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Participation in adaptation initiatives (MapServer)
Title Adaptation_initiatives
Author
Subject This map shows the activities of the European cities in adaptation planning and their commitment to adaptation, shown through participation in adaptation initiatives and projects.
Keywords adaptation; urban; city
Copyright Text Reckien et al. (2018); Covenant of Mayors; European Commission; 100 Resilient Cities; C40 Cities; UNISDR
Registered first time 14 Feb 2020
Service Description

The data has been collected from various sources and mapped :

- Urban Audit cities with adaptation action plans: data provided by Diana Reckien, University of Twente: Reckien D, Salvia M, Heidrich O, Church JM, Pietrapertosa F, De Gregorio-Hurtado S, ValentinaD'Alonzo V, Foley A, Simoes SG, Krkoška Lorencová E, Orru H, Orru K, Wejs A, Flacke J, Olazabal M, Geneletti D, Feliu E, Vasilie S, Nador C, Krook-Riekkola A, Matosović M, Fokaides PA, Ioannou BI, Flamos A, Spyridaki N-A, Balzan MV, Fülöp O, Paspaldzhiev I, Grafakos S, Dawson R (2018). How are cities planning to respond to climate change? Assessment of local climate plans from 885 cities in the EU-28. Journal of Cleaner Production. DOI: https://doi.org/10.1016/j.jclepro.2018.03.220

- Signatories to Covenant of Mayors: https://www.covenantofmayors.eu/about/covenant-community/signatories.html

- Cities participating in EU Life Programme: in house EEA analysis based on the information held in Climate-ADAPT, provided by EASME and available through Life programme webpages: https://ec.europa.eu/easme/en/life

- Cities participating in EU-funded research projects: in-house EEA analysis based on the infomration held in Climate-ADAPT and provided by DG RTD

- Participants in 100 Resilient Cities: http://www.100resilientcities.org/

- Participants in C40 Cities: https://www.c40.org/

Description
SRS 102100
Extent -2841494.5678000003,0,4866854.519000001,10044793.518399999
Layers Urban Audit cities with an adaptation action plan (approved by July 2017),Signatories to Covenant of Mayors (by July 2018),Municipalities and regions participating in adaptation projects within EU Life programme (by September 2018),Municipalities participating in EU funded research projects on adaptation (by June 2017),Participants in 100 Resilient Cities (by July 2018),Participants in C40 Cities
Map Name Adaptation initiatives
Category
Annual number of combined summer days (Tmax>30 C) and tropical nights (Tmin>20 C) per year (1987-2016 average). (MapServer)
Title Annual_Number_Hot_Days_Warm_Nights
Author
Subject The combination of hot days and warm nights is particularly dangerous to human health, as the high temperatures during night time does not allow for the cities and buildings to cool down. Such combination, if lasting over several days (heatwaves) can have severe health implications or even be fatal to the elderly, babies or those in poor health. Therefore, the knowledge of the occurrence of such c
Keywords temperature; heat; thermal comfort; health
Copyright Text E-OBS dataset is from the EU-FP6 project ENSEMBLES (http://ensembles-eu.metoffice.com), data is provided by the European Climate Assessment and Dataset (ECA&D) project (http://www.ecad.eu). Updated fr
Registered first time 14 Feb 2020
Service Description
The average number of combined hot days and tropical nights in the period 1987-2016 was calculated based on the E-OBS dataset , which is agridded data with 0.25° spatial resolution, based on over 10,000 stations across Europe. The value of the nearest-distance grid point to the city centroid was used as the value for that city.
Description
Annual number of combined summer days (maximum temperature over 30 degrees Celsius) and warm nights (minimum temperature over 20 degrees Celsius) (average from the period 1987 - 2016)
SRS 102100
Extent -2412697.121283252,3372915.259233929,3715702.8929682076,10952085.097847171
Layers Number of combined summer days (Tmax over 30° C) and tropical nights (Tmin over 20° C) per year (1987-2016 average)
Map Name Combined summer days (Tmax over 30° C) and tropical nights (Tmin over 20° C)
Category
Areas burnt by wildfires between 2000 and 2017 (MapServer)
Title AreasBurntByWildfires
Author
Subject The extent of areas directly affected by wildfires in the past can be used as one of the indications, where the danger of wildfires may persist or increase in the future under the changing climate.
Keywords forest fire; wildfire; distaster; land cover
Copyright Text EFFIS; JRC
Registered first time 14 Feb 2020
Service Description
The burnt areas were obtained as polygons from the European Forest Fire Information System (EFFIS) of the European Commission Joint Research Centre (JRC) (http://effis.jrc.ec.europa.eu). The polygons were dissolved in order to create one layer for all the years; therefore, if an area has been affected by fires on multiple occassions, it will just present as affected by fires.
Description
Areas affected by wildfires, i.e. burnt areas (2000 - 2017)
SRS 102100
Extent -1994619.0207999982,-2411686.0505000018,6168436.195099998,10324587.072300002
Layers Areas affected by wildfires (2000 - 2017)
Map Name Areas affected by wildfires (2000 - 2017)
Category
Average forest fire danger (1981 - 2010) (MapServer)
Title AverageForestDanger
Author
Subject see https://www.eea.europa.eu/data-and-maps/indicators/forest-fire-danger-2/assessment
Keywords wildfires; fores fires; disaster; risk
Copyright Text EEA; Seasonal severity rating index provided by Joint Research Centre (JRC)
Registered first time 14 Feb 2020
Service Description

see https://www.eea.europa.eu/data-and-maps/indicators/forest-fire-danger-2/assessment

Description
Average forest fire danger (1981 - 2010; seasonal severity rating)
SRS 102100
Extent -2623848.0427,3630418.9924999997,6534365.721900001,11466846.305799998
Layers Average forest fire danger (1981 - 2010; seasonal severity rating)
Map Name Average forest fire danger (1981 - 2010; seasonal severity rating)
Category
cities_adaptation_initiatives_2019 (MapServer)
Title cities_adaptation_initiatives_2019
Author
Subject
Keywords
Copyright Text
Registered first time 14 Feb 2020
Service Description
Description
SRS 102100
Extent -2839602.0533999987,3205002.3973000012,4160724.042100001,10044793.518399999
Layers Signatories to Covenant of Mayors for Climate and Energy on adaptation (June 2019) ,Participants in Making Cities Resilient campaign (UNDRR),Participants in C40 cities (on adaptation-related initiatives),Participants in 100 Resilient Cities (Rockefeller Foundation),Winners of the European Green City/Green Leaf Award
Map Name Cities and local authorities participating in adaptation initiatives (by mid-2019)
Category
City population (MapServer)
Title City_Population
Author
Subject The number of people living in the city affects the need for natural resources, such as drinking water. Therefore, the size of the city population is directly relevant to adaptation planning, e.g. in the context of water scarcity.
Keywords population
Copyright Text Eurostat; Google Public Data Tool
Registered first time 14 Feb 2020
Service Description
Data on population was obtained from Eurostat (Population on 1 January by age groups and sex - cities and greater cities (urb_cpop1; http://appsso.eurostat.ec.europa.eu/nui/show.do?dataset=urb_cpop1&lang=en)) and joined to the Urban Audit 2011-2014 cities' centroids (https://ec.europa.eu/eurostat/web/gisco/geodata/reference-data/administrative-units-statistical-units/urban-audit#ua11-14). Where no data was available from Eurostat, this was supplemented by internet search (Google Public data Tool). This is associated with some uncertainty about the quality of data, and also means that the population is from various years.
Description
Number of people living in the city, recorded for various years.
SRS 102100
Extent -2412697.121283341,3372915.2592421174,3715702.8929676972,10952085.097966254
Layers City population (various years)
Map Name City population (various years)
Category
climatic_suitability_tiger_mosquito_cities_high_res (MapServer)
Title climatic_suitability_tiger_mosquito_cities_high_res
Author
Subject
Keywords
Copyright Text Copernicus C3S, VITO, DeRidder et al., 2015, ECDC (2009)
Registered first time 14 Feb 2020
Service Description

