WMS This dataset shows the forest global coverage with canopy density higher than 10%. It is derived from the Current forest coverage from the World Resource Institute Atlas of Forest Landscape Restoration Opportunities, by extracting the 'close forest', 'open forest' and the 'woodlands' classes. Reference period 2000-2009. EPSG:4326 -180.0 180.0 -90.0 90.0 ECO-DRR:Forest_flood_all This dataset shows the areas where forest restoration (open and closed forests, as well as woodlands) could be undertaken for reducing flood exposure. Restoration opportunities areas were considered for areas where the ecosystem coverage was below the median. EPSG:4326 -180.0 180.0 -90.0 90.0 AccessMod:Travel_time_composite The problem of maternal mortality is a recurring problem in Sub-Saharan Africa and the poorest population and residing away from health centers is the most affected. AccessMod is a computer program designed to help these countries examine the geographical aspect of their health system. And so to map the physical accessibility in terms of travel time to the health center. The accessibility map helps to guide decision-makers by showing them areas of low access, ie places where the population must walk to reach health centers. Increasing accessibility in these places will at the same time improve maternal health. EPSG:4326 -17.55393284087355 49.9629004390912 -23.67057144852623 23.53082146804556 UN_Sahel:USAID_CMM_bivariate_exp_frag The first mechanism of compound risk assessment—a bivariate map—integrates the fragility and climate data before they are mapped to show the intersection of fragility and climate risks at the first administrative division level for all countries for which data are available. This approach allows comparison of countries across all fragility categories on a single map. However, it loses some granularity in the subnational climate exposure data, which must be aggregated to the first administrative division to be cross-tabulated with the fragility data. The bivariate map uses the total climate exposure data and total fragility data. It is created using the first administrative division polygon files from the Global Administrative Areas database version 2.8. In this map, each country’s national-level fragility category is applied to all administrative divisions in the country. The bivariate map thus retains the five fragility categories seen in the total fragility measure, classifying areas as having low, some, moderate, high, or highest fragility. The climate exposure category is based on a k-median clustering algorithm. The median climate exposure score for each administrative division is determined with spatial analysis in ArcGIS. These median scores are then clustered into five categories of low to high exposure using the k-median algorithm contained in Stata.15. The cross-tabulation of the five fragility categories and the five climate exposure categories are represented on the final bivariate map. The total exposure to climate retain the five exposure categories of low to high exposure (1 to 5) based on the k-median clustering algorithm. The median climate exposure score for each administrative division is determined with spatial analysis in ArcGIS. These median scores are then clustered into five categories of low to high exposure using the k-median algorithm contained in Stata. The climate exposure portion of this study aims to identify places most likely vulnerable to a combination of climate hazards. Geographic location makes some countries more susceptible to climate hazards. Within countries, some areas, such as the coasts, have more exposure to certain kinds of climate hazards. This study assesses climate hazard exposure using historical data on the frequency and magnitude of six hazards, including cyclones, flood events, wildfire events, rainfall anomalies, and chronic aridity. The sixth hazard is a measure of low-elevation coastal zones, which may be susceptible to storm surges and future sea-level rise. The goal is to identify places that in the recent historical record have faced high exposure to climaterelated hazards. This seeks to get a snapshot of places of chronic concern over a long enough time period to say that these are places that have been historically affected by climate-related hazards. The indicators included here use the most recent data available. Some of these indicators, like cyclones, have longerterm data available, while others like floods have data for fewer years. In all cases, this project uses the most recent and broadest set of years for which global data are available to get the best snapshot of climate exposure in the recent past, as close as possible to the present. The total fragility data retain the five fragility categories seen in the total fragility measure, classifying areas as having low, some, moderate, high, or highest fragility. To help achieve this study’s goal of identifying the intersection of fragility and climate risks globally, this study develops a new fragility measure. Developing a new measure specifically for this purpose provides a comprehensive fragility measure while avoiding use of existing fragility measures that include environmental indicators and thus should not be overlaid on climate hazards. This study uses open-source data to create a measure of state fragility that is similar in composition and outcome to USAID’s internal methods and framework for analyzing fragility.11 Like USAID’s internal measure, the new measure assesses fragility in state effectiveness and legitimacy in four key spheres: political, security, economic, and social. This is based on an understanding of fragility as being rooted in poor state capacity and poor state-society relationships, both of which can contribute to instability. Poor state capacity and state-society relationships can lead to and perpetuate other forms of overt instability, including conflict or an inability to address and mitigate stresses such as a changing environment, difficult global financial situations, or conflict in neighboring states. This <a href="https://pdf.usaid.gov/pdf_docs/PA00TBFH.pdf"target=_blank>document</a> provide a detailed approach on the generation of this bivariate map and its attributes. EPSG:4326 -179.99999999999994 180.00000000000014 -90.0 83.65833282470703 text/plain grid_data_core:cities_geonames EPSG:4326 -180.914413452148 181.156951904297 -47.0626029968262 78.8466567993164 text/plain risk_niger_undp:codemi_h EPSG:4326 0.1675000015994978 16.000833334932196 11.697500041616369 23.52416670828256 MapX_UNDP:forest_height 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. EPSG:4326 -117.0005 180.0005 -32.0005 32.0005 text/plain MapX_UNDP:forest_int_index_19 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/> EPSG:4326 -81.70875583396088 153.42801388207823 -20.582447168229265 25.91760211162399 text/plain MapX_UNDP:forest_struct_cond_ind_19 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/> EPSG:4326 -81.70875583396088 153.4280138820783 -20.582447168229265 25.91760211162399 text/plain wesr_biodiversity:forint100 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/" target="_blank">CC-4.0 Attribution</a>.<br/> EPSG:4326 -81.007003784197 141.016129418236 -9.999861112471002 23.384477504501 text/plain cc_and_health:temperatures_2023_hydro Mean predicted temperatures in 2023 in the Central African Republic. Reference layer. Hydrography. EPSG:4326 14.278348129129038 27.53442276957556 2.0622926391050047 11.049277995 unea_pollution:08_inorganic_poll_halpern_KNB_WGS84 Non-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_WGS84 WCS GeoTIFF EPSG:4326 CRS:84 -179.99998890825066 180.0014259209304 -89.90931632117805 83.62149081142242 text/plain unea_pollution:08_ocean_pollution_halpern_KNB_WGS84 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. 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_WGS84 WCS GeoTIFF ecosystem EPSG:4326 CRS:84 -179.9999997890976 179.99802237886038 -89.99765318080647 85.2846797894314 text/plain unea_pollution:08_plumes_fert_halpern_KNB_WGS84 Input 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_WGS84 WCS GeoTIFF EPSG:4326 CRS:84 -179.99999948447567 179.99443435918158 -57.923325435334434 84.90643464931345 text/plain unea_pollution:08_plumes_pest_halpern_KNB_WGS84 Pesticides 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_WGS84 WCS GeoTIFF EPSG:4326 CRS:84 -179.99999948447567 179.99443435918158 -57.923325435334434 84.90643464931345 text/plain unea_pollution:08_shipping_halpern_KNB_WGS84 Vessel 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_WGS84 WCS GeoTIFF EPSG:4326 CRS:84 -179.99999978909742 179.99802237886044 -89.99765318080641 85.2846797894314 text/plain wesr_climat:12month_anomaly_Global_ea_2t_202001-202012_1991-2020_v02 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=overview 12month_anomaly_Global_ea_2t_202001-202012_1991-2020_v02.grib WCS GeoTIFF EPSG:4326 CRS:84 -179.875 180.125 -90.125 90.125 wesr_climat:12month_anomaly_Global_ea_tp_202001-202012_1991-2020_v02 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=overview 12month_anomaly_Global_ea_tp_202001-202012_1991-2020_v02.grib WCS GeoTIFF EPSG:4326 CRS:84 -179.875 180.125 -90.125 90.125 unea_pollution:13_global_cumul_impact_all_pressures_KNB_WGS84 This 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_WGS84 WCS GeoTIFF cumulative impact all pressures Pollution Ocean EPSG:4326 CRS:84 -179.99999978928923 179.99802237904845 -89.99765318081656 85.28467978960619 text/plain unea_pollution:13_ocean_acidification Changes 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_acidification WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 text/plain unea_pollution:13_ocean_pollution_halpern_KNB_WGS84 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. 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_WGS84 WCS GeoTIFF EPSG:4326 CRS:84 -179.9999992159167 179.99646810995122 -89.94351550779915 89.94750143086456 text/plain MapX_UNDP:13_ocean_pollution_pressures_halpern_KNB_WGS84 This 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_2013 WCS GeoTIFF EPSG:4326 CRS:84 -179.9999997892864 179.99802237904566 -89.9976531808165 85.28467978960349 text/plain unea_pollution:13_plumes_fert_halpern_KNB_WGS84 Input 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_WGS84 WCS GeoTIFF EPSG:4326 CRS:84 -179.99999869409874 179.9994455075789 -57.96420981377538 84.9958245860871 text/plain unea_pollution:13_plumes_pest_halpern_KNB_WGS84 Pesticides 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_WGS84 WCS GeoTIFF EPSG:4326 CRS:84 -179.99999869409874 179.9994455075789 -57.96420981377538 84.9958245860871 text/plain unea_pollution:13_shipping_halpern_KNB_WGS84 Vessel 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_WGS84 WCS GeoTIFF EPSG:4326 CRS:84 -179.99999978909761 179.99802237886036 -85.28265369039616 89.9996792798417 text/plain hotspots:20130401 20130401 WCS GeoTIFF EPSG:32632 CRS:84 7.983671848622689 8.218081323368821 46.30059103015211 46.46654729416933 hotspots:20130401_originals 20130401_originals WCS GeoTIFF EPSG:32632 CRS:84 7.1100148908217635 10.103215000055275 44.996576646230245 47.04130233299387 UNILIEGE:20160708Classes Classification 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" 20160708Classes WCS GeoTIFF EPSG:32618 CRS:84 -74.86103149540197 -74.77551708391071 7.565680150087606 7.624603274629289 text/plain UNILIEGE:20170703Classes Classification 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" 20170703Classes WCS GeoTIFF EPSG:32618 CRS:84 -74.86103149540197 -74.77551708391071 7.565680150087606 7.624603274629289 text/plain UNILIEGE:20180623Classes Classification 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" 20180623Classes WCS GeoTIFF EPSG:32618 CRS:84 -74.86103149540197 -74.77551708391071 7.565680150087606 7.624603274629289 text/plain UNILIEGE:20181230Classes Classification 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" 20181230Classes WCS GeoTIFF EPSG:32618 CRS:84 -74.86103149540197 -74.77551708391071 7.565680150087606 7.624603274629289 text/plain wesr_mountains:2019-09_albedo 2019-09_albedo WCS GeoTIFF EPSG:4326 CRS:84 -180.004464286 179.995535715 -59.995535714 80.004464286 wesr_mountains:2019-12_albedo 2019-12_albedo WCS GeoTIFF EPSG:4326 CRS:84 -180.004464286 179.995535715 -59.995535714 80.004464286 UNILIEGE:20190124Classes Classification 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" 20190124Classes WCS GeoTIFF EPSG:32618 CRS:84 -74.86103149540197 -74.77551708391071 7.565680150087606 7.624603274629289 text/plain UNILIEGE:20190618Classes Classification 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" 20190618Classes WCS GeoTIFF EPSG:32618 CRS:84 -74.8619381795564 -74.77524438684844 7.565137269898085 7.625960417972978 text/plain wesr_mountains:2020-03_albedo 2020-03_albedo WCS GeoTIFF EPSG:4326 CRS:84 -180.004464286 179.995535715 -59.995535714 80.004464286 wesr_mountains:2020-06_albedo 2020-06_albedo WCS GeoTIFF EPSG:4326 CRS:84 -180.004464286 179.995535715 -59.995535714 80.004464286 MapX_UNDP:Avitabile_AGB_Map 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.13139 Avitabile_AGB_Map WCS GeoTIFF EPSG:4326 CRS:84 -112.85865304959007 154.9996695694099 -56.259938619115786 39.99839086388421 text/plain TEEB:BAU_LULC2030_wgs84_opt BAU_LULC2030_wgs84_opt WCS GeoTIFF EPSG:4326 CRS:84 34.553167266769464 37.239835341004294 -10.038048450490551 -7.663994368030097 TEEB:BHU_HQ_30-10 New map generated at GRID HQ_30-10 WCS GeoTIFF EPSG:4326 CRS:84 88.733529924 92.146928137 26.684796595 28.247811534 TEEB:BHU_Water_yield_2010 Layer received from TEEB partner in Bhutan in Q2 2020. wateryeild_2010_wgs_opt WCS GeoTIFF EPSG:4326 CRS:84 88.737534326 92.146924212 26.685110159 28.245314268 TEEB:BHU_Water_yield_2030 Layer received from TEEB partner in Bhutan Q2 2020 wateryeild_2030_wgs_opt WCS GeoTIFF EPSG:4326 CRS:84 88.737534326 92.146924212 26.685110159 28.245314268 TEEB:BHU_carbonSeq_upper_2010 Carbon 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_opt WCS GeoTIFF EPSG:3857 CRS:84 88.73352726023123 92.1470305182156 26.684993773827255 28.248035723321824 TEEB:BHU_carbonSeq_upper_2030 Carbon 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_opt WCS GeoTIFF EPSG:3857 CRS:84 88.73352726023123 92.1470305182156 26.684993773827255 28.248035723321824 dakar_waste:BSI 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 Management Remote Sensing GIS Citizen Science Multi-Criteria Analysis Illegal Dumping EPSG:4326 CRS:84 -17.382029187007333 -17.2719124771499 14.733719739640827 14.84106039798925 MAPX:Ba200702 Ba200702 WCS GeoTIFF EPSG:4326 CRS:84 0.166015625 15.995117188 11.6953125 23.525390625 MAPX:Ba200804 Ba200804 WCS GeoTIFF EPSG:4326 CRS:84 0.166015625 15.995117188 11.6953125 23.525390625 unep_storymap:Barcelona_ua2012_wgs_Warsaw features Barcelona_ua2012_wgs EPSG:4326 CRS:84 1.5574475003518657 2.670868499618607 41.185901499757 41.760135499971355 MapX_UNDP:Bare_ground_change_1982-2016 This 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_1982-2016 WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 text/plain peatlands:CAFI_Congo_Basin_Condition 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 Initiative Congo_Basin_Condition_wgs84_opt WCS GeoTIFF EPSG:4326 CRS:84 5.602366924286013 31.305988384304207 -13.455401562813996 13.078121459231383 peatlands:CAFI_FNF_2015_sinu The 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 Initiative CAFI_FNF_2015_sinu_wgs84_opt WCS GeoTIFF EPSG:4326 CRS:84 5.354407954878081 31.7723896460075 -13.459105669480616 13.083556355951599 peatlands:CAFI_change_map_2015_2020_sinu_wgs84_opt 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 Initiative CAFI_change_map_2015_2020_sinu_wgs84_opt WCS GeoTIFF EPSG:4326 CRS:84 5.354407954878081 31.7723896460075 -13.459105669480616 13.083556355951599 MapX_UNDP:CGMFC-21_mangroves_forest_cover_a2000mfw_opt Please 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.1244 a2000mfw_opt WCS GeoTIFF EPSG:4326 CRS:84 -179.9506858152323 179.98793546539514 -38.76215863757685 32.35012047466632 text/plain MapX_UNDP:CGMFC-21_mangroves_forest_cover_a2014 A2014_opt WCS GeoTIFF EPSG:4326 CRS:84 -179.950685815232 179.98793546876746 -38.762158638242894 32.350120473757 UN_Sahel:CHELSA_bio10_01_opt 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 1.2 CHELSA_bio10_01_opt WCS GeoTIFF EPSG:4326 CRS:84 -180.00013888885002 179.99985967115003 -90.00013888884999 83.99986041515001 text/plain UN_Sahel:CHELSA_bio10_04_opt 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 1.2 CHELSA_bio10_04_opt WCS GeoTIFF EPSG:4326 CRS:84 -180.00013888885002 179.99985967115003 -90.00013888884999 83.99986041515001 text/plain UN_Sahel:CHELSA_bio10_12_opt 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 1.2 CHELSA_bio10_12_opt WCS GeoTIFF EPSG:4326 CRS:84 -180.0 179.99985967115003 -90.0 83.99986041515001 text/plain UN_Sahel:CHELSA_bio10_15_opt 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 1.2 CHELSA_bio10_15_opt WCS GeoTIFF EPSG:4326 CRS:84 -180.00013888885002 179.99985967115003 -90.00013888884999 83.99986041515001 text/plain UN_Sahel:CHELSA_bio_mon_ACCESS1-0_rcp45_r1i1p1_g025.nc_12_2041-2060_V1.2_opt Accumulated 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 1.2 CHELSA_bio_mon_ACCESS1-0_rcp45_r1i1p1_g025.nc_12_2041-2060_V1.2_opt WCS GeoTIFF EPSG:4326 CRS:84 -180.00013888885002 179.99985967115003 -90.00013888884999 83.99986041515001 text/plain UN_Sahel:CHELSA_bio_mon_ACCESS1-0_rcp45_r1i1p1_g025.nc_15_2041-2060_V1.2_opt 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) 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 1.2 CHELSA_bio_mon_ACCESS1-0_rcp45_r1i1p1_g025.nc_15_2041-2060_V1.2_opt WCS GeoTIFF EPSG:4326 CRS:84 -180.00013888885002 179.99985967115003 -90.00013888884999 83.99986041515001 text/plain UN_Sahel:CHELSA_bio_mon_ACCESS1-0_rcp45_r1i1p1_g025.nc_1_2041-2060_V1.2_opt Mean 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 1.2 CHELSA_bio_mon_ACCESS1-0_rcp45_r1i1p1_g025.nc_1_2041-2060_V1.2_opt WCS GeoTIFF EPSG:4326 CRS:84 -180.00013888885002 179.99985967115003 -90.00013888884999 83.99986041515001 text/plain UN_Sahel:CHELSA_bio_mon_ACCESS1-0_rcp45_r1i1p1_g025.nc_4_2041-2060_V1.2_opt Standard 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 1.2 CHELSA_bio_mon_ACCESS1-0_rcp45_r1i1p1_g025.nc_4_2041-2060_V1.2_opt WCS GeoTIFF EPSG:4326 CRS:84 -180.00013888885002 179.99985967115003 -90.00013888884999 83.99986041515001 text/plain UN_Sahel:CHELSA_bio_mon_ACCESS1-0_rcp85_r1i1p1_g025.nc_12_2041-2060_V1.2_opt Accumulated 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 1.2 CHELSA_bio_mon_ACCESS1-0_rcp85_r1i1p1_g025.nc_12_2041-2060_V1.2_opt WCS GeoTIFF EPSG:4326 CRS:84 -180.00013888885002 179.99985967115003 -90.00013888884999 83.99986041515001 text/plain UN_Sahel:CHELSA_bio_mon_ACCESS1-0_rcp85_r1i1p1_g025.nc_15_2041-2060_V1.2_opt 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) 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 1.2 CHELSA_bio_mon_ACCESS1-0_rcp85_r1i1p1_g025.nc_15_2041-2060_V1.2_opt WCS GeoTIFF EPSG:4326 CRS:84 -180.00013888885002 179.99985967115003 -90.00013888884999 83.99986041515001 text/plain UN_Sahel:CHELSA_bio_mon_ACCESS1-0_rcp85_r1i1p1_g025.nc_1_2041-2060_V1.2_opt Mean 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 1.2 CHELSA_bio_mon_ACCESS1-0_rcp85_r1i1p1_g025.nc_1_2041-2060_V1.2_opt WCS GeoTIFF EPSG:4326 CRS:84 -180.00013888885002 179.99985967115003 -90.00013888884999 83.99986041515001 text/plain UN_Sahel:CHELSA_bio_mon_ACCESS1-0_rcp85_r1i1p1_g025.nc_4_2041-2060_V1.2_opt Standard 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 1.2 CHELSA_bio_mon_ACCESS1-0_rcp85_r1i1p1_g025.nc_4_2041-2060_V1.2_opt WCS GeoTIFF EPSG:4326 CRS:84 -180.00013888885002 179.99985967115003 -90.00013888884999 83.99986041515001 text/plain UN_Sahel:CHELSA_shc_5_1979-2013_opt Unit=(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 1.2 CHELSA_shc_5_1979-2013_opt WCS GeoTIFF EPSG:4326 CRS:84 -180.00013888885002 179.99985967115003 -90.00013888884999 83.99986041515001 text/plain MAPX:CHIRPS_Anomaly_2-Monthly_Oct-Nov_2016_(mm) https://earlywarning.usgs.gov/fews/ewx/index.html?region=af CHIRPS_Anomaly_2-Monthly_Oct-Nov_2016_(mm) WCS GeoTIFF EPSG:4326 CRS:84 -19.125063083055668 52.6296893492118 -35.37238257858172 38.256795423617206 MAPX:CHIRPS_Anomaly_3-Monthly_Apr-May-Jun_2016_(mm) CHIRPS_Anomaly_3-Monthly_Apr-May-Jun_2016_(mm) WCS GeoTIFF EPSG:4326 CRS:84 -19.125063083055668 52.6296893492118 -35.37238257858172 38.256795423617206 MAPX:CHIRPS_Anomaly_3-Monthly_Apr-May-Jun_2017_(mm) CHIRPS_Anomaly_3-Monthly_Apr-May-Jun_2017_(mm) WCS GeoTIFF EPSG:4326 CRS:84 -19.125063083055668 52.6296893492118 -35.37238257858172 38.256795423617206 MAPX:CHIRPS_Anomaly_Monthly_Oct_2017_(mm) https://earlywarning.usgs.gov/fews/ewx/index.html?region=af CHIRPS_Anomaly_Monthly_Oct_2017_(mm) WCS GeoTIFF EPSG:4326 CRS:84 -19.125063083055668 52.6296893492118 -35.37238257858172 38.256795423617206 Medicip:CRI_pesato_02_03 CRI_pesato_02_03_opt WCS GeoTIFF EPSG:4326 CRS:84 -5.3554163761242854 35.977083623875714 30.264583696518883 45.50708369651888 wesr_cca:CobVeg_180615 Layer provided by Ministry of Environment of Peru. The following link has information on the methodology used to produce the national land use cover map. https://www.minam.gob.pe/patrimonio-natural/wp-content/uploads/sites/6/2013/10/MAPA-NACIONAL-DE-COBERTURA-VEGETAL-FINAL.compressed.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. features CobVeg_180615 EPSG:32718 CRS:84 -81.64209769673542 -68.47323079023445 -18.40713299058302 -0.0383709144748494 wesr_biodiversity:Copernicus_PROBAV_LC100_global_v3.0.1_2019-nrt_Tree-CoverFraction-layer_EPSG-4326_opt 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.3939050 Copernicus_PROBAV_LC100_global_v3.0.1_2019-nrt_Tree-CoverFraction-layer_EPSG-4326_opt WCS GeoTIFF EPSG:4326 CRS:84 -180.0 179.9999999999988 -59.999999999999545 80.0 unep_storymap:Corine_LCov_Warsaw Corine_LCov WCS GeoTIFF EPSG:3035 CRS:84 -81.23107933735224 93.48635817033637 -29.129342538404806 71.33111662209056 peatlands:Crezee_2022_Map_comparison_opt 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.uk Crezee_2022_Map_comparison_opt WCS GeoTIFF EPSG:4326 CRS:84 14.99985 25.72774272 -5.000394440000001 3.30574472 peatlands:Crezee_2022_Median_Carbon_density_2000runs_opt 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.uk Crezee_2022_Median_Carbon_density_2000runs_opt WCS GeoTIFF EPSG:4326 CRS:84 14.999619457 24.000289446 -5.015743389 3.511065288 peatlands:Crezee_2022_Median_Peat_thickness_RF_100runs_opt 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.uk Crezee_2022_Median_Peat_thickness_RF_100runs_opt WCS GeoTIFF EPSG:4326 CRS:84 14.999619457 23.800414295 -5.014845073999999 3.51061613 peatlands:Crezee_2022_Probability_HardwoodDominatedPeatSwamp_opt 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.uk Probability_2022_HardwoodDominatedPeatSwamp_opt WCS GeoTIFF EPSG:4326 CRS:84 15.0 25.72744828 -4.999916949999999 3.30533333 peatlands:Crezee_2022_Probability_NonPeatFormingForest 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.uk Probability_2022_NonPeatFormingForest_opt WCS GeoTIFF EPSG:4326 CRS:84 15.0 25.72744828 -4.999916949999999 3.30533333 peatlands:Crezee_2022_Probability_PalmDominatedPeatSwamp 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.uk Probability_2022_PalmDominatedPeatSwamp_opt WCS GeoTIFF EPSG:4326 CRS:84 15.0 25.72744828 -4.999916949999999 3.30533333 peatlands:Crezee_2022_Probability_Savanna 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.uk Probability_2022_Savanna_opt WCS GeoTIFF EPSG:4326 CRS:84 15.0 25.72744828 -4.999916949999999 3.30533333 peatlands:Crezee_2022_Probability_Water 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.uk Probability_2022_Water_opt WCS GeoTIFF EPSG:4326 CRS:84 15.0 25.72744828 -4.999916949999999 3.30533333 peatlands:Crezee_2022_Smoothed_7x7_Classification_Most_likely_class_opt 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.uk Crezee_2022_Smoothed_7x7_Classification_Most_likely_class_opt WCS GeoTIFF EPSG:4326 CRS:84 15.0 25.72744828 -4.999916949999999 3.30533333 peatlands:Crezee_2022_Uncertainty_Carbon_density_2000runs_opt 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.uk Crezee_2022_Uncertainty_Carbon_density_2000runs_opt WCS GeoTIFF EPSG:4326 CRS:84 14.999619457 24.000289446 -5.015743389 3.511065288 peatlands:Crezee_2022_Uncertainty_Peat_thickness_RF_100runs_opt 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.uk Crezee_2022_Uncertainty_Peat_thickness_RF_100runs_opt WCS GeoTIFF EPSG:4326 CRS:84 14.999619457 23.800414295 -5.014845073999999 3.51061613 peatlands:Crezee_2022_Unsmoothed_Classification_Most_likely_class_opt 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.uk Crezee_2022_Unsmoothed_Classification_Most_likely_class_opt WCS GeoTIFF EPSG:4326 CRS:84 15.0 25.72744828 -4.999916949999999 3.30533333 wesr_cca:Degraded lands Layer 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_2019 WCS GeoTIFF EPSG:32718 CRS:84 -82.811770420087 -67.70058473717306 -19.050723822685583 0.5435574567185314 UN_Sahel:Diff_CHELSA_bio_mon_ACCESS1-0_rcp45_r1i1p1_g025.nc_1_2041-2060_V12_opt Anomaly [°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 1.2 Diff_CHELSA_bio_mon_ACCESS1-0_rcp45_r1i1p1_g025.nc_1_2041-2060_V12_opt WCS GeoTIFF EPSG:4326 CRS:84 -180.00013888885002 179.99985967115003 -90.00013888884999 83.99986041515001 text/plain UN_Sahel:Diff_CHELSA_bio_mon_ACCESS1-0_rcp85_r1i1p1_g025.nc_1_2041-2060_V12_opt Anomaly [°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 1.2 Diff_CHELSA_bio_mon_ACCESS1-0_rcp85_r1i1p1_g025.nc_1_2041-2060_V12_opt WCS GeoTIFF EPSG:4326 CRS:84 -180.00013888885002 179.99985967115003 -90.00013888884999 83.99986041515001 text/plain risk_niger_undp:Dogondoutchi_Duration_78_T100 Dogondoutchi_Duration_78_T100_opt_deflate WCS GeoTIFF EPSG:4326 CRS:84 2.431818181818182 6.068181818181818 11.93181818181818 15.568181818181818 