Soil and terrain suitability for maize dataset, at about 1 km resolution, investigates soil suitability and both soil and terrain suitability for maize, under rain-fed condition, with high and low inputs. The Soil and terrain suitability for crops datasets are part of the GAEZ v4 Theme 1 Land and Water Resources, Soil Suitability sub-theme. In the GAEZ v4 the Soil Suitability assessment sub-theme, soil qualities are evaluated for each crop and input/management level and for different water supply systems (rain-fed and irrigated). The results are crop and input level specific soil suitability ratings, which are used for the crop suitability and attainable yield assessment presented in Theme 4. Terrain suitability is also estimated according to terrain-slope classes and location-specific rainfall amounts and rainfall-concentration characteristics to account for soil erosion risks related to crop cover dynamics. Soil and terrain suitability are combined in an edaphic rating factor by crop, water supply type and input level, which is an important input for the estimation of agro-ecological crop suitability and attainable yields presented in Theme 4. For further details, please refer to the GAEZ v4 Model Documentation.

AEZ classification by climate, soil, terrain and land cover (57 classes) at about 1 km resolution at the equator, using different climate data source and based on different Representative Concentration Pathways (RCPs) according to the time period as follows: - climate data source CRUTS32 based on historical data for the time period 1981-2010; - climate data source ENSEMBLE based on the Representative Concentration Pathway RCP8.5 for time periods 2041-2070 and 2071-2100. The AEZ classification by climate, soil, terrain and land cover dataset is part of the GAEZ v4 Theme 1 Land and Water Resources, Agro-Ecological Zones sub-theme. The agro-ecological zones classification provides a characterization of bio-physical resources relevant to agricultural production systems. AEZ definitions and map classes follow a rigorous methodology and an explicit set of principles. The inventory combines spatial layers of thermal and moisture regimes with broad categories of soil/terrain qualities. It also indicates locations of areas with irrigated soils and shows land with severely limiting bio-physical constraints including very cold and very dry (desert) areas as well as areas with very steep terrain or very poor soil/terrain conditions. For further details, please refer to the GAEZ v4 Model Documentation.

The Koeppen-Geiger climate classification is based on a subdivision of terrestrial climates into five major types, which are represented by the capital letters A (tropical), B (dry), C (temperate), D (cold), and E (polar). Each of these climate types, except for B, is defined by temperature criteria. Type B designates climates in which the controlling factor on vegetation is dryness (rather than coldness). Dry climates are divided into arid (BW) and semi-arid (BS) subtypes. Other climate types are sub-divided according to seasonal precipitation characteristics. The level-2 classification distinguishes 14 classes. CRUTS32 as climate data source is used based on historical data for the time period 1981-2010. The Koeppen-Geiger climate classification dataset is part of the GAEZ v4 Theme 2 Agro-climatic Resources, Climate Classification sub-theme. For additional information on agro-climatic resources and indicators, please to Chapter 3 of the GAEZ v4 Model Documentation.

The Thermal zones dataset, at about 9 km resolution, reflects actual monthly temperature conditions throughout the year and serve as input to crop suitability assessment. CRUTS32 as climate data source is used based on historical data for the time period 1981-2010. The Thermal zones dataset is part of the GAEZ v4 Theme 2 Agro-climatic Resources, Climate Classification sub-theme. For additional information on agro-climatic resources and indicators, please to Chapter 3 of the GAEZ v4 Model Documentation.

Crop suitability index in classes, current cropland in grid cell for wheat, cotton and sugarcane under irrigated conditions and high input level, with and without CO2 fertilization, using different climate data source and based on different Representative Concentration Pathways (RCPs) according to the time period as follows: - climate data source CRUTS32 based on historical data for the time period 1981-2010; - climate data source ENSEMBLE based on the Representative Concentration Pathway RCP8.5 for time period 2041-2070. The Crop suitability index in classes dataset is part of the GAEZ v4 Suitability and Attainable Yield - Suitability Class. For additional information, please refer to the GAEZ v4 Model Documentation.

In GAEZ, the procedures proposed by Nelson and Outcalt (1987) are applied to calculate an air frost index (FI) which is used to characterize climate-derived permafrost conditions into four classes: (i) Continuous permafrost (ii) Discontinuous permafrost (iii) Sporadic permafrost (iv) No permafrost Reference permafrost zones are determined based on prevailing daily mean air temperature (Ta). The air frost index (FI) is calculated and used to characterize permafrost areas. For this calculation, accumulated degree-days, above and below 0oC, are used to calculate the thawing index (DDT) and the freezing index (DDF). CRUTS32 as climate data source is used based on historical data for the time period 1981-2010. The Permafrost zones classification dataset is part of the GAEZ v4 Theme 2 Agro-Climatic Resources, Climate Classification sub-theme. For additional information on agro-climatic resources and indicators, please to Chapter 3 of the GAEZ v4 Model Documentation.

Production for the year 2010 for wheat, cotton and sugarcane under rain-fed, irrigated and total water supply conditions. The Crop Production dataset, at about 5 km resolution, is part of the GAEZ v4 Actual Yields and Production - Area, Yield and Production sub-theme. For additional information, please refer to the GAEZ v4 Model Documentation.

The Multi-cropping classification dataset at about 9 km resolution represents a classification in terms of sequential multi-cropping zones under rain-fed conditions. CRUTS32 as climate data source is used based on historical data for the time period 1981-2010. The Multi-cropping classification dataset is part of the GAEZ v4 Theme 2 Agro-climatic Resources, Climate Classification sub-theme. In the GAEZ crop suitability analysis, the LUTs considered refer to single cropping of sole crops, i.e., each crop is presumed to occupy the land only once a year and in pure stand. Consequently, in areas where the growing periods are sufficiently long to allow more than one crop to be grown in the same year or season, single crop yields of annual crops do not reflect the full potential of total time available each year for rain-fed or irrigated crop production. To assess the multiple cropping potential, a number of multiple cropping zones have been defined through matching both growth cycle and temperature requirements of individual suitable crops with time available for crop growth. For rain-fed conditions this period is approximated by the LGP, i.e., the number of days during which both temperature and moisture conditions permit crop growth. Under irrigation conditions the length of the temperature growing period and annual accumulated temperature sums are decisive. For additional information on agro-climatic resources and indicators, please to Chapter 3 of the GAEZ v4 Model Documentation.

Crop suitability index in classes, all land in grid cell for wheat, cotton and sugarcane under rain-fed conditions and high input level, with and without CO2 fertilization, using different climate data source and based on different Representative Concentration Pathways (RCPs) according to the time period as follows: - climate data source CRUTS32 based on historical data for the time period 1981-2010; - climate data source ENSEMBLE based on the Representative Concentration Pathway RCP8.5 for time period 2041-2070. The Crop suitability index in classes dataset is part of the GAEZ v4 Theme 4 Suitability and Attainable Yield, Suitability Class sub-theme. For additional information, please refer to the GAEZ v4 Model Documentation.

Moisture regime classification at about 9 km resolution at the equator, using different climate data source and based on different Representative Concentration Pathways (RCPs) according to the time period as follows: - climate data source CRUTS32 based on historical data for the time period 1981-2010; - climate data source ENSEMBLE based on the Representative Concentration Pathway RCP8.5 for time periods 2041-2070 and 2071-2100. The Moisture regime classification dataset is part of the GAEZ v4 Theme 1 Land and Water Resources, Agro-Ecological Zones sub-theme. The agro-ecological zones classification provides a characterization of bio-physical resources relevant to agricultural production systems. AEZ definitions and map classes follow a rigorous methodology and an explicit set of principles. The inventory combines spatial layers of thermal and moisture regimes with broad categories of soil/terrain qualities. It also indicates locations of areas with irrigated soils and shows land with severely limiting bio-physical constraints including very cold and very dry (desert) areas as well as areas with very steep terrain or very poor soil/terrain conditions. For further details, please refer to the GAEZ v4 Model Documentation.

Output density (potential production divided by total grid cell area) for wheat, cotton and sugarcane under rain-fed conditions and high input level, with and without CO2 fertilization, using different climate data source and based on different Representative Concentration Pathways (RCPs) according to the time period as follows: - climate data source CRUTS32 based on historical data for the time period 1981-2010; - climate data source ENSEMBLE based on the Representative Concentration Pathway RCP8.5 for time period 2041-2070. The Output density (potential production divided by total grid cell area) dataset is part of the GAEZ v4 Theme 4 Suitability and Attainable Yield, Agro-ecological Attainable Yield sub-theme. For additional information, please refer to the GAEZ v4 Model Documentation.

Classification by thermal climates and thermal zones dataset at about 9 km resolution at the equator. CRUTS32 as climate data source is used based on historical data for the time period 1981-2010. The Classification by thermal climates and thermal zones dataset is part of the GAEZ v4 Theme 2 Agro-climatic Resources - Climate Classification sub-theme. For additional information on agro-climatic resources and indicators, please to Chapter 3 of the GAEZ v4 Model Documentation.

Annual temperature sum for days with Ta >5 °C (degree-days) at about 9 km resolution at the equator, using different climate data source and based on different Representative Concentration Pathways (RCPs) according to the time period as follows: - climate data source CRUTS32 based on historical data for the time period 1981-2010; - climate data source ENSEMBLE based on the Representative Concentration Pathway RCP8.5 for time periods 2041-2070 and 2071-2100. The Annual temperature sum for days with Ta >5 °C (degree-days) dataset is part of the GAEZ v4 Agro-climatic Resources - Thermal Regime sub-theme. For additional information, please refer to the GAEZ v4 Model Documentation.

The Thermal climates dataset, at about 9 km resolution, represents major latitudinal climatic zones based on monthly temperatures reduced to sea level. CRUTS32 as climate data source is used based on historical data for the time period 1981-2010. The Thermal climates dataset is part of the GAEZ v4 Theme 2 Agro-climatic Resources, Climate Classification sub-theme. For additional information on agro-climatic resources and indicators, please to Chapter 3 of the GAEZ v4 Model Documentation.

The Multi-cropping classification dataset at about 9 km resolution represents a classification in terms of sequential multi-cropping zones under irrigated conditions. CRUTS32 as climate data source is used based on historical data for the time period 1981-2010. The Multi-cropping classification dataset is part of the GAEZ v4 Theme 2 Agro-climatic Resources, Climate classification sub-theme. In the GAEZ crop suitability analysis, the LUTs considered refer to single cropping of sole crops, i.e., each crop is presumed to occupy the land only once a year and in pure stand. Consequently, in areas where the growing periods are sufficiently long to allow more than one crop to be grown in the same year or season, single crop yields of annual crops do not reflect the full potential of total time available each year for rain-fed or irrigated crop production. To assess the multiple cropping potential, a number of multiple cropping zones have been defined through matching both growth cycle and temperature requirements of individual suitable crops with time available for crop growth. For rain-fed conditions this period is approximated by the LGP, i.e., the number of days during which both temperature and moisture conditions permit crop growth. Under irrigation conditions the length of the temperature growing period and annual accumulated temperature sums are decisive. For additional information on agro-climatic resources and indicators, please to Chapter 3 of the GAEZ v4 Model Documentation.

Simplified AEZ classification (33 classes) at about 1 km resolution at the equator, using different climate data source and based on different Representative Concentration Pathways (RCPs) according to the time period as follows: - climate data source CRUTS32 based on historical data for the time period 1981-2010; - climate data source ENSEMBLE based on the Representative Concentration Pathway RCP8.5 for time periods 2041-2070 and 2071-2100. The Simplified AEZ classification dataset is part of the GAEZ v4 Theme 1 Land and Water Resources, Agro-Ecological Zones sub-theme. The agro-ecological zones classification provides a characterization of bio-physical resources relevant to agricultural production systems. AEZ definitions and map classes follow a rigorous methodology and an explicit set of principles. The inventory combines spatial layers of thermal and moisture regimes with broad categories of soil/terrain qualities. It also indicates locations of areas with irrigated soils and shows land with severely limiting bio-physical constraints including very cold and very dry (desert) areas as well as areas with very steep terrain or very poor soil/terrain conditions. For further details, please refer to the GAEZ v4 Model Documentation.

The ConvLSTM model was trained on simulated data instead of observations (emulator).

See supporting file S4W_ERFA_Drammen_ReadMe.txt, found at 10.5281/zenodo.22233710 (DOI)

No description.

This dataset covers 155 stations. There is one simulation for each historical and climate scenario for all stations covered, and for each calibration strategy.

The ConvLSTM model was trained on simulated data instead of observations (emulator).

See supporting file S4W_ERFA_Seine_ReadMe.txt, found at 10.5281/zenodo.21997573 (DOI)

The ConvLSTM model was trained based on simulated data instead of observations (emulator) ; The predictions are performed following an autoregressive approach (previous output used as input for next steps) ;

In addition to the baseline simulation 1989-2022, the dataset includes results for future “what-if” climate scenarios representing periods of five and seven consecutive dry years. These scenarios were generated stochastically based on the statistical characteristics of historical 1989-2022 monthly precipitation and potential evapotranspiration (PET). The stochastic simulation was used to construct a plausible sequence of 7 consecutive dry years, representing a prolonged multi-year drought stress on the water system. The scenario is therefore not intended as a specific climate projection or forecast, but as a stress-test scenario used to assess how the basin and its water management system could respond to an extended period of persistently dry climatic conditions.

See supporting file S4W_ERFA_CS-N0W_EastAnglia_D42_ReadMe.txt, found at 10.5281/zenodo.21646744 (DOI)

The ConvLSTM model was trained based on simulated data instead of observations (emulator) ; The predictions are performed following an autoregressive approach (previous output used as input for next steps) ;

No description.

See supporting file S4W_ERFA_E-FLAG_EastAnglia_D42_ReadMe.txt, found at 10.5281/zenodo.21649392 (DOI)

The ConvLSTM model was trained based on simulated data instead of observations (emulator) ; The predictions are performed following an autoregressive approach (previous output used as input for next steps) ;

See supporting file S4W_ERFA_Europe_ReadMe.txt, found at 10.5281/zenodo.21670346 (DOI)

No description.

The ConvLSTM model was trained on simulated data instead of observations (emulator).

No description.

The ConvLSTM model was trained on simulated data instead of observations (emulator).

Model predictions on SOM-content under arable land( Report: https://edepot.wur.nl/556312). Predictions with a model ensemble at fixed depths of 15, 45, 80 and 120 cm. Predictions using an ensemble of Random Forests and Gradient Boosting, with covariates from the SoilGrids 2017 model (Hengl et al. 2017) and a spatial cross-validation procedure. The model was applied on log-transformed observations of SOC. Models result were back- transformed and converted to SOM by multiplying SOC predictions with 1.743. Prediction

The vector data set is based on the FAO-UNESCO Soil Map of the World. The Digitized Soil Map of the World, at 1:5.000.000 scale, is in the Geographic projection (Latitude - Longitude) intersected with a template containing water related features (coastlines, lakes, glaciers and double-lined rivers). The Digital Soil Map of the World (except for the continent of Africa) was intersected with the Country Boundaries map from the World Data Bank II (with country boundaries updated to January 1994 at 1:3 000 000 scale), obtained from the US Government. For Africa, the country boundaries are derived from the FAO Country Boundaries on the original FAO/UNESCO Soil Map of the World. Country boundaries in both cases were checked and adjusted in certain places on the basis of FAO and UN conventions. The maps (jpg and pdf) are also available on the FAO soil Portal together with their reports for the following regions : North America, Mexico and Central America, South America, Europe, Asia, South Asia, Nortah and Central Asia, Sourtheast Asia and Australasia. [http://www.fao.org/soils-portal/soil-survey/soil-maps-and-databases/faounesco-soil-map-of-the-world/en/]

Land use units in Botswana, that share geographical overlapping with the Okavango Basin. Source: The Botswana National Atlas (The Department of Survey and Mapping). This dataset is part of the GIS Database for the Environment Protection and Sustainable Management of the Okavango River Basin project (EPSMO). Detailed information on the database can be found in the “GIS Database for the EPSMO Project” document produced by Luis Veríssimo (FAO consultant) in July 2009, and here available for download.

The Gridded Livestock of the World (GLW) is a global livestock mapping project by the Food and Agriculture Organization's Animal Production and Health Division (FAO-AGA). It describes the global spatial distribution of cattle, buffaloes, sheep, goats, pigs and poultry. The most-detailed sub-national census data are disaggregated based on statistical relations with some environmental variables in similar agro-ecological zones.

This dataset forms part of a global livestock mapping project by the Food and Agriculture Organization's Animal Production and Health Division (FAO-AGA). The methodology and sources of data are fully described in a document entitled: "The Gridded Livestock of the World FAO (2007)". In summary, for each country the most recent available sub-national livestock census data and corresponding administrative boundaries have been collected. These are then converted into densities, excluding land unsuitable for livestock (either monogastric or ruminant), to provide the 'observed' data. The data are then disaggregated based on statistical relations with some environmental variables in similar agro-ecological zones to produce the 'predicted' distribution. The predicted data are further manipulated to match national census totals for the year 2000 and the year 2005 according to the FAOSTAT database. The project includes: a global network of data providers on livestock and sub-national boundaries; an Oracle database in which these data are managed and processed; a system for predicting livestock distributions based on environmental data and an interactive web application, the Global Livestock Production and Health Atlas (GLiPHA - http://www.fao.org/ag/aga/glipha/index.jsp), through which data are viewed and disseminated. The files are in a raster GRID format, with an ArcGis layer file and an ArcView legend file. Pixel values represent actual densities (per square kilometre). Projection details are given in the metadata. The map should ideally be viewed with the overlay of national boundaries, water bodies and unsuitable land. All of these supplementary data are available in accompanying zip files. These data have been produced by FAO's Animal Production and Health Division in collaboration with ERGO and the TALA research group, University of Oxford, UK.

This dataset forms part of a global livestock mapping project by the Food and Agriculture Organization's Animal Production and Health Division (FAO-AGA). The methodology and sources of data are fully described in a document entitled: "The Gridded Livestock of the World FAO (2007)". In summary, for each country the most recent available sub-national livestock census data and corresponding administrative boundaries have been collected. These are then converted into densities, excluding land unsuitable for livestock (either monogastric or ruminant), to provide the 'observed' data. The data are then disaggregated based on statistical relations with some environmental variables in similar agro-ecological zones to produce the 'predicted' distribution. The predicted data are further manipulated to match national census totals for the year 2000 and the year 2005 according to the FAOSTAT database. The project includes: a global network of data providers on livestock and sub-national boundaries; an Oracle database in which these data are managed and processed; a system for predicting livestock distributions based on environmental data and an interactive web application, the Global Livestock Production and Health Atlas (GLiPHA - http://kids.fao.org/glipha/), through which data are viewed and disseminated. The files are in a raster GRID format, with an ArcGis layer file and an ArcView legend file. Pixel values represent actual densities (per square kilometre). Projection details are given in the metadata. The map should ideally be viewed with the overlay of national boundaries, water bodies and unsuitable land. All of these supplementary data are available in accompanying zip files. These data have been produced by FAO's Animal Production and Health Division in collaboration with ERGO and the TALA research group, University of Oxford, UK.

This dataset forms part of a global livestock mapping project by the Food and Agriculture Organization's Animal Production and Health Division (FAO-AGA). The methodology and sources of data are fully described in a document entitled: "The Gridded Livestock of the World FAO (2007)". In summary, for each country the most recent available sub-national livestock census data and corresponding administrative boundaries have been collected. These are then converted into densities, excluding land unsuitable for livestock (either monogastric or ruminant), to provide the 'observed' data. The data are then disaggregated based on statistical relations with some environmental variables in similar agro-ecological zones to produce the 'predicted' distribution. The predicted data are further manipulated to match national census totals for the year 2000 and the year 2005 according to the FAOSTAT database. The project includes: a global network of data providers on livestock and sub-national boundaries; an Oracle database in which these data are managed and processed; a system for predicting livestock distributions based on environmental data and an interactive web application, the Global Livestock Production and Health Atlas (GLiPHA - http://www.fao.org/ag/aga/glipha/index.jsp), through which data are viewed and disseminated. The files are in a raster GRID format, with an ArcGis layer file and an ArcView legend file. Pixel values represent actual densities (per square kilometre). Projection details are given in the metadata. The map should ideally be viewed with the overlay of national boundaries, water bodies and unsuitable land. All of these supplementary data are available in accompanying zip files. These data have been produced by FAO's Animal Production and Health Division in collaboration with ERGO and the TALA research group, University of Oxford, UK.

This dataset forms part of a global livestock mapping project by the Food and Agriculture Organization's Animal Production and Health Division (FAO-AGA). The methodology and sources of data are fully described in a document entitled: "The Gridded Livestock of the World FAO (2007)". In summary, for each country the most recent available sub-national livestock census data and corresponding administrative boundaries have been collected. These are then converted into densities, excluding land unsuitable for livestock (either monogastric or ruminant), to provide the 'observed' data. The data are then disaggregated based on statistical relations with some environmental variables in similar agro-ecological zones to produce the 'predicted' distribution. The predicted data are further manipulated to match national census totals for the year 2000 and the year 2005 according to the FAOSTAT database. The project includes: a global network of data providers on livestock and sub-national boundaries; an Oracle database in which these data are managed and processed; a system for predicting livestock distributions based on environmental data and an interactive web application, the Global Livestock Production and Health Atlas (GLiPHA - http://www.fao.org/ag/aga/glipha/index.jsp), through which data are viewed and disseminated. The files are in a raster GRID format, with an ArcGis layer file and an ArcView legend file. Pixel values represent actual densities (per square kilometre). Projection details are given in the metadata. The map should ideally be viewed with the overlay of national boundaries, water bodies and unsuitable land. All of these supplementary data are available in accompanying zip files. These data have been produced by FAO's Animal Production and Health Division in collaboration with ERGO and the TALA research group, University of Oxford, UK.

This dataset forms part of a global livestock mapping project by the Food and Agriculture Organization's Animal Production and Health Division (FAO-AGA). The methodology and sources of data are fully described in a document entitled: "The Gridded Livestock of the World FAO (2007)". In summary, for each country the most recent available sub-national livestock census data and corresponding administrative boundaries have been collected. These are then converted into densities, excluding land unsuitable for livestock (either monogastric or ruminant), to provide the 'observed' data. The data are then disaggregated based on statistical relations with some environmental variables in similar agro-ecological zones to produce the 'predicted' distribution. The predicted data are further manipulated to match national census totals for the year 2000 and the year 2005 according to the FAOSTAT database. The project includes: a global network of data providers on livestock and sub-national boundaries; an Oracle database in which these data are managed and processed; a system for predicting livestock distributions based on environmental data and an interactive web application, the Global Livestock Production and Health Atlas (GLiPHA - http://www.fao.org/ag/aga/glipha/index.jsp), through which data are viewed and disseminated. The files are in a raster GRID format, with an ArcGis layer file and an ArcView legend file. Pixel values represent actual densities (per square kilometre). Projection details are given in the metadata. The map should ideally be viewed with the overlay of national boundaries, water bodies and unsuitable land. All of these supplementary data are available in accompanying zip files. These data have been produced by FAO's Animal Production and Health Division in collaboration with ERGO and the TALA research group, University of Oxford, UK.

This dataset forms part of a global livestock mapping project by the Food and Agriculture Organization's Animal Production and Health Division (FAO-AGA). The methodology and sources of data are fully described in a document entitled: "The Gridded Livestock of the World FAO (2006)". In summary, for each country the most recent available sub-national livestock census data and corresponding administrative boundaries have been collected. These are then converted into densities, excluding land unsuitable for livestock (either monogastric or ruminant), to provide the 'observed' data. The data are then disaggregated based on statistical relations with some environmental variables in similar agro-ecological zones to produce the 'predicted' distribution. The predicted data are further manipulated to match national census totals for the year 2000 according to the FAOSTAT database. The project includes: a global network of data providers on livestock and sub-national boundaries; an Oracle database in which these data are managed and processed; a system for predicting livestock distributions based on environmental data and an interactive web application, the Global Livestock Production and Health Atlas (GLiPHA - http://www.fao.org/ag/aga/glipha/index.jsp), through which data are viewed and disseminated. The files are in a raster GRID format, with an ArcGis layer file and an ArcView legend file. Pixel values represent actual densities (per square kilometre). Projection details are given in the metadata. The map should ideally be viewed with the overlay of national boundaries, water bodies and unsuitable land. All of these supplementary data are available in three accompanying zip files. These data have been produced by FAO's Animal Production and Health Division in collaboration with ERGO and the TALA research group, University of Oxford, UK.

This dataset forms part of a global livestock mapping project by the Food and Agriculture Organization's Animal Production and Health Division (FAO-AGA). The methodology and sources of data are fully described in a document entitled: "The Gridded Livestock of the World FAO (2007)". In summary, for each country the most recent available sub-national livestock census data and corresponding administrative boundaries have been collected. These are then converted into densities, excluding land unsuitable for livestock (either monogastric or ruminant), to provide the 'observed' data. The data are then disaggregated based on statistical relations with some environmental variables in similar agro-ecological zones to produce the 'predicted' distribution. The predicted data are further manipulated to match national census totals for the year 2000 and the year 2005 according to the FAOSTAT database. The project includes: a global network of data providers on livestock and sub-national boundaries; an Oracle database in which these data are managed and processed; a system for predicting livestock distributions based on environmental data and an interactive web application, the Global Livestock Production and Health Atlas (GLiPHA - http://www.fao.org/ag/aga/glipha/index.jsp), through which data are viewed and disseminated. The files are in a raster GRID format, with an ArcGis layer file and an ArcView legend file. Pixel values represent actual densities (per square kilometre). Projection details are given in the metadata. The map should ideally be viewed with the overlay of national boundaries, water bodies and unsuitable land. All of these supplementary data are available in accompanying zip files. These data have been produced by FAO's Animal Production and Health Division in collaboration with ERGO and the TALA research group, University of Oxford, UK.

Eco-climatic zones were produced taking into account land-surface and atmospheric characteristics (the photosynthetic activity, the temperature of the earth?s surface, the atmospheric drying power, the rainfall, the potential evapotranspiration, the length of growing period for plants, the surface elevation). Most of these features could be accurately predicted using satellite data; some others needed a combination of remotely sensed and ground-based data. Remotely sensed data were acquired by NOAA-AVHRR sensor and by Meteosat satellite. The AVHRR satellite data were available in dekadal images for a 13 year series from 1982-1994. Meteosat imagery runs from 1988 to 1997. The information layer was produced for FAO in January 1999 by Environmental Research Group Oxford (ERGO Ltd) in collaboration with the Trypanosomosis and Land Use in Africa (TALA) research group at the Department of Zoology, University of Oxford.

