Joint Research Centre Data Catalogue - datasetsA RSS feed containing the selected datasets of the Joint Research Centre Data Catalogue.ODCAT2024 European Commission2024-03-28T12:08:30Z3574020ESDACArsenic in European topsoils2024-03-28T12:08:30Z2024-03-28T12:08:30ZMaps of Arsenic (As) in EU topsoils at 250m resolution. In addition, we provide the exceedance probabilities for the threshold of 1.5, 20 and 45 mg kg-1 . We also provide comparison with GEMAS data on As.DROUGHTGDO Standardized Precipitation Index ERA5, 3-month accumulation period (SPI-3) (version 1.0.0)2024-03-25T17:04:28Z2024-03-26T14:51:02ZThe 3-month Standardized Precipitation Index (SPI-3) is a meteorological drought indicator to monitor precipitation anomalies over 3-month accumulation periods and is a proxy indicator for immediate impacts of droughts, such as reduced soil moisture, snowpack, and flow in smaller creeks, and for medium-term impacts, for example reduced stream flow and reservoir storage. This indicator is calculated with one month total precipitation accumulation period (SPI-1), derived from the fifth generation ECMWF reanalysis for the global climate and weather ERA5 (https://confluence.ecmwf.int/display/CKB/The+family+of+ERA5+datasets) hourly precipitation data; using a reference period between the year 1991 and 2020. Data at the moment have a monthly computation but in the future version 2.0.0 they will be actualized every 10 days, thus giving a more actual overview of the indicator than the classical end-of-month computation. The accumulation periods will remain however monthly, with monthly windows (28-, 29-, 30-, or 31-day periods) starting on the 1st, 11th and 21st day of the month. The SPI represents precipitation anomalies at a given location and time, as the deviation from the long-term mean in a standard-normal distribution, with more negative values representing a more severe drought. SPI is calculated by comparing the observed total precipitation amounts for an accumulation period of a number of months, indicated by a number following SPI-, with the long-term “climatological” rainfall distribution for that same period of the year, derived from historical rainfall records in a specific reference period. A given accumulation period corresponds to different potential impacts of a drought, with shorter accumulation periods generally representing fast-changing aspects of the water system such as surface soil moisture and flow in small rivers, and longer accumulation periods representing slower changing aspects of the water system such as groundwater and baseflow in larger rivers. However, the exact relationship between accumulation period and impacts depends on the natural environment (e.g. geology, soils) and human interference (e.g. irrigation). Details on the calculation of the SPI are given in the indicator factsheet.DROUGHTGDO Standardized Precipitation Index ERA5, 1-month accumulation period (SPI-1) (version 1.0.0)2024-03-25T16:58:03Z2024-03-26T14:50:46ZThe 1-month Standardized Precipitation Index (SPI-1) is an indicator used to monitor meteorological drought based on precipitation anomalies over 1-month accumulation periods. SPI-1 serves as a proxy indicator for immediate impacts of droughts such as reduced soil moisture, snowpack, and flow in smaller creeks. This indicator is calculated with one month total precipitation accumulation period (SPI-1), derived from the fifth generation ECMWF reanalysis for the global climate and weather ERA5 (https://confluence.ecmwf.int/display/CKB/The+family+of+ERA5+datasets) hourly precipitation data; using a reference period between the year 1991 and 2020. Data at the moment have a monthly computation but in the future version 2.0.0 they will be actualized every 10 days, thus giving a more actual overview of the indicator than the classical end-of-month computation. The accumulation periods will remain however monthly, with monthly windows (28-, 29-, 30-, or 31-day periods) starting on the 1st, 11th and 21st day of the month. The SPI represents precipitation anomalies at a given location and time, as the deviation from the long-term mean in a standard-normal distribution, with more negative values representing a more severe drought. SPI is calculated by comparing the observed total precipitation amounts for an accumulation period of a number of months, indicated by a number following SPI-, with the long-term “climatological” rainfall distribution for that same period of the year, derived from historical rainfall records in a specific reference period. A given accumulation period corresponds to different potential impacts of a drought, with shorter accumulation periods generally representing fast-changing aspects of the water system such as surface soil moisture and flow in small rivers, and longer accumulation periods representing slower changing aspects of the water system such as groundwater and baseflow in larger rivers. However, the exact relationship between accumulation period and impacts depends on the natural environment (e.g. geology, soils) and human interference (e.g. irrigation). Details on the calculation of the SPI are given in the indicator factsheet.DROUGHTEDO 10-day Snow Masks (based on VIIRS, version 1.0.0)2024-03-25T17:33:55Z2024-03-26T14:50:22ZThe Snow Masks used in the European Drought Observatory (EDO) are a collection of time-varying binary