risk_niger_undp:Dogondoutchi_Duration_78_T25 Dogondoutchi_Duration_78_T25_opt_deflate WCS GeoTIFF EPSG:4326 CRS:84 2.431818181818182 6.068181818181818 11.93181818181818 15.568181818181818 risk_niger_undp:Dogondoutchi_Duration_78_T250 Dogondoutchi_Duration_78_T250_opt_deflate WCS GeoTIFF EPSG:4326 CRS:84 2.431818181818182 6.068181818181818 11.93181818181818 15.568181818181818 risk_niger_undp:Dogondoutchi_Duration_78_T50 Dogondoutchi_Duration_78_T50_opt_deflate WCS GeoTIFF EPSG:4326 CRS:84 2.431818181818182 6.068181818181818 11.93181818181818 15.568181818181818 risk_niger_undp:Dogondoutchi_Intensity_78_T100 Dogondoutchi_Intensity_78_T100_opt_deflate WCS GeoTIFF EPSG:4326 CRS:84 2.431818181818182 6.068181818181818 11.93181818181818 15.568181818181818 risk_niger_undp:Dogondoutchi_Intensity_78_T25 Dogondoutchi_Intensity_78_T25_opt_deflate WCS GeoTIFF EPSG:4326 CRS:84 2.431818181818182 6.068181818181818 11.93181818181818 15.568181818181818 risk_niger_undp:Dogondoutchi_Intensity_78_T250 Dogondoutchi_Intensity_78_T250_opt_deflate WCS GeoTIFF EPSG:4326 CRS:84 2.431818181818182 6.068181818181818 11.93181818181818 15.568181818181818 risk_niger_undp:Dogondoutchi_Intensity_78_T50 Dogondoutchi_Intensity_78_T50_opt_deflate WCS GeoTIFF EPSG:4326 CRS:84 2.431818181818182 6.068181818181818 11.93181818181818 15.568181818181818 risk_niger_undp:Dogondoutchi_Severity_78_T100 Dogondoutchi_Severity_78_T100_opt_deflate WCS GeoTIFF EPSG:4326 CRS:84 2.431818181818182 6.068181818181818 11.93181818181818 15.568181818181818 risk_niger_undp:Dogondoutchi_Severity_78_T25 Dogondoutchi_Severity_78_T25_opt_deflate WCS GeoTIFF EPSG:4326 CRS:84 2.431818181818182 6.068181818181818 11.93181818181818 15.568181818181818 risk_niger_undp:Dogondoutchi_Severity_78_T250 Dogondoutchi_Severity_78_T250_opt_deflate WCS GeoTIFF EPSG:4326 CRS:84 2.431818181818182 6.068181818181818 11.93181818181818 15.568181818181818 risk_niger_undp:Dogondoutchi_Severity_78_T50 Dogondoutchi_Severity_78_T50_opt_deflate WCS GeoTIFF EPSG:4326 CRS:84 2.431818181818182 6.068181818181818 11.93181818181818 15.568181818181818 sand:Dredging_points-2012-2024vf This layer maps global dredging activities from 2012 to 2024, including sand and gravel extraction zones across marine and coastal regions. It provides spatial insights into industrial footprints, temporal trends, and potential overlaps with protected areas and sensitive ecosystems. The dataset supports environmental impact assessments and sustainable ocean planning. features Dredging_points-2012-2024vf EPSG:4326 CRS:84 -176.7239999999999 178.0480000000001 -54.81699999999994 79.69800000000004 MapX_UNDP:ESACCI-LC-L4-LCCS-Map-300m-P1Y-1992_2015-v2.0.7_2015 The 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_2015 WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.00000000002865 -90.00000000001432 90.0 text/plain grid_data_core:ESA_WB-Map-v30 Global map of open permanent water bodies at 300m spatial resolution derived from the full ENVISAT-ASAR dataset between 2005 and 2010. WCS GeoTIFF ESA_WB-Map-v30 lake river EPSG:4326 CRS:84 -180.00000033927267 179.9999897345785 -89.99999503692558 90.0 MAPX:ET2017_1-10_usgs Calculated through raster calculator as average of 10 specific rasters, each per month from jan to oct 2017. ET2017_1-10 WCS GeoTIFF EPSG:4326 CRS:84 -20.008449316024837 55.00689406692973 -38.00309218093754 40.004371177405076 Medicip:EmodNet_HA_Vessel_density_2019avg EmodNet_HA_Vessel_density_2019avg_Part1_st5_Medcut_opt WCS GeoTIFF EPSG:3035 CRS:84 -8.270934566444502 40.671063797404635 26.199426516464257 46.83058065129969 wesr_risk:Eq_physexp Eq_phex WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 UN_Sahel:FEWS_NET_LH_World Livelihood zone maps define geographic areas of a country where people generally share similar options for obtaining food and income and similar access to markets. An understanding of geographic livelihood systems is a key component of FEWS NET’s food security analysis. The maps are produced through multi-day workshops during which food security stakeholders and country experts identify zones. Factors considered include agro-climatology, elevation, land-cover, market accessibility, sources of food, and major economic activities. A livelihood zone map is typically accompanied by a livelihood description which outlines the key characteristics of each zone. In some cases, maps may also be accompanied by livelihood profiles, which are in-depth descriptions of the characteristics of wealth groups within each zone. The zone maps are available for download as PDFs, PNGs, and GIS shapefiles. The shapefiles and maps are all produced and maintained by FEWS NET. Please consult the logos in maps for the full list of organizations who participated in and/or funded the zoning workshop. For more information on FEWS NET’s livelihood approach and to view our other livelihood products, please click <a href="https://fews.net/sectors-topics/sectors/livelihoods"target=_blank>here</a>. <br><br> Data sourced from <a href="http://fews.net/fews-data/335"target=_blank>FEWS NET Data Center</a>. features FEWS_NET_LH_World EPSG:4326 CRS:84 -90.12866210916894 74.87943118699972 -30.668964385997583 38.48342611122613 MapX_UNDP:FSII_merged 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_merged WCS GeoTIFF EPSG:4326 CRS:84 -81.70887178141503 153.4281182773073 -20.582648947388527 25.91783325130279 text/plain unep_storymap:Focal_varity_WGS84 Focal varity. Based on 18 key geomorphic features. Focal_varity_WGS84 WCS GeoTIFF focal varity EPSG:4326 CRS:84 -179.99999952227077 179.99994967041263 -88.20698944113573 89.99999999999997 MapX_UNDP:GEOCARBON_Global_Forest_AGB_10072015 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.htm GEOCARBON_Global_Forest_AGB_10072015 WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -56.02000000000001 84.0 text/plain MAPX:GHA_GNDVI1990 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 mining GNDVI1990_opt WCS GeoTIFF EPSG:4326 CRS:84 -2.941804951 -1.42736896 9.626453977 11.030613419 text/plain MAPX:GHA_GNDVI2000 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 Ghana GNDVI2000_opt WCS GeoTIFF EPSG:4326 CRS:84 -2.941804951 -1.42736896 9.626453977 11.030613419 text/plain MAPX:GHA_GNDVI2010 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_opt WCS GeoTIFF EPSG:4326 CRS:84 -2.941804951 -1.42736896 9.626453977 11.030613419 text/plain MAPX:GHA_GNDVI2019 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 area GNDVI2019_opt WCS GeoTIFF EPSG:4326 CRS:84 -2.941804951 -1.42736896 9.626453977 11.030613419 text/plain MAPX:GHA_LULC1990 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_opt WCS GeoTIFF EPSG:4326 CRS:84 -2.941804951 -1.42736896 9.626453977 11.030613419 text/plain MAPX:GHA_LULC2000 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_opt WCS GeoTIFF EPSG:4326 CRS:84 -2.941942292 -1.427242947 9.62626436 11.030913256 text/plain MAPX:GHA_LULC2010 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_opt WCS GeoTIFF EPSG:4326 CRS:84 -2.941952153 -1.4272597149999997 9.62648323 11.03077215 text/plain MAPX:GHA_LULC2019 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 hazards LULC2019_opt WCS GeoTIFF EPSG:4326 CRS:84 -2.941804951 -1.42736896 9.626453977 11.030613419 text/plain MAPX:GHA_Stream_Order Stream_Order_opt WCS GeoTIFF EPSG:4326 CRS:84 -2.942754639 -1.420219031 9.62256316 11.034631553 MAPX:GHA_Stream_Profile Stream_Profile_opt WCS GeoTIFF EPSG:4326 CRS:84 -2.942754639 -1.420219031 9.62256316 11.034631553 grid_data_core_pro:GHSL_pop_count_2018_wnodata_opt Population 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_opt WCS GeoTIFF EPSG:4326 CRS:84 -179.999999999928 179.999999999928 -59.99999999424 84.00833333903574 MAPX:GHS_BUILT_V_E2020_GLOBE_R2023A_4326_3ss_V1_0_opt 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 2016 GHS_BUILT_V_E2020_GLOBE_R2023A_4326_3ss_V1_0_opt WCS GeoTIFF EPSG:4326 CRS:84 -180.00041593133002 180.00124930938549 -89.09291610516487 89.09208317767352 grid_data_core_pro:GHS_POP_E2020_GLOBE_R2022A_54009_1000_V1_0_wgs84_opt The 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 2016 GHS_POP_E2020_GLOBE_R2022A_54009_1000_V1_0_wgs84_opt WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.00833333318934 -60.01666666090665 84.01666667236907 MAPX:GHS_POP_E2020_GLOBE_R2023A_4326_3ss_V1_0_opt 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 2016 GHS_POP_E2020_GLOBE_R2023A_4326_3ss_V1_0_opt WCS GeoTIFF EPSG:4326 CRS:84 -180.00041593133002 180.0012493094487 -89.09291610520242 89.09208317767579 wesr_climat:GHS_POP_GPW41975 GHS_POP_GPW41975_GLOBE_R2015A_54009_1k_v1_0_wgs84_opt WCS GeoTIFF EPSG:4326 CRS:84 -179.99999680823143 179.99218561802442 -55.98302144283261 83.63964763391371 wesr_climat:GHS_POP_GPW41990 GHS_POP_GPW41990_GLOBE_R2015A_54009_1k_v1_0_wgs84_opt WCS GeoTIFF EPSG:4326 CRS:84 -179.99999680823143 179.99218561802442 -55.98302144283261 83.63964763391371 wesr_climat:GHS_POP_GPW42000 GHS_POP_GPW42000_GLOBE_R2015A_54009_1k_v1_0_wgs84_opt WCS GeoTIFF EPSG:4326 CRS:84 -179.99999680823143 179.99218561802442 -55.98302144283261 83.63964763391371 wesr_climat:GHS_POP_GPW42015 GHS_POP_GPW42015_GLOBE_R2015A_54009_1k_v1_0_wgs84_opt WCS GeoTIFF EPSG:4326 CRS:84 -179.99999680823143 179.99218561802442 -55.98302144283261 83.63964763391371 ECO-DRR:GLO_ecodrr features GLO_ecodrr EPSG:4326 CRS:84 -180.0 180.0 -60.35499999999999 84.43 ECO-DRR:GLO_flo_for6_13 GLO_flo_for6_13 WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 ECO-DRR:GLO_flo_for6_13_P3 GLO_flo_for6_13 WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 ECO-DRR:GLO_flo_for6_46 GLO_flo_for6_46 WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 ECO-DRR:GLO_flo_for6_46_P3 GLO_flo_for6_46 WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 ECO-DRR:GLO_flo_for9_1112 GLO_flo_for9_1112 WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 ECO-DRR:GLO_flo_for9_1112_P3 GLO_flo_for9_1112 WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 ECO-DRR:GLO_lds_for6_13 GLO_lds_for6_13 WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 ECO-DRR:GLO_lds_for6_13_P3 GLO_lds_for6_13 WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 ECO-DRR:GLO_lds_for6_46 GLO_lds_for6_46 WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 ECO-DRR:GLO_lds_for6_46_P3 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CRS:84 -180.0 180.0 -90.0 90.0 ECO-DRR:GLO_tcs_for6_46 GLO_tcs_for6_46 WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 ECO-DRR:GLO_tcs_for6_46_P3 GLO_tcs_for6_46 WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 ECO-DRR:GLO_tcs_for9_1112 GLO_tcs_for9_1112 WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 ECO-DRR:GLO_tcs_for9_1112_P3 GLO_tcs_for9_1112 WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 ECO-DRR:GLO_tcs_man6_13 GLO_tcs_man6_13 WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 ECO-DRR:GLO_tcs_man6_13_P3 GLO_tcs_man6_13 WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 ECO-DRR:GLO_tcs_man6_46 GLO_tcs_man6_46 WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 ECO-DRR:GLO_tcs_man6_46_P3 GLO_tcs_man6_46 WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 ECO-DRR:GLO_tcs_man9_1112 GLO_tcs_man9_1112 WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 ECO-DRR:GLO_tcs_man9_1112_P3 GLO_tcs_man9_1112 WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 ECO-DRR:GLO_tcs_sea6_13 GLO_tcs_sea6_13 WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 ECO-DRR:GLO_tcs_sea6_13_P3 GLO_tcs_sea6_13 WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 ECO-DRR:GLO_tcs_sea6_46 GLO_tcs_sea6_46 WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 ECO-DRR:GLO_tcs_sea6_46_P3 GLO_tcs_sea6_46 WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 ECO-DRR:GLO_tcs_sea9_1112 GLO_tcs_sea9_1112 WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 ECO-DRR:GLO_tcs_sea9_1112_P3 GLO_tcs_sea9_1112 WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 ECO-DRR:GLO_tcw_for6_13 GLO_tcw_for6_13 WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 ECO-DRR:GLO_tcw_for6_13_P3 GLO_tcw_for6_13 WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 ECO-DRR:GLO_tcw_for6_46 GLO_tcw_for6_46 WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 ECO-DRR:GLO_tcw_for6_46_P3 GLO_tcw_for6_46 WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 ECO-DRR:GLO_tcw_for9_1112 GLO_tcw_for9_1112 WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 ECO-DRR:GLO_tcw_for9_1112_P3 GLO_tcw_for9_1112 WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 ECO-DRR:GLO_tsu_cor6_13 GLO_tsu_cor6_13 WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 ECO-DRR:GLO_tsu_cor6_13_P3 GLO_tsu_cor6_13 WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 ECO-DRR:GLO_tsu_cor6_46 GLO_tsu_cor6_46 WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 ECO-DRR:GLO_tsu_cor6_46_P3 GLO_tsu_cor6_46 WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 ECO-DRR:GLO_tsu_cor9_1112 GLO_tsu_cor9_1112 WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 ECO-DRR:GLO_tsu_cor9_1112_P3 GLO_tsu_cor9_1112 WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 ECO-DRR:GLO_tsu_for6_13 GLO_tsu_for6_13 WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 ECO-DRR:GLO_tsu_for6_13_P3 GLO_tsu_for6_13 WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 ECO-DRR:GLO_tsu_for6_46 GLO_tsu_for6_46 WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -90.0 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GLO_tsu_sea6_13 WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 ECO-DRR:GLO_tsu_sea6_46 GLO_tsu_sea6_46 WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 ECO-DRR:GLO_tsu_sea6_46_P3 GLO_tsu_sea6_46 WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 ECO-DRR:GLO_tsu_sea9_1112 GLO_tsu_sea9_1112 WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 ECO-DRR:GLO_tsu_sea9_1112_P3 GLO_tsu_sea9_1112 WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 MapX_UNDP:GSOCmapV1 Soil 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/ GSOCmapV1 WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.00833189299993 -70.91725146799998 83.616081247 text/plain MapX_UNDP:GSOCmapV150 Soil 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/ GSOCv150webopt WCS GeoTIFF EPSG:4326 CRS:84 -180.0 179.99999856000005 -70.91725146799998 83.616081247 MapX_UNDP:Glb_Cattle_CC2006_AD This 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_AD WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 text/plain MapX_UNDP:Glb_GTAD_2006 This 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_2006 WCS GeoTIFF EPSG:4326 CRS:84 -180.00000000000003 180.00000000000003 -90.00000000000001 90.00000000000001 text/plain MapX_UNDP:Glb_SHAD_2006 This 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_2006 WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 text/plain unea_pollution:GlobalPopCarbonEmis2014 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.V1 GlobalPopCarbonEmis2014 WCS GeoTIFF global carbon emissions population density spatial distribution 0.1°resolution EPSG:4326 CRS:84 -180.0 180.0 -59.99999999999177 83.60000000000822 text/plain UN_Sahel:GlobalR_NoPol_opt The 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_opt WCS GeoTIFF EPSG:4326 CRS:84 -179.99999999999997 179.99999999999986 -58.49228515076997 72.25771484922997 text/plain unep_storymap:HELSINKI_UA2012_WGS84_Warsaw features HELSKINKI_UA2012_WGS84 EPSG:4326 CRS:84 24.126390699633447 25.525388499641736 59.903163910578456 60.84077849941336 ECO-DRR:HRSL_wGHSL_pop_count ghrsl22a WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.00833333318934 -59.999999994239985 84.00833333903576 grid_data_core_pro:HRSL_wGHSL_pop_count_2018_wnodata_opt Population 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_opt WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.00833333318934 -59.999999994239985 84.00833333903576 wesr_pollution:HgT_2015_allsectors_025d__g_km2_opt 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_opt WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 wesr_pollution:HgT_2015_allsectors_025d__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_opt WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 MapX_UNDP:Human_pressures_wgs_opt_opt <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_opt WCS GeoTIFF EPSG:4326 CRS:84 -179.99051999985159 179.99257988732398 -81.82870752472896 81.82264272729027 wesr_mountains:K1-KAPOS K1-KAPOS WCS GeoTIFF EPSG:4326 CRS:84 -179.99998890825066 180.00001109174872 -55.90638888888891 83.32902777777751 grid_data_core:K2_new_YG K2_new_YG WCS GeoTIFF EPSG:4326 CRS:84 -180.02083 179.97859 -59.47894000000001 83.64583 wesr_mountains:K3-ELU-LF K3-ELU-LF WCS GeoTIFF EPSG:4326 CRS:84 -180.0000000000001 179.99999999999926 -56.00013888888891 83.62486111111086 MapX_UNDP_nd:KBA_GeoTiff_clear v2 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_clear WCS GeoTIFF EPSG:4326 CRS:84 -186.93396181851392 188.54498490150434 -87.90113898196397 95.19838541667612 MapX_UNDP_nd:KBA_masked_v1 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_masked WCS GeoTIFF EPSG:4326 CRS:84 -180.9599047118093 181.50122715994834 -154.2757462501222 154.73152172943685 text/plain unccd:K_factor_with_Ksat The 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.2023.168249 K_factor_with_Ksat_opt WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -62.0 87.37 ECO-DRR:Kerala_Flood_100_Yrs_Historical Kerala State flood hazard map - Historical. Maximum expected flood event water height in a 100 years return period, expressed in cm. h100i WCS GeoTIFF EPSG:4326 CRS:84 74.85875 77.49291666666667 8.25375 12.842916666666673 ECO-DRR:Kerala_Flood_100_Yrs_RCP85 Kerala State flood hazard map - RCP 8.5 scenario. Maximum expected flood event water height in a 100 years return period, expressed in cm. h100i WCS GeoTIFF EPSG:4326 CRS:84 74.85958333333333 77.49208333333334 8.25375 12.842916666666673 ECO-DRR:Kerala_Flood_10_Yrs_Historical Kerala State flood hazard map - Historical. Maximum expected flood event water height in a 10 years return period, expressed in cm. h10i WCS GeoTIFF EPSG:4326 CRS:84 74.85875 77.49291666666667 8.25375 12.842916666666673 ECO-DRR:Kerala_Flood_10_Yrs_RCP85 Kerala State flood hazard map - RCP 8.5 scenario. Maximum expected flood event water height in a 10 years return period, expressed in cm. h10i WCS GeoTIFF EPSG:4326 CRS:84 74.85958333333333 77.49208333333334 8.25375 12.842916666666673 ECO-DRR:Kerala_Flood_200_Yrs_Historical Kerala State flood hazard map - Historical. Maximum expected flood event water height in a 200 years return period, expressed in cm. h200i WCS GeoTIFF EPSG:4326 CRS:84 74.85875 77.49291666666667 8.25375 12.842916666666673 ECO-DRR:Kerala_Flood_200_Yrs_RCP85 Kerala State flood hazard map - RCP 8.5 scenario. Maximum expected flood event water height in a 200 years return period, expressed in cm. h200i WCS GeoTIFF EPSG:4326 CRS:84 74.85958333333333 77.49208333333334 8.25375 12.842916666666673 ECO-DRR:Kerala_Flood_25_Yrs_Historical Kerala State flood hazard map - Historical. Maximum expected flood event water height in a 25 years return period, expressed in cm. h25i WCS GeoTIFF EPSG:4326 CRS:84 74.85875 77.49291666666667 8.25375 12.842916666666673 ECO-DRR:Kerala_Flood_25_Yrs_RCP85 Kerala State flood hazard map - RCP 8.5 scenario. Maximum expected flood event water height in a 25 years return period, expressed in cm. h25i WCS GeoTIFF EPSG:4326 CRS:84 74.85958333333333 77.49208333333334 8.25375 12.842916666666673 ECO-DRR:Kerala_Flood_500_Yrs_Historical Kerala State flood hazard map - Historical. Maximum expected flood event water height in a 500 years return period, expressed in cm. h500i WCS GeoTIFF EPSG:4326 CRS:84 74.85875 77.49291666666667 8.25375 12.842916666666673 ECO-DRR:Kerala_Flood_500_Yrs_RCP85 Kerala State flood hazard map - RCP 8.5 scenario. Maximum expected flood event water height in a 500 years return period, expressed in cm. h500i WCS GeoTIFF EPSG:4326 CRS:84 74.85958333333333 77.49208333333334 8.25375 12.842916666666673 ECO-DRR:Kerala_Flood_50_Yrs_Historical Kerala State flood hazard map - Historical. Maximum expected flood event water height in a 50 years return period, expressed in cm. h50i WCS GeoTIFF EPSG:4326 CRS:84 74.85875 77.49291666666667 8.25375 12.842916666666673 ECO-DRR:Kerala_Flood_50_Yrs_RCP85 Kerala State flood hazard map - RCP 8.5 scenario. Maximum expected flood event water height in a 50 years return period, expressed in cm. h50i WCS GeoTIFF EPSG:4326 CRS:84 74.85958333333333 77.49208333333334 8.25375 12.842916666666673 ECO-DRR:Kerala_Global_Land_Cover_2009 ESA Global Land Cover 2009 for Kerala State. glc2009 WCS GeoTIFF EPSG:4326 CRS:84 74.85972222222227 77.49305555555561 8.254166666666677 12.843055555555566 ECO-DRR:Kerala_Hillshade Hillshade on Kerala MERIT DEM 240-30 codemh WCS GeoTIFF EPSG:4326 CRS:84 74.85875 77.49291666666667 8.25375 12.842916666666673 ECO-DRR:Kerala_Hillshade_Multi codemhm WCS GeoTIFF EPSG:4326 CRS:84 74.85875 77.49291666666667 8.25375 12.842916666666673 ECO-DRR:Kerala_MERIT_DEM codem WCS GeoTIFF EPSG:4326 CRS:84 74.85875 77.49291666666667 8.25375 12.842916666666673 ECO-DRR:Kerala_SWBD SRTM Water Body Data for Kerala State swbdkalk WCS GeoTIFF EPSG:4326 CRS:84 74.85874450133333 77.49289430933334 8.253748373333334 12.84288566933333 TEEB:Kilombero_LULC2014_wgs84_opt Kilombero_LULC2014_wgs84_opt WCS GeoTIFF EPSG:4326 CRS:84 34.553167266769464 37.239835341004294 -10.038048450490551 -7.663994368030097 sdc_test:L8_mosaic L8_mosaic WCS GeoTIFF EPSG:4326 CRS:84 5.7348574 10.667580872 45.675510856 47.916276664 dakar_waste:LST_mean 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 Management Remote Sensing GIS Citizen Science Multi-Criteria Analysis Illegal Dumping EPSG:4326 CRS:84 -17.55611637429027 -17.19121319151092 14.634522359756563 14.86324689854362 MAPX_nd:LandCover_opt Source: 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_opt WCS GeoTIFF EPSG:4326 CRS:84 24.15069586900006 35.94791809216606 3.4874709340000436 12.237470934700044 text/plain wesr_cca:MAPA_NACIONAL_DE_ECOSISTEMAS Layer provided by Ministry of Environment of Peru. This link has the documentation, definitions and methods of the national map of ecosystems. https://geoservidor.minam.gob.pe/wp-content/uploads/2017/06/MEMORIA-DESCRIPTIVA-MAPA-DE-ECOSISTEMAS.pdf features MAPA_NACIONAL_DE_ECOSISTEMAS EPSG:32718 CRS:84 -81.64209769673546 -68.4732307911745 -18.407132990582912 -0.0383709135756495 medprogramme:MOR_cei_opt Exposure-related variables: Land Cover, Population density Area: Morocco Child Project: SCCF Source: GEF-MedProgramme MOR_cei_opt WCS GeoTIFF EPSG:4326 CRS:84 -6.243055555556 -3.8036111091600007 34.6680215 35.922465945448 medprogramme:MOR_cfi_opt The 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-MedProgramme MOR_cfi_opt WCS GeoTIFF EPSG:4326 CRS:84 -6.243194443839 -3.804305552999 34.6680215 35.920521501002 medprogramme:MOR_cri_opt Multiplication of 3 sub-indexes: Coastal Vulnerability Index Coastal Exposure Index Coastal Forcing Index Area: Morocco Child Project: SCCF Source: GEF-MedProgramme MOR_cri_opt WCS GeoTIFF EPSG:4326 CRS:84 -6.243055555556 -3.8041666647160004 34.6680215 35.920521501002 medprogramme:MOR_cvi_only_env_opt 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-MedProgramme MOR_cvi_only_env_opt WCS GeoTIFF EPSG:4326 CRS:84 -6.243055555556 -3.8041666647160004 34.513854833210004 35.922465945448 medprogramme:MOR_cvi_opt The 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-MedProgramme MOR_cvi_opt WCS GeoTIFF EPSG:4326 CRS:84 -6.243055555556 -3.8041666647160004 34.6680215 35.922465945448 grid_data_core:MSR_50M WCS GeoTIFF MSR_50M EPSG:4326 CRS:84 -179.99999999999997 179.99999999996405 -89.99999999998201 90.0 MAPX:Mogadisho_Landsat7_20140616-531 Landsat 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_20140616-531 WCS GeoTIFF EPSG:32638 CRS:84 45.19537208608338 45.47928595286078 1.9551716669929171 2.1415394320566414 wesr_cca:MovMasa_COSTA_SIERRA Layer 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.pdf MovMasa_COSTA_SIERRA WCS GeoTIFF EPSG:32718 CRS:84 -81.64209767609421 -68.74570664904627 -18.407041570165664 -3.3742169907173394 unep_storymap:NATURA_UA2012_WGS84_Warsaw features NATURA_UA2012_WGS84 EPSG:4326 CRS:84 -32.36805685315556 34.101526762283285 27.587675100452497 70.02586482791324 ECO-DRR:NAT_flo_for6_13 NAT_flo_for6_13 WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 ECO-DRR:NAT_flo_for6_46 NAT_flo_for6_46 WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 ECO-DRR:NAT_flo_for9_1112 NAT_flo_for9_1112 WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 ECO-DRR:NAT_lds_for6_13 NAT_lds_for6_13 WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 ECO-DRR:NAT_lds_for6_46 NAT_lds_for6_46 WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 ECO-DRR:NAT_lds_for9_1112 NAT_lds_for9_1112 WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 ECO-DRR:NAT_tcs_cor6_13 NAT_tcs_cor6_13 WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 ECO-DRR:NAT_tcs_cor6_46 NAT_tcs_cor6_46 WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 ECO-DRR:NAT_tcs_cor9_1112 NAT_tcs_cor9_1112 WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 ECO-DRR:NAT_tcs_for6_13 NAT_tcs_for6_13 WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 ECO-DRR:NAT_tcs_for6_46 NAT_tcs_for6_46 WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 ECO-DRR:NAT_tcs_for9_1112 NAT_tcs_for9_1112 WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 ECO-DRR:NAT_tcs_man6_13 NAT_tcs_man6_13 WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 ECO-DRR:NAT_tcs_man6_46 NAT_tcs_man6_46 WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 ECO-DRR:NAT_tcs_man9_1112 NAT_tcs_man9_1112 WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 ECO-DRR:NAT_tcs_sea6_13 NAT_tcs_sea6_13 WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 ECO-DRR:NAT_tcs_sea6_46 NAT_tcs_sea6_46 WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 ECO-DRR:NAT_tcs_sea9_1112 NAT_tcs_sea9_1112 WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 ECO-DRR:NAT_tcw_for6_13 NAT_tcw_for6_13 WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 ECO-DRR:NAT_tcw_for6_46 NAT_tcw_for6_46 WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 ECO-DRR:NAT_tcw_for9_1112 NAT_tcw_for9_1112 WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 ECO-DRR:NAT_tsu_cor6_13 NAT_tsu_cor6_13 WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 ECO-DRR:NAT_tsu_cor6_46 NAT_tsu_cor6_46 WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 ECO-DRR:NAT_tsu_cor9_1112 NAT_tsu_cor9_1112 WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 ECO-DRR:NAT_tsu_for6_13 NAT_tsu_for6_13 WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 ECO-DRR:NAT_tsu_for6_46 NAT_tsu_for6_46 WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 ECO-DRR:NAT_tsu_for9_1112 NAT_tsu_for9_1112 WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 ECO-DRR:NAT_tsu_man6_13 NAT_tsu_man6_13 WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 ECO-DRR:NAT_tsu_man6_46 NAT_tsu_man6_46 WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 ECO-DRR:NAT_tsu_man9_1112 NAT_tsu_man9_1112 WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 ECO-DRR:NAT_tsu_sea6_13 NAT_tsu_sea6_13 WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 ECO-DRR:NAT_tsu_sea6_46 NAT_tsu_sea6_46 WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 ECO-DRR:NAT_tsu_sea9_1112 NAT_tsu_sea9_1112 WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 dakar_waste:NDWI This 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 Management Remote Sensing GIS Citizen Science Multi-Criteria Analysis Illegal Dumping EPSG:4326 CRS:84 -17.382029187007333 -17.2719124771499 14.733719739640827 14.84106039798925 mapx_chemobs:NVG_2000 NVG_2000_opt WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 MAPX_nd:Nile_ET_opt Source: 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_opt WCS GeoTIFF EPSG:4326 CRS:84 23.550893783569364 39.804861615169365 -4.0330696105956605 31.60211402020434 text/plain AccessMod:POP_FACEBOOK_Lekoumou_2019_3km_UTM_opt POP_FACEBOOK_Lekoumou_2019_3km_UTM_opt WCS GeoTIFF EPSG:32633 CRS:84 12.79605904597841 14.350819887086997 -4.052850314579793 -2.0880026703818597 AccessMod:POP_FACEBOOK_Lekoumou_2019_92m_UTM_opt POP_FACEBOOK_Lekoumou_2019_92m_UTM_opt WCS GeoTIFF EPSG:32633 CRS:84 12.79605904597841 14.350819887086997 -4.052850314579793 -2.0880026703818597 AccessMod:POP_FACEBOOK_Sangha_2019_92m_UTM_opt POP_FACEBOOK_Sangha_2019_92m_UTM_opt WCS GeoTIFF EPSG:32633 CRS:84 13.123883469958477 16.999368548454783 0.0353725084228712 2.722913660432471 AccessMod:POP_FACEBOOK_Sangha_2019_Agg3km_UTM_opt POP_FACEBOOK_Sangha_2019_Agg3km_UTM_opt WCS GeoTIFF EPSG:32633 CRS:84 13.123883469599217 16.999368548094683 0.0353725081516256 2.722913660161077 AccessMod:POP_LEKOUMOU_2019_finale_Worlpop_opt POP_LEKOUMOU_2019_finale_Worlpop_opt WCS GeoTIFF EPSG:4326 CRS:84 12.796059046335326 14.35138242981778 -4.052797249963222 -2.088002670078685 AccessMod:POP_SANGHA_2019_finale_Worlpop_opt POP_SANGHA_2019_finale_Worlpop_opt WCS GeoTIFF EPSG:4326 CRS:84 13.12388346995083 16.999434297770676 0.0357446152097114 2.722915245020809 UN_Sahel:Perc_Diff_CHELSA_bio_mon_ACCESS1-0_rcp45_r1i1p1_g025.nc_12_2041-2060_V12_opt Anomaly [%] 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 1.2 Perc_Diff_CHELSA_bio_mon_ACCESS1-0_rcp45_r1i1p1_g025.nc_12_2041-2060_V12_opt WCS GeoTIFF EPSG:4326 CRS:84 -180.00013888885002 179.99985967115003 -90.00013888884999 83.99986041515001 text/plain UN_Sahel:Perc_Diff_CHELSA_bio_mon_ACCESS1-0_rcp85_r1i1p1_g025.nc_12_2041-2060_V12_opt Anomaly [%] 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 1.2 Perc_Diff_CHELSA_bio_mon_ACCESS1-0_rcp85_r1i1p1_g025.nc_12_2041-2060_V12_opt WCS GeoTIFF EPSG:4326 CRS:84 -180.00013888885002 179.99985967115003 -90.00013888884999 83.99986041515001 text/plain wesr_cca:PeruFFDiversity2017 Layer 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. PeruFFDiversity2017 WCS GeoTIFF EPSG:32718 CRS:84 -82.71417274508761 -65.41909110049332 -18.737938819947015 0.9308945156352922 wesr_cca:Peru_ACD Layer 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_ACD WCS GeoTIFF EPSG:32718 CRS:84 -82.72089353764397 -65.41079891321573 -18.887043202719365 0.930894533727649 AccessMod:Pop_Togo_wgs84_opt WCS GeoTIFF EPSG:4326 CRS:84 -0.1462858377440157 1.8324070818850766 5.639838970946034 11.150878574337117 text/plain MAPX:Population_Density_opt 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_opt WCS GeoTIFF EPSG:4326 CRS:84 24.15069586900006 35.95069586900014 3.48747093435 12.23747093435006 text/plain wesr_mountains:RGI_Global This layer contains the Randolph Glacier Inventory (RGI) version 6 (released in July 2017), which is a global inventory of glacier outlines representative of the situation at the beginning of the XXIst century (see details in the detailed description mentioned hereafter). It is a subset of the Global Land Ice Measurements from Space initiative (GLIMS). The complete description of the Randolph Glacier Inventory (RGI) can be found here: http://www.glims.org/RGI/00_rgi60_TechnicalNote.pdf. The attribute table contains 22 fields for each glacier, including: a unique RGI code (RGIId); the corresponding GLIMS database code (GLIMSId); the date or date ranges of the source from which the outline was taken (BgnDate, EndDate), the majority being after 1998; the coordinates of a point representing the location of the glacier (CenLon, CenLat); the region (01Region) or sub-region (02Region) to which the glacier belongs; its area (Area) in km2; its minimum (Zmin), maximum (Zmax) and median (Zmed) elevation in meters; the Mean slope of the glacier surface (slope); the mean orientation (aspect) of the glacier; the length (Lmax) of the longest surface flowline of the glacier; the name of the glacier as well as several other specific fields. features RGI_Global EPSG:4326 CRS:84 -179.93483169799998 179.7511910390001 -78.30914570999994 83.60812590700004 UN_Sahel:RUSLE_KFactor_v1.1_25km The 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. 2013 RUSLE_KFactor_v1.1_25km WCS GeoTIFF EPSG:4326 CRS:84 -180.0 179.99999999999943 -56.6229166666667 83.58541666666642 text/plain UN_Sahel:RUSLE_SoilLoss_v1.1_yr2012_25km Assessment 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/4756 RUSLE_SoilLoss_v1.1_yr2012_25km WCS GeoTIFF EPSG:4326 CRS:84 -180.0 179.99999999999943 -56.6229166666667 83.58541666666642 text/plain TEEB:SAGCOT_2030INT_wgs84_opt SAGCOT_2030INT_wgs84_opt WCS GeoTIFF EPSG:4326 CRS:84 34.553167266769464 37.239835341004294 -10.038048450490551 -7.663994368030097 dakar_waste:SAVI 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 Management Remote Sensing GIS Citizen Science Multi-Criteria Analysis Illegal Dumping EPSG:4326 CRS:84 -17.382029187007333 -17.2719124771499 14.733719739640827 14.84106039798925 MapX_UNDP:SCI_merged 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_merged WCS GeoTIFF EPSG:4326 CRS:84 -81.70887178141503 153.42811827730736 -20.58264894738855 25.91783325130279 text/plain wesr_cca:SOM_ICPAC_prec_trend_perc_Apr_Jun_2008_2018_opt 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.en SOM_ICPAC_prec_trend_perc_Apr_Jun_2008_2018_opt WCS GeoTIFF EPSG:4326 CRS:84 40.93000030517578 51.43000124394894 -1.7000011578202248 11.999999046325684 wesr_cca:SOM_ICPAC_prec_trend_perc_Jan_Dec_2008_2018_opt 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.en SOM_ICPAC_prec_trend_perc_Jan_Dec_2008_2018_opt WCS GeoTIFF EPSG:4326 CRS:84 40.93000030517578 51.43000124394894 -1.7000011578202248 11.999999046325684 wesr_cca:SOM_ICPAC_prec_trend_perc_Oct_Dec_2008_2018_opt 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.en SOM_ICPAC_prec_trend_perc_Oct_Dec_2008_2018_opt WCS GeoTIFF EPSG:4326 CRS:84 40.93000030517578 51.43000124394894 -1.7000011578202248 11.999999046325684 MAPX_nd:SSD_Wetlands_opt 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_opt WCS GeoTIFF EPSG:4326 CRS:84 24.150695869 35.950696484488 3.4874709339480017 12.237471390348 text/plain MAPX:SSD_dem90_opt This 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_opt WCS GeoTIFF EPSG:4326 CRS:84 24.150833333259754 35.94749999992215 3.48750000002876 12.237500000025591 text/plain MAPX_nd:SSD_treecover_opt 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_opt WCS GeoTIFF EPSG:4326 CRS:84 24.147119842068182 36.000094869180025 3.4562049048399057 12.24945847146474 text/plain MapX_UNDP:Short_vegetation_change_1982-2016 This 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_1982-2016 WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 text/plain MAPX:South_Sudan_SRTM30meters_opt This 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_opt WCS GeoTIFF EPSG:4326 CRS:84 24.139861111111113 35.941250000000004 3.492916666666666 12.219027777777777 text/plain unccd:TLU30s_AD_2010_v2_1_opt The 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_opt WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 AccessMod:Travel_time_final_Benin_opt Travel_time_final_Benin_opt WCS GeoTIFF EPSG:4326 CRS:84 0.7572938644426767 3.847729210168526 6.232483045915507 12.414186738808102 AccessMod:Travel_time_final_Burundi_opt Travel_time_final_Burundi_opt WCS GeoTIFF EPSG:4326 CRS:84 28.995910775304033 30.8592085848395 -4.477617488031152 -2.305992223429439 AccessMod:Travel_time_final_CIV_opt Travel_time_final_CIV_opt WCS GeoTIFF EPSG:4326 CRS:84 -8.58395416756133 -2.4925843424414804 4.361274354409544 10.745411290664995 AccessMod:Travel_time_final_Guinee2_opt Travel_time_final_Guinee2_opt WCS GeoTIFF EPSG:4326 CRS:84 -15.12552847903117 -7.622479365710555 7.153551977476363 12.71750787814737 AccessMod:Travel_time_final_Lekoumou_opt Travel_time_final_Lekoumou_opt WCS GeoTIFF EPSG:4326 CRS:84 12.796059046335326 14.35138242981778 -4.052797249963222 -2.088002670078685 AccessMod:Travel_time_final_Madagascar_1_opt Travel_time_final_Madagascar_1_opt WCS GeoTIFF EPSG:32738 CRS:84 44.24783271042902 49.9629004390912 -23.67057144852623 -11.945018130107131 AccessMod:Travel_time_final_Sangha_opt Travel_time_final_Sangha_opt WCS GeoTIFF EPSG:4326 CRS:84 13.12388346995083 16.999434297770676 0.0357446152097114 2.722915245020809 AccessMod:Travel_time_final_Senegal2_opt Travel_time_final_Senegal2_opt WCS GeoTIFF EPSG:4326 CRS:84 -17.55393284087355 -11.27780942134996 12.29686893595293 16.693373854449966 AccessMod:Travel_time_final_Sudan_opt Travel_time_final_Sudan_opt WCS GeoTIFF EPSG:4326 CRS:84 21.168981361587942 38.777586812876365 8.582606902113872 23.1625652956636 AccessMod:Travel_time_final_Tchad_opt Travel_time_final_Tchad_opt WCS GeoTIFF EPSG:4326 CRS:84 13.057871509865597 24.14820348041431 7.4132735664299965 23.53082146804556 AccessMod:Travel_time_final_Togo_opt Travel_time_final_Togo_opt WCS GeoTIFF EPSG:4326 CRS:84 -0.1462858377361148 1.832407081892993 5.639838970959496 11.150878574350621 MapX_UNDP:Tree_canopy_change_1982-2016 This 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_1982-2016 WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 text/plain grid_data_core_pro:UN0_administrative_boundaries_2018 United Nations map (known as UNmap) is a worldwide geospatial database consisting of country and geographic name information on a global scale. The data is designed for the production of cartographic documents and maps, including their dissemination via public electronic networks, for the Secretariat of the United Nations.The United Nations maintains the Data as a courtesy to those who may choose to access the Data. The Data is provided “as is”, without warranty of any kind, either express or implied, including, without limitation, warranties of merchantability, fitness for a particular purpose and non-infringement.<br> <br> Disclaimers: <br> - The boundaries and names shown and the designations used on this map do not imply official endorsement or acceptance by the United Nations. <br> - The designations employed and the presentation of material on this map do not imply the expression of any opinion whatsoever on the part of the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries. <br> - Dotted line represents approximately the Line of Control in Jammu and Kashmir agreed upon by India and Pakistan. <br> - The final status of Jammu and Kashmir has not yet been agreed upon by the parties. <br> - Final boundary between the Republic of Sudan and the Republic of South Sudan has not yet been determined. <br> - Final status of the Abyei area is not yet determined. <br> - A dispute exists between the Governments of Argentina and the United Kingdom of Great Britain and Northern Ireland concerning sovereignty over the Falkland Islands (Malvinas).<br> <br> Generalization parametrisation for the data is developed based on the work of Douglas and Peucker (1973), Wang (1996) and the Polynomial Approximation with Exponential Kernel algorithm.The adequate generalized data should be used for the intended dissemination scale and not rely on software or platform-automated generalization as some specific geographic features are removed at scales. For instance, the region of Abyei is not included at the scale of 1:25 million but is included at lower scales.<br> <br> Maps produced using this layer should be featured with the appropriate disclaimer depending on the shown area.<br><br> Source: United Nations International and Administrative Boundaries Resources features BNDL EPSG:4326 CRS:84 -180.00000543699997 180.0000000000001 -89.90000152599998 83.62741851600003 text/plain grid_data_core_pro:UN0_administrative_boundaries_2020 United Nations map geospatial database (and hereafter referred to as the “Data”) is a worldwide geospatial database consisting of country and geographic name information on a global scale. The data is designed for the production of cartographic documents and maps, including their dissemination via public electronic networks, for the Secretariat of the United Nations. The United Nations maintains the Data as a courtesy to those who may choose to access the Data (“Users”). The Data is provided “as is”, without warranty of any kind, either express or implied, including, without limitation, warranties of merchantability, fitness for a particular purpose and non-infringement. The designations employed and the presentation of material contained in the Data do not imply the expression of any opinion whatsoever on the part of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.<br> <br> Disclaimers: <br> - The boundaries and names shown and the designations used on this map do not imply official endorsement or acceptance by the United Nations. <br> - The designations employed and the presentation of material on this map do not imply the expression of any opinion whatsoever on the part of the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.<br> - Dotted line represents approximately the Line of Control in Jammu and Kashmir agreed upon by India and Pakistan. The final status of Jammu and Kashmir has not yet been agreed upon by the parties. <br> - Final boundary between the Republic of Sudan and the Republic of South Sudan has not yet been determined. <br> - Final status of the Abyei area is not yet determined. <br> - A dispute exists between the Governments of Argentina and the United Kingdom of Great Britain and Northern Ireland concerning sovereignty over the Falkland Islands (Malvinas).<br> <br> Generalization parametrization for the Data is developed based on the work of Douglas and Peucker (1973), Wang (1996) and the Polynomial Approximation with Exponential Kernel algorithm. The adequate generalized Data should be used for the intended dissemination scale and not rely on software or platform-automated generalization as some specific geographic features are removed at scales. For example, the region of Abyei is not included at the scale of 1:25 million but is included at larger scales.<br> <br> Maps produced using this layer should be featured with the appropriate disclaimer depending on the shown area.<br><br> Source: United Nations International and Administrative Boundaries Resources features BNDL EPSG:4326 CRS:84 -180.0000054369999 180.000028958 -89.90000152599998 83.62741851600002 grid_data_core_pro:UN0_administrative_boundaries_polygons_2018 United Nations map (known as UNmap) is a worldwide geospatial database consisting of country and geographic name information on a global scale. The data is designed for the production of cartographic documents and maps, including their dissemination via public electronic networks, for the Secretariat of the United Nations.The United Nations maintains the Data as a courtesy to those who may choose to access the Data. The Data is provided “as is”, without warranty of any kind, either express or implied, including, without limitation, warranties of merchantability, fitness for a particular purpose and non-infringement. Disclaimers: - The boundaries and names shown and the designations used on this map do not imply official endorsement or acceptance by the United Nations. - The designations employed and the presentation of material on this map do not imply the expression of any opinion whatsoever on the part of the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries. - Dotted line represents approximately the Line of Control in Jammu and Kashmir agreed upon by India and Pakistan. - The final status of Jammu and Kashmir has not yet been agreed upon by the parties. - Final boundary between the Republic of Sudan and the Republic of South Sudan has not yet been determined. - Final status of the Abyei area is not yet determined. - A dispute exists between the Governments of Argentina and the United Kingdom of Great Britain and Northern Ireland concerning sovereignty over the Falkland Islands (Malvinas). Generalization parametrisation for the data is developed based on the work of Douglas and Peucker (1973), Wang (1996) and the Polynomial Approximation with Exponential Kernel algorithm.The adequate generalized data should be used for the intended dissemination scale and not rely on software or platform-automated generalization as some specific geographic features are removed at scales. For instance, the region of Abyei is not included at the scale of 1:25 million but is included at lower scales. Maps produced using this layer should be featured with the appropriate disclaimer depending on the shown area. Source: United Nations International and Administrative Boundaries Resources features BNDA_CTY EPSG:4326 CRS:84 -180.00000543699997 180.00000000000009 -89.90000152599998 83.62741851600008 text/plain UN_Sahel:USAID_CMM_exp_0-001_v The map displays total exposure to climate at province level scale using the first administrative division polygon files from the Global Administrative Areas database version 2.8. The map retains the five exposure categories of low to high exposure (1 to 5) based on the k-median clustering algorithm. The median climate exposure score for each administrative division is determined with spatial analysis in ArcGIS. These median scores are then clustered into five categories of low to high exposure using the k-median algorithm contained in Stata. The climate exposure portion of this study aims to identify places most likely vulnerable to a combination of climate hazards. Geographic location makes some countries more susceptible to climate hazards. Within countries, some areas, such as the coasts, have more exposure to certain kinds of climate hazards. This study assesses climate hazard exposure using historical data on the frequency and magnitude of six hazards, including cyclones, flood events, wildfire events, rainfall anomalies, and chronic aridity. The sixth hazard is a measure of low-elevation coastal zones, which may be susceptible to storm surges and future sea-level rise. The goal is to identify places that in the recent historical record have faced high exposure to climaterelated hazards. This seeks to get a snapshot of places of chronic concern over a long enough time period to say that these are places that have been historically affected by climate-related hazards. The indicators included here use the most recent data available. Some of these indicators, like cyclones, have longerterm data available, while others like floods have data for fewer years. In all cases, this project uses the most recent and broadest set of years for which global data are available to get the best snapshot of climate exposure in the recent past, as close as possible to the present. This <a href="https://pdf.usaid.gov/pdf_docs/PA00TBFH.pdf"target=_blank>document</a> outlines the process of mapping individual climate hazards, the rationale for including particular indicators, and the process of calculating total climate exposure. features USAID_CMM_bivariate_exp_frag_0-001_v EPSG:4326 CRS:84 -179.99999999999994 180.00000000000014 -90.0 83.65833282470703 text/plain UN_Sahel:USAID_CMM_frag_0-001_v The map displays total fragility data at province level scale using the first administrative division polygon files from the Global Administrative Areas database version 2.8. The map retains the five fragility categories seen in the total fragility measure, classifying areas as having low, some, moderate, high, or highest fragility. To help achieve this study’s goal of identifying the intersection of fragility and climate risks globally, this study develops a new fragility measure. Developing a new measure specifically for this purpose provides a comprehensive fragility measure while avoiding use of existing fragility measures that include environmental indicators and thus should not be overlaid on climate hazards. This study uses open-source data to create a measure of state fragility that is similar in composition and outcome to USAID’s internal methods and framework for analyzing fragility.11 Like USAID’s internal measure, the new measure assesses fragility in state effectiveness and legitimacy in four key spheres: political, security, economic, and social. This is based on an understanding of fragility as being rooted in poor state capacity and poor state-society relationships, both of which can contribute to instability. Poor state capacity and state-society relationships can lead to and perpetuate other forms of overt instability, including conflict or an inability to address and mitigate stresses such as a changing environment, difficult global financial situations, or conflict in neighboring states. This <a href="https://pdf.usaid.gov/pdf_docs/PA00TBFH.pdf"target=_blank>document</a> outlines the indicators, data sources, and process used to assess countries’ relative state capacity and state-society relationship on a global scale. features USAID_CMM_bivariate_exp_frag_0-001_v EPSG:4326 CRS:84 -179.99999999999994 180.00000000000014 -90.0 83.65833282470703 text/plain sand:WDPA_2024_MPA This layer presents globally distributed marine protected areas (MPAs) from the World Database on Protected Areas (WDPA). It includes designated zones across all ocean basins, highlighting conservation efforts aimed at safeguarding biodiversity, regulating resource use, and supporting climate resilience. The dataset is essential for assessing overlaps between protected zones and industrial activities such as sand dredging, fishing, and offshore development. features WDPA_2024_MPA EPSG:4326 CRS:84 -179.9999999999 180.0000000001 -85.41188720023403 86.45371110997979 NEAT_plus:WE_reclass_opt Data 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 Moist WE_reclass_opt WCS GeoTIFF EPSG:4326 CRS:84 -179.999999 179.999334901 -55.99953000000001 83.624039629 text/plain unep_storymap:WordLandCover WordLandCover WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 grid_data_core_pro:_2012_ungiwg_bnd_ln_01 International boundary resources are prepared by United Nations Cartographic Section. The 1:1million dataset derived initially from VMAP0 has been corrected to better reflect the cartographic practise of UN Cartographic Section. features _2012_ungiwg_bnd_ln_01 ungiwg boundary EPSG:4326 CRS:84 -133.207870483398 142.383773803711 -52.3656349182129 70.7053146362305 grid_data_core_pro:_2012_ungiwg_cnt_ply_01 International boundary resources are prepared by United Nations Cartographic Section. The 1:1million dataset derived initially from VMAP0 has been corrected to better reflect the cartographic practise of UN Cartographic Section. _2012_ungiwg_cnt_ply_01 features boundary ungiwg EPSG:4326 CRS:84 -181.800003051758 181.800003051758 -90.7676391601562 84.4950561523438 grid_data_core_pro:_2012_ungiwg_cst_ln_01 International boundary resources are prepared by United Nations Cartographic Section. The 1:1million dataset derived initially from VMAP0 has been corrected to better reflect the cartographic practise of UN Cartographic Section. features _2012_ungiwg_cst_ln_01 boundary coast EPSG:4326 CRS:84 -181.800003051758 181.800003051758 -90.7676391601562 84.4950561523438 grid_data_core_pro:_2012_ungiwg_lks_ply_01 _2012_ungiwg_lks_ply_01 features lake water body ungiwg EPSG:4326 CRS:84 -125.12333 109.965 -17.9886099999999 67.04695 MapX_UNDP:accessibility_to_cities_2015 This 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_2015 WCS GeoTIFF EPSG:4326 CRS:84 -180.0 179.99985600000002 -60.0 84.999942 text/plain afg_transboundary:afg_biodiversity_intactness_index Map_of_Biodiversity_Intactness_Index_final_opt_deflate_v2 WCS GeoTIFF EPSG:4326 CRS:84 68.224999007 75.158332313 35.441666285 39.858332934 afg_transboundary:afg_flood_mortality_risk fl1010irmt_clipped_final_opt_deflate_v2 WCS GeoTIFF EPSG:4326 CRS:84 68.249999901 75.166666565 35.49999995 39.833333281 afg_transboundary:afg_le1010ipey le1010ipeykx_clipped_final_opt_deflate_v2 WCS GeoTIFF EPSG:4326 CRS:84 68.208333532 75.125000204 35.458333434 39.833333437 MAPX:afghanistan_wind afghanistan_wind_opt_deflate WCS GeoTIFF EPSG:4326 CRS:84 57.467697464 80.618624748 22.275675475 39.589800476 MapX_UNDP:africaFI 