The dataset was developed following the methodologies of Yoshihide Wada using open-source data normalized to the base year 2000 and downscaled to basin level. Uncertainties stem from input data resolution, spatial disaggregation based on population distribution, nationally aggregated recycling rates, and the lack of seasonal variability, with error propagation through the EDev and TDev indicators to gross and net estimates. Despite these limitations, the consistent framework ensures temporal and spatial comparability across basins and SSP scenarios. Wada, Y., van Beek, L.P.H., Viviroli, D. et al.: Global monthly water stress: 2. water demand and severity of water stress. Water Resources Research, 47(7), 2011

No description.

No description.

The data entails a set of indicators for assessing climate risks and impacts on integrated water resources systems. This first tier is a set of indicators that are directly derived from readily available datasets from e.g. the Copernicus Climate Data Store.

No description.

Precipitation deficit is a common measure for severity of agricultural drought, for example in the Netherlands, but it is also used internationally (Narasimhan, 2005). The precipitation deficit addresses the need for drought monitoring, which was expressed by several river basins, including the Drammen, East Anglia and Danube (Hegdahl, 2023).

See supporting file S4W_ERFA_D26_ReadMe.txt

No description.

This dataset covers 155 stations, with 17 stations downstream of the reservoirs. There is one simulation for each climate scenario for all stations covered, the different management scenarios outputs are only provided for the 17 influenced stations.

Grid with estimated maximum available soil moisture in mm/m with a spatial resolution of 5 * 5 arc minutes. Information with regard to maximum available soil moisture was calculated from the "Derived Soil Properties" of the "Digital Soil Map of the World" which contains raster information on soil moisture in different classes.

Grids with monthly values of coefficient of variation of precipitation for the period 1961-1990, at 5 arc min. Based on: New, M., Lister, D., Hulme, M. and Makin, I., 2002: A high-resolution data set of surface climate over global land areas. Climate Research 21:1-25

This dataset includes elemental and isotopic data, environmental parameters, and fractional abundances of branched glycerol dialkyl glycerol tetraethers (brGDGTs) and branched glycerol monoalkyl glycerol tetraethers (brGMGTs) in the soils, Suspended Particulate Matter (SPM), and sediments collected along the Seine River basin in Northwest France. Samples (n=237) were collected in high-flow (over 250 m³/s) and low-flow (below 250 m³/s) periods from the three zones (river, upstream estuary and downstream estuary) of the Seine River basin from 2015 to 2021. Elemental and isotopic analyses were performed by an elemental analyzer coupled with an isotope ratio mass spectrometer. The brGDGTs and brGMGTs were analyzed using high pressure liquid chromatography coupled with mass spectrometry with an atmospheric pressure chemical ionization source in selected ion monitoring mode. Using this dataset, we aim to (1) investigate the sources of brGDGTs and brGMGTs along the land-sea continuum, (2) identify the environmental factors influencing the distribution of these molecules, and (3) evaluate the applicability of brGMGTs as an indicator for riverine runoff.

The RAR file is composed of all files related to the Digital Bathymetric Model (DBM) and the Digital Surface Model (DSM). Among these files there are: the original rasters files; the files digitally modified to correctly represent all the hydraulic characteristics of the Duero River and those urban characteristics of Zamora; the rasters that represent the error made in both models, calculated by geostatistics; the files related to the geostatistics process. Also, it also includes the integration of both models (DBM plus DSM) to form a High Resolution Urban Digital Model (HRDUM) of the Duero River in it passes through Zamora.

Location of the Upper Rhine aquifers according to the type of rock medium (porosity) and yield. The latter reflects the amount of water or flow that can be extracted from the groundwater contained in the aquifer. It is categorised as follows: a few litres/s = low yield; ~10 l/s = medium yield; above 10 l/s = high yield.

The data portal offered by the Environment Agency serves as a comprehensive platform for visualizing and accessing a wide array of open hydrology data in England. It incorporates an extensive range of information, including river flow, river level, rainfall, and groundwater level readings obtained from open hydrometric stations. Users can delve into monthly time series for Groundwater Level, daily mean flow time series for river flow, 15-minute time series for river level, and daily total time series for total Rainfall. Additionally, the portal provides detailed Water Quality data, encompassing Dissolved Oxygen, Conductivity, pH, Salinity, Temperature, Chlorophyll, Ammonia, Ammonium, Nitrate, Fluorescent dissolved organic matter, Blue-Green Algae, and Turbidity readings, both for open and historic sites, offering a wide understanding of hydrological and water quality dynamics.

The eFLaG dataset, available at the Environmental Information Data Centre, displays predictive models of forthcoming river flow, groundwater levels, and recharge patterns for 200 catchments, 54 boreholes, and 558 groundwater areas across Great Britain and Northern Ireland. It offers unified climate and hydrological forecasts derived from UKCP18, serving as a valuable resource for the water industry in strategizing for water resources and droughts. Moreover, this dataset holds potential for diverse applications across various sectors beyond the water industry.

The national hydrological database for professional users to download observations according to self-defined criteria. The variables include water level, water discharge, precipitation, snow water equivalent, snow depth, soil moisture, groundwater level, pumping and air temperature. The earliest observation is traced back to 1800s. The data has the information from ca. 1800 stations that are both active and inactive.

The compressed files are seasonal forecasts of discharge based on ECMWF System4 (1981-2003) and GLOSEA5 (1996-2003) for the Koutsoulidis catchment, Crete, Greece. The location of the Koutsoulidis catchment as well as the methodology of data development and validation are described in the open access publication by Grillakis et al., 2018 (https://www.mdpi.com/2073-4441/10/11/1593). Seasonal forecasts of Standardized Precipitation Index (SPI) based on ECMWF System4 (1981-2009) and GLOSEA5 (1996-2009) for the Messara catchment are also included. A description of the study site and methodology is included in the poster attached in the compressed file (EGU201712072-Koutroulis).

seNorge2018 is a collection of observational gridded datasets over Norway for daily total precipitation, daily mean, maximum, and minimum temperatures. The seNorge family of observational gridded datasets developed at the Norwegian Meteorological Institute (MET Norway) has a 20-year-long history and seNorge2018 is its newest member, the first providing daily minimum and maximum temperatures. seNorge datasets are used for a wide range of applications in climatology, hydrology, and meteorology.

This dataset presents historic flood levels marked on buildings and other structures in the Rhine river delta. These flood marks provide maximum water levels across the region from the late sixteenth to late twentieth centuries, complementing instrumental measurement records in both space and time. Besides the locations (RD, spatial accuracy ~1 m) and elevations (NAP, vertical accuracy ~0.03 m), the dataset provides the epigraphic inscriptions of the marks and relevant information (type, age) on the constructions holding the marks. In total, the inventory contains 131 flood marks from 46 locations spread across 32 towns. The data provide tangible evidence of historic flood events and are useful in quantitative analyses of past flood patterns and magnitudes in the Rhine river delta.

This data set provides a stochastic event set of flood inundation depth maps (fluvial flood hazard footprints) for the German part of the Danube catchment for current and future climate in GEOTIFF format. The maps provide inundation depth information in cm above ground level on a 100 m grid along the major rivers (4150 km) based on 2D hydro-numeric simulations. Flood event sets are derived for the historical period (1970-1990) and two RCPs (4.5 and 8.5) for the near future (2020-2049) and far future (2070-2099) for four CORDEX models. These flood event sets are created within continuous long-term simulations of a coupled model chain including the IMAGE stochastic multi-variable, multi-site weather generator, the eco-hydrological model SWIM and 1D river network coupled with 2D hydro-numeric hinterland inundation model. Key usages of the data are large-scale flood risk assessment, future flood risk assessment and flood risk management with long-term perspective. The data have been produced within the OASIS+ demonstrator project 'Future Danube Multi Hazard and Risk Model' funded by Climate-KIC in the period from January 2016 to December 2017.

The national hydrological database for automated retrieve data of both historical and real-time observations as well as metadata. It includes water level, water discharge, snow depth, groundwater, water temperature and air temperature. The earliest observation is traced back to 1800s. The data has the information from ca. 1800 stations that are both active and inactive. In total there are ca. 8500 time series available.

The Environment Agency National LIDAR Programme provides accurate elevation data at 1m spatial resolution for all of England.

The csv contains river discharge measured at 17 stations in the River Rhine in Germany. Start and stop day are 1. Jan 2023 and 1. June 2024. The data are daily values computed at the University of Bonn averaging data in GMT time. 15 minutes values are delivered by BfG German Federal Agency for Hydrology. The data will be used in the next Confluence Run. Data of the 4 main tributaries of the Rhine: Neckar, Main, Lahn and Moselle will be added soon in the same file.

Raster maps (pixel size 1 × 1 km) of monthly and annual average precipitation, temperature, actual evapotranspiration, and total runoff, as considered in the SIMPA II hydrological model used by the Duero River Basin Authority.

Macro-plastic concentrations in the water column of the river Rhine increase with higher discharge

WINFORE is a dataset of standardized index values of the climatic water balance (SPEI - Standardized Precipitation Evapotranspiration Index). This is based on the daily temperature and precipitation fields from SPARTACUS, from which the potential evapotranspiration and the climatic water balance are derived.

HySN2018v2005ERA5 is a meteorological dataset for Norway and parts of neighbouring countries at 1x1 km2 and daily time steps, covering the period 1960-2020. Variables included are downward short- and longwave radiation (rsds and rlds, respectively), relative humidity (hurs) and surface pressure (ps). The dataset is created based on seNorge2018v20.05 (Lussana et al., 2019) and ERA5 (Hersbach et al., 2020; Bell et al., 2021). HySN2018v2005ERA5 is an updated and extended version of the HySN dataset (Erlandsen et al., 2019). The files are in netCDF4 format, and the map projection is UTM zone33N.

The national dataset of historical weather and climate data. This data includes quality controlled daily, monthly, and yearly measurements of temperature, precipitation, and wind data. Other information, like metadata about weather stations, is also available.

Digital files with altimeter information of the LiDAR point cloud, distributed in 2x2 km-long files. Point clouds were captured with flights using LiDAR sensors with a density of 0,5 points/m2, or higher. Initially, the unclassified files are published and updated once processed. All the files are coloured with either RGB or infrared using orthophotos taken at the same time of LiDAR data. Depending on the set, 1x1 km-long files with a higher density of points/m2 are available.

The national hydrological database with a user-friendly interface for general public to view and download of both historical and real-time observations as well as metadata. The variables include water level, water discharge, reservoir volume, reservoir water level, snow water equivalent, snow depth, surface temperature, soil moisture, groundwater level, groundwater temperature, river water temperature and air temperature. The earliest observation is traced back to 1800s. The data has the information from ca. 1800 stations that are both active and inactive.

This dataset contains dissolved oxygen (DO) concentrations, stable oxygen isotope ratios of DO (δ¹⁸ODO), particulate organic carbon (POC) concentrations, and respiration/photosynthesis (R/P) ratios, along with corresponding parameters (temperature, δ¹⁸OH2O, nitrate) collected from the Danube River and its key tributaries during five seasonal sampling campaigns in 2023 and 2024. Water samples were collected using a weighted 2 L sampling bottle submerged 1–2 meters below the surface, with sampling conducted from the river center via bridges or passenger boats, and occasionally from the riverbank. In situ temperature measurements were taken with a multiparameter instrument (HQ40d, HACH™, Loveland, CO, USA). δ¹⁸ODO was analyzed using a modified automated equilibration system (Gasbench II, ThermoFisher Scientific™) coupled to a DELTA V Advantage isotope ratio mass spectrometer (IRMS, ThermoFisher Scientific™). This dataset captures seasonal variations in DO dynamics and provides valuable insights into oxygen sources and sinks within the Danube River. The data support the study of biogeochemical cycling in large river systems and can inform ecosystem management and conservation strategies in the face of environmental and climate change.

Database that contains the geographical objects related to hydrography: surface water (canals, lakes, reservoirs, upwelling, waterfalls, etc.), artificial objects that interact with hydrography (dams, coastal structures, etc.) and hydrographic network (nodes, network stretches). It has been made from the integration of data from the IGN and from sources from the General State Administration (General Water Directorate; Marine Hydrographic Institute, etc.), including the coding of Rivers of Spain classified according to modified Pfafstetter of the General Directorate of Water and CEDEX (Ministry for Ecological Transition).

Long-term ice phenology records of Danube River (East Central Europe) between 1774-2017

This series is an amalgamation of all the water abstraction data sets previously held on Data.Gov.UK (DGUK), thus consolidating all abstraction statistical data into one location on DGUK.

The data set contains an SQL-dump of a PostgreSQL data base. This data base contains concentrations of hazardous substances and other water quality pa-rameters in different environmental compartments: • river water (water and suspended sedi-ments) • ground water • waste water (treated and untreated) and sewage sludge • storm water runoff from combined and separate sewer systems • atmospheric deposition • soil Data from many different data sources were collect-ed, checked and combined and meta data were harmonized to allow for a combined data evalua-tion.

The groundwater dataset provides an overview of the country's groundwater wells and sources, and groundwater quality. It is primarily the well drilling companies that supply reports on wells, and the data is recorded for a specific template in accordance with regulations. Reported well data are quality assured by NGU. In Norwegian only.

These data provide details of all permit details as required under the Environmental Permit Regulation. Information is held for all permit holders and covers all substances that are controlled. These data are a taken from the Environment Agency’s Public Register and contains three tiers of data for all active permits: site and general, effluent, and determinant limits.

The dataset regroups ground station observations, satellite images, rain radar observations, weather forecasting models and land-sea and relief masks.

Population by country and projections until 2100

The STARS4Water project generates sets of indicators for assessing climate risks and impacts on integrated water resources system. The first tier is directly derived from global datasets with only limited post-processing.

No description.

Provides semi-continuous observations for a large number of medium and large-sized lakes, according to the Global Lakes and Wetlands Database (GLWD) or otherwise of specific environmental monitoring interest. 10-daily observations are available in near real time in the spatial resolution of 100 m and with the temporal extent from 2019 to 2024.

The LSTM and RF models were trained based on simulated data instead of observations (emulator) ; The models were trained pixel wise

Annual groundwater level data are available in cases where we have received permission from a database manager to post annual groundwater level data.

The ESA CCI Lakes dataset contains lake water level , extent, temperature, ice cover, water quality, chlorophyll and turbidity from multiple satellite missions.

historical time series of common climate indices (e.g. ENSO)

This data was prepared based on the data available in the "Net irrigation requirement under different climate scenarios using AquaCrop over Europe" repository (Busschaert et al., 2022, DOI: 10.5281/zendo.6760976), so, the data quality of the basic dataset.

E-OBS is a daily gridded land-only observational dataset over Europe. The blended time series from the station network of the European Climate Assessment Dataset (ECAD) project form the basis for the E-OBS gridded dataset. All station data are sourced directly from the European National Meteorological and Hydrological Services (NMHSs) or other data holding institutions. Max, mean and min T, Precipitation, Relative humidity, Solar radiation, Wind speed

Model predictions on SOM-content under arable land (see: Report: https://edepot.wur.nl/556312 ): 'Predictions with a model ensemble at fixed depths of 15, 45, 80 and 120 cm on a 250m grid. Predictions using an ensemble of Random Forests and Gradient Boosting, with covariates from the SoilGrids 2017 model (Hengl et al. 2017) and a spatial cross-validation procedure. The model was applied on log-transformed observations of SOC. Models result were back- transformed and converted to SOM by multiplying SOC predictions with 1.743.'

The FGGD CV of LGP map is a global raster datalayer with a resolution of 5 arc-minutes. Each pixel contains an average coefficient of variation of LGP for the pixel area over the period 1901-1966. The data are from FAO and IIASA, 2000, Global agro-ecological zones, as reported in FAO and IIASA, 2007, Mapping biophysical factors that influence agricultural production and rural vulnerability, by H. von Velthuizen et al.

The FGGD terrain slope map is a global raster datalayer with a resolution of 5 arc-minutes. Each pixel contains a terrain slope class value for the pixel area. The data are from U.S. Geological Survey, 1993, GTOPO 30, as reported in FAO and IIASA, 2000, Global agro-ecological zones, and FAO and IIASA, 2007, Mapping biophysical factors that influence agricultural production and rural vulnerability, by H. von Velthuizen et al.

The FGGD soils map is a global raster datalayer with a resolution of 5 arc-minutes. Each pixel contains a class value for the major soil group found in the pixel area. The data are from FAO and IIASA, 2000, Global agro-ecological zones, as reported in FAO and IIASA, 2007, Mapping biophysical factors that influence agricultural production and rural vulnerability, by H. von Velthuizen et al.

The FGGD severe environmental constraints map is a global raster datalayer with a resolution of 5 arc-minutes. Pixels with no severe environmental constraints contain a value of zero. Each other pixel contains a cumulative class value that shows which environmental constraint is binding in the pixel area. The data are from FAO and IIASA, 2000, Global agro-ecological zones, as reported in FAO and IIASA, 2007, Mapping biophysical factors that influence agricultural production and rural vulnerability, by H. von Velthuizen et al.

The FGGD terrain slope constraints map is a global raster datalayer with a resolution of 5 arc-minutes. Each pixel contains a class value for the degree to which terrain slope constrains agricultural production potential in the pixel area. The data are from FAO and IIASA, 2000, Global agro-ecological zones, as reported in FAO and IIASA, 2007, Mapping biophysical factors that influence agricultural production and rural vulnerability, by H. von Velthuizen et al.

The FGGD CV of LGP map is a global raster datalayer with a resolution of 5 arc-minutes. Each pixel contains an average coefficient of variation of LGP for the pixel area over the period 1901-1996. The data are from FAO and IIASA, 2000, Global agro-ecological zones, as reported in FAO and IIASA, 2007, Mapping biophysical factors that influence agricultural production and rural vulnerability, by H. von Velthuizen et al.

The FGGD climate constraints map is a global raster datalayer with a resolution of 5 arc-minutes. Each pixel contains a class value for the degree to which climate constrains agricultural production potential in the pixel area. The data are from FAO and IIASA, 2000, Global agro-ecological zones, as reported in FAO and IIASA, 2007, Mapping biophysical factors that influence agricultural production and rural vulnerability, by H. von Velthuizen et al.

The FGGD soil constraints map is a global raster datalayer with a resolution of 5 arc-minutes. Each pixel contains a class value for the degree to which soil characteristics constrain agricultural production potential in the pixel area. The data are from FAO and IIASA, 2000, Global agro-ecological zones, as reported in FAO and IIASA, 2007, Mapping biophysical factors that influence agricultural production and rural vulnerability, by H. von Velthuizen et al.

The FGGD LGP zone map is a global raster datalayer with a resolution of 5 arc-minutes. Each pixel contains a class value for the dominant LGP zone found in the pixel. The data are from FAO and IIASA, 2000, Global agro-ecological zones, as reported in FAO and IIASA, 2007, Mapping biophysical factors that influence agricultural production and rural vulnerability, by H. von Velthuizen et al.

Vulnerable population identified by the prevalence of diseases (malaria, cough and diarrhea) as indicator for food security, in sample of households in East Africa study area. Data based on DHS and MICS surveys. In defining vulnerability, WFP (2009) and IFPRI (2012) have been followed and combined with indicators for food security with health indicators that signal vulnerability in a physical sense. IFPRI's Global Hunger Index uses three indicators to measure hunger: the number of adults being undernourished, the number of children that have low weight for age, and child mortality. Other classifications of food security use the variety of the diet as an indicator, combined with anthropometric data on children. However, in the DHS data there were no information available on child mortality, nor on dietary composition. Given these data limitations, data on nutritional status of women (Body Mass Index, BMI) for women and children (weight for age and weight for height) have been used as indicators for food security. These data were combined with data on morbidity among adults and children, specifically the occurrence of malaria, cough, and diarrhea. Combinations of indicators have led to a classification of households as being very vulnerable, vulnerable, nearly vulnerable and not vulnerable. This data set was produced in the framework of the "Climate change predictions in Sub-Saharan Africa: impacts and adaptations (ClimAfrica)" project, Work Package 5 (WP5). More information on ClimAfrica project is provided in the Supplemental Information section of this metadata. This study in WP5 aimed to identify, locate and characterize groups that are vulnerable for climate change conditions in two country clusters; one in West Africa (Benin, Burkina Faso, Côte d'Ivoire, Ghana, and Togo) and one in East Africa (Sudan, South Sudan and Uganda). Data used for the study include the Demographic and Health Surveys (DHS) , the Multi Indicator Cluster Survey (MICS) and the Afrobarometer surveys for the socio-economic variables and grid level data on agro-ecological and climatic conditions.

Vulnerable population identified by the nutritional status of women (BMI) as indicator for food security, in sample of households in East Africa study area. Data based on DHS and MICS surveys. In defining vulnerability, WFP (2009) and IFPRI (2012) have been followed and combined with indicators for food security with health indicators that signal vulnerability in a physical sense. IFPRI's Global Hunger Index uses three indicators to measure hunger: the number of adults being undernourished, the number of children that have low weight for age, and child mortality. Other classifications of food security use the variety of the diet as an indicator, combined with anthropometric data on children. However, in the DHS data there were no information available on child mortality, nor on dietary composition. Given these data limitations, data on nutritional status of women (Body Mass Index, BMI) for women and children (weight for age) have been used as indicators for food security. These data were combined with data on morbidity among adults and children, specifically the occurrence of malaria, cough, and diarrhea. Combinations of indicators have led to a classification of households as being very vulnerable, vulnerable, nearly vulnerable and not vulnerable. This data set was produced in the framework of the "Climate change predictions in Sub-Saharan Africa: impacts and adaptations (ClimAfrica)" project, Work Package 5 (WP5). More information on ClimAfrica project is provided in the Supplemental Information section of this metadata. This study in WP5 aimed to identify, locate and characterize groups that are vulnerable for climate change conditions in two country clusters; one in West Africa (Benin, Burkina Faso, Côte d'Ivoire, Ghana, and Togo) and one in East Africa (Sudan, South Sudan and Uganda). Data used for the study include the Demographic and Health Surveys (DHS) , the Multi Indicator Cluster Survey (MICS) and the Afrobarometer surveys for the socio-economic variables and grid level data on agro-ecological and climatic conditions.

Vulnerable population identified by the nutritional status of women (BMI) as indicator for food security, in sample of households in West Africa study area. Data based on DHS and MICS surveys. In defining vulnerability, WFP (2009) and IFPRI (2012) have been followed and combined with indicators for food security with health indicators that signal vulnerability in a physical sense. IFPRI's Global Hunger Index uses three indicators to measure hunger: the number of adults being undernourished, the number of children that have low weight for age, and child mortality. Other classifications of food security use the variety of the diet as an indicator, combined with anthropometric data on children. However, in the DHS data there were no information available on child mortality, nor on dietary composition. Given these data limitations, data on nutritional status of women (Body Mass Index, BMI) for women and children (weight for age) have been used as indicators for food security. These data were combined with data on morbidity among adults and children, specifically the occurrence of malaria, cough, and diarrhea. Combinations of indicators have led to a classification of households as being very vulnerable, vulnerable, nearly vulnerable and not vulnerable. This data set was produced in the framework of the "Climate change predictions in Sub-Saharan Africa: impacts and adaptations (ClimAfrica)"project, Work Package 5 (WP5). More information on ClimAfrica project is provided in the Supplemental Information section of this metadata. This study in WP5 aimed to identify, locate and characterize groups that are vulnerable for climate change conditions in two country clusters; one in West Africa (Benin, Burkina Faso, Côte d'Ivoire, Ghana, and Togo) and one in East Africa (Sudan, South Sudan and Uganda). Data used for the study include the Demographic and Health Surveys (DHS) , the Multi Indicator Cluster Survey (MICS) and the Afrobarometer surveys for the socio-economic variables and grid level data on agro-ecological and climatic conditions.

In defining vulnerability, WFP (2009) and IFPRI (2012) have been followed and combined with indicators for food security with health indicators that signal vulnerability in a physical sense. IFPRI's Global Hunger Index uses three indicators to measure hunger: the number of adults being undernourished, the number of children that have low weight for age, and child mortality. Other classifications of food security use the variety of the diet as an indicator, combined with anthropometric data on children. However, in the DHS data there were no information available on child mortality, nor on dietary composition. Given these data limitations, data on nutritional status of women (Body Mass Index, BMI) for women and children (weight for age) have been used as indicators for food security. These data were combined with data on morbidity among adults and children, specifically the occurrence of malaria, cough, and diarrhea. Combinations of indicators have led to a classification of households as being very vulnerable, vulnerable, nearly vulnerable and not vulnerable. The Afrobarometer surveys did not include data on the BMI of adults nor weights for children. Here, the reported times the household went without food in the year were used prior to the date the survey was conducted as vulnerability indicator. The study area of households vulnerability included: rural, urban and total population. This data set was produced in the framework of the "Climate change predictions in Sub-Saharan Africa: impacts and adaptations (ClimAfrica)" project, Work Package 5 (WP5). More information on ClimAfrica project is provided in the Supplemental Information section of this metadata. This study in WP5 aimed to identify, locate and characterize groups that are vulnerable for climate change conditions in two country clusters; one in West Africa (Benin, Burkina Faso, Côte d'Ivoire, Ghana, and Togo) and one in East Africa (Sudan, South Sudan and Uganda). Data used for the study include the Demographic and Health Surveys (DHS) , the Multi Indicator Cluster Survey (MICS) and the Afrobarometer surveys for the socio-economic variables and grid level data on agro-ecological and climatic conditions.

In defining vulnerability, WFP (2009) and IFPRI (2012) have been followed and combined with indicators for food security with health indicators that signal vulnerability in a physical sense. IFPRI's Global Hunger Index uses three indicators to measure hunger: the number of adults being undernourished, the number of children that have low weight for age, and child mortality. Other classifications of food security use the variety of the diet as an indicator, combined with anthropometric data on children. However, in the DHS data there were no information available on child mortality, nor on dietary composition. Given these data limitations, data on nutritional status of women (Body Mass Index, BMI) for women and children (weight for age) have been used as indicators for food security. These data were combined with data on morbidity among adults and children, specifically the occurrence of malaria, cough, and diarrhea. Combinations of indicators have led to a classification of households as being very vulnerable, vulnerable, nearly vulnerable and not vulnerable. The Afrobarometer surveys did not include data on the BMI of adults nor weights for children. Here, the reported times the household went without food in the year were used prior to the date the survey was conducted as vulnerability indicator. The study area of households vulnerability included: rural, urban and total population. This data set was produced in the framework of the "Climate change predictions in Sub-Saharan Africa: impacts and adaptations (ClimAfrica)" project, Work Package 5 (WP5). More information on ClimAfrica project is provided in the Supplemental Information section of this metadata. This study in WP5 aimed to identify, locate and characterize groups that are vulnerable for climate change conditions in two country clusters; one in West Africa (Benin, Burkina Faso, Côte d'Ivoire, Ghana, and Togo) and one in East Africa (Sudan, South Sudan and Uganda). Data used for the study include the Demographic and Health Surveys (DHS) , the Multi Indicator Cluster Survey (MICS) and the Afrobarometer surveys for the socio-economic variables and grid level data on agro-ecological and climatic conditions.

