSD_Wetlands_optRectifiedGridCoverage24.150695869 3.487470933948001735.950696484488 12.23747139034824.150695869 3.487470933948001735.950696484488 12.237471390348Winter precipitations change (%) for the period 2020 - 2039 under the RCP 2.6 scenarioProjected change of precipitations for the specified season and reference period, under the specified scenario. Data provided by MedECC AtlasProjected change of precipitations for the specified season and reference period, under the specified scenario with respect to the reference period (1980-1999). Data provided by MedECC Atlas.pr_djf_NEARREF_rcp26_optWCSGeoTIFFMedicip__pr_djf_NEARREF_rcp26_optRectifiedGridCoverage-12.0 27.537.0 49.0-12.0 27.537.0 49.0Winter precipitations change (%) for the period 2020 - 2039 under the RCP 8.5 scenarioProjected change of precipitations for the specified season and reference period, under the specified scenario with respect to the reference period (1980-1999). Data provided by MedECC Atlas.pr_djf_NEARREF_rcp85_optWCSGeoTIFFMedicip__pr_djf_NEARREF_rcp85_optRectifiedGridCoverage-12.0 27.537.0 49.0-12.0 27.537.0 49.0Winter precipitations change (%) for the period 2040 - 2059 under the RCP 2.6 scenarioProjected change of precipitations for the specified season and reference period, under the specified scenario with respect to the reference period (1980-1999). Data provided by MedECC Atlas.pr_djf_MIDREF_rcp26_optWCSGeoTIFFMedicip__pr_djf_MIDREF_rcp26_optRectifiedGridCoverage-12.0 27.537.0 49.0-12.0 27.537.0 49.0Winter precipitations change (%) for the period 2040 - 2059 under the RCP 8.5 scenarioProjected change of precipitations for the specified season and reference period, under the specified scenario with respect to the reference period (1980-1999). Data provided by MedECC Atlas.pr_djf_MIDREF_rcp85_optWCSGeoTIFFMedicip__pr_djf_MIDREF_rcp85_optRectifiedGridCoverage-12.0 27.537.0 49.0-12.0 27.537.0 49.0Winter precipitations change (%) for the period 2080 - 2099 under the RCP 2.6 scenarioProjected change of precipitations for the specified season and reference period, under the specified scenario with respect to the reference period (1980-1999). Data provided by MedECC Atlas.pr_djf_ENDREF_rcp26_optWCSGeoTIFFMedicip__pr_djf_ENDREF_rcp26_optRectifiedGridCoverage-12.0 27.537.0 49.0-12.0 27.537.0 49.0Winter precipitations change (%) for the period 2080 - 2099 under the RCP 8.5 scenarioProjected change of precipitations for the specified season and reference period, under the specified scenario with respect to the reference period (1980-1999). Data provided by MedECC Atlas.pr_djf_ENDREF_rcp85_optWCSGeoTIFFMedicip__pr_djf_ENDREF_rcp85_optRectifiedGridCoverage-12.0 27.537.0 49.0-12.0 27.537.0 49.0Winter temperature change (°C) for the period 2020 - 2039 under the RCP 2.6 scenarioProjected change of temperature for the specified season and reference period, under the specified scenario with respect to the reference period (1980-1999). Data provided by MedECC Atlas.tas_djf_NEARREF_rcp26_optWCSGeoTIFFMedicip__tas_djf_NEARREF_rcp26_optRectifiedGridCoverage-12.0 27.537.0 49.0-12.0 27.537.0 49.0Winter temperature change (°C) for the period 2020 - 2039 under the RCP 8.5 scenarioProjected change of temperature for the specified season and reference period, under the specified scenario with respect to the reference period (1980-1999). Data provided by MedECC Atlas.tas_djf_NEARREF_rcp85_optWCSGeoTIFFMedicip__tas_djf_NEARREF_rcp85_optRectifiedGridCoverage-12.0 27.537.0 49.0-12.0 27.537.0 49.0Winter temperature change (°C) for the period 2040 - 2059 under the RCP 2.6 scenarioProjected change of temperature for the specified season and reference period, under the specified scenario with respect to the reference period (1980-1999). Data provided by MedECC Atlas.tas_djf_MIDREF_rcp26_optWCSGeoTIFFMedicip__tas_djf_MIDREF_rcp26_optRectifiedGridCoverage-12.0 27.537.0 49.0-12.0 27.537.0 49.0Winter temperature change (°C) for the period 2040 - 2059 under the RCP 8.5 scenarioProjected change of temperature for the specified season and reference period, under the specified scenario with respect to the reference period (1980-1999). Data provided by MedECC Atlas.tas_djf_MIDREF_rcp85_optWCSGeoTIFFMedicip__tas_djf_MIDREF_rcp85_optRectifiedGridCoverage-12.0 27.537.0 49.0-12.0 27.537.0 49.0Winter temperature change (°C) for the period 2080 - 2099 under the RCP 2.6 scenarioProjected change of temperature for the specified season and reference period, under the specified scenario with respect to the reference period (1980-1999). Data provided by MedECC Atlas.tas_djf_ENDREF_rcp26_optWCSGeoTIFFMedicip__tas_djf_ENDREF_rcp26_optRectifiedGridCoverage-12.0 27.537.0 49.0-12.0 27.537.0 49.0Winter temperature change (°C) for the period 2080 - 2099 under the RCP 8.5 scenarioProjected change of temperature for the specified season and reference period, under the specified scenario with respect to the reference period (1980-1999). Data provided by MedECC Atlas.tas_djf_ENDREF_rcp85_optWCSGeoTIFFMedicip__tas_djf_ENDREF_rcp85_optRectifiedGridCoverage-12.0 27.537.0 49.0-12.0 27.537.0 49.0Woodland total area in km2forestwlkm2aWCSGeoTIFFECO-DRR__forestwlkm2aRectifiedGridCoverage-180.0 -90.0180.0 90.0-180.0 -90.0180.0 90.0Word Land Cover FAO ?WordLandCoverWCSGeoTIFFunep_storymap__WordLandCoverRectifiedGridCoverage-180.0 -90.0180.0 90.0-180.0 -90.0180.0 90.0World Crop Dominancewld__crop_dominanceWCSGeoTIFFMAPX__wld_crop_dominanceRectifiedGridCoverage-180.01636766952797 -89.98702927013751180.9506393039613 90.00542337852794-180.01636766952797 -89.98702927013751180.9506393039613 90.00542337852794World Database of Key Biodiversity Areas (KBA)Key Biodiversity Areas (KBA) are 'sites contributing significantly to the global persistence of biodiversity’, in terrestrial, freshwater and marine ecosystems. <br> The Global Standard for the Identification of Key Biodiversity Areas (IUCN 2016) sets out globally agreed criteria for the identification of KBAs worldwide. The KBA Standard establishes a consultative, science-based process for KBA identification, founded on the consistent application of global criteria with quantitative thresholds that have been developed through an extensive consultation exercise spanning several years. The Standard is also available in French and Spanish. <br> Sites qualify as global KBAs if they meet one or more of 11 criteria, clustered into five categories: threatened biodiversity; geographically restricted biodiversity; ecological integrity; biological processes; and, irreplaceability. The KBA criteria can be applied to species and ecosystems in terrestrial, inland water and marine environments. Although not all KBA criteria may be relevant to all elements of biodiversity, the thresholds associated with each of the criteria may be applied across all taxonomic groups (other than micro-organisms) and ecosystems. <br> The KBA identification process is a highly inclusive, consultative and bottom-up exercise. Although anyone with appropriate scientific data may propose a site to qualify as a KBA, consultation with stakeholders at the national level (both non-governmental and governmental organizations) is required during the proposal process. KBA identification should build off the existing network of KBAs (including Important Bird and Biodiversity Areas and Alliance for Zero Extinction sites) and new data should seek to strengthen and expand the network of these sites. Any site proposal must undergo independent scientific review. This is followed by the official site nomination with full documentation meeting the Documentation Standards for KBAs. Sites confirmed by the KBA Secretariat to qualify as KBAs then appear on this website. <br> The consultation process to develop A Global Standard for the Identification of Key Biodiversity Areas was led by the IUCN WCPA-SSC Joint Task Force on Biodiversity and Protected Areas. It involved a Framing workshop; technical workshops on Criteria and Delineation, quantitative Thresholds, and Governance of the process to identify KBAs; a second Governance meeting; 14 separate regional consultations held around the world; and interviews with end users of KBA data. A first public consultation draft was posted in 2014, generating more than 1200 comments and responses. The second consultation draft on the Standard was posted in 2015, generating an additional 600 comments and responses. More information on the consultation process to develop the KBA Standard can be found on the Joint Task Force website.KBA_GeoTiff_clearWCSGeoTIFFMapX_UNDP_nd__KBA_GeoTiff_clear v2RectifiedGridCoverage-186.93396181851392 -87.90113898196397188.54498490150434 95.19838541667612-186.93396181851392 -87.90113898196397188.54498490150434 95.19838541667612World Ecosystem Data - World Climate Regions DataData modified for visualisation purpose from the original: World Climate Regions Data in World Ecosystem Data. Originally downloded from https://esri.maps.arcgis.com/home/item.html?id=3bfa1aa4cd9844d5a0922540210da25b Sayre et al. 2020. An assessment of the representation of ecosystems in global protected areas using new maps of World Climate Regions and World Ecosystems - Global Ecology and Conservation. More information at: https://www.sciencedirect.com/science/article/pii/S2351989419307231?via%3Dihub Data reclassified based on the following schema: Existing classes from "Temp_Moist" -> New classes Boreal Desert, Boreal Dry, Boreal Moist, Polar Desert, Polar Dry, Polar Moist -> Boreal/Polar Cool Temperate Moist -> Cool Temperate Moist Cool Temperate Dry and Desert -> Cool Temperate Dry & Desert Warm Temperate Moist -> Warm Temperate Moist Warm Temperate Moist, Warm Temperate Desert -> Warm Temperate Dry & Desert Tropical Desert, Sub Tropical Desert -> Tropical & Sub Tropical Desert Tropical Dry, Sub Tropical Dry -> Tropical & Sub Tropical Dry Tropical Moist, Sub Tropical Moist -> Tropical & Sub Tropical MoistWE_reclass_optWCSGeoTIFFNEAT_plus__WE_reclass_optRectifiedGridCoverage-179.999999 -55.99953000000001179.999334901 83.624039629-179.999999 -55.99953000000001179.999334901 83.624039629World Multi Study Crop Maskwld__multi_study_crop_maskWCSGeoTIFFMAPX__wld_multi_study_crop_maskRectifiedGridCoverage-180.254454098528 -89.98798806770512180.95361419547302 90.25445409852799-180.254454098528 -89.98798806770512180.95361419547302 90.25445409852799Year precipitations 2000 - Central African RepublicTotal precipitations observed in 2000 in the Central African Republic.Precipitationscc_and_health__precip_2000_cafRectifiedGridCoverage14.417960310118321 2.21883875177521827.47048138837497 11.01334538330533314.417960310118321 2.21883875177521827.47048138837497 11.013345383305333Year precipitations 2001 - Central African RepublicTotal precipitations observed in 2001 in the Central African Republic.Precipitationscc_and_health__precip_2001_cafRectifiedGridCoverage14.417960310118321 2.21883875177521827.47048138837497 11.01334538330533314.417960310118321 2.21883875177521827.47048138837497 11.013345383305333Year precipitations 2002 - Central African RepublicTotal precipitations observed in 2002 in the Central African Republic.Precipitationscc_and_health__precip_2002_cafRectifiedGridCoverage14.417960310118321 2.21883875177521827.47048138837497 11.01334538330533314.417960310118321 2.21883875177521827.47048138837497 11.013345383305333Year precipitations 2003 - Central African RepublicTotal precipitations observed in 2003 in the Central African Republic.Precipitationscc_and_health__precip_2003_cafRectifiedGridCoverage14.417960310118321 2.21883875177521827.47048138837497 11.01334538330533314.417960310118321 2.21883875177521827.47048138837497 11.013345383305333Year precipitations 2004 - Central African RepublicTotal precipitations observed in 2004 in the Central African Republic.Precipitationscc_and_health__precip_2004_cafRectifiedGridCoverage14.417960310118321 2.21883875177521827.47048138837497 11.01334538330533314.417960310118321 2.21883875177521827.47048138837497 11.013345383305333Year precipitations 2005 - Central African RepublicTotal precipitations observed in 2005 in the Central African Republic.Precipitationscc_and_health__precip_2005_cafRectifiedGridCoverage14.417960310118321 2.21883875177521827.47048138837497 11.01334538330533314.417960310118321 2.21883875177521827.47048138837497 11.013345383305333Year precipitations 2006 - Central African RepublicTotal precipitations observed in 2006 in the Central African Republic.Precipitationscc_and_health__precip_2006_cafRectifiedGridCoverage14.417960310118321 2.21883875177521827.47048138837497 11.01334538330533314.417960310118321 2.21883875177521827.47048138837497 11.013345383305333Year precipitations 2007 - Central African RepublicTotal precipitations observed in 2007 in the Central African Republic.Precipitationscc_and_health__precip_2007_cafRectifiedGridCoverage14.417960310118321 2.21883875177521827.47048138837497 11.01334538330533314.417960310118321 2.21883875177521827.47048138837497 11.013345383305333Year precipitations 2008 - Central African RepublicTotal precipitations observed in 2008 in the Central African Republic.Precipitationscc_and_health__precip_2008_cafRectifiedGridCoverage14.417960310118321 2.21883875177521827.47048138837497 11.01334538330533314.417960310118321 2.21883875177521827.47048138837497 11.013345383305333Year precipitations 2009 - Central African RepublicTotal precipitations observed in 2009 in the Central African Republic.Precipitationscc_and_health__precip_2009_cafRectifiedGridCoverage14.417960310118321 2.21883875177521827.47048138837497 11.01334538330533314.417960310118321 2.21883875177521827.47048138837497 11.013345383305333Year precipitations 2010 - Central African RepublicTotal precipitations observed in 2010 in the Central African