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/> africaFI WCS GeoTIFF EPSG:4326 CRS:84 4.291832979587211 35.40579740803462 -7.903071018846619 6.8343008700661745 MapX_UNDP:africaSCI 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/> africaSCI WCS GeoTIFF EPSG:4326 CRS:84 4.291832979587211 35.40579740803463 -7.903071018846623 6.8343008700661745 MapX_UNDP:africaSCI3 africaSCI WCS GeoTIFF EPSG:4326 CRS:84 4.291563438357412 35.4060669442033 -7.915216499533859 6.84482706260207 MapX_UNDP:africa_forheight 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_opt WCS GeoTIFF EPSG:4326 CRS:84 -26.0005 61.0005 -32.0005 32.0005 text/plain sand:aggregate_consumption_per_year_Mt aggregate_consumption_per_year_Mt WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -89.9999999999884 90.0000000000116 UN_Sahel:ai_et0_opt 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.community ai_et0_opt WCS GeoTIFF EPSG:4326 CRS:84 -180.0 179.99999999999983 -59.99999999999994 90.0 mapx_chemobs:albania_remote_site_screening_res50 albania_remote_site_screening_res50 WCS GeoTIFF EPSG:4326 CRS:84 19.262757000000047 21.057257000000046 39.6460501110001 42.6610501110001 text/plain mapx_chemobs:algeria_remote_site_screening_res50 algeria_remote_site_screening_res50 WCS GeoTIFF EPSG:4326 CRS:84 -8.66662930766872 11.997370692331282 19.060734741000026 37.09373474100003 text/plain unccd:all_2010_yld_opt 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_opt WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 MapX_UNDP:areas_global_significance_restoration_opt <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_opt WCS GeoTIFF EPSG:4326 CRS:84 -179.87896164729798 179.85992227851924 -72.35059978902385 85.07288135595564 wesr_climat:asb_year_0_ssp245 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_ssp245 WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 text/plain wesr_climat:asb_year_0_ssp585 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_ssp585 WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 text/plain MapX_UNDP:asiaFI 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/> asiaFI WCS GeoTIFF EPSG:4326 CRS:84 90.32202306186224 153.42801388207823 -11.768033998374744 25.91760211162399 MapX_UNDP:asiaSCI 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/> asiaSCI WCS GeoTIFF EPSG:4326 CRS:84 90.32202306186224 153.4280138820783 -11.768033998374701 25.91760211162399 MapX_UNDP:asiaSCI3 asiaSCI WCS GeoTIFF EPSG:4326 CRS:84 90.32175330967124 153.42828368100916 -11.847003516704188 26.038457275126742 MapX_UNDP:asia_forheight 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_opt WCS GeoTIFF EPSG:4326 CRS:84 60.9995 180.0005 -32.0005 32.0005 text/plain MapX_UNDP_nd:baccini_carbon_change_global_opt 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_opt WCS GeoTIFF EPSG:4326 CRS:84 -119.85892985444444 170.00430119527778 -23.402497460000003 23.400000000555554 text/plain unccd:barea2003 This layer was produced by UNEP/GRID-Geneva in December 2023. burned area fire EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 unccd:barea2004 This layer was produced by UNEP/GRID-Geneva in December 2023. burned area fire EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 unccd:barea2005 This layer was produced by UNEP/GRID-Geneva in December 2023. burned area fire EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 unccd:barea2006 This layer was produced by UNEP/GRID-Geneva in December 2023. burned area fire EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 unccd:barea2007 This layer was produced by UNEP/GRID-Geneva in December 2023. burned area fire EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 unccd:barea2008 This layer was produced by UNEP/GRID-Geneva in December 2023. burned area fire EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 unccd:barea2009 This layer was produced by UNEP/GRID-Geneva in December 2023. burned area fire EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 unccd:barea2010 This layer was produced by UNEP/GRID-Geneva in December 2023. burned area fire EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 unccd:barea2011 This layer was produced by UNEP/GRID-Geneva in December 2023. burned area fire EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 unccd:barea2012 This layer was produced by UNEP/GRID-Geneva in December 2023. burned area fire EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 unccd:barea2013 This layer was produced by UNEP/GRID-Geneva in December 2023. burned area fire EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 unccd:barea2014 This layer was produced by UNEP/GRID-Geneva in December 2023. burned area fire EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 unccd:barea2015 This layer was produced by UNEP/GRID-Geneva in December 2023. burned area fire EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 unccd:barea2016 This layer was produced by UNEP/GRID-Geneva in December 2023. burned area fire EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 unccd:barea2017 This layer was produced by UNEP/GRID-Geneva in December 2023. burned area fire EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 unccd:barea2018 This layer was produced by UNEP/GRID-Geneva in December 2023. burned area fire EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 unccd:barea2019 This layer was produced by UNEP/GRID-Geneva in December 2023. burned area fire EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 unccd:barea2020 This layer was produced by UNEP/GRID-Geneva in December 2023. burned area fire EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 unccd:barea2021 This layer was produced by UNEP/GRID-Geneva in December 2023. burned area fire EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 unccd:barea2022 This layer was produced by UNEP/GRID-Geneva in December 2023. burned area fire EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 medqsr:below_5m_asl LECZ, i.e. areas at less then 5 m above sea level with highest risk to be impacted by flooding. Below_5m_ASL WCS GeoTIFF Seafloor topography Bathymetry EPSG:4326 CRS:84 12.114305555555555 20.265138888888888 39.64486111111111 45.89486111111111 wesr_biodiversity:biodiv_intact_ind The 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/10.5519/0009936 biodiv_intact_ind WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -60.0 90.0 MapX_UNDP:biomass_carbon_density_opt <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_opt WCS GeoTIFF EPSG:4326 CRS:84 -179.99994506847912 179.99993096842002 -59.59552039034499 83.66748265638508 mapx_chemobs:bosnia_herzegovina_remote_site_screening_res50 bosnia_herzegovina_remote_site_screening_res50 WCS GeoTIFF EPSG:4326 CRS:84 15.728732108000047 19.621732108000046 42.55653964800005 45.276539648000046 text/plain mapx_chemobs:botswana_remote_site_screening_res50 botswana_remote_site_screening_res50 WCS GeoTIFF EPSG:4326 CRS:84 19.99936526400007 29.36086526400007 -26.90059308399997 -17.77859308399997 text/plain grid_data_core_pro:buref09 WCS GeoTIFF buref09 EPSG:4326 CRS:84 -180.0 180.00818933 -59.999999994239985 84.00827573576002 risk_southeast_senegal:burn The 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. burn EPSG:4326 CRS:84 -13.239460488881921 -11.342218608821492 12.307817707721565 13.461254532531031 risk_southeast_senegal:burn_1995_2004 Forest 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_2004 WCS GeoTIFF EPSG:4326 CRS:84 -13.238921499711449 -11.345452543844322 12.308356696892037 13.463410489212919 risk_southeast_senegal:burn_2005_2014 Forest 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_2014 WCS GeoTIFF EPSG:4326 CRS:84 -13.238921499711449 -11.345452543844322 12.308356696892037 13.463410489212919 risk_southeast_senegal:burn_2015_2024 Forest 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_2024 WCS GeoTIFF EPSG:4326 CRS:84 -13.238921499711449 -11.345452543844322 12.308356696892037 13.463410489212919 risk_southeast_senegal:burn_risk This 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). burn EPSG:4326 CRS:84 -13.239460488881921 -11.342398271878317 12.307817707721565 13.461074869474206 risk_southeast_senegal:burn_vulnerability Vulnerability 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. burn EPSG:4326 CRS:84 -13.239460488881921 -11.342218608821492 12.307817707721565 13.461254532531031 MAPX:c181125_mantapala c181125_Mantapala_Geo_TIF_wgs84_v2_opt_deflate WCS GeoTIFF EPSG:4326 CRS:84 28.847679478 28.918080924 -9.533699678 -9.487133772 MapX_UNDP_nd:c2000 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> c2000 WCS GeoTIFF EPSG:4326 CRS:84 -95.26972112443687 167.81411628372408 -26.368039400717038 25.126944105146272 text/plain MapX_UNDP_nd:c2013 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> c2013 WCS GeoTIFF EPSG:4326 CRS:84 -95.26972112443687 167.81411628372408 -26.368039400717038 25.126944105146272 text/plain wesr_mountains:c_gls_ALDH_2019-20_hiver_raster_opt 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_opt WCS GeoTIFF albedo EPSG:4326 CRS:84 -180.0044642857143 179.9955357142039 -59.995535714253904 80.00446428571429 wesr_mountains:c_gls_ALDH_2019_automne_raster_opt 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_opt WCS GeoTIFF albedo EPSG:4326 CRS:84 -180.0044642857143 179.9955357142039 -59.995535714253904 80.00446428571429 wesr_mountains:c_gls_ALDH_2019_ete_raster_opt 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_opt WCS GeoTIFF albedo EPSG:4326 CRS:84 -180.0044642857143 179.9955357142039 -59.995535714253904 80.00446428571429 wesr_mountains:c_gls_ALDH_2020_printemps_raster_opt 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_opt WCS GeoTIFF albedo EPSG:4326 CRS:84 -180.0044642857143 179.9955357142039 -59.995535714253904 80.00446428571429 dakar_waste:carte_non_ponderee 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 Management Remote Sensing GIS Citizen Science Multi-Criteria Analysis Illegal Dumping EPSG:32628 CRS:84 -17.36716999545483 -17.286595649628648 14.763385550148119 14.822825777160343 dakar_waste:carte_ponderee 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 Management Remote Sensing GIS Citizen Science Multi-Criteria Analysis Illegal Dumping EPSG:32628 CRS:84 -17.36716999545483 -17.286595649628648 14.763385550148119 14.822825777160343 grid_data_core:city15k_gis features city15k_gis EPSG:4326 CRS:84 -177.952224731445 181.142211914062 -47.0626029968262 78.8466567993164 grid_data_core:city1k_gis features city1k_gis EPSG:4326 CRS:84 -180.914413452148 181.156951904297 -38.1779518127441 71.8351974487305 text/plain grid_data_core:city5k_gis features city5k_gis EPSG:4326 CRS:84 -179.953155517578 181.152160644531 -44.4762802124023 72.2679595947266 grid_data_core:city_esri features city_esri EPSG:4326 CRS:84 -177.928436279297 180.998764038086 -55.4569625854492 78.8649673461914 risk_niger_undp:codem codem_trans_opt_deflate WCS GeoTIFF EPSG:4326 CRS:84 0.1675000015994978 16.000833334932196 11.697500041616369 23.52416670828256 risk_niger_undp:codemi codemi WCS GeoTIFF EPSG:4326 CRS:84 0.1675000015994978 16.000833334932196 11.697500041616369 23.52416670828256 risk_niger_undp:codemih codemih WCS GeoTIFF EPSG:4326 CRS:84 0.1675000015994978 16.000833334932196 11.697500041616369 23.52416670828256 MapX_UNDP:col_forint100 Forest Integrity (100m res.) in Colombia (Montana State University). col_forint100 WCS GeoTIFF EPSG:4326 CRS:84 -79.758379943848 -66.21837994384799 -4.470114545847 11.205885454153 text/plain wesr_biodiversity:col_forint100 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/" target="_blank">CC-4.0 Attribution</a>.<br/> col_forint100 WCS GeoTIFF EPSG:4326 CRS:84 -79.758379943848 -66.21837994384799 -4.470114545847 11.205885454153 text/plain MapX_UNDP:conservation_biodiversity_carbon_opt <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_opt WCS GeoTIFF EPSG:4326 CRS:84 -179.995201157628 179.8859620772232 -84.69466806618583 85.91771612300938 MapX_UNDP:conservation_biodiversity_carbon_water_opt <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_opt WCS GeoTIFF EPSG:4326 CRS:84 -179.995201157628 179.8859620772232 -84.69466806618583 85.91771612300938 MapX_UNDP:conservation_biodiversity_water_opt <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_opt WCS GeoTIFF EPSG:4326 CRS:84 -179.995201157628 179.8859620772232 -84.69466806618583 85.91771612300938 ECO-DRR:coralkm2a coralkm2a WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 ECO-DRR:coralkm2a_P3 coralkm2a WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 wesr_climat:cru_pre_01_10_anom9120 Average 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_anom9120 WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 wesr_climat:cru_pre_01_10_anom_1901_1920 Average 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_1920 WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 wesr_climat:cru_pre_01_10_anom_91_00 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_00 WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 wesr_climat:cru_pre_01_10_mean 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_mean WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 wesr_climat:cru_pre_11_20_anom9120 Average 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_anom9120 WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 wesr_climat:cru_pre_11_20_anom_01_10 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_10 WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 wesr_climat:cru_pre_11_20_anom_1901_1920 Average 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_1920 WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 wesr_climat:cru_pre_11_20_mean 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_mean WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 wesr_climat:cru_pre_21_22_anom9120 Average 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_anom9120 WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 wesr_climat:cru_pre_21_22_anom_11_20 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_20 WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 wesr_climat:cru_pre_21_22_anom_1901_1920 Average 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_1920 WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 wesr_climat:cru_pre_21_22_mean 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_mean WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 wesr_climat:cru_pre_91_00_anom9120 Average 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_anom9120 WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 wesr_climat:cru_pre_91_00_anom_1901_1920 Average 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_1920 WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 wesr_climat:cru_pre_91_00_anom_81_90 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_90 WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 wesr_climat:cru_pre_91_00_mean 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_mean WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 wesr_climat:cru_pre_91_20_mean 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_mean WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 wesr_climat:cru_pre_91_22_lm 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_lm WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 wesr_climat:cru_pre_91_22_mean 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_mean WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 wesr_climat:cru_scpdsi_01_10_mean 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_mean WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 wesr_climat:cru_scpdsi_11_20_mean 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_mean WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 wesr_climat:cru_scpdsi_11_mean 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_mean WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 wesr_climat:cru_scpdsi_12_mean 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_mean WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 wesr_climat:cru_scpdsi_13_mean 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_mean WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 wesr_climat:cru_scpdsi_14_mean 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_mean WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 wesr_climat:cru_scpdsi_15_mean 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_mean WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 wesr_climat:cru_scpdsi_16_mean 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_mean WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 wesr_climat:cru_scpdsi_17_mean 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_mean WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 wesr_climat:cru_scpdsi_18_mean 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_mean WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 wesr_climat:cru_scpdsi_19_mean 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_mean WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 wesr_climat:cru_scpdsi_20_mean 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_mean WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 wesr_climat:cru_scpdsi_21_22_mean 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_mean WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 wesr_climat:cru_scpdsi_21_mean 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_mean WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 wesr_climat:cru_scpdsi_22_mean 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_mean WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 wesr_climat:cru_scpdsi_61_70_mean 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_mean WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 wesr_climat:cru_scpdsi_71_80_mean 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_mean WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 wesr_climat:cru_scpdsi_81_90_mean 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_mean WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 wesr_climat:cru_scpdsi_91_00_mean 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_mean WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 wesr_climat:cru_tmp_01_10_anom9120 Average 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_anom9120 WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 wesr_climat:cru_tmp_01_10_anom_1901_1920 Average 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_1920 WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 wesr_climat:cru_tmp_01_10_anom_91_00 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_00 WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 wesr_climat:cru_tmp_01_10_lm 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_lm WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 wesr_climat:cru_tmp_01_10_mean 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_mean WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 wesr_climat:cru_tmp_11_20_anom9120 Average 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_anom9120 WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 wesr_climat:cru_tmp_11_20_anom_01_10 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_10 WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 wesr_climat:cru_tmp_11_20_anom_1901_1920 Average 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_1920 WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 wesr_climat:cru_tmp_11_20_lm 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_lm WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 wesr_climat:cru_tmp_11_20_mean 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_mean WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 wesr_climat:cru_tmp_21_22_anom9120 Average 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_anom9120 WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 wesr_climat:cru_tmp_21_22_anom_11_20 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_20 WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 wesr_climat:cru_tmp_21_22_anom_1901_1920 Average 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_1920 WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 wesr_climat:cru_tmp_21_22_mean 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_mean WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 wesr_climat:cru_tmp_91_00_anom9120 Average 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_anom9120 WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 wesr_climat:cru_tmp_91_00_anom_1901_1920 Average 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_1920 WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 wesr_climat:cru_tmp_91_00_anom_81_90 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_90 WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 wesr_climat:cru_tmp_91_00_lm 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_lm WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 wesr_climat:cru_tmp_91_00_mean 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_mean WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 wesr_climat:cru_tmp_91_20_3coldest_mean 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_mean WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 wesr_climat:cru_tmp_91_20_3hottest_mean 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_mean WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 wesr_climat:cru_tmp_91_20_mean 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_mean WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 wesr_climat:cru_tmp_91_22_lm 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_lm WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 wesr_climat:cru_tmp_91_22_mean 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_mean WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 ECO-DRR:cs_freqa_P3 cs_freqa WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 ECO-DRR:cs_freqaf cs_freqaf WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 ECO-DRR:cs_phex cs_phex WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 ECO-DRR:cs_phex_P3 cs_phex WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 LWP:current_wc2_0_2_5m_tavg_annual_opt Average 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_opt WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 text/plain ECO-DRR:cy_freqa_P3 cy_freqa WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 ECO-DRR:cy_freqaf cy_freqaf WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 wesr_risk:cy_frequency This 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_frequency WCS GeoTIFF EPSG:4326 CRS:84 -180.0 179.99599307699998 -90.00000000000003 83.99199665399996 wesr_risk:cy_hazard_1000_yrp 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_yrp WCS GeoTIFF EPSG:4326 CRS:84 -180.13566066745048 179.9077507461337 -55.13566066745048 59.904585330559826 wesr_risk:cy_hazard_100_yrp 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_yrp WCS GeoTIFF EPSG:4326 CRS:84 -180.13566066745048 179.9077507461337 -55.13566066745048 59.904585330559826 wesr_risk:cy_hazard_250_yrp 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_yrp WCS GeoTIFF EPSG:4326 CRS:84 -180.13566066745048 179.9077507461337 -55.13566066745048 59.904585330559826 wesr_risk:cy_hazard_500_yrp 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_yrp WCS GeoTIFF EPSG:4326 CRS:84 -180.13566066745048 179.9077507461337 -55.13566066745048 59.904585330559826 wesr_risk:cy_hazard_50_yrp 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_yrp WCS GeoTIFF EPSG:4326 CRS:84 -180.13566066745048 179.9077507461337 -55.13566066745048 59.904585330559826 wesr_risk:cy_intensity cy_intensity WCS GeoTIFF EPSG:4326 CRS:84 -180.0 179.99599307699998 -90.00000000000003 83.99199665399996 ECO-DRR:cy_phex cy_phex WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 ECO-DRR:cy_phex_P3 cy_phex WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 wesr_risk:cy_physexp This 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_physexp WCS GeoTIFF EPSG:4326 CRS:84 -180.0 179.99999999985602 -90.0 83.99999999993042 wesr_risk:cy_risk This 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_risk WCS GeoTIFF EPSG:4326 CRS:84 -180.0 179.99999999985602 -90.0 83.99999999993042 wesr_risk:cy_sumwinds cy_sumwinds WCS GeoTIFF EPSG:4326 CRS:84 -180.0 179.99599307699998 -90.00000000000003 83.99199665399996 MapX_UNDP_nd:degraded_areas_PER National 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.pdf degrad_nac_wgs WCS GeoTIFF EPSG:4326 CRS:84 -82.8117704208033 -67.7006922413798 -19.05097182878838 0.5435622953186765 MapX_UNDP:dem90 This 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/> dem90 WCS GeoTIFF EPSG:4326 CRS:84 -179.99999999999966 179.99999999986994 -59.999999999948244 82.99999999999997 text/plain risk_niger_undp:dfo dfo_opt_deflate WCS GeoTIFF EPSG:4326 CRS:84 0.1675000015994978 16.000833334932196 11.697500041616369 23.52416670828256 wesr_climat:diff_ssp245_ssp585 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). diff_ssp245_ssp585 WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 text/plain LWP:disp_sresa2_2050_10min_int1000_opt This 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_opt WCS GeoTIFF EPSG:4326 CRS:84 -180.0003335 180.0003865 -59.16733350000001 83.66628549999999 text/plain MAPX_nd:drc_hansen_cover_clipped_mines_2013 The 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_ WCS GeoTIFF EPSG:4326 CRS:84 12.383333300292 30.841583300292 -13.455676000047001 5.166573999953 MAPX_nd:drc_hansen_cover_clipped_mines_2014 The 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_new WCS GeoTIFF EPSG:4326 CRS:84 12.383333300292 30.808333300292 -13.455676000047001 5.033323999953 MAPX_nd:drc_hansen_cover_clipped_mines_2015 The 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_ WCS GeoTIFF EPSG:4326 CRS:84 12.383333300292 30.808333300292 -13.455676000047001 5.033323999953 MAPX_nd:drc_hansen_cover_clipped_mines_2016 The 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_ WCS GeoTIFF EPSG:4326 CRS:84 10.0 30.80825 -20.0 5.033249999999999 risk_southeast_senegal:drought In 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 hazard EPSG:4326 CRS:84 -13.239460488881921 -11.342218608821492 12.307817707721565 13.461254532531031 risk_southeast_senegal:drought_risk Drought 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 risk EPSG:4326 CRS:84 -13.239445601904379 -11.342383384900774 12.307811091287101 13.461068253039743 risk_southeast_senegal:drought_vulnerability Cultivated 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 vulnerability EPSG:32628 CRS:84 -13.243602482435989 -11.336189526734906 12.296668462171974 13.472239258295431 dakar_waste:dumping_suscep_noLST Solid Waste Management Remote Sensing GIS Citizen Science Multi-Criteria Analysis Illegal Dumpin EPSG:32628 CRS:84 -17.36711980308572 -17.286639327624464 14.763413721162385 14.822762633451076 grid_data_core:dust_extreme_events_1980_1986_output_opt dust_extreme_events_1980_1986_output_opt WCS GeoTIFF EPSG:4326 CRS:84 -17.499999999999943 75.00000000000006 -1.4999999999999432 45.00000000000006 grid_data_core:dust_extreme_events_1980_1986_output_opt_1 dust_extreme_events_1980_1986_output_opt WCS GeoTIFF EPSG:4326 CRS:84 -17.499999999999943 75.00000000000006 -1.4999999999999432 45.00000000000006 unccd:dust_extreme_events_2017_2022_output dust_extreme_events_2017_2022_output WCS GeoTIFF EPSG:4326 CRS:84 -17.499999999999943 75.00000000000006 -1.4999999999999432 45.00000000000006 unccd:dust_slope_map_1980_2022_output_opt dust_slope_map_1980_2022_output_opt WCS GeoTIFF EPSG:4326 CRS:84 -17.499999999999943 75.00000000000006 -1.4999999999999432 45.00000000000006 grid_data_core:dust_strong_events_1980_1986_output dust_strong_events_1980_1986_output WCS GeoTIFF EPSG:4326 CRS:84 -17.499999999999943 75.00000000000006 -1.4999999999999432 45.00000000000006 grid_data_core:dust_strong_events_2017_2022_output dust_strong_events_2017_2022_output WCS GeoTIFF EPSG:4326 CRS:84 -17.499999999999943 75.00000000000006 -1.4999999999999432 45.00000000000006 MapX_UNDP:ecu_forint100 Forest 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_forint100 WCS GeoTIFF EPSG:4326 CRS:84 -81.007003784197 -75.189781557321 -5.015087128068 1.4260239881959995 text/plain wesr_biodiversity:ecu_forint100 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/" target="_blank">CC-4.0 Attribution</a>.