The General Global Map of Seafloor Bedrock Geology (seafloorgeol) shows the global distribution of seafloor bedrock, the boundaries of the continental shelf and continental slope, the axes of the oceanic ridges and other marine geological features. Sedimentary deposits are not shown. The map is based on extracts from Bouysse et al. (2010) © CGMW, and Bryan & Ernst (2008) using Esri Basemap, HERE, Garmin, FAO, NOAA, USGS, © OpenStreetMap contributors and the GIS User Community.
This dataset accompanies the publication "Archetypes of agri-environmental potential: a multi-scale typology for spatial stratification and upscaling in Europe" by Michael Beckmann, Gregor Didenko, James M. Bullock, Anna F. Cord, Anne Paulus, Guy Ziv and Tomáš Václavík. Developing spatially-targeted policies for farmland in the European Union (EU) requires synthesized, spatially-explicit knowledge of agricultural systems and their environmental conditions. Such synthesis needs to be flexible and scalable in a way that allows the generalization of European landscapes and their agricultural potential into spatial units that are informative at any given resolution and extent. In recent years, typologies of agricultural lands have been substantially improved, however, agriculturally relevant aspects have yet to be included. We here provide a spatial classification approach for identifying archetypal patterns of agri-environmental potential in Europe based on machine-learning clustering of 17 variables on bioclimatic conditions, soil characteristics and topographical parameters. We improve existing typologies by (1) including more recent biophysical data (e.g. agriculturally-important soil parameters), (2) employing a fully data-driven approach that reduces subjectivity in identifying archetypal patterns, and (3) providing a scalable approach suitable both for the entire European continent as well as smaller geographical extents. We demonstrate the utility and scalability of our typology by comparing the archetypes with independent data on cropland cover and field size at the European scale and in three regional case studies in Germany, Czechia and Spain. The resulting archetypes can be used to support spatial stratification, upscaling and designation of more spatially-targeted agricultural policies, such as those in the context of the EU’s Common Agricultural Policy post-2020. Continental application - SOM k400 The regional application clustered European land into 400 smaller and more homogeneous agri-environmental archetypes than in the case of SOM k20. The sizes of clusters ranged from 2,230 km² (0.04% of the study area) for cluster 381 to 34,000 km² (0.5% of the study area) for cluster 184, with a median of 15,068 km², which is close to 1/400 of the total study area. Smaller clusters tended to be more heterogeneous (lower QE), but the overall cluster quality was uniformly distributed across Europe and higher than in the case of k20. A correlation of input variables with the clusters’ mean QE showed that QE was positively associated with annual precipitation, soil coarse fragments, terrain ruggedness and elevation. Therefore, agri-environmental potential with high values of these variables, located along the coast of Norway, Northern UK and the Alpine region, were also more heterogeneous and thus less likely to form homogeneous archetypes.
This dataset accompanies the publication "Archetypes of agri-environmental potential: a multi-scale typology for spatial stratification and upscaling in Europe" by Michael Beckmann, Gregor Didenko, James M. Bullock, Anna F. Cord, Anne Paulus, Guy Ziv and Tomáš Václavík. Developing spatially-targeted policies for farmland in the European Union (EU) requires synthesized, spatially-explicit knowledge of agricultural systems and their environmental conditions. Such synthesis needs to be flexible and scalable in a way that allows the generalization of European landscapes and their agricultural potential into spatial units that are informative at any given resolution and extent. In recent years, typologies of agricultural lands have been substantially improved, however, agriculturally relevant aspects have yet to be included. We here provide a spatial classification approach for identifying archetypal patterns of agri-environmental potential in Europe based on machine-learning clustering of 17 variables on bioclimatic conditions, soil characteristics and topographical parameters. We improve existing typologies by (1) including more recent biophysical data (e.g. agriculturally-important soil parameters), (2) employing a fully data-driven approach that reduces subjectivity in identifying archetypal patterns, and (3) providing a scalable approach suitable both for the entire European continent as well as smaller geographical extents. We demonstrate the utility and scalability of our typology by comparing the archetypes with independent data on cropland cover and field size at the European scale and in three regional case studies in Germany, Czechia and Spain. The resulting archetypes can be used to support spatial stratification, upscaling and designation of more spatially-targeted agricultural policies, such as those in the context of the EU’s Common Agricultural Policy post-2020. Continental application - SOM k20 The identified archetypes of agri-environmental potential showed a relatively even geographical distribution and their coverage ranged from 1.0% (Cluster 20 with 62,000 km²) to 10.1% (Cluster 10 with 640,000 km²) of European land. The largest clusters, 4 (542,000 km²) and 10 (640,000 km²), were in Northern Finland and Russia, suggesting that there is a relatively homogenous space of environmental conditions over a large area, although much of it with low agricultural potential. The highest quantization error was found in clusters 19 and 20, located along the coast of Norway and the northern UK, and also at the coast of Spain, Portugal and the Alpine region. These archetypes were the most heterogeneous, clustering agri-environmental potential with a wide range of conditions, especially elevation and precipitation.