Vulnerable population identified by the nutritional status of children (weight for age and weight for height) as indicators for food security, in sample of households in East Africa study area. Data based on DHS and MICS surveys. In defining vulnerability, WFP (2009) and IFPRI (2012) have been followed and combined with indicators for food security with health indicators that signal vulnerability in a physical sense. IFPRI's Global Hunger Index uses three indicators to measure hunger: the number of adults being undernourished, the number of children that have low weight for age, and child mortality. Other classifications of food security use the variety of the diet as an indicator, combined with anthropometric data on children. However, in the DHS data there were no information available on child mortality, nor on dietary composition. Given these data limitations, data on nutritional status of women (Body Mass Index, BMI) for women and children (weight for age and weight for height) have been used as indicators for food security. These data were combined with data on morbidity among adults and children, specifically the occurrence of malaria, cough, and diarrhea. Combinations of indicators have led to a classification of households as being very vulnerable, vulnerable, nearly vulnerable and not vulnerable. This data set was produced in the framework of the "Climate change predictions in Sub-Saharan Africa: impacts and adaptations (ClimAfrica)" project, Work Package 5 (WP5). More information on ClimAfrica project is provided in the Supplemental Information section of this metadata. This study in WP5 aimed to identify, locate and characterize groups that are vulnerable for climate change conditions in two country clusters; one in West Africa (Benin, Burkina Faso, Côte d'Ivoire, Ghana, and Togo) and one in East Africa (Sudan, South Sudan and Uganda). Data used for the study include the Demographic and Health Surveys (DHS) , the Multi Indicator Cluster Survey (MICS) and the Afrobarometer surveys for the socio-economic variables and grid level data on agro-ecological and climatic conditions.

Vulnerable population identified by the nutritional status of children (weight for age and weight for height) as indicators for food security, in sample of households in West Africa study area. Data based on DHS and MICS surveys. In defining vulnerability, WFP (2009) and IFPRI (2012) have been followed and combined with indicators for food security with health indicators that signal vulnerability in a physical sense. IFPRI's Global Hunger Index uses three indicators to measure hunger: the number of adults being undernourished, the number of children that have low weight for age, and child mortality. Other classifications of food security use the variety of the diet as an indicator, combined with anthropometric data on children. However, in the DHS data there were no information available on child mortality, nor on dietary composition. Given these data limitations, data on nutritional status of women (Body Mass Index, BMI) for women and children (weight for age and weight for height) have been used as indicators for food security. These data were combined with data on morbidity among adults and children, specifically the occurrence of malaria, cough, and diarrhea. Combinations of indicators have led to a classification of households as being very vulnerable, vulnerable, nearly vulnerable and not vulnerable. This data set was produced in the framework of the "Climate change predictions in Sub-Saharan Africa: impacts and adaptations (ClimAfrica)" project, Work Package 5 (WP5). More information on ClimAfrica project is provided in the Supplemental Information section of this metadata. This study in WP5 aimed to identify, locate and characterize groups that are vulnerable for climate change conditions in two country clusters; one in West Africa (Benin, Burkina Faso, Côte d’Ivoire, Ghana, and Togo) and one in East Africa (Sudan, South Sudan and Uganda). Data used for the study include the Demographic and Health Surveys (DHS) , the Multi Indicator Cluster Survey (MICS) and the Afrobarometer surveys for the socio-economic variables and grid level data on agro-ecological and climatic conditions.

The soil degradation index is based on the Global Assessment of Human-induced Soil Degradation (GLASOD) (Oldeman et al., 1991). The UNEP-funded GLASOD project produced a world map of human-induced soil degradation. Data were compiled in cooperation with a large number of soil scientists throughout the world, using uniform guidelines and international correlation. The status of soil degradation was mapped within physiographic units, based on expert judgments, indicating the type, extent, degree, rate and main causes of the degradation process. To compare the impact of land degradation between different sites we created a land degradation index that attributes the following weights to area shares of the soil degradation classes 'light'= 1, 'moderate'=2, 'severe' = 3 and 'very severe' = 4. Next, we scaled the index between a range of 0-1. Combining classes and area shares in a single land degradation index is common practice in many other peer reviewed studies (e.g. Leiwen et al., 2005; McCoubrey, 1998; Pace at al., 2008; Safriel, 1999; Sonneveld and Dent, 2009), which gives us, sufficient confidence to apply the index for our analysis. This dataset has been used to complement the survey data and included in the ''Climate change predictions in Sub-Saharan Africa: impacts and adaptations (ClimAfrica)'' project, Work Package 5 (WP5). More information on ClimAfrica project is provided in the Supplemental Information section of this metadata. This study in WP5 aimed to identify, locate and characterize groups that are vulnerable for climate change conditions in two country clusters; one in West Africa (Benin, Burkina Faso, Côte d'Ivoire, Ghana, and Togo) and one in East Africa (Sudan, South Sudan and Uganda). Data used for the study include the Demographic and Health Surveys (DHS) , the Multi Indicator Cluster Survey (MICS) and the Afrobarometer surveys for the socio-economic variables and grid level data on agro-ecological and climatic conditions.

The soil degradation index is based on the Global Assessment of Human-induced Soil Degradation (GLASOD) (Oldeman et al., 1991). The UNEP-funded GLASOD project produced a world map of human-induced soil degradation. Data were compiled in cooperation with a large number of soil scientists throughout the world, using uniform guidelines and international correlation. The status of soil degradation was mapped within physiographic units, based on expert judgments, indicating the type, extent, degree, rate and main causes of the degradation process. To compare the impact of land degradation between different sites we created a land degradation index that attributes the following weights to area shares of the soil degradation classes 'light'= 1, 'moderate'=2, 'severeâ' = 3 and 'very severe' = 4. Next, we scaled the index between a range of 0-1. Combining classes and area shares in a single land degradation index is common practice in many other peer reviewed studies (e.g. Leiwen et al., 2005; McCoubrey, 1998; Pace at al., 2008; Safriel, 1999; Sonneveld and Dent, 2009), which gives us, sufficient confidence to apply the index for our analysis. This dataset has been used to complement the survey data and included in the "Climate change predictions in Sub-Saharan Africa: impacts and adaptations (ClimAfrica)" project, Work Package 5 (WP5). More information on ClimAfrica project is provided in the Supplemental Information section of this metadata. This study in WP5 aimed to identify, locate and characterize groups that are vulnerable for climate change conditions in two country clusters; one in West Africa (Benin, Burkina Faso, Côte d'Ivoire, Ghana, and Togo) and one in East Africa (Sudan, South Sudan and Uganda). Data used for the study include the Demographic and Health Surveys (DHS) , the Multi Indicator Cluster Survey (MICS) and the Afrobarometer surveys for the socio-economic variables and grid level data on agro-ecological and climatic conditions.

The “malnourishment index” relates to the degree of food insecurity of a certain region in 2010. A community characterized by scarce food quality supply and thus subject to malnutrition and starvation of large part of its members is prone to suffer from climate change impact on food production. The index results from the second cluster of the Principal Component Analysis preformed among 14 potential variables. The analysis identify four dominant variables, namely “percentage of underweighted children”, “percentage of stunted children”, “diet diversification index” and “animal protein supply”, assigning a weight of 0.25 to the “percentage of underweighted children” and the “percentage of stunted children”, 0.3 to the “animal protein supply” and 0.2 to the “diet diversification index”. Before to perform the analysis the variables were score-standardized (converted to distribution with average of 0 and standard deviation of 1; “diet diversification index” and “animal protein supply” with inverse method) in order to be comparable. The first administrative level data for “percentage of underweighted children” (more than two standard deviations below the mean weight-for-age score of the NCHS/CDC/WHO international reference population) and “percentage of stunted children” (more than two standard deviations below the mean height-for-age score of the NCHS/CDC/WHO international reference population) were derived from the Global Database on Child Growth and Malnutrition of WHO/UNICEF (data range from 1998 to 2012). When subnational data were not available, were used the national values from UNICEF database. Such national figures were used also to normalize to 2010 the values recorded by WHO/UNICEF. Tabular data were linked by first administrative unit to the first administrative boundaries shapefile (FAO/GAUL) and then converted into raster format (resolution 0.5 arc-minute). The country based values for the other two variables were collected from FAO statistics like the average of the period 2008-2012. Tabular data were linked by country to the national boundaries shapefile (FAO/GAUL) and then converted into raster format (resolution 0.5 arc-minute). Malnourishment illustrates the problems of food insecurity and hunger of a population, which has serious consequences on people's physical condition and very negative impacts on the mental and physical development of children. Countries which have worst diet parameters are more sensitive to the effects of the climate change. Indeed low animal protein consumption and low diet diversification (dominated by cereals) are indicators of the lack of alternative food source than local cereals production. This dataset has been produced in the framework of the “Climate change predictions in Sub-Saharan Africa: impacts and adaptations (ClimAfrica)” project, Work Package 4 (WP4). More information on ClimAfrica project is provided in the Supplemental Information section of this metadata.

The “governance index” is related to potential effectiveness of any governmental response during and after exposure to climate stress in 2010. Generally well-structured governmental institutions are able to enhance the resilience of its members and citizen promoting adaptation strategies and action. The index results from the first cluster of the Principal Component Analysis preformed among 18 potential variables. The analysis identifies seven dominant variables, namely “openness to external assistance”, “regulatory quality”, “government effectiveness”, “voice and accountability”, “failed state”, “perceived corruption” and “criminal rate”, assigning equal weight of 0.1525 except to “criminal rate” that retrieve a weight of 0.085. Before to perform the analysis “criminal rate” was log transformed to shorten the extreme variation and then all variables were score-standardized (converted to distribution with average of 0 and standard deviation of 1; inverse method was applied for “criminal rate”) in order to be comparable. All the variables are country base and the tabular data were linked by country to the national boundaries shapefile (FAO/GAUL) and then converted into raster format (resolution 0.5 arc-minute). The variable “openness to external assistance” was compute as the average values of the period 2008-2012 of the KOF Index of Globalization that serves as a proxy for a country’s level of global integration. It uses data on three dimensions of globalization (economic, social and political) to create an overall globalization score between zero and 100 that measures a country’s level of integration in the global system. The variables “regulatory quality”, “government effectiveness” and “voice and accountability” are gathered from the World Bank’s Worldwide Governance Indicators, considering the average of the period 2008-2012. “Regulatory quality” captures perceptions of the ability of the government to formulate and implement sound policies and regulations that permit and promote private sector development. “Government effectiveness” captures perceptions of the quality of public services, the quality of the civil service and the degree of its independence from political pressures, the quality of policy formulation and implementation, and the credibility of the government's commitment to such policies. “Voice and accountability captures perceptions of the extent to which a country's citizens are able to participate in selecting their government, as well as freedom of expression, freedom of association, and a free media. The “failed state” variable is based on the average 2008-2012 of the fragile state index that is a compilation of 12 socio-economic and political variables made by the Fund for Peace. It aims to capture the vulnerability to any kind of stress of a certain state. The “perceived corruption” variable is based on the homonym index published by Transparency International (NGO) averaged from 2008 to 2012. It assesses the level of corruption using qualitative surveys. Finally the “criminal rate” records the average rate (per 100,000 people) of unlawful death, as defined and assessed by the UN Office on Drugs and Crime, in the period 2008-2012. A country with a higher degree of global integration will be better positioned to obtain disaster assistance from the international community. Autarkic or less well-integrated countries may be less able or willing to receive outside assistance in the event of extreme weather events. Such assistance could take the form of aid from bilateral or multilateral donors or international humanitarian organizations, or it could be in the form of remittances from overseas family or diasporas of the affected population. The Worldwide Governance Indicators define the level of expected assistance to citizens to better coping and manage with climate stressors and disaster events in situations measuring the good governance degree of a state. The failed state index measures the state ability to implement adaptation strategies measuring its vulnerability. People living in countries with higher level of corruption are thought to have more difficulties recovering from climate change impacts, due to limited governmental support reaching affected population compared to states with lower level of corruption. Corruption can further be of particular importance when it comes to the distribution of and access to emergency relief resources. Finally the criminal rate is included because violence severely limits a government’s ability to deliver assistance and indicate a lack of authority by central government. This dataset has been produced in the framework of the “Climate change predictions in Sub-Saharan Africa: impacts and adaptations (ClimAfrica)” project, Work Package 4 (WP4). More information on ClimAfrica project is provided in the Supplemental Information section of this metadata.

The “richness index” represents the level of economical wellbeing a country certain area in 2010. Regions with higher income per capita and low poverty rate and more access to market are wealthier and are therefore better able to prepare for and respond to adversity. The index results from the second cluster of the Principal Component Analysis preformed among 9 potential variables. The analysis identifies four dominant variables, namely “GDPppp per capita”, “agriculture share GDP per agriculture sector worker”, “poverty rate” and “market accessibility”, assigning weights of 0.33, 0.26, 0.25 and 0.16, respectively. Before to perform the analysis all variables were log transformed (except the “agriculture share GDP per agriculture sector worker”) to shorten the extreme variation and then were score-standardized (converted to distribution with average of 0 and standard deviation of 1; inverse method was applied for the “poverty rate” and “market accessibility”) in order to be comparable. The 0.5 arc-minute grid total GDPppp is based on the night time light satellite imagery of NOAA (see Ghosh, T., Powell, R., Elvidge, C. D., Baugh, K. E., Sutton, P. C., & Anderson, S. (2010).Shedding light on the global distribution of economic activity. The Open Geography Journal (3), 148-161) and adjusted to national total as recorded by International Monetary Fund for 2010. The “GDPppp per capita” was calculated dividing the total GDPppp by the population in each pixel. Further, a focal statistic ran to determine mean values within 10 km. This had a smoothing effect and represents some of the extended influence of intense economic activity for the local people. Country based data for “agriculture share GDP per agriculture sector worker” were calculated from GDPppp (data from International Monetary Fund) fraction from agriculture activity (measured by World Bank) divided by the number of worker in the agriculture sector (data from World Bank). The tabular data represents the average of the period 2008-2012 and were linked by country unit to the national boundaries shapefile (FAO/GAUL) and then converted into raster format (resolution 0.5 arc-minute). The first administrative level data for the “poverty rate” were estimated by NOAA for 2003 using nighttime lights satellite imagery. Tabular data were linked by first administrative unit to the first administrative boundaries shapefile (FAO/GAUL) and then converted into raster format (resolution 0.5 arc-minute). The 0.5 arc-minute grid “market accessibility” measures the travel distance in minutes to large cities (with population greater than 50,000 people). This dataset was developed by the European Commission and the World Bank to represent access to markets, schools, hospitals, etc.. The dataset capture the connectivity and the concentration of economic activity (in 2000). Markets may be important for a variety of reasons, including their abilities to spread risk and increase incomes. Markets are a means of linking people both spatially and over time. That is, they allow shocks (and risks) to be spread over wider areas. In particular, markets should make households less vulnerable to (localized) covariate shocks. This dataset has been produced in the framework of the “Climate change predictions in Sub-Saharan Africa: impacts and adaptations (ClimAfrica)” project, Work Package 4 (WP4). More information on ClimAfrica project is provided in the Supplemental Information section of this metadata.

The “agriculture resources sensitivity” represents the agriculture potential in 2010. This potential is measured by the availability of land and food production per capita, and the main thread to agriculture land, represented by desertification risk. The index results from the second cluster of the Principal Component Analysis preformed among 16 potential variables. The analysis identify four dominant variables, namely “potential rain-fed food production per capita”, “cropland crowding”, “desertification index” and “topographic resources availability”, assigning respectively the weights of 0.29, 0.29, 0.27 and 0.15. Before to perform the analysis all the variables were log transformed to shorten the extreme variation and then were score-standardized (converted to distribution with average of 0 and standard deviation of 1; all variables with inverse method except “desertification index”) in order to be comparable. The 5 arc-minute grid “potential rain-fed food production per capita” of 2007 was gathered from FAO GeoNetwork and then sampled at 0.5 arc-minutes. It was multiplied by crop land occurrence dataset, extracted from the FAO Global Land Cover-SHARE dataset of 2014 and divided by population grid in order to compute the per capita values. The 0.5 arc-minute grid “cropland crowding” of 2010 was produced dividing the crop land occurrence (FAO Global Land Cover-SHARE) by the population. The 0.5 arc-minute grid “desertification index” of 2000 was measured in terms of number of months recording values less than 0.75 of the ratio between precipitation (current monthly average) and potential evapo-transpiration (PET, current monthly average). Data of precipitation and PET were gathered from Worldclim and from CGIAR Consortium for Spatial Information, respectively. Finally the 0.5 arc-minute grid “topographic resources availability” was produced within the ClimAfrica project based on SRTM DEM of NASA. The “potential rain-fed food production per capita” measures the availability of food in a certain area produced with subsistence techniques. Cells with low food production are sensitive to climate change impacts because the low input agriculture (dominant in Africa) may not produced sufficient food quantities to support the local populations. The “cropland crowding” is an indicator that assess the availability of crop land hectares per 1,000 people. Sensitive areas are where few crop lands are shared by large population. The “desertification index” assesses the climatological risk of a certain area to be subjected to desertification due to lack of rainfall. Such areas are more sensitive to lost crop land and thus food production quantities due to climate change impacts. The “topographic resources availability” is the percentage of each cell with slopes equal to or lower than 15 %. Landscapes strongly dissected contain less land with agriculture values than plain landscapes. The scarcity of agriculture land may increase the fragility of a system because unable to increase to crop surface to cope with climate change impacts. This dataset has been produced in the framework of the “Climate change predictions in Sub-Saharan Africa: impacts and adaptations (ClimAfrica)” project, Work Package 4 (WP4). More information on ClimAfrica project is provided in the Supplemental Information section of this metadata.

The “natural resources sensitivity” symbolizes the ecosystem vitality and degree of conservation in 2010. Deforestation and loss of water resources quality may render certain areas more sensitive to climate stressors on account of the loss of normal vegetation cover, the depletion of biodiversity, the reduction in ecosystem services and significant loss of beneficial assets. The index results from the first cluster of the Principal Component Analysis preformed among 16 potential variables. The analysis identify five dominant variables, namely “water availability per capita”, “net primary production”, “forest accessibility”, “vegetation continuity” and “climatic resources availability”, assigning respectively the weights of 0.19, 0.21, 0.165, 0.21 and 0.225. Before to perform the analysis the variables “water availability per capita”, “forest accessibility” and “vegetation continuity” were log transformed to shorten the extreme variation and then together with the other two variables were score-standardized (converted to distribution with average of 0 and standard deviation of 1; all variables with inverse method) in order to be comparable. The 6 arc-minute grid “water availability per capita” of 2005 was computed by sum of the run-off and discharge grids produced by World Water Development Report II and then sampled at 0.5 arc-minutes. A focal statistic ran with a radius of 55 cells (about 50 Km). This had a smoothing effect and represents some of the extend influence of major rivers as a resources for local people. To calculate the available water per capita it was then divided by the population. The 5 arc-minute grid “net primary production” of 2000 was gathered from FAO GeoNetwork and sampled at 0.5 arc-minute. Also in this case a focal statistic ran with a radius of 22 cells (about 20 Km) in order to represents the extend effect of primary production as natural resources for local people. The 0.5 arc-minute grid “forest accessibility” was build using the grid of travel distance in minutes to large cities (which one with population greater than 50,000 people), produced by the European Commission and the World Bank to represent the connectivity in 2000, and the grid of forest occurrence, extracted from the FAO Global Land Cover-SHARE dataset of 2014. The result measures the distance in minutes between forest and cities, thus is a proxy for remoteness and naturalness of forest. The 0.125 arc-minute grid “vegetation continuity” of 2010 were collected from University of Maryland and NASA and sampled at 0.5 arc-minute. A focal statistic ran with a radius of 55 cells (about 50 Km). This had a smoothing effect and represents some of the extend influence of vegetation concentration as a resources for local people. Finally the 0.5 arc-minute grid “climatic resources availability” was produced within the ClimAfrica project. The “water availability per capita” represents the potential water available per people in a certain area. We can consider the area with small values more sensitive to climatic stress, because lack a buffer of water resources, precious in a prevalently rain-fed agricultural system like in Africa. The “net primary production” and the “vegetation continuity” are proxies of the potential vegetal productivity available in a certain area. Moreover “vegetation continuity” is an indicator of abundance of natural ecosystem services that can reduce the sensitivity of human-environment systems. The “forest accessibility” assessing the distance between human and natural system measure the anthropogenic degree of a forest. A forest recording a high anthropogenic degree (thus near in terms of minute from a city) may potentially be threaded by human activity and thus represent a fragile ecosystem. Finally the “climatic resources availability” is an indicator of the climatic potential for biomass production. It is based on the climatically determined biomass productivity index that is a proxy for the atmospheric energy available for biomass production, as expressed by accumulated temperature, adjusted for drought stress. This dataset has been produced in the framework of the “Climate change predictions in Sub-Saharan Africa: impacts and adaptations (ClimAfrica)” project, Work Package 4 (WP4). More information on ClimAfrica project is provided in the Supplemental Information section of this metadata.

The “element at risk index” describes the distribution in 2010 of the density of element (i.e. people, livestock unit or crop land) that are potentially threaded by climatic stress. When a climatic stress impact a densely populated (in terms of element at risk) the impact is likely to be more severe than it would be in areas with fewer element. The index results from the second cluster of the Principal Component Analysis (PCA) performed among potential 10 variables. The analysis identify three dominant variables, namely “people”, “livestock unit” and “crop land”, assigning a weight of 0.3 to people and weights of 0.35 to the other two variables. Before to perform the analysis the variables were log transformed to shorten the extreme variation and then score-standardized (converted to distribution with average of 0 and standard deviation of 1) in order to be comparable. The 0.5 arc-minute grid of population distribution in 2010 were gathered from Worldpop project website. The methodological approach used by Worldpop Project is described in Tatem et al 2007 (DOI: 10.1371/journal.pone.0001298). The values were adjusted in order to have national population totals equal to the UN Population and Demographic Office estimation per Country (World Population Prospect - the 2012 Revision). The 3 arc-minute grid of livestock distribution were gathered from FAO GeoNetwork (cattle, sheep, goats, pigs and poultry distribution), sampled at 0.5 arc-minute. The values were adjusted to national total livestock abundance in 2010 (FAO stats). The single species abundances were converted to livestock unit (LSU) according FAO methodology. The 0.5 arc-minute grid of crop land distribution were collected from FAO Global Land Cover-SHARE of 2014. The area with less than 1 people and 1 LSU for cell (about 1 Km square) and without cropland were masked and removed from the analysis for all layer because considered not exposed to climatic stress. This dataset has been produced in the framework of the “Climate change predictions in Sub-Saharan Africa: impacts and adaptations (ClimAfrica)” project, Work Package 4 (WP4). More information on ClimAfrica project is provided in the Supplemental Information section of this metadata.

The “financial development index” symbolizes the degree of financial development of a country in 2010. Well-developed financial systems may reduce climate change impact because it underlying the diffusion of services and tertiary economic activity that reduce the dependence to agriculture income of a certain population. The index results from the first cluster of the Principal Component Analysis preformed among 9 potential variables. The analysis identifies three dominant variables, namely “investment per capita”, “global commerce volume per capita” and “gross national saving per capita”, assigning weights of 0.35, 0.35 and 0.3, respectively. Before to perform the analysis all variables were log transformed to shorten the extreme variation and then were score-standardized (converted to distribution with average of 0 and standard deviation of 1) in order to be comparable. Country based data for “investment per capita” (expressed as a ratio of total investment in current local currency and GDP in current local currency. Investment or gross capital formation is measured by the total value of the gross fixed capital formation and changes in inventories and acquisitions less disposals of valuables for a unit or sector), “global commerce volume per capita” (expressed as a ratio of commerce volume in current local currency and GDP in current local currency. Commerce volume is the sum of exports and imports of goods and services) and “gross national saving per capita” (expressed as a ratio of gross national savings in current local currency and GDP in current local currency. Gross national saving is gross disposable income less final consumption expenditure after taking account of an adjustment for pension funds) were collected jointly from International Monetary Fund and World Bank (for global commerce volumes) and records the average of the period 2008-2012. The variables represent the share of GDP, thus they were multiplied by total GDPppp in order to have absolute value in international dollars and then divided by population to calculate the per capita values of each variable. The tabular data were linked by country unit to the national boundaries shapefile (FAO/GAUL) and then converted into raster format (resolution 0.5 arc-minute). Investment and global commerce per capita are proxy of economic transition out of agriculture, while national gross saving represents the financial resources buffer that can facilitate the implementation of climate change adaptation strategies. This dataset has been produced in the framework of the “Climate change predictions in Sub-Saharan Africa: impacts and adaptations (ClimAfrica)” project, Work Package 4 (WP4). More information on ClimAfrica project is provided in the Supplemental Information section of this metadata.

The “environmental management index” symbolize the impacts of institutional management on ecosystem and thus the ability of state institution to enhance the resilience to environmental and climate change in 2010. Institution able to manage efficiently the biodiversity and habitat conservation and to administer water related problems may have the capacity to face the impact of climate change on agriculture and water resources. Moreover ecosystem diversity and conservation is an indicator of environmental health, which maintains environmental services and natural balance among species. These ecological qualities are useful for communities when trying to keep or improve adaptation processes. The index results from the third cluster of the Principal Component Analysis preformed among 18 potential variables. The analysis identifies two dominant variables, namely “habitat and biodiversity management” and “water management”, assigning weights of 0.6 and 0.4, respectively. Before to perform the analysis the variables were log transformed to shorten the extreme variation and then were score-standardized (converted to distribution with average of 0 and standard deviation of 1) in order to be comparable. The country based values of the two variables were obtained from sub-indicators of the Environmental Performance Index (EPI) developed by Yale University. The “habitat and biodiversity management” includes four indicators: Critical Habitat Protection, Terrestrial Protected Areas (National Biome Weight), Terrestrial Protected Areas (Global Biome Weight), and Marine Protected Areas, whereas the “water management” include the only Wastewater treatment indicator. The data represents the averaged value for the period 2008-2012. EPI indicators use a “proximity-to-target” methodology, which assesses how close a particular country is to an identified policy target. Thus, scores are on a scale of 0 to 100 by simple arithmetic calculation, with 0 being the farthest from the target and 100 being closest to the target. This dataset has been produced in the framework of the “Climate change predictions in Sub-Saharan Africa: impacts and adaptations (ClimAfrica)” project, Work Package 4 (WP4). More information on ClimAfrica project is provided in the Supplemental Information section of this metadata.