In the framework of the Copernicus C3S SIS European Health, VITO has provided to the Climate Data Store 100m resolution hourly temperature data for 100 European cities, based on simulations with the urban climate model UrbClim (De Ridder et al., 2015). From this data set, climate suitability maps (0-100%) of Aedes albopictusare generated based on annual precipitation and the average temperature in January and during the summer period JJAfor the years 2008-2009, following ECDC (2009). This approach considers empirical suitability functions, which link a number of climate variables to the suitability of a habitat. The suitability for tiger mosquito is zero when the annual rainfall is lower than 450 mm, and maximum suitability is reached when the annual rainfall is higher than 800 mm. For summer temperatures, the suitability is zero when temperatures were lower than 15°C and higher than 30°C, and maximum suitability is between 20°C and 25°C. For January temperatures, the suitability is zero when temperatures were lower than -1°C and maximum when temperatures were higher than 3°C.The different suitability functions are then entered into a weighted linear combination approach and the results were rescaled to a range between 0 and 100.

Description
Climate suitability maps for the tiger mosquito for the 100 cities involved in the Copernicus C3S EU Health activity
SRS 102100
Extent -2787968.533105738,4280801.7375052385,4580131.466894262,10255801.737505239
Layers Climatic suitability for tiger mosquito (on a 0-100 scale)
Map Name Modelled climatic suitability for Aedes albopictus (tiger mosquito) in 100 European cities
Category
90th percentile (P90) climatic suitability for tiger mosquito in 100 European cities (MapServer)
Title climatic_suitability_tiger_mosquito_cities_P90_final
Author
Subject Climate suitability maps for the tiger mosquito for the 100 cities involved in the Copernicus C3S EU Health activity
Keywords climate change; health; vector-borne diseases; city; urban
Copyright Text Copernicus C3S, VITO, DeRidder et al., 2015, ECDC (2009)
Registered first time 14 Feb 2020
Service Description

In the framework of the Copernicus C3S SIS European Health, VITO has provided to the Climate Data Store 100m resolution hourly temperature data for 100 European cities, based on simulations with the urban climate model UrbClim (De Ridder et al., 2015). From this data set, climate suitability maps (0-100%) of Aedes albopictusare generated based on annual precipitation and the average temperature in January and during the summer period JJAfor the years 2008-2009, following ECDC (2009). This approach considers empirical suitability functions, which link a number of climate variables to the suitability of a habitat. The suitability for tiger mosquito is zero when the annual rainfall is lower than 450 mm, and maximum suitability is reached when the annual rainfall is higher than 800 mm. For summer temperatures, the suitability is zero when temperatures were lower than 15°C and higher than 30°C, and maximum suitability is between 20°C and 25°C. For January temperatures, the suitability is zero when temperatures were lower than -1°C and maximum when temperatures were higher than 3°C.The different suitability functions are then entered into a weighted linear combination approach and the results were rescaled to a range between 0 and 100.

The P90 indicator represents the specific exposure of single cities and is independent of the model domain or size of a city. Since it is the 90th percentile, there are a number of grid cells (areas) in a city with an even higher suitability value, so it can be considered a rather conservative value.

Description
In the framework of the Copernicus C3S SIS European Health, VITO has provided to the Climate Data Store 100m resolution hourly temperature data for 100 European cities, based on simulations with the urban climate model UrbClim (De Ridder et al., 2015). From this data set, climate suitability maps (0-100%) of Aedes albopictus are generated based on annual precipitation and the average temperature in January and during the summer period JJA for the years 2008-2009, following ECDC (2009). This approach considers empirical suitability functions, which link a number of (aggregated) climate variables to the suitability of a habitat for a given vector species, e.g. for a species to be active a minimum threshold of temperature is required below which the species is not active. Similarly some species cannot overwinter if the winter is too cold (e.g. January temperature lower than a given value).
SRS 102100
Extent -2430004.6303999983,4399767.607299998,3108867.6207000017,9380898.5216
Layers Climatic suitability for tiger mosquito (Aedes albopictus) 2008-2009
Map Name Modelling of climate suitability for Aedes albopictus in 100 European cities
Category
Coastal_flooding_1m (MapServer)
Title Coastal_flooding_1m
Author
Subject
Keywords
Copyright Text Urban Morphological Zone (EEA), DIVA dataset, Hydro1k model
Registered first time 14 Feb 2020
Service Description
The data on maximum storm surge heights (100-year event of actual climate) as generated by the ‘Dynamic and interactive vulnerability assessment’ (DIVA) project were used to determine actual medium inundation heights. To account for the effects of climate change, 1 m of potential sea level rise until 2100 was calculated on top of these values35. In order to determine potentially inundated areas, the Hydro1k digital elevation model was utilised. By identifying the cells below the respective regional inundation threshold, continuous areas have been delineated, which can be considered as potentially at risk of storm surge-related inundation. The inundation information has been combined with the urban area of all coastal cities. For each city, the proportion of the area affected by a 1-metre sea level rise is computed and represented by coloured dots in the map. As this indicator is simply based on elevation by identifying low-lying areas, and does not take into account existing adaptation measures like dikes, the actual risk is likely to be much lower than the indicator shows.
Description
SRS 102100
Extent -1019420,4285099.100097656,3183014.700073242,10952153.100097656
Layers Percentage of Urban Morphological Zone (UMZ) potentially affected by coastal flooding, assuming a sea level rise of 1m
Map Name Coastal_flooding_1m
Category
Percentage of core city area inundated by sea level rise of 1m, assuming no coastal defences (MapServer)
Title coastal_inundation_city_area_1m
Author
Subject The map shows the percentage of cities' administrative area (core city) iinundated by the sea level rise of 1 metre, without any coastal flooding defences present.
Keywords climate change; adaptation; sea level rise; coastal flooding; urban; city
Copyright Text CReSIS (Centre for Remote Sensing of Ice Sheets) 2018, Lawrence, Kansas, USA. Digital Media. http://data.cresis.ku.edu/. Eurostat Urban Audit 2018 spatial units: https://ec.europa.eu/eurostat/web/gisc
Registered first time 14 Feb 2020
Service Description

The map shows the percentage of cities' administrative area (core city) inundated by the sea level rise of 1 metre, without any coastal flooding defences present.