Republic.Precipitationscc_and_health__precip_2010_cafRectifiedGridCoverage14.417960310118321 2.21883875177521827.47048138837497 11.01334538330533314.417960310118321 2.21883875177521827.47048138837497 11.013345383305333Year precipitations 2011 - Central African RepublicTotal precipitations observed in 2011 in the Central African Republic.Precipitationscc_and_health__precip_2011_cafRectifiedGridCoverage14.417960310118321 2.21883875177521827.47048138837497 11.01334538330533314.417960310118321 2.21883875177521827.47048138837497 11.013345383305333Year precipitations 2012 - Central African RepublicTotal precipitations observed in 2012 in the Central African Republic.Precipitationscc_and_health__precip_2012_cafRectifiedGridCoverage14.417960310118321 2.21883875177521827.47048138837497 11.01334538330533314.417960310118321 2.21883875177521827.47048138837497 11.013345383305333Year precipitations 2013 - Central African RepublicTotal precipitations observed in 2013 in the Central African Republic.Precipitationscc_and_health__precip_2013_cafRectifiedGridCoverage14.417960310118321 2.21883875177521827.47048138837497 11.01334538330533314.417960310118321 2.21883875177521827.47048138837497 11.013345383305333Year precipitations 2014 - Central African RepublicTotal precipitations observed in 2014 in the Central African Republic.Precipitationscc_and_health__precip_2014_cafRectifiedGridCoverage14.417960310118321 2.21883875177521827.47048138837497 11.01334538330533314.417960310118321 2.21883875177521827.47048138837497 11.013345383305333Year precipitations 2015 - Central African RepublicTotal precipitations observed in 2015 in the Central African Republic.Precipitationscc_and_health__precip_2015_cafRectifiedGridCoverage14.417960310118321 2.21883875177521827.47048138837497 11.01334538330533314.417960310118321 2.21883875177521827.47048138837497 11.013345383305333Year precipitations 2016 - Central African RepublicTotal precipitations observed in 2016 in the Central African Republic.Precipitationscc_and_health__precip_2016_cafRectifiedGridCoverage14.417960310118321 2.21883875177521827.47048138837497 11.01334538330533314.417960310118321 2.21883875177521827.47048138837497 11.013345383305333Year precipitations 2017 - Central African RepublicTotal precipitations observed in 2017 in the Central African Republic.Precipitationscc_and_health__precip_2017_cafRectifiedGridCoverage14.417960310118321 2.21883875177521827.47048138837497 11.01334538330533314.417960310118321 2.21883875177521827.47048138837497 11.013345383305333Year precipitations 2018 - Central African RepublicTotal precipitations observed in 2018 in the Central African Republic.Precipitationscc_and_health__precip_2018_cafRectifiedGridCoverage14.417960310118321 2.21883875177521827.47048138837497 11.01334538330533314.417960310118321 2.21883875177521827.47048138837497 11.013345383305333Year precipitations 2019 - Central African RepublicTotal precipitations observed in 2019 in the Central African Republic.Precipitationscc_and_health__precip_2019_cafRectifiedGridCoverage14.417960310118321 2.21883875177521827.47048138837497 11.01334538330533314.417960310118321 2.21883875177521827.47048138837497 11.013345383305333Year precipitations 2020 - Central African RepublicTotal precipitations observed in 2020 in the Central African Republic.Precipitationscc_and_health__precip_2020_cafRectifiedGridCoverage14.417960310118321 2.21883875177521827.47048138837497 11.01334538330533314.417960310118321 2.21883875177521827.47048138837497 11.013345383305333Year precipitations 2021 - Reference - Central African RepublicTotal precipitations observed in 2021 in the Central African Republic. Reference layer.Precipitationscc_and_health__precip_2021_ref_cafRectifiedGridCoverage14.41796031 2.21883875227.470481388 11.01334538314.41796031 2.21883875227.470481388 11.013345383Yearly emissions of CH4 - 1970 (EDGAR v6.0)EDGARv6.0 provides emissions of the three main greenhouse gases (CO2, CH4, N2O) and fluorinated gases (coming soon) per sector and country. CO2 emissions are provided separately for CO2_excl_short-cycle_org_C and CO2_short-cycle_org_C. Emissions of CO2_excl_short-cycle_org_C include all fossil CO2 sources, such as fossil fuel combustion, non-metallic mineral processes (e.g. cement production), metal (ferrous and non-ferrous) production processes, urea production, agricultural liming and solvents use. Large scale biomass burning with Savannah burning, forest fires, and sources and sinks from land-use, land-use change and forestry (LULUCF) are excluded. For the energy related sectors the activity data are primarily based on IEA data from IEA (2019) World Energy Balances, (Internet: link), All rights reserved, as modified by Joint Research Centre, European Commission, whereas the activity data for the agricultural sectors originate primarily from FAO (2020) (Internet: link). United States Geological Survey (USGS), International Fertiliser Association (IFA), Gas Flaring Reduction Partnership (GGFR)/U.S. National Oceanic and Atmospheric Administration (NOAA), UNFCCC and World Steel Association (worldsteel) recent statistics are also used for activity data. Additional information can be found in Crippa et al. (2021). Compared to EDGARv5.0 the following updates have been included: updates of all activity data up to 2018; inclusion of CO2 emissions from coal mining based on the IPCC 2019 refinements; new international shipping proxies and monthly profiles (Jalkanen et al. (2012), Johansson et al. (2017)). Reference: Crippa, Monica; Guizzardi, Diego; Muntean, Marilena; Schaaf, Edwin; Lo Vullo, Eleonora; Solazzo, Efisio; Monforti-Ferrario, Fabio; Olivier, Jos; Vignati, Elisabetta (2021): EDGAR v6.0 Greenhouse Gas Emissions. European Commission, Joint Research Centre (JRC) [Dataset] PID: http://data.europa.eu/89h/97a67d67-c62e-4826-b873-9d972c4f670b https://edgar.jrc.ec.europa.eu/dataset_ghg60v60_CH4_1970_optWCSGeoTIFFwesr_climat__v60_CH4_1970_optRectifiedGridCoverage-180.0 -88.79999999998984179.99999999997954 90.0-180.0 -88.79999999998984179.99999999997954 90.0Yearly emissions of CH4 - 1980 (EDGAR v6.0)EDGARv6.0 provides emissions of the three main greenhouse gases (CO2, CH4, N2O) and fluorinated gases (coming soon) per sector and country. CO2 emissions are provided separately for CO2_excl_short-cycle_org_C and CO2_short-cycle_org_C. Emissions of CO2_excl_short-cycle_org_C include all fossil CO2 sources, such as fossil fuel combustion, non-metallic mineral processes (e.g. cement production), metal (ferrous and non-ferrous) production processes, urea production, agricultural liming and solvents use. Large scale biomass burning with Savannah burning, forest fires, and sources and sinks from land-use, land-use change and forestry (LULUCF) are excluded. For the energy related sectors the activity data are primarily based on IEA data from IEA (2019) World Energy Balances, (Internet: link), All rights reserved, as modified by Joint Research Centre, European Commission, whereas the activity data for the agricultural sectors originate primarily from FAO (2020) (Internet: link). United States Geological Survey (USGS), International Fertiliser Association (IFA), Gas Flaring Reduction Partnership (GGFR)/U.S. National Oceanic and Atmospheric Administration (NOAA), UNFCCC and World Steel Association (worldsteel) recent statistics are also used for activity data. Additional information can be found in Crippa et al. (2021). Compared to EDGARv5.0 the following updates have been included: updates of all activity data up to 2018; inclusion of CO2 emissions from coal mining based on the IPCC 2019 refinements; new international shipping proxies and monthly profiles (Jalkanen et al. (2012), Johansson et al. (2017)). Reference: Crippa, Monica; Guizzardi, Diego; Muntean, Marilena; Schaaf, Edwin; Lo Vullo, Eleonora; Solazzo, Efisio; Monforti-Ferrario, Fabio; Olivier, Jos; Vignati, Elisabetta (2021): EDGAR v6.0 Greenhouse Gas Emissions. European Commission, Joint Research Centre (JRC) [Dataset] PID: http://data.europa.eu/89h/97a67d67-c62e-4826-b873-9d972c4f670b https://edgar.jrc.ec.europa.eu/dataset_ghg60v60_CH4_1980_optWCSGeoTIFFwesr_climat__v60_CH4_1980_optRectifiedGridCoverage-180.0 -88.79999999998984179.99999999997954 90.0-180.0 -88.79999999998984179.99999999997954 90.0Yearly emissions of CH4 - 1990 (EDGAR v6.0)EDGARv6.0 provides emissions of the three main greenhouse gases (CO2, CH4, N2O) and fluorinated gases (coming soon) per sector and country. CO2 emissions are provided separately for CO2_excl_short-cycle_org_C and CO2_short-cycle_org_C. Emissions of CO2_excl_short-cycle_org_C include all fossil CO2 sources, such as fossil fuel combustion, non-metallic mineral processes (e.g. cement production), metal (ferrous and non-ferrous) production processes, urea production, agricultural liming and solvents use. Large scale biomass burning with Savannah burning, forest fires, and sources and sinks from land-use, land-use change and forestry (LULUCF) are excluded. For the energy related sectors the activity data are primarily based on IEA data from IEA (2019) World Energy Balances, (Internet: link), All rights reserved, as modified by Joint Research Centre, European Commission, whereas the activity data for the agricultural sectors originate primarily from FAO (2020) (Internet: link). United States Geological Survey (USGS), International Fertiliser Association (IFA), Gas Flaring Reduction Partnership (GGFR)/U.S. National Oceanic and Atmospheric Administration (NOAA), UNFCCC and World Steel Association (worldsteel) recent statistics are also used for activity data. Additional information can be found in Crippa et al. (2021). Compared to EDGARv5.0 the following updates have been included: updates of all activity data up to 2018; inclusion of CO2 emissions from coal mining based on the IPCC 2019 refinements; new international shipping proxies and monthly profiles (Jalkanen et al. (2012), Johansson et al. (2017)). Reference: Crippa, Monica; Guizzardi, Diego; Muntean, Marilena; Schaaf, Edwin; Lo Vullo, Eleonora; Solazzo, Efisio; Monforti-Ferrario, Fabio; Olivier, Jos; Vignati, Elisabetta (2021): EDGAR v6.0 Greenhouse Gas Emissions. European Commission, Joint Research Centre (JRC) [Dataset] PID: http://data.europa.eu/89h/97a67d67-c62e-4826-b873-9d972c4f670b https://edgar.jrc.ec.europa.eu/dataset_ghg60v60_CH4_1990_optWCSGeoTIFFwesr_climat__v60_CH4_1990_optRectifiedGridCoverage-180.0 -88.79999999998984179.99999999997954 90.0-180.0 -88.79999999998984179.99999999997954 90.0Yearly emissions of CH4 - 2000 (EDGAR v6.0)EDGARv6.0 provides emissions of the three main greenhouse gases (CO2, CH4, N2O) and fluorinated gases (coming soon) per sector and country. CO2 emissions are provided separately for CO2_excl_short-cycle_org_C and CO2_short-cycle_org_C. Emissions of CO2_excl_short-cycle_org_C include all fossil CO2 sources, such as fossil fuel combustion, non-metallic mineral processes (e.g. cement production), metal (ferrous and non-ferrous) production processes, urea production, agricultural liming and solvents use. Large scale biomass burning with Savannah burning, forest fires, and sources and sinks from land-use, land-use change and forestry (LULUCF) are excluded. For the energy related sectors the activity data are primarily based on IEA data from IEA (2019) World Energy Balances, (Internet: link), All rights reserved, as modified by Joint Research Centre, European Commission, whereas the activity data for the agricultural sectors originate primarily from FAO (2020) (Internet: link). United States Geological Survey (USGS), International Fertiliser Association (IFA), Gas Flaring Reduction Partnership (GGFR)/U.S. National Oceanic and Atmospheric Administration (NOAA), UNFCCC and World Steel Association (worldsteel) recent statistics are also used for activity data. Additional information can be found in Crippa et al. (2021). Compared to EDGARv5.0 the following updates have been included: updates of all activity data up to 2018; inclusion of CO2 emissions from coal mining based on the IPCC 2019 refinements; new international shipping proxies and monthly profiles (Jalkanen et al. (2012), Johansson et al. (2017)). Reference: Crippa, Monica; Guizzardi, Diego; Muntean, Marilena; Schaaf, Edwin; Lo Vullo, Eleonora; Solazzo, Efisio; Monforti-Ferrario, Fabio; Olivier, Jos; Vignati, Elisabetta (2021): EDGAR v6.0 Greenhouse Gas Emissions. European Commission, Joint Research Centre (JRC) [Dataset] PID: http://data.europa.eu/89h/97a67d67-c62e-4826-b873-9d972c4f670b https://edgar.jrc.ec.europa.eu/dataset_ghg60v60_CH4_2000_optWCSGeoTIFFwesr_climat__v60_CH4_2000_optRectifiedGridCoverage-180.0 -88.79999999998984179.99999999997954 90.0-180.0 -88.79999999998984179.99999999997954 90.0Yearly emissions of CH4 - 2010 (EDGAR v6.0)EDGARv6.0 provides emissions of the three main greenhouse gases (CO2, CH4, N2O) and fluorinated gases (coming soon) per sector and country. CO2 emissions are provided separately for CO2_excl_short-cycle_org_C and CO2_short-cycle_org_C. Emissions of CO2_excl_short-cycle_org_C include all fossil CO2 sources, such as fossil fuel combustion, non-metallic mineral processes (e.g. cement production), metal (ferrous and non-ferrous) production processes, urea production, agricultural liming and solvents use. Large scale biomass burning with Savannah burning, forest fires, and sources and sinks from land-use, land-use change and forestry (LULUCF) are excluded. For the energy related sectors the activity data are primarily based on IEA data from IEA (2019) World Energy Balances, (Internet: link), All rights reserved, as modified by Joint Research Centre, European Commission, whereas the activity data for the agricultural sectors originate primarily from FAO (2020) (Internet: link). United States