<br/> ecu_forint100 WCS GeoTIFF EPSG:4326 CRS:84 -81.007003784197 -75.189781557321 -5.015087128068 1.4260239881959995 text/plain MAPX_nd:ecuador_ecosistemas_clean_ecosistema ecd_ecosistemas_ecuador_84_a features EPSG:4326 CRS:84 -81.09264033289094 -75.1950953254417 -5.015146735772655 1.4627334137699841 MAPX_nd:ecuador_ecosistemas_clean_fragmentation ecd_ecosistemas_ecuador_84_a features EPSG:4326 CRS:84 -81.09264033289094 -75.1950953254417 -5.015146735772655 1.4627334137699841 unea_pollution:edg10_co edg10_co WCS GeoTIFF Carbon Monoxide EPSG:4326 CRS:84 -180.05 179.95 -77.95 88.15000000000002 unea_pollution:edg10_nx 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 / year edg10_nx WCS GeoTIFF nitrogen oxides nitric oxide (NO) nitrogen dioxide (NO2) EPSG:4326 CRS:84 -180.05 179.95 -77.95 88.15000000000002 unea_pollution:edg70_co 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 / year edg70_co WCS GeoTIFF Carbon Monoxide Air polluants EPSG:4326 CRS:84 -180.05 179.95 -77.95 88.15000000000002 unea_pollution:edg70_nx 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 / year edg70_nx WCS GeoTIFF nitrogen oxides nitric oxide (NO) nitrogen dioxide (NO2) EPSG:4326 CRS:84 -180.05 179.95 -77.95 88.15000000000002 grid_data_core:edg90_co Global 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 / year edg90_co WCS GeoTIFF Carbon Monoxide EPSG:4326 CRS:84 -180.05 179.95 -77.95 88.15000000000002 unea_pollution:edg90_nx 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 / year edg90_nx WCS GeoTIFF nitrogen oxides nitrogen dioxide (NO2) nitric oxide (NO) EPSG:4326 CRS:84 -180.05 179.95 -77.95 88.15000000000002 mapx_chemobs:egypt_remote_site_screening_res50 egypt_remote_site_screening_res50 WCS GeoTIFF EPSG:4326 CRS:84 24.86526973696999 36.24276973696999 21.99984000000001 31.66734000000001 text/plain Medicip:emodnet_open_maplibrary_mediseh_posidonia emodnet_open_maplibrary_mediseh_posidonia_opt WCS GeoTIFF EPSG:3857 CRS:84 -6.557323000000001 42.965205499824044 29.437318067526018 47.30077599999999 grid_data_core:esri_boundaries2015 features esri_boundaries2015 EPSG:4326 CRS:84 -181.800003051758 181.800003051758 -90.8683242797852 84.533821105957 grid_data_core:esri_urban_areas esri_urban_areas features EPSG:4326 CRS:84 -176.982559204102 180.237594604492 -46.4931335449219 69.942024230957 mapx_chemobs:eswatini_remote_site_screening_res50 eswatini_remote_site_screening_res50 WCS GeoTIFF EPSG:4326 CRS:84 30.794110000000003 32.13711000000001 -27.317150000000023 -25.719650000000023 text/plain UN_Sahel:et0_yr_opt Potential 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_opt WCS GeoTIFF EPSG:4326 CRS:84 -180.0 179.99999998616659 -59.99999997599997 90.00000001798031 mapx_chemobs:eth_natural_hazards_exposure eth_natural_hazards_exposure_4326 features EPSG:4326 CRS:84 33.0000777214893 47.701938752897306 3.4041562230000286 14.894164106000062 mapx_chemobs:eth_wb_buffer_100m features eth_wb_buffer_100m_4326 EPSG:4326 CRS:84 33.00029156227385 47.276572438492494 3.4089243300132126 14.866325792845133 mapx_chemobs:ethiopia_remote_site_screening_res50 ethiopia_remote_site_screening_res50 WCS GeoTIFF EPSG:4326 CRS:84 32.99994 47.94344 3.4043378313011186 14.869337831301118 text/plain Medicip:exposure_pesato_02_03 exposure_pesato_02_03_opt WCS GeoTIFF EPSG:4326 CRS:84 -5.3554163761242854 35.977083623875714 30.264583696518883 45.50708369651888 wesr_risk:fires_density_lastyear 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. Fi2025dy WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 wesr_risk:fires_density_total 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_2025dt WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 wesr_risk:fl1010ipey fl1010ipey WCS GeoTIFF EPSG:4326 CRS:84 -180.0 179.99999999985602 -90.0 83.99999999993042 ECO-DRR:fl_freq fl_freq WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 ECO-DRR:fl_freq_P3 fl_freq WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 UNDAC-PAK:fl_freq_P3_pak_opt fl_freq_P3_pak_opt WCS GeoTIFF EPSG:4326 CRS:84 60.879999999999995 75.38 23.709999999999994 36.9 wesr_risk:fl_hazard_1000_yrp 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_yrp WCS GeoTIFF EPSG:4326 CRS:84 -141.09900045782885 180.08131581575455 -56.085762831133565 60.09233546603441 wesr_risk:fl_hazard_100_yrp 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_yrp WCS GeoTIFF EPSG:4326 CRS:84 -141.09900045782885 180.08131581575455 -56.085762831133565 60.09233546603441 wesr_risk:fl_hazard_200_yrp 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_yrp WCS GeoTIFF EPSG:4326 CRS:84 -141.09900045782885 180.08131581575455 -56.085762831133565 60.09233546603441 wesr_risk:fl_hazard_25_yrp 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_yrp WCS GeoTIFF EPSG:4326 CRS:84 -141.09900045782885 180.08131581575455 -56.085762831133565 60.09233546603441 wesr_risk:fl_hazard_500_yrp 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_yrp WCS GeoTIFF EPSG:4326 CRS:84 -141.09900045782885 180.08131581575455 -56.085762831133565 60.09233546603441 wesr_risk:fl_hazard_50_yrp 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_yrp WCS GeoTIFF EPSG:4326 CRS:84 -141.09900045782885 180.08131581575455 -56.085762831133565 60.09233546603441 ECO-DRR:fl_phex fl_phex WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 ECO-DRR:fl_phex_P3 fl_phex WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 UNDAC-PAK:fl_phex_P3_pak_opt fl_phex_P3_pak_opt WCS GeoTIFF EPSG:4326 CRS:84 60.879999999999995 75.38 23.709999999999994 36.9 wesr_risk:fl_risk This 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_risk WCS GeoTIFF EPSG:4326 CRS:84 -180.0 179.99999999999858 -90.0 83.99999999999932 risk_southeast_senegal:flood The 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 hazard EPSG:4326 CRS:84 -13.239460488881921 -11.342218608821492 12.307817707721565 13.461254532531031 risk_southeast_senegal:flood_1995_2004 Flood 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_2004 WCS GeoTIFF EPSG:32628 CRS:84 -13.243723483393213 -11.335952171241116 12.296539942972935 13.472448719878967 risk_southeast_senegal:flood_2005_2014 Flood 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_2014 WCS GeoTIFF EPSG:32628 CRS:84 -13.243723483393213 -11.335952171241116 12.296539942972935 13.472448719878967 risk_southeast_senegal:flood_2015_2024 Flood 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_2024 WCS GeoTIFF EPSG:32628 CRS:84 -13.243723483393213 -11.335952171241116 12.296539942972935 13.472448719878967 risk_southeast_senegal:flood_risk Analysis 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 risk EPSG:4326 CRS:84 -13.239494659264894 -11.34243244226129 12.307771971227796 13.461029132980437 risk_southeast_senegal:flood_vulnerability In 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 vulnerability EPSG:32628 CRS:84 -13.24387253466399 -11.335905049138686 12.296396064761913 13.472511381334042 Medicip:forcing_pesato_02_03 forcing_pesato_02_03_opt WCS GeoTIFF EPSG:4326 CRS:84 -5.3554163761242854 35.977083623875714 30.264583696518883 45.50708369651888 MapX_UNDP:forest_biodiversity_intactness_index_naturemap_2020_opt <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_opt WCS GeoTIFF EPSG:4326 CRS:84 -179.991848215593 179.9875281634017 -81.8206539215438 81.8207424633558 tropisco:forest_loss_su_2022_v1 forest_loss_su_2022_v1 WCS GeoTIFF EPSG:4326 CRS:84 -58.06766256156945 -53.98639912628145 1.833557839460103 6.011499880852531 MapX_UNDP:forest_management_layer_v3_2_opt <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_opt WCS GeoTIFF EPSG:4326 CRS:84 -179.9999885408441 179.99982972004844 -60.00069958862515 80.00049603174595 ECO-DRR:forestcfkm2a forestcfkm2a WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 ECO-DRR:forestkm2a_P3 forestkm2a WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 ECO-DRR:forestofkm2a forestofkm2a WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 ECO-DRR:forestwlkm2a forestwlkm2a WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 rs_for_asgm:frac_cats_2016_kamituga frac_cats_2016_kamituga WCS GeoTIFF EPSG:32735 CRS:84 28.141845709394754 28.214936578763222 -3.221071413308412 -3.0368306131003737 rs_for_asgm:frac_cats_2016_kamituga_west frac_cats_2016_kamituga_west WCS GeoTIFF EPSG:32735 CRS:84 28.166978184291047 28.181206954060517 -3.0756872701433142 -3.06427470634831 rs_for_asgm:frac_cats_2017_kamituga frac_cats_2017_kamituga WCS GeoTIFF EPSG:32735 CRS:84 28.141845709394754 28.214936578763222 -3.221071413308412 -3.0368306131003737 rs_for_asgm:frac_cats_2017_kamituga_west frac_cats_2017_kamituga_west WCS GeoTIFF EPSG:32735 CRS:84 28.166978184291047 28.181206954060517 -3.0756872701433142 -3.06427470634831 rs_for_asgm:frac_cats_2018_kamituga frac_cats_2018_kamituga WCS GeoTIFF EPSG:32735 CRS:84 28.141845709394754 28.214936578763222 -3.221071413308412 -3.0368306131003737 rs_for_asgm:frac_cats_2018_kamituga_west frac_cats_2018_kamituga_west WCS GeoTIFF EPSG:32735 CRS:84 28.166978184291047 28.181206954060517 -3.0756872701433142 -3.06427470634831 rs_for_asgm:frac_cats_2019_kamituga frac_cats_2019_kamituga WCS GeoTIFF EPSG:32735 CRS:84 28.141845709394754 28.214936578763222 -3.221071413308412 -3.0368306131003737 rs_for_asgm:frac_cats_2019_kamituga_west frac_cats_2019_kamituga_west WCS GeoTIFF EPSG:32735 CRS:84 28.166978184291047 28.181206954060517 -3.0756872701433142 -3.06427470634831 rs_for_asgm:frac_cats_2020_kamituga frac_cats_2020_kamituga WCS GeoTIFF EPSG:32735 CRS:84 28.141845709394754 28.214936578763222 -3.221071413308412 -3.0368306131003737 rs_for_asgm:frac_cats_2020_kamituga_west frac_cats_2020_kamituga_west WCS GeoTIFF EPSG:32735 CRS:84 28.166978184291047 28.181206954060517 -3.0756872701433142 -3.06427470634831 rs_for_asgm:frac_cats_2021_kamituga frac_cats_2021_kamituga WCS GeoTIFF EPSG:32735 CRS:84 28.141845709394754 28.214936578763222 -3.221071413308412 -3.0368306131003737 rs_for_asgm:frac_cats_2021_kamituga_west frac_cats_2021_kamituga_west WCS GeoTIFF EPSG:32735 CRS:84 28.166978184291047 28.181206954060517 -3.0756872701433142 -3.06427470634831 rs_for_asgm:frac_cats_transition_2016_2017_kamituga frac_cats_transition_2016_2017_kamituga WCS GeoTIFF EPSG:32735 CRS:84 28.141845709394754 28.214936578763222 -3.221071413308412 -3.0368306131003737 rs_for_asgm:frac_cats_transition_2017_2018_kamituga frac_cats_transition_2017_2018_kamituga WCS GeoTIFF EPSG:32735 CRS:84 28.141845709394754 28.214936578763222 -3.221071413308412 -3.0368306131003737 rs_for_asgm:frac_cats_transition_2018_2019_kamituga frac_cats_transition_2018_2019_kamituga WCS GeoTIFF EPSG:32735 CRS:84 28.141845709394754 28.214936578763222 -3.221071413308412 -3.0368306131003737 rs_for_asgm:frac_cats_transition_2019_2020_kamituga frac_cats_transition_2019_2020_kamituga WCS GeoTIFF EPSG:32735 CRS:84 28.141845709394754 28.214936578763222 -3.221071413308412 -3.0368306131003737 rs_for_asgm:frac_cats_transition_2020_2021_kamituga frac_cats_transition_2020_2021_kamituga WCS GeoTIFF EPSG:32735 CRS:84 28.141845709394754 28.214936578763222 -3.221071413308412 -3.0368306131003737 MapX_UNDP_nd:frag_2000_1000_25_0 The 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_0 WCS GeoTIFF EPSG:4326 CRS:84 -119.36170706306382 179.99995545035347 -34.213866389533806 30.001378 text/plain MapX_UNDP_nd:frag_2000_500_25_0 The 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_0 WCS GeoTIFF EPSG:4326 CRS:84 -119.36170706306382 179.99995545035347 -34.213866389533806 30.001378 text/plain MapX_UNDP_nd:frag_2012_1000_25_0 The 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_0 WCS GeoTIFF EPSG:4326 CRS:84 -119.36170706306382 179.99995545035347 -34.213866389533806 30.001378 text/plain MapX_UNDP_nd:frag_2012_500_25_0 The 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_0 WCS GeoTIFF EPSG:4326 CRS:84 -119.36170706306382 179.99995545035347 -34.213866389533806 30.001378 text/plain LWP:future_Av_annual_temp_Av_18_models_opt 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_opt WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.00288 -60.0 90.00120000000001 text/plain grid_data_core_pro:g2015_2014_0 features g2015_2014_0 EPSG:4326 CRS:84 -181.800003051758 181.800018310547 -90.7676467895508 84.4950561523438 grid_data_core_pro:g2015_2014_1 g2015_2014_1 features EPSG:4326 CRS:84 -181.800003051758 181.800018310547 -60.199836730957 84.3429794311523 grid_data_core_pro:g2015_2014_2 features g2015_2014_2 EPSG:4326 CRS:84 -181.800003051758 181.800018310547 -60.1909866333008 82.5640869140625 MapX_UNDP:gHMv1_1990_wgs Detailed 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#.X5ChM0JKjUI gHMv1_1990_wgs WCS GeoTIFF EPSG:4326 CRS:84 -180.0 179.99932064640564 -75.00027807853427 85.00013997386212 MapX_UNDP:gHMv1_2015_wgs Detailed 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#.X5ChM0JKjUI gHMv1_2015_wgs WCS GeoTIFF EPSG:4326 CRS:84 -180.0 179.99932064640564 -75.00027807853427 85.00013997386212 MapX_UNDP:gHMv1_2017_wgs Detailed 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#.X5ChM0JKjUI gHMv1_2017_wgs WCS GeoTIFF EPSG:4326 CRS:84 -180.0 179.99932064640564 -75.00027807853427 85.00013997386212 mapx_chemobs:gab_natural_hazards_exposure features gba_natural_hazards_4326 EPSG:4326 CRS:84 8.695480635376585 14.526885997845666 -3.9616657924700505 2.315041494652071 mapx_chemobs:gab_wb_buffer_100m features gab_wb_buffer_100m_4326 EPSG:4326 CRS:84 8.705816679771605 14.383836049311741 -3.801030948400662 2.3177509999999604 grid_data_core_pro:gadm36_1_0.001_shp - Edited by GRID - simplified DP 0.001 on QGIS using Simplify features gadm36_1_0-001 EPSG:4326 CRS:84 -179.99999999999994 180.0 -90.0 83.65833282470703 text/plain MAPX_nd:gadm36_2 Source: https://gadm.org/data.html features gadm36_2 EPSG:4326 CRS:84 -179.99999999999994 180.0 -90.0 83.65833282470703 wesr_risk:gar_eco_exp The 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_exp WCS GeoTIFF EPSG:4326 CRS:84 -179.99954 179.95786999999999 -55.71600000000001 81.82433 unccd:gdp2019_wgs84_opt The 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/17004523/1 gdp2019_wgs84_opt WCS GeoTIFF EPSG:4326 CRS:84 -179.99999957737955 179.99807001075922 -65.03459342294055 75.00630943315629 wesr_cca:geonode_drought_econ_exposure_icpac This 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_exposure WCS GeoTIFF EPSG:4326 CRS:84 21.838947 51.440099935812 -11.745700000296004 23.14286 text/plain wesr_cca:geonode_physical_exposition_to_drought_events This 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 Laboratory geonode_physical_exposition_to_drought_events WCS GeoTIFF EPSG:4326 CRS:84 21.838947 51.440099935812 -11.745700000296004 23.14286 text/plain paprac:ghs_built_surf_100m_1975 The 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-ABB0-4F8B7B1D72EA built-up EPSG:4326 CRS:84 -23.264767173073253 52.90404528839234 16.259077704195818 51.21395264452012 paprac:ghs_built_surf_100m_1980 The 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-ABB0-4F8B7B1D72EA built-up EPSG:4326 CRS:84 -23.264767173073253 52.90404528839234 16.259077704195818 51.21395264452012 paprac:ghs_built_surf_100m_1985 The 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-ABB0-4F8B7B1D72EA built-up EPSG:4326 CRS:84 -23.264767173073253 52.90404528839234 16.259077704195818 51.21395264452012 paprac:ghs_built_surf_100m_1990 The 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-ABB0-4F8B7B1D72EA built-up EPSG:4326 CRS:84 -23.264767173073253 52.90404528839234 16.259077704195818 51.21395264452012 paprac:ghs_built_surf_100m_1995 The 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-ABB0-4F8B7B1D72EA built-up EPSG:4326 CRS:84 -23.264767173073253 52.90404528839234 16.259077704195818 51.21395264452012 paprac:ghs_built_surf_100m_2000 The 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-ABB0-4F8B7B1D72EA built-up EPSG:4326 CRS:84 -23.264767173073253 52.90404528839234 16.259077704195818 51.21395264452012 paprac:ghs_built_surf_100m_2005 The 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-ABB0-4F8B7B1D72EA built-up EPSG:4326 CRS:84 -23.264767173073253 52.90404528839234 16.259077704195818 51.21395264452012 paprac:ghs_built_surf_100m_2010 The 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-ABB0-4F8B7B1D72EA built-up EPSG:4326 CRS:84 -23.264767173073253 52.90404528839234 16.259077704195818 51.21395264452012 paprac:ghs_built_surf_100m_2015 The 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-ABB0-4F8B7B1D72EA built-up EPSG:4326 CRS:84 -23.264767173073253 52.90404528839234 16.259077704195818 51.21395264452012 paprac:ghs_built_surf_100m_2020 The 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-ABB0-4F8B7B1D72EA built-up EPSG:4326 CRS:84 -23.264767173073253 52.90404528839234 16.259077704195818 51.21395264452012 paprac:ghs_built_surf_100m_2025 The 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-ABB0-4F8B7B1D72EA built-up EPSG:4326 CRS:84 -23.264767173073253 52.90404528839234 16.259077704195818 51.21395264452012 paprac:ghs_built_surf_100m_2030 The 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-ABB0-4F8B7B1D72EA built-up EPSG:4326 CRS:84 -23.264767173073253 52.90404528839234 16.259077704195818 51.21395264452012 wesr_mountains:glims_polygons features glims_polygons EPSG:4326 CRS:84 -179.818145 179.820642 -80.085534 83.608126 MapX_UNDP:global_cumul_impact_2013_minus_2008_wgs Human 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#s1 global_cumul_impact_2013_minus_2008_wgs WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.00000000000006 -79.0 84.0 text/plain MapX_UNDP:global_grid_prob_expansion_2030_unused The Global Grid of Probabilities of Urban Expansion to 2030 presents spatially explicit probabilistic forecasts of global urban land cover change from 2000 to 2030 at a 2.5 arc-minute resolution. For each grid cell that is non-urban in 2000, a Monte-Carlo model assigned a probability of becoming urban by the year 2030. The authors first extracted urban extent circa 2000 from the NASA MODIS Land Cover Type Product Version 5, which provides a conservative estimate of global urban land cover. The authors then used population densities from the Global Rural-Urban Mapping Project, Version 1 (GRUMPv1) to create the population density driver map. They estimated the amount of new urban land in each United Nations region by 2030 in a Monte-Carlo fashion based on present empirical distribution of regional urban population densities and probability density functions of projected regional population and GDP values for 2030. To facilitate integration with other data products, CIESIN reprojected the data from Goode's Homolosine to Geographic WGS84 projection. We recommend using the Goode Homolosine equal area projection for data analysis. NOTES CONCERNING THE DATA Although regions were not excluded in the creation of these data some small urban areas, such as Tromso and Norilsk, are missing. This is a result of resampling the input land-cover data from 463 meters to 5 kilometers as described in the documentation. ACCESSING THE DATA The data may be downloaded via the World Wide Web at http://sedac.ciesin.columbia.edu/data/set/lulc-global-grid-prob-urban-expansion-2030. The data are available in a compressed zipfile of GeoTIFF format. The downloaded files need to be uncompressed in a single folder using either WinZip (Windows file compression utility) or a similar application before it can be accessed by your GIS software package. DATA FORMAT Two files containing probability of urban expansion grids in GeoTIFF format are available in the download. The WGS84 archive is in geographic coordinates of decimal degrees based on the World Geodetic System spheroid of 1984. The Goode Homolosine archive is in projected coordinates based on the pseudocylindrical, equal-area, composite map, Goode Homolosine projection. File naming convention: urban-expansion-2000-to-2030-wgs84 WGS 1984, with 0.048125633 decimal degree resolution urban-expansion-2000-to-2030-goode Goode Homolosine, with 5 kilometer resolution global_grid_prob_expansion_2030 WCS GeoTIFF EPSG:4326 CRS:84 -179.99997803468415 180.02788233587063 -55.922762245435266 83.97845284575614 MapX_UNDP:global_habitat_v004_opt <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_opt WCS GeoTIFF EPSG:4326 CRS:84 -179.99917662946004 179.9994217980732 -81.81826968418555 81.81855607761675 cc_and_health:global_rcp85_h10glob_CentreAfrique_opt This 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_opt WCS GeoTIFF EPSG:4326 CRS:84 14.420833333 27.463333333 2.2241666670000004 11.0075 grid_data_core:gpw-v4-population-density-adjusted-to-2015-unwpp-country-totals_2015 WCS GeoTIFF gpw-v4-population-density-adjusted-to-2015-unwpp-country-totals_2015 EPSG:4326 CRS:84 -180.0 180.00000000000244 -59.9999999999918 85.0000000000092 wesr_risk:gshap gshap WCS GeoTIFF EPSG:4326 CRS:84 -180.00000000000006 180.09999999999997 -65.0 90.10000000000002 risk_niger_undp:h1000ic h1000ic_opt_deflate WCS GeoTIFF EPSG:4326 CRS:84 0.1675000015994978 16.000833334932196 11.697500041616369 23.52416670828256 risk_niger_undp:h100ic h100ic_opt_deflate WCS GeoTIFF EPSG:4326 CRS:84 0.1675000015994978 16.000833334932196 11.697500041616369 23.52416670828256 risk_niger_undp:h200ic h200ic_opt_deflate WCS GeoTIFF EPSG:4326 CRS:84 0.1675000015994978 16.000833334932196 11.697500041616369 23.52416670828256 risk_niger_undp:h25ic h25ic_opt_deflate WCS GeoTIFF EPSG:4326 CRS:84 0.1675000015994978 16.000833334932196 11.697500041616369 23.52416670828256 risk_niger_undp:h500ic h500ic_opt_deflate WCS GeoTIFF EPSG:4326 CRS:84 0.1675000015994978 16.000833334932196 11.697500041616369 23.52416670828256 risk_niger_undp:h50ic h50ic_opt_deflate WCS GeoTIFF EPSG:4326 CRS:84 0.1675000015994978 16.000833334932196 11.697500041616369 23.52416670828256 risk_southeast_senegal:heat The 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. heat EPSG:4326 CRS:84 -13.239460488881921 -11.342218608821492 12.307817707721565 13.461254532531031 risk_southeast_senegal:heat_1995_2004 Extreme 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_2004 WCS GeoTIFF EPSG:4326 CRS:84 -13.239460488881921 -11.342218608821492 12.307817707721565 13.461254532531031 risk_southeast_senegal:heat_2005_2014 Extreme 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_2014 WCS GeoTIFF EPSG:4326 CRS:84 -13.239460488881921 -11.342218608821492 12.307817707721565 13.461254532531031 risk_southeast_senegal:heat_2015_2024 Extreme 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_2024 WCS GeoTIFF EPSG:4326 CRS:84 -13.239460488881921 -11.342218608821492 12.307817707721565 13.461254532531031 risk_southeast_senegal:heat_risk In 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 au EPSG:4326 CRS:84 -13.254663620065084 -11.324971709823716 12.30783537839622 13.461101944475129 risk_southeast_senegal:heat_vulnerability Vulnerability 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 vulnerability EPSG:32628 CRS:84 -13.243470490606473 -11.336069905109735 12.296796567458285 13.472353901206636 MapX_UNDP:hfp1993 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]. hfp1993 WCS GeoTIFF EPSG:4326 CRS:84 -179.99999995447251 179.99661098079287 -65.25013301721049 89.89762321520959 text/plain wesr_biodiversity:hfp2000_merisINT Brooke 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.3600547 hfp2000_merisINT_wgs84_opt WCS GeoTIFF EPSG:4326 CRS:84 -179.99999995447251 179.99661098079287 -65.25013301721049 89.89762321520959 MapX_UNDP:hfp2000_merisINT_subsetsGlobal 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_subsetsGlobal WCS GeoTIFF EPSG:4326 CRS:84 -81.71613729134685 153.42326553667291 -20.618092964606667 26.043388408099844 text/plain