The Gross Domestic Product per capita (gross domestic product divided by mid-year population converted to international dollars, using purchasing power parity rates) has been identified as an important determinant of susceptibility and vulnerability by different authors and used in the Disaster Risk Index 2004 (Peduzzi et al. 2009, Schneiderbauer 2007, UNDP 2004) and is commonly used as an indicator for a country’s economic development (e.g. Human Development Index). Despite some criticisms (Brooks et al. 2005) it is still considered useful to estimate a population’s susceptibility to harm, as limited monetary resources are seen as an important factor of vulnerability. However, collection of data on economic variables, especially sub-national income levels, is problematic, due to various shortcomings in the data collection process. Additionally, the informal economy is often excluded from official statistics. Night time lights satellite imagery of NOAA grid provides an alternative means for measuring economic activity. NOAA scientists developed a model for creating a world map of estimated total (formal plus informal) economic activity. Regression models were developed to calibrate the sum of lights to official measures of economic activity at the sub-national level for some target Country and at the national level for other countries of the world, and subsequently regression coefficients were derived. Multiplying the regression coefficients with the sum of lights provided estimates of total economic activity, which were spatially distributed to generate a 30 arc-second map of total economic activity (see Ghosh, T., Powell, R., Elvidge, C. D., Baugh, K. E., Sutton, P. C., & Anderson, S. (2010).Shedding light on the global distribution of economic activity. The Open Geography Journal (3), 148-161). We adjusted the GDP to the total national GDPppp amount as recorded by IMF (International Monetary Fund) for 2010 and we divided it by the population layer from Worldpop Project. Further, we ran a focal statistics analysis to determine mean values within 10 cell (5 arc-minute, about 10 Km) of each grid cell. This had a smoothing effect and represents some of the extended influence of intense economic activity for local people. Finally we apply a mask to remove the area with population below 1 people per square Km. This dataset has been produced in the framework of the "Climate change predictions in Sub-Saharan Africa: impacts and adaptations (ClimAfrica)" project, Work Package 4 (WP4). More information on ClimAfrica project is provided in the Supplemental Information section of this metadata.

Vulnerable population identified by the prevalence of diseases (malaria, cough and diarrhea) as indicator for food security, in sample of households in West Africa study area. Data based on DHS and MICS surveys. In defining vulnerability, WFP (2009) and IFPRI (2012) have been followed and combined with indicators for food security with health indicators that signal vulnerability in a physical sense. IFPRI's Global Hunger Index uses three indicators to measure hunger: the number of adults being undernourished, the number of children that have low weight for age, and child mortality. Other classifications of food security use the variety of the diet as an indicator, combined with anthropometric data on children. However, in the DHS data there were no information available on child mortality, nor on dietary composition. Given these data limitations, data on nutritional status of women (Body Mass Index, BMI) for women and children (weight for age and weight for height) have been used as indicators for food security. These data were combined with data on morbidity among adults and children, specifically the occurrence of malaria, cough, and diarrhea. Combinations of indicators have led to a classification of households as being very vulnerable, vulnerable, nearly vulnerable and not vulnerable. This data set was produced in the framework of the "Climate change predictions in Sub-Saharan Africa: impacts and adaptations (ClimAfrica)" project, Work Package 5 (WP5). More information on ClimAfrica project is provided in the Supplemental Information section of this metadata. This study in WP5 aimed to identify, locate and characterize groups that are vulnerable for climate change conditions in two country clusters; one in West Africa (Benin, Burkina Faso, Côte d'Ivoire, Ghana, and Togo) and one in East Africa (Sudan, South Sudan and Uganda). Data used for the study include the Demographic and Health Surveys (DHS) , the Multi Indicator Cluster Survey (MICS) and the Afrobarometer surveys for the socio-economic variables and grid level data on agro-ecological and climatic conditions.

The FGGD irrigated areas map is a global raster datalayer with a resolution of 5 arc-minutes. Each pixel in the map contains a value representing the percentage of the area equipped for irrigation. The data are a IIASA modification of FAO and University of Kessel (2002), Digital Global Map of Irrigated Areas v. 2.1. Please for the latest version refer to www.fao.org/ag/agl/aglw/aquastat/irrigationmap/index10.stm

The ALOS Global Digital Surface Model (AW3D30) is a global dataset generated from images collected using the Panchromatic Remote-sensing Instrument for Stereo Mapping (PRISM) aboard the Advanced Land Observing Satellite (ALOS) from 2006 to 2011. As described by the Japan Aerospace Exploration Agency: The Japan Aerospace Exploration Agency (JAXA) releases the global digital surface model (DSM) dataset with a horizontal resolution of approx. 30-meter mesh (1 arcsec) free of charge.

In 2023 ESA WorldCereal released the first dynamic system to provide cropland and crop type maps at 10 meter spatial resolution. The WorldCereal consortium succeeded in providing detailed and seasonally updated global maps of temporary crops, crop type (maize, winter cereals and spring cereals) and irrigation for the year 2021.

The MOD16A2 V105 product provides information about 8-day global terrestrial evapotranspiration at 1km pixel resolution. Evapotranspiration (ET) is the sum of evaporation and plant transpiration from the Earth's surface to the atmosphere. With long-term ET data, the effects of changes in climate, land use, and ecosystems disturbances can be quantified. Temporal coverage 2001-01-01 to 2023-02-09. Deprecated

Global Human Settlement Layer, 2020 at 100 m resolution

EUBUCCO is a scientific database of individual building footprints for 200+ million buildings across the 27 European Union countries and Switzerland, together with three main attributes – building type, height and construction year

International financial statistics per country

The Copernicus DEM is a Digital Surface Model (DSM) that represents the surface of the Earth including buildings, infrastructure and vegetation. The Copernicus DEM is provided in 3 different instances: EEA-10, GLO-30 and GLO-90. Data were acquired through the TanDEM-X mission between 2011 and 2015. The datasets were made available for use in 2019 and will be maintained until 2026. Resolution is 10m for Europe and 30m globally.

The Copernicus DEM is a Digital Surface Model (DSM) that represents the surface of the Earth including buildings, infrastructure and vegetation. The Copernicus DEM is provided in 3 different instances: EEA-10, GLO-30 and GLO-90. Data were acquired through the TanDEM-X mission between 2011 and 2015. The datasets were made available for use in 2019 and will be maintained until 2026. Resolution is 10m for Europe and 30m globally.

Land surface temperature derived from passive microwave at 1km resolution, downscaled to 100m using optical imagery

Soil moisture derived from passive microwave at 1km resolution, downscaled to 100m using Sentinel-2

GSOCmap is the first global soil organic carbon map ever produced through a consultative and participatory process involving member countries, which makes this map totally new and unique. In fact, the map was prepared by member countries, under the guidance of the Intergovernmental Technical Panel on Soils and the Global Soil Partnership Secretariat. Countries agreed on the methodology to produce the map and were trained on modern tools and methodologies to develop national maps. The Global Soil Partnership then gathered all national maps to produce the final product, ensuring a thorough harmonization process. Note: The dataset comes along with a technical report which presents methodologies and the process of compiling the Global Soil organic Carbon Map. The link to download the report is available in the on-line resources.

On the basis of soil parameters provided by the Harmonized World Soil Database (HWSD) seven key soil qualities important for crop production have been derived, namely: nutrient availability, nutrient retention capacity, rooting conditions, oxygen availability to roots, excess salts, toxicities, and workability. Soil qualities are related to the agricultural use of the soil and more specifically to specific crop requirements and tolerances. For the illustration of soil qualities, maize was selected as reference crop because of its global importance and wide geographical distribution. Workability (SQ7) Diagnostic characteristics to indicate soil workability vary by type of management applied. Workability or ease of tillage depends on interrelated soil characteristics such as texture, structure, organic matter content, soil consistence/bulk density, the occurrence of gravel or stones in the profile or at the soil surface, and the presence of continuous hard rock at shallow depth as well as rock outcrops. Some soils are easy to work independent of moisture conditions, other soils are only manageable at an adequate moisture status, in particular for manual cultivation or light machinery. Irregular soil depth, gravel and stones in the profile and rock outcrops, might prevent the use of heavy farm machinery. The soil constraints related to soil texture and soil structure are particularly affecting low and intermediate input farming LUTs, while the constraints related to irregular soil depth and stony and rocky soil conditions are foremost affecting mechanized land preparation and harvesting operations, of high-level input mechanized farming LUTs. Workability constraints are therefore handled differently for low/intermediate and high inputs. The workability soil quality SQ7 includes physical hindrance to cultivation, and limitations to cultivation imposed by texture/clay mineralogy. The soil quality SQ7 is derived by combining the most limiting soil/soil phase attribute with the average of the remaining attribute coditions. Soil phases considered in the quantification of SQ7 are stony, lithic, petric, petrocalcic, petroferric, fragipan and duripan (FAO ‘74), and lithic, petroferric, rudic, skeletic, duripan and fragipan (FAO’90). Note that the classes used in the Soil Quality evaluation are: 1: No or slight limitations 2: Moderate limitations 3: Sever limitations 4: Very severe limitations 5: Mainly non-soil 6: Permafrost area 7: Water bodies Remember that classes are qualitative not quantitative. Only classes 1 to 4 are corresponding to an assessment of soil limitations for plant growth. Class 1 is generally rated between 80 and 100% of the growth potential, class 2 between 60 and 80%, class 3 between 40 and 60%, and class 4 less than 40%.

On the basis of soil parameters provided by the Harmonized World Soil Database (HWSD) seven key soil qualities important for crop production have been derived, namely: nutrient availability, nutrient retention capacity, rooting conditions, oxygen availability to roots, excess salts, toxicities, and workability. Soil qualities are related to the agricultural use of the soil and more specifically to specific crop requirements and tolerances. For the illustration of soil qualities, maize was selected as reference crop because of its global importance and wide geographical distribution. Toxicities (SQ.6) Low pH leads to acidity related toxicities, e.g., aluminum, iron, manganese toxicities, and to various deficiencies, e.g., of phosphorus and molybdenum. Calcareous soils exhibit generally micronutrient deficiencies, for instance of iron, manganese, and zinc and in some cases toxicity of molybdenum. Gypsum strongly limits available soil moisture. Tolerance of crops to calcium carbonate and gypsum varies widely (FAO, 1990; Sys, 1993). Low pH and high calcium carbonate and gypsum are mutually exclusive. Acidity related toxicities such as aluminum toxicities and micro-nutrient deficiencies are accounted for respectively in SQ1, nutrient availability, and in SQ2, nutrient retention capacity. This soil quality SQ6 is therefore only including calcium carbonate and gypsum related toxicities. The most limiting of the combination of excess calcium carbonate and gypsum in the soil, and occurrence of petrocalcic and petrogypsic soil phases is selected for the quantification of SQ6. Note that the classes used in the Soil Quality evaluation are: 1: No or slight limitations 2: Moderate limitations 3: Sever limitations 4: Very severe limitations 5: Mainly non-soil 6: Permafrost area 7: Water bodies Remember that classes are qualitative not quantitative. Only classes 1 to 4 are corresponding to an assessment of soil limitations for plant growth. Class 1 is generally rated between 80 and 100% of the growth potential, class 2 between 60 and 80%, class 3 between 40 and 60%, and class 4 less than 40%.

On the basis of soil parameters provided by the Harmonized World Soil Database (HWSD) seven key soil qualities important for crop production have been derived, namely: nutrient availability, nutrient retention capacity, rooting conditions, oxygen availability to roots, excess salts, toxicities, and workability. Soil qualities are related to the agricultural use of the soil and more specifically to specific crop requirements and tolerances. For the illustration of soil qualities, maize was selected as reference crop because of its global importance and wide geographical distribution. Nutrient retention capacity (SQ2) Nutrient retention capacity is of particular importance for the effectiveness of fertilizer applications and is therefore of special relevance for intermediate and high input level cropping conditions. Nutrient retention capacity refers to the capacity of the soil to retain added nutrients against losses caused by leaching. Plant nutrients are held in the soil on the exchange sites provided by the clay fraction, organic matter and the clay-humus complex. Losses vary with the intensity of leaching which is determined by the rate of drainage of soil moisture through the soil profile. Soil texture affects nutrient retention capacity in two ways, through its effects on available exchange sites on the clay minerals and by soil permeability. The soil characteristics used for topsoil are respectively: Organic Carbon (OC), Soil Texture (Text), Base Saturation (BS), Cation Exchange Capacity of soil (CECsoil), pH, and Cation Exchange Capacity of clay fraction (CECclay). Soil pH serves as indicator for aluminum toxicity and for micro-nutrient deficiencies. The most limitingof these soil characteristic is combined with the average of the remaining less limiting soil characteristics to estimate nutrient retention capacity SQ2. Note that the classes used in the Soil Quality evaluation are: 1: No or slight limitations 2: Moderate limitations 3: Sever limitations 4: Very severe limitations 5: Mainly non-soil 6: Permafrost area 7: Water bodies Remember that classes are qualitative not quantitative. Only classes 1 to 4 are corresponding to an assessment of soil limitations for plant growth. Class 1 is generally rated between 80 and 100% of the growth potential, class 2 between 60 and 80%, class 3 between 40 and 60%, and class 4 less than 40%.

On the basis of soil parameters provided by the Harmonized World Soil Database (HWSD) seven key soil qualities important for crop production have been derived, namely: nutrient availability, nutrient retention capacity, rooting conditions, oxygen availability to roots, excess salts, toxicities, and workability. Soil qualities are related to the agricultural use of the soil and more specifically to specific crop requirements and tolerances. For the illustration of soil qualities, maize was selected as reference crop because of its global importance and wide geographical distribution. Rooting conditions (SQ3) Rooting conditions include effective soil depth (cm) and effective soil volume (vol. %) related to presence of gravel and stoniness. Rooting conditions may be affected by the presence of a soil phase either limiting the effective rooting depth or decreasing the effective volume accessible for root penetration. Rooting conditions address various relations between soil conditions of the rooting zone and crop growth. The following factors are considered in the evaluation: Adequacy of foothold, i.e., sufficient soil depth for the crop for anchoring; available soil volume and penetrability of the soil for roots to extract nutrients; space for root and tuber crops for expansion and economic yield in the soil; and absence of shrinking and swelling properties (vertic) affecting root and tuber crops. Soil depth/volume limitations affect root penetration and may constrain yield formation (roots and tubers). Relevant soil properties considered are: soil depth, soil texture/structure, vertic properties, gelic properties, petric properties and presence of coarse fragments. This soil quality is estimated by multiplying of the soil depth limitation with the most limiting soil or soil phase property Soil phases that relevant for rooting conditions vary somewhat with source of soil map and soil classification used. In the HWSD these are: FAO 74 soil phases: stony, lithic, petric, petrocalcic, petrogypsic, petroferric, fragipan and duripan. FAO 90 soil phases: rudic, lithic, pertroferric, placic, skeletic, fragipan and duripan. ESB soil phases and other soil depth/volume related characteristics: stony, lithic, petrocalcic, petroferric, fragipan and duripan, and presence of gravel or concretions, obstacles to roots (6 classes), and impermeable layers (4 classes). Note that the classes used in the Soil Quality evaluation are: 1: No or slight limitations 2: Moderate limitations 3: Sever limitations 4: Very severe limitations 5: Mainly non-soil 6: Permafrost area 7: Water bodies Remember that classes are qualitative not quantitative. Only classes 1 to 4 are corresponding to an assessment of soil limitations for plant growth. Class 1 is generally rated between 80 and 100% of the growth potential, class 2 between 60 and 80%, class 3 between 40 and 60%, and class 4 less than 40%.

On the basis of soil parameters provided by the Harmonized World Soil Database (HWSD) seven key soil qualities important for crop production have been derived, namely: nutrient availability, nutrient retention capacity, rooting conditions, oxygen availability to roots, excess salts, toxicities, and workability. Soil qualities are related to the agricultural use of the soil and more specifically to specific crop requirements and tolerances. For the illustration of soil qualities, maize was selected as reference crop because of its global importance and wide geographical distribution. Oxygen availability (SQ4) Oxygen availability in soils is largely defined by drainage characteristics of soils. The determination of soil drainage classes is based on procedures developed at FAO (FAO 1995). These procedures take into account soil type, soil texture, soil phases and terrain slope. Apart from drainage characteristics, the soil quality of oxygen availability may be influenced by soil and terrain characteristics that are defined through the occurrence of specific soil phases. These include for the FAO ‘74 classification soil phases indicating phreatic conditions, and for the FAO ’90 classification soil phases indicating respectively phreatic, anthraquic, inundic, or placic conditions. Note that the classes used in the Soil Quality evaluation are: 1: No or slight limitations 2: Moderate limitations 3: Sever limitations 4: Very severe limitations 5: Mainly non-soil 6: Permafrost area 7: Water bodies Remember that classes are qualitative not quantitative. Only classes 1 to 4 are corresponding to an assessment of soil limitations for plant growth. Class 1 is generally rated between 80 and 100% of the growth potential, class 2 between 60 and 80%, class 3 between 40 and 60%, and class 4 less than 40%.

On the basis of soil parameters provided by the Harmonized World Soil Database (HWSD) seven key soil qualities important for crop production have been derived, namely: nutrient availability, nutrient retention capacity, rooting conditions, oxygen availability to roots, excess salts, toxicities, and workability. Soil qualities are related to the agricultural use of the soil and more specifically to specific crop requirements and tolerances. For the illustration of soil qualities, maize was selected as reference crop because of its global importance and wide geographical distribution. Excess salts (SQ5) Accumulation of salts may cause salinity. Excess of free salts referred to as soil salinity is measured as Electric Conductivity (EC in dS/m) or as saturation of the exchange complex with sodium ions, which is referred to as sodicity or sodium alkalinity and is measured as Exchangeable Sodium Percentage (ESP). Salinity affects crops through inhibiting the uptake of water. Moderate salinity affects growth and reduces yields; high salinity levels may kill the crop. Sodicity causes sodium toxicity and affects soil structure leading to massive or coarse columnar structure with low permeability. Apart from soil salinity and sodicity, conditions indicated by saline (salic) and sodic soil phases may affect crop growth and yields. In case of simultaneous occurrence of saline (salic) and sodic soils the limitations are combined. The most limiting of the combined soil salinity and/or sodicity conditions and occurrence of saline (salic) and/or sodic soil phase is selected. Note that the classes used in the Soil Quality evaluation are: 1: No or slight limitations 2: Moderate limitations 3: Sever limitations 4: Very severe limitations 5: Mainly non-soil 6: Permafrost area 7: Water bodies Remember that classes are qualitative not quantitative. Only classes 1 to 4 are corresponding to an assessment of soil limitations for plant growth. Class 1 is generally rated between 80 and 100% of the growth potential, class 2 between 60 and 80%, class 3 between 40 and 60%, and class 4 less than 40%.

The combination of yield gap and poverty rates into a single map enables to identify best feasible modalities for agricultural development, potential investment, and resource allocation. - Yield gap provides important information for identifying causes of food insecurity and addressing rural poverty. Yield and production gaps have been estimated by comparing at a spatially detailed level of 5 arc-minutes the agro-ecological attainable yield and production of 22 major crops/crop groups, simulated under the historical climate of 1981-2010, with actual yields and production obtained by downscaling for the years 2000 and 2010 statistical data of main food, feed, and fiber crops. - The poverty map portrays poverty rates at the country level. Poverty rates vary from 12.7 to 76.2 percent. Accordingly, the higher percentage of poverty in a particular area, the poorer population is located there.

The combination of yield gap and poverty rates into a single map enables to identify best feasible modalities for agricultural development, potential investment, and resource allocation. - Yield gap provides important information for identifying causes of food insecurity and addressing rural poverty. Yield and production gaps have been estimated by comparing at a spatially detailed level of 5 arc-minutes the agro-ecological attainable yield and production of 22 major crops/crop groups, simulated under the historical climate of 1981-2010, with actual yields and production obtained by downscaling for the years 2000 and 2010 statistical data of main food, feed, and fiber crops. - The poverty map portrays poverty rates at the country level. Accordingly, the higher percentage of poverty in a particular area, the poorer population is located there.

Geo-referenced point database on dams in Africa.

This map represents the spatial distribution of land under irrigation which is affected by some degree of salinization. It was produced by combining FAO AQUASTAT country statistics regarding irrigated areas affected by salinization with spatial information on irrigated areas where precipitation is not sufficient to leach away salt residues that are built up in the soil due to irrigation. It was assumed that the risk of salinization of irrigated areas can occur only in areas with an Aridity Index lower than 0.65 (where the Aridity Index is defined as Yearly Precipitation divided by Yearly Reference Evapotranspiration).

This map builds upon work done by Dixon (Dixon et al., 2001) in an attempt to propose a general classification of major farming systems. Based on a combination of global dataset, the map proposes a harmonized classification of major agricultural systems used as the basis for the analysis of SOLAW's Systems at risk. The map is based on an interpretation of global land cover data, combined with thematic datasets showing irrigated land and paddy rice extent. The map is published in SOLAW Report: The state of the world's land and water resources for food and agriculture - Managing systems at risk (2011) and SOLAW Thematic Report 15: Sustainable options for addressing land and water problems - a problem tree and case studies (links in the online resources section).

This map shows the extent of land area equipped for irrigation -expressed as percentage- around the turn of the 20th century according to the Global Map of Irrigation Areas (version 4.0.1), together with areas of rainfed agriculture. Data are available from AQUASTAT - programme of the Land and Water Division of the Food and Agriculture Organization of the United Nations.

GlobWat uses spatially distributed input layers consisting of monthly precipitation, number of wet days per month, coefficient of variation of precipitation, monthly reference evapotranspiration, maximum soil moisture storage capacity, maximum percolation flux, irrigated areas, land use, and areas of open water and wetlands. All these input layers are based on freely available spatial dataset with a resolution of 10 arc minutes for the climate dataset and 5 arc minutes for all the terrain and land dataset (data sources are provided in the downloadable files). The water balance is calculated in two steps. First a vertical water balance is calculated that includes rainfall dependent evapotranspiration and evapotranspiration from crops under irrigated circumstances (for which it is assumed that it can be provided by surface water or groundwater). In a second stage, a horizontal water balance is calculated to correct for incremental evapotranspiration from open water and wetlands and to calculate discharges from river (sub-) basins taking into consideration the water needed for irrigation.

The gridded Climatic Research Unit (CRU) Time-series (TS) data version 4.06 data are month-by-month variations in climate over the period 1901-2021, provided on high-resolution (0.5x0.5 degree) grids.The CRU TS4.06 variables are cloud cover, diurnal temperature range, frost day frequency, wet day frequency, potential evapotranspiration (PET), precipitation, daily mean temperature, monthly average daily maximum and minimum temperature, and vapour pressure for the period January 1901 - December 2021.

Dynamic World is a 10m near-real-time (NRT) Land Use/Land Cover (LULC) dataset that includes class probabilities and label information for nine classes. Predictions are available from 2015-06-27 to present.

Annual dynamics of global land cover and its long-term changes from 1982 to 2015

The Global Lake/Reservoir Storage Time Series is derived from the Surface Water Height Time Series and Surface Water Extent Mask Time Series products.

HydroATLAS offers a global compendium of hydro-environmental characteristics for all sub-basins of HydroBASINS, all river reaches of HydroRIVERS, and all lake polygons of HydroLAKES.

Waterbase is the generic name given to the EEA's databases on the status and quality of Europe's rivers, lakes, groundwater bodies and transitional, coastal and marine waters, on the quantity of Europe's water resources, and on the emissions to surface waters from point and diffuse sources of pollution.

Percent of saturated soil moisture, 25km resolution

The Global Freshwater Quality Database GEMStat provides scientifically-sound data and information on the state and trend of global inland water quality. As operational part of the GEMS/Water Programme of the United Nations Environment Programme (UNEP), GEMStat is hosted by the GEMS/Water Data Centre (GWDC) within the International Centre for Water Resources and Global Change (ICWRGC) in Koblenz, Germany.

Dynamic World is a 10m near-real-time (NRT) Land Use/Land Cover (LULC) dataset that includes class probabilities and label information for nine classes.

Provides for Northern Hemisphere daily maps of the fraction of snow cover on ground (also in forested areas) per pixel in percentage (0% – 100%). The data is available in near real time with a pixel spacing of about 1 km and with the temporal extent from January 2018 to present.

This map provides a representation of levels of water scarcity by major hydrological basin, expressed in terms of the ratio between irrigation water that is consumed by plants through evapotranspiration and renewable fresh water resources. Contrarily to previous water scarcity maps, this map uses consumptive use of water rather than water withdrawal. Renewable fresh water resources as well as net irrigation water requirements in the river basin are calculated through a water balance model, with information regarding climate, soils and irrigated agriculture as input data. The legend distinguishes three classes: • Water scarcity in river basins where evapotranspiration due to irrigation is less than 10% of the total renewable water resources is classified as low; • Water scarcity in river basins where evapotranspiration due to irrigation is in between 10% and 20% of the total renewable water resources is classified as moderate; • Water scarcity in river basins where evapotranspiration due to irrigation is more than 20% of the total renewable water resources is classified as high.

This map shows to which extent rainfed and irrigated agricultural systems as identified on SOLAW Map 1.3: "Major agricultural systems" suffer from land and / or water scarcity. Land scarcity in rainfed agriculture was assessed by comparing the rural population density, (obtained from GRUMP 2000, adjusted for UN data, excluding the urban areas indicated on the GRUMP dataset) with the suitability for rainfed crops as mapped for the Global Agro-ecological Zones 2000. Since land that is very suitable for rainfed agriculture can sustain more people than land that is not suitable, it was assumed that each suitability class has its own carrying capacity regarding population. On the map, land is considered scarce if the population density is higher that the highest quintile in the density distribution for each suitability class. Land scarce areas in climates with an Aridity Index lower than 0.5 (where the Aridity Index is defined as Yearly Precipitation divided by Yearly Reference Evapotranspiration) are considered both land and water scarce. Water scarcity in irrigated areas was assessed by combining the Map 1.2: Global distribution of physical water scarcity with the Global Map of Irrigation Areas. The areas equipped for irrigation are considered water scarce if already more than 10% of the renewable water resources in the river basin is consumed by irrigated crops.