Description
The map shows the percentage of cities' administrative area (core city) inundated by the sea level rise of 1 metre, without any coastal flooding defences present.
SRS 102100
Extent -2864105.9497999996,4125566.1010000035,3681391.331599999,10952085.097999997
Layers Percentage of core city area inundated under 1m sea level rise scenario (no defences)
Map Name Exposure of coastal cities to 1m sea level rise
Category
Percentage of core city area inundated by sea level rise of 2m, assuming no coastal defences (MapServer)
Title coastal_inundation_city_area_2m
Author
Subject The map shows the percentage of cities' administrative area (core city) iinundated by the sea level rise of 2 metres, without any coastal flooding defences present.
Keywords climate change; adaptation; sea level rise; coastal flooding; urban; city
Copyright Text CReSIS (Centre for Remote Sensing of Ice Sheets) 2018, Lawrence, Kansas, USA. Digital Media. http://data.cresis.ku.edu/. Eurostat Urban Audit 2018 spatial units: https://ec.europa.eu/eurostat/web/gisc
Registered first time 14 Feb 2020
Service Description

The map shows the percentage of cities' administrative area (core city) iinundated by the sea level rise of 2 metres, without any coastal flooding defences present.

Description
SRS 102100
Extent -2864105.9497999996,4125566.1010000035,3681391.331599999,10952085.097999997
Layers Percentage of core city area inundated under 2m sea level rise scenario (no defences)
Map Name Exposure of coastal cities to 2m sea level rise
Category
Annual number of cooling degree days (average for the period 1990-2015) (MapServer)
Title CoolingDegreeDays
Author
Subject Cooling Degree Days (CDD) is a measurement designed to quantify the demand for energy needed to cool a building in order to keep it at a comfortable temperature. In this report, it is defined as the sum of the difference in degrees between 21 °C and the mean temperature over the year, for the days when the mean daily temperature is higher than 21 °C. The number of CDDs is useful in differentiating
Keywords temperature; heat; thermal comfort
Copyright Text E-OBS dataset is from the EU-FP6 project ENSEMBLES (http://ensembles-eu.metoffice.com), data is provided by the European Climate Assessment and Dataset (ECA&D) project (http://www.ecad.eu). Updated fr
Registered first time 14 Feb 2020
Service Description
The cooling degree days were calculated based on the E-OBS dataset , which is agridded data with 0.25° spatial resolution, based on over 10,000 stations across Europe. The value of the nearest-distance grid point to the city centroid was used as the value for that city.
Description
Annual number of cooling degree days (average from the period 1990 - 2015).
SRS 102100
Extent -6794650.951511465,-2384711.8986121863,6178462.671310332,10952085.097966254
Layers Annual number of cooling degree days (1990 - 2015 average)
Map Name CoolingDegreeDays_1990_2015_average
Category
countries_WEI (MapServer)
Title countries_WEI
Author
Subject Water Exploitation Index by country
Keywords Water
Copyright Text © Service Copyright EEA Copenhagen
Registered first time 14 Feb 2020
Service Description
Water Exploitation Index by country
Description
Water Exploitation Index by country
SRS 3395
Extent -2731050.0034000017,3191604.1382,4989305.2117,11425554.877999999
Layers Countries
Map Name Water Exploitation Index
Category
Price of a m³ of domestic water (MapServer)
Title DomesticWaterPrice
Author
Subject Many locations in Europe are already affectd, or will be affected in the future by water scarcity. Pricing of water is used as a mechanism to control demand; however, high water prices may negatively affect those on lowest incomes. Therefore, it is important to consider water pricing, and its fairness, in the context of adaptation to the changing climate.
Keywords water; water scarcity; water availability
Copyright Text Tapia etl. (2017) based on Eurostat
Registered first time 14 Feb 2020
Service Description
The price of domestic water was obtained from Tapia et al. (2017), based on Eurostat data ([urb_cenv]; http://appsso.eurostat.ec.europa.eu/nui/submitViewTableAction.do). Various years were considered in order to ensure the greatest possible spatial coverage.
Description
Price of domestic water to consumers, in Euro per cubic meter
SRS 102100
Extent -2412697.121283341,3372915.2592421174,3715702.8929676972,10952085.097966254
Layers Price of domestic water (Euro per cubic meter; various years)
Map Name Price of domestic water (Euro per cubic meter; various years)
Category
Change factor of maximum drought in 571 European cities between 1951-2000 and 2051-2100. (MapServer)
Title droughts_change_factor
Author
Subject The map shows the projected change in the maximum droughts experienced by cities.
Keywords climate change; cities; drought; adaptation
Copyright Text Guerreiro, S. B., et al., 2018, ‘Future heat-waves, droughts and floods in 571 European cities’, Environmental Research Letters 13(3), p. 034009 (DOI: 10.1088/1748-9326/aaaad3).
Registered first time 07 Sep 2020
Service Description

The 12-month scale Drought Severity Index (DSI-12) was used. It is based on cumulative monthly precipitation anomalies, where the absolute deficit (in mm) is divided by the mean annual rainfall and multiplied by 100. DSI-12 is a rainfall index and therefore does not account for an increase in drought due to increasing temperatures (and subsequently potential evaporation). The map shows the ratio of the maximum DSI-12 in the future to the maximum DSI-12 in the historical period. Based on 50 climate model projections from the Coupled Model Intercomparison Project Phase 5 (CMIP5; (Taylor et al., 2012), under the RCP8.5 climate scenario.

Description
The 12-month scale Drought Severity Index (DSI-12) was used. It is based on cumulative monthly precipitation anomalies, where the absolute deficit (in mm) is divided by the mean annual rainfall and multiplied by 100. DSI-12 is a rainfall index and therefore does not account for an increase in drought due to increasing temperatures (and subsequently potential evaporation). The map shows the ratio of the maximum DSI-12 in the future to the maximum DSI-12 in the historical period. Based on 50 climate model projections from the Coupled Model Intercomparison Project Phase 5 (CMIP5; (Taylor et al., 2012), under the RCP8.5 climate scenario.
SRS 102100
Extent -1044275.5639999993,4118270.5491999984,3713940.0375000015,10959110.719400005
Layers Low impact scenario (10th percentile),Medium impact scenario (50th percentile),High impact scenario (90th percentile)
Map Name Change factor of maximum drought between 1951-2000 and 2051-2100
Category
Cities, local and subnational authorities participating in EU-funded projects on adaptation by 2019 (MapServer)
Title EU_funded_projects_on_adaptation_local_subnational_level
Author
Subject The map provides an overview of the cities, other local authorities and subnational authorities participating in EU funded projects suporting research, knowledge exchange, adaptation planning or or implementation of adaptation actions.
Keywords adaptation; EU; climate change; research; knowledge exchange; adaptation planning; adaptation implementation
Copyright Text Climate-ADAPT; Life projects: http://ec.europa.eu/environment/life/project/Projects/index.cfm plus direct communication with EASME; Interreg projects: https://www.keep.eu/ (June 2019); Research projec
Registered first time 14 Feb 2020
Service Description

The map provides an overview of the cities, other local authorities and subnational authorities participating in EU funded projects suporting research, knowledge exchange, adaptation planning or or implementation of adaptation actions.It is based on publically available information in various European Commission's databases.