MapX_UNDP_nd:hfp2000_meris_wgs 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 hfp2000_meris_wgs WCS GeoTIFF EPSG:4326 CRS:84 -179.99997804642848 180.00508149566073 -65.24741434935741 89.8976232152096 MapX_UNDP:hfp2000_meris_wgs_subset To come hfp2000_meris_wgs_subset WCS GeoTIFF EPSG:4326 CRS:84 -81.71613498761286 153.4232678403954 -20.61982884080102 26.041652531903203 wesr_biodiversity:hfp2005_merisINT Brooke 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.3600547 hfp2005_merisINT_wgs84_opt WCS GeoTIFF EPSG:4326 CRS:84 -179.99999995447251 179.99661098079287 -65.25013301721049 89.89762321520959 MapX_UNDP:hfp2005_merisINT_subsetsGlobal 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_subsetsGlobal WCS GeoTIFF EPSG:4326 CRS:84 -81.71613729134685 153.42326553667291 -20.618092964606667 26.043388408099844 text/plain MapX_UNDP_nd:hfp2005_meris_wgs 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 hfp2005_meris_wgs WCS GeoTIFF EPSG:4326 CRS:84 -179.99997804642848 180.00508149566073 -65.24741434935741 89.8976232152096 MapX_UNDP:hfp2005_meris_wgs_subset To come hfp2005_meris_wgs_subset WCS GeoTIFF EPSG:4326 CRS:84 -81.71613498761286 153.4232678403954 -20.61982884080102 26.041652531903203 MapX_UNDP:hfp2009 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]. hfp2009 WCS GeoTIFF EPSG:4326 CRS:84 -179.99999995447251 179.99661098079287 -65.25013301721049 89.89762321520959 text/plain wesr_biodiversity:hfp2010_merisINT Brooke 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.3600547 hfp2010_merisINT_wgs84_opt WCS GeoTIFF EPSG:4326 CRS:84 -179.99999995447251 179.99661098079287 -65.25013301721049 89.89762321520959 MapX_UNDP:hfp2010_merisINT_subsetsGlobal 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_subsetsGlobal WCS GeoTIFF EPSG:4326 CRS:84 -81.71613729134685 153.42326553667291 -20.618092964606667 26.043388408099844 text/plain MapX_UNDP_nd:hfp2010_meris_wgs 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 hfp2010_meris_wgs WCS GeoTIFF EPSG:4326 CRS:84 -179.99997804642848 180.00508149566073 -65.24741434935741 89.8976232152096 MapX_UNDP:hfp2010_meris_wgs_subset To come hfp2010_meris_wgs_subset WCS GeoTIFF EPSG:4326 CRS:84 -81.71613498761286 153.4232678403954 -20.61982884080102 26.041652531903203 wesr_biodiversity:hfp2013_merisINT Brooke 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.3600547 hfp2013_merisINT_wgs84_opt WCS GeoTIFF EPSG:4326 CRS:84 -179.99999995447251 179.99661098079287 -65.25013301721049 89.89762321520959 MapX_UNDP:hfp2013_merisINT_subsetsGlobal 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_subsetsGlobal WCS GeoTIFF EPSG:4326 CRS:84 -81.71613729134685 153.42326553667291 -20.618092964606667 26.043388408099844 text/plain MapX_UNDP_nd:hfp2013_meris_wgs 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 hfp2013_meris_wgs WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 MapX_UNDP:hfp2013_meris_wgs_subset To come hfp2013_meris_wgs_subset WCS GeoTIFF EPSG:4326 CRS:84 -81.71613498761286 153.4232678403954 -20.61982884080102 26.041652531903203 MapX_UNDP:hfp_difference_93-09 hfp_difference WCS GeoTIFF EPSG:4326 CRS:84 -179.99999995447251 179.99661098079287 -65.25013301721049 89.89762321520959 text/plain mapx_chemobs:hwsd_texture hwsd_texture_opt WCS GeoTIFF EPSG:4326 CRS:84 -180.0 179.99999998559997 -88.999999992912 88.999999999968 wesr_cca:icpac_worldpop_2019_population_gha the data indicates to the estimated total number of people per grid-cell at a resolution of 1 Km. icpac_worldpop_2019_population_gha WCS GeoTIFF EPSG:4326 CRS:84 21.838947 51.4196463067 -11.745699999198003 23.14286 text/plain MapX_UNDP:indo_forint100 Forest 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_forint100 WCS GeoTIFF EPSG:4326 CRS:84 100.0 141.016129418236 -9.999861112471002 23.384477504501 text/plain wesr_biodiversity:indo_forint100 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/" target="_blank">CC-4.0 Attribution</a>.<br/> indo_forint100 WCS GeoTIFF EPSG:4326 CRS:84 100.0 141.016129418236 -9.999861112471002 23.384477504501 text/plain MapX_UNDP:int_pct_conv_in_pas_wgs 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.aap9565 int_pct_conv_in_pas_wgs WCS GeoTIFF EPSG:4326 CRS:84 -179.99998854073996 179.99795085448952 -69.47709354376937 83.70375566498902 text/plain mapx_chemobs:ken_natural_hazards ken_all_hazards_merged_dissolve_4326_opt WCS GeoTIFF EPSG:4326 CRS:84 33.89349461411385 41.911157303972296 -4.680416726482791 4.632479077612832 mapx_chemobs:ken_natural_hazards_exposure features ken_natural_hazards_exposure_fixed_buffer_10cm EPSG:32737 CRS:84 33.89349371338194 41.91109517384215 -4.680309318350787 4.632446574486288 mapx_chemobs:ken_slope dem90_ken_reproj_slope_deg_wgs84_opt WCS GeoTIFF EPSG:4326 CRS:84 33.88974434855757 41.937083228471195 -4.739402006732216 5.081234702368131 mapx_chemobs:ken_wb_buffer ken_wb_buffer_4326_opt WCS GeoTIFF EPSG:4326 CRS:84 33.85213190746376 41.922488963485065 -4.735138369617019 5.104984389948367 mapx_chemobs:ken_wb_buffer_100m features ken_wb_buffer_100m EPSG:4326 CRS:84 33.872286018827374 41.91822660197737 -4.735135036822349 5.094192528564251 wesr_risk:lakes_glwd features glwd_1 EPSG:4326 CRS:84 -178.871 179.995 -54.6059 81.9818 wesr_sdg:land_degradation_global_GeoEssential This 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 VLab sdg1531_global_deflate_opt WCS GeoTIFF EPSG:4326 CRS:84 -180.002170844 180.00217056900001 -55.920126210999996 83.63539874 text/plain risk_niger_undp:lc_niger lc_niger_opt WCS GeoTIFF EPSG:4326 CRS:84 0.167162698412664 16.001488095238045 11.696924603174637 23.524305555555575 wesr_risk:le1010ieey This 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). le1010ieey WCS GeoTIFF EPSG:4326 CRS:84 -180.0 179.99999999999983 -90.0 89.99166666666659 wesr_risk:le1010ipey This 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). le1010ipey WCS GeoTIFF EPSG:4326 CRS:84 -180.0 179.99999999999983 -90.0 89.99166666666659 mapx_chemobs:lebanon_remote_site_screening_res50 lebanon_remote_site_screening_res50 WCS GeoTIFF EPSG:4326 CRS:84 35.103656 36.606156 33.05658 34.69208 text/plain mapx_chemobs:lesotho_remote_site_screening_res50 lesotho_remote_site_screening_res50 WCS GeoTIFF EPSG:4326 CRS:84 27.011148168000023 29.457148168000025 -30.675262557999954 -28.570762557999952 text/plain wesr_risk:ls_ecoexp2 This 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_ecoexp2 WCS GeoTIFF EPSG:4326 CRS:84 -180.0 179.99999999999983 -90.0 89.99166666666659 wesr_risk:ls_eq This 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_eq WCS GeoTIFF EPSG:4326 CRS:84 -180.0 179.9916666666665 -56.01339243789985 89.99494089543342 ECO-DRR:ls_freq2af ls_freq2af WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 ECO-DRR:ls_freq2af_P3 ls_freq2af WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 UNDAC-PAK:ls_freq2af_P3_pak_opt ls_freq2af_P3_pak_opt WCS GeoTIFF EPSG:4326 CRS:84 60.879999999999995 75.38 23.709999999999994 36.9 ECO-DRR:ls_phex ls_phex WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 ECO-DRR:ls_phex_P3 ls_phex WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 UNDAC-PAK:ls_phex_P3_pak_opt ls_phex_P3_pak_opt WCS GeoTIFF EPSG:4326 CRS:84 60.879999999999995 75.38 23.709999999999994 36.9 wesr_risk:ls_physexp2 This 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_physexp2 WCS GeoTIFF EPSG:4326 CRS:84 -180.0 179.99999999999983 -90.0 89.99166666666659 wesr_risk:ls_pr This 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_pr WCS GeoTIFF EPSG:4326 CRS:84 -180.0 179.9916666666665 -56.01339243789985 89.99494089543342 wesr_risk:ls_risk This 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_risk WCS GeoTIFF EPSG:4326 CRS:84 -181.0 180.9999999999999 -90.0 89.99999999999994 wesr_mountains:m201912-202001-02_hiver_opt 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_opt WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.000293053458 -60.00000000570601 80.002258848399 wesr_mountains:m202003-05_printemps_opt 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_opt WCS GeoTIFF evapotranspiration EPSG:4326 CRS:84 -180.0 180.000293053458 -60.00000000570601 80.002258848399 wesr_mountains:m202006-08_ete_opt 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_opt WCS GeoTIFF evapotranspiration EPSG:4326 CRS:84 -180.0 180.000293053458 -60.00000000570601 80.002258848399 wesr_mountains:m202009-11_automne_opt 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_opt WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.000293053458 -60.00000000570601 80.002258848399 mapx_chemobs:madagascar_remote_site_screening_res50 madagascar_remote_site_screening_res50 WCS GeoTIFF EPSG:4326 CRS:84 43.191379547000075 50.48187954700008 -25.608412065819937 -11.948412065819937 text/plain sand:mafaru_2014 mafaru_2014 WCS GeoTIFF EPSG:3857 CRS:84 73.43606112249947 73.51117370374618 5.8013253113891965 5.859057400767438 sand:mafaru_2024 mafaru_2024 WCS GeoTIFF EPSG:3857 CRS:84 73.43606112249947 73.51117370374618 5.8013253113891965 5.859057400767438 mapx_chemobs:malawi_remote_site_screening_res50 malawi_remote_site_screening_res50 WCS GeoTIFF EPSG:4326 CRS:84 32.67395 35.93195 -17.130540000000007 -9.367540000000007 text/plain ECO-DRR:mangrokm2a mangrokm2a WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 ECO-DRR:mangrokm2a_P3 mangrokm2a WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 MapX_UNDP:mangroves_SOCS_0_100cm_30m_opt_deflate mangroves_SOCS_0_100cm_30m_opt_deflate WCS GeoTIFF EPSG:4326 CRS:84 -180.0 179.99999999998727 -39.00080943399745 32.999190566 mapx_chemobs:mauritius_remote_site_screening_res50 mauritius_remote_site_screening_res50 WCS GeoTIFF EPSG:4326 CRS:84 56.58525666700009 63.50025666700009 -20.52580569099994 -10.336805690999938 text/plain cc_and_health:mean_temp_2000_caf Mean observed temperatures in 2000 in the Central African Republic. Temperature Mean temperature EPSG:4326 CRS:84 14.373044545912343 27.488447694057356 2.1559566818868507 11.049277994670113 cc_and_health:mean_temp_2001_caf Mean observed temperatures in 2001 in the Central African Republic. Temperature Mean temperature EPSG:4326 CRS:84 14.373044545912343 27.488447694057356 2.1559566818868507 11.049277994670113 cc_and_health:mean_temp_2002_caf Mean observed temperatures in 2002 in the Central African Republic. Temperature Mean temperature EPSG:4326 CRS:84 14.373044545912343 27.488447694057356 2.1559566818868507 11.049277994670113 cc_and_health:mean_temp_2003_caf Mean observed temperatures in 2003 in the Central African Republic. Temperature Mean temperature EPSG:4326 CRS:84 14.373044545912343 27.488447694057356 2.1559566818868507 11.049277994670113 cc_and_health:mean_temp_2004_caf Mean observed temperatures in 2004 in the Central African Republic. Temperature Mean temperature EPSG:4326 CRS:84 14.373044545912343 27.488447694057356 2.1559566818868507 11.049277994670113 cc_and_health:mean_temp_2005_caf Mean observed temperatures in 2005 in the Central African Republic. Temperature Mean temperature EPSG:4326 CRS:84 14.373044545912343 27.488447694057356 2.1559566818868507 11.049277994670113 cc_and_health:mean_temp_2006_caf Mean observed temperatures in 2006 in the Central African Republic. Temperature Mean temperature EPSG:4326 CRS:84 14.373044545912343 27.488447694057356 2.1559566818868507 11.049277994670113 cc_and_health:mean_temp_2007_caf Mean observed temperatures in 2007 in the Central African Republic. Temperature Mean temperature EPSG:4326 CRS:84 14.373044545912343 27.488447694057356 2.1559566818868507 11.049277994670113 cc_and_health:mean_temp_2008_caf Mean observed temperatures in 2008 in the Central African Republic. Temperature Mean temperature EPSG:4326 CRS:84 14.373044545912343 27.488447694057356 2.1559566818868507 11.049277994670113 cc_and_health:mean_temp_2009_caf Mean observed temperatures in 2009 in the Central African Republic. Temperature Mean temperature EPSG:4326 CRS:84 14.373044545912343 27.488447694057356 2.1559566818868507 11.049277994670113 cc_and_health:mean_temp_2010_caf Mean observed temperatures in 2010 in the Central African Republic. Temperature Mean temperature EPSG:4326 CRS:84 14.373044545912343 27.488447694057356 2.1559566818868507 11.049277994670113 cc_and_health:mean_temp_2011_caf Mean observed temperatures in 2011 in the Central African Republic. Temperature Mean temperature EPSG:4326 CRS:84 14.373044545912343 27.488447694057356 2.1559566818868507 11.049277994670113 cc_and_health:mean_temp_2012_caf Mean observed temperatures in 2012 in the Central African Republic. Temperature Mean temperature EPSG:4326 CRS:84 14.373044545912343 27.488447694057356 2.1559566818868507 11.049277994670113 cc_and_health:mean_temp_2013_caf Mean observed temperatures in 2013 in the Central African Republic. Temperature Mean temperature EPSG:4326 CRS:84 14.373044545912343 27.488447694057356 2.1559566818868507 11.049277994670113 cc_and_health:mean_temp_2014_caf Mean observed temperatures in 2014 in the Central African Republic. Temperature Mean temperature EPSG:4326 CRS:84 14.373044545912343 27.488447694057356 2.1559566818868507 11.049277994670113 cc_and_health:mean_temp_2015_caf Mean observed temperatures in 2015 in the Central African Republic. Temperature Mean temperature EPSG:4326 CRS:84 14.373044545912343 27.488447694057356 2.1559566818868507 11.049277994670113 cc_and_health:mean_temp_2016_caf Mean observed temperatures in 2016 in the Central African Republic. Temperature Mean temperature EPSG:4326 CRS:84 14.373044545912343 27.488447694057356 2.1559566818868507 11.049277994670113 cc_and_health:mean_temp_2017_caf Mean observed temperatures in 2017 in the Central African Republic. Temperature Mean temperature EPSG:4326 CRS:84 14.373044545912343 27.488447694057356 2.1559566818868507 11.049277994670113 cc_and_health:mean_temp_2018_caf Mean observed temperatures in 2018 in the Central African Republic. Temperature Mean temperature EPSG:4326 CRS:84 14.373044545912343 27.488447694057356 2.1559566818868507 11.049277994670113 cc_and_health:mean_temp_2019_caf Mean observed temperatures in 2019 in the Central African Republic. Temperature Mean temperature EPSG:4326 CRS:84 14.373044545912343 27.488447694057356 2.1559566818868507 11.049277994670113 cc_and_health:mean_temp_2020_caf Mean observed temperatures in 2020 in the Central African Republic. Temperature Mean temperature EPSG:4326 CRS:84 14.373044545912343 27.488447694057356 2.1559566818868507 11.049277994670113 cc_and_health:mean_temp_predict_2020_caf Mean predicted temperatures in 2020 in the Central African Republic. Temperature Mean temperature EPSG:4326 CRS:84 14.373044545912343 27.488447694057356 2.1559566818868507 11.049277994670113 cc_and_health:mean_temp_predict_2021_caf Mean predicted temperatures in 2021 in the Central African Republic. Temperature Mean temperature EPSG:4326 CRS:84 14.373044545912343 27.488447694057356 2.1559566818868507 11.049277994670113 cc_and_health:mean_temp_predict_2022_caf Mean predicted temperatures in 2022 in the Central African Republic. Temperature Mean temperature EPSG:4326 CRS:84 14.373044545912343 27.488447694057356 2.1559566818868507 11.049277994670113 cc_and_health:mean_temp_predict_2023_ref_caf Mean predicted temperatures in 2023 in the Central African Republic. Reference layer. Temperature Mean temperature EPSG:4326 CRS:84 14.373044546 27.488447694 2.1559566819999993 11.049277995 Medicip:med_00_ogs_nut_an-fc-m-avrg_year_2020 med_00_ogs_nut_an-fc-m-avrg_year_2020_opt WCS GeoTIFF EPSG:4326 CRS:84 -5.5625 36.3125 30.16667 46.0 Medicip:med_00_ogs_nut_an-fc-m-avrg_year_2020_po3 med_00_ogs_nut_an-fc-m-avrg_year_2020_po3_opt WCS GeoTIFF EPSG:4326 CRS:84 -5.562499842 36.312501272 30.166666665 46.000001273 unea_pollution:mercure_em 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/km2 mercure_em WCS GeoTIFF amap EPSG:4326 CRS:84 -180.0 180.0 -55.0 84.5 wesr_risk:ml_risk This 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_risk WCS GeoTIFF EPSG:4326 CRS:84 -180.0 179.99999999985602 -90.0 83.99999999993042 mapx_chemobs:montenegro_remote_site_screening_res50 montenegro_remote_site_screening_res50 WCS GeoTIFF EPSG:4326 CRS:84 18.43355958000009 20.35205958000009 41.84389394527755 43.55839394527755 text/plain mapx_chemobs:morocco_remote_site_screening_res50 morocco_remote_site_screening_res50 WCS GeoTIFF EPSG:4326 CRS:84 -13.168084726852316 -0.9980847268523156 27.664466294000036 35.921466294000034 text/plain rs_for_asgm:mosaic_2016_kamituga mosaic_2016_kamituga WCS GeoTIFF EPSG:32735 CRS:84 28.141845709394754 28.214936578763222 -3.221071413308412 -3.0368306131003737 rs_for_asgm:mosaic_2016_kamituga_west mosaic_2016_kamituga_west WCS GeoTIFF EPSG:32735 CRS:84 28.166978184291047 28.181206954060517 -3.0756872701433142 -3.06427470634831 rs_for_asgm:mosaic_2017_kamituga mosaic_2017_kamituga WCS GeoTIFF EPSG:32735 CRS:84 28.141845709394754 28.214936578763222 -3.221071413308412 -3.0368306131003737 rs_for_asgm:mosaic_2017_kamituga_west mosaic_2017_kamituga_west WCS GeoTIFF EPSG:32735 CRS:84 28.166978184291047 28.181206954060517 -3.0756872701433142 -3.06427470634831 rs_for_asgm:mosaic_2018_kamituga mosaic_2018_kamituga WCS GeoTIFF EPSG:32735 CRS:84 28.141845709394754 28.214936578763222 -3.221071413308412 -3.0368306131003737 rs_for_asgm:mosaic_2018_kamituga_west mosaic_2018_kamituga_west WCS GeoTIFF EPSG:32735 CRS:84 28.166978184291047 28.181206954060517 -3.0756872701433142 -3.06427470634831 rs_for_asgm:mosaic_2019_kamituga mosaic_2019_kamituga WCS GeoTIFF EPSG:32735 CRS:84 28.141845709394754 28.214936578763222 -3.221071413308412 -3.0368306131003737 rs_for_asgm:mosaic_2019_kamituga_west mosaic_2019_kamituga_west WCS GeoTIFF EPSG:32735 CRS:84 28.166978184291047 28.181206954060517 -3.0756872701433142 -3.06427470634831 rs_for_asgm:mosaic_2020_kamituga mosaic_2020_kamituga WCS GeoTIFF EPSG:32735 CRS:84 28.141845709394754 28.214936578763222 -3.221071413308412 -3.0368306131003737 rs_for_asgm:mosaic_2020_kamituga_west mosaic_2020_kamituga_west WCS GeoTIFF EPSG:32735 CRS:84 28.166978184291047 28.181206954060517 -3.0756872701433142 -3.06427470634831 rs_for_asgm:mosaic_2021_kamituga mosaic_2021_kamituga WCS GeoTIFF EPSG:32735 CRS:84 28.141845709394754 28.214936578763222 -3.221071413308412 -3.0368306131003737 rs_for_asgm:mosaic_2021_kamituga_west mosaic_2021_kamituga_west WCS GeoTIFF EPSG:32735 CRS:84 28.166978184291047 28.181206954060517 -3.0756872701433142 -3.06427470634831 mapx_chemobs:mozambique_remote_site_screening_res50 mozambique_remote_site_screening_res50 WCS GeoTIFF EPSG:4326 CRS:84 30.217320000000004 40.84082000000001 -26.86869848999992 -10.472198489999924 text/plain risk_southeast_senegal:multirisk The 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. multirisk EPSG:4326 CRS:84 -13.239460488881921 -11.342398271878315 12.307817707721565 13.461074869474206 MapX_UNDP:murray_intertidal_global 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_global WCS GeoTIFF EPSG:4326 CRS:84 -179.9997453923923 179.9997453923915 -60.00000496232559 60.000004962325846 text/plain mapx_chemobs:namibia_remote_site_screening_res50 namibia_remote_site_screening_res50 WCS GeoTIFF EPSG:4326 CRS:84 11.715630531000063 25.262630531000063 -28.969192951999922 -16.963192951999922 text/plain MAPX:nasa_firms_fires_nrt NASA's Fire Information for Resource Management System (FIRMS) distributes Near Real-Time (NRT) active fire data within 3 hours of satellite overpass from NASA's Moderate Resolution Imaging Spectroradiometer (MODIS) and NASA's Visible Infrared Imaging Radiometer Suite (VIIRS). MODIS NRT active fire products (MCD14DL) are processed using the standard MOD14/MYD14 Fire and Thermal Anomalies product. Each MODIS active fire location represents the center of a 1km pixel that is flagged by the algorithm as containing one or more fires within the pixel. VIIRS NRT 375 m active fire products (VNP14IMGTDL_NRT, VJ114IMGTDL_NRT, VJ214IMGTDL_NRT) complements MODIS fire detections but the improved spatial resolution of the 375 m data provides a greater response of fires over relatively small areas. The 375 m data also has improved nighttime performance. MODIS Collection 6 NRT Hotspot/Active Fire Detections MCD14DL. Available on-line. doi: 10.5067/FIRMS/MODIS/MCD14DL.NRT.006 VIIRS (S-NPP) I Band 375 m Active Fire Product NRT VNP14IMGT. Available on-line. doi: 10.5067/FIRMS/VIIRS/VNP14IMGT.NRT.001. VIIRS (NOAA-20/JPSS-1) I Band 375 m Active Fire Product NRT VJ114IMGTDL_NRT. Available on-line. doi: 10.5067/FIRMS/VIIRS/VJ114IMGT_NRT.002. VIIRS (NOAA-21/JPSS-2) I Band 375 m Active Fire Product NRT VJ214IMGTDL_NRT. Available on-line. doi: 10.5067/VIIRS/VJ214IMG_NRT.002. features nasa_firms_fires_nrt EPSG:4326 CRS:84 -175.1784210205078 178.45079040527344 -87.84051513671875 71.26011657714844 grid_data_core:ne_10m_admin_0_countries ne_10m_admin_0_countries features EPSG:4326 CRS:84 -181.800003051758 181.800018310547 -90.8681716918945 84.5022735595703 grid_data_core:ne_10m_urban_areas features ne_10m_urban_areas EPSG:4326 CRS:84 -159.671325683594 179.713973999023 -44.1649017333984 70.3367004394531 sand:new_road_aggregate_change__2030 sand EPSG:3857 CRS:84 -13.2995832582623 -12.904858119938874 8.093137521714812 8.716250167822336 grid_data_core:new_trend_map new_trend_map WCS GeoTIFF EPSG:4326 CRS:84 -17.5 75.0 -1.5 45.0 LWP:novel_disp_0to1_normalisedq_opt Climate 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_opt WCS GeoTIFF EPSG:4326 CRS:84 -180.0003335 179.9996665 -59.08371450000001 83.66628549999999 text/plain LWP:novel_sresa2_2050_ENSEMB_10min_int1000_opt This 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.1559299 novel_sresa2_2050_ENSEMB_10min_int1000_opt WCS GeoTIFF EPSG:4326 CRS:84 -180.0003335 180.0003865 -59.16733350000001 83.66628549999999 text/plain wesr_risk:nparks features nparks EPSG:4326 CRS:84 -180.0 179.999 -55.9836 83.675 MAPX_nd:npl_ls_susceptibility_dd Ls_susceptibility_DD_trans WCS GeoTIFF EPSG:4326 CRS:84 84.3751220733584 86.69125415435332 26.895844097850706 28.776169868535888 unea_pollution_nd:oil20100325 On 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. oil20100325 WCS GeoTIFF gulf of mexico deepwater EPSG:4326 CRS:84 -96.4439 -81.18219999999984 23.45089999999986 36.9407 MapX_UNDP:overall_cropsuit_i_1981-2010 This 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_1981-2010 WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -60.0 90.0 text/plain MapX_UNDP:overall_cropsuit_i_2011-2040 This 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_2011-2040 WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -60.0 90.0 text/plain MapX_UNDP:overall_cropsuit_i_2071-2100 This 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_2071-2100 WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -60.0 90.0 text/plain MapX_UNDP:overall_cropsuit_i_diff_19812010_20112040 This 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_20112040 WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -60.0 90.0 text/plain MapX_UNDP:overall_cropsuit_i_diff_19812010_20712100 This 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_20712100 WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -60.0 90.0 text/plain MapX_UNDP:overall_cropsuit_i_diff_20112040_20712100 This 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_20712100 WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -60.0 90.0 text/plain peatlands:peatGPA22WGS_2cl_opt The 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_opt WCS GeoTIFF EPSG:4326 CRS:84 -179.766242816 179.733393112 -74.342809401 78.32251340541809 wesr_risk:pga_1500_yrp 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_yrp WCS GeoTIFF EPSG:4326 CRS:84 -180.25 180.25 -90.25 90.25 wesr_risk:pga_2475_yrp 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_yrp WCS GeoTIFF EPSG:4326 CRS:84 -180.25 180.25 -90.25 90.25 wesr_risk:pga_250_yrp 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_yrp WCS GeoTIFF EPSG:4326 CRS:84 -180.25 180.25 -90.25 90.25 wesr_risk:pga_475_yrp 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_yrp WCS GeoTIFF EPSG:4326 CRS:84 -180.25 180.25 -90.25 90.25 wesr_risk:pga_975_yrp 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_yrp WCS GeoTIFF EPSG:4326 CRS:84 -180.25 180.25 -90.25 90.25 unea_pollution:ph_aut World 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/l ph_aut WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -78.0 90.0 unea_pollution:ph_spr World 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/l ph_spr WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -78.0 90.0 unea_pollution:ph_summ World 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/l ph_summ WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -78.0 90.0 unea_pollution:ph_win World 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/l ph_win WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -78.0 90.0 risk_niger_undp:popV10a popV10a_opt_deflate WCS GeoTIFF EPSG:4326 CRS:84 0.1734108579448161 13.816738733944817 11.647777821277227 21.183607340277227 grid_data_core_pro:pop_landscan2011 WCS GeoTIFF pop_landscan2011 EPSG:4326 CRS:84 -180.0 179.99999999999858 -90.0 83.99999999999932 wesr_climat:pop_perc_2015_1975 pop_perc_2015_1975_wgs84_opt WCS GeoTIFF EPSG:4326 CRS:84 -179.99999680823143 179.99218561802442 -55.98302144283261 83.63964763391371 dakar_waste:popu_2025_Dakar 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 Management Remote Sensing GIS Citizen Science Multi-Criteria Analysis Illegal Dumping EPSG:4326 CRS:84 -17.574657994490252 -17.17229869382342 14.62972698943294 14.86844154301869 dakar_waste:popu_2030_Dakar 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 Management Remote Sensing GIS Citizen Science Multi-Criteria Analysis Illegal Dumping EPSG:4326 CRS:84 -17.574657994490252 -17.17229869382342 14.62972698943294 14.86844154301869 MapX_UNDP:potential_clean_water_prov_opt <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_opt WCS GeoTIFF EPSG:4326 CRS:84 -179.99999999990345 179.97221821435372 -56.016220913563274 83.99999999995521 Medicip:pr_45_46_65 Yearly