Reference evapotranspiration per month with a spatial resolution of 0.1 degree. Unit: mm month-1. The dataset contains monthly values for global land areas, excluding Antarctica, since 1979. The dataset has been prepared according to the FAO Penman - Monteith method as described in FAO Irrigation and Drainage Paper 56. The input variables are part of the Agrometeorological indicators dataset produced by the European Centre for Medium-Range Weather Forecasts (ECMWF) through the Copernicus Climate Change Service (C3S). The Agrometeorological indicators dataset provides daily surface meteorological data for the period from 1979 to present as input for agriculture and agro-ecological studies. This dataset is based on the hourly ECMWF ERA5 data at surface level and is referred to as AgERA5. References: https://doi.org/10.24381/cds.6c68c9bb The Copernicus Climate Change Service (C3S) aims to combine observations of the climate system with the latest science to develop authoritative, quality-assured information about the past, current and future states of the climate in Europe and worldwide. ECMWF operates the Copernicus Climate Change Service on behalf of the European Union and will bring together expertise from across Europe to deliver the service.

This map, compiled to support the analysis of SOLAW report concerning trends and current use of water use in agriculture, shows the percentage of irrigated area supplied by groundwater. Irrigation mainly relies on renewable freshwater resources, either surface water or groundwater. However, while the extent of irrigation and related water uses are reported in statistical databases or by model simulations, information on the source of irrigation water is still very rare. A recent global inventory undertaken by FAO and the University of Bonn reports that 113 million ha, or 38 percent of the total area equipped for irrigation of 301 million ha, is irrigated by groundwater (Siebert et al, 2010).

Total volume of liquid water (mm3) precipitated over the period 00h-24h local time per unit of area (mm2), per day. Unit: mm day-1. The Precipitation flux variable is part of the Agrometeorological indicators dataset produced by the European Centre for Medium-Range Weather Forecasts (ECMWF) through the Copernicus Climate Change Service (C3S). The Agrometeorological indicators dataset provides daily surface meteorological data for the period from 1979 to present as input for agriculture and agro-ecological studies. This dataset is based on the hourly ECMWF ERA5 data at surface level and is referred to as AgERA5. References: https://doi.org/10.24381/cds.6c68c9bb The Copernicus Climate Change Service (C3S) aims to combine observations of the climate system with the latest science to develop authoritative, quality-assured information about the past, current and future states of the climate in Europe and worldwide. ECMWF operates the Copernicus Climate Change Service on behalf of the European Union and will bring together expertise from across Europe to deliver the service.

Total volume of liquid water (mm3) precipitated over the period 00h-24h local time per unit of area (mm2), per month. Unit: mm month-1. The Precipitation flux variable is part of the Agrometeorological indicators dataset produced by the European Centre for Medium-Range Weather Forecasts (ECMWF) through the Copernicus Climate Change Service (C3S). The Agrometeorological indicators dataset provides daily surface meteorological data for the period from 1979 to present as input for agriculture and agro-ecological studies. This dataset is based on the hourly ECMWF ERA5 data at surface level and is referred to as AgERA5. References: https://doi.org/10.24381/cds.6c68c9bb The Copernicus Climate Change Service (C3S) aims to combine observations of the climate system with the latest science to develop authoritative, quality-assured information about the past, current and future states of the climate in Europe and worldwide. ECMWF operates the Copernicus Climate Change Service on behalf of the European Union and will bring together expertise from across Europe to deliver the service.

Reference evapotranspiration per year with a spatial resolution of 0.1 degree. Unit: mm year-1. The dataset contains annual values for global land areas, excluding Antarctica, since 1979. The dataset has been prepared according to the FAO Penman - Monteith method as described in FAO Irrigation and Drainage Paper 56. The input variables are part of the Agrometeorological indicators dataset produced by the European Centre for Medium-Range Weather Forecasts (ECMWF) through the Copernicus Climate Change Service (C3S). The Agrometeorological indicators dataset provides daily surface meteorological data for the period from 1979 to present as input for agriculture and agro-ecological studies. This dataset is based on the hourly ECMWF ERA5 data at surface level and is referred to as AgERA5. References: https://doi.org/10.24381/cds.6c68c9bb The Copernicus Climate Change Service (C3S) aims to combine observations of the climate system with the latest science to develop authoritative, quality-assured information about the past, current and future states of the climate in Europe and worldwide. ECMWF operates the Copernicus Climate Change Service on behalf of the European Union and will bring together expertise from across Europe to deliver the service.

Total volume of liquid water (mm3) precipitated over the period 00h-24h local time per unit of area (mm2), per year. Unit: mm year-1. The Precipitation flux variable is part of the Agrometeorological indicators dataset produced by the European Centre for Medium-Range Weather Forecasts (ECMWF) through the Copernicus Climate Change Service (C3S). The Agrometeorological indicators dataset provides daily surface meteorological data for the period from 1979 to present as input for agriculture and agro-ecological studies. This dataset is based on the hourly ECMWF ERA5 data at surface level and is referred to as AgERA5. References: https://doi.org/10.24381/cds.6c68c9bb The Copernicus Climate Change Service (C3S) aims to combine observations of the climate system with the latest science to develop authoritative, quality-assured information about the past, current and future states of the climate in Europe and worldwide. ECMWF operates the Copernicus Climate Change Service on behalf of the European Union and will bring together expertise from across Europe to deliver the service.

Reference evapotranspiration per day with a spatial resolution of 0.1 degree. Unit: mm day-1. The dataset contains daily values for global land areas, excluding Antarctica, since 1979. The dataset has been prepared according to the FAO Penman - Monteith method as described in FAO Irrigation and Drainage Paper 56. The input variables are part of the Agrometeorological indicators dataset produced by the European Centre for Medium-Range Weather Forecasts (ECMWF) through the Copernicus Climate Change Service (C3S). The Agrometeorological indicators dataset provides daily surface meteorological data for the period from 1979 to present as input for agriculture and agro-ecological studies. This dataset is based on the hourly ECMWF ERA5 data at surface level and is referred to as AgERA5. References: https://doi.org/10.24381/cds.6c68c9bb The Copernicus Climate Change Service (C3S) aims to combine observations of the climate system with the latest science to develop authoritative, quality-assured information about the past, current and future states of the climate in Europe and worldwide. ECMWF operates the Copernicus Climate Change Service on behalf of the European Union and will bring together expertise from across Europe to deliver the service.

Total volume of liquid water (mm3) precipitated over the period 00h-24h local time per unit of area (mm2), per dekade. Unit: mm dekade-1. The Precipitation flux variable is part of the Agrometeorological indicators dataset produced by the European Centre for Medium-Range Weather Forecasts (ECMWF) through the Copernicus Climate Change Service (C3S). The Agrometeorological indicators dataset provides daily surface meteorological data for the period from 1979 to present as input for agriculture and agro-ecological studies. This dataset is based on the hourly ECMWF ERA5 data at surface level and is referred to as AgERA5. References: https://doi.org/10.24381/cds.6c68c9bb The Copernicus Climate Change Service (C3S) aims to combine observations of the climate system with the latest science to develop authoritative, quality-assured information about the past, current and future states of the climate in Europe and worldwide. ECMWF operates the Copernicus Climate Change Service on behalf of the European Union and will bring together expertise from across Europe to deliver the service.

Reference evapotranspiration per dekade with a spatial resolution of 0.1 degree. Unit: mm dekad-1. The dataset contains dekadal values for global land areas, excluding Antarctica, since 1979. The dataset has been prepared according to the FAO Penman - Monteith method as described in FAO Irrigation and Drainage Paper 56. The input variables are part of the Agrometeorological indicators dataset produced by the European Centre for Medium-Range Weather Forecasts (ECMWF) through the Copernicus Climate Change Service (C3S). The Agrometeorological indicators dataset provides daily surface meteorological data for the period from 1979 to present as input for agriculture and agro-ecological studies. This dataset is based on the hourly ECMWF ERA5 data at surface level and is referred to as AgERA5. References: https://doi.org/10.24381/cds.6c68c9bb The Copernicus Climate Change Service (C3S) aims to combine observations of the climate system with the latest science to develop authoritative, quality-assured information about the past, current and future states of the climate in Europe and worldwide. ECMWF operates the Copernicus Climate Change Service on behalf of the European Union and will bring together expertise from across Europe to deliver the service.

Minimum air temperature calculated at a height of 2 metres above the surface. Unit: K. The Minimum air temperature variable is part of the Agrometeorological indicators dataset produced by the European Centre for Medium-Range Weather Forecasts (ECMWF) through the Copernicus Climate Change Service (C3S). The Agrometeorological indicators dataset provides daily surface meteorological data for the period from 1979 to present as input for agriculture and agro-ecological studies. This dataset is based on the hourly ECMWF ERA5 data at surface level and is referred to as AgERA5. References: https://doi.org/10.24381/cds.6c68c9bb The Copernicus Climate Change Service (C3S) aims to combine observations of the climate system with the latest science to develop authoritative, quality-assured information about the past, current and future states of the climate in Europe and worldwide. ECMWF operates the Copernicus Climate Change Service on behalf of the European Union and will bring together expertise from across Europe to deliver the service.

Maximum air temperature calculated at a height of 2 metres above the surface. Unit: K. The Maximum air temperature variable is part of the Agrometeorological indicators dataset produced by the European Centre for Medium-Range Weather Forecasts (ECMWF) through the Copernicus Climate Change Service (C3S). The Agrometeorological indicators dataset provides daily surface meteorological data for the period from 1979 to present as input for agriculture and agro-ecological studies. This dataset is based on the hourly ECMWF ERA5 data at surface level and is referred to as AgERA5. References: https://doi.org/10.24381/cds.6c68c9bb The Copernicus Climate Change Service (C3S) aims to combine observations of the climate system with the latest science to develop authoritative, quality-assured information about the past, current and future states of the climate in Europe and worldwide. ECMWF operates the Copernicus Climate Change Service on behalf of the European Union and will bring together expertise from across Europe to deliver the service.

Relative humidity at 12h (local time) at a height of 2 metres above the surface. This variable describes the amount of water vapour present in air expressed as a percentage of the amount needed for saturation at the same temperature. Unit: %. The Relative humidity variable is part of the Agrometeorological indicators dataset produced by the European Centre for Medium-Range Weather Forecasts (ECMWF) through the Copernicus Climate Change Service (C3S). The Agrometeorological indicators dataset provides daily surface meteorological data for the period from 1979 to present as input for agriculture and agro-ecological studies. This dataset is based on the hourly ECMWF ERA5 data at surface level and is referred to as AgERA5. References: https://doi.org/10.24381/cds.6c68c9bb The Copernicus Climate Change Service (C3S) aims to combine observations of the climate system with the latest science to develop authoritative, quality-assured information about the past, current and future states of the climate in Europe and worldwide. ECMWF operates the Copernicus Climate Change Service on behalf of the European Union and will bring together expertise from across Europe to deliver the service.

Relative humidity at 09h (local time) at a height of 2 metres above the surface. This variable describes the amount of water vapour present in air expressed as a percentage of the amount needed for saturation at the same temperature. Unit: %. The Relative humidity variable is part of the Agrometeorological indicators dataset produced by the European Centre for Medium-Range Weather Forecasts (ECMWF) through the Copernicus Climate Change Service (C3S). The Agrometeorological indicators dataset provides daily surface meteorological data for the period from 1979 to present as input for agriculture and agro-ecological studies. This dataset is based on the hourly ECMWF ERA5 data at surface level and is referred to as AgERA5. References: https://doi.org/10.24381/cds.6c68c9bb The Copernicus Climate Change Service (C3S) aims to combine observations of the climate system with the latest science to develop authoritative, quality-assured information about the past, current and future states of the climate in Europe and worldwide. ECMWF operates the Copernicus Climate Change Service on behalf of the European Union and will bring together expertise from across Europe to deliver the service.

Relative humidity at 18h (local time) at a height of 2 metres above the surface. This variable describes the amount of water vapour present in air expressed as a percentage of the amount needed for saturation at the same temperature. Unit: %. The Relative humidity variable is part of the Agrometeorological indicators dataset produced by the European Centre for Medium-Range Weather Forecasts (ECMWF) through the Copernicus Climate Change Service (C3S). The Agrometeorological indicators dataset provides daily surface meteorological data for the period from 1979 to present as input for agriculture and agro-ecological studies. This dataset is based on the hourly ECMWF ERA5 data at surface level and is referred to as AgERA5. References: https://doi.org/10.24381/cds.6c68c9bb The Copernicus Climate Change Service (C3S) aims to combine observations of the climate system with the latest science to develop authoritative, quality-assured information about the past, current and future states of the climate in Europe and worldwide. ECMWF operates the Copernicus Climate Change Service on behalf of the European Union and will bring together expertise from across Europe to deliver the service.

Relative humidity at 15h (local time) at a height of 2 metres above the surface. This variable describes the amount of water vapour present in air expressed as a percentage of the amount needed for saturation at the same temperature. Unit: %. The Relative humidity variable is part of the Agrometeorological indicators dataset produced by the European Centre for Medium-Range Weather Forecasts (ECMWF) through the Copernicus Climate Change Service (C3S). The Agrometeorological indicators dataset provides daily surface meteorological data for the period from 1979 to present as input for agriculture and agro-ecological studies. This dataset is based on the hourly ECMWF ERA5 data at surface level and is referred to as AgERA5. References: https://doi.org/10.24381/cds.6c68c9bb The Copernicus Climate Change Service (C3S) aims to combine observations of the climate system with the latest science to develop authoritative, quality-assured information about the past, current and future states of the climate in Europe and worldwide. ECMWF operates the Copernicus Climate Change Service on behalf of the European Union and will bring together expertise from across Europe to deliver the service.

Mean wind speed at a height of 10 metres above the surface over the period 00h-24h local time. Unit: m s-1. The Wind Speed variable is part of the Agrometeorological indicators dataset produced by the European Centre for Medium-Range Weather Forecasts (ECMWF) through the Copernicus Climate Change Service (C3S). The Agrometeorological indicators dataset provides daily surface meteorological data for the period from 1979 to present as input for agriculture and agro-ecological studies. This dataset is based on the hourly ECMWF ERA5 data at surface level and is referred to as AgERA5. References: https://doi.org/10.24381/cds.6c68c9bb The Copernicus Climate Change Service (C3S) aims to combine observations of the climate system with the latest science to develop authoritative, quality-assured information about the past, current and future states of the climate in Europe and worldwide. ECMWF operates the Copernicus Climate Change Service on behalf of the European Union and will bring together expertise from across Europe to deliver the service.

Relative humidity at 06h (local time) at a height of 2 metres above the surface. This variable describes the amount of water vapour present in air expressed as a percentage of the amount needed for saturation at the same temperature. Unit: %. The Relative humidity variable is part of the Agrometeorological indicators dataset produced by the European Centre for Medium-Range Weather Forecasts (ECMWF) through the Copernicus Climate Change Service (C3S). The Agrometeorological indicators dataset provides daily surface meteorological data for the period from 1979 to present as input for agriculture and agro-ecological studies. This dataset is based on the hourly ECMWF ERA5 data at surface level and is referred to as AgERA5. References: https://doi.org/10.24381/cds.6c68c9bb The Copernicus Climate Change Service (C3S) aims to combine observations of the climate system with the latest science to develop authoritative, quality-assured information about the past, current and future states of the climate in Europe and worldwide. ECMWF operates the Copernicus Climate Change Service on behalf of the European Union and will bring together expertise from across Europe to deliver the service.

The raster dataset consists of a 1km score grid for major cereals storage location, produced under the scope of FAO’s Hand-in-Hand Initiative, Geographical Information Systems - Multicriteria Decision Analysis for value chain infrastructure location. Major cereals include barley, millet, wheat, maize, sorghum, and rice. The location score is achieved by processing sub-model outputs that characterize logistical factors for selected crop warehouse location: • Supply: Crop. • Demand: Human population density, Major cities population (national and bordering countries). • Infrastructure/accessibility: main transportation infrastructure. It consists of an arithmetic weighted sum of normalized grids (0 to 100): ("Crop Production" * 0.4) + ("Human Population Density" * 0.2) + (“Major Cities Accessibility” * 0.3) + (”Regional Cities Weighted Accessibility” *0.1 )

The 1km raster dataset represents top location score areas filtered by exclusive criteria: access to finance, distance to major roads and access to IT. The layer is produced under the scope of FAO’s Hand-in-Hand Initiative, Geographical Information Systems - Multicriteria Decision Analysis for value chain infrastructure location. The location score is achieved by processing sub-model outputs characterizing logistical factors for crop warehouse siting: Supply, demand, Infrastructure/accessibility. Access to finance and roads are defined using a linear distance threshold: • Banks - approx. 20 km (0.18 degree) buffer radius. • Major roads - approx. 2 km (0.018 degree) buffer radius. Access to IT is characterized applying the mobile broadband coverage map.

The 1km raster dataset represents top location score areas filtered by exclusive criteria: access to finance, distance to major roads and access to IT. The layer is produced under the scope of FAO’s Hand-in-Hand Initiative, Geographical Information Systems - Multicriteria Decision Analysis for value chain infrastructure location. The location score is achieved by processing sub-model outputs characterizing logistical factors for crop warehouse siting: Supply, demand, Infrastructure/accessibility. Access to finance and roads are defined using a linear distance threshold: • Banks - approx. 20 km (0.18 degree) buffer radius. • Major roads - approx. 2 km (0.018 degree) buffer radius. Access to IT is characterized applying the mobile broadband coverage map.

The raster dataset consists of a 1km score grid for vegetables storage location, produced under the scope of FAO’s Hand-in-Hand Initiative, Geographical Information Systems - Multicriteria Decision Analysis for value chain infrastructure location. The location score is achieved by processing sub-model outputs that characterize logistical factors for selected crop warehouse location: • Supply: Crop. • Demand: Human population density, Major cities population (national and bordering countries). • Infrastructure/accessibility: main transportation infrastructure. It consists of an arithmetic weighted sum of normalized grids (0 to 100): ("Crop Production" * 0.4) + ("Human Population Density" * 0.2) + (“Major Cities Accessibility” * 0.3) + (”Regional Cities Weighted Accessibility” *0.1 )

The raster dataset consists of a 1km score grid for fruits and nuts storage location, produced under the scope of FAO’s Hand-in-Hand Initiative, Geographical Information Systems - Multicriteria Decision Analysis for value chain infrastructure location. The location score is achieved by processing sub-model outputs that characterize logistical factors for selected crop warehouse location: • Supply: Crop. • Demand: Human population density, Major cities population (national and bordering countries). • Infrastructure/accessibility: main transportation infrastructure. It consists of an arithmetic weighted sum of normalized grids (0 to 100): ("Crop Production" * 0.4) + ("Human Population Density" * 0.2) + (“Major Cities Accessibility” * 0.3) + (”Regional Cities Weighted Accessibility” *0.1 )

The 1km raster dataset represents top location score areas filtered by exclusive criteria: access to finance, distance to major roads and access to IT (mobile broadband connection). The layer is produced under the scope of FAO’s Hand-in-Hand Initiative, Geographical Information Systems - Multicriteria Decision Analysis for value chain infrastructure location. The location score is achieved by processing sub-model outputs characterizing logistical factors for crop warehouse siting: Supply, demand, Infrastructure/accessibility. Access to finance and roads are defined using a linear distance threshold: • Banks - approx. 20 km (0.18 degree) buffer radius. • Major roads - approx. 2 km (0.018 degree) buffer radius. Access to IT is characterized applying the mobile broadband coverage map.

The raster dataset consists of a 1km score grid for cereal storage location, produced under the scope of FAO’s Hand-in-Hand Initiative, Geographical Information Systems - Multicriteria Decision Analysis for value chain infrastructure location. The location score is achieved by processing sub-model outputs that characterize logistical factors for selected crop warehouse location: • Supply: Crop. • Demand: Human population density, Major cities population (national and bordering countries). • Infrastructure/accessibility: main transportation infrastructure. It consists of an arithmetic weighted sum of normalized grids (0 to 100): ("Crop Production" * 0.4) + ("Human Population Density" * 0.2) + (“Major Cities Accessibility” * 0.3) + (”Regional Cities Weighted Accessibility” *0.1 )

Total amount of energy provided by solar radiation at the surface over the period 00-24h local time per unit area and time. Unit: J m-2 day-1. The Solar Radiation Flux variable is part of the Agrometeorological indicators dataset produced by the European Centre for Medium-Range Weather Forecasts (ECMWF) through the Copernicus Climate Change Service (C3S). The Agrometeorological indicators dataset provides daily surface meteorological data for the period from 1979 to present as input for agriculture and agro-ecological studies. This dataset is based on the hourly ECMWF ERA5 data at surface level and is referred to as AgERA5. References: https://doi.org/10.24381/cds.6c68c9bb The Copernicus Climate Change Service (C3S) aims to combine observations of the climate system with the latest science to develop authoritative, quality-assured information about the past, current and future states of the climate in Europe and worldwide. ECMWF operates the Copernicus Climate Change Service on behalf of the European Union and will bring together expertise from across Europe to deliver the service.

The 1km raster dataset represents top location score areas filtered by exclusive criteria: access to finance, distance to major roads and access to IT. The layer is produced under the scope of FAO’s Hand-in-Hand Initiative, Geographical Information Systems - Multicriteria Decision Analysis for value chain infrastructure location. The location score is achieved by processing sub-model outputs characterizing logistical factors for crop warehouse siting: Supply, demand, Infrastructure/accessibility. Access to finance and roads are defined using a linear distance threshold: • Banks - approx. 20 km (0.18 degree) buffer radius. • Major roads - approx. 2 km (0.018 degree) buffer radius. Access to IT is characterized applying the mobile broadband coverage map.

A set of 2 vector layers representing the Okavango River (Macro) Basin and the main sub-basins. Source: Data generated under the GIS EPSMO program. This dataset is part of the GIS Database for the Environment Protection and Sustainable Management of the Okavango River Basin project (EPSMO). Detailed information on the database can be found in the “GIS Database for the EPSMO Project” document produced by Luis Veríssimo (FAO consultant) in July 2009, and here available for download.

Okavango Megabasin drainage network, including permanent, seasonal, ephemeral and dry water courses. Source: Data generated under the GIS EPSMO program. This dataset is part of the GIS Database for the Environment Protection and Sustainable Management of the Okavango River Basin project (EPSMO). Detailed information on the database can be found in the “GIS Database for the EPSMO Project” document produced by Luis Veríssimo (FAO consultant) in July 2009, and here available for download.

This raster layer represents the land cover classification within the Okavango Basin. Source: Generated under the GIS EPSMO program (Classification based on MODIS Vegetation Continues Fields data from 2000-2005). This dataset is part of the GIS Database for the Environment Protection and Sustainable Management of the Okavango River Basin project (EPSMO). Detailed information on the database can be found in the “GIS Database for the EPSMO Project” document produced by Luis Veríssimo (FAO consultant) in July 2009, and here available for download.

Location of fall, reservoirs, springs and other features within the Okavango River Basin. Source: Digital Chart of the World (DCW). This dataset is part of the GIS Database for the Environment Protection and Sustainable Management of the Okavango River Basin project (EPSMO). Detailed information on the database can be found in the “GIS Database for the EPSMO Project” document produced by Luis Veríssimo (FAO consultant) in July 2009, and here available for download.

A set of 4 vector data, derived from the World Database on Protected Areas WDPA/IUCN (2009), representing le following layers: -Protected Areas; -Conservation managed units; -Hunting Management Areas; -Forest Reserves. Source: WDPA/IUCN (2009). The WDPA Annual Release includes all nationally designated (e.g. National Parks, Nature Reserves) and internationally recognized protected areas (e.g. UNESCO World Heritage Sites, Wetlands of International Importance) currently held in the WDPA, up to end of February 2009. This static version contains all data improvements made to the WDPA since the previous annual release in December 2007. This dataset is part of the GIS Database for the Environment Protection and Sustainable Management of the Okavango River Basin project (EPSMO). Detailed information on the database can be found in the “GIS Database for the EPSMO Project” document produced by Luis Veríssimo (FAO consultant) in July 2009, and here available for download.

Location of large irrigation schemes, in Namibia, that share geographical overlapping with the Okavango Basin. Source: Ministry of Agriculture, Water and Forestry of Namibia. This dataset is part of the GIS Database for the Environment Protection and Sustainable Management of the Okavango River Basin project (EPSMO). Detailed information on the database can be found in the GIS Database for the EPSMO Project document produced by Luis Veríssimo (FAO consultant) in July 2009, and here available for download.

This raster layer is the hill shade in the CORB-contributing area, in the reference scale of 1/200 000. It was created by interpolation of the DTM model with 90m pixel resolution. This dataset is part of the GIS Database for the Environment Protection and Sustainable Management of the Okavango River Basin project (EPSMO). Detailed information on the database can be found in the “GIS Database for the EPSMO Project” document produced by Luis Veríssimo (FAO consultant) in July 2009, and here available for download.

A general characterization of moisture conditions is achieved through the concept of length of growing period (LGP), i.e. the period during the year when both moisture availability and temperature are conducive to crop growth. Thus, in a formal sense, LGP refers to the number of days within the period of temperatures above 5°C when moisture conditions are considered adequate. Under rain-fed conditions, the begin of the LGP is linked to the start of the rainy season. For establishing crops, 0.4 - 0.5 times the level of reference evapotranspiration is considered sufficient to meet water requirements of dryland crops (FAO 1978-81a; 1979; 1992a). This layer is a result of the Agro-ecological Zones (AEZ) methodology for land productivity assessments and is part of the Global Agro-ecological Zoning Database.

The EDO Soil Moisture Anomaly (SMA) indicator is computed every 10-day as anomalies of soil moisture index derived from the JRC’s in-house LISFLOOD hydrological model.

The JRC has published the first continental map of crops grown in the European Union at 10-m resolution. This map combines Copernicus Sentinel-1 satellite observations and in-situ LUCAS 2018 Copernicus data using machine learning and cloud computing.

EuroCrops is a dataset collection combining all publicly available self-declared crop reporting datasets from countries of the European Union.

The JRC Global Surface Watter Mapping Layers v1.4 contains maps of the location and temporal distribution of surface water from 1984 to 2021 and provides statistics on the extent and change of those water surfaces.