Description
Cities, other local and sub-national authorities participating in research, knowledge exchange or implementation projects (Interreg, Life and Framework Programme/Horizon2020 research projects) by 2019.
SRS 102100
Extent -2858194.049899999,3218511.0823,3710646.1543999985,9611718.315899998
Layers Authorities participating in Interreg projects,Authorities participating in research projects,Authorities participating in LIFE projects
Map Name Cities, local and regional authorities participating in EU funded projects by 2019
Category
Projected change in the percentage of summer (May-September) days classified as heatwave days between the historical period (1951–2000) and the future period (2051–2100) in 571 European cities (MapServer)
Title Heatwave_days_change
Author
Subject The map presents the projected change in the percentage of summer days classified as heatwaves, therefore indicating the projected future risks to human health.
Keywords climate change; heatwaves; cities
Copyright Text Guerreiro, S. B., et al., 2018, ‘Future heat-waves, droughts and floods in 571 European cities’, Environmental Research Letters 13(3), p. 034009 (DOI: 10.1088/1748-9326/aaaad3).
Registered first time 07 Sep 2020
Service Description

Heatwaves were defined as three consecutive days where both the maximum and the minimum temperature exceed their respective 95th percentile from the historical period. The analysis is based on 50 climate model projections from the Coupled Model Intercomparison Project Phase 5 (CMIP5) (Taylor et al., 2012), under the RCP8.5 climate scenario.

Description
SRS 102100
Extent -1044275.5639999993,4118270.5491999984,3713940.0375000015,10959110.719400005
Layers Low impact scenario (10th percentile),Medium impact scenario (50th percentile),High impact scenario (90th percentile)
Map Name Projected change in the percentage of summer days classified as heatwave days between 1951-2000 and
Category
Number of hot summer days (Tmax > 35 degrees celsius) per year (1987 - 2016 average). (MapServer)
Title Hot_Summer_Days
Author
Subject High temperatures are dangerous to human health, as and can even be fatal to the elderly, babies or those in poor health, especially if lasting longer than one day. High daytime temperatures may bear particular relevance to the health of workers in outdoor or high-temperature indoor settings. It has also been estimated that as many as 40 % of deaths associated with heat occur on isolated hot days
Keywords temperature; heat; thermal comfort; health
Copyright Text E-OBS dataset is from the EU-FP6 project ENSEMBLES (http://ensembles-eu.metoffice.com), data is provided by the European Climate Assessment and Dataset (ECA&D) project (http://www.ecad.eu). Updated fr
Registered first time 14 Feb 2020
Service Description
The average number of hot summer days in the period 1987-2016 was calculated based on the E-OBS dataset , which is agridded data with 0.25° spatial resolution, based on over 10,000 stations across Europe. The value of the nearest-distance grid point to the city centroid was used as the value for that city.
Description
Number of days per year, where maximum temperature exceeds 35 degrees Celsius (average from the period 1987 - 2016).
SRS 102100
Extent -2412697.121283252,3372915.259233929,3715702.8929682076,10952085.097847171
Layers Number of hot summer days (Tmax over 35° C) per year (1987-2016 average)
Map Name Number of hot summer days (Tmax over 35° C) per year (1987-2016 average)
Category
Percentage of impervious area within the core city (2015) (MapServer)
Title Impervious_Areas_Core_City
Author
Subject The percentage of sealed area in the city affects the impacts of climate-related hazards. Heavy rainfall may lead to flooding, if the water can not infiltrate into the ground, and if its amount exceeds the capacity of the drainage system. Also, artificial surfaces tend to get hotter than vegetation, contributing to the urban heat island effect. Therefore, it is important to consider the amount of
Keywords impervious area; soil sealing
Copyright Text Eurostat; Copernicus
Registered first time 14 Feb 2020
Service Description
The area of soil sealing is taken from the Copernicus pan-European soil-sealing layer 2015 which containsthe fraction of sealed soil per 1 ha cell(https://land.copernicus.eu/pan-european/high-resolution-layers/imperviousness/status-maps/2015/view). The basic reference unit for the processing is the core city based on UrbanAudit - eurostat (http://ec.europa.eu/eurostat/web/gisco/geodata/reference-data/administrative-units-statistical-units/urban-audit,http://ec.europa.eu/eurostat/statistics-explained/index.php?title=European_cities_-_spatial_dimension&oldid=213219#Core_cities) To compute the mean soil-sealing degree of city’s urban area by means of zonal statistics, the soil-sealing mosaic of Europe was overlaid with these reference unit objects.
Description
Percentage of impervious areas in the core city (the city's administrative area) in 2015
SRS 102100
Extent -2412697.121283341,3372915.2592421174,3715702.8929676972,10952085.097966254
Layers Percentage of impervious areas in core city (2015)
Map Name Percentage of impervious areas in core city (2015)
Category
Land use in urban floodplains (MapServer)
Title land_use_urban_floodplains
Author
Subject The map shows the extent of the area potentially at flood risk in European cities, focussing on 'urban' and industrial/commercial land uses.
Keywords urban; city; floodplain; flood risk; adaptation; land use; spatial planning
Copyright Text Copernicus Land Monitoring Service Urban Atlas 2012 https://land.copernicus.eu/local/urban-atlas Dottori, F., Alfieri, L., Salamon, P., Bianchi, A., Feyen, L., Lorini, V. (2016): Flood hazard map fo
Registered first time 14 Feb 2020
Service Description
Description
The map shows the extent of areas within European cities (cities included in Urban Audit/city statistics) which are located within potential floodplains. The potential floodplain captures the area that could be flooded during a flood event with a return period of one in 100 years, as well as the river area. It was derived by adding two spatial layers: 1) JRC flood hazard map for Europe 100-year return period (JRC, 2016), based on LISFLOOD model results (Burek et al., 2013) and Copernicus Potential Riparian Zone layer from the dataset: Delineation of Riparian Zone. The map also shows the percentage of the areas at flood risk which are classed in the Copernicus Land Monitoring Service Urban Atlas as 'urban fabric' (class 1.1) or 'industrial, commercial, public, military, private and transport units' (class 1.2). This shows the potential extent of exposure of these land uses to river flooding.
SRS 102100
Extent -2412697.1213000007,4182973.606200002,3715702.892999999,10952085.097999997
Layers Size of the potential floodplain within the core city (square kilometre),Percentage of the potential floodplain classed as 'urban fabric' (UA 2012 class 1.1),Percentage of the potential floodplain classed as 'industrial, commercial etc' (UA 2012 class 1.2)
Map Name Land use in urban floodplains (based on Copernicus Urban Atlas 2012)
Category
Observed trends in maximum annual five-day consecutive precipitation in summer (1960 - 2015) (MapServer)
Title ObservedPrecipitationSummer
Author
Subject Heavy precipitation events have become more intense and more frequent in Europe on average, but there are important differences across regions, seasons, time periods, heavy precipitation indices and underlying datasets. Studies generally agree that heavy precipitation has become more intense in northern and north-eastern Europe since the 1950s, even though not all changes are statistically signifi
Keywords precipitation; climate change
Copyright Text EEA; KNMI (E-OBS gridded dataset)
Registered first time 14 Feb 2020
Service Description

Heavy precipitation is defined as the maximum annual five-day consecutive precipitation. Trends are calculated for the period between 1960 and 2015. Trends are calculated using a median of pairwise slopes algorithm.see: https://www.eea.europa.eu/data-and-maps/indicators/precipitation-extremes-in-europe-3/assessment