Geological Survey (USGS), International Fertiliser Association (IFA), Gas Flaring Reduction Partnership (GGFR)/U.S. National Oceanic and Atmospheric Administration (NOAA), UNFCCC and World Steel Association (worldsteel) recent statistics are also used for activity data. Additional information can be found in Crippa et al. (2021). Compared to EDGARv5.0 the following updates have been included: updates of all activity data up to 2018; inclusion of CO2 emissions from coal mining based on the IPCC 2019 refinements; new international shipping proxies and monthly profiles (Jalkanen et al. (2012), Johansson et al. (2017)). Reference: Crippa, Monica; Guizzardi, Diego; Muntean, Marilena; Schaaf, Edwin; Lo Vullo, Eleonora; Solazzo, Efisio; Monforti-Ferrario, Fabio; Olivier, Jos; Vignati, Elisabetta (2021): EDGAR v6.0 Greenhouse Gas Emissions. European Commission, Joint Research Centre (JRC) [Dataset] PID: http://data.europa.eu/89h/97a67d67-c62e-4826-b873-9d972c4f670b https://edgar.jrc.ec.europa.eu/dataset_ghg60v60_CH4_2010_optWCSGeoTIFFwesr_climat__v60_CH4_2010_optRectifiedGridCoverage-180.0 -85.59999999999002179.99999999997954 90.0-180.0 -85.59999999999002179.99999999997954 90.0Yearly emissions of CH4 - 2018 (EDGAR v6.0)EDGARv6.0 provides emissions of the three main greenhouse gases (CO2, CH4, N2O) and fluorinated gases (coming soon) per sector and country. CO2 emissions are provided separately for CO2_excl_short-cycle_org_C and CO2_short-cycle_org_C. Emissions of CO2_excl_short-cycle_org_C include all fossil CO2 sources, such as fossil fuel combustion, non-metallic mineral processes (e.g. cement production), metal (ferrous and non-ferrous) production processes, urea production, agricultural liming and solvents use. Large scale biomass burning with Savannah burning, forest fires, and sources and sinks from land-use, land-use change and forestry (LULUCF) are excluded. For the energy related sectors the activity data are primarily based on IEA data from IEA (2019) World Energy Balances, (Internet: link), All rights reserved, as modified by Joint Research Centre, European Commission, whereas the activity data for the agricultural sectors originate primarily from FAO (2020) (Internet: link). United States Geological Survey (USGS), International Fertiliser Association (IFA), Gas Flaring Reduction Partnership (GGFR)/U.S. National Oceanic and Atmospheric Administration (NOAA), UNFCCC and World Steel Association (worldsteel) recent statistics are also used for activity data. Additional information can be found in Crippa et al. (2021). Compared to EDGARv5.0 the following updates have been included: updates of all activity data up to 2018; inclusion of CO2 emissions from coal mining based on the IPCC 2019 refinements; new international shipping proxies and monthly profiles (Jalkanen et al. (2012), Johansson et al. (2017)). Reference: Crippa, Monica; Guizzardi, Diego; Muntean, Marilena; Schaaf, Edwin; Lo Vullo, Eleonora; Solazzo, Efisio; Monforti-Ferrario, Fabio; Olivier, Jos; Vignati, Elisabetta (2021): EDGAR v6.0 Greenhouse Gas Emissions. European Commission, Joint Research Centre (JRC) [Dataset] PID: http://data.europa.eu/89h/97a67d67-c62e-4826-b873-9d972c4f670b https://edgar.jrc.ec.europa.eu/dataset_ghg60v60_CH4_2018_optWCSGeoTIFFwesr_climat__v60_CH4_2018_optRectifiedGridCoverage-180.0 -85.19999999999004179.99999999997954 90.0-180.0 -85.19999999999004179.99999999997954 90.0Yearly emissions of CO2 (excluding short cycle organic Carbon) - 1970 (EDGAR v6.0)EDGARv6.0 provides emissions of the three main greenhouse gases (CO2, CH4, N2O) and fluorinated gases (coming soon) per sector and country. CO2 emissions are provided separately for CO2_excl_short-cycle_org_C and CO2_short-cycle_org_C. Emissions of CO2_excl_short-cycle_org_C include all fossil CO2 sources, such as fossil fuel combustion, non-metallic mineral processes (e.g. cement production), metal (ferrous and non-ferrous) production processes, urea production, agricultural liming and solvents use. Large scale biomass burning with Savannah burning, forest fires, and sources and sinks from land-use, land-use change and forestry (LULUCF) are excluded. For the energy related sectors the activity data are primarily based on IEA data from IEA (2019) World Energy Balances, (Internet: link), All rights reserved, as modified by Joint Research Centre, European Commission, whereas the activity data for the agricultural sectors originate primarily from FAO (2020) (Internet: link). United States Geological Survey (USGS), International Fertiliser Association (IFA), Gas Flaring Reduction Partnership (GGFR)/U.S. National Oceanic and Atmospheric Administration (NOAA), UNFCCC and World Steel Association (worldsteel) recent statistics are also used for activity data. Additional information can be found in Crippa et al. (2021). Compared to EDGARv5.0 the following updates have been included: updates of all activity data up to 2018; inclusion of CO2 emissions from coal mining based on the IPCC 2019 refinements; new international shipping proxies and monthly profiles (Jalkanen et al. (2012), Johansson et al. (2017)). Reference: Crippa, Monica; Guizzardi, Diego; Muntean, Marilena; Schaaf, Edwin; Lo Vullo, Eleonora; Solazzo, Efisio; Monforti-Ferrario, Fabio; Olivier, Jos; Vignati, Elisabetta (2021): EDGAR v6.0 Greenhouse Gas Emissions. European Commission, Joint Research Centre (JRC) [Dataset] PID: http://data.europa.eu/89h/97a67d67-c62e-4826-b873-9d972c4f670b https://edgar.jrc.ec.europa.eu/dataset_ghg60v60_CO2_excl_short-cycle_org_C_1970_optWCSGeoTIFFwesr_climat__v60_CO2_excl_short-cycle_org_C_1970_optRectifiedGridCoverage-180.0 -88.79999999998984179.99999999997954 90.0-180.0 -88.79999999998984179.99999999997954 90.0Yearly emissions of CO2 (excluding short cycle organic Carbon) - 1980 (EDGAR v6.0)EDGARv6.0 provides emissions of the three main greenhouse gases (CO2, CH4, N2O) and fluorinated gases (coming soon) per sector and country. CO2 emissions are provided separately for CO2_excl_short-cycle_org_C and CO2_short-cycle_org_C. Emissions of CO2_excl_short-cycle_org_C include all fossil CO2 sources, such as fossil fuel combustion, non-metallic mineral processes (e.g. cement production), metal (ferrous and non-ferrous) production processes, urea production, agricultural liming and solvents use. Large scale biomass burning with Savannah burning, forest fires, and sources and sinks from land-use, land-use change and forestry (LULUCF) are excluded. For the energy related sectors the activity data are primarily based on IEA data from IEA (2019) World Energy Balances, (Internet: link), All rights reserved, as modified by Joint Research Centre, European Commission, whereas the activity data for the agricultural sectors originate primarily from FAO (2020) (Internet: link). United States Geological Survey (USGS), International Fertiliser Association (IFA), Gas Flaring Reduction Partnership (GGFR)/U.S. National Oceanic and Atmospheric Administration (NOAA), UNFCCC and World Steel Association (worldsteel) recent statistics are also used for activity data. Additional information can be found in Crippa et al. (2021). Compared to EDGARv5.0 the following updates have been included: updates of all activity data up to 2018; inclusion of CO2 emissions from coal mining based on the IPCC 2019 refinements; new international shipping proxies and monthly profiles (Jalkanen et al. (2012), Johansson et al. (2017)). Reference: Crippa, Monica; Guizzardi, Diego; Muntean, Marilena; Schaaf, Edwin; Lo Vullo, Eleonora; Solazzo, Efisio; Monforti-Ferrario, Fabio; Olivier, Jos; Vignati, Elisabetta (2021): EDGAR v6.0 Greenhouse Gas Emissions. European Commission, Joint Research Centre (JRC) [Dataset] PID: http://data.europa.eu/89h/97a67d67-c62e-4826-b873-9d972c4f670b https://edgar.jrc.ec.europa.eu/dataset_ghg60v60_CO2_excl_short-cycle_org_C_1980_optWCSGeoTIFFwesr_climat__v60_CO2_excl_short-cycle_org_C_1980_optRectifiedGridCoverage-180.0 -88.79999999998984179.99999999997954 90.0-180.0 -88.79999999998984179.99999999997954 90.0Yearly emissions of CO2 (excluding short cycle organic Carbon) - 1990 (EDGAR v6.0)EDGARv6.0 provides emissions of the three main greenhouse gases (CO2, CH4, N2O) and fluorinated gases (coming soon) per sector and country. CO2 emissions are provided separately for CO2_excl_short-cycle_org_C and CO2_short-cycle_org_C. Emissions of CO2_excl_short-cycle_org_C include all fossil CO2 sources, such as fossil fuel combustion, non-metallic mineral processes (e.g. cement production), metal (ferrous and non-ferrous) production processes, urea production, agricultural liming and solvents use. Large scale biomass burning with Savannah burning, forest fires, and sources and sinks from land-use, land-use change and forestry (LULUCF) are excluded. For the energy related sectors the activity data are primarily based on IEA data from IEA (2019) World Energy Balances, (Internet: link), All rights reserved, as modified by Joint Research Centre, European Commission, whereas the activity data for the agricultural sectors originate primarily from FAO (2020) (Internet: link). United States Geological Survey (USGS), International Fertiliser Association (IFA), Gas Flaring Reduction Partnership (GGFR)/U.S. National Oceanic and Atmospheric Administration (NOAA), UNFCCC and World Steel Association (worldsteel) recent statistics are also used for activity data. Additional information can be found in Crippa et al. (2021). Compared to EDGARv5.0 the following updates have been included: updates of all activity data up to 2018; inclusion of CO2 emissions from coal mining based on the IPCC 2019 refinements; new international shipping proxies and monthly profiles (Jalkanen et al. (2012), Johansson et al. (2017)). Reference: Crippa, Monica; Guizzardi, Diego; Muntean, Marilena; Schaaf, Edwin; Lo Vullo, Eleonora; Solazzo, Efisio; Monforti-Ferrario, Fabio; Olivier, Jos; Vignati, Elisabetta (2021): EDGAR v6.0 Greenhouse Gas Emissions. European Commission, Joint Research Centre (JRC) [Dataset] PID: http://data.europa.eu/89h/97a67d67-c62e-4826-b873-9d972c4f670b https://edgar.jrc.ec.europa.eu/dataset_ghg60v60_CO2_excl_short-cycle_org_C_1990_optWCSGeoTIFFwesr_climat__v60_CO2_excl_short-cycle_org_C_1990_optRectifiedGridCoverage-180.0 -88.79999999998984179.99999999997954 90.0-180.0 -88.79999999998984179.99999999997954 90.0Yearly emissions of CO2 (excluding short cycle organic Carbon) - 2000 (EDGAR v6.0)EDGARv6.0 provides emissions of the three main greenhouse gases (CO2, CH4, N2O) and fluorinated gases (coming soon) per sector and country. CO2 emissions are provided separately for CO2_excl_short-cycle_org_C and CO2_short-cycle_org_C. Emissions of CO2_excl_short-cycle_org_C include all fossil CO2 sources, such as fossil fuel combustion, non-metallic mineral processes (e.g. cement production), metal (ferrous and non-ferrous) production processes, urea production, agricultural liming and solvents use. Large scale biomass burning with Savannah burning, forest fires, and sources and sinks from land-use, land-use change and forestry (LULUCF) are excluded. For the energy related sectors the activity data are primarily based on IEA data from IEA (2019) World Energy Balances, (Internet: link), All rights reserved, as modified by Joint Research Centre, European Commission, whereas the activity data for the agricultural sectors originate primarily from FAO (2020) (Internet: link). United States Geological Survey (USGS), International Fertiliser Association (IFA), Gas Flaring Reduction Partnership (GGFR)/U.S. National Oceanic and Atmospheric Administration (NOAA), UNFCCC and World Steel Association (worldsteel) recent statistics are also used for activity data. Additional information can be found in Crippa et al. (2021). Compared to EDGARv5.0 the following updates have been included: updates of all activity data up to 2018; inclusion of CO2 emissions from coal mining based on the IPCC 2019 refinements; new international shipping proxies and monthly profiles (Jalkanen et al. (2012), Johansson et al. (2017)). Reference: Crippa, Monica; Guizzardi, Diego; Muntean, Marilena; Schaaf, Edwin; Lo Vullo, Eleonora; Solazzo, Efisio; Monforti-Ferrario, Fabio; Olivier, Jos; Vignati, Elisabetta (2021): EDGAR v6.0 Greenhouse Gas Emissions. European Commission, Joint Research Centre (JRC) [Dataset] PID: http://data.europa.eu/89h/97a67d67-c62e-4826-b873-9d972c4f670b https://edgar.jrc.ec.europa.eu/dataset_ghg60v60_CO2_excl_short-cycle_org_C_2000_optWCSGeoTIFFwesr_climat__v60_CO2_excl_short-cycle_org_C_2000_optRectifiedGridCoverage-180.0 -88.79999999998984179.99999999997954 90.0-180.0 -88.79999999998984179.99999999997954 90.0Yearly emissions of CO2 (excluding short cycle organic Carbon) - 2010 (EDGAR v6.0)EDGARv6.0 provides emissions of the three main greenhouse gases (CO2, CH4, N2O) and fluorinated gases (coming soon) per sector and country. CO2 emissions are provided separately for CO2_excl_short-cycle_org_C and CO2_short-cycle_org_C. Emissions of CO2_excl_short-cycle_org_C include all fossil CO2 sources, such as fossil fuel combustion, non-metallic mineral processes (e.g. cement production), metal (ferrous and non-ferrous) production processes, urea production, agricultural liming and solvents use. Large scale biomass burning with Savannah burning, forest fires, and sources and sinks from land-use, land-use change and forestry (LULUCF) are excluded. For the energy related sectors the activity data are primarily based on IEA data from IEA (2019) World Energy Balances, (Internet: link), All rights reserved, as modified by Joint Research Centre, European Commission, whereas the activity data for the agricultural sectors originate primarily from FAO (2020) (Internet: link). United States Geological Survey (USGS), International Fertiliser Association (IFA), Gas Flaring Reduction Partnership (GGFR)/U.S. National Oceanic and Atmospheric Administration (NOAA), UNFCCC and World Steel Association (worldsteel) recent statistics are also used for activity data. Additional information can be found in Crippa et al. (2021). Compared to EDGARv5.0 the following updates have been included: updates of all activity data up to 2018; inclusion of CO2 emissions from coal mining based on the IPCC 2019 refinements; new international shipping proxies and monthly profiles (Jalkanen et al. (2012), Johansson et al. (2017)). Reference: Crippa, Monica; Guizzardi, Diego; Muntean, Marilena; Schaaf, Edwin; Lo Vullo, Eleonora; Solazzo, Efisio; Monforti-Ferrario, Fabio; Olivier, Jos; Vignati, Elisabetta (2021): EDGAR v6.0 Greenhouse Gas Emissions. European Commission, Joint Research Centre (JRC) [Dataset] PID: http://data.europa.eu/89h/97a67d67-c62e-4826-b873-9d972c4f670b https://edgar.jrc.ec.europa.eu/dataset_ghg60v60_CO2_excl_short-cycle_org_C_2010_optWCSGeoTIFFwesr_climat__v60_CO2_excl_short-cycle_org_C_2010_optRectifiedGridCoverage-180.0 -85.59999999999002179.99999999997954 90.0-180.0 -85.59999999999002179.99999999997954 90.0Yearly emissions of CO2 (excluding short cycle organic Carbon) - 2018 (EDGAR v6.0)EDGARv6.0 provides emissions of the three main greenhouse gases (CO2, CH4, N2O) and fluorinated gases (coming soon) per sector and country. CO2 emissions are provided separately for CO2_excl_short-cycle_org_C and CO2_short-cycle_org_C. Emissions of CO2_excl_short-cycle_org_C include all fossil CO2 sources, such as fossil fuel combustion, non-metallic mineral processes (e.g. cement production), metal (ferrous and non-ferrous) production processes, urea production, agricultural liming and solvents use. Large scale biomass burning with Savannah burning, forest fires, and sources and sinks from land-use, land-use change and forestry (LULUCF) are excluded. For the energy related sectors the activity data are primarily based on IEA data from IEA (2019) World Energy Balances, (Internet: link), All rights reserved, as modified by Joint Research Centre, European Commission, whereas the activity data for the agricultural sectors originate primarily from FAO (2020) (Internet: link). United States Geological Survey (USGS), International Fertiliser Association (IFA), Gas Flaring Reduction Partnership (GGFR)/U.S. National Oceanic and Atmospheric Administration (NOAA), UNFCCC and World Steel Association (worldsteel) recent statistics are also used for activity data. Additional information can be found in Crippa et al. (2021). Compared to EDGARv5.0 the following updates have been included: updates of all activity data up to 2018; inclusion of CO2 emissions from coal mining based on the IPCC 2019 refinements; new international shipping proxies and monthly profiles (Jalkanen et al. (2012), Johansson et al. (2017)). Reference: Crippa, Monica; Guizzardi, Diego; Muntean, Marilena; Schaaf, Edwin; Lo Vullo, Eleonora; Solazzo, Efisio; Monforti-Ferrario, Fabio; Olivier, Jos; Vignati, Elisabetta (2021): EDGAR v6.0 Greenhouse Gas Emissions. European Commission, Joint Research Centre (JRC) [Dataset] PID: http://data.europa.eu/89h/97a67d67-c62e-4826-b873-9d972c4f670b https://edgar.jrc.ec.europa.eu/dataset_ghg60v60_CO2_excl_short-cycle_org_C_2018_optWCSGeoTIFFwesr_climat__v60_CO2_excl_short-cycle_org_C_2018_optRectifiedGridCoverage-180.0 -85.19999999999004179.99999999997954 90.0-180.0 -85.19999999999004179.99999999997954 90.0Yearly emissions of CO2 (short cycle organic Carbon) - 1970 (EDGAR v6.0)EDGARv6.0 provides emissions of the three main greenhouse gases (CO2, CH4, N2O) and fluorinated gases (coming soon) per sector and country. CO2 emissions are provided separately for CO2_excl_short-cycle_org_C and CO2_short-cycle_org_C. Emissions of CO2_excl_short-cycle_org_C include all fossil CO2 sources, such as fossil fuel combustion, non-metallic mineral processes (e.g. cement production), metal (ferrous and non-ferrous) production processes, urea production, agricultural liming and solvents use. Large scale biomass burning with Savannah burning, forest fires, and sources and sinks from land-use, land-use change and forestry (LULUCF) are excluded. For the energy related sectors the activity data are primarily based on IEA data from IEA (2019) World Energy Balances, (Internet: link), All rights reserved, as modified by Joint Research Centre, European Commission, whereas the activity data for the agricultural sectors originate primarily from FAO (2020) (Internet: link). United States Geological Survey (USGS), International Fertiliser Association (IFA), Gas Flaring Reduction Partnership (GGFR)/U.S. National Oceanic and Atmospheric Administration (NOAA), UNFCCC and World Steel Association (worldsteel) recent statistics are also used for activity data. Additional information can be found in Crippa et al. (2021). Compared to EDGARv5.0 the following updates have been included: updates of all activity data up to 2018; inclusion of CO2 emissions from coal mining based on the IPCC 2019 refinements; new international shipping proxies and monthly profiles (Jalkanen et al. (2012), Johansson et al. (2017)). Reference: Crippa, Monica; Guizzardi, Diego; Muntean, Marilena; Schaaf, Edwin; Lo Vullo, Eleonora; Solazzo, Efisio; Monforti-Ferrario, Fabio; Olivier, Jos; Vignati, Elisabetta (2021): EDGAR v6.0 Greenhouse Gas Emissions. European Commission, Joint Research Centre (JRC) [Dataset] PID: http://data.europa.eu/89h/97a67d67-c62e-4826-b873-9d972c4f670b https://edgar.jrc.ec.europa.eu/dataset_ghg60v60_CO2_org_short-cycle_C_1970_optWCSGeoTIFFwesr_climat__v60_CO2_org_short-cycle_C_1970_optRectifiedGridCoverage-180.0 -55.699999999992485179.99999999997954 76.5-180.0 -55.699999999992485179.99999999997954 76.5Yearly emissions of CO2 (short cycle organic Carbon) - 1980 (EDGAR v6.0)EDGARv6.0 provides emissions of the three main greenhouse gases (CO2, CH4, N2O) and fluorinated gases (coming soon) per sector and country. CO2 emissions are provided separately for CO2_excl_short-cycle_org_C and CO2_short-cycle_org_C. Emissions of CO2_excl_short-cycle_org_C include all fossil CO2 sources, such as fossil fuel combustion, non-metallic mineral processes (e.g. cement production), metal (ferrous and non-ferrous) production processes, urea production, agricultural liming and solvents use. Large scale biomass burning with Savannah burning, forest fires, and sources and sinks from land-use, land-use change and forestry (LULUCF) are excluded. For the energy related sectors the activity data are primarily based on IEA data from IEA (2019) World Energy Balances, (Internet: link), All rights reserved, as modified by Joint Research Centre, European Commission, whereas the activity data for the agricultural sectors originate primarily from FAO (2020) (Internet: link). United States Geological Survey (USGS), International Fertiliser Association (IFA), Gas Flaring Reduction Partnership (GGFR)/U.S. National Oceanic and Atmospheric Administration (NOAA), UNFCCC and World Steel Association (worldsteel) recent statistics are also used for activity data. Additional information can be found in Crippa et al. (2021). Compared to EDGARv5.0 the following updates have been included: updates of all activity data up to 2018; inclusion of CO2 emissions from coal mining based on the IPCC 2019 refinements; new international shipping proxies and monthly profiles (Jalkanen et al. (2012), Johansson et al. (2017)). Reference: Crippa, Monica; Guizzardi, Diego; Muntean, Marilena; Schaaf, Edwin; Lo Vullo, Eleonora; Solazzo, Efisio; Monforti-Ferrario, Fabio; Olivier, Jos; Vignati, Elisabetta (2021): EDGAR v6.0 Greenhouse Gas Emissions. European Commission, Joint Research Centre (JRC) [Dataset] PID: http://data.europa.eu/89h/97a67d67-c62e-4826-b873-9d972c4f670b https://edgar.jrc.ec.europa.eu/dataset_ghg60v60_CO2_org_short-cycle_C_1980_optWCSGeoTIFFwesr_climat__v60_CO2_org_short-cycle_C_1980_optRectifiedGridCoverage-180.0 -55.699999999992485179.99999999997954 76.5-180.0 -55.699999999992485179.99999999997954 76.5Yearly emissions of CO2 (short cycle organic Carbon) - 1990 (EDGAR v6.0)EDGARv6.0 provides emissions of the three main greenhouse gases (CO2, CH4, N2O) and fluorinated gases (coming soon) per sector and country. CO2 emissions are provided separately for CO2_excl_short-cycle_org_C and CO2_short-cycle_org_C. Emissions of CO2_excl_short-cycle_org_C include all fossil CO2 sources, such as fossil fuel combustion, non-metallic mineral processes (e.g. cement production), metal (ferrous and non-ferrous) production processes, urea production, agricultural liming and solvents use. Large scale biomass burning with Savannah burning, forest fires, and sources and sinks from land-use, land-use change and forestry (LULUCF) are excluded. For the energy related sectors the activity data are primarily based on IEA data from IEA (2019) World Energy Balances, (Internet: link), All rights reserved, as modified by Joint Research Centre, European Commission, whereas the activity data for the agricultural sectors originate primarily from FAO (2020) (Internet: link). United States Geological Survey (USGS), International Fertiliser Association (IFA), Gas Flaring Reduction Partnership (GGFR)/U.S. National Oceanic and Atmospheric Administration (NOAA), UNFCCC and World Steel Association (worldsteel) recent statistics are also used for activity data. Additional information can be found in Crippa et al. (2021). Compared to EDGARv5.0 the following updates have been included: updates of all activity data up to 2018; inclusion of CO2 emissions from coal mining based on the IPCC 2019 refinements; new international shipping proxies and monthly profiles (Jalkanen et al. (2012), Johansson et al. (2017)). Reference: Crippa, Monica; Guizzardi, Diego; Muntean, Marilena; Schaaf, Edwin; Lo Vullo, Eleonora; Solazzo, Efisio; Monforti-Ferrario, Fabio; Olivier, Jos; Vignati, Elisabetta (2021): EDGAR v6.0 Greenhouse Gas Emissions. European Commission, Joint Research Centre (JRC) [Dataset] PID: http://data.europa.eu/89h/97a67d67-c62e-4826-b873-9d972c4f670b https://edgar.jrc.ec.europa.eu/dataset_ghg60v60_CO2_org_short-cycle_C_1990_optWCSGeoTIFFwesr_climat__v60_CO2_org_short-cycle_C_1990_optRectifiedGridCoverage-180.0 -55.899999999992474179.99999999997954 76.5-180.0 -55.899999999992474179.99999999997954 76.5Yearly emissions of CO2 (short cycle organic Carbon) - 2000 (EDGAR v6.0)EDGARv6.0 provides emissions of the three main greenhouse gases (CO2, CH4, N2O) and fluorinated gases (coming soon) per sector and country. CO2 emissions are provided separately for CO2_excl_short-cycle_org_C and CO2_short-cycle_org_C. Emissions of CO2_excl_short-cycle_org_C include all fossil CO2 sources, such as fossil fuel combustion, non-metallic mineral processes (e.g. cement production), metal (ferrous and non-ferrous) production processes, urea production, agricultural liming and solvents use. Large scale biomass burning with Savannah burning, forest fires, and sources and sinks from land-use, land-use change and forestry (LULUCF) are excluded. For the energy related sectors the activity data are primarily based on IEA data from IEA (2019) World Energy Balances, (Internet: link), All rights reserved, as modified by Joint Research Centre, European Commission, whereas the activity data for the agricultural sectors originate primarily from FAO (2020) (Internet: link). United States Geological Survey (USGS), International Fertiliser Association (IFA), Gas Flaring Reduction Partnership (GGFR)/U.S. National Oceanic and Atmospheric Administration (NOAA), UNFCCC and World Steel Association (worldsteel) recent statistics are also used for activity data. Additional information can be found in Crippa et al. (2021). Compared to EDGARv5.0 the following updates have been included: updates of all activity data up to 2018; inclusion of CO2 emissions from coal mining based on the IPCC 2019 refinements; new international shipping proxies and monthly profiles (Jalkanen et al. (2012), Johansson et al. (2017)). Reference: Crippa, Monica; Guizzardi, Diego; Muntean, Marilena; Schaaf, Edwin; Lo Vullo, Eleonora; Solazzo, Efisio; Monforti-Ferrario, Fabio; Olivier, Jos; Vignati, Elisabetta (2021): EDGAR v6.0 Greenhouse Gas Emissions. European Commission, Joint Research Centre (JRC) [Dataset] PID: http://data.europa.eu/89h/97a67d67-c62e-4826-b873-9d972c4f670b https://edgar.jrc.ec.europa.eu/dataset_ghg60v60_CO2_org_short-cycle_C_2000_optWCSGeoTIFFwesr_climat__v60_CO2_org_short-cycle_C_2000_optRectifiedGridCoverage-180.0 -55.59999999999249179.99999999997954 76.5-180.0 -55.59999999999249179.99999999997954 76.5Yearly emissions of CO2 (short cycle organic Carbon) - 2010 (EDGAR v6.0)EDGARv6.0 provides emissions of the three main greenhouse gases (CO2, CH4, N2O) and fluorinated gases (coming soon) per sector and country. CO2 emissions are provided separately for CO2_excl_short-cycle_org_C and CO2_short-cycle_org_C. Emissions of CO2_excl_short-cycle_org_C include all fossil CO2 sources, such as fossil fuel combustion, non-metallic mineral processes (e.g. cement production), metal (ferrous and non-ferrous) production processes, urea production, agricultural liming and solvents use. Large scale biomass burning with Savannah burning, forest fires, and sources and sinks from land-use, land-use change and forestry (LULUCF) are excluded. For the energy related sectors the activity data are primarily based on IEA data from IEA (2019) World Energy Balances, (Internet: link), All rights reserved, as modified by Joint Research Centre, European Commission, whereas the activity data for the agricultural sectors originate primarily from FAO (2020) (Internet: link). United States Geological