GRACE Tellus Monthly Mass Grids provides monthly gravitational anomalies relative to a 2004-2010 time-mean baseline. The data contained in this dataset are units of "Equivalent Water Thickness" which represent the deviations of mass in terms of vertical extent of water in centimeters. .

GSMaP (Global Satellite Mapping of Precipitation) developed by the JAXA[17]

This dataset provides water variables and indicators based on hydrological impact modelling, forced by bias adjusted regional climate simulations from the European Coordinated Regional Climate Downscaling Experiment (EURO-CORDEX). The dataset contains Essential Climate Variable (ECV) data in the form of daily mean river discharge and a set of climate impact indicators (CIIs) for both water quantity and quality. ECV datasets provide the empirical evidence needed to understand the current climate and predict future changes. CIIs contain condensed climate information which facilitate relatively quick and efficient subsequent analysis. Therefore, CIIs make climate information accessible to application focussed users within a sector. The ECVs and CIIs provided here were derived within the water management sectoral information service to address questions specific to the water sector. However, the products are provided in a generic form and are relevant for a range of sectors, for example agriculture and energy.

11 classes of land cover derived from Sentinel-2 imagery. Resolution is 10m.

The map summarizes the status of the land use and land cover at global scale. It was compiled according to the current GAEZ (Global Agro-Ecological Zones) -2009 approach, developed by FAO in collaboration with IIASA (International Institute for Applied Systems Analysis).The current Global AEZ (GAEZ-2009) offers a standardized framework for the characterization of climate, soil and terrain conditions relevant to agricultural production, which can be applied at global to sub-national levels. The map is based on six geographic datasets: - GLC2000 land cover database at 30 arc-sec (http://www-gvm.jrc.it/glc2000), using regional and global legends (JRC, 2006); - an IFPRI global land cover categorization providing 17 land cover classes at 30 arc-sec. (IFPRI, 2002), based on a reinterpretation of the Global Land Cover Characteristics Database (GLCC ver. 2.0), EROS Data Centre (EDC, 2000); - FAO’s Global Forest Resources Assessment 2000 and 2005 (FAO, 2001; FAO, 2006) at 30 arc-sec. resolution; - digital Global Map of Irrigated Areas (GMIA) version 4.0 of (FAO/University of Frankfurt) at 5’ by 5’ latitude/longitude resolution, providing by grid-cell the percentage land area equipped with irrigation infrastructure (Siebert et al., 2007); - a spatial population density inventory (30-arc seconds) for year 2000 developed by FAO-SDRN, based on spatial data of LANDSCAN 2003, with calibration to UN 2000 population figures. An iterative calculation procedure has been implemented to estimate land cover class weights, consistent with aggregate FAO land statistics and spatial land cover patterns obtained from remotely sensed data, allowing the quantification of major land use/land cover shares in individual 5’ by 5’ latitude/longitude grid cells.

On 8 November 2013, Typhoon Haiyan made landfall in the Philippines causing widespread devastation, tremendous loss of life and catastrophic impact on infrastructure and natural resources. As a result of the devastating damage to agriculture and fisheries sectors and the severe impact on lives and livelihoods FAO has declared Level 3 Emergency Response. In response to the call for needs assessment and coordinating humanitarian assistance in agriculture and fisheries sectors the FAO, Land and Water Division Geospatial Unit collected a number of datasets from various sources including FAO, WFP, UNITAR-UNOSAT, EC-JRC, USDA, USGS as well as updated reports from national agencies as well as the National Disaster Risk Reduction and Management council. Severity zones were mapped using data from the EC-JRC updates based on the Public Storm Warning Signals (PSWC) in four main levels: S1-very high for PSWC #1, S2-high for PSWC #2, S3-medium for PSWC #3 and S4-medium-low for PSWC#4. The severity area mask was calibrated using remote sensing data combined with the data reported on November 13, 2013 by the National Disaster Risk Reduction and Management Council of Philippines. The severity intensity rate was classified proportionally with the affected population figures. The data on crop production, harvested area, yield for major crops were collected from FAO, Global Spatial Database of Agricultural Land-Use Statistics AgroMaps ( http://kids.fao.org/agromaps ), FAOSTAT ( http://faostat.fao.org/) and Bureau of Agricultural Statistics of Philippines (http://countrystat.bas.gov.ph ). Data on arable land and permanent crops were derived from the FAO Global Land Cover Share database (Beta-version) and data on the area by district were derived by the FAO Global Administrative Units Layer, GAUL version 2013-12 (www.fao.org/geonetwork). In addition data on crop calendars to identify crop growing stage were collected from the FAO-IIASA Global Agro-Ecological Zones Data portal, GAEZ (www.fao.org/nr/gaez) , FAO Crop Calendar from Data@Fao.org (https://data.apps.fao.org/) and FAO Agricultural Market Information System, AMIS (http://www.amis-outlook.org/) .Philippines rice crop calendar was provided by the Philippines Rice Research Institute (PhilRice-DA), International Rice Research Institute (IRRI). The assessment was done by first georefencing, harmonizing and creating a central database in the UTM WGS 84 reference system. Areas of crop growth stages were analyzed according to their stage at the time of the event. Major season and secondary season for rice were considered for analysis at the time of the event. The areas of standing rice at the moment of the event were mapped using the district level crop calendars for the major season. Also the planted rice areas for the second season were mapped. The affected area was then calculated considering both these areas which were considered as area loss, calibrated by the severity intensity rate class. The extent (ha) and fraction (%) of the potential affected crop areas was calculated by intersecting the tropical cyclone severity areas with the administrative layers, arable land and permanent crops, annual harvested area, yield and production for year 2012 (which was used as a proxy to assess the planted area). Major crops which were affected include Rice Paddy, Coconut and Sugar Cane. The maps show the impacts of the tropical cyclone on these crops. The outputs were classified in 5 classes of affected areas: less the 10%, 10-25%, 25-50%, 50-75%, and >75%. The tabular information provides information on the estimated affected area for Rice Paddy (extent in ha and share in percentage) by Typhoon “YOLANDA” (HAIYAN) severity Level and by District (updated on 15 November 2013). The affected planted areas for each severity zone were calculated as a fraction of the planted area versus by severity zone by administrative unit area. In addition, the analysis was done for major livestock, pig, poultry and cattle, affected population. Sources: Agromaps, FAOSTAT, CountrySTAT Philippines, GAEZ, AMIS, GLC-SHARE, FAO NRL Geospatial Unit datasets, Data@FAO.ORG, EC-JRC, ESA GlobCover, UNITAR, UNOSAT, WFP, National Disaster Risk Reduction and Management Council, Republic of Philippines, Philippines rice crop calendar, Philippines Rice Research Institute (PhilRice-DA), International Rice Research Institute (IRRI) sponsored by the DA-National Rice Program via the Rice Self Sufficiency Program (RSSP) and the Global Rice Science Partnership (GRiSP), the CGIAR Research Program on Rice.

A set of 13 raster layers representing the monthly and annual, average precipitation for the Okavango Rive Basin. Source: Africa Water Resources Database (FAO). This dataset is part of the GIS Database for the Environment Protection and Sustainable Management of the Okavango River Basin project (EPSMO). Detailed information on the database can be found in the “GIS Database for the EPSMO Project” document produced by Luis Veríssimo (FAO consultant) in July 2009, and here available for download.

This raster layer represents the slope model of the CORB-Contributing area, in the reference scale of 1/200 000. The creation of this layer was achieved by interpolating the DTM raster layer. Units are organized by degrees. This dataset is part of the GIS Database for the Environment Protection and Sustainable Management of the Okavango River Basin project (EPSMO). Detailed information on the database can be found in the “GIS Database for the EPSMO Project” document produced by Luis Veríssimo (FAO consultant) in July 2009, and here available for download.

This raster layer represents the Digital Elevation Model (DEM) of the CORB-Contributing area, in the reference scale of 1/200 000. It was created by converting raw SRTM data into a finalized raster layer for the CORB-Contributing area with 90m pixel resolution. This dataset is part of the GIS Database for the Environment Protection and Sustainable Management of the Okavango River Basin project (EPSMO). Detailed information on the database can be found in the “GIS Database for the EPSMO Project” document produced by Luis Veríssimo (FAO consultant) in July 2009, and here available for download.

This raster layer represents the aspect model of the CORB-Contributing area, in the reference scale of 1/200 000. Aspect is the direction that a slope faces. It identifies the steepest downslope direction at a location on a surface. Source: The creation of this layer was achieved by interpolating the DTM raster layer. This dataset is part of the GIS Database for the Environment Protection and Sustainable Management of the Okavango River Basin project (EPSMO). Detailed information on the database can be found in the “GIS Database for the EPSMO Project” document produced by Luis Veríssimo (FAO consultant) in July 2009, and here available for download.

This vector layer represents the geologic units in the CORB-Contributing area of Angola. Source: Data provided by SINFIC, based on the Topographic Map of Angola (Laboratorio Nacional de Investigação Científica Tropica, 1982). This dataset is part of the GIS Database for the Environment Protection and Sustainable Management of the Okavango River Basin project (EPSMO). Detailed information on the database can be found in the “GIS Database for the EPSMO Project” document produced by Luis Veríssimo (FAO consultant) in July 2009, and here available for download.

Population database compiled on a 30x30” lat/long grid, for the years: 1998, 2000 and 2007. within the Okavango Basin. Census counts (at sub-national level) were apportioned to each grid cell based on likelihood coefficients, which are based on proximity to roads, slope, land cover, nighttime lights, and other information.Source: Oak Ridge National Laboratory (ORNL). This dataset is part of the GIS Database for the Environment Protection and Sustainable Management of the Okavango River Basin project (EPSMO). Detailed information on the database can be found in the “GIS Database for the EPSMO Project” document produced by Luis Veríssimo (FAO consultant) in July 2009, and here available for download.

Geologic Provinces and Units within the Okavango River Basin. Source: U.S. Geological Survey (USGS). This dataset is part of the GIS Database for the Environment Protection and Sustainable Management of the Okavango River Basin project (EPSMO). Detailed information on the database can be found in the “GIS Database for the EPSMO Project” document produced by Luis Veríssimo (FAO consultant) in July 2009, and here available for download.

Dune fields and major pans within the Okavango River Basin. Source: Digital Chart of the World (DCW). This dataset is part of the GIS Database for the Environment Protection and Sustainable Management of the Okavango River Basin project (EPSMO). Detailed information on the database can be found in the “GIS Database for the EPSMO Project” document produced by Luis Veríssimo (FAO consultant) in July 2009, and here available for download.

Dominant soil types within the Okavango Basin. Source: SOTER/FAO. This dataset is part of the GIS Database for the Environment Protection and Sustainable Management of the Okavango River Basin project (EPSMO). Detailed information on the database can be found in the “GIS Database for the EPSMO Project” document produced by Luis Veríssimo (FAO consultant) in July 2009, and here available for download.

Delineation of aquifer systems in Namibia, that share geographical overlapping with the Okavango Basin. Source: Ministry of Agriculture, Water and Forestry. This dataset is part of the GIS Database for the Environment Protection and Sustainable Management of the Okavango River Basin project (EPSMO). Detailed information on the database can be found in the “GIS Database for the EPSMO Project” document produced by Luis Veríssimo (FAO consultant) in July 2009, and here available for download.

Delineation of aquifer systems in Angola, that share geographical overlapping with the Okavango Basin. Source: Generated for the EPSMO project based on hydrogeological map of Angola provided by Direccao de Geologia e Minas at 1/1000000. This dataset is part of the GIS Database for the Environment Protection and Sustainable Management of the Okavango River Basin project (EPSMO). Detailed information on the database can be found in the “GIS Database for the EPSMO Project” document produced by Luis Veríssimo (FAO consultant) in July 2009, and here available for download.

Airports and airstrips within the Okavango Basin. Source: Digital Chart of the World (DCW) and update vectorization from ONC charts. This dataset is part of the GIS Database for the Environment Protection and Sustainable Management of the Okavango River Basin project (EPSMO). Detailed information on the database can be found in the “GIS Database for the EPSMO Project” document produced by Luis Veríssimo (FAO consultant) in July 2009, and here available for download.

Roads, tracks and railroad lines within the Okavango Basin. Source: Digital Chart of the World (DCW) with partial update editing from remote sense data. This dataset is part of the GIS Database for the Environment Protection and Sustainable Management of the Okavango River Basin project (EPSMO). Detailed information on the database can be found in the “GIS Database for the EPSMO Project” document produced by Luis Veríssimo (FAO consultant) in July 2009, and here available for download.

This vector layer represents the Okavango Delta System Ramsar site within the Okavango River Basin. Source: The RAMSAR Convention on Wetlands. This dataset is part of the GIS Database for the Environment Protection and Sustainable Management of the Okavango River Basin project (EPSMO). Detailed information on the database can be found in the “GIS Database for the EPSMO Project” document produced by Luis Veríssimo (FAO consultant) in July 2009, and here available for download.

Digital Terrain Model within the Okavango Basin. Source: Data generated under the GIS EPSMO program (base:SRTM data). This dataset is part of the GIS Database for the Environment Protection and Sustainable Management of the Okavango River Basin project (EPSMO). Detailed information on the database can be found in the “GIS Database for the EPSMO Project” document produced by Luis Veríssimo (FAO consultant) in July 2009, and here available for download.

A set of 2 vector layers representing the contour lines at every 100m and 200m within the Okavango Basin. Source: Contour 100m generated under the GIS EPSMO program, by interpolation of the DTM model, and further operations of smoothing tolerance. Contour 200m generated under the GIS EPSMO program, by vectorization of existing topographic maps at 1/1 000 000. This dataset is part of the GIS Database for the Environment Protection and Sustainable Management of the Okavango River Basin project (EPSMO). Detailed information on the database can be found in the “GIS Database for the EPSMO Project” document produced by Luis Veríssimo (FAO consultant) in July 2009, and here available for download.

Elevation points in meters within the Okavango Basin. Source: Generated under the GIS EPSMO program, by vectorization of existing topographic maps at 1/1 000 000. This dataset is part of the GIS Database for the Environment Protection and Sustainable Management of the Okavango River Basin project (EPSMO). Detailed information on the database can be found in the “GIS Database for the EPSMO Project” document produced by Luis Veríssimo (FAO consultant) in July 2009, and here available for download.

This raster layer represents the slope model within the Okavango Basin. Source: Data generated under the GIS EPSMO program (base:OB_DTM). This dataset is part of the GIS Database for the Environment Protection and Sustainable Management of the Okavango River Basin project (EPSMO). Detailed information on the database can be found in the “GIS Database for the EPSMO Project” document produced by Luis Veríssimo (FAO consultant) in July 2009, and here available for download.

This raster layer represents the hill shade model within the Okavango Basin. Source: Data generated under the GIS EPSMO program (base:OB_DTM). This dataset is part of the GIS Database for the Environment Protection and Sustainable Management of the Okavango River Basin project (EPSMO). Detailed information on the database can be found in the “GIS Database for the EPSMO Project” document produced by Luis Veríssimo (FAO consultant) in July 2009, and here available for download.

Town lands in Namibia, that share geographical overlapping with the Okavango Basin. Source: Ministry of Agriculture, Water and Forestry of Namibia. This dataset is part of the GIS Database for the Environment Protection and Sustainable Management of the Okavango River Basin project (EPSMO). Detailed information on the database can be found in the “GIS Database for the EPSMO Project” document produced by Luis Veríssimo (FAO consultant) in July 2009, and here available for download.

Delineation of Communal Conservancies in Namibia, that share geographical overlapping with the Okavango Basin. Source: Ministry of Agriculture, Water and Forestry of Namibia. This dataset is part of the GIS Database for the Environment Protection and Sustainable Management of the Okavango River Basin project (EPSMO). Detailed information on the database can be found in the “GIS Database for the EPSMO Project” document produced by Luis Veríssimo (FAO consultant) in July 2009, and here available for download.

This vector layer (line) – reference scale 1/200 000 – is a drainage network model of the CORB-Contributing area. In the attributes table, each drainage line is linked to the micro catchment in which it resides. The creation of this layer was achieved by converting the Steam Segmentation grid layer into vector layer. This dataset is part of the GIS Database for the Environment Protection and Sustainable Management of the Okavango River Basin project (EPSMO). Detailed information on the database can be found in the “GIS Database for the EPSMO Project” document produced by Luis Veríssimo (FAO consultant) in July 2009, and here available for download.

This vector layer (line) – reference scale 1/200 000 – is a drainage network model of the CORB-Contributing area. In the attributes table, each drainage line is linked to the micro catchment in which it resides. The creation of this layer was achieved by filtering the Drainage Network model with visual analyses of topographic maps and DTM model. This dataset is part of the GIS Database for the Environment Protection and Sustainable Management of the Okavango River Basin project (EPSMO). Detailed information on the database can be found in the “GIS Database for the EPSMO Project” document produced by Luis Veríssimo (FAO consultant) in July 2009, and here available for download.

This vector layer (point) represents the points at which stream segments intersect in the CORB-Contributing area, and can aid in the identified of locations for potential hydrological monitoring. This was created by an interpolation of all stream intersections in the Drainage Line layer. This dataset is part of the GIS Database for the Environment Protection and Sustainable Management of the Okavango River Basin project (EPSMO). Detailed information on the database can be found in the “GIS Database for the EPSMO Project” document produced by Luis Veríssimo (FAO consultant) in July 2009, and here available for download.

This vector layer (line) – reference scale 1/200 000 – is a infered sub-surface drainage network model of the CORB-Contributing area. In the attributes table, each drainage line is linked to the micro catchment in which it resides. The creation of this layer was achieved by filtering the Drainage Network model with the Surface Drainage Network model. This dataset is part of the GIS Database for the Environment Protection and Sustainable Management of the Okavango River Basin project (EPSMO). Detailed information on the database can be found in the “GIS Database for the EPSMO Project” document produced by Luis Veríssimo (FAO consultant) in July 2009, and here available for download.

This vector layer (polygon) - reference scale 1/200 000 - also identifies the catchment area for each stream segment in the CORB-Contributing area. The value for each polygon corresponds to the value carried by the stream segment that drains that area, defined in the Stream Segmentation grid. Source: This layer was created by converting the Catchment Grid raster layer into vector format. This dataset is part of the GIS Database for the Environment Protection and Sustainable Management of the Okavango River Basin project (EPSMO). Detailed information on the database can be found in the “GIS Database for the EPSMO Project” document produced by Luis Veríssimo (FAO consultant) in July 2009, and here available for download.

This raster layer (90m resolution) identifies the catchment area for each stream segment in the CORB-Contributing area. Each cell in the catchment area has a value corresponding to the value of the stream segment that drains that area, defined in the Stream Segmentation grid. Source: This layer was created by computations of the Flow Direction and Stream Segmentation raster grids. This dataset is part of the GIS Database for the Environment Protection and Sustainable Management of the Okavango River Basin project (EPSMO). Detailed information on the database can be found in the “GIS Database for the EPSMO Project” document produced by Luis Veríssimo (FAO consultant) in July 2009, and here available for download.

This raster layer (90m resolution) identifies stream segments in the CORB-Contributing area. Each segment has a specific grid code, which will be fundamental in later modelling of the hydrological network. Source: This layer was created by computations of the Flow Direction and Stream Segmentation raster grids. This dataset is part of the GIS Database for the Environment Protection and Sustainable Management of the Okavango River Basin project (EPSMO). Detailed information on the database can be found in the “GIS Database for the EPSMO Project” document produced by Luis Veríssimo (FAO consultant) in July 2009, and here available for download.

This raster layer (90m resolution) represents the flow accumulation in the CORB-Contributing area. This raster was created by interpolating the Flow Direction raster grid to discern the accumulated number of cells with flow direction upstream of a cell, for each cell in the input grid. This dataset is part of the GIS Database for the Environment Protection and Sustainable Management of the Okavango River Basin project (EPSMO). Detailed information on the database can be found in the “GIS Database for the EPSMO Project” document produced by Luis Veríssimo (FAO consultant) in July 2009, and here available for download.

This vector layer (polygon) – reference scale 1/200 000 – is a sub-basin delineation of the CORB-Contributing area, incorporating basins of the main tributaries and sub- tributaries of the Kubango/Okavango river. It was created by a supervised aggregation of micro basin units from the Catchment MicroBasins layer. This dataset is part of the GIS Database for the Environment Protection and Sustainable Management of the Okavango River Basin project (EPSMO). Detailed information on the database can be found in the “GIS Database for the EPSMO Project” document produced by Luis Veríssimo (FAO consultant) in July 2009, and here available for download.

This vector layer (polygon) – reference scale 1/200 000 – is an aggregated sub-basin delineation of the CORB-Contributing area,incorporating basins of the main tributaries only. It was created by aggregation of the sub basin units from the Sub Basins Level III. This dataset is part of the GIS Database for the Environment Protection and Sustainable Management of the Okavango River Basin project (EPSMO). Detailed information on the database can be found in the “GIS Database for the EPSMO Project” document produced by Luis Veríssimo (FAO consultant) in July 2009, and here available for download.

This raster layer (90m resolution) represents the flow direction in the CORB-Contributing area. The values in the cells of the grid indicate the direction of flow from that cell. This layer was created from the SRTM DTM (digital terrain model). In order to eliminate error, a “Fill Sinks” function was conducted on the SRTM DTM of the basin prior to the creation of this layer. Source: SRTM DTM (digital terrain model). This dataset is part of the GIS Database for the Environment Protection and Sustainable Management of the Okavango River Basin project (EPSMO). Detailed information on the database can be found in the “GIS Database for the EPSMO Project” document produced by Luis Veríssimo (FAO consultant) in July 2009, and here available for download.

This raster layer (90m resolution) represents streams in the CORB-Contributing area. This layer was created by interpolating the Flow Accumulation raster. All cells in the input Flow Accumulation grid that have values greater than a defined threshold have a value of “1” for Stream Definition. This dataset is part of the GIS Database for the Environment Protection and Sustainable Management of the Okavango River Basin project (EPSMO). Detailed information on the database can be found in the “GIS Database for the EPSMO Project” document produced by Luis Veríssimo (FAO consultant) in July 2009, and here available for download.

Infrastructures including vector layers that represent the network of roads, gravel roads, thacks, railroad lines and build-up areas in the CORB-contributing area of Angola. Source: Data provided by SINFIC, based on the Topographic Map of Angola (IGCA). This dataset is part of the GIS Database for the Environment Protection and Sustainable Management of the Okavango River Basin project (EPSMO). Detailed information on the database can be found in the “GIS Database for the EPSMO Project” document produced by Luis Veríssimo (FAO consultant) in July 2009, and here available for download.

Distribution of Ecoregions within the Okavango Basin. Source: World Wild Life Fund (WWF). This dataset is part of the GIS Database for the Environment Protection and Sustainable Management of the Okavango River Basin project (EPSMO). Detailed information on the database can be found in the “GIS Database for the EPSMO Project” document produced by Luis Veríssimo (FAO consultant) in July 2009, and here available for download.

This vector layer delineates the phytogeographic units within the Okavango Basin. Source: Several (FAO, WCMC, Phytogeographic Chart of Angola). Irreconcilable adjustments between Namibia and Botswana. This dataset is part of the GIS Database for the Environment Protection and Sustainable Management of the Okavango River Basin project (EPSMO). Detailed information on the database can be found in the “GIS Database for the EPSMO Project” document produced by Luis Veríssimo (FAO consultant) in July 2009, and here available for download.

Vegetation classes according to White's (1983) classification within the Okavango Basin. Source: Vegetation Map of Africa, White 1983, UNESCO/AETFAT/UNSO. This dataset is part of the GIS Database for the Environment Protection and Sustainable Management of the Okavango River Basin project (EPSMO). Detailed information on the database can be found in the “GIS Database for the EPSMO Project” document produced by Luis Veríssimo (FAO consultant) in July 2009, and here available for download.

Complete set of 23 medium scale topographic maps, that share geographical overlapping with the Okavango Basin. Source: Instituto de Geodesia e Cartografia de Angola (IGCA), Luanda. Original data georeferenced under the GIS EPSMO program. This dataset is part of the GIS Database for the Environment Protection and Sustainable Management of the Okavango River Basin project (EPSMO). Detailed information on the database can be found in the “GIS Database for the EPSMO Project” document produced by Luis Veríssimo (FAO consultant) in July 2009, and here available for download.

Partial coverage of band compositions data of Landsat MSS, Landsat TM and Landsat ETM, in the decadal intervals of 1972-87 (MSS), 1987-97 (TM), 1999-2008 (ETM). Source: Free of charge acquisition (Global Land Cover Facility) and pre processing under the GIS EPSMO program. This dataset is part of the GIS Database for the Environment Protection and Sustainable Management of the Okavango River Basin project (EPSMO). Detailed information on the database can be found in the “GIS Database for the EPSMO Project” document produced by Luis Veríssimo (FAO consultant) in July 2009, and here available for download.

A set of 13 ortho-image maps was elaborated for the main settlements in the basin at the nominal scale of 1/5 000 (the coverage is constrained by the free availability of data). Source: Free of charge acquisition and pre processing under the GIS EPSMO program. This dataset is part of the GIS Database for the Environment Protection and Sustainable Management of the Okavango River Basin project (EPSMO). Detailed information on the database can be found in the “GIS Database for the EPSMO Project” document produced by Luis Veríssimo (FAO consultant) in July 2009, and here available for download.

Geology and Hydrogeology Map of Angola, that shares geographical overlapping with the Okavango Basin. Source: Instituto de Geodesia e Cartografia de Angola (IGCA), Luanda. Original data georeferenced under the GIS EPSMO program. This dataset is part of the GIS Database for the Environment Protection and Sustainable Management of the Okavango River Basin project (EPSMO). Detailed information on the database can be found in the “GIS Database for the EPSMO Project” document produced by Luis Veríssimo (FAO consultant) in July 2009, and here available for download.

A set of 6 medium scale (250k) topographic maps, that share geographical overlapping with the Okavango Basin. Source: The Surveyor General, Harare, Zimbabwe. Original data georeferenced under the GIS EPSMO program. This dataset is part of the GIS Database for the Environment Protection and Sustainable Management of the Okavango River Basin project (EPSMO). Detailed information on the database can be found in the “GIS Database for the EPSMO Project” document produced by Luis Veríssimo (FAO consultant) in July 2009, and here available for download.

Toponomy Gazetteers within the Okavango River Basin. Source: DCW and GNS. This dataset is part of the GIS Database for the Environment Protection and Sustainable Management of the Okavango River Basin project (EPSMO). Detailed information on the database can be found in the “GIS Database for the EPSMO Project” document produced by Luis Veríssimo (FAO consultant) in July 2009, and here available for download.