Description
SRS 102100
Extent -3235222.701200001,3484090.7578999996,6574807.425000001,15670499.581899999
Layers Rx5d (mm/decade)
Map Name Observed trends in maximum annual five-day consecutive precipitation in summer (1960 - 2015)
Category
Observed trends in maximum annual five-day consecutive precipitation in winter (1960 - 2015) (MapServer)
Title ObservedPrecipitationWinter
Author
Subject Heavy precipitation events have become more intense and more frequent in Europe on average, but there are important differences across regions, seasons, time periods, heavy precipitation indices and underlying datasets. Studies generally agree that heavy precipitation has become more intense in northern and north-eastern Europe since the 1950s, even though not all changes are statistically signifi
Keywords precipitation; climate change
Copyright Text EEA; KNMI (E-OBS gridded dataset)
Registered first time 14 Feb 2020
Service Description

Heavy precipitation is defined as the maximum annual five-day consecutive precipitation. Trends are calculated for the period between 1960 and 2015. Trends are calculated using a median of pairwise slopes algorithm.

see: https://www.eea.europa.eu/data-and-maps/indicators/precipitation-extremes-in-europe-3/assessment

Description
SRS 102100
Extent -3235222.701200001,3484090.7578999996,6574807.425000001,15670499.581899999
Layers Rx5d (mm per decade)
Map Name Observed trends in maximum annual five day consecutive precipitation in winter (1960 - 2016)
Category
Observed trends in frequency of meteorological droughts (1950 - 2012; events per decade) (MapServer)
Title ObservedTrendsDroughts1950_2012
Author
Subject Trends in frequency (upper) and severity (lower) of meteorological droughts between 1950 and 2012. Trends are based on a combination of three different drought indices - SPI, SPEI and RDI accumulated over 12-month periods. see https://www.eea.europa.eu/data-and-maps/indicators/river-flow-drought-2/assessment
Keywords drought; water scarcity; climate change
Copyright Text JRC; Spinoni et al. (2015) https://www.sciencedirect.com/science/article/pii/S0921818115000284?via%3Dihub
Registered first time 14 Feb 2020
Service Description

see https://www.eea.europa.eu/data-and-maps/indicators/river-flow-drought-2/assessment