Temperature time series with high spatial and temporal resolutions are important for several applications. The new MODIS Land Surface Temperature (LST) collection 6 provides numerous improvements compared to collection 5. However, being remotely sensed data in the thermal range, LST shows gaps in cloud-covered areas. With a novel method [1] we fully reconstructed the daily global MODIS LST products MOD11A1/MYD11A1 (spatial resolution: 1 km). For this, we combined temporal and spatial interpolation, using emissivity and elevation as covariates for the spatial interpolation. The data are provided in GeoTIFF format. The Coordinate Reference System (CRS) is identical to the MOD11A1/MYD11A1 product (Sinusoidal) as provided by NASA. In WKT as reported by GDAL: PROJCRS["unnamed", BASEGEOGCRS["Unknown datum based upon the custom spheroid", DATUM["Not specified (based on custom spheroid)", ELLIPSOID["Custom spheroid",6371007.181,0, LENGTHUNIT["metre",1, ID["EPSG",9001]]]], PRIMEM["Greenwich",0, ANGLEUNIT["degree",0.0174532925199433, ID["EPSG",9122]]]], CONVERSION["unnamed", METHOD["Sinusoidal"], PARAMETER["Longitude of natural origin",0, ANGLEUNIT["degree",0.0174532925199433], ID["EPSG",8802]], PARAMETER["False easting",0, LENGTHUNIT["Meter",1], ID["EPSG",8806]], PARAMETER["False northing",0, LENGTHUNIT["Meter",1], ID["EPSG",8807]]], CS[Cartesian,2], AXIS["easting",east, ORDER[1], LENGTHUNIT["Meter",1]], AXIS["northing",north, ORDER[2], LENGTHUNIT["Meter",1]]] Acknowledgments: We are grateful to the NASA Land Processes Distributed Active Archive Center (LP DAAC) for making the MODIS LST data available. The dataset is based on MODIS Collection V006. Meaning of pixel values: The pixel values are coded in Kelvin * 50 Data type: raster, UInt16 Spatial resolution: 926.62543314 m Spatial extent Sinusoidal (W, S, E, N): 0, 4447802.079066, 2223901.039533, 6671703.118599 Spatial extent in EPSG:4326 (W, S, E, N): 0, 40, 40, 60 [1] Metz M., Andreo V., Neteler M. (2017): A new fully gap-free time series of Land Surface Temperature from MODIS LST data. Remote Sensing, 9(12):1333. DOI: http://dx.doi.org/10.3390/rs9121333
The GEMStat Dashboard visualizes the water quality of global water bodies on different spatial and temporal scales, based on data from the GEMStat Database . Available data includes the parameters dissolved oxygen, nitrogen, phosphorus and pH, all core parameters of the UN SDG 6.3.2 water quality indicator. The data are voluntarily provided by countries and organizations worldwide within the framework of the GEMS/Water Programme of the United Nations Environment Programme (UNEP) .
The World-wide Hydrogeological Mapping and Assessment Programme (WHYMAP) provides data and information about the earth´s major groundwater resources. The WHYMAP Viewer provides access to the topics “Groundwater Resources of the World", "World-wide River and Groundwater Basins", "World-wide Groundwater Vulnerability", "Karst Aquifers of the World", and to the “World-wide Hydrogeological Map Information System (WHYMIS)”.
The GEMStat Data Portal provides access to freshwater quality data and statistical vizualisations at different spatial scales. The data are voluntarily provided by countries and organizations worldwide within the framework of the GEMS/Water Programme of the United Nations Environment Programme (UNEP) .
The Global Runoff Data Centre is an International data centre operating under the auspices of the World Meteorological Organization (WMO). Its primary objective consists in supporting the water and climate related programmes and projects of the United Nations, its specialised agencies and the scientific research community by collecting and disseminating hydrological data across national borders in a long-term perspective.
The Annual Characteristics and Long-Term Statistics offer basic hydrological statistics of timeseries data of the gauging stations being represented in the Global Runoff Database. Annual characteristics are derived from monthly discharge data, either through aggregated daily data or originally provided monthly data. Long-term statistics and long-term variability are derived from annual characteristics too.
The General Global Map of Seafloor Bedrock Geology (seafloorgeol) shows the global distribution of seafloor bedrock, the boundaries of the continental shelf and continental slope, the axes of the oceanic ridges and other marine geological features. Sedimentary deposits are not shown. The map is based on extracts from Bouysse et al. (2010) © CGMW, and Bryan & Ernst (2008) using Esri Basemap, HERE, Garmin, FAO, NOAA, USGS, © OpenStreetMap contributors and the GIS User Community.