Complete set of 27 (500k) and 8 (200k) medium scale topographic maps, that share geographical overlapping with the Okavango Basin. Source: International Russian topo maps georeferenced under the GIS EPSMO program. This dataset is part of the GIS Database for the Environment Protection and Sustainable Management of the Okavango River Basin project (EPSMO). Detailed information on the database can be found in the “GIS Database for the EPSMO Project” document produced by Luis Veríssimo (FAO consultant) in July 2009, and here available for download.

Complete set of 16 medium scale topographic maps, that share geographical overlapping with the Okavango Basin. Source: Instituto de Geodesia e Cartografia de Angola (IGCA), Luanda. Original data georeferenced under the GIS EPSMO program. This dataset is part of the GIS Database for the Environment Protection and Sustainable Management of the Okavango River Basin project (EPSMO). Detailed information on the database can be found in the “GIS Database for the EPSMO Project” document produced by Luis Veríssimo (FAO consultant) in July 2009, and here available for download.

This layer represents the spatial distribution of rain measuring stations, in Namibia, that share geographical overlapping with the Okavango Basin. Source: Ministry of Agriculture, Water and Forestry of Namibia. This dataset is part of the GIS Database for the Environment Protection and Sustainable Management of the Okavango River Basin project (EPSMO). Detailed information on the database can be found in the “GIS Database for the EPSMO Project” document produced by Luis Veríssimo (FAO consultant) in July 2009, and here available for download.

A set of 15 raster layers representing the monthly, annual, mean and average temperature for the Okavango Rive Basin. Source: Africa Water Resources Database (FAO). This dataset is part of the GIS Database for the Environment Protection and Sustainable Management of the Okavango River Basin project (EPSMO). Detailed information on the database can be found in the “GIS Database for the EPSMO Project” document produced by Luis Veríssimo (FAO consultant) in July 2009, and here available for download.

This layer represents the spatial distribution of Government Farms, as of 2005, in Namibia, that share geographical overlapping with the Okavango Basin. Source: Ministry of Agriculture, Water and Forestry of Namibia. This dataset is part of the GIS Database for the Environment Protection and Sustainable Management of the Okavango River Basin project (EPSMO). Detailed information on the GIS Database can be found in the “GIS Database for the EPSMO Project” document produced by Luis Veríssimo (FAO consultant) in July 2009, and here available for download.

This layer represents the spatial distribution of agriculture research stations, as of 2003, in Namibia, within the Okavango Basin. Source: Ministry of Agriculture, Water and Forestry of Namibia. This dataset is part of the GIS Database for the Environment Protection and Sustainable Management of the Okavango River Basin project (EPSMO). Detailed information on the GIS Database can be found in the “GIS Database for the EPSMO Project” document produced by Luis Veríssimo (FAO consultant) in July 2009, and here available for download.

This layer represents the spatial distribution of resettlement Farms, as of 2005, in Namibia, that share geographical overlapping with the Okavango Basin. Source: Ministry of Agriculture, Water and Forestry of Namibia. This dataset is part of the GIS Database for the Environment Protection and Sustainable Management of the Okavango River Basin project (EPSMO). Detailed information on the GIS Database can be found in the “GIS Database for the EPSMO Project” document produced by Luis Veríssimo (FAO consultant) in July 2009, and here available for download.

This layer represents the spatial distribution of veterinary and game fences, that share geographical overlapping with the Okavango Basin. Source: Data generated under the GIS EPSMO program. This dataset is part of the GIS Database for the Environment Protection and Sustainable Management of the Okavango River Basin project (EPSMO). Detailed information on the database can be found in the “GIS Database for the EPSMO Project” document produced by Luis Veríssimo (FAO consultant) in July 2009, and here available for download.

This layer represents the spatial distribution of Farmers Associations, as of 2005, in Namibia, that share geographical overlapping with the Okavango Basin. Source: Ministry of Agriculture, Water and Forestry of Namibia. This dataset is part of the GIS Database for the Environment Protection and Sustainable Management of the Okavango River Basin project (EPSMO). Detailed information on the database can be found in the “GIS Database for the EPSMO Project” document produced by Luis Veríssimo (FAO consultant) in July 2009, and here available for download.

This layer represents the spatial distribution of veterinarian offices, in Namibia, that share geographical overlapping with the Okavango Basin. Source: Ministry of Agriculture, Water and Forestry of Namibia. This dataset is part of the GIS Database for the Environment Protection and Sustainable Management of the Okavango River Basin project (EPSMO). Detailed information on the database can be found in the “GIS Database for the EPSMO Project” document produced by Luis Veríssimo (FAO consultant) in July 2009, and here available for download.

This layer represents the political administration level 1 boundaries (level of municipality, (Region) and subdistrict) in the 4 riparian countries, that share geographical overlapping with the Okavango Basin: Angola, Namibia, Botswana and Zimbabwe. Source: Generated under the GIS EPSMO program. This dataset is part of the GIS Database for the Environment Protection and Sustainable Management of the Okavango River Basin project (EPSMO). Detailed information on the GIS Database can be found in the “GIS Database for the EPSMO Project” document produced by Luis Veríssimo (FAO consultant) in July 2009, and here available for download.

This layer represents the political regional boundaries (level of Province, Region and District) in the 4 riparian countries that share geographical overlapping with the Okavango Basin: Angola, Namibia, Botswana and Zimbabwe. Source: Digital Chart of the World (DCW) with further editing corrections for a more accurate spatial definition. This dataset is part of the GIS Database for the Environment Protection and Sustainable Management of the Okavango River Basin project (EPSMO). Detailed information on the GIS Database can be found in the “GIS Database for the EPSMO Project” document produced by Luis Veríssimo (FAO consultant) in July 2009, and here available for download.

This layer represents the country boundaries of the 4 riparian countries that share geographical overlapping with the Okavango Basin: Angola, Namibia, Botswana and Zimbabwe. Source: Digital Chart of the World (DCW) with further editing corrections for a more accurate spatial definition. This dataset is part of the GIS Database for the Environment Protection and Sustainable Management of the Okavango River Basin project (EPSMO). Detailed information on the GIS Database can be found in the “GIS Database for the EPSMO Project” document produced by Luis Veríssimo (FAO consultant) in July 2009, and here available for download.

Location of boreholes, in Namibia, that share geographical overlapping with the Okavango Basin. Source: Ministry of Agriculture, Water and Forestry of Namibia. This dataset is part of the GIS Database for the Environment Protection and Sustainable Management of the Okavango River Basin project (EPSMO). Detailed information on the database can be found in the “GIS Database for the EPSMO Project” document produced by Luis Veríssimo (FAO consultant) in July 2009, and here available for download.

This layer represents the political administration level 2 boundaries (level of Commune, and Constituency) in Angola and Namibia, that share geographical overlapping with the Okavango Basin. Source: Generated under the GIS EPSMO program. This dataset is part of the GIS Database for the Environment Protection and Sustainable Management of the Okavango River Basin project (EPSMO). Detailed information on the GIS Database can be found in the “GIS Database for the EPSMO Project” document produced by Luis Veríssimo (FAO consultant) in July 2009, and here available for download.

Delineation of power lines in Namibia, that share geographical overlapping with the Okavango Basin. Source: Ministry of Agriculture, Water and Forestry of Namibia. This dataset is part of the GIS Database for the Environment Protection and Sustainable Management of the Okavango River Basin project (EPSMO). Detailed information on the database can be found in the “GIS Database for the EPSMO Project” document produced by Luis Veríssimo (FAO consultant) in July 2009, and here available for download.

Water pipelines, in Namibia, that share geographical overlapping with the Okavango Basin. Source: Ministry of Agriculture, Water and Forestry of Namibia. This dataset is part of the GIS Database for the Environment Protection and Sustainable Management of the Okavango River Basin project (EPSMO). Detailed information on the database can be found in the “GIS Database for the EPSMO Project” document produced by Luis Veríssimo (FAO consultant) in July 2009, and here available for download.

Small reservoires and other point features related to inland waters within the Okavango Basin. Source: Digital Chart of the World (DCW). This dataset is part of the GIS Database for the Environment Protection and Sustainable Management of the Okavango River Basin project (EPSMO). Detailed information on the database can be found in the “GIS Database for the EPSMO Project” document produced by Luis Veríssimo (FAO consultant) in July 2009, and here available for download.

State water schemes, in Namibia, that share geographical overlapping with the Okavango Basin. Source: Ministry of Agriculture, Water and Forestry of Namibia. This dataset is part of the GIS Database for the Environment Protection and Sustainable Management of the Okavango River Basin project (EPSMO). Detailed information on the database can be found in the “GIS Database for the EPSMO Project” document produced by Luis Veríssimo (FAO consultant) in July 2009, and here available for download.

Hydrographical related toponomy within Okavango River Basin. Source: Africa Water Resources Database (FAO). This dataset is part of the GIS Database for the Environment Protection and Sustainable Management of the Okavango River Basin project (EPSMO). Detailed information on the database can be found in the “GIS Database for the EPSMO Project” document produced by Luis Veríssimo (FAO consultant) in July 2009, and here available for download.

The raster dataset of easily available water has a spatial resolution of 5 * 5 arc minutes and is in geographic projection. Information with regard to available water was obtained from the "Derived Soil Properties" of the FAO-UNESCO Soil Map of the World which contains raster information on soil properties.

River network and a drainage model with catchments and drainage lines for Europe (derived from EU-DEM).

The EC JRC global map of forest cover provides a spatially explicit representation of forest presence and absence for the year 2020 at 10m spatial resolution.

The Global 1-km Downscaled Population Base Year and Projection Grids Based on the Shared Socioeconomic Pathways, Revision 01, data set consists of global urban, rural, and total populaton for the base year 2000, and population projections at ten-year intervals for 2010-2100 at a resolution of 1-km (about 30 arc-seconds), consistent both quantitatively and qualitatively with the SSPs.

20 years of means of climate indices (e.g. mean annual temperature), calculated from daily time series from the RCMs under the EUROCORDEX

The dataset includes the projected global gridded land cover (excluding the Antarctic) for the period of 2015-2100 at 0.05-degree resolution and 5-year time step under fifteen SSP-RCP scenarios driven by five GCMs (i.e., gfdl, hadgem, ipsl, miroc, and noresm), using the Global Change Analysis Model (GCAM) and a geospatial downscaling model (Demeter). The data has been stored in self-describing NetCDF format. The dataset also includes the mean and standard deviation of the results driven by the five GCMs. More specifically, the data in each year includes grid-explicit fraction (in percent) of each of the 32 plant functional types that are widely used in current Earth system models.

The global map of transboundary aquifers (TBAs) shows the surface delineation of aquifers extending over country borders.

The Global Runoff Data Base (GRDB) comprises river discharge data of well over 10,000 gauging stations from all over the world.

GlobTemperature is an ESA project that provides access to multipurpose satellite-based land, lake and ice surface temperature data sets. Historical period and resolutions vary.

Soil moisture content in the surface layer (0.5-2 cm) express in degree of saturation (0 – 100 %) generated from ASCAT Metop-B at 12.5 km sampling, processed shortly after each satellite orbit completion.

Estimates of population density with age/sex breakdowns for each 100m x 100m grid square on the planet.

The Food and Agriculture Organization of the United Nations (FAO) with the collaboration of the International Institute for Applied Systems Analysis (IIASA), has developed a system that enables rational land-use planning on the basis of an inventory of land resources and evaluation of biophysical limitations and potentials. This is referred to as the Agro-ecological Zones (AEZ) methodology.

The map shows the total annual water withdrawal. Water withdrawals are downscaled to to a five arc-minute grid. Water is considered scarce when the withdrawals exceed 40% of the renewable resource. According to statistics compiled by FAO (FAOSTAT), several countries in North Africa, the Middle East and Central Asia withdraw more water than their total renewable resources. Domestic water withdrawals are downscaled by applying the per capita domestic water use to population of each pixel. Industrial water withdrawals were downscaled by using the industrial water use per unit GDP and applying downscaled information on GDP. Water consumption is assumed to be 30% of domestic use and 10% of industrial use. Finally, agricultural water consumption is assumed to be the crop water deficit in irrigated areas generated in the AEZ analysis and water used for livestock consumption, applied to a global spatial data set of livestock distribution prepared by FAO. Source of the map: GAEZ 2009 and AQUASTAT; downscaling simulations by authors.

The map shows the ratio of total withdrawals to the available renewable water resource. Renewable water resources are downscaled to a five arc-minute grid. Water is considered scarce when the withdrawals exceed 40% of the renewable resource. According to statistics compiled by FAO (FAOSTAT),several countries in North Africa, the Middle East and Central Asia withdraw more water than their total renewable resources. Domestic water withdrawals are downscaled by applying the per capita domestic water use to population of each pixel. Industrial water withdrawals were downscaled by using the industrial water use per unit GDP and applying downscaled information on GDP. Water consumption is assumed to be 30% of domestic use and 10% of industrial use. Finally, agricultural water consumption is assumed to be the crop water deficit in irrigated areas generated in the AEZ analysis and water used for livestock consumption, applied to a global spatial data set of livestock distribution prepared by FAO.Source of the map: GAEZ 2009 and AQUASTAT; downscaling simulations by authors.

The Food and Agriculture Organization of the United Nations (FAO) with the collaboration of the International Institute for Applied Systems Analysis (IIASA), has developed a system that enables rational land-use planning on the basis of an inventory of land resources and evaluation of biophysical limitations and potentials. This is referred to as the Agro-ecological Zones (AEZ) methodology.

The Food and Agriculture Organization of the United Nations (FAO) with the collaboration of the International Institute for Applied Systems Analysis (IIASA), has developed a system that enables rational land-use planning on the basis of an inventory of land resources and evaluation of biophysical limitations and potentials. This is referred to as the Agro-ecological Zones (AEZ) methodology.

The Food and Agriculture Organization of the United Nations (FAO) with the collaboration of the International Institute for Applied Systems Analysis (IIASA), has developed a system that enables rational land-use planning on the basis of an inventory of land resources and evaluation of biophysical limitations and potentials. This is referred to as the Agro-ecological Zones (AEZ) methodology.

The Food and Agriculture Organization of the United Nations (FAO) with the collaboration of the International Institute for Applied Systems Analysis (IIASA), has developed a system that enables rational land-use planning on the basis of an inventory of land resources and evaluation of biophysical limitations and potentials. This is referred to as the Agro-ecological Zones (AEZ) methodology.

The Food and Agriculture Organization of the United Nations (FAO) with the collaboration of the International Institute for Applied Systems Analysis (IIASA), has developed a system that enables rational land-use planning on the basis of an inventory of land resources and evaluation of biophysical limitations and potentials. This is referred to as the Agro-ecological Zones (AEZ) methodology.

The Food and Agriculture Organization of the United Nations (FAO) with the collaboration of the International Institute for Applied Systems Analysis (IIASA), has developed a system that enables rational land-use planning on the basis of an inventory of land resources and evaluation of biophysical limitations and potentials. This is referred to as the Agro-ecological Zones (AEZ) methodology.

The Food and Agriculture Organization of the United Nations (FAO) with the collaboration of the International Institute for Applied Systems Analysis (IIASA), has developed a system that enables rational land-use planning on the basis of an inventory of land resources and evaluation of biophysical limitations and potentials. This is referred to as the Agro-ecological Zones (AEZ) methodology.

The Food and Agriculture Organization of the United Nations (FAO) with the collaboration of the International Institute for Applied Systems Analysis (IIASA), has developed a system that enables rational land-use planning on the basis of an inventory of land resources and evaluation of biophysical limitations and potentials. This is referred to as the Agro-ecological Zones (AEZ) methodology.

The Food and Agriculture Organization of the United Nations (FAO) with the collaboration of the International Institute for Applied Systems Analysis (IIASA), has developed a system that enables rational land-use planning on the basis of an inventory of land resources and evaluation of biophysical limitations and potentials. This is referred to as the Agro-ecological Zones (AEZ) methodology.

The Food and Agriculture Organization of the United Nations (FAO) with the collaboration of the International Institute for Applied Systems Analysis (IIASA), has developed a system that enables rational land-use planning on the basis of an inventory of land resources and evaluation of biophysical limitations and potentials. This is referred to as the Agro-ecological Zones (AEZ) methodology.

The Food and Agriculture Organization of the United Nations (FAO) with the collaboration of the International Institute for Applied Systems Analysis (IIASA), has developed a system that enables rational land-use planning on the basis of an inventory of land resources and evaluation of biophysical limitations and potentials. This is referred to as the Agro-ecological Zones (AEZ) methodology.

The Food and Agriculture Organization of the United Nations (FAO) with the collaboration of the International Institute for Applied Systems Analysis (IIASA), has developed a system that enables rational land-use planning on the basis of an inventory of land resources and evaluation of biophysical limitations and potentials. This is referred to as the Agro-ecological Zones (AEZ) methodology.

The Food and Agriculture Organization of the United Nations (FAO) with the collaboration of the International Institute for Applied Systems Analysis (IIASA), has developed a system that enables rational land-use planning on the basis of an inventory of land resources and evaluation of biophysical limitations and potentials. This is referred to as the Agro-ecological Zones (AEZ) methodology.

The Food and Agriculture Organization of the United Nations (FAO) with the collaboration of the International Institute for Applied Systems Analysis (IIASA), has developed a system that enables rational land-use planning on the basis of an inventory of land resources and evaluation of biophysical limitations and potentials. This is referred to as the Agro-ecological Zones (AEZ) methodology.

The Food and Agriculture Organization of the United Nations (FAO) with the collaboration of the International Institute for Applied Systems Analysis (IIASA), has developed a system that enables rational land-use planning on the basis of an inventory of land resources and evaluation of biophysical limitations and potentials. This is referred to as the Agro-ecological Zones (AEZ) methodology.

The Food and Agriculture Organization of the United Nations (FAO) with the collaboration of the International Institute for Applied Systems Analysis (IIASA), has developed a system that enables rational land-use planning on the basis of an inventory of land resources and evaluation of biophysical limitations and potentials. This is referred to as the Agro-ecological Zones (AEZ) methodology.

The Food and Agriculture Organization of the United Nations (FAO) with the collaboration of the International Institute for Applied Systems Analysis (IIASA), has developed a system that enables rational land-use planning on the basis of an inventory of land resources and evaluation of biophysical limitations and potentials. This is referred to as the Agro-ecological Zones (AEZ) methodology.

The Food and Agriculture Organization of the United Nations (FAO) with the collaboration of the International Institute for Applied Systems Analysis (IIASA), has developed a system that enables rational land-use planning on the basis of an inventory of land resources and evaluation of biophysical limitations and potentials. This is referred to as the Agro-ecological Zones (AEZ) methodology.

The Food and Agriculture Organization of the United Nations (FAO) with the collaboration of the International Institute for Applied Systems Analysis (IIASA), has developed a system that enables rational land-use planning on the basis of an inventory of land resources and evaluation of biophysical limitations and potentials. This is referred to as the Agro-ecological Zones (AEZ) methodology.

The Food and Agriculture Organization of the United Nations (FAO) with the collaboration of the International Institute for Applied Systems Analysis (IIASA), has developed a system that enables rational land-use planning on the basis of an inventory of land resources and evaluation of biophysical limitations and potentials. This is referred to as the Agro-ecological Zones (AEZ) methodology.

The Food and Agriculture Organization of the United Nations (FAO) with the collaboration of the International Institute for Applied Systems Analysis (IIASA), has developed a system that enables rational land-use planning on the basis of an inventory of land resources and evaluation of biophysical limitations and potentials. This is referred to as the Agro-ecological Zones (AEZ) methodology.

The Food and Agriculture Organization of the United Nations (FAO) with the collaboration of the International Institute for Applied Systems Analysis (IIASA), has developed a system that enables rational land-use planning on the basis of an inventory of land resources and evaluation of biophysical limitations and potentials. This is referred to as the Agro-ecological Zones (AEZ) methodology.

The Food and Agriculture Organization of the United Nations (FAO) with the collaboration of the International Institute for Applied Systems Analysis (IIASA), has developed a system that enables rational land-use planning on the basis of an inventory of land resources and evaluation of biophysical limitations and potentials. This is referred to as the Agro-ecological Zones (AEZ) methodology.

The Food and Agriculture Organization of the United Nations (FAO) with the collaboration of the International Institute for Applied Systems Analysis (IIASA), has developed a system that enables rational land-use planning on the basis of an inventory of land resources and evaluation of biophysical limitations and potentials. This is referred to as the Agro-ecological Zones (AEZ) methodology.

The Food and Agriculture Organization of the United Nations (FAO) with the collaboration of the International Institute for Applied Systems Analysis (IIASA), has developed a system that enables rational land-use planning on the basis of an inventory of land resources and evaluation of biophysical limitations and potentials. This is referred to as the Agro-ecological Zones (AEZ) methodology.

The Food and Agriculture Organization of the United Nations (FAO) with the collaboration of the International Institute for Applied Systems Analysis (IIASA), has developed a system that enables rational land-use planning on the basis of an inventory of land resources and evaluation of biophysical limitations and potentials. This is referred to as the Agro-ecological Zones (AEZ) methodology.

The Food and Agriculture Organization of the United Nations (FAO) with the collaboration of the International Institute for Applied Systems Analysis (IIASA), has developed a system that enables rational land-use planning on the basis of an inventory of land resources and evaluation of biophysical limitations and potentials. This is referred to as the Agro-ecological Zones (AEZ) methodology.

The Food and Agriculture Organization of the United Nations (FAO) with the collaboration of the International Institute for Applied Systems Analysis (IIASA), has developed a system that enables rational land-use planning on the basis of an inventory of land resources and evaluation of biophysical limitations and potentials. This is referred to as the Agro-ecological Zones (AEZ) methodology.

The Food and Agriculture Organization of the United Nations (FAO) with the collaboration of the International Institute for Applied Systems Analysis (IIASA), has developed a system that enables rational land-use planning on the basis of an inventory of land resources and evaluation of biophysical limitations and potentials. This is referred to as the Agro-ecological Zones (AEZ) methodology.

The Food and Agriculture Organization of the United Nations (FAO) with the collaboration of the International Institute for Applied Systems Analysis (IIASA), has developed a system that enables rational land-use planning on the basis of an inventory of land resources and evaluation of biophysical limitations and potentials. This is referred to as the Agro-ecological Zones (AEZ) methodology.

The Food and Agriculture Organization of the United Nations (FAO) with the collaboration of the International Institute for Applied Systems Analysis (IIASA), has developed a system that enables rational land-use planning on the basis of an inventory of land resources and evaluation of biophysical limitations and potentials. This is referred to as the Agro-ecological Zones (AEZ) methodology.

The Food and Agriculture Organization of the United Nations (FAO) with the collaboration of the International Institute for Applied Systems Analysis (IIASA), has developed a system that enables rational land-use planning on the basis of an inventory of land resources and evaluation of biophysical limitations and potentials. This is referred to as the Agro-ecological Zones (AEZ) methodology.

The Food and Agriculture Organization of the United Nations (FAO) with the collaboration of the International Institute for Applied Systems Analysis (IIASA), has developed a system that enables rational land-use planning on the basis of an inventory of land resources and evaluation of biophysical limitations and potentials. This is referred to as the Agro-ecological Zones (AEZ) methodology.

The raster dataset of suitability for rainfed wheat with intermediate inputs has a spatial resolution of 5 * 5 arc minutes and is in geographic projection. Information with regard to wheat suitability was obtained from the "Derived Soil Properties" of the FAO-UNESCO Soil Map of the World which contains raster information on soil properties.

The Food and Agriculture Organization of the United Nations (FAO) with the collaboration of the International Institute for Applied Systems Analysis (IIASA), has developed a system that enables rational land-use planning on the basis of an inventory of land resources and evaluation of biophysical limitations and potentials. This is referred to as the Agro-ecological Zones (AEZ) methodology.

The Food and Agriculture Organization of the United Nations (FAO) with the collaboration of the International Institute for Applied Systems Analysis (IIASA), has developed a system that enables rational land-use planning on the basis of an inventory of land resources and evaluation of biophysical limitations and potentials. This is referred to as the Agro-ecological Zones (AEZ) methodology.

The Food and Agriculture Organization of the United Nations (FAO) with the collaboration of the International Institute for Applied Systems Analysis (IIASA), has developed a system that enables rational land-use planning on the basis of an inventory of land resources and evaluation of biophysical limitations and potentials. This is referred to as the Agro-ecological Zones (AEZ) methodology.

The Food and Agriculture Organization of the United Nations (FAO) with the collaboration of the International Institute for Applied Systems Analysis (IIASA), has developed a system that enables rational land-use planning on the basis of an inventory of land resources and evaluation of biophysical limitations and potentials. This is referred to as the Agro-ecological Zones (AEZ) methodology.

The Food and Agriculture Organization of the United Nations (FAO) with the collaboration of the International Institute for Applied Systems Analysis (IIASA), has developed a system that enables rational land-use planning on the basis of an inventory of land resources and evaluation of biophysical limitations and potentials. This is referred to as the Agro-ecological Zones (AEZ) methodology.

The Food and Agriculture Organization of the United Nations (FAO) with the collaboration of the International Institute for Applied Systems Analysis (IIASA), has developed a system that enables rational land-use planning on the basis of an inventory of land resources and evaluation of biophysical limitations and potentials. This is referred to as the Agro-ecological Zones (AEZ) methodology.

The Food and Agriculture Organization of the United Nations (FAO) with the collaboration of the International Institute for Applied Systems Analysis (IIASA), has developed a system that enables rational land-use planning on the basis of an inventory of land resources and evaluation of biophysical limitations and potentials. This is referred to as the Agro-ecological Zones (AEZ) methodology.

The Food and Agriculture Organization of the United Nations (FAO) with the collaboration of the International Institute for Applied Systems Analysis (IIASA), has developed a system that enables rational land-use planning on the basis of an inventory of land resources and evaluation of biophysical limitations and potentials. This is referred to as the Agro-ecological Zones (AEZ) methodology.

The Food and Agriculture Organization of the United Nations (FAO) with the collaboration of the International Institute for Applied Systems Analysis (IIASA), has developed a system that enables rational land-use planning on the basis of an inventory of land resources and evaluation of biophysical limitations and potentials. This is referred to as the Agro-ecological Zones (AEZ) methodology.

The Food and Agriculture Organization of the United Nations (FAO) with the collaboration of the International Institute for Applied Systems Analysis (IIASA), has developed a system that enables rational land-use planning on the basis of an inventory of land resources and evaluation of biophysical limitations and potentials. This is referred to as the Agro-ecological Zones (AEZ) methodology.