precipitation anomalies (mm) for the period 2045-2065 based on the climate scenario RCP4.5 (reference 1986-2005). precipitations EPSG:4326 CRS:84 -26.619999885559082 75.4600133895874 -6.819999933242798 45.10000681877136 Medicip:pr_45_81_00 Yearly precipitation anomalies (mm) for the period 2081-2100 based on the climate scenario RCP4.5 (reference 1986-2005). precipitations EPSG:4326 CRS:84 -26.619999885559082 75.4600133895874 -6.819999933242798 45.10000681877136 Medicip:pr_45_86_05 Yearly precipitation averages in the reference period 1986-2005 based on the climate scenario RCP4.5. precipitations EPSG:4326 CRS:84 -26.619999885559082 75.4600133895874 -6.819999933242798 45.10000681877136 Medicip:pr_85_46_65 Yearly precipitation anomalies (mm) for the period 2045-2065 based on the climate scenario RCP8.5 (reference 1986-2005). precipitations EPSG:4326 CRS:84 -26.619999885559082 75.4600133895874 -6.819999933242798 45.10000681877136 Medicip:pr_85_81_00 Yearly precipitation anomalies (mm) for the period 2081-2100 based on the climate scenario RCP8.5 (reference 1986-2005). precipitations EPSG:4326 CRS:84 -26.619999885559082 75.4600133895874 -6.819999933242798 45.10000681877136 Medicip:pr_85_86_05 Yearly precipitation averages in the reference period 1986-2005 based on the climate scenario RCP8.5. precipitations EPSG:4326 CRS:84 -26.619999885559082 75.4600133895874 -6.819999933242798 45.10000681877136 Medicip:pr_djf_ENDREF_rcp26_opt Projected 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_opt WCS GeoTIFF EPSG:4326 CRS:84 -12.0 37.0 27.5 49.0 Medicip:pr_djf_ENDREF_rcp85_opt Projected 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_opt WCS GeoTIFF EPSG:4326 CRS:84 -12.0 37.0 27.5 49.0 Medicip:pr_djf_MIDREF_rcp26_opt Projected 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_opt WCS GeoTIFF EPSG:4326 CRS:84 -12.0 37.0 27.5 49.0 Medicip:pr_djf_MIDREF_rcp85_opt Projected 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_opt WCS GeoTIFF EPSG:4326 CRS:84 -12.0 37.0 27.5 49.0 Medicip:pr_djf_NEARREF_rcp26_opt Projected 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_opt WCS GeoTIFF EPSG:4326 CRS:84 -12.0 37.0 27.5 49.0 Medicip:pr_djf_NEARREF_rcp85_opt Projected 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_opt WCS GeoTIFF EPSG:4326 CRS:84 -12.0 37.0 27.5 49.0 Medicip:pr_jja_ENDREF_rcp26_opt Projected 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_opt WCS GeoTIFF EPSG:4326 CRS:84 -12.0 37.0 27.5 49.0 Medicip:pr_jja_ENDREF_rcp85_opt Projected 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_opt WCS GeoTIFF EPSG:4326 CRS:84 -12.0 37.0 27.5 49.0 Medicip:pr_jja_MIDREF_rcp26_opt Projected 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_opt WCS GeoTIFF EPSG:4326 CRS:84 -12.0 37.0 27.5 49.0 Medicip:pr_jja_MIDREF_rcp85_opt Projected 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_opt WCS GeoTIFF EPSG:4326 CRS:84 -12.0 37.0 27.5 49.0 Medicip:pr_jja_NEARREF_rcp26_opt Projected 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_opt WCS GeoTIFF EPSG:4326 CRS:84 -12.0 37.0 27.5 49.0 Medicip:pr_jja_NEARREF_rcp85_opt Projected 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_opt WCS GeoTIFF EPSG:4326 CRS:84 -12.0 37.0 27.5 49.0 Medicip:pr_mam_ENDREF_rcp26_opt Projected 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_opt WCS GeoTIFF EPSG:4326 CRS:84 -12.0 37.0 27.5 49.0 Medicip:pr_mam_ENDREF_rcp85_opt Projected 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_opt WCS GeoTIFF EPSG:4326 CRS:84 -12.0 37.0 27.5 49.0 Medicip:pr_mam_MIDREF_rcp26_opt Projected 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_opt WCS GeoTIFF EPSG:4326 CRS:84 -12.0 37.0 27.5 49.0 Medicip:pr_mam_MIDREF_rcp85_opt Projected 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_opt WCS GeoTIFF EPSG:4326 CRS:84 -12.0 37.0 27.5 49.0 Medicip:pr_mam_NEARREF_rcp26_opt Projected 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_opt WCS GeoTIFF EPSG:4326 CRS:84 -12.0 37.0 27.5 49.0 Medicip:pr_mam_NEARREF_rcp85_opt Projected 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_opt WCS GeoTIFF EPSG:4326 CRS:84 -12.0 37.0 27.5 49.0 Medicip:pr_son_ENDREF_rcp26_opt Projected 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_opt WCS GeoTIFF EPSG:4326 CRS:84 -12.0 37.0 27.5 49.0 Medicip:pr_son_ENDREF_rcp85_opt Projected 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_opt WCS GeoTIFF EPSG:4326 CRS:84 -12.0 37.0 27.5 49.0 Medicip:pr_son_MIDREF_rcp26_opt Projected 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_opt WCS GeoTIFF EPSG:4326 CRS:84 -12.0 37.0 27.5 49.0 Medicip:pr_son_MIDREF_rcp85_opt Projected 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_opt WCS GeoTIFF EPSG:4326 CRS:84 -12.0 37.0 27.5 49.0 Medicip:pr_son_NEARREF_rcp26_opt Projected 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_opt WCS GeoTIFF EPSG:4326 CRS:84 -12.0 37.0 27.5 49.0 Medicip:pr_son_NEARREF_rcp85_opt Projected 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_opt WCS GeoTIFF EPSG:4326 CRS:84 -12.0 37.0 27.5 49.0 cc_and_health:precip_2000_caf Total precipitations observed in 2000 in the Central African Republic. Precipitations EPSG:4326 CRS:84 14.417960310118321 27.47048138837497 2.218838751775218 11.013345383305333 cc_and_health:precip_2001_caf Total precipitations observed in 2001 in the Central African Republic. Precipitations EPSG:4326 CRS:84 14.417960310118321 27.47048138837497 2.218838751775218 11.013345383305333 cc_and_health:precip_2002_caf Total precipitations observed in 2002 in the Central African Republic. Precipitations EPSG:4326 CRS:84 14.417960310118321 27.47048138837497 2.218838751775218 11.013345383305333 cc_and_health:precip_2003_caf Total precipitations observed in 2003 in the Central African Republic. Precipitations EPSG:4326 CRS:84 14.417960310118321 27.47048138837497 2.218838751775218 11.013345383305333 cc_and_health:precip_2004_caf Total precipitations observed in 2004 in the Central African Republic. Precipitations EPSG:4326 CRS:84 14.417960310118321 27.47048138837497 2.218838751775218 11.013345383305333 cc_and_health:precip_2005_caf Total precipitations observed in 2005 in the Central African Republic. Precipitations EPSG:4326 CRS:84 14.417960310118321 27.47048138837497 2.218838751775218 11.013345383305333 cc_and_health:precip_2006_caf Total precipitations observed in 2006 in the Central African Republic. Precipitations EPSG:4326 CRS:84 14.417960310118321 27.47048138837497 2.218838751775218 11.013345383305333 cc_and_health:precip_2007_caf Total precipitations observed in 2007 in the Central African Republic. Precipitations EPSG:4326 CRS:84 14.417960310118321 27.47048138837497 2.218838751775218 11.013345383305333 cc_and_health:precip_2008_caf Total precipitations observed in 2008 in the Central African Republic. Precipitations EPSG:4326 CRS:84 14.417960310118321 27.47048138837497 2.218838751775218 11.013345383305333 cc_and_health:precip_2009_caf Total precipitations observed in 2009 in the Central African Republic. Precipitations EPSG:4326 CRS:84 14.417960310118321 27.47048138837497 2.218838751775218 11.013345383305333 cc_and_health:precip_2010_caf Total precipitations observed in 2010 in the Central African Republic. Precipitations EPSG:4326 CRS:84 14.417960310118321 27.47048138837497 2.218838751775218 11.013345383305333 cc_and_health:precip_2011_caf Total precipitations observed in 2011 in the Central African Republic. Precipitations EPSG:4326 CRS:84 14.417960310118321 27.47048138837497 2.218838751775218 11.013345383305333 cc_and_health:precip_2012_caf Total precipitations observed in 2012 in the Central African Republic. Precipitations EPSG:4326 CRS:84 14.417960310118321 27.47048138837497 2.218838751775218 11.013345383305333 cc_and_health:precip_2013_caf Total precipitations observed in 2013 in the Central African Republic. Precipitations EPSG:4326 CRS:84 14.417960310118321 27.47048138837497 2.218838751775218 11.013345383305333 cc_and_health:precip_2014_caf Total precipitations observed in 2014 in the Central African Republic. Precipitations EPSG:4326 CRS:84 14.417960310118321 27.47048138837497 2.218838751775218 11.013345383305333 cc_and_health:precip_2015_caf Total precipitations observed in 2015 in the Central African Republic. Precipitations EPSG:4326 CRS:84 14.417960310118321 27.47048138837497 2.218838751775218 11.013345383305333 cc_and_health:precip_2016_caf Total precipitations observed in 2016 in the Central African Republic. Precipitations EPSG:4326 CRS:84 14.417960310118321 27.47048138837497 2.218838751775218 11.013345383305333 cc_and_health:precip_2017_caf Total precipitations observed in 2017 in the Central African Republic. Precipitations EPSG:4326 CRS:84 14.417960310118321 27.47048138837497 2.218838751775218 11.013345383305333 cc_and_health:precip_2018_caf Total precipitations observed in 2018 in the Central African Republic. Precipitations EPSG:4326 CRS:84 14.417960310118321 27.47048138837497 2.218838751775218 11.013345383305333 cc_and_health:precip_2019_caf Total precipitations observed in 2019 in the Central African Republic. Precipitations EPSG:4326 CRS:84 14.417960310118321 27.47048138837497 2.218838751775218 11.013345383305333 cc_and_health:precip_2020_caf Total precipitations observed in 2020 in the Central African Republic. Precipitations EPSG:4326 CRS:84 14.417960310118321 27.47048138837497 2.218838751775218 11.013345383305333 cc_and_health:precip_2021_ref_caf Total precipitations observed in 2021 in the Central African Republic. Reference layer. Precipitations EPSG:4326 CRS:84 14.41796031 27.470481388 2.218838752 11.013345383 unea_pollution:prot2 The World Database on Protected Areas (WDPA) is the most comprehensive global database on terrestrial and marine protected areas. prot2 WCS GeoTIFF IUCN WCMC EPSG:4326 CRS:84 -179.91959083499992 179.77206144400006 -55.61075097999992 83.13924347000008 text/plain unea_pollution_nd:prot_marine_iucn IUCN 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 legislation prot_marine_iucn WCS GeoTIFF WCMC reserve national park Ramsar Biosphere Heritage EPSG:4326 CRS:84 -179.99998853999992 179.99999706000008 -63.99999999999994 83.73332742400007 MapX_UNDP:prrarity 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> prrarity WCS GeoTIFF EPSG:4326 CRS:84 -179.99999501081106 180.00000498918894 -89.99967927984171 90.00032072015829 text/plain MapX_UNDP:rarity_weighted_richness_opt <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_opt WCS GeoTIFF EPSG:4326 CRS:84 -179.995201157628 179.8859620772232 -84.69466806618583 85.91771612300938 grid_data_core:realized_clean_water_prov_opt <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_opt WCS GeoTIFF EPSG:4326 CRS:84 -179.58826144943154 179.5477094321325 -55.983070494175905 83.99999999995521 MapX_UNDP:richness 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> richness WCS GeoTIFF EPSG:4326 CRS:84 -179.99999501081106 180.00000498918894 -89.99967927984171 90.00032072015829 text/plain wesr_risk:rivers features river EPSG:4326 CRS:84 -161.879 176.873 -49.7278 71.53 grid_data_core:roads_esri features roads_esri EPSG:4326 CRS:84 -159.789321899414 180.214218139648 -34.6280746459961 70.44384765625 MapX_UNDP:rrarity 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> rrarity WCS GeoTIFF EPSG:4326 CRS:84 -179.99999501081106 180.00000498918894 -89.99967927984171 90.00032072015829 text/plain MapX_UNDP:samericaFI2 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/> samericaFI2 WCS GeoTIFF EPSG:4326 CRS:84 -81.70875583396088 -34.24721833519705 -20.582447168229265 13.047396176983916 MapX_UNDP:samericaFI3 samericaFI_opt2 WCS GeoTIFF EPSG:4326 CRS:84 -81.70902543656312 -34.24694876580037 -20.630653749343182 13.092434648616072 MapX_UNDP:samericaSCI2 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/> samericaSCI2 WCS GeoTIFF EPSG:4326 CRS:84 -81.70875583396088 -34.24721833519705 -20.582447168229265 13.047396176983916 MapX_UNDP:samericaSCI3 samericaSCI WCS GeoTIFF EPSG:4326 CRS:84 -81.70902543656312 -34.24694876580037 -20.63065374934316 13.092434648616072 MapX_UNDP:samerica_forheight samerica_forheight_opt WCS GeoTIFF EPSG:4326 CRS:84 -117.0005 -33.9995 -32.0005 32.0005 UN_Sahel:scPDSI.cru_ts403_1960-1969 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_Mean60s WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 text/plain UN_Sahel:scPDSI.cru_ts403_1970-1979 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_Mean70s WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 text/plain UN_Sahel:scPDSI.cru_ts403_1980-1989 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_Mean80s WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 text/plain UN_Sahel:scPDSI.cru_ts403_1990-1999 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_Mean90s WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 text/plain UN_Sahel:scPDSI.cru_ts403_2000-2009 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_Mean00s WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 text/plain UN_Sahel:scPDSI.cru_ts403_2010-2018 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_Mean10s WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 text/plain UN_Sahel:sdg1531_sahel_opt This 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 VLab sdg1531_sahel_opt WCS GeoTIFF EPSG:4326 CRS:84 -25.357194800661915 51.4128293216946 -0.0336866878891016 37.54284158619938 text/plain ECO-DRR:seagrakm2a seagrakm2a WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 ECO-DRR:seagrakm2a_P3 seagrakm2a WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 mapx_chemobs:sen_natural_hazards sen_all_hazards_merged_dissolve_4326_opt WCS GeoTIFF EPSG:4326 CRS:84 -17.553355291229398 -11.281251799204284 12.295673564888503 16.692700606714585 mapx_chemobs:sen_natural_hazards_exposure features sen_natural_hazards_exposure_fixed_buffer_10cm EPSG:32628 CRS:84 -17.553356309302842 -11.281279712807224 12.295762807806764 16.6921903507492 mapx_chemobs:sen_slope dem90_sen_reproj_slope_deg_wgs84_opt WCS GeoTIFF EPSG:4326 CRS:84 -17.593971748225744 -11.270009072589186 12.283748773075564 16.72443886379466 mapx_chemobs:sen_wb_buffer sen_wb_buffer_4326_opt WCS GeoTIFF EPSG:4326 CRS:84 -17.55246539577028 -11.263233441297636 12.283484750909368 16.697303506881106 mapx_chemobs:sen_wb_buffer_100m features sen_wb_buffer_100m EPSG:4326 CRS:84 -17.528482082499128 -11.332683451847313 12.295406935298832 16.69730488090794 MAPX:sentinel2_sierra_leone_20170119 SierraLeone_TrueColor_20170119_high_fullTile_opt_deflate WCS GeoTIFF EPSG:4326 CRS:84 -12.278532425848166 -11.274042950588264 8.036744648101147 9.036590153051195 MAPX:sentinel2_sierra_leone_20180208 SierraLeone_TrueColor_20180208_high_fullTile_opt_deflate WCS GeoTIFF EPSG:4326 CRS:84 -12.278532425848166 -11.274042950588264 8.036744648101147 9.036590153051195 TEEB:sequest_fut_BAU_wgs84_opt sequest_fut_BAU_wgs84_opt WCS GeoTIFF EPSG:4326 CRS:84 34.553167266769464 37.239835341004294 -10.038048450490551 -7.663994368030097 TEEB:sequest_fut_BAUwc_wgs84_opt sequest_fut_BAUwc_wgs84_opt WCS GeoTIFF EPSG:4326 CRS:84 34.553167266769464 37.239835341004294 -10.038048450490551 -7.663994368030097 TEEB:sequest_fut_SAGCOT_wgs84_opt sequest_fut_SAGCOT_wgs84_opt WCS GeoTIFF EPSG:4326 CRS:84 34.553167266769464 37.239835341004294 -10.038048450490551 -7.663994368030097 TEEB:sequest_fut_SAGCOTint_wgs84_opt sequest_fut_SAGCOTint_wgs84_opt WCS GeoTIFF EPSG:4326 CRS:84 34.553167266769464 37.239835341004294 -10.038048450490551 -7.663994368030097 TEEB:sequest_fut_SAGCOTwc_wgs84_opt sequest_fut_SAGCOTwc_wgs84_opt WCS GeoTIFF EPSG:4326 CRS:84 34.553167266769464 37.239835341004294 -10.038048450490551 -7.663994368030097 mapx_chemobs:seychelles_remote_site_screening_res50 seychelles_remote_site_screening_res50 WCS GeoTIFF EPSG:4326 CRS:84 46.19920349100001 56.279703491000014 -10.193519002999965 -3.7115190029999647 text/plain hotspots:site_0016_visual_20150227_lc08 site_0016_visual_20150227_lc08 WCS GeoTIFF EPSG:32636 CRS:84 32.60687054879944 32.907211866267744 0.0210345058649999 0.3499948245599756 hotspots:site_0336_19860908_math site_0336_19860908_math WCS GeoTIFF EPSG:32634 CRS:84 21.804453393351142 22.972377367092143 2.163950346428799 3.360478793316272 hotspots:site_0336_19860908_math_float site_0336_19860908_math_float WCS GeoTIFF EPSG:32634 CRS:84 21.804453393351142 22.972377367092143 2.163950346428799 3.360478793316272 hotspots:site_0336_19860908_math_uint16 site_0336_19860908_math_uint16 WCS GeoTIFF EPSG:32634 CRS:84 21.804453393351142 22.972377367092143 2.163950346428799 3.360478793316272 hotspots:site_0336_19860908_visual site_0336_19860908_visual WCS GeoTIFF EPSG:32634 CRS:84 21.804453393351142 22.972377367092143 2.163950346428799 3.360478793316272 hotspots:site_0336_19860908_visual_average site_0336_19860908_visual_average WCS GeoTIFF EPSG:32634 CRS:84 21.804453393351142 22.972377367092143 2.163950346428799 3.360478793316272 hotspots:site_0336_19860908_visual_bilinear site_0336_19860908_visual_bilinear WCS GeoTIFF EPSG:32634 CRS:84 21.804453393351142 22.972377367092143 2.163950346428799 3.360478793316272 hotspots:site_0336_19860908_visual_gauss site_0336_19860908_visual_gauss WCS GeoTIFF EPSG:32634 CRS:84 21.804453393351142 22.972377367092143 2.163950346428799 3.360478793316272 hotspots:site_0336_19860908_visual_mode site_0336_19860908_visual_mode WCS GeoTIFF EPSG:32634 CRS:84 21.804453393351142 22.972377367092143 2.163950346428799 3.360478793316272 hotspots:site_0336_19860908_visual_nearest site_0336_19860908_visual_nearest WCS GeoTIFF EPSG:32634 CRS:84 21.804453393351142 22.972377367092143 2.163950346428799 3.360478793316272 hotspots:site_0336_19860908_visual_rms site_0336_19860908_visual_rms WCS GeoTIFF EPSG:32634 CRS:84 21.804453393351142 22.972377367092143 2.163950346428799 3.360478793316272 hotspots:site_0336_20010128_enhanced site_0336_20010128_enhanced WCS GeoTIFF EPSG:32634 CRS:84 21.804453393351142 22.972377367092143 2.163950346428799 3.360478793316272 hotspots:site_0336_20010128_enhanced_cog site_0336_20010128_enhanced_cog WCS GeoTIFF EPSG:32634 CRS:84 21.804453393351142 22.972377367092143 2.163950346428799 3.360478793316272 hotspots:site_0336_20010128_raw site_0336_20010128_raw WCS GeoTIFF EPSG:32634 CRS:84 21.804453393351142 22.972377367092143 2.163950346428799 3.360478793316272 hotspots:site_0336_20010128_raw_cog site_0336_20010128_raw_cog WCS GeoTIFF EPSG:32634 CRS:84 21.804453393351142 22.972377367092143 2.163950346428799 3.360478793316272 MapX_UNDP:soc_dif_hi This 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_hi WCS GeoTIFF EPSG:4326 CRS:84 -180.0 179.9999424 -62.002087813 87.37080495400002 text/plain MapX_UNDP:soc_dif_me This 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_me WCS GeoTIFF EPSG:4326 CRS:84 -180.0 179.9999424 -62.002087813 87.37080495400002 text/plain mapx_chemobs:south_africa_remote_site_screening_res50 south_africa_remote_site_screening_res50 WCS GeoTIFF EPSG:4326 CRS:84 16.447713889000067 38.00071388900007 -46.979109999999956 -22.126609999999957 text/plain MapX_UNDP:species_richness_opt This 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_opt WCS GeoTIFF EPSG:4326 CRS:84 -179.995201157628 179.8859620772232 -84.69466806618583 85.91771612300938 wesr_risk:spectral_acceleration_02_s_1500_yrp spectral_acceleration_02_s_1500_yrp WCS GeoTIFF EPSG:4326 CRS:84 -180.25 180.25 -90.25 90.25 wesr_risk:spectral_acceleration_02_s_2475_yrp spectral_acceleration_02_s_2475_yrp WCS GeoTIFF EPSG:4326 CRS:84 -180.25 180.25 -90.25 90.25 wesr_risk:spectral_acceleration_02_s_250_yrp spectral_acceleration_02_s_250_yrp WCS GeoTIFF EPSG:4326 CRS:84 -180.25 180.25 -90.25 90.25 wesr_risk:spectral_acceleration_02_s_475_yrp spectral_acceleration_02_s_475_yrp WCS GeoTIFF EPSG:4326 CRS:84 -180.25 180.25 -90.25 90.25 wesr_risk:spectral_acceleration_02_s_975_yrp spectral_acceleration_02_s_975_yrp WCS GeoTIFF EPSG:4326 CRS:84 -180.25 180.25 -90.25 90.25 wesr_risk:spectral_acceleration_05_s_1500_yrp spectral_acceleration_05_s_1500_yrp WCS GeoTIFF EPSG:4326 CRS:84 -180.25 180.25 -90.25 90.25 wesr_risk:spectral_acceleration_05_s_2475_yrp spectral_acceleration_05_s_2475_yrp WCS GeoTIFF EPSG:4326 CRS:84 -180.25 180.25 -90.25 90.25 wesr_risk:spectral_acceleration_05_s_250_yrp spectral_acceleration_05_s_250_yrp WCS GeoTIFF EPSG:4326 CRS:84 -180.25 180.25 -90.25 90.25 wesr_risk:spectral_acceleration_05_s_475_yrp spectral_acceleration_05_s_475_yrp WCS GeoTIFF EPSG:4326 CRS:84 -180.25 180.25 -90.25 90.25 wesr_risk:spectral_acceleration_05_s_975_yrp spectral_acceleration_05_s_975_yrp WCS GeoTIFF EPSG:4326 CRS:84 -180.25 180.25 -90.25 90.25 wesr_risk:spectral_acceleration_10_s_1500_yrp spectral_acceleration_10_s_1500_yrp WCS GeoTIFF EPSG:4326 CRS:84 -180.25 180.25 -90.25 90.25 wesr_risk:spectral_acceleration_10_s_2475_yrp spectral_acceleration_10_s_2475_yrp WCS GeoTIFF EPSG:4326 CRS:84 -180.25 180.25 -90.25 90.25 wesr_risk:spectral_acceleration_10_s_250_yrp spectral_acceleration_10_s_250_yrp WCS GeoTIFF EPSG:4326 CRS:84 -180.25 180.25 -90.25 90.25 wesr_risk:spectral_acceleration_10_s_475_yrp spectral_acceleration_10_s_475_yrp WCS GeoTIFF EPSG:4326 CRS:84 -180.25 180.25 -90.25 90.25 wesr_risk:spectral_acceleration_10_s_975_yrp spectral_acceleration_10_s_975_yrp WCS GeoTIFF EPSG:4326 CRS:84 -180.25 180.25 -90.25 90.25 LWP:sresa2_bccr_bcm2_0_5km_2041-2060_debiasedmean_tas_av_12month_opt 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_bccr_bcm2_0_5km_2041-2060_debiasedmean_tas_av_12month_opt WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.00002016000002 -60.0 90.00000840000001 text/plain LWP:sresa2_cccma_cgcm3_1_5km_2041-2060_debiasedmean_tas_av_12month_opt 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_cccma_cgcm3_1_5km_2041-2060_debiasedmean_tas_av_12month_opt WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.00002016000002 -60.0 90.00000840000001 text/plain LWP:sresa2_cnrm_cm3_5km_2041-2060_debiasedmean_tas_av_12month_opt 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_cnrm_cm3_5km_2041-2060_debiasedmean_tas_av_12month_opt WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.00002016000002 -60.0 90.00000840000001 text/plain LWP:sresa2_csiro_mk3_0_5km_2041-2060_debiasedmean_tas_av_12month_opt 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_csiro_mk3_0_5km_2041-2060_debiasedmean_tas_av_12month_opt WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.00002016000002 -60.0 90.00000840000001 text/plain LWP:sresa2_csiro_mk3_5_5km_2041-2060_debiasedmean_tas_av_12month_opt 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_csiro_mk3_5_5km_2041-2060_debiasedmean_tas_av_12month_opt WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.00002016000002 -60.0 90.00000840000001 text/plain LWP:sresa2_gfdl_cm2_0_5km_2041-2060_debiasedmean_tas_av_12mon_opt 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_gfdl_cm2_0_5km_2041-2060_debiasedmean_tas_av_12mon_opt WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.00002016000002 -60.0 90.00000840000001 text/plain LWP:sresa2_gfdl_cm2_1_5km_2041-2060_debiasedmean_tas_av_12m_opt 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_gfdl_cm2_1_5km_2041-2060_debiasedmean_tas_av_12m_opt WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.00002016000002 -60.0 90.00000840000001 text/plain LWP:sresa2_giss_model_e_r_5km_2041-2060_debiasedmean_tas_av_12m_opt 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_giss_model_e_r_5km_2041-2060_debiasedmean_tas_av_12m_opt WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.00288 -60.0 90.00120000000001 text/plain LWP:sresa2_ingv_echam4_5km_2041-2060_debiasedmean_tas_av_12m_opt 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_ingv_echam4_5km_2041-2060_debiasedmean_tas_av_12m_opt WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.00002016000002 -60.0 90.00000840000001 text/plain LWP:sresa2_inmcm3_0_5km_2041-2060_debiasedmean_tas_av_12m_opt 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_inmcm3_0_5km_2041-2060_debiasedmean_tas_av_12m_opt WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.00002016000002 -60.0 90.00000840000001 text/plain LWP:sresa2_ipsl_cm4_5km_2041-2060_debiasedmean_tas_av_12m_opt 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_ipsl_cm4_5km_2041-2060_debiasedmean_tas_av_12m_opt WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.00002016000002 -60.0 90.00000840000001 text/plain LWP:sresa2_miroc3_2_medres_5km_2041-2060_debiasedmean_tas_av_12m_opt 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_miroc3_2_medres_5km_2041-2060_debiasedmean_tas_av_12m_opt WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.00002016000002 -60.0 90.00000840000001 text/plain LWP:sresa2_miub_echo_g_5km_2041-2060_debiasedmean_tas_av12mon_opt 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_miub_echo_g_5km_2041-2060_debiasedmean_tas_av12mon_opt WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.00002016000002 -60.0 90.00000840000001 text/plain LWP:sresa2_mpi_echam5_5km_2041-2060_debiasedmean_tas_av12mon_opt 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_mpi_echam5_5km_2041-2060_debiasedmean_tas_av12mon_opt WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.00002016000002 -60.0 90.00000840000001 text/plain LWP:sresa2_mri_cgcm2_3_2a_5km_2041-2060_debiasedmean_tas_12mav_opt 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_mri_cgcm2_3_2a_5km_2041-2060_debiasedmean_tas_12mav_opt WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.00002016000002 -60.0 90.00000840000001 text/plain LWP:sresa2_ncar_ccsm3_0_5km_2041-2060_debiasedmean_tas_av12_opt 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_ncar_ccsm3_0_5km_2041-2060_debiasedmean_tas_av12_opt WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.00002016000002 -60.0 90.00000840000001 text/plain LWP:sresa2_ncar_pcm1_5km_2041-2060_debiasedmean_tas_av12_opt 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_ncar_pcm1_5km_2041-2060_debiasedmean_tas_av12_opt WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.00002016000002 -60.0 90.00000840000001 text/plain