Survey (USGS), International Fertiliser Association (IFA), Gas Flaring Reduction Partnership (GGFR)/U.S. National Oceanic and Atmospheric Administration (NOAA), UNFCCC and World Steel Association (worldsteel) recent statistics are also used for activity data. Additional information can be found in Crippa et al. (2021). Compared to EDGARv5.0 the following updates have been included: updates of all activity data up to 2018; inclusion of CO2 emissions from coal mining based on the IPCC 2019 refinements; new international shipping proxies and monthly profiles (Jalkanen et al. (2012), Johansson et al. (2017)). Reference: Crippa, Monica; Guizzardi, Diego; Muntean, Marilena; Schaaf, Edwin; Lo Vullo, Eleonora; Solazzo, Efisio; Monforti-Ferrario, Fabio; Olivier, Jos; Vignati, Elisabetta (2021): EDGAR v6.0 Greenhouse Gas Emissions. European Commission, Joint Research Centre (JRC) [Dataset] PID: http://data.europa.eu/89h/97a67d67-c62e-4826-b873-9d972c4f670b https://edgar.jrc.ec.europa.eu/dataset_ghg60v60_CO2_org_short-cycle_C_2010_optWCSGeoTIFFwesr_climat__v60_CO2_org_short-cycle_C_2010_optRectifiedGridCoverage-180.0 -55.59999999999239179.99999999997954 78.3-180.0 -55.59999999999239179.99999999997954 78.3Yearly emissions of CO2 (short cycle organic Carbon) - 2018 (EDGAR v6.0)EDGARv6.0 provides emissions of the three main greenhouse gases (CO2, CH4, N2O) and fluorinated gases (coming soon) per sector and country. CO2 emissions are provided separately for CO2_excl_short-cycle_org_C and CO2_short-cycle_org_C. Emissions of CO2_excl_short-cycle_org_C include all fossil CO2 sources, such as fossil fuel combustion, non-metallic mineral processes (e.g. cement production), metal (ferrous and non-ferrous) production processes, urea production, agricultural liming and solvents use. Large scale biomass burning with Savannah burning, forest fires, and sources and sinks from land-use, land-use change and forestry (LULUCF) are excluded. For the energy related sectors the activity data are primarily based on IEA data from IEA (2019) World Energy Balances, (Internet: link), All rights reserved, as modified by Joint Research Centre, European Commission, whereas the activity data for the agricultural sectors originate primarily from FAO (2020) (Internet: link). United States Geological Survey (USGS), International Fertiliser Association (IFA), Gas Flaring Reduction Partnership (GGFR)/U.S. National Oceanic and Atmospheric Administration (NOAA), UNFCCC and World Steel Association (worldsteel) recent statistics are also used for activity data. Additional information can be found in Crippa et al. (2021). Compared to EDGARv5.0 the following updates have been included: updates of all activity data up to 2018; inclusion of CO2 emissions from coal mining based on the IPCC 2019 refinements; new international shipping proxies and monthly profiles (Jalkanen et al. (2012), Johansson et al. (2017)). Reference: Crippa, Monica; Guizzardi, Diego; Muntean, Marilena; Schaaf, Edwin; Lo Vullo, Eleonora; Solazzo, Efisio; Monforti-Ferrario, Fabio; Olivier, Jos; Vignati, Elisabetta (2021): EDGAR v6.0 Greenhouse Gas Emissions. European Commission, Joint Research Centre (JRC) [Dataset] PID: http://data.europa.eu/89h/97a67d67-c62e-4826-b873-9d972c4f670b https://edgar.jrc.ec.europa.eu/dataset_ghg60v60_CO2_org_short-cycle_C_2018_optWCSGeoTIFFwesr_climat__v60_CO2_org_short-cycle_C_2018_optRectifiedGridCoverage-180.0 -55.89999999999222179.99999999997954 81.19999999999999-180.0 -55.89999999999222179.99999999997954 81.19999999999999Yearly emissions of N2O - 1970 (EDGAR v6.0)EDGARv6.0 provides emissions of the three main greenhouse gases (CO2, CH4, N2O) and fluorinated gases (coming soon) per sector and country. CO2 emissions are provided separately for CO2_excl_short-cycle_org_C and CO2_short-cycle_org_C. Emissions of CO2_excl_short-cycle_org_C include all fossil CO2 sources, such as fossil fuel combustion, non-metallic mineral processes (e.g. cement production), metal (ferrous and non-ferrous) production processes, urea production, agricultural liming and solvents use. Large scale biomass burning with Savannah burning, forest fires, and sources and sinks from land-use, land-use change and forestry (LULUCF) are excluded. For the energy related sectors the activity data are primarily based on IEA data from IEA (2019) World Energy Balances, (Internet: link), All rights reserved, as modified by Joint Research Centre, European Commission, whereas the activity data for the agricultural sectors originate primarily from FAO (2020) (Internet: link). United States Geological Survey (USGS), International Fertiliser Association (IFA), Gas Flaring Reduction Partnership (GGFR)/U.S. National Oceanic and Atmospheric Administration (NOAA), UNFCCC and World Steel Association (worldsteel) recent statistics are also used for activity data. Additional information can be found in Crippa et al. (2021). Compared to EDGARv5.0 the following updates have been included: updates of all activity data up to 2018; inclusion of CO2 emissions from coal mining based on the IPCC 2019 refinements; new international shipping proxies and monthly profiles (Jalkanen et al. (2012), Johansson et al. (2017)). Reference: Crippa, Monica; Guizzardi, Diego; Muntean, Marilena; Schaaf, Edwin; Lo Vullo, Eleonora; Solazzo, Efisio; Monforti-Ferrario, Fabio; Olivier, Jos; Vignati, Elisabetta (2021): EDGAR v6.0 Greenhouse Gas Emissions. European Commission, Joint Research Centre (JRC) [Dataset] PID: http://data.europa.eu/89h/97a67d67-c62e-4826-b873-9d972c4f670b https://edgar.jrc.ec.europa.eu/dataset_ghg60v60_N2O_1970_optWCSGeoTIFFwesr_climat__v60_N2O_1970_optRectifiedGridCoverage-180.0 -89.99999999998977179.99999999997954 90.0-180.0 -89.99999999998977179.99999999997954 90.0Yearly emissions of N2O - 1980 (EDGAR v6.0)EDGARv6.0 provides emissions of the three main greenhouse gases (CO2, CH4, N2O) and fluorinated gases (coming soon) per sector and country. CO2 emissions are provided separately for CO2_excl_short-cycle_org_C and CO2_short-cycle_org_C. Emissions of CO2_excl_short-cycle_org_C include all fossil CO2 sources, such as fossil fuel combustion, non-metallic mineral processes (e.g. cement production), metal (ferrous and non-ferrous) production processes, urea production, agricultural liming and solvents use. Large scale biomass burning with Savannah burning, forest fires, and sources and sinks from land-use, land-use change and forestry (LULUCF) are excluded. For the energy related sectors the activity data are primarily based on IEA data from IEA (2019) World Energy Balances, (Internet: link), All rights reserved, as modified by Joint Research Centre, European Commission, whereas the activity data for the agricultural sectors originate primarily from FAO (2020) (Internet: link). United States Geological Survey (USGS), International Fertiliser Association (IFA), Gas Flaring Reduction Partnership (GGFR)/U.S. National Oceanic and Atmospheric Administration (NOAA), UNFCCC and World Steel Association (worldsteel) recent statistics are also used for activity data. Additional information can be found in Crippa et al. (2021). Compared to EDGARv5.0 the following updates have been included: updates of all activity data up to 2018; inclusion of CO2 emissions from coal mining based on the IPCC 2019 refinements; new international shipping proxies and monthly profiles (Jalkanen et al. (2012), Johansson et al. (2017)). Reference: Crippa, Monica; Guizzardi, Diego; Muntean, Marilena; Schaaf, Edwin; Lo Vullo, Eleonora; Solazzo, Efisio; Monforti-Ferrario, Fabio; Olivier, Jos; Vignati, Elisabetta (2021): EDGAR v6.0 Greenhouse Gas Emissions. European Commission, Joint Research Centre (JRC) [Dataset] PID: http://data.europa.eu/89h/97a67d67-c62e-4826-b873-9d972c4f670b https://edgar.jrc.ec.europa.eu/dataset_ghg60v60_N2O_1980_optWCSGeoTIFFwesr_climat__v60_N2O_1980_optRectifiedGridCoverage-180.0 -89.99999999998977179.99999999997954 90.0-180.0 -89.99999999998977179.99999999997954 90.0Yearly emissions of N2O - 1990 (EDGAR v6.0)EDGARv6.0 provides emissions of the three main greenhouse gases (CO2, CH4, N2O) and fluorinated gases (coming soon) per sector and country. CO2 emissions are provided separately for CO2_excl_short-cycle_org_C and CO2_short-cycle_org_C. Emissions of CO2_excl_short-cycle_org_C include all fossil CO2 sources, such as fossil fuel combustion, non-metallic mineral processes (e.g. cement production), metal (ferrous and non-ferrous) production processes, urea production, agricultural liming and solvents use. Large scale biomass burning with Savannah burning, forest fires, and sources and sinks from land-use, land-use change and forestry (LULUCF) are excluded. For the energy related sectors the activity data are primarily based on IEA data from IEA (2019) World Energy Balances, (Internet: link), All rights reserved, as modified by Joint Research Centre, European Commission, whereas the activity data for the agricultural sectors originate primarily from FAO (2020) (Internet: link). United States Geological Survey (USGS), International Fertiliser Association (IFA), Gas Flaring Reduction Partnership (GGFR)/U.S. National Oceanic and Atmospheric Administration (NOAA), UNFCCC and World Steel Association (worldsteel) recent statistics are also used for activity data. Additional information can be found in Crippa et al. (2021). Compared to EDGARv5.0 the following updates have been included: updates of all activity data up to 2018; inclusion of CO2 emissions from coal mining based on the IPCC 2019 refinements; new international shipping proxies and monthly profiles (Jalkanen et al. (2012), Johansson et al. (2017)). Reference: Crippa, Monica; Guizzardi, Diego; Muntean, Marilena; Schaaf, Edwin; Lo Vullo, Eleonora; Solazzo, Efisio; Monforti-Ferrario, Fabio; Olivier, Jos; Vignati, Elisabetta (2021): EDGAR v6.0 Greenhouse Gas Emissions. European Commission, Joint Research Centre (JRC) [Dataset] PID: http://data.europa.eu/89h/97a67d67-c62e-4826-b873-9d972c4f670b https://edgar.jrc.ec.europa.eu/dataset_ghg60v60_N2O_1990_optWCSGeoTIFFwesr_climat__v60_N2O_1990_optRectifiedGridCoverage-180.0 -89.99999999998977179.99999999997954 90.0-180.0 -89.99999999998977179.99999999997954 90.0Yearly emissions of N2O - 2000 (EDGAR v6.0)EDGARv6.0 provides emissions of the three main greenhouse gases (CO2, CH4, N2O) and fluorinated gases (coming soon) per sector and country. CO2 emissions are provided separately for CO2_excl_short-cycle_org_C and CO2_short-cycle_org_C. Emissions of CO2_excl_short-cycle_org_C include all fossil CO2 sources, such as fossil fuel combustion, non-metallic mineral processes (e.g. cement production), metal (ferrous and non-ferrous) production processes, urea production, agricultural liming and solvents use. Large scale biomass burning with Savannah burning, forest fires, and sources and sinks from land-use, land-use change and forestry (LULUCF) are excluded. For the energy related sectors the activity data are primarily based on IEA data from IEA (2019) World Energy Balances, (Internet: link), All rights reserved, as modified by Joint Research Centre, European Commission, whereas the activity data for the agricultural sectors originate primarily from FAO (2020) (Internet: link). United States Geological Survey (USGS), International Fertiliser Association (IFA), Gas Flaring Reduction Partnership (GGFR)/U.S. National Oceanic and Atmospheric Administration (NOAA), UNFCCC and World Steel Association (worldsteel) recent statistics are also used for activity data. Additional information can be found in Crippa et al. (2021). Compared to EDGARv5.0 the following updates have been included: updates of all activity data up to 2018; inclusion of CO2 emissions from coal mining based on the IPCC 2019 refinements; new international shipping proxies and monthly profiles (Jalkanen et al. (2012), Johansson et al. (2017)). Reference: Crippa, Monica; Guizzardi, Diego; Muntean, Marilena; Schaaf, Edwin; Lo Vullo, Eleonora; Solazzo, Efisio; Monforti-Ferrario, Fabio; Olivier, Jos; Vignati, Elisabetta (2021): EDGAR v6.0 Greenhouse Gas Emissions. European Commission, Joint Research Centre (JRC) [Dataset] PID: http://data.europa.eu/89h/97a67d67-c62e-4826-b873-9d972c4f670b https://edgar.jrc.ec.europa.eu/dataset_ghg60v60_N2O_2000_optWCSGeoTIFFwesr_climat__v60_N2O_2000_optRectifiedGridCoverage-180.0 -89.99999999998977179.99999999997954 90.0-180.0 -89.99999999998977179.99999999997954 90.0Yearly emissions of N2O - 2010 (EDGAR v6.0)EDGARv6.0 provides emissions of the three main greenhouse gases (CO2, CH4, N2O) and fluorinated gases (coming soon) per sector and country. CO2 emissions are provided separately for CO2_excl_short-cycle_org_C and CO2_short-cycle_org_C. Emissions of CO2_excl_short-cycle_org_C include all fossil CO2 sources, such as fossil fuel combustion, non-metallic mineral processes (e.g. cement production), metal (ferrous and non-ferrous) production processes, urea production, agricultural liming and solvents use. Large scale biomass burning with Savannah burning, forest fires, and sources and sinks from land-use, land-use change and forestry (LULUCF) are excluded. For the energy related sectors the activity data are primarily based on IEA data from IEA (2019) World Energy Balances, (Internet: link), All rights reserved, as modified by Joint Research Centre, European Commission, whereas the activity data for the agricultural sectors originate primarily from FAO (2020) (Internet: link). United States Geological Survey (USGS), International Fertiliser Association (IFA), Gas Flaring Reduction Partnership (GGFR)/U.S. National Oceanic and Atmospheric Administration (NOAA), UNFCCC and World Steel Association (worldsteel) recent statistics are also used for activity data. Additional