Description
SRS 102100
Extent -2671667.7790385657,4110355.570454051,7365332.226744868,11488299.604556052
Layers Events per decade
Map Name Observed trends in frequency of meteorological droughts (1950 - 2012
Category
Percentage of city area affected by wildfires (2000 - 2017) (MapServer)
Title Percentage_Area_Affected_By_Wildfires
Author
Subject The extent of areas directly affected by wildfires in the past can be used as one of the indications, where the danger of wildfires may persist or increase in the future under the changing climate.
Keywords wildfires; forest fires; disaster
Copyright Text EFFIS; JRC; Eurostat
Registered first time 14 Feb 2020
Service Description
The burnt areas were obtained as polygons from the European Forest Fire Information System (EFFIS) of the European Commission Joint Research Centre (JRC) (http://effis.jrc.ec.europa.eu). The polygons were dissolved in order to create one layer for all the years; therefore, if an area has been affected by fires on multiple occassions, it will just present as affected by fires.The areas affected by fires were overlaid with the polygons representing the Urban Audit cities' administrative boundaries (2011-2014 dataset from Eurostat: https://ec.europa.eu/eurostat/web/gisco/geodata/reference-data/administrative-units-statistical-units/urban-audit#ua11-14). This allowed calculation of the total percentage of area that was affected by wildfires in the period of 2000-2017 in the city's administrative area.
Description
Percentage of the city area directly affected by wildfires in the period 2000 - 2017.
SRS 102100
Extent -2412697.121283341,3372915.2592421174,3715702.8929676972,10952085.097966254
Layers Percentage of city area affected by wildfires (2000 - 2017)
Map Name Percentage of city area affected by wildfires (2000 - 2017)
Category
Percentage of children under the age of 5 in the city population (2014) (MapServer)
Title Percentage_Children_Under_5
Author
Subject Children tend to be more severely affected by climate-relatred hazards, e.g heatwaves and flooding, than healthy adults. The high proportion of children in the population may increase a vulnerability of the city and should be considered in adaptation planning
Keywords Demography,age,vulnerability,health
Copyright Text Eurostat
Registered first time 14 Feb 2020
Service Description
Tabular data was obtained from Eurostat (Population on 1 January by age groups and sex - cities and greater cities (urb_cpop1; http://appsso.eurostat.ec.europa.eu/nui/show.do?dataset=urb_cpop1&lang=en)) and joined to the Urban Audit 2011-2014 cities' centroids (https://ec.europa.eu/eurostat/web/gisco/geodata/reference-data/administrative-units-statistical-units/urban-audit#ua11-14) .
Description
percentage of children under 5 years old in the city population (2014)
SRS 102100
Extent -2412697.121283252,3372915.259233929,3715702.8929682076,10952085.097847171
Layers Percentage of children under 5 years old in the population (2014)
Map Name Percentage of children under 5 years old in the population (2014)
Category
Percentage of people born in another country in the city (2014) (MapServer)
Title Percentage_Foreign_Born_Population
Author
Subject People born in a different country to the country of residence may be more vulnerable to climate-relaed hazards such as heatwaves and flooding. This is because they may not speak the official language, or their knowledge of this language is not sufficient to understand the warnings and communicate with the emergency services. Also, these people may be less familiar with the area and the specificit
Keywords social vulnerability; country of origin
Copyright Text Eurostat
Registered first time 14 Feb 2020
Service Description
The data on 'EU foreigners' and 'Non-EU foreigners' for 2014 was was downloaded for Urban Audit cities from Eurostat (http://appsso.eurostat.ec.europa.eu/nui/submitViewTableAction.do), added together and joined spatially with the Urban Audit 2011-14 city centroids, also downloaded from Eurostat (https://ec.europa.eu/eurostat/web/gisco/geodata/reference-data/administrative-units-statistical-units/urban-audit#ua11-14)
Description
Percentage of people born in another country in the city's population (2014)
SRS 102100
Extent -6794650.951511465,-2384711.8986121863,6178462.671310332,10952085.097966254
Layers Percentage of people born in another country in the population (2014)
Map Name Percentage of people born in another country in the population (2014)
Category
Percentage of green space in the urban morphological zone (2012) (MapServer)
Title Percentage_Green_Space_in_UMZ
Author
Subject The presence and proportion of green space in densely built-up urban areas is an important aspect of adaptation to climate change. Green space regulates the microclimate of the city, reducing the temperatures. It also reduces the risk of flooding related to heavy precipitation, as vegetation and permeable surfaces retain and infiltrate rainwater, reducing the amount that comes into the drainage sy
Keywords green space; green infrastructure
Copyright Text Eurostat; Copernicus; EEA
Registered first time 14 Feb 2020
Service Description
Data on green space was obtained from Copernicus Urban Atlas 2012 (https://land.copernicus.eu/local/urban-atlas/urban-atlas-2012). The following Urban Atlas classes were treated as green space: 11230 (discontinuous low desnity urban fabric); 11240 (discontinuous very low density urban fabric); 14100 (green urban areas); 14200 (sports and leisure facilities); 20000 (agricultural areas, semi-natural areas and wetlands); and 30000 (forests). Urban Morphological Area (UMZ) is a reference unit for city morphology (as the best approximation of the ‘real’ city form, which often does not correspond to the administrative delineation). UMZ was intersected with the polygon representive the administrative boundary of the city (urban Audity core city - polygon, version 2011-2014: http://ec.europa.eu/eurostat/web/gisco/geodata/reference-data/administrative-units-statistical-units/urban-audit) to identify the UMZ within the core city. The total area of the green space per UMZ within the core city was calculated and divided by the area of the UMZ within the core city.
Description
Percentage of green space in Urban Morphological Zone (densely built-up areas within city's administrative area).
SRS 102100
Extent -2412697.121283252,3372915.259233929,3715702.8929682076,10952085.097847171
Layers Percentage of green space in the urban morphological zone (2012)
Map Name Percentage of green space in the urban morphological zone (2012)
Category
Percentage of impervious area within the Urban Morphological zone of the city (2012) (MapServer)
Title Percentage_Impervious_Area_UMZ_2012
Author
Subject The percentage of sealed area in the city affects the impacts of climate-related hazards. Heavy rainfall may lead to flooding, if the water can not infiltrate into the ground, and if its amount exceeds the capacity of the drainage system. Also, artificial surfaces tend to get hotter than vegetation, contributing to the urban heat island effect. Therefore, it is important to consider the amount of
Keywords soils sealing; impervious area
Copyright Text Eurostat; Copernicus
Registered first time 14 Feb 2020
Service Description
The area of soil sealing is taken from the Copernicus pan-European soil-sealing layer 2015 which containsthe fraction of sealed soil per 1 ha cell(https://land.copernicus.eu/pan-european/high-resolution-layers/imperviousness/status-maps/2015/view). The basic reference unit for the processing is the core city based on UrbanAudit - eurostat (http://ec.europa.eu/eurostat/web/gisco/geodata/reference-data/administrative-units-statistical-units/urban-audit,http://ec.europa.eu/eurostat/statistics-explained/index.php?title=European_cities_-_spatial_dimension&oldid=213219#Core_cities)and the Urban Morphological Area(a reference unit for city morphology (as the best approximation of the ‘real’ city form, which often does not correspond to the administrative delineation).To compute the mean soil-sealing degree of city’s urban area by means of zonal statistics, the soil-sealing mosaic of Europe was overlaid with theextent of UMZ within the core city.
Description
Percentage of impervious areas within Urban Morphological Zone (densely built-up urban area within the city's administrative boundaries)
SRS 102100
Extent -2412697.121283341,3372915.2592421174,3715702.8929676972,10952085.097966254
Layers Percentage of impervious area within Urban Morphological Zone (2015)
Map Name Percentage of impervious area within Urban Morphological Zone (2015)
Category
Percentage of lone-parent households in the city (2014) (MapServer)
Title Percentage_Lone_Parent_Households
Author
Subject Lone-parent households tend to be more vulnerable to climate-related hazards such as flooding or heatwaves. This is due to their usually lower socio-economic status, compared to average population, and to the burden of childcare resting on one person making it ore difficult to e.g. prepare the house for aproaching flood. Considering the type of households in the city, including the proportion of v
Keywords social vulnerability; socio-economic status
Copyright Text Eurostat
Registered first time 14 Feb 2020
Service Description
The data on proportion of households that are loneparenthouseholds [urb_clivcon] for 2014 was downloaded for Urban Audit citiesfrom Eurostat (http://appsso.eurostat.ec.europa.eu/nui/submitViewTableAction.do) and joined spatially with the Urban Audit 2011-14 city centroids, also downloaded from Eurostat (https://ec.europa.eu/eurostat/web/gisco/geodata/reference-data/administrative-units-statistical-units/urban-audit#ua11-14)
Description
SRS 102100
Extent -6794650.951511465,-2384711.8986121863,6178462.671310332,10952085.097966254
Layers Percentage of lone parent households (2014)
Map Name Percentage of lone parent households (2014)
Category
Percentage of lone-pensioner households in the city (2004) (MapServer)
Title Percentage_Lone_Pensioner_Households
Author
Subject Older people living alone tend to be more vulnerable to climate-related hazards such as heatwaves or flooding than the population on average. Considering the type of households in the city, including the proportion of vulnerable households, is an important aspect of planning of adaptation to climate change.
Keywords demography; age; social vulnerability
Copyright Text Eurostat
Registered first time 14 Feb 2020
Service Description
The data on proportion of households that are lone pensioner households [urb_clivcon] for 2014 was downloaded for Urban Audit citie from Eurostat (http://appsso.eurostat.ec.europa.eu/nui/submitViewTableAction.do) and joined spatially with the Urban Audit 2011-14 city centroids, also downloaded from Eurostat (https://ec.europa.eu/eurostat/web/gisco/geodata/reference-data/administrative-units-statistical-units/urban-audit#ua11-14)
Description
Percentage of lone-pensioner households in the city (2014)
SRS 102100
Extent -2412697.121283252,3372915.259233929,3715702.8929682076,10952085.097847171
Layers Percentage of lone-pensioner households (2014)
Map Name Percentage of lone-pensioner households (2014)
Category
Percentage of people aged 75 or older in the city population (2014) (MapServer)
Title Percentage_People_75_Or_Older
Author
Subject Older people tend to be more affected by climate-related hazards, mainly heatwaves but also flooding. The number of older people and their poportionin the population should be considered in planning adaptation to climate change in order to design and implement appropriate actions.
Keywords Demography; age; vulnerability
Copyright Text Eurostat
Registered first time 14 Feb 2020
Service Description
Data on number of people 75 years old or older was obtained from Eurostat (Population on 1 January by age groups and sex - cities and greater cities (urb_cpop1; http://appsso.eurostat.ec.europa.eu/nui/show.do?dataset=urb_cpop1&lang=en)) and joined to the Urban Audit 2011-2014 cities' centroids (https://ec.europa.eu/eurostat/web/gisco/geodata/reference-data/administrative-units-statistical-units/urban-audit#ua11-14) .
Description
Percentage of people 75 years old or older in the city population (2014).
SRS 102100
Extent -2412697.121283252,3372915.259233929,3715702.8929682076,10952085.097847171
Layers Percentage of of people 75 years old or older in the population (2014)
Map Name Percentage of of people 75 years old or older in the population (2014)
Category
Percentage of working-age population in the city with higher education (2014) (MapServer)
Title Percentage_People_Higher_Education
Author
Subject Level of education is one of the indicators of socio-economic status, which in turn translates into vulnebaility to climate-related hazards such as heatwaves and flooding. Those with lower education levels tend to have lower incomes, which affects where and how they live as well as their ability to afford adaptation measures for their households, People with lower levels of education tend to have
Keywords social vulnerability; socio-economic status
Copyright Text Eurostat
Registered first time 14 Feb 2020
Service Description
The data on 'Persons aged 25-64 with ISCED level 5, 6, 7 or 8 as the highest level of education, from 2014 onwards' [urb_ceduc] for 2014 was was downloaded for Urban Audit cities from Eurostat (http://appsso.eurostat.ec.europa.eu/nui/submitViewTableAction.do) and joined spatially with the Urban Audit 2011-14 city centroids, also downloaded from Eurostat (https://ec.europa.eu/eurostat/web/gisco/geodata/reference-data/administrative-units-statistical-units/urban-audit#ua11-14)
Description
Percentage of people with higher education (ISCED level 5) in the working-age population (2014)
SRS 102100
Extent -6794650.951511465,-2384711.8986121863,6178462.671310332,10952085.097966254
Layers Percentage of working-age population with higher education (2014)
Map Name Percentage of working-age population with higher education (2014)
Category
Percentage of city's population affected by wildfires 2000 - 2017 (MapServer)
Title Percentage_Population_Affected_By_Wildfires
Author
Subject The proportion of population directly affected by wildfires in the past can be used as one of the indications, where the danger of wildfires may persist or increase in the future under the changing climate.
Keywords wildfires; forest fires; disaster;
Copyright Text EFFIS; JRS; Eurostat
Registered first time 14 Feb 2020
Service Description
The burnt areas were obtained as polygons from the European Forest Fire Information System (EFFIS) of the European Commission Joint Research Centre (JRC) (http://effis.jrc.ec.europa.eu). The polygons were dissolved in order to create one layer for all the years; therefore, if an area has been affected by fires on multiple occassions, it will just present as affected by fires.The areas affected by fires were overlaid with the polygons representing the Urban Audit cities' administrative boundaries (2011-2014 dataset from Eurostat: https://ec.europa.eu/eurostat/web/gisco/geodata/reference-data/administrative-units-statistical-units/urban-audit#ua11-14) to limit the spatial extent af analysis. Within the cities' administrative boundaries, the burnt areas were overlaid with the Geostat 1km grid population data for 2011 (https://ec.europa.eu/eurostat/web/gisco/geodata/reference-data/population-distribution-demography/geostat). The population within the extent of burnt areas was divided by the total population for the city to calculate the percentage.
Description
Percentage of the city's population affected by wildfires (2000 - 2017)
SRS 102100
Extent -2412697.121283341,3372915.2592421174,3715702.8929676972,10952085.097966254
Layers Percentage of population affected by wildfires (2000 - 2017)
Map Name Percentage of population affected by wildfires (2000 - 2017)
Category
Urban population directly afected by forest fires 2000-2018 (MapServer)
Title population_affected_forest_fires_2000_2018
Author
Subject The map shows the population of cities living within areas burnt by forest fires between 2000 and 2018.
Keywords forest fires; burnt areas; population; wildfires
Copyright Text JRC EFFIS https://effis.jrc.ec.europa.eu/about-effis/technical-background/rapid-damage-assessment/ Eurostat Geostat 2011 (v 2.0.1) https://ec.europa.eu/eurostat/web/gisco/geodata/reference-data/popula
Registered first time 14 Feb 2020
Service Description