spatial layers (one for each 10-day period of the year) used to depict the location and extent of snow on the ground. The input data for calculating the snow masks is the NASA VNP10A1 product (https://nsidc.org/data/vnp10a1/versions/1). Data values are 0: Snow-free, 1: Snow-covered.DROUGHTGDO 10-day static Crop Masks (based on ASAP, version 1.0.0)2024-03-25T17:33:49Z2024-03-26T14:15:38ZThe Crop Masks used in the European Drought Observatory (EDO) and Global Drought Observatory (GDO) consist of 36 binary spatial layers (one for each 10-day period of the year) used to depict the location and extent of crop in its growing season. The input data for calculating the crop masks is the cropland and rangeland mask available through ASAP (Anomaly hot Spots of Agricultural Production, https://agricultural-production-hotspots.ec.europa.eu/). Data values are 0: Non-active, 1: Active.DROUGHTGDO Standardized Precipitation Index ERA5, 6-month accumulation period (SPI-6) (version 1.0.0)2024-03-25T17:16:11Z2024-03-26T14:02:57ZThe 6-month Standardized Precipitation Index (SPI-6) is a meteorological drought indicator to monitor precipitation anomalies over 6-month accumulation periods and is a proxy indicator for medium-term impacts, for example reduced stream flow and reservoir storage. This indicator is calculated with one month total precipitation accumulation period (SPI-1), derived from the fifth generation ECMWF reanalysis for the global climate and weather ERA5 (https://confluence.ecmwf.int/display/CKB/The+family+of+ERA5+datasets) hourly precipitation data; using a reference period between the year 1991 and 2020. The SPI represents precipitation anomalies at a given location and time, as the deviation from the long-term mean in a standard-normal distribution, with more negative values representing a more severe drought. SPI is calculated by comparing the observed total precipitation amounts for an accumulation period of a number of months, indicated by a number following SPI-, with the long-term “climatological” rainfall distribution for that same period of the year, derived from historical rainfall records in a specific reference period. A given accumulation period corresponds to different potential impacts of a drought, with shorter accumulation periods generally representing fast-changing aspects of the water system such as surface soil moisture and flow in small rivers, and longer accumulation periods representing slower changing aspects of the water system such as groundwater and baseflow in larger rivers. However, the exact relationship between accumulation period and impacts depends on the natural environment (e.g. geology, soils) and human interference (e.g. irrigation). Details on the calculation of the SPI are given in the indicator factsheet.DROUGHTGDO Standardized Precipitation Index ERA5, 12-month accumulation period (SPI-12) (version 1.0.0)2024-03-25T17:16:24Z2024-03-26T14:02:49ZThe 12-month Standardized Precipitation Index (SPI-12) is a meteorological drought indicator to monitor precipitation anomalies over 12-month accumulation periods and is a proxy indicator for medium-term impacts, for example reduced stream flow and reservoir storage, and for long-term impacts, for example reduced reservoir and groundwater recharge. This indicator is calculated with one month total precipitation accumulation period (SPI-1), derived from the fifth generation ECMWF reanalysis for the global climate and weather ERA5 (https://confluence.ecmwf.int/display/CKB/The+family+of+ERA5+datasets) hourly precipitation data; using a reference period between the year 1991 and 2020. The SPI represents precipitation anomalies at a given location and time, as the deviation from the long-term mean in a standard-normal distribution, with more negative values representing a more severe drought. SPI is calculated by comparing the observed total precipitation amounts for an accumulation period of a number of months, indicated by a number following SPI-, with the long-term “climatological” rainfall distribution for that same period of the year, derived from historical rainfall records in a specific reference period. A given accumulation period corresponds to different potential impacts of a drought, with shorter accumulation periods generally representing fast-changing aspects of the water system such as surface soil moisture and flow in small rivers, and longer accumulation periods representing slower changing aspects of the water system such as groundwater and baseflow in larger rivers. However, the exact relationship between accumulation period and impacts depends on the natural environment (e.g. geology, soils) and human interference (e.g. irrigation). Details on the calculation of the SPI are given in the indicator factsheet.DROUGHTGDO Standardized Precipitation Index ERA5, 9-month accumulation period (SPI-9) (version 1.0.0)2024-03-25T17:16:18Z2024-03-26T14:02:44ZThe 9-month Standardized Precipitation Index (SPI-9) is a meteorological drought indicator to monitor precipitation anomalies over 9-month accumulation periods and is a proxy indicator for medium-term impacts, for example reduced stream flow and reservoir storage. This indicator is calculated with one month total precipitation accumulation period (SPI-1), derived from the fifth generation ECMWF reanalysis for the global climate and weather ERA5 (https://confluence.ecmwf.int/display/CKB/The+family+of+ERA5+datasets) hourly precipitation data; using a reference period between the year 1991 and 2020. The SPI represents precipitation anomalies at a given location and time, as the deviation from the long-term mean in a standard-normal distribution, with more negative values representing a more severe drought. SPI is calculated by comparing the observed total precipitation amounts for an accumulation period of a number of months, indicated by a number following SPI-, with the long-term “climatological” rainfall distribution for that same period of the year, derived from historical rainfall records in a specific reference period. A given accumulation period corresponds to different potential impacts of a drought, with shorter accumulation periods generally representing fast-changing aspects of the water system such as surface soil moisture and flow in small rivers, and longer accumulation periods representing slower changing aspects of the water system such as groundwater and baseflow in larger rivers. However, the exact relationship between accumulation period and impacts depends on the natural environment (e.g. geology, soils) and human interference (e.g. irrigation). Details on the calculation of the SPI are given in the indicator factsheet.DROUGHTGDO Standardized Precipitation Index ERA5, 24-month accumulation period (SPI-24) (version 1.0.0)2024-03-25T17:16:31Z2024-03-26T14:02:37ZThe 24-month Standardized Precipitation Index (SPI-24) is a meteorological drought indicator to monitor precipitation anomalies over 24-month accumulation periods and is a proxy indicator for long-term impacts, for example, reduced reservoir and groundwater recharge. This indicator is calculated with one month total precipitation accumulation period (SPI-1), derived from the fifth generation ECMWF reanalysis for the global climate and weather ERA5 (https://confluence.ecmwf.int/display/CKB/The+family+of+ERA5+datasets) hourly precipitation data; using a reference period between the year 1991 and 2020. The SPI represents precipitation anomalies at a given location and time, as the deviation from the long-term mean in a standard-normal distribution, with more negative values representing a more severe drought. SPI is calculated by comparing the observed total precipitation amounts for an accumulation period of a number of months, indicated by a number following SPI-, with the long-term “climatological” rainfall distribution for that same period of the year, derived from historical rainfall records in a specific reference period. A given accumulation period corresponds to different potential impacts of a drought, with shorter accumulation periods generally representing fast-changing aspects of the water system such as surface soil moisture and flow in small rivers, and longer accumulation periods representing slower changing aspects of the water system such as groundwater and baseflow in larger rivers. However, the exact relationship between accumulation period and impacts depends on the natural environment (e.g. geology, soils) and human interference (e.g. irrigation). Details on the calculation of the SPI are given in the indicator factsheet.DROUGHTGDO Standardized Precipitation Index ERA5, 48-month accumulation period (SPI-48) (version 1.0.0)2024-03-25T17:16:38Z2024-03-26T14:02:28ZThe 48-month Standardized Precipitation Index (SPI-48) is a meteorological drought indicator to monitor precipitation anomalies over 48-month accumulation periods and is a proxy indicator for long-term impacts, for example, reduced reservoir and groundwater recharge. This indicator is calculated with one month total precipitation accumulation period (SPI-1), derived from the fifth generation ECMWF reanalysis for the global climate and weather ERA5 (https://confluence.ecmwf.int/display/CKB/The+family+of+ERA5+datasets) hourly precipitation data; using a reference period between the year 1991 and 2020. The SPI represents precipitation anomalies at a given location and time, as the deviation from the long-term mean in a standard-normal distribution, with more negative values representing a more severe drought. SPI is calculated by comparing the observed total precipitation amounts for an accumulation period of a number of months, indicated by a number following SPI-, with the long-term “climatological” rainfall distribution for that same period of the year, derived from historical rainfall records in a specific reference period. A given accumulation period corresponds to different potential impacts of a drought, with shorter accumulation periods generally representing fast-changing aspects of the water system such as surface soil moisture and flow in small rivers, and longer accumulation periods representing slower changing aspects of the water system such as groundwater and baseflow in larger rivers. However, the exact relationship between accumulation period and impacts depends on the natural environment (e.g. geology, soils) and human interference (e.g. irrigation). Details on the calculation of the SPI are given in the indicator factsheet.DROUGHTGDO Meteorological Drought Tracking (version 1.0.1)2024-02-06T09:54:34Z2024-03-26T12:25:04ZThis indicator outlines persistent anomaly conditions of low precipitation, as discrete events in space and time, at the global scale and at near real-time, every ten days.