information can be found in Crippa et al. (2021). Compared to EDGARv5.0 the following updates have been included: updates of all activity data up to 2018; inclusion of CO2 emissions from coal mining based on the IPCC 2019 refinements; new international shipping proxies and monthly profiles (Jalkanen et al. (2012), Johansson et al. (2017)). Reference: Crippa, Monica; Guizzardi, Diego; Muntean, Marilena; Schaaf, Edwin; Lo Vullo, Eleonora; Solazzo, Efisio; Monforti-Ferrario, Fabio; Olivier, Jos; Vignati, Elisabetta (2021): EDGAR v6.0 Greenhouse Gas Emissions. European Commission, Joint Research Centre (JRC) [Dataset] PID: http://data.europa.eu/89h/97a67d67-c62e-4826-b873-9d972c4f670b https://edgar.jrc.ec.europa.eu/dataset_ghg60v60_N2O_2010_optWCSGeoTIFFwesr_climat__v60_N2O_2010_optRectifiedGridCoverage-180.0 -89.99999999998977179.99999999997954 90.0-180.0 -89.99999999998977179.99999999997954 90.0Yearly emissions of N2O - 2018 (EDGAR v6.0)EDGARv6.0 provides emissions of the three main greenhouse gases (CO2, CH4, N2O) and fluorinated gases (coming soon) per sector and country. CO2 emissions are provided separately for CO2_excl_short-cycle_org_C and CO2_short-cycle_org_C. Emissions of CO2_excl_short-cycle_org_C include all fossil CO2 sources, such as fossil fuel combustion, non-metallic mineral processes (e.g. cement production), metal (ferrous and non-ferrous) production processes, urea production, agricultural liming and solvents use. Large scale biomass burning with Savannah burning, forest fires, and sources and sinks from land-use, land-use change and forestry (LULUCF) are excluded. For the energy related sectors the activity data are primarily based on IEA data from IEA (2019) World Energy Balances, (Internet: link), All rights reserved, as modified by Joint Research Centre, European Commission, whereas the activity data for the agricultural sectors originate primarily from FAO (2020) (Internet: link). United States Geological Survey (USGS), International Fertiliser Association (IFA), Gas Flaring Reduction Partnership (GGFR)/U.S. National Oceanic and Atmospheric Administration (NOAA), UNFCCC and World Steel Association (worldsteel) recent statistics are also used for activity data. Additional information can be found in Crippa et al. (2021). Compared to EDGARv5.0 the following updates have been included: updates of all activity data up to 2018; inclusion of CO2 emissions from coal mining based on the IPCC 2019 refinements; new international shipping proxies and monthly profiles (Jalkanen et al. (2012), Johansson et al. (2017)). Reference: Crippa, Monica; Guizzardi, Diego; Muntean, Marilena; Schaaf, Edwin; Lo Vullo, Eleonora; Solazzo, Efisio; Monforti-Ferrario, Fabio; Olivier, Jos; Vignati, Elisabetta (2021): EDGAR v6.0 Greenhouse Gas Emissions. European Commission, Joint Research Centre (JRC) [Dataset] PID: http://data.europa.eu/89h/97a67d67-c62e-4826-b873-9d972c4f670b https://edgar.jrc.ec.europa.eu/dataset_ghg60v60_N2O_2018_optWCSGeoTIFFwesr_climat__v60_N2O_2018_optRectifiedGridCoverage-180.0 -89.99999999998977179.99999999997954 90.0-180.0 -89.99999999998977179.99999999997954 90.0afg_biodiversity_intactness_indexMap_of_Biodiversity_Intactness_Index_final_opt_deflate_v2WCSGeoTIFFafg_transboundary__afg_biodiversity_intactness_indexRectifiedGridCoverage68.224999007 35.44166628575.158332313 39.85833293468.224999007 35.44166628575.158332313 39.858332934afg_flood_mortality_riskfl1010irmt_clipped_final_opt_deflate_v2WCSGeoTIFFafg_transboundary__afg_flood_mortality_riskRectifiedGridCoverage68.249999901 35.4999999575.166666565 39.83333328168.249999901 35.4999999575.166666565 39.833333281afg_le1010ipeyle1010ipeykx_clipped_final_opt_deflate_v2WCSGeoTIFFafg_transboundary__afg_le1010ipeyRectifiedGridCoverage68.208333532 35.45833343475.125000204 39.83333343768.208333532 35.45833343475.125000204 39.833333437afghanistan_windafghanistan_wind_opt_deflateWCSGeoTIFFMAPX__afghanistan_windRectifiedGridCoverage57.467697464 22.27567547580.618624748 39.58980047657.467697464 22.27567547580.618624748 39.589800476africaSCI3africaSCIWCSGeoTIFFMapX_UNDP__africaSCI3RectifiedGridCoverage4.291563438357412 -7.91521649953385935.4060669442033 6.844827062602074.291563438357412 -7.91521649953385935.4060669442033 6.84482706260207aggregate_consumption_per_year_Mtaggregate_consumption_per_year_MtWCSGeoTIFFsand__aggregate_consumption_per_year_MtRectifiedGridCoverage-180.0 -89.9999999999884180.0 90.0000000000116-180.0 -89.9999999999884180.0 90.0000000000116albania_remote_site_screening_res50albania_remote_site_screening_res50WCSGeoTIFFmapx_chemobs__albania_remote_site_screening_res50RectifiedGridCoverage19.262757000000047 39.646050111000121.057257000000046 42.661050111000119.262757000000047 39.646050111000121.057257000000046 42.6610501110001algeria_remote_site_screening_res50algeria_remote_site_screening_res50WCSGeoTIFFmapx_chemobs__algeria_remote_site_screening_res50RectifiedGridCoverage-8.666629307668718 19.06073474100002611.99737069233128 37.09373474100003-8.66662930766872 19.06073474100002611.997370692331282 37.09373474100003asiaSCI3asiaSCIWCSGeoTIFFMapX_UNDP__asiaSCI3RectifiedGridCoverage90.32175330967124 -11.847003516704188153.42828368100916 26.03845727512674290.32175330967124 -11.847003516704188153.42828368100916 26.038457275126742bosnia_herzegovina_remote_site_screening_res50bosnia_herzegovina_remote_site_screening_res50WCSGeoTIFFmapx_chemobs__bosnia_herzegovina_remote_site_screening_res50RectifiedGridCoverage15.728732108000047 42.5565396480000519.621732108000046 45.27653964800004615.728732108000047 42.5565396480000519.621732108000046 45.276539648000046botswana_remote_site_screening_res50botswana_remote_site_screening_res50WCSGeoTIFFmapx_chemobs__botswana_remote_site_screening_res50RectifiedGridCoverage19.99936526400007 -26.9005930839999729.36086526400007 -17.7785930839999719.99936526400007 -26.9005930839999729.36086526400007 -17.77859308399997c181125_mantapalac181125_Mantapala_Geo_TIF_wgs84_v2_opt_deflateWCSGeoTIFFMAPX__c181125_mantapalaRectifiedGridCoverage28.847679478 -9.53369967828.918080924 -9.48713377228.847679478 -9.53369967828.918080924 -9.487133772codemcodem_trans_opt_deflateWCSGeoTIFFrisk_niger_undp__codemRectifiedGridCoverage0.16750000159949785 11.69750004161636916.000833334932196 23.524166708282560.1675000015994978 11.69750004161636916.000833334932196 23.52416670828256codemicodemiWCSGeoTIFFrisk_niger_undp__codemiRectifiedGridCoverage0.16750000159949785 11.69750004161636916.000833334932196 23.524166708282560.1675000015994978 11.69750004161636916.000833334932196 23.52416670828256codemihcodemihWCSGeoTIFFrisk_niger_undp__codemihRectifiedGridCoverage0.16750000159949785 11.69750004161636916.000833334932196 23.524166708282560.1675000015994978 11.69750004161636916.000833334932196 23.52416670828256col_forint100The forest integrrity index is derived by overlaying the human footprint (Venter et al. 2016) on the forest structural condition. The name is consistent with the concept of ecological integrity. Ecological integrity has been defined as, “the system’s capacity to maintain structure and ecosystem functions using processes and elements characteristic for its ecoregion.” (Parks Canada 2008). This capacity is a result of the climate, soil, topography, biota and other biophysical properties of the ecoregion and the extent to which these properties are not altered by modern human pressures. Consistent with this definition, the forest integrity index is based on on the structural complexity of a stand relative to the natural potential of the ecoregion and level of human pressure. Thus, forest of high integrity are relatively tall, high in canopy cover, older, and with relatively low human pressure. An increasing number of studies have shown that human pressure in various forms can have negative effects on native species. Thus, high integrity forests may be uniquely important for conservation because they support species and processes that are require well-developed forests and are sensitive to human activities. Such forests often also have high economic value and have likely been preferentially converted to more intense human land uses. Thus, identifying remaining areas of high forest integrity is important for conservation planning.<br><br>Data is provided by Montana State University.<br/><br>License information: <a href "https://creativecommons.org/licenses/by/4.0/" target="_blank">CC-4.0 Attribution</a>.<br/>col_forint100WCSGeoTIFFwesr_biodiversity__col_forint100RectifiedGridCoverage-79.758379943848 -4.470114545847-66.21837994384799 11.205885454153-79.758379943848 -4.470114545847-66.21837994384799 11.205885454153dfodfo_opt_deflateWCSGeoTIFFrisk_niger_undp__dfoRectifiedGridCoverage0.16750000159949785 11.69750004161636916.000833334932196 23.524166708282560.1675000015994978 11.69750004161636916.000833334932196 23.52416670828256dumping_suscep_noLSTSolid Waste ManagementRemote SensingGISCitizen ScienceMulti-Criteria AnalysisIllegal Dumpindakar_waste__dumping_suscep_noLSTRectifiedGridCoverage245244.17762321254 1633500.0000000012253844.6474574117 1639980.354014607-17.36711980308572 14.763413721162385-17.286639327624464 14.822762633451076dust_extreme_events_1980_1986_output_optdust_extreme_events_1980_1986_output_optWCSGeoTIFFgrid_data_core__dust_extreme_events_1980_1986_output_optRectifiedGridCoverage-17.499999999999943 -1.499999999999943275.00000000000006 45.00000000000006-17.499999999999943 -1.499999999999943275.00000000000006 45.00000000000006dust_extreme_events_1980_1986_output_opt_1dust_extreme_events_1980_1986_output_optWCSGeoTIFFgrid_data_core__dust_extreme_events_1980_1986_output_opt_1RectifiedGridCoverage-17.499999999999943 -1.499999999999943275.00000000000006 45.00000000000006-17.499999999999943 -1.499999999999943275.00000000000006 45.00000000000006dust_strong_events_1980_1986_outputdust_strong_events_1980_1986_outputWCSGeoTIFFgrid_data_core__dust_strong_events_1980_1986_outputRectifiedGridCoverage-17.499999999999943 -1.499999999999943275.00000000000006 45.00000000000006-17.499999999999943 -1.499999999999943275.00000000000006 45.00000000000006dust_strong_events_2017_2022_outputdust_strong_events_2017_2022_outputWCSGeoTIFFgrid_data_core__dust_strong_events_2017_2022_outputRectifiedGridCoverage-17.499999999999943 -1.499999999999943275.00000000000006 45.00000000000006-17.499999999999943 -1.499999999999943275.00000000000006 45.00000000000006ecu_forint100The forest integrrity index is derived by overlaying the human footprint (Venter et al. 2016) on the forest structural condition. The name is consistent with the concept of ecological integrity. Ecological integrity has been defined as, “the system’s capacity to maintain structure and ecosystem functions using processes and elements characteristic for its ecoregion.” (Parks Canada 2008). This capacity is a result of the climate, soil, topography, biota and other biophysical properties of the ecoregion and the extent to which these properties are not altered by modern human pressures. Consistent with this definition, the forest integrity index is based on on the structural complexity of a stand relative to the natural potential of the ecoregion and level of human pressure. Thus, forest of high integrity are relatively tall, high in canopy cover, older, and with relatively low human pressure. An increasing number of studies have shown that human pressure in various forms can have negative effects on native species. Thus, high integrity forests may be uniquely important for conservation because they support species and processes that are require well-developed forests and are sensitive to human activities. Such forests often also have high economic value and have likely been preferentially converted to more intense human land uses. Thus, identifying remaining areas of high forest integrity is important for conservation planning.<br><br>Data is provided by Montana State University.<br/><br>License information: <a href "https://creativecommons.org/licenses/by/4.0/" target="_blank">CC-4.0 Attribution</a>.