The Food and Agriculture Organization of the United Nations (FAO) with the collaboration of the International Institute for Applied Systems Analysis (IIASA), has developed a system that enables rational land-use planning on the basis of an inventory of land resources and evaluation of biophysical limitations and potentials. This is referred to as the Agro-ecological Zones (AEZ) methodology.

The thermal climates represent major latitudinal climatic zones. They were obtained through classifying monthly temperatures corrected to sea level (with an assumed lapse rate: 0.55°C/100m). The latitudinal thermal climates distinguished in global AEZ are the following: tropics, subtropics with summer rainfall, subtropics with winter rainfall, temperate, boreal and polar/arctic. The temperate and boreal belts have been further subdivided according to continentality into three classes, namely: oceanic, sub-continental and continental. Data derived from analysis over 37 years.

The Food and Agriculture Organization of the United Nations (FAO) with the collaboration of the International Institute for Applied Systems Analysis (IIASA), has developed a system that enables rational land-use planning on the basis of an inventory of land resources and evaluation of biophysical limitations and potentials. This is referred to as the Agro-ecological Zones (AEZ) methodology.

The Food and Agriculture Organization of the United Nations (FAO) with the collaboration of the International Institute for Applied Systems Analysis (IIASA), has developed a system that enables rational land-use planning on the basis of an inventory of land resources and evaluation of biophysical limitations and potentials. This is referred to as the Agro-ecological Zones (AEZ) methodology.

The Food and Agriculture Organization of the United Nations (FAO) with the collaboration of the International Institute for Applied Systems Analysis (IIASA), has developed a system that enables rational land-use planning on the basis of an inventory of land resources and evaluation of biophysical limitations and potentials. This is referred to as the Agro-ecological Zones (AEZ) methodology.

The Food and Agriculture Organization of the United Nations (FAO) with the collaboration of the International Institute for Applied Systems Analysis (IIASA), has developed a system that enables rational land-use planning on the basis of an inventory of land resources and evaluation of biophysical limitations and potentials. This is referred to as the Agro-ecological Zones (AEZ) methodology.

The Food and Agriculture Organization of the United Nations (FAO) with the collaboration of the International Institute for Applied Systems Analysis (IIASA), has developed a system that enables rational land-use planning on the basis of an inventory of land resources and evaluation of biophysical limitations and potentials. This is referred to as the Agro-ecological Zones (AEZ) methodology.

The Food and Agriculture Organization of the United Nations (FAO) with the collaboration of the International Institute for Applied Systems Analysis (IIASA), has developed a system that enables rational land-use planning on the basis of an inventory of land resources and evaluation of biophysical limitations and potentials. This is referred to as the Agro-ecological Zones (AEZ) methodology.

The Food and Agriculture Organization of the United Nations (FAO) with the collaboration of the International Institute for Applied Systems Analysis (IIASA), has developed a system that enables rational land-use planning on the basis of an inventory of land resources and evaluation of biophysical limitations and potentials. This is referred to as the Agro-ecological Zones (AEZ) methodology.

The Food and Agriculture Organization of the United Nations (FAO) with the collaboration of the International Institute for Applied Systems Analysis (IIASA), has developed a system that enables rational land-use planning on the basis of an inventory of land resources and evaluation of biophysical limitations and potentials. This is referred to as the Agro-ecological Zones (AEZ) methodology.

The Food and Agriculture Organization of the United Nations (FAO) with the collaboration of the International Institute for Applied Systems Analysis (IIASA), has developed a system that enables rational land-use planning on the basis of an inventory of land resources and evaluation of biophysical limitations and potentials. This is referred to as the Agro-ecological Zones (AEZ) methodology.

The Food and Agriculture Organization of the United Nations (FAO) with the collaboration of the International Institute for Applied Systems Analysis (IIASA), has developed a system that enables rational land-use planning on the basis of an inventory of land resources and evaluation of biophysical limitations and potentials. This is referred to as the Agro-ecological Zones (AEZ) methodology.

The Food and Agriculture Organization of the United Nations (FAO) with the collaboration of the International Institute for Applied Systems Analysis (IIASA), has developed a system that enables rational land-use planning on the basis of an inventory of land resources and evaluation of biophysical limitations and potentials. This is referred to as the Agro-ecological Zones (AEZ) methodology.

The Food and Agriculture Organization of the United Nations (FAO) with the collaboration of the International Institute for Applied Systems Analysis (IIASA), has developed a system that enables rational land-use planning on the basis of an inventory of land resources and evaluation of biophysical limitations and potentials. This is referred to as the Agro-ecological Zones (AEZ) methodology.

The Food and Agriculture Organization of the United Nations (FAO) with the collaboration of the International Institute for Applied Systems Analysis (IIASA), has developed a system that enables rational land-use planning on the basis of an inventory of land resources and evaluation of biophysical limitations and potentials. This is referred to as the Agro-ecological Zones (AEZ) methodology.

The Food and Agriculture Organization of the United Nations (FAO) with the collaboration of the International Institute for Applied Systems Analysis (IIASA), has developed a system that enables rational land-use planning on the basis of an inventory of land resources and evaluation of biophysical limitations and potentials. This is referred to as the Agro-ecological Zones (AEZ) methodology.

The Food and Agriculture Organization of the United Nations (FAO) with the collaboration of the International Institute for Applied Systems Analysis (IIASA), has developed a system that enables rational land-use planning on the basis of an inventory of land resources and evaluation of biophysical limitations and potentials. This is referred to as the Agro-ecological Zones (AEZ) methodology.

The Food and Agriculture Organization of the United Nations (FAO) with the collaboration of the International Institute for Applied Systems Analysis (IIASA), has developed a system that enables rational land-use planning on the basis of an inventory of land resources and evaluation of biophysical limitations and potentials. This is referred to as the Agro-ecological Zones (AEZ) methodology.

The Food and Agriculture Organization of the United Nations (FAO) with the collaboration of the International Institute for Applied Systems Analysis (IIASA), has developed a system that enables rational land-use planning on the basis of an inventory of land resources and evaluation of biophysical limitations and potentials. This is referred to as the Agro-ecological Zones (AEZ) methodology.

The Food and Agriculture Organization of the United Nations (FAO) with the collaboration of the International Institute for Applied Systems Analysis (IIASA), has developed a system that enables rational land-use planning on the basis of an inventory of land resources and evaluation of biophysical limitations and potentials. This is referred to as the Agro-ecological Zones (AEZ) methodology.

The Food and Agriculture Organization of the United Nations (FAO) with the collaboration of the International Institute for Applied Systems Analysis (IIASA), has developed a system that enables rational land-use planning on the basis of an inventory of land resources and evaluation of biophysical limitations and potentials. This is referred to as the Agro-ecological Zones (AEZ) methodology.

The Food and Agriculture Organization of the United Nations (FAO) with the collaboration of the International Institute for Applied Systems Analysis (IIASA), has developed a system that enables rational land-use planning on the basis of an inventory of land resources and evaluation of biophysical limitations and potentials. This is referred to as the Agro-ecological Zones (AEZ) methodology.

The Food and Agriculture Organization of the United Nations (FAO) with the collaboration of the International Institute for Applied Systems Analysis (IIASA), has developed a system that enables rational land-use planning on the basis of an inventory of land resources and evaluation of biophysical limitations and potentials. This is referred to as the Agro-ecological Zones (AEZ) methodology.

The Food and Agriculture Organization of the United Nations (FAO) with the collaboration of the International Institute for Applied Systems Analysis (IIASA), has developed a system that enables rational land-use planning on the basis of an inventory of land resources and evaluation of biophysical limitations and potentials. This is referred to as the Agro-ecological Zones (AEZ) methodology.

As part of the ESA Land Cover Climate Change Initiative (CCI) project a new set of yearly Global Land Cover Maps have been produced. Each pixel value corresponds to the classification of a land cover class defined based on the UN Land Cover Classification System (LCCS). The reliability of the classifications made are documented by the four quality flags (decribed further in the Product User Guide) that accompany these maps.

The database contains long time-series of demographic and socio-economic variables and indicators for EU27 regions at various statistical scales (NUTS1, NUTS2, NUTS3, metro regions). Upon data availability, regions in EFTA and candidate countries are also covered. ARDECO is updated on a quarterly basis. Data includes Population, Domestic Product, Employment, Labour Cost, Labour Productivity, Capital Formation, Capital Stock, Households. The main source is EUROSTATS

ASTER GDEM was generated using stereo-pair images collected by the ASTER instrument onboard Terra. Resolution is 30m. ASTER GDEM coverage spans from 83 degrees north latitude to 83 degrees south, encompassing 99 percent of Earth's landmass.

CMORPH (Climate Prediction Center Morphing Technique) generates global precipitation products based on PMW and IR with 8 km spatial and 0.5-hourly temporal resolution. An archive is available from 1988 until present.

IMERG (Integrated MultisatellitE Retrievals from GPM) precipitation product was developed by NASA in collaboration with JAXA

MSWEP (Multi-Source Weighted-Ensemble Precipitation) is a long-term global satellite precipitation product with high spatiotemporal resolution. MSWEP is a historical record for more than 40 years (from 1979 to 2020)

As part of the ESA Land Cover Climate Change Initiative (CCI) project a new set of yearly Global Land Cover Maps have been produced. Each pixel value corresponds to the classification of a land cover class defined based on the UN Land Cover Classification System (LCCS). The reliability of the classifications made are documented by the four quality flags (decribed further in the Product User Guide) that accompany these maps.

MOD10A1 and MYD10A1 are tiles of daily snow cover at 500 m spatial resolution. The daily observation selected from multiple observations in a MOD10A1 (or MYD10A1) cell is the observation acquired nearest nadir and having the greatest coverage of the grid cell. The daily MOD10A1 and MYD10A1 snow products are tiles of data gridded in the sinusoidal projection. Tiles are approximately 1200 x1200 km in area. A single scientific data set (SDS) of snow cover and a single SDS of QA data along with local and global attributes comprise the data product file. The daily level 3 snow product is the result of selecting an observation from the multiple observations mapped to a cell of the MOD10_L2G (or MYD10_L2G) product. See the validation webpage for details on the validation and validation definitions.

HILDA+ (HIstoric Land Dynamics Assessment+) is a global dataset on annual land use/cover change between 1960 and 2019 at 1 km spatial resolution. It is based on a data-driven reconstruction approach and integrates multiple open data streams (from high-resolution remote sensing, long-term land use reconstructions and statistics). It covers six generic land use/cover categories: 1: Urban areas, 2: Cropland, 3: Pasture/rangeland, 4: Forest, 5: Unmanaged grass/shrubland, 6: Sparse/no vegetation.

The World Settlement Footprint (WSF) 2019 is a 10m resolution binary mask outlining the extent of human settlements globally

This is the original PCR-GLOBWB simulation data that was used to derive the results for the Human water consumption intensifies hydrological drought worldwide paper by Wada, van Beek, Wanders and Bierkens in Environmental Research Letters. (2018-04-17)

MERIT is a merger of existing DEMs (NASA SRTM3 DEM, JAXA AW3D DEM, Viewfinder Panoramas' DEM). With error removal especially aimed at geoscience applications which are terrain-dependent. Resolution is 90m.

SM2RAIN (Soil Moisture TO RAIN) is a newly developed “bottom-up” method estimating precipitation over large areas with spatial resolution of 0.1∘ (∼10 km) on a global scale.

The Global Precipitation Climatology Project (GPCP) consists of monthly satellite-gauge and associated precipitation error estimates and covers the period January 1979 to the present. The general approach is to combine the precipitation information available from each of several satellite and in situ sources into a final merged product, taking advantage of the strengths of each data type: passive Microwave estimates are based on SSMI/SSMIS data; infrared precipitation estimates are included, using GOES data and POES data; as well as other low earth orbit data and insitu observations. Data are provided on a 2.5 degree grid.

The Terra Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature/Emissivity 8-Day (MOD11A2) Version 6.1 product provides an average 8-day per-pixel Land Surface Temperature and Emissivity (LSTand E) with a 1 kilometer (km) spatial resolution in a 1,200 by 1,200 km grid. Each pixel value in the MOD11A2 is a simple average of all the corresponding MOD11A1 LST pixels collected within that 8-day period.

A virtual time machine that maps the location and temporal distribution of water surfaces at the global scale over the past 3.8 decades, and provides statistics on their extent and change to support better informed water-management decision-making.

Soil moisture derived from the Backus-Gilbert interpolated radiometer brightness temperature measurements, posted at 9 km.

CHIRPS (Climate Hazards Group InfraRed Precipitation with Station data) provides precipitation products with a high spatial resolution of 0.05° from 50°S to 50°N across the world since 1981.

FLUXNET scientists use the eddy covariance technique to measure the cycling of carbon, water, and energy between the biosphere and atmosphere.

Daily Snow Cover Extent (SCE) at 500 m resolution. A global mosaic derived from interpolated MODIS snow information of the Platforms Terra (MOD10A1) and Aqua (MYD10A1).

The Shuttle Radar Topography Mission (SRTM, see Farr et al. 2007) digital elevation data is an international research effort that obtained digital elevation models on a near-global scale. This SRTM V3 product (SRTM Plus) is provided by NASA JPL at a resolution of 1 arc-second (approximately 30m).

This dataset is one derivative product from GAEZ v4 for year 2015 that includes gridded (5-arcminute resolution) irrigated and rainfed crop harvested area and crop yield for 26 different crops/crop categories (please see GAEZ_2015_Dimension_crops.csv for the full list). For each country, the production for each crop was extracted from the FAOSTAT database (http://www.fao.org/faostat/en/#data/QC; data downloaded April 2020). The three years average (2009-2011) of annual national crop production data was considered to represent 2010 national crop production, and the three years average (2014-2016) of annual crop production data to represent 2015 national crop production. The method for updating crop production from 2010 to 2015 was the same as described for crop harvested area, except using the 2010 national crop areas, A_(P_(m,2010))^k and the crop production ratio FP,m,i. Unit of measure Crop Production: 1000 tonnes (10e6 kg) per 5-arcminute grid cell More information about this global gridded crop harvest area, crop production, and crop yield and selected crops dataset is available in the full metadata at https://data.apps.fao.org/map/catalog/srv/eng/catalog.search#/metadata/74bab234-3a1e-442d-93cf-0b34859e9a60

This dataset is one derivative product from GAEZ v4 for year 2015 that includes gridded (5-arcminute resolution) irrigated and rainfed crop production and crop yield for 26 different crops/crop categories (please see GAEZ_2015_Dimenision_crops.csv for the full list). For each country, the harvested area for each crop was extracted from the FAOSTAT database (http://www.fao.org/faostat/en/#data/QC; data downloaded April 2020). The three years average (2009-2011) of annual national crop harvested area data was considered to represent 2010 national crop harvest area, and the three years average (2014-2016) of annual crop harvested area data to represent 2015 national crop harvest area. The GAEZ v4.0 global gridded (5-arcminute) 2010 crop harvest area product (Fischer et al., 2021) was used as base layer for generating the updated crop harvested area for year 2015. For each grid cell, the crop harvested area in the 2010 product was multiplied by the fractional change from 2010 to 2015 in harvested area for that crop and country. In cases where a grid cell shares its area between multiple countries, the country area-fraction weighted-average was used for the calculation of the given crop. Note, a cell table of country fractions in the given cell (f_(m,i)^k) was based on the Global Administrative Unit Layers (GAUL) of FAO (http://www.fao.org/geonetwork/srv/en/metadata.show?id=12691). Unit of measure Crop harvested area: 1000 ha (10e7 m2) per 5-arcminute grid cell More information about this global gridded crop harvest area, crop production, and crop yield and selected crops dataset is available in the full metadata at https://data.apps.fao.org/map/catalog/srv/eng/catalog.search#/metadata/74bab234-3a1e-442d-93cf-0b34859e9a60

This dataset, GAEZ+_2015, provides global, gridded (5-arcminute resolution) irrigated and rainfed crop harvested areas, irrigated and rainfed crop production, and irrigated and rainfed crop yield for 26 different crops/crop categories (please see GAEZ_2015 crops.csv for the full list). This new product is an update to 2015 of the Global Agro-Ecological Zones (GAEZ) Version 4 data of c.2010 crop harvested area (Fischer et al. 2012; note GAEZ v4.0 is not published, this citation is to v3.0). This product is based on national statistics and ignores changes in the spatial distribution of cropland. It was developed as an intermediate product for studies that rely on GAEZ data, providing more recent data on production, yield, and harvested areas. It is not meant to depict the spatial distribution of crops at the pixel level in 2015 accurately, and will become obsolete as soon as a more formal update of GAEZ becomes available that updates pixel level data using the GAEZ methodology. This new product updates the 2010 GAEZ crop harvest area, crop production, and crop yield maps (Fischer et al., 2012), using national-scale data on the fractional change in crop harvested area and production from 2010 to 2015, based on statistics for 160 crops from FAOSTAT (http://www.fao.org/faostat/en/#data/QA). More information on the methodology is available in the Data Quality sections of this metadata.

The Database for Hydrological Time Series of Inland Waters (DAHITI) provides hydrologial information on lakes, reservoirs, rivers, and wetlands derived from satellite data.

DLR Map of Human Settlements based on SAR data

Global Urban Heat Island (UHI) Data Set

The Danubis Water Quality Database of the ICPDR contains combined measurements from the TransNational Monitoring Network (TNMN) and the Joint Danube Surveys from 2001 (JDS1), 2007 (JDS2) and 2013 (JDSE3). The TNMN includes 79 monitoring locations with up to three sampling points across the Danube and its main tributaries river. The minimum sampling frequency is 12 times per year for chemical determinands in water and twice a year for biological parameters.

The International Hydrogeological Map of Europe 1:1,500,000 constitutes the only coherent general map of groundwater resources throughout Europe. Groundwater is distinguished according to productivity, aquifer rock type (porous or fissured rocks) and lithology of water-bearing strata. Moreover the map series includes the position of relevant tectonic fractures.

The Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks- Climate Data Record (PERSIANN-CDR) provides daily rainfall estimates at a spatial resolution of 0.25 degrees in the latitude band 60S - 60N from 1983 to the near-present. The precipitation estimate is produced using the PERSIANN algorithm on GridSat-B1 infrared satellite data, and the training of the artificial neural network is done using the National Centers for Environmental Prediction (NCEP) stage IV hourly precipitation data. The PERSIANN-CDR is adjusted using the Global Precipitation Climatology Project (GPCP) monthly product version 2.2 (GPCPv2.2), so that the PERSIANN-CDR monthly means degraded to 2.5 degree resolution match GPCPv2.2. PERSIANN CDR is a Climate Data Record, which the National Research Council (NRC) defines as a time series of measurements of sufficient length, consistency, and continuity to determine climate variability and change.

As part of the the World Bank's review of its rural development strategy, the Bank sought the assistance of the Food and Agriculture Organization of the United Nations (FAO) in evaluating how farming systems might change and adapt over the next thirty years. Amongst other objectives, the World Bank asked FAO to provide guidance on priorities for investment in food security, poverty reduction, and economic growth, and in particular to identify promising approaches and technologies that will contribute to these goals. The results of the study are summarized in a set of seven documents, comprising six regional reports and the global overview contained in this volume. This document, which synthesises the results of the six regional analyses as well as discussing global trends, cross-cutting issues and possible implementation modalities, presents an overview of the complete study. This document is supplemented by two case study reports of development issues of importance to farming systems globally.

As part of the the World Bank's review of its rural development strategy, the Bank sought the assistance of the Food and Agriculture Organization of the United Nations (FAO) in evaluating how farming systems might change and adapt over the next thirty years. Amongst other objectives, the World Bank asked FAO to provide guidance on priorities for investment in food security, poverty reduction, and economic growth, and in particular to identify promising approaches and technologies that will contribute to these goals. The results of the study are summarized in a set of seven documents, comprising six regional reports and the global overview contained in this volume. This document, which synthesises the results of the six regional analyses as well as discussing global trends, cross-cutting issues and possible implementation modalities, presents an overview of the complete study. This document is supplemented by two case study reports of development issues of importance to farming systems globally.

As part of the the World Bank's review of its rural development strategy, the Bank sought the assistance of the Food and Agriculture Organization of the United Nations (FAO) in evaluating how farming systems might change and adapt over the next thirty years. Amongst other objectives, the World Bank asked FAO to provide guidance on priorities for investment in food security, poverty reduction, and economic growth, and in particular to identify promising approaches and technologies that will contribute to these goals. The results of the study are summarized in a set of seven documents, comprising six regional reports and the global overview contained in this volume. This document, which synthesises the results of the six regional analyses as well as discussing global trends, cross-cutting issues and possible implementation modalities, presents an overview of the complete study. This document is supplemented by two case study reports of development issues of importance to farming systems globally.

The G-REALM (Global Reservoirs and Lakes Monitor) product service provides near-real time data on water level variations of lakes, reservoirs and rivers larger than 100 km2 around the world (264 stations) based on multi-sensor satellite altimetry.

Gridded daily precipitation obtained by merging soil moisture-derived and passive microwave rainfall estimatesobtained through the application of SM2RAIN to the SSMASCAT-A/B-NRT-O12.5 (H16and H101) soil moisture products, with the H-AUX-23 rainfall estimates.

Agricultural production and land statistics are available at national scale from FAOSTAT database, but these statistical data cannot capture the spatial heterogeneity of agricultural production systems at fine resolutions within country boundaries. In this case a “downscaling” method is needed for plausible attribution of aggregate national production statistics to individual spatial units (grid cells) by applying formal methods that account for land characteristics, assess possible production options and can use available evidence from observed or inferred geo-spatial information, including remotely sensed land cover, soil, climate and vegetation distribution, population density and distribution, etc. Theme 5 spatial layers include mapped distributions of harvested area, yield and production at 5 arc-minute resolution for 26 major crops/crop groups, separately in rain-fed and irrigated cropland. Country totals are based on FAO statistics for the years 2009-2011. Also included are estimates of the spatial distribution of total crop production value and the production values of major crop groups (cereals, root crops, oil crops), all valued at year 2000 international prices, separately for rain-fed and irrigated cropland. This theme is organized into two main sub-themes: (1) Area, Yield and Production, and (2) Aggregate Crop Production Value. GAEZ methodology development, data base compilation, production of results and establishing the Data Portal were accomplished in close technical collaboration and with inputs of the International Institute for Applied Systems Analysis (IIASA). For further details, please refer to the GAEZ v4 Model Documentation.

Soil information, from the global to the local scale, has often been the one missing biophysical information layer, the absence of which has added to the uncertainties of predicting potentials and constraints for food and fiber production. The lack of reliable and harmonized soil data has considerably hampered land degradation assessments, environmental impact studies and adapted sustainable land management interventions. Recognizing the urgent need for improved soil information worldwide, particularly in the context of the Climate Change Convention and the Kyoto Protocol for soil carbon measurements and the immediate requirement for the FAO/IIASA Global Agro-ecological Assessment study (GAEZ v3.0), the Food and Agriculture Organization of the United Nations (FAO) and the International Institute for Applied Systems Analysis (IIASA) took the initiativeof combining the recently collected vast volumes of regional and national updates of soil information with the information already contained within the 1:5,000,000 scale FAOUNESCO Digital Soil Map of the World, into a new comprehensive Harmonized World Soil Database (HWSD). This database was achieved in partnership with: • ISRIC-World Soil Information together with FAO, which were responsible for the development of regional soil and terrain databases and the WISE soil profile database; • the European Soil Bureau Network, which had recently completed a major update of soil information for Europe and northern Eurasia, and • the Institute of Soil Science, Chinese Academy of Sciences which provided the recent 1:1,000,000 scale Soil Map of China.

The surface temperature product at 120 m resolution is generated from the Landsat Collection 2 Level-1 thermal infrared bands and several other EO products.

As part of the the World Bank's review of its rural development strategy, the Bank sought the assistance of the Food and Agriculture Organization of the United Nations (FAO) in evaluating how farming systems might change and adapt over the next thirty years. Amongst other objectives, the World Bank asked FAO to provide guidance on priorities for investment in food security, poverty reduction, and economic growth, and in particular to identify promising approaches and technologies that will contribute to these goals. The results of the study are summarized in a set of seven documents, comprising six regional reports and the global overview contained in this volume. This document, which synthesises the results of the six regional analyses as well as discussing global trends, cross-cutting issues and possible implementation modalities, presents an overview of the complete study. This document is supplemented by two case study reports of development issues of importance to farming systems globally.

As part of the the World Bank's review of its rural development strategy, the Bank sought the assistance of the Food and Agriculture Organization of the United Nations (FAO) in evaluating how farming systems might change and adapt over the next thirty years. Amongst other objectives, the World Bank asked FAO to provide guidance on priorities for investment in food security, poverty reduction, and economic growth, and in particular to identify promising approaches and technologies that will contribute to these goals. The results of the study are summarized in a set of seven documents, comprising six regional reports and the global overview contained in this volume. This document, which synthesises the results of the six regional analyses as well as discussing global trends, cross-cutting issues and possible implementation modalities, presents an overview of the complete study. This document is supplemented by two case study reports of development issues of importance to farming systems globally.

This dataset forms part of a global livestock mapping project by the Food and Agriculture Organization's Animal Production and Health Division (FAO-AGA). The methodology and sources of data are fully described in a document entitled: "The Gridded Livestock of the World FAO (2007)". In summary, for each country the most recent available sub-national livestock census data and corresponding administrative boundaries have been collected. These are then converted into densities, excluding land unsuitable for livestock (either monogastric or ruminant), to provide the 'observed' data. The data are then disaggregated based on statistical relations with some environmental variables in similar agro-ecological zones to produce the 'predicted' distribution. The predicted data are further manipulated to match national census totals for the year 2000 and the year 2005 according to the FAOSTAT database. The project includes: a global network of data providers on livestock and sub-national boundaries; an Oracle database in which these data are managed and processed; a system for predicting livestock distributions based on environmental data and an interactive web application, the Global Livestock Production and Health Atlas (GLiPHA - http://www.fao.org/ag/aga/glipha/index.jsp), through which data are viewed and disseminated. The files are in a raster GRID format, with an ArcGis layer file and an ArcView legend file. Pixel values represent actual densities (per square kilometre). Projection details are given in the metadata. The map should ideally be viewed with the overlay of national boundaries, water bodies and unsuitable land. All of these supplementary data are available in accompanying zip files. These data have been produced by FAO's Animal Production and Health Division in collaboration with ERGO and the TALA research group, University of Oxford, UK.