By providing information on the duration and spatial extent of droughts, it allows for a structured and well-defined analysis of individual droughts, e.g. to follow evolution in space-time, to associate impacts, to link with other natural hazards, to search for patterns, etc.
Derived from the ERA5 (fifth generation) reanalysis, drought events are identified with a three-dimensional density-based clustering algorithm (DBSCAN) on SPI-3.
Events are classified as "consolidated" if their outline at a given ten-day period is stable through updates, or "provisional", when the spatio-temporal outline is likely to change with further updates.
The technical details describing the methods for the computation of the indicator can be found in Cammalleri, et al. An event-oriented database of meteorological droughts in Europe based on spatio-temporal clustering. Sci Rep 13, 3145 (2023).LUISAPrototype Functional Rural Areas2024-03-26T09:44:09Z2024-03-26T10:45:50ZThis dataset describes the FRAGs (Functional Rural Area at the Grid level) and FRAUs (Functional Rural Area at the local administrative Unit level) as described in the JRC working paper Dijkstra, Jacobs-Crisioni (2023), Developing a definition of Functional Rural Areas in the EU. It also contains an overview of the matching between FRAGs and the LAU-2 units from which the FRAUs are composed.
RESOLUTION: 1:1000000.
COMPLETENESS: 100%.
POLICY CONTEXT: Regional and urban policies.
METHODOLOGY: Functional rural areas cover all the territory outside functional urban areas. They are constructed in three steps. First, we define rural centres: they are the largest town or village within a 10-minute drive. Second, we create catchment areas by assigning every grid cell to the nearby rural centre that has the greatest gravitational pull. Third, we combine small and nearby catchment areas. We combine catchment area until each has at least 25 000 inhabitants or is more than an hour’s drive away from the surrounding catchment areas. We also combine catchment areas that have centres that are less than a 30-minute drive apart, even if they have a population of at least 25 000 inhabitants. Next, we show that functional rural areas are more harmonised in terms of population and area size than LAUs and NUTS-3 regions. The analysis of population change and of the distance to the nearest school shows that the results by functional area are less volatile than the results per LAU and show more detail than the results per NUTS-3 regions. Functional rural areas can inform policies that promote access to services and that respond to demographic change. They can also be used to inform transport infrastructure investments and public transport provision.
DATA SOURCES: Settlement definitions according to degrees of urbanisation, Geostat 2011. Population based on JRC-Geostat 2018. FUAs from provisional 2021 FUA boundaries. Network connectivity and travel times from Tom Tom freeflow impedances.