<br/>ecu_forint100WCSGeoTIFFwesr_biodiversity__ecu_forint100RectifiedGridCoverage-81.007003784197 -5.015087128068-75.189781557321 1.4260239881959995-81.007003784197 -5.015087128068-75.189781557321 1.4260239881959995egypt_remote_site_screening_res50egypt_remote_site_screening_res50WCSGeoTIFFmapx_chemobs__egypt_remote_site_screening_res50RectifiedGridCoverage24.86526973696999 21.9998400000000136.24276973696999 31.6673400000000124.86526973696999 21.9998400000000136.24276973696999 31.66734000000001emodnet_open_maplibrary_mediseh_posidonia_optemodnet_open_maplibrary_mediseh_posidonia_optWCSGeoTIFFMedicip__emodnet_open_maplibrary_mediseh_posidoniaRectifiedGridCoverage-729957.8573270211 3431425.4482482844782864.798068769 5991307.305507091-6.557323000000001 29.43731806752601842.965205499824044 47.30077599999999eswatini_remote_site_screening_res50eswatini_remote_site_screening_res50WCSGeoTIFFmapx_chemobs__eswatini_remote_site_screening_res50RectifiedGridCoverage30.794110000000003 -27.31715000000002332.13711000000001 -25.71965000000002330.794110000000003 -27.31715000000002332.13711000000001 -25.719650000000023ethiopia_remote_site_screening_res50ethiopia_remote_site_screening_res50WCSGeoTIFFmapx_chemobs__ethiopia_remote_site_screening_res50RectifiedGridCoverage32.99994 3.404337831301118647.94344 14.86933783130111832.99994 3.404337831301118647.94344 14.869337831301118exposure_pesato_02_03exposure_pesato_02_03_optWCSGeoTIFFMedicip__exposure_pesato_02_03RectifiedGridCoverage-5.3554163761242854 30.26458369651888335.977083623875714 45.50708369651888-5.3554163761242854 30.26458369651888335.977083623875714 45.50708369651888forcing_pesato_02_03forcing_pesato_02_03_optWCSGeoTIFFMedicip__forcing_pesato_02_03RectifiedGridCoverage-5.3554163761242854 30.26458369651888335.977083623875714 45.50708369651888-5.3554163761242854 30.26458369651888335.977083623875714 45.50708369651888forest_loss_su_2022_v1forest_loss_su_2022_v1WCSGeoTIFFtropisco__forest_loss_su_2022_v1RectifiedGridCoverage-58.06766256156945 1.833557839460103-53.98639912628145 6.011499880852531-58.06766256156945 1.833557839460103-53.98639912628145 6.011499880852531frac_cats_2016_kamitugafrac_cats_2016_kamitugaWCSGeoTIFFrs_for_asgm__frac_cats_2016_kamitugaRectifiedGridCoverage626890.0 9643900.0634990.0 9664260.028.141845709394754 -3.22107141330841228.214936578763222 -3.0368306131003737frac_cats_2016_kamituga_westfrac_cats_2016_kamituga_westWCSGeoTIFFrs_for_asgm__frac_cats_2016_kamituga_westRectifiedGridCoverage629680.0 9659970.0631260.0 9661230.028.166978184291047 -3.075687270143314228.181206954060517 -3.06427470634831frac_cats_2017_kamitugafrac_cats_2017_kamitugaWCSGeoTIFFrs_for_asgm__frac_cats_2017_kamitugaRectifiedGridCoverage626890.0 9643900.0634990.0 9664260.028.141845709394754 -3.22107141330841228.214936578763222 -3.0368306131003737frac_cats_2017_kamituga_westfrac_cats_2017_kamituga_westWCSGeoTIFFrs_for_asgm__frac_cats_2017_kamituga_westRectifiedGridCoverage629680.0 9659970.0631260.0 9661230.028.166978184291047 -3.075687270143314228.181206954060517 -3.06427470634831frac_cats_2018_kamitugafrac_cats_2018_kamitugaWCSGeoTIFFrs_for_asgm__frac_cats_2018_kamitugaRectifiedGridCoverage626890.0 9643900.0634990.0 9664260.028.141845709394754 -3.22107141330841228.214936578763222 -3.0368306131003737frac_cats_2018_kamituga_westfrac_cats_2018_kamituga_westWCSGeoTIFFrs_for_asgm__frac_cats_2018_kamituga_westRectifiedGridCoverage629680.0 9659970.0631260.0 9661230.028.166978184291047 -3.075687270143314228.181206954060517 -3.06427470634831frac_cats_2019_kamitugafrac_cats_2019_kamitugaWCSGeoTIFFrs_for_asgm__frac_cats_2019_kamitugaRectifiedGridCoverage626890.0 9643900.0634990.0 9664260.028.141845709394754 -3.22107141330841228.214936578763222 -3.0368306131003737frac_cats_2019_kamituga_westfrac_cats_2019_kamituga_westWCSGeoTIFFrs_for_asgm__frac_cats_2019_kamituga_westRectifiedGridCoverage629680.0 9659970.0631260.0 9661230.028.166978184291047 -3.075687270143314228.181206954060517 -3.06427470634831frac_cats_2020_kamitugafrac_cats_2020_kamitugaWCSGeoTIFFrs_for_asgm__frac_cats_2020_kamitugaRectifiedGridCoverage626890.0 9643900.0634990.0 9664260.028.141845709394754 -3.22107141330841228.214936578763222 -3.0368306131003737frac_cats_2020_kamituga_westfrac_cats_2020_kamituga_westWCSGeoTIFFrs_for_asgm__frac_cats_2020_kamituga_westRectifiedGridCoverage629680.0 9659970.0631260.0 9661230.028.166978184291047 -3.075687270143314228.181206954060517 -3.06427470634831frac_cats_2021_kamitugafrac_cats_2021_kamitugaWCSGeoTIFFrs_for_asgm__frac_cats_2021_kamitugaRectifiedGridCoverage626890.0 9643900.0634990.0 9664260.028.141845709394754 -3.22107141330841228.214936578763222 -3.0368306131003737frac_cats_2021_kamituga_westfrac_cats_2021_kamituga_westWCSGeoTIFFrs_for_asgm__frac_cats_2021_kamituga_westRectifiedGridCoverage629680.0 9659970.0631260.0 9661230.028.166978184291047 -3.075687270143314228.181206954060517 -3.06427470634831frac_cats_transition_2016_2017_kamitugafrac_cats_transition_2016_2017_kamitugaWCSGeoTIFFrs_for_asgm__frac_cats_transition_2016_2017_kamitugaRectifiedGridCoverage626890.0 9643900.0634990.0 9664260.028.141845709394754 -3.22107141330841228.214936578763222 -3.0368306131003737frac_cats_transition_2017_2018_kamitugafrac_cats_transition_2017_2018_kamitugaWCSGeoTIFFrs_for_asgm__frac_cats_transition_2017_2018_kamitugaRectifiedGridCoverage626890.0 9643900.0634990.0 9664260.028.141845709394754 -3.22107141330841228.214936578763222 -3.0368306131003737frac_cats_transition_2018_2019_kamitugafrac_cats_transition_2018_2019_kamitugaWCSGeoTIFFrs_for_asgm__frac_cats_transition_2018_2019_kamitugaRectifiedGridCoverage626890.0 9643900.0634990.0 9664260.028.141845709394754 -3.22107141330841228.214936578763222 -3.0368306131003737frac_cats_transition_2019_2020_kamitugafrac_cats_transition_2019_2020_kamitugaWCSGeoTIFFrs_for_asgm__frac_cats_transition_2019_2020_kamitugaRectifiedGridCoverage626890.0 9643900.0634990.0 9664260.028.141845709394754 -3.22107141330841228.214936578763222 -3.0368306131003737frac_cats_transition_2020_2021_kamitugafrac_cats_transition_2020_2021_kamitugaWCSGeoTIFFrs_for_asgm__frac_cats_transition_2020_2021_kamitugaRectifiedGridCoverage626890.0 9643900.0634990.0 9664260.028.141845709394754 -3.22107141330841228.214936578763222 -3.0368306131003737gpcc_precipitation_anomaly_annual_19912020Monthly Precipitation anomaly (mm, difference from the 1991-2020 average). Data shown are the median of the following one data sets: GPCC. White areas indicate where there were too few data to reliably estimate the percentiles.gpcc_precipitation_anomaly_annual_19912020WCSImageMosaicwmo__gpcc_precipitation_anomaly_annual_19912020RectifiedGridCoverage-180.0 -90.0180.0 90.0-180.0 -90.0180.0 90.0gpcc_precipitation_quantile_12month_19912020Precipitation quantiles are based on the twelve months aggregated GPCC Monitoring Product and First Guess Monthly product. The baseline period is 1991-2020, using Full Data Monthly in its latest version. Quality controlled rain gauge (in situ) data are used and the quality control protocol depends on the data set. The percentiles are not calculated for those grid cells, where the precipitation total aggregated in the reference period is below ten millimetres.gpcc_precipitation_quantile_12month_19912020WCSImageMosaicwmo__gpcc_precipitation_quantile_12month_19912020RectifiedGridCoverage-180.0 -90.0180.0 90.0-180.0 -90.0180.0 90.0h1000ich1000ic_opt_deflateWCSGeoTIFFrisk_niger_undp__h1000icRectifiedGridCoverage0.16750000159949785 11.69750004161636916.000833334932196 23.524166708282560.1675000015994978 11.69750004161636916.000833334932196 23.52416670828256h100ich100ic_opt_deflateWCSGeoTIFFrisk_niger_undp__h100icRectifiedGridCoverage0.16750000159949785 11.69750004161636916.000833334932196 23.524166708282560.1675000015994978 11.69750004161636916.000833334932196 23.52416670828256h200ich200ic_opt_deflateWCSGeoTIFFrisk_niger_undp__h200icRectifiedGridCoverage0.16750000159949785 11.69750004161636916.000833334932196 23.524166708282560.1675000015994978 11.69750004161636916.000833334932196 23.52416670828256h25ich25ic_opt_deflateWCSGeoTIFFrisk_niger_undp__h25icRectifiedGridCoverage0.16750000159949785 11.69750004161636916.000833334932196 23.524166708282560.1675000015994978 11.69750004161636916.000833334932196 23.52416670828256h500ich500ic_opt_deflateWCSGeoTIFFrisk_niger_undp__h500icRectifiedGridCoverage0.16750000159949785 11.69750004161636916.000833334932196 23.524166708282560.1675000015994978 11.69750004161636916.000833334932196 23.52416670828256h50ich50ic_opt_deflateWCSGeoTIFFrisk_niger_undp__h50icRectifiedGridCoverage0.16750000159949785 11.69750004161636916.000833334932196 23.524166708282560.1675000015994978 11.69750004161636916.000833334932196 23.52416670828256hfp image mosaic 93-09hfpxWCSImageMosaicMapX_UNDP__hfpx_mosaic_93-09RectifiedGridCoverage-179.99999995447251 -65.25013301721049179.99661098079287 89.89762321520959-179.99999995447251 -65.25013301721049179.99661098079287 89.89762321520959hfp2000_meris_wgsVenter, O. et al., 2016. Sixteen years of change in the global terrestrial human footprint and implications for biodiversity conservation. Nature Communications, 7, pp.1–11hfp2000_meris_wgsWCSGeoTIFFMapX_UNDP_nd__hfp2000_meris_wgsRectifiedGridCoverage-179.99997804642848 -65.24741434935741180.00508149566073 89.8976232152096-179.99997804642848 -65.24741434935741180.00508149566073 89.8976232152096hfp2005_meris_wgsVenter, O. et al., 2016. Sixteen years of change in the global terrestrial human footprint and implications for biodiversity conservation. Nature Communications, 7, pp.1–11hfp2005_meris_wgsWCSGeoTIFFMapX_UNDP_nd__hfp2005_meris_wgsRectifiedGridCoverage-179.99997804642848 -65.24741434935741180.00508149566073 89.8976232152096-179.99997804642848 -65.24741434935741180.00508149566073 89.8976232152096hfp2010_meris_wgsVenter, O. et al., 2016. Sixteen years of change in the global terrestrial human footprint and implications for biodiversity conservation. Nature Communications, 7, pp.1–11hfp2010_meris_wgsWCSGeoTIFFMapX_UNDP_nd__hfp2010_meris_wgsRectifiedGridCoverage-179.99997804642848 -65.24741434935741180.00508149566073 89.8976232152096-179.99997804642848 -65.24741434935741180.00508149566073 89.8976232152096hfp2013_meris_wgsVenter, O. et al., 2016. Sixteen years of change in the global terrestrial human footprint and implications for biodiversity conservation. Nature Communications, 7, pp.1–11hfp2013_meris_wgsWCSGeoTIFFMapX_UNDP_nd__hfp2013_meris_wgsRectifiedGridCoverage-179.99997804642848 -90.00129442462195179.995662211005 90.00123534642266-180.0 -90.0180.0 90.0hwsd_texturehwsd_texture_optWCSGeoTIFFmapx_chemobs__hwsd_textureRectifiedGridCoverage-180.0 -88.999999992912179.99999998559997 88.999999999968-180.0 -88.999999992912179.99999998559997 88.999999999968indo_forint100The forest integrrity index is derived by overlaying the human footprint (Venter et al. 2016) on the forest structural condition. The name is consistent with the concept of ecological integrity. Ecological integrity has been defined as, “the system’s capacity to maintain structure and ecosystem functions using processes and elements characteristic for its ecoregion.” (Parks Canada 2008). This capacity is a result of the climate, soil, topography, biota and other biophysical properties of the ecoregion and the extent to which these properties are not altered by modern human pressures. Consistent with this definition, the forest integrity index is based on on the structural complexity of a stand relative to the natural potential of the ecoregion and level of human pressure. Thus, forest of high integrity are relatively tall, high in canopy cover, older, and with relatively low human pressure. An increasing number of studies have shown that human pressure in various forms can have negative effects on native species. Thus, high integrity forests may be uniquely important for conservation because they support species and processes that are require well-developed forests and are sensitive to human activities. Such forests often also have high economic value and have likely been preferentially converted to more intense human land uses. Thus, identifying remaining areas of high forest integrity is important for conservation planning.<br><br>Data is provided by Montana State University.<br/><br>License information: <a href "https://creativecommons.org/licenses/by/4.0/" target="_blank">CC-4.0 Attribution</a>.<br/>indo_forint100WCSGeoTIFFwesr_biodiversity__indo_forint100RectifiedGridCoverage100.0 -9.999861112471002141.016129418236 23.384477504501100.0 -9.999861112471002141.016129418236 23.384477504501ken_natural_hazardsken_all_hazards_merged_dissolve_4326_optWCSGeoTIFFmapx_chemobs__ken_natural_hazardsRectifiedGridCoverage33.89349461411385 -4.68041672648279141.911157303972296 4.63247907761283233.89349461411385 -4.68041672648279141.911157303972296 4.632479077612832ken_slopedem90_ken_reproj_slope_deg_wgs84_optWCSGeoTIFFmapx_chemobs__ken_slopeRectifiedGridCoverage33.88974434855757 -4.73940200673221641.937083228471195 5.08123470236813133.88974434855757 -4.73940200673221641.937083228471195 5.081234702368131ken_wb_bufferken_wb_buffer_4326_optWCSGeoTIFFmapx_chemobs__ken_wb_bufferRectifiedGridCoverage33.85213190746376 -4.73513836961701941.922488963485065 5.10498438994836733.85213190746376 -4.73513836961701941.922488963485065 5.104984389948367land_cover_copernicus_africa_2015c_gls_LC100-LCCS_201501010000_AFRI_PROBAV_1.0.1WCSGeoTIFFMAPX__land_cover_copernicus_africa_2015RectifiedGridCoverage-30.00049603174603 -34.9995039682538859.99950396825387 45.00049603174603-30.00049603174603 -34.9995039682538859.99950396825387 45.00049603174603land_degradation_global_GeoEssentialThis dataset has been generated using the Trends.Earth model to asses Land Degradation according to the UN Metadata Guideline for SDG. <br> More information on <a href= "http://trends.earth/docs/en/" target="_blank">Trends.Earth</a>. <br> More information on <a href="https://knowledge.unccd.int/topics/sustainable-development-goals-sdgs/sdg-indicator-1531" target= "_blank">Land Degradation</a>. <br> More information on <a href="https://unstats.un.org/sdgs/metadata/files/Metadata-15-03-01.pdf" target="_blank">SDG15.3.1</a>. <br> <br> Generated using the GEOEssential VLabsdg1531_global_deflate_optWCSGeoTIFFwesr_sdg__land_degradation_global_GeoEssentialRectifiedGridCoverage-180.002170844 -55.920126210999996180.00217056900001 83.63539874-180.002170844 -55.920126210999996180.00217056900001 83.63539874lc_nigerlc_niger_optWCSGeoTIFFrisk_niger_undp__lc_nigerRectifiedGridCoverage0.16716269841266396 11.69692460317463716.001488095238045 23.5243055555555750.167162698412664 11.69692460317463716.001488095238045 23.524305555555575lebanon_remote_site_screening_res50lebanon_remote_site_screening_res50WCSGeoTIFFmapx_chemobs__lebanon_remote_site_screening_res50RectifiedGridCoverage35.103656 33.0565836.606156 34.6920835.103656 33.0565836.606156 34.69208lesotho_remote_site_screening_res50lesotho_remote_site_screening_res50WCSGeoTIFFmapx_chemobs__lesotho_remote_site_screening_res50RectifiedGridCoverage27.011148168000023 -30.67526255799995429.457148168000025 -28.57076255799995227.011148168000023 -30.67526255799995429.457148168000025 -28.570762557999952madagascar_remote_site_screening_res50madagascar_remote_site_screening_res50WCSGeoTIFFmapx_chemobs__madagascar_remote_site_screening_res50RectifiedGridCoverage43.191379547000075 -25.60841206581993750.48187954700008 -11.94841206581993743.191379547000075 -25.60841206581993750.48187954700008 -11.948412065819937mafaru_2014mafaru_2014WCSGeoTIFFsand__mafaru_2014RectifiedGridCoverage8174864.930020355 646906.87559785348183226.424316907 653366.998815972473.43606112249947 5.801325311389196573.51117370374618 5.859057400767438mafaru_2024mafaru_2024WCSGeoTIFFsand__mafaru_2024RectifiedGridCoverage8174864.930020355 646906.87559785348183226.424316907 653366.998815972473.43606112249947 5.801325311389196573.51117370374618 5.859057400767438malawi_remote_site_screening_res50malawi_remote_site_screening_res50WCSGeoTIFFmapx_chemobs__malawi_remote_site_screening_res50RectifiedGridCoverage32.67395 -17.13054000000000735.93195 -9.36754000000000732.67395 -17.13054000000000735.93195 -9.367540000000007mangroves_SOCS_0_100cm_30m_opt_deflatemangroves_SOCS_0_100cm_30m_opt_deflateWCSGeoTIFFMapX_UNDP__mangroves_SOCS_0_100cm_30m_opt_deflateRectifiedGridCoverage-180.0 -39.00080943399745179.99999999998727 32.999190566-180.0 -39.00080943399745179.99999999998727 32.999190566mauritius_remote_site_screening_res50mauritius_remote_site_screening_res50WCSGeoTIFFmapx_chemobs__mauritius_remote_site_screening_res50RectifiedGridCoverage56.58525666700009 -20.5258056909999463.50025666700009 -10.33680569099993856.58525666700009 -20.5258056909999463.50025666700009 -10.336805690999938med_00_ogs_nut_an-fc-m-avrg_year_2020med_00_ogs_nut_an-fc-m-avrg_year_2020_optWCSGeoTIFFMedicip__med_00_ogs_nut_an-fc-m-avrg_year_2020RectifiedGridCoverage-5.5625 30.1666736.3125 46.0-5.5625 30.1666736.3125 46.0med_00_ogs_nut_an-fc-m-avrg_year_2020_po3med_00_ogs_nut_an-fc-m-avrg_year_2020_po3_optWCSGeoTIFFMedicip__med_00_ogs_nut_an-fc-m-avrg_year_2020_po3RectifiedGridCoverage-5.562499842 30.16666666536.312501272 46.000001273-5.562499842 30.16666666536.312501272 46.000001273montenegro_remote_site_screening_res50montenegro_remote_site_screening_res50WCSGeoTIFFmapx_chemobs__montenegro_remote_site_screening_res50RectifiedGridCoverage18.43355958000009 41.8438939452775520.35205958000009 43.5583939452775518.43355958000009 41.8438939452775520.35205958000009 43.55839394527755morocco_remote_site_screening_res50morocco_remote_site_screening_res50WCSGeoTIFFmapx_chemobs__morocco_remote_site_screening_res50RectifiedGridCoverage-13.168084726852316 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-3.06427470634831mosaic_2020_kamitugamosaic_2020_kamitugaWCSGeoTIFFrs_for_asgm__mosaic_2020_kamitugaRectifiedGridCoverage626890.0 9643900.0634990.0 9664260.028.141845709394754 -3.22107141330841228.214936578763222 -3.0368306131003737mosaic_2020_kamituga_westmosaic_2020_kamituga_westWCSGeoTIFFrs_for_asgm__mosaic_2020_kamituga_westRectifiedGridCoverage629680.0 9659970.0631260.0 9661230.028.166978184291047 -3.075687270143314228.181206954060517 -3.06427470634831mosaic_2021_kamitugamosaic_2021_kamitugaWCSGeoTIFFrs_for_asgm__mosaic_2021_kamitugaRectifiedGridCoverage626890.0 9643900.0634990.0 9664260.028.141845709394754 -3.22107141330841228.214936578763222 -3.0368306131003737mosaic_2021_kamituga_westmosaic_2021_kamituga_westWCSGeoTIFFrs_for_asgm__mosaic_2021_kamituga_westRectifiedGridCoverage629680.0 9659970.0631260.0 9661230.028.166978184291047 -3.075687270143314228.181206954060517 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28.75255357860686npl_ls_susceptibility_ddLs_susceptibility_DD_transWCSGeoTIFFMAPX_nd__npl_ls_susceptibility_ddRectifiedGridCoverage84.3751220733584 26.89584409785070686.69125415435332 28.77616986853588884.3751220733584 26.89584409785070686.69125415435332 28.776169868535888popV10apopV10a_opt_deflateWCSGeoTIFFrisk_niger_undp__popV10aRectifiedGridCoverage0.17341085794481614 11.64777782127722713.816738733944817 21.1836073402772270.1734108579448161 11.64777782127722713.816738733944817 21.183607340277227pop_landscan2011WCSGeoTIFFpop_landscan2011grid_data_core_pro__pop_landscan2011RectifiedGridCoverage-180.0 -90.0179.99999999999858 83.99999999999932-180.0 -90.0179.99999999999858 83.99999999999932pop_perc_2015_1975pop_perc_2015_1975_wgs84_optWCSGeoTIFFwesr_climat__pop_perc_2015_1975RectifiedGridCoverage-179.99999680823143 -55.98302144283261179.99218561802442 83.63964763391371-179.99999680823143 -55.98302144283261179.99218561802442 83.63964763391371population density 2015 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12.295673564888503-11.281251799204284 16.692700606714585sen_slopedem90_sen_reproj_slope_deg_wgs84_optWCSGeoTIFFmapx_chemobs__sen_slopeRectifiedGridCoverage-17.593971748225744 12.283748773075564-11.270009072589186 16.72443886379466-17.593971748225744 12.283748773075564-11.270009072589186 16.72443886379466sen_wb_buffersen_wb_buffer_4326_optWCSGeoTIFFmapx_chemobs__sen_wb_bufferRectifiedGridCoverage-17.55246539577028 12.283484750909368-11.263233441297636 16.697303506881106-17.55246539577028 12.283484750909368-11.263233441297636 16.697303506881106sentinel2_sierra_leone_20170119SierraLeone_TrueColor_20170119_high_fullTile_opt_deflateWCSGeoTIFFMAPX__sentinel2_sierra_leone_20170119RectifiedGridCoverage-12.278532425848166 8.036744648101147-11.274042950588264 9.036590153051195-12.278532425848166 8.036744648101147-11.274042950588264 9.036590153051195sentinel2_sierra_leone_20180208SierraLeone_TrueColor_20180208_high_fullTile_opt_deflateWCSGeoTIFFMAPX__sentinel2_sierra_leone_20180208RectifiedGridCoverage-12.278532425848166 8.036744648101147-11.274042950588264 9.036590153051195-12.278532425848166 8.036744648101147-11.274042950588264 9.036590153051195seychelles_remote_site_screening_res50seychelles_remote_site_screening_res50WCSGeoTIFFmapx_chemobs__seychelles_remote_site_screening_res50RectifiedGridCoverage46.19920349100001 -10.19351900299996556.279703491000014 -3.711519002999964746.19920349100001 -10.19351900299996556.279703491000014 -3.7115190029999647site_0016_visual_20150227_lc08site_0016_visual_20150227_lc08WCSGeoTIFFhotspots__site_0016_visual_20150227_lc08RectifiedGridCoverage456255.0 2325.0489675.0 38685.032.60687054879944 0.021034505864999932.907211866267744 0.3499948245599756site_0060_Landsatsite_0060_LandsatWCSImageMosaictest_antonio_mosaics__site_0060_LandsatRectifiedGridCoverage295612.8957496233 893555.6061067106368932.8957496233 972815.6061067106-10.858176899138208 8.079534492312552-10.189521743955197 8.79879002262406site_0060_Landsat_tomsite_0060_Landsat_tomWCSImageMosaictest_imagemosaic_tom__site_0060_Landsat_tomRectifiedGridCoverage295612.8957496233 893555.6061067106368932.8957496233 972815.6061067106-10.858176899138208 8.079534492312552-10.189521743955197 8.79879002262406site_0336_19860908_mathsite_0336_19860908_mathWCSGeoTIFFhotspots__site_0336_19860908_mathRectifiedGridCoverage589455.0 239325.0719145.0 371475.021.804453393351142 2.16395034642879922.972377367092143 3.360478793316272site_0336_19860908_math_floatsite_0336_19860908_math_floatWCSGeoTIFFhotspots__site_0336_19860908_math_floatRectifiedGridCoverage589455.0 239325.0719145.0 371475.021.804453393351142 2.16395034642879922.972377367092143 3.360478793316272site_0336_19860908_math_uint16site_0336_19860908_math_uint16WCSGeoTIFFhotspots__site_0336_19860908_math_uint16RectifiedGridCoverage589455.0 239325.0719145.0 371475.021.804453393351142 2.16395034642879922.972377367092143 3.360478793316272site_0336_19860908_visualsite_0336_19860908_visualWCSGeoTIFFhotspots__site_0336_19860908_visualRectifiedGridCoverage589455.0 239325.0719145.0 371475.021.804453393351142 2.16395034642879922.972377367092143 3.360478793316272site_0336_19860908_visual_averagesite_0336_19860908_visual_averageWCSGeoTIFFhotspots__site_0336_19860908_visual_averageRectifiedGridCoverage589455.0 239325.0719145.0 371475.021.804453393351142 2.16395034642879922.972377367092143 3.360478793316272site_0336_19860908_visual_bilinearsite_0336_19860908_visual_bilinearWCSGeoTIFFhotspots__site_0336_19860908_visual_bilinearRectifiedGridCoverage589455.0 239325.0719145.0 371475.021.804453393351142 2.16395034642879922.972377367092143 3.360478793316272site_0336_19860908_visual_gausssite_0336_19860908_visual_gaussWCSGeoTIFFhotspots__site_0336_19860908_visual_gaussRectifiedGridCoverage589455.0 239325.0719145.0 371475.021.804453393351142 2.16395034642879922.972377367092143 3.360478793316272site_0336_19860908_visual_modesite_0336_19860908_visual_modeWCSGeoTIFFhotspots__site_0336_19860908_visual_modeRectifiedGridCoverage589455.0 239325.0719145.0 371475.021.804453393351142 2.16395034642879922.972377367092143 3.360478793316272site_0336_19860908_visual_nearestsite_0336_19860908_visual_nearestWCSGeoTIFFhotspots__site_0336_19860908_visual_nearestRectifiedGridCoverage589455.0 239325.0719145.0 371475.021.804453393351142 2.16395034642879922.972377367092143 3.360478793316272site_0336_19860908_visual_rmssite_0336_19860908_visual_rmsWCSGeoTIFFhotspots__site_0336_19860908_visual_rmsRectifiedGridCoverage589455.0 239325.0719145.0 371475.021.804453393351142 2.16395034642879922.972377367092143 3.360478793316272site_0336_20010128_enhancedsite_0336_20010128_enhancedWCSGeoTIFFhotspots__site_0336_20010128_enhancedRectifiedGridCoverage589455.0 239325.0719145.0 371475.021.804453393351142 2.16395034642879922.972377367092143 3.360478793316272site_0336_20010128_enhanced_cogsite_0336_20010128_enhanced_cogWCSGeoTIFFhotspots__site_0336_20010128_enhanced_cogRectifiedGridCoverage589455.0 239325.0719145.0 371475.021.804453393351142 2.16395034642879922.972377367092143 3.360478793316272site_0336_20010128_rawsite_0336_20010128_rawWCSGeoTIFFhotspots__site_0336_20010128_rawRectifiedGridCoverage589455.0 239325.0719145.0 371475.021.804453393351142 2.16395034642879922.972377367092143 3.360478793316272site_0336_20010128_raw_cogsite_0336_20010128_raw_cogWCSGeoTIFFhotspots__site_0336_20010128_raw_cogRectifiedGridCoverage589455.0 239325.0719145.0 371475.021.804453393351142 2.16395034642879922.972377367092143 3.360478793316272south_africa_remote_site_screening_res50south_africa_remote_site_screening_res50WCSGeoTIFFmapx_chemobs__south_africa_remote_site_screening_res50RectifiedGridCoverage16.447713889000067 -46.97910999999995638.00071388900007 -22.12660999999995716.447713889000067 -46.97910999999995638.00071388900007 -22.126609999999957tanzania_remote_site_screening_res50tanzania_remote_site_screening_res50WCSGeoTIFFmapx_chemobs__tanzania_remote_site_screening_res50RectifiedGridCoverage29.340830109000006 -11.76143204299994840.44333010900001 -0.984432042999946529.340830109000006 -11.76143204299994840.44333010900001 -0.9844320429999465test_ssd057580465010_01_P001_PSHWCSImageMosaicMAPX__test_ssdRectifiedGridCoverage32.447270327263716 10.40028187889219432.5506862684502 10.49844583178311632.447270327263716 10.40028187889219432.5506862684502 10.498445831783116test_swipe_20190927_843bis20190927_843bisWCSGeoTIFFwesr_swipe__20190927_843bisRectifiedGridCoverage199980.0 5990200.0309780.0 6200020.0149.66386433258654 -36.21435579772867150.93246853305004 -34.29772543003008total_extreme_events_Automn_output_opttotal_extreme_events_Automn_output_optWCSGeoTIFFunccd__total_extreme_events_Automn_output_optRectifiedGridCoverage-17.999999999999943 -1.499999999999943275.00000000000006 45.00000000000006-17.999999999999943 -1.499999999999943275.00000000000006 45.00000000000006total_extreme_events_spring_output_opttotal_extreme_events_spring_output_optWCSGeoTIFFunccd__total_extreme_events_spring_output_optRectifiedGridCoverage-17.999999999999943 -1.499999999999943275.00000000000006 45.00000000000006-17.999999999999943 -1.499999999999943275.00000000000006 45.00000000000006total_extreme_events_summer_output_opttotal_extreme_events_summer_output_optWCSGeoTIFFunccd__total_extreme_events_summer_output_optRectifiedGridCoverage-17.999999999999943 -1.499999999999943275.00000000000006 45.00000000000006-17.999999999999943 -1.499999999999943275.00000000000006 45.00000000000006total_extreme_events_winter_output_opttotal_extreme_events_winter_output_optWCSGeoTIFFgrid_data_core__total_extreme_events_winter_output_optRectifiedGridCoverage-17.999999999999943 -1.499999999999943275.00000000000006 45.00000000000006-17.999999999999943 -1.499999999999943275.00000000000006 45.00000000000006total_strong_events_Automn_output_opttotal_strong_events_Automn_output_optWCSGeoTIFFgrid_data_core__total_strong_events_Automn_output_optRectifiedGridCoverage-17.999999999999943 -1.499999999999943275.00000000000006 45.00000000000006-17.999999999999943 -1.499999999999943275.00000000000006 45.00000000000006total_strong_events_spring_output_opttotal_strong_events_spring_output_optWCSGeoTIFFgrid_data_core__total_strong_events_spring_output_optRectifiedGridCoverage-17.999999999999943 -1.499999999999943275.00000000000006 45.00000000000006-17.999999999999943 -1.499999999999943275.00000000000006 45.00000000000006total_strong_events_winter_output_opttotal_strong_events_winter_output_optWCSGeoTIFFunccd__total_strong_events_winter_output_optRectifiedGridCoverage-17.999999999999943 -1.499999999999943275.00000000000006 45.00000000000006-17.999999999999943 -1.499999999999943275.00000000000006 45.00000000000006tsm_col_zaragoza_20210118tsm_col_zaragoza_20210118WCSGeoTIFFrs_for_asgm__tsm_col_zaragoza_20210118RectifiedGridCoverage514880.0 837860.0523170.0 844510.0-74.8651063820499 7.579889039548307-74.7899249680916 7.640072688469065tsm_col_zaragoza_20210309tsm_col_zaragoza_20210309WCSGeoTIFFrs_for_asgm__tsm_col_zaragoza_20210309RectifiedGridCoverage514880.0 837860.0523170.0 844510.0-74.8651063820499 7.579889039548307-74.7899249680916 7.640072688469065tsm_col_zaragoza_20210617tsm_col_zaragoza_20210617WCSGeoTIFFrs_for_asgm__tsm_col_zaragoza_20210617RectifiedGridCoverage514880.0 837860.0523170.0 844510.0-74.8651063820499 7.579889039548307-74.7899249680916 7.640072688469065tsm_col_zaragoza_20210712tsm_col_zaragoza_20210712WCSGeoTIFFrs_for_asgm__tsm_col_zaragoza_20210712RectifiedGridCoverage514880.0 837860.0523170.0 844510.0-74.8651063820499 7.579889039548307-74.7899249680916 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