LWP:sresa2_ukmo_hadcm3_5km_2041-2060_debiasedmean_tas_av12_opt "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_opt WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.00002016000002 -60.0 90.00000840000001 text/plain mapx_chemobs:tanzania_remote_site_screening_res50 tanzania_remote_site_screening_res50 WCS GeoTIFF EPSG:4326 CRS:84 29.340830109000006 40.44333010900001 -11.761432042999948 -0.9844320429999465 text/plain Medicip:tas_45_46_65 Temperature anomalies (°C) for the period 2045-2065 based on the climate scenario RCP4.5 (reference 1986-2005). temperatures EPSG:4326 CRS:84 -26.619999885559082 75.4600133895874 -6.819999933242798 45.10000681877136 Medicip:tas_45_81_00 Temperature anomalies (°C) for the period 2081-2100 based on the climate scenario RCP4.5 (reference 1986-2005). temperatures EPSG:4326 CRS:84 -26.619999885559082 75.4600133895874 -6.819999933242798 45.10000681877136 Medicip:tas_45_86_05 Temperatures averages in the reference period 1986-2005 based on the climate scenario RCP4.5. temperatures EPSG:4326 CRS:84 -26.619999885559082 75.4600133895874 -6.819999933242798 45.10000681877136 Medicip:tas_85_46_65 Temperature anomalies (°C) for the period 2045-2065 based on the climate scenario RCP8.5 (reference 1986-2005). temperatures EPSG:4326 CRS:84 -26.619999885559082 75.4600133895874 -6.819999933242798 45.10000681877136 Medicip:tas_85_81_00 Temperature anomalies (°C) for the period 2081-2100 based on the climate scenario RCP8.5 (reference 1986-2005). temperatures EPSG:4326 CRS:84 -26.619999885559082 75.4600133895874 -6.819999933242798 45.10000681877136 Medicip:tas_85_86_05 Temperatures averages in the reference period 1986-2005 based on the climate scenario RCP8.5. temperatures EPSG:4326 CRS:84 -26.619999885559082 75.4600133895874 -6.819999933242798 45.10000681877136 Medicip:tas_djf_ENDREF_rcp26_opt Projected 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_opt WCS GeoTIFF EPSG:4326 CRS:84 -12.0 37.0 27.5 49.0 Medicip:tas_djf_ENDREF_rcp85_opt Projected 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_opt WCS GeoTIFF EPSG:4326 CRS:84 -12.0 37.0 27.5 49.0 Medicip:tas_djf_MIDREF_rcp26_opt Projected 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_opt WCS GeoTIFF EPSG:4326 CRS:84 -12.0 37.0 27.5 49.0 Medicip:tas_djf_MIDREF_rcp85_opt Projected 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_opt WCS GeoTIFF EPSG:4326 CRS:84 -12.0 37.0 27.5 49.0 Medicip:tas_djf_NEARREF_rcp26_opt Projected 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_opt WCS GeoTIFF EPSG:4326 CRS:84 -12.0 37.0 27.5 49.0 Medicip:tas_djf_NEARREF_rcp85_opt Projected 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_opt WCS GeoTIFF EPSG:4326 CRS:84 -12.0 37.0 27.5 49.0 Medicip:tas_jja_ENDREF_rcp26_opt Projected 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_opt WCS GeoTIFF EPSG:4326 CRS:84 -12.0 37.0 27.5 49.0 Medicip:tas_jja_ENDREF_rcp85_opt Projected 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_opt WCS GeoTIFF EPSG:4326 CRS:84 -12.0 37.0 27.5 49.0 Medicip:tas_jja_MIDREF_rcp26_opt Projected 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_opt WCS GeoTIFF EPSG:4326 CRS:84 -12.0 37.0 27.5 49.0 Medicip:tas_jja_MIDREF_rcp85_opt Projected 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_opt WCS GeoTIFF EPSG:4326 CRS:84 -12.0 37.0 27.5 49.0 Medicip:tas_jja_NEARREF_rcp26_opt Projected 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_opt WCS GeoTIFF EPSG:4326 CRS:84 -12.0 37.0 27.5 49.0 Medicip:tas_jja_NEARREF_rcp85_opt Projected 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_opt WCS GeoTIFF EPSG:4326 CRS:84 -12.0 37.0 27.5 49.0 Medicip:tas_mam_ENDREF_rcp26_opt Projected 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_opt WCS GeoTIFF EPSG:4326 CRS:84 -12.0 37.0 27.5 49.0 Medicip:tas_mam_ENDREF_rcp85_opt Projected 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_opt WCS GeoTIFF EPSG:4326 CRS:84 -12.0 37.0 27.5 49.0 Medicip:tas_mam_MIDREF_rcp26_opt Projected 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_opt WCS GeoTIFF EPSG:4326 CRS:84 -12.0 37.0 27.5 49.0 Medicip:tas_mam_MIDREF_rcp85_opt Projected 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_opt WCS GeoTIFF EPSG:4326 CRS:84 -12.0 37.0 27.5 49.0 Medicip:tas_mam_NEARREF_rcp26_opt Projected 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_opt WCS GeoTIFF EPSG:4326 CRS:84 -12.0 37.0 27.5 49.0 Medicip:tas_mam_NEARREF_rcp85_opt Projected 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_opt WCS GeoTIFF EPSG:4326 CRS:84 -12.0 37.0 27.5 49.0 Medicip:tas_son_ENDREF_rcp26_opt Projected 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_opt WCS GeoTIFF EPSG:4326 CRS:84 -12.0 37.0 27.5 49.0 Medicip:tas_son_ENDREF_rcp85_opt Projected 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_opt WCS GeoTIFF EPSG:4326 CRS:84 -12.0 37.0 27.5 49.0 Medicip:tas_son_MIDREF_rcp26_opt Projected 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_opt WCS GeoTIFF EPSG:4326 CRS:84 -12.0 37.0 27.5 49.0 Medicip:tas_son_MIDREF_rcp85_opt Projected 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_opt WCS GeoTIFF EPSG:4326 CRS:84 -12.0 37.0 27.5 49.0 Medicip:tas_son_NEARREF_rcp26_opt Projected 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_opt WCS GeoTIFF EPSG:4326 CRS:84 -12.0 37.0 27.5 49.0 Medicip:tas_son_NEARREF_rcp85_opt Projected 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_opt WCS GeoTIFF EPSG:4326 CRS:84 -12.0 37.0 27.5 49.0 cc_and_health:temp_2023_x_precip_2021_caf This 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. Precipitations Temperature EPSG:4326 CRS:84 14.41796031 27.470481388 2.218838752 11.013345383 MapX_UNDP:threatened_species_richness_opt This 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_opt WCS GeoTIFF EPSG:4326 CRS:84 -179.995201157628 179.8859620772232 -84.69466806618583 85.91771612300938 unccd:total_extreme_events_Automn_output_opt total_extreme_events_Automn_output_opt WCS GeoTIFF EPSG:4326 CRS:84 -17.999999999999943 75.00000000000006 -1.4999999999999432 45.00000000000006 unccd:total_extreme_events_spring_output_opt total_extreme_events_spring_output_opt WCS GeoTIFF EPSG:4326 CRS:84 -17.999999999999943 75.00000000000006 -1.4999999999999432 45.00000000000006 unccd:total_extreme_events_summer_output_opt total_extreme_events_summer_output_opt WCS GeoTIFF EPSG:4326 CRS:84 -17.999999999999943 75.00000000000006 -1.4999999999999432 45.00000000000006 grid_data_core:total_extreme_events_winter_output_opt total_extreme_events_winter_output_opt WCS GeoTIFF EPSG:4326 CRS:84 -17.999999999999943 75.00000000000006 -1.4999999999999432 45.00000000000006 grid_data_core:total_strong_events_Automn_output_opt total_strong_events_Automn_output_opt WCS GeoTIFF EPSG:4326 CRS:84 -17.999999999999943 75.00000000000006 -1.4999999999999432 45.00000000000006 grid_data_core:total_strong_events_spring_output_opt total_strong_events_spring_output_opt WCS GeoTIFF EPSG:4326 CRS:84 -17.999999999999943 75.00000000000006 -1.4999999999999432 45.00000000000006 grid_data_core:total_strong_events_summer_output_opt total_strong_events_summer_output_opt WCS GeoTIFF EPSG:4326 CRS:84 -17.999999999999943 75.00000000000006 -1.4999999999999432 45.00000000000006 unccd:total_strong_events_winter_output_opt total_strong_events_winter_output_opt WCS GeoTIFF EPSG:4326 CRS:84 -17.999999999999943 75.00000000000006 -1.4999999999999432 45.00000000000006 peatlands:tree_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_opt WCS GeoTIFF EPSG:4326 CRS:84 10.960797274810812 31.421883855574613 -13.500019913075064 5.46454493380093 peatlands:tree_mosaic_mask_wgs84_opt Forest 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_opt WCS GeoTIFF EPSG:4326 CRS:84 11.089197577536725 31.362924324738536 -13.463462043356328 5.428044711016847 MapX_UNDP:trop-subtrop_wetlandv2_2016_cifor_opt This 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_opt WCS GeoTIFF EPSG:4326 CRS:84 -179.99999777114783 179.99926251758217 -60.001279841670446 40.00080455854803 text/plain wesr_risk:ts_ecoexp This 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_ecoexp WCS GeoTIFF EPSG:4326 CRS:84 -180.0 179.99999999985602 -90.0 83.99999999993042 ECO-DRR:ts_freqaf ts_freqaf WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 ECO-DRR:ts_freqaf_P3 ts_freqaf WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 ECO-DRR:ts_phex ts_phex WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 ECO-DRR:ts_phex_P3 ts_phex WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 wesr_risk:ts_physexp This 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_physexp WCS GeoTIFF EPSG:4326 CRS:84 -180.0 179.99999999985602 -90.0 83.99999999993042 rs_for_asgm:tsm_col_zaragoza_20210118 tsm_col_zaragoza_20210118 WCS GeoTIFF EPSG:32618 CRS:84 -74.8651063820499 -74.7899249680916 7.579889039548307 7.640072688469065 rs_for_asgm:tsm_col_zaragoza_20210309 tsm_col_zaragoza_20210309 WCS GeoTIFF EPSG:32618 CRS:84 -74.8651063820499 -74.7899249680916 7.579889039548307 7.640072688469065 rs_for_asgm:tsm_col_zaragoza_20210617 tsm_col_zaragoza_20210617 WCS GeoTIFF EPSG:32618 CRS:84 -74.8651063820499 -74.7899249680916 7.579889039548307 7.640072688469065 rs_for_asgm:tsm_col_zaragoza_20210712 tsm_col_zaragoza_20210712 WCS GeoTIFF EPSG:32618 CRS:84 -74.8651063820499 -74.7899249680916 7.579889039548307 7.640072688469065 rs_for_asgm:tsm_col_zaragoza_20210821 tsm_col_zaragoza_20210821 WCS GeoTIFF EPSG:32618 CRS:84 -74.8651063820499 -74.7899249680916 7.579889039548307 7.640072688469065 rs_for_asgm:tsm_col_zaragoza_20210925 tsm_col_zaragoza_20210925 WCS GeoTIFF EPSG:32618 CRS:84 -74.8651063820499 -74.7899249680916 7.579889039548307 7.640072688469065 rs_for_asgm:tsm_col_zaragoza_20211025 tsm_col_zaragoza_20211025 WCS GeoTIFF EPSG:32618 CRS:84 -74.8651063820499 -74.7899249680916 7.579889039548307 7.640072688469065 rs_for_asgm:tsm_mambo_bado_20190220 tsm_mambo_bado_20190220 WCS GeoTIFF EPSG:32735 CRS:84 27.5809572578242 27.610087434421537 1.3227299095392917 1.351053414069064 rs_for_asgm:tsm_mambo_bado_20190312 tsm_mambo_bado_20190312 WCS GeoTIFF EPSG:32735 CRS:84 27.5809572578242 27.610087434421537 1.3227299095392917 1.351053414069064 rs_for_asgm:tsm_mambo_bado_20190824 tsm_mambo_bado_20190824 WCS GeoTIFF EPSG:32735 CRS:84 27.5809572578242 27.610087434421537 1.3227299095392917 1.351053414069064 rs_for_asgm:tsm_mambo_bado_20190903 tsm_mambo_bado_20190903 WCS GeoTIFF EPSG:32735 CRS:84 27.5809572578242 27.610087434421537 1.3227299095392917 1.351053414069064 rs_for_asgm:tsm_mambo_bado_20191227 tsm_mambo_bado_20191227 WCS GeoTIFF EPSG:32735 CRS:84 27.5809572578242 27.610087434421537 1.3227299095392917 1.351053414069064 rs_for_asgm:tsm_mambo_bado_20200116 tsm_mambo_bado_20200116 WCS GeoTIFF EPSG:32735 CRS:84 27.5809572578242 27.610087434421537 1.3227299095392917 1.351053414069064 rs_for_asgm:tsm_mambo_bado_20200331 tsm_mambo_bado_20200331 WCS GeoTIFF EPSG:32735 CRS:84 27.5809572578242 27.610087434421537 1.3227299095392917 1.351053414069064 rs_for_asgm:tsm_mambo_bado_20200515 tsm_mambo_bado_20200515 WCS GeoTIFF EPSG:32735 CRS:84 27.5809572578242 27.610087434421537 1.3227299095392917 1.351053414069064 rs_for_asgm:tsm_mambo_bado_20200624 tsm_mambo_bado_20200624 WCS GeoTIFF EPSG:32735 CRS:84 27.5809572578242 27.610087434421537 1.3227299095392917 1.351053414069064 rs_for_asgm:tsm_mambo_bado_20200724 tsm_mambo_bado_20200724 WCS GeoTIFF EPSG:32735 CRS:84 27.5809572578242 27.610087434421537 1.3227299095392917 1.351053414069064 rs_for_asgm:tsm_mambo_bado_20200922 tsm_mambo_bado_20200922 WCS GeoTIFF EPSG:32735 CRS:84 27.5809572578242 27.610087434421537 1.3227299095392917 1.351053414069064 rs_for_asgm:tsm_mambo_bado_20201201 tsm_mambo_bado_20201201 WCS GeoTIFF EPSG:32735 CRS:84 27.5809572578242 27.610087434421537 1.3227299095392917 1.351053414069064 rs_for_asgm:tsm_mambo_bado_20210130 tsm_mambo_bado_20210130 WCS GeoTIFF EPSG:32735 CRS:84 27.5809572578242 27.610087434421537 1.3227299095392917 1.351053414069064 rs_for_asgm:tsm_mambo_bado_20210311 tsm_mambo_bado_20210311 WCS GeoTIFF EPSG:32735 CRS:84 27.5809572578242 27.610087434421537 1.3227299095392917 1.351053414069064 rs_for_asgm:tsm_mambo_bado_20210410 tsm_mambo_bado_20210410 WCS GeoTIFF EPSG:32735 CRS:84 27.5809572578242 27.610087434421537 1.3227299095392917 1.351053414069064 rs_for_asgm:tsm_mambo_bado_20210520 tsm_mambo_bado_20210520 WCS GeoTIFF EPSG:32735 CRS:84 27.5809572578242 27.610087434421537 1.3227299095392917 1.351053414069064 rs_for_asgm:tsm_mambo_bado_20210709 tsm_mambo_bado_20210709 WCS GeoTIFF EPSG:32735 CRS:84 27.5809572578242 27.610087434421537 1.3227299095392917 1.351053414069064 mapx_chemobs:tunisia_remote_site_screening_res50 tunisia_remote_site_screening_res50 WCS GeoTIFF EPSG:4326 CRS:84 7.5281449999999825 11.598144999999983 30.245430054000035 37.543930054000036 text/plain mapx_chemobs:tza_natural_hazards tza_all_hazards_merged_dissolve_4326_opt WCS GeoTIFF EPSG:4326 CRS:84 29.441079233725127 40.450152794534304 -11.772081707731523 -0.981548292482449 mapx_chemobs:tza_natural_hazards_exposure features tza_natural_hazards_exposure_fixed_buffer_10cm EPSG:32737 CRS:84 29.441078353308356 40.45015746710648 -11.771764043500042 -0.9815888211757782 mapx_chemobs:tza_slope dem90_tza_reproj_slope_deg_wgs84_opt WCS GeoTIFF EPSG:4326 CRS:84 29.12132096256551 40.47638687416472 -11.910880187327674 -0.9716805428762081 mapx_chemobs:tza_wb_buffer tza_wb_buffer_4326_opt WCS GeoTIFF EPSG:4326 CRS:84 29.42290987105417 40.54185706243741 -12.249352377620772 0.418418794759549 mapx_chemobs:tza_wb_buffer_100m features tza_wb_buffer_100m EPSG:4326 CRS:84 29.602962897647817 40.5327712775776 -12.231888476493026 0.416259661526923 grid_data_core_pro:u4_modis500 WCS GeoTIFF u4_modis500 EPSG:4326 CRS:84 -180.0020835 180.0267165 -89.99148149999999 90.0020835 grid_data_core_pro:ungiwg_bnd_ln_01m UNmap is a global multi-scale geospatial dataset that is designed to serve as a base geodatabase for map production and web mapping to meet the needs of the United Nations Secretariat in supporting the work of the Security Council and field operations. It consists of basic geospatial information at different scales. features ungiwg_bnd_ln_01m EPSG:4326 CRS:84 -142.412094116211 142.429565429688 -55.743522644043 70.7178955078125 grid_data_core_pro:ungiwg_bnd_ply_01m UNmap is a global multi-scale geospatial dataset that is designed to serve as a base geodatabase for map production and web mapping to meet the needs of the United Nations Secretariat in supporting the work of the Security Council and field operations. It consists of basic geospatial information at different scales. ungiwg_bnd_ply_01m features EPSG:4326 CRS:84 -181.800018310547 181.800018310547 -90.7676467895508 84.4950561523438 grid_data_core_pro:ungiwg_cst_ln_01m UNmap is a global multi-scale geospatial dataset that is designed to serve as a base geodatabase for map production and web mapping to meet the needs of the United Nations Secretariat in supporting the work of the Security Council and field operations. It consists of basic geospatial information at different scales. ungiwg_cst_ln_01m features EPSG:4326 CRS:84 -181.800018310547 181.800018310547 -86.4279861450195 84.3074951171875 wesr_risk:urban_assets features v0_urban EPSG:4326 CRS:84 -179.13 178.443 -53.1674 70.9927 wesr_climat:v432_CH4_1970_opt_deflate v432_CH4_1970_opt_deflate WCS GeoTIFF EPSG:4326 CRS:84 -180.05 179.95 -77.95 88.15000000000002 wesr_climat:v432_CH4_1990_opt_deflate v432_CH4_1990_opt_deflate WCS GeoTIFF EPSG:4326 CRS:84 -180.05 179.95 -77.95 88.15000000000002 wesr_climat:v432_CH4_2012_opt_deflate v432_CH4_2012_opt_deflate WCS GeoTIFF EPSG:4326 CRS:84 -180.05 179.95 -77.95 88.15000000000002 wesr_climat:v432_CO2_excl_C_1970_opt_deflate v432_CO2_excl_C_1970_opt_deflate WCS GeoTIFF EPSG:4326 CRS:84 -180.05 179.95 -77.95 88.15000000000002 wesr_climat:v432_CO2_excl_C_1990_opt_deflate v432_CO2_excl_C_1990_opt_deflate WCS GeoTIFF EPSG:4326 CRS:84 -180.05 179.95 -77.95 88.15000000000002 wesr_climat:v432_CO2_excl_C_2012_opt_deflate v432_CO2_excl_C_2012_opt_deflate WCS GeoTIFF EPSG:4326 CRS:84 -180.05 179.95 -77.95 88.15000000000002 wesr_climat:v60_CH4_1970_opt 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_ghg60 v60_CH4_1970_opt WCS GeoTIFF EPSG:4326 CRS:84 -180.0 179.99999999997954 -88.79999999998984 90.0 wesr_climat:v60_CH4_1980_opt 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_ghg60 v60_CH4_1980_opt WCS GeoTIFF EPSG:4326 CRS:84 -180.0 179.99999999997954 -88.79999999998984 90.0 wesr_climat:v60_CH4_1990_opt 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_ghg60 v60_CH4_1990_opt WCS GeoTIFF EPSG:4326 CRS:84 -180.0 179.99999999997954 -88.79999999998984 90.0 wesr_climat:v60_CH4_2000_opt 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_ghg60 v60_CH4_2000_opt WCS GeoTIFF EPSG:4326 CRS:84 -180.0 179.99999999997954 -88.79999999998984 90.0 wesr_climat:v60_CH4_2010_opt 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_ghg60 v60_CH4_2010_opt WCS GeoTIFF EPSG:4326 CRS:84 -180.0 179.99999999997954 -85.59999999999002 90.0 wesr_climat:v60_CH4_2018_opt 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_ghg60 v60_CH4_2018_opt WCS GeoTIFF EPSG:4326 CRS:84 -180.0 179.99999999997954 -85.19999999999004 90.0 wesr_climat:v60_CO2_excl_short-cycle_org_C_1970_opt 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_ghg60 v60_CO2_excl_short-cycle_org_C_1970_opt WCS GeoTIFF EPSG:4326 CRS:84 -180.0 179.99999999997954 -88.79999999998984 90.0 wesr_climat:v60_CO2_excl_short-cycle_org_C_1980_opt 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_ghg60 v60_CO2_excl_short-cycle_org_C_1980_opt WCS GeoTIFF EPSG:4326 CRS:84 -180.0 179.99999999997954 -88.79999999998984 90.0 wesr_climat:v60_CO2_excl_short-cycle_org_C_1990_opt 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_ghg60 v60_CO2_excl_short-cycle_org_C_1990_opt WCS GeoTIFF EPSG:4326 CRS:84 -180.0 179.99999999997954 -88.79999999998984 90.0 wesr_climat:v60_CO2_excl_short-cycle_org_C_2000_opt 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_ghg60 v60_CO2_excl_short-cycle_org_C_2000_opt WCS GeoTIFF EPSG:4326 CRS:84 -180.0 179.99999999997954 -88.79999999998984 90.0 wesr_climat:v60_CO2_excl_short-cycle_org_C_2010_opt 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_ghg60 v60_CO2_excl_short-cycle_org_C_2010_opt WCS GeoTIFF EPSG:4326 CRS:84 -180.0 179.99999999997954 -85.59999999999002 90.0 wesr_climat:v60_CO2_excl_short-cycle_org_C_2018_opt 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_ghg60 v60_CO2_excl_short-cycle_org_C_2018_opt WCS GeoTIFF EPSG:4326 CRS:84 -180.0 179.99999999997954 -85.19999999999004 90.0 wesr_climat:v60_CO2_org_short-cycle_C_1970_opt 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_ghg60 v60_CO2_org_short-cycle_C_1970_opt WCS GeoTIFF EPSG:4326 CRS:84 -180.0 179.99999999997954 -55.699999999992485 76.5 wesr_climat:v60_CO2_org_short-cycle_C_1980_opt 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_ghg60 v60_CO2_org_short-cycle_C_1980_opt WCS GeoTIFF EPSG:4326 CRS:84 -180.0 179.99999999997954 -55.699999999992485 76.5 wesr_climat:v60_CO2_org_short-cycle_C_1990_opt 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_ghg60 v60_CO2_org_short-cycle_C_1990_opt WCS GeoTIFF EPSG:4326 CRS:84 -180.0 179.99999999997954 -55.899999999992474 76.5 wesr_climat:v60_CO2_org_short-cycle_C_2000_opt 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_ghg60 v60_CO2_org_short-cycle_C_2000_opt WCS GeoTIFF EPSG:4326 CRS:84 -180.0 179.99999999997954 -55.59999999999249 76.5 wesr_climat:v60_CO2_org_short-cycle_C_2010_opt 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_ghg60 v60_CO2_org_short-cycle_C_2010_opt WCS GeoTIFF EPSG:4326 CRS:84 -180.0 179.99999999997954 -55.59999999999239 78.3 wesr_climat:v60_CO2_org_short-cycle_C_2018_opt 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_ghg60 v60_CO2_org_short-cycle_C_2018_opt WCS GeoTIFF EPSG:4326 CRS:84 -180.0 179.99999999997954 -55.89999999999222 81.19999999999999 wesr_climat:v60_N2O_1970_opt 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_ghg60 v60_N2O_1970_opt WCS GeoTIFF EPSG:4326 CRS:84 -180.0 179.99999999997954 -89.99999999998977 90.0 wesr_climat:v60_N2O_1980_opt 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_ghg60 v60_N2O_1980_opt WCS GeoTIFF EPSG:4326 CRS:84 -180.0 179.99999999997954 -89.99999999998977 90.0 wesr_climat:v60_N2O_1990_opt 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_ghg60 v60_N2O_1990_opt WCS GeoTIFF EPSG:4326 CRS:84 -180.0 179.99999999997954 -89.99999999998977 90.0 wesr_climat:v60_N2O_2000_opt 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_ghg60 v60_N2O_2000_opt WCS GeoTIFF EPSG:4326 CRS:84 -180.0 179.99999999997954 -89.99999999998977 90.0 wesr_climat:v60_N2O_2010_opt 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_ghg60 v60_N2O_2010_opt WCS GeoTIFF EPSG:4326 CRS:84 -180.0 179.99999999997954 -89.99999999998977 90.0 wesr_climat:v60_N2O_2018_opt 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_ghg60 v60_N2O_2018_opt WCS GeoTIFF EPSG:4326 CRS:84 -180.0 179.99999999997954 -89.99999999998977 90.0 cc_and_health:vNaturalPolygons features vNaturalPolygons EPSG:32634 CRS:84 14.278348129129038 26.929306202145593 2.1686254796591595 10.991564938682107 cc_and_health:vWaterLines_river_only features vWaterLines_river_only EPSG:32634 CRS:84 14.311495526534397 27.53442276957556 2.0622926391050047 11.020842231641868 dakar_waste:validation_overlay Solid Waste Management Remote Sensing GIS Citizen Science Multi-Criteria Analysis Illegal Dumping EPSG:32628 CRS:84 -17.367169957539442 -17.28660005665432 14.763385552886751 14.822822501580214 risk_southeast_senegal:vhi_1995_2004 The 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_2004 WCS GeoTIFF EPSG:4326 CRS:84 -13.239460488881921 -11.342218608821492 12.307817707721565 13.461254532531031 risk_southeast_senegal:vhi_2005_2014 The 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_2014 WCS GeoTIFF EPSG:4326 CRS:84 -13.239460488881921 -11.342218608821492 12.307817707721565 13.461254532531031 risk_southeast_senegal:vhi_2015_2024 The 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_2024 WCS GeoTIFF EPSG:4326 CRS:84 -13.239460488881921 -11.342218608821492 12.307817707721565 13.461254532531031 Medicip:vulnerability_pesato_02_03 vulnerability_pesato_02_03_opt WCS GeoTIFF EPSG:4326 CRS:84 -5.3554163761242854 35.977083623875714 30.264583696518883 45.50708369651888 MapX_UNDP:vulnerable_soil_organic_carbon_density_opt vulnerable_soil_organic_carbon_density_opt WCS GeoTIFF EPSG:4326 CRS:84 -179.9999463166003 179.9999737471548 -81.81909663334687 81.81933800928408 sand:wayport_2014 sand EPSG:3857 CRS:84 -13.304775967345588 -13.281558766114637 8.485148634917662 8.499123593990518 sand:wayport_2020 sand EPSG:3857 CRS:84 -13.302697292288268 -13.28362142178019 8.485153978191043 8.49912097880625 wesr_climat:wesr_climate_perma1 wesr_climate_perma1_opt_deflate WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 wesr_climat:wesr_climate_perma1_5 wesr_climate_perma1_5_opt_deflate WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 wesr_climat:wesr_climate_perma2 wesr_climate_perma2_opt_deflate WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 wesr_climat:wesr_climate_perma3 wesr_climate_perma3_opt_deflate WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 wesr_climat:wesr_climate_perma4 wesr_climate_perma4_opt_deflate WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 wesr_climat:wesr_climate_perma5 wesr_climate_perma5_opt_deflate WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 wesr_climat:wesr_climate_perma6 wesr_climate_perma6_opt_deflate WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 wesr_climat:wesr_climate_perma_histo wesr_climate_perma_histo_opt_deflate WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 wesr_climat:wesr_climate_rcp45 wesr_climate_rcp45_opt_deflate WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 wesr_climat:wesr_climate_slr_tropex_n wesr_climate_slr_tropex_n_opt_deflate WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -85.0 85.0 wesr_climat:wesr_climate_sta_an wesr_climate_sta_an_opt_deflate WCS GeoTIFF EPSG:4326 CRS:84 -179.99999999999997 180.00000000000003 -89.99999999999994 90.00000000000006 wesr_climat:wesr_climate_sta_nov wesr_climate_sta_nov_opt_deflate WCS GeoTIFF EPSG:4326 CRS:84 -179.99999999999997 180.00000000000003 -89.99999999999994 90.00000000000006 unea_pollution:who25 The 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. who25 WCS GeoTIFF Particulate Matters Particulate Matters EPSG:4326 CRS:84 -180.03333333333333 179.96666666673863 -55.93333333333335 70.06666666669184 MAPX:wld__pvout_opt_deflate wld__pvout_opt_deflate WCS GeoTIFF EPSG:4326 CRS:84 -180.0 180.0 -55.0 60.0 MAPX:wld_cy1010irmt_cl_wgs84 wld__cy1010irmt_cl_wgs84 WCS GeoTIFF EPSG:4326 CRS:84 -180.0 179.99999999985602 -90.0 83.99999999993042 MAPX_nd:wld_richness_10km_mammals_threatened 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_threatened WCS GeoTIFF EPSG:4326 CRS:84 -179.49349086582234 179.52408225466056 -56.61353702601424 83.78813167493983 MAPX_nd:wld_richness_mammals_10km 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_10km WCS GeoTIFF EPSG:4326 CRS:84 -179.50889388818507 179.53549311992452 -85.69633751266441 83.78813167493983 grid_data_core:wpop_2010_glob WCS GeoTIFF wpop_2010_glob EPSG:4326 CRS:84 -174.54561564127565 176.85438295312437 -59.484294439157466 53.56570510864255 MAPX:y2016_modisSSEBopETv4_anomaly_pct Data downloaded directly from USGS. y2016_modisSSEBopETv4_anomaly_pct WCS GeoTIFF EPSG:4326 CRS:84 -20.00844965760001 55.006893565199974 -38.005350777400054 40.002112414399946 mapx_chemobs:zambia_remote_site_screening_res50 zambia_remote_site_screening_res50 WCS GeoTIFF EPSG:4326 CRS:84 21.994957999999993 33.70545799999999 -18.079247604999956 -8.192247604999954 text/plain mapx_chemobs:zimbabwe_remote_site_screening_res50 zimbabwe_remote_site_screening_res50 WCS GeoTIFF EPSG:4326 CRS:84 25.237080000000006 33.05608000000001 -22.417830000000002 -15.608830000000003 text/plain