The map shows thetotalpopulation of cities living within areas burnt by forest fires between 2000 and 2018.The areas burnt between the beginning of 2000 and the end of 2018 , obtained from JRC's EFFIS, were merged togather and overlaid with the Geostat population grid from 2011.

Description
The map shows thetotalpopulation of cities living within areas burnt by forest fires between 2000 and 2018.The areas burnt between the beginning of 2000 and the end of 2018 , obtained from JRC's EFFIS, were merged togather and overlaid with the Geostat population grid from 2011.
SRS 102100
Extent -6794650.9515,-2384711.898600001,6178462.671300001,10952085.097999997
Layers Total population within burnt areas 2000-2018
Map Name Urban population directly affected by forest fires 2000-2018
Category
Urban population living in potential floodplains (MapServer)
Title population_urban_floodplains
Author
Subject The map shows the proportion of the population living in flood risk areas in European cities.
Keywords flooding; flood risk; urban; city; climate change; adaptation
Copyright Text Population data: Geostat 2011 (v 2.0.1); Copernicus, 2019, ‘Copernicus Riparian Zones’ (https://land.copernicus.eu/local/riparian-zones); Dottori, F., Alfieri, L., Salamon, P., Bianchi, A., Feyen, L.
Registered first time 14 Feb 2020
Service Description
Proportion of the population living in flood risk areas in the European cities included in Urban Audit (city statistics) was calculated based on the 1km grid population data (Geostat 2011) and the potential floodplains. The potential floodplain captures the area that could be flooded during a flood event with a return period of one in 100 years, as well as the river area. It was derived by adding two spatial layers: 1) JRC flood hazard map for Europe 100-year return period (JRC, 2016), based on LISFLOOD model results (Burek et al., 2013) and Copernicus Potential Riparian Zone layer from the dataset: Delineation of Riparian Zone.. The map provides an European overivew of the number of people living in cities whi may be at risk of flooding and emphasises the need to adapt.
Description
Proportion of the population living in flood risk areas in the European cities included in Urban Audit (city statistics) was calculated based on the 1km grid population data (Geostat 2011) and the potential floodplains. The potential floodplain captures the area that could be flooded during a flood event with a return period of one in 100 years, as well as the river area. It was derived by adding two spatial layers: 1) JRC flood hazard map for Europe 100-year return period (JRC, 2016), based on LISFLOOD model results (Burek et al., 2013) and Copernicus Potential Riparian Zone layer from the dataset: Delineation of Riparian Zone.. The map provides an European overivew of the number of people living in cities whi may be at risk of flooding and emphasises the need to adapt.
SRS 102100
Extent -2412697.1213000007,4182973.606200002,3715702.892999999,10952085.097999997
Layers Percentage of city's population living in the floodplain
Map Name Population in urban floodplains
Category
Change in the frequency of flooding events under projected sea level rise (MapServer)
Title Projected_Change_Frequency_Coastal_Flooding
Author
Subject This map shows the estimated multiplication factor, by which the frequency of flooding events of a given height is likely to change due to projected regional sea relative level rise. The probable frequency of flooding from the sea is an important consideration for the See also: https://www.eea.europa.eu/data-and-maps/indicators/sea-level-rise-5/assessment
Keywords coastal flooding; sea level rise; climate change
Copyright Text EEA after Summary of AR5 regional projections and allowances provided by Antarctic Climate & Ecosystems Cooperative Research Centre
Registered first time 14 Feb 2020
Service Description
This map shows the estimated multiplication factor, by which the frequency of flooding events of a given height changes between 2010 and 2100 due to projected regional sea relative level rise under the RCP4.5 scenario. Values larger than 1 indicate an increase in flooding frequency. See also: https://www.eea.europa.eu/data-and-maps/indicators/sea-level-rise-5/assessmen
Description
SRS 102100
Extent -1048262.2490000017,4286870.236000001,3462036.1636999995,11396564.447700001
Layers Multiplication factor
Map Name Change in the frequency of flooding events under projected sea level rise
Category
Projected change in relative sea level (RCP 4.5; 2081-2100 compared to 1986-2005; metres) (MapServer)
Title Projected_change_in_relative_sea_level_in_2081_2100
Author
Subject see https://www.eea.europa.eu/data-and-maps/indicators/sea-level-rise-5/assessment
Keywords coast; coastal flooding; sea level rise
Copyright Text EEA; Integrated Climate Data Centre (ICDC)
Registered first time 14 Feb 2020
Service Description

see https://www.eea.europa.eu/data-and-maps/indicators/sea-level-rise-5/assessment