LEVEL OF AGGREGATION: Functional Rural Areas
UNCERTAINTY AND LIMITATIONS: Data represent likely functionally autonomous areas, with a loose definition of functional autonomy. Not validated empirically.DATAMThe impacts of the Africa Continental Free Trade Area on the Kenyan economy2024-03-26T07:50:49Z2024-03-26T07:50:49ZEmploying an economy-wide model (JRC DEMETRA), the analysis provides a detailed characterization of the AfCFTA impacts on the Kenyan economy in the 2021-2035 timeframe. It considers two liberalization scenarios — tariff-only liberalization and tariff&Non-Tariff Measures (NTMs) liberalization — across four potential liberalization schedules defined by alternative government revenue, food security and economic development objectives. To capture the responses to the AfCFTA establishment occurring outside Kenya, results from a continental-level assessment are linked to the DEMETRA model to determine changes in international markets. The study findings show that the tariff-only liberalization leads to moderate positive outcomes, encouraging trade in commodities where Kenya already has a comparative advantage, namely cash crops. Moreover, there is a decrease in the production of food crops which are substituted by their imported variety signalling an increase in import dependency in this area. The tariff&NTMs liberalization induces a more significant reduction in trade costs thus stimulating trade. Exports of cash crops continue to have the highest expansion rate followed by manufacturing products. At the same time, there is an important growth of imports in manufacturing and processed food, which determines the output of many activities in this area to reduce relative to baseline values. DATAMThe impacts of the Africa Continental Free Trade Area on the Tanzanian economy2024-03-26T07:40:35Z2024-03-26T07:40:35ZThis study was conducted by the European Union’s Joint Research Centre (JRC) in collaboration with the University of Dodoma (UDOM), the Sokoine University of Agriculture (SUA) and the Mzumbe University (MU). Embedding insights from a continental-level study into a country economy-wide model, the analysis enables a detailed description of the African Continental Free Trade Area (AfCFTA) impacts on the Tanzanian economy and on households.DROUGHTGDO Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) (VIIRS) (version 1.0.0)2023-03-01T10:50:13Z2024-03-25T17:36:39ZThe FAPAR indicator used in the Copernicus Global Drought Observatory (GDO) is the satellite-measured biophysical variable Fraction of Absorbed Photosynthetically Active Radiation (FAPAR, sometimes written as fAPAR or FPAR), composited for 10-day intervals, from its long-term mean values. FAPAR is one of the 50 so-called “Essential Climate Variables” (ECVs) that have been defined by the Global Climate Observing System (GCOS) as being both feasible for global climate observation, and important to support the work of the United Nations Framework Convention on Climate Change (UNFCCC) and the Intergovernmental Panel on Climate Change (IPCC) (Bojinski et al., 2014). FAPAR values and their anomalies have been shown to be good indicators for detecting and assessing drought impacts on plant canopies, such as agricultural crops and natural vegetation (Gobron et al., 2005), and thus provide information that is potentially useful for water and agricultural management purposes. Data considered for the GDO FAPAR indicator are based on the Visible Infrared Imaging Radiometer Suite (VIIRS). The resolution of input data is 500 m, while the resolution of data made available for download in GDO is 0.083 decimal degrees (1/12 decimal degree).DROUGHTEDO Combined Drought Indicator (CDI) (version 1.6.1)2020-11-10T09:35:44Z2024-03-25T16:02:55ZCombined Drought Indicator (CDI) based on SPI, soil moisture and fAPAR, to identify areas with potential to suffer agricultural drought, areas where the vegetation is already affected by drought conditions, and areas in the recovery process to normal conditions after a drought episode. The method is based on 5 impact levels. These levels are: "Watch" when a relevant precipitation shortage is observed, "Warning" when this precipitation shortage comes with a soil moisture anomaly, "Alert" when these two conditions are accompanied with an anomaly in the vegetation condition, "Partial recovery" when after a drought episode, the meteorological conditions are recovered to normal but not the vegetation conditions, "Full recovery" when meteorological and vegetation normal conditions are recovered. Currently, historical data are being reprocessed.
IMPORTANT WARNING: this dataset is not computed or displayed in EDO anymore. Please, consider the latest CDI release instead.ESDACSediments removal costs2024-03-25T12:09:44Z2024-03-25T12:09:44ZFor the entire EU and UK, we provide estimates of sediments removal costs based on three quantification methods: a) Flat rate assignment from modelled sediment delivery b) Regional assignment from modelled sediment delivery c) Sediment remediation costs from extrapolated potential reservoir capacity losses.ESDACLand suitability in temperate Europe2024-03-25T12:09:44Z2024-03-25T12:09:44ZThis datasets includes land suitability maps for several crops and land uses (14 crops , 7 fruit trees, 3 land-use types) in the temperate continental climate of Europe. To model the land suitability we used geospatial data depicting seventeen eco-pedological indicators (e.g. soil texture, pH, porosity, temperature, precipitation, slope). To evaluate how the land is utilized, the suitability maps have been spatially cross-tabulated with a crop map.REMdbREM data bank - Year 20202024-03-15T06:50:41Z2024-03-25T05:58:18ZEnvironmental radioactivity data from the Radioactivity Environmental Monitoring (REM) data bank, referred to year 2020.REMdbREM data bank - Year 20152024-03-15T06:34:09Z2024-03-25T05:57:58ZEnvironmental radioactivity data from the Radioactivity Environmental Monitoring (REM) data bank, referred to year 2015.