Description
The map shows the projected change in relative sea level in 2081-2100 compared to 1986-2005 for the medium-low emission scenario RCP4.5 based on an ensemble of CMIP5 climate models. Projections consider land movement due to glacial isostatic adjustment but not land subsidence due to human activities. No projections are available for the Black Sea.
SRS 102100
Extent -20037507.842788246,-30294029.286906082,20190224.281878795,30240971.458386153
Layers Projected change in relative sea level (RCP 4.5; 2081-2100 compared to 1986-2005; metres)
Map Name Projected change in relative sea level (RCP 4.5; 2081-2100 compared to 1986-2005; metres)
Category
Projected trends in drought frequency (RCP 8.5; 2041-2071) (MapServer)
Title ProjectedDroughtFrequency2041_2071
Author
Subject see https://www.eea.europa.eu/data-and-maps/indicators/river-flow-drought-2/assessment
Keywords drought; water scarcity; climate change
Copyright Text EEA; Regional climate model (RCM) simulations (dataset URL is not available) provided by EURO-CORDEX; Future meteorological drought: projections of regional climate models for Europe provided by Unive
Registered first time 14 Feb 2020
Service Description

see https://www.eea.europa.eu/data-and-maps/indicators/river-flow-drought-2/assessment

Description
Projected trends in drought frequency (RCP 8.5; 2041-2017; months/30-year period)
SRS 102100
Extent -3445655.6910805944,2755456.7276874855,6216872.311545409,15308823.026181107
Layers Projected trends in drought frequency (RCP 8.5; 2041-2017; months per 30-year period)
Map Name Projected trends in drought frequency (RCP 8.5; 2041-2017; months per 30-year period)
Category
Projected trends in drought frequency (RCP 8.5; 2071-2100) (MapServer)
Title ProjectedDroughtFrequency2071_2100
Author
Subject see https://www.eea.europa.eu/data-and-maps/indicators/river-flow-drought-2/assessment
Keywords drought; water scarcity; climate change
Copyright Text EEA; Regional climate model (RCM) simulations (dataset URL is not available) provided by EURO-CORDEX; Future meteorological drought: projections of regional climate models for Europe provided by Unive
Registered first time 14 Feb 2020
Service Description

see https://www.eea.europa.eu/data-and-maps/indicators/river-flow-drought-2/assessment

Description
Projected trends in drought frequency (RCP 8.5; 2071-2100; months/30-year period)
SRS 102100
Extent -3445655.6910805944,2755456.7276874855,6216872.311545409,15308823.026181107
Layers Projected trends in drought frequency (RCP 8.5; 2071-2100; months per 30-year period)
Map Name Projected trends in drought frequency (RCP 8.5; 2071-2100; months per 30-year period)
Category
Projected forest fire danger (2071-2100; RCP 4.5) (MapServer)
Title ProjectedForestFireDanger
Author
Subject see https://www.eea.europa.eu/data-and-maps/indicators/forest-fire-danger-2/assessment
Keywords fires; forest fires; wildfires; disaster
Copyright Text EEA; Seasonal severity rating index provided by Joint Research Centre (JRC)
Registered first time 14 Feb 2020
Service Description

see https://www.eea.europa.eu/data-and-maps/indicators/forest-fire-danger-2/assessment

Description
Projected forest fire danger (2071-2100; RCP 4.5; seasonal severity rating)
SRS 102100
Extent -2656821.209899999,3630418.9926000014,6534365.721900001,11466846.306000002
Layers Projected forest fire danger (2071-2100; RCP 4.5; seasonal severity rating)
Map Name Projected forest fire danger (2071-2100; RCP 4.5; seasonal severity rating)
Category
Projected number of extreme heatwaves (2020-2052; RCP 8.5) (MapServer)
Title ProjectedHeatwaves2020_2052
Author
Subject see https://www.eea.europa.eu/data-and-maps/indicators/global-and-european-temperature-8/assessment
Keywords high temperatures; heat; heatwaves; climate change
Copyright Text EEA; Number of heat waves provided by Joint Research Centre (JRC)
Registered first time 14 Feb 2020
Service Description

see https://www.eea.europa.eu/data-and-maps/indicators/global-and-european-temperature-8/assessment

Description
Number of extreme heat waves in future climates
SRS 102100
Extent -1139771.6721,3996575.6262999997,6335209.799800001,12844696.769399999
Layers Projected number of extreme heatwaves (2020-2052; RCP 8.5; number in 33 years)
Map Name Projected number of extreme heatwaves (2020-2052; RCP 8.5; number in 33 years)
Category
Projected number of extreme heatwaves (2068-2100; RCP 8.5) (MapServer)
Title ProjectedHeatwaves2068_2100
Author
Subject see https://www.eea.europa.eu/data-and-maps/indicators/global-and-european-temperature-8/assessment
Keywords heat high temperatures; heatwaves; climate change
Copyright Text EEA; Number of heat waves provided by Joint Research Centre (JRC)
Registered first time 14 Feb 2020
Service Description

see https://www.eea.europa.eu/data-and-maps/indicators/global-and-european-temperature-8/assessment

Description
Projected number of extreme heatwaves (2068-2100; RCP 8.5; number in 33 years)
SRS 102100
Extent -1970325.1690084673,-2314594.1357208714,7040590.446324727,30240971.95838615
Layers Projected number of extreme heatwaves (2068-2100; RCP 8.5; number in 33 years)
Map Name Projected number of extreme heatwaves (2068-2100; RCP 8.5; number in 33 years)
Category
Projected changes in heavy precipitation in winter (from 1971-2000 to 2071-2100; RCP 8.5) (MapServer)
Title ProjectedHeavyPrecipitationWinter_Final
Author
Subject Projected changes in heavy precipitation in winter (December, January, February). see https://www.eea.europa.eu/data-and-maps/indicators/precipitation-extremes-in-europe-3/assessment
Keywords climate change,heavy precipitation
Copyright Text EEA; Datasource: http://www.euro-cordex.net/060378/index.php.en
Registered first time 14 Feb 2020
Service Description

Projected changes in heavy precipitation (in %) in winter and summer from 1971-2000 to 2071–2100 for the RCP8.5 scenario based on the ensemble mean of different regional climate models (RCMs) nested in different general circulation models (GCMs).

see https://www.eea.europa.eu/data-and-maps/indicators/precipitation-extremes-in-europe-3/assessment

Description
Projected change in heavy precipitation in winter (from 1971-2000 to 2071-2100; RCP 8.5; %)
SRS 102100
Extent -3771504.34807611,3074351.686637735,6519755.117852432,11653068.753103828
Layers Projected change in heavy precipitation in winter (from 1971-2000 to 2071-2100; RCP 8.5; %)
Map Name Projected change in heavy precipitation in winter (from 1971-2000 to 2071-2100; RCP 8.5; %)
Category
Changes in the 10-year high river flow for European cities with large river basins (ratio between 2051-2100 and 1951-2000 flows) (MapServer)
Title q10_river_flow_change
Author
Subject Changes in the 10-year high river flow for European cities with large river basins (ratio between 2051-2100 and 1951-2000 flows)
Keywords climate change; flooding; cities
Copyright Text Guerreiro, S. B., et al., 2018, ‘Future heat-waves, droughts and floods in 571 European cities’, Environmental Research Letters 13(3), p. 034009 (DOI: 10.1088/1748-9326/aaaad3).
Registered first time 07 Sep 2020
Service Description

The 10-year high flow corresponds with the 1 in 10-year return periods of annual maximum daily discharge. The changes are calculated as the projected 2051–2100 10-year high flow divided by 1951–2000 10-year high flow. They are shown for low (10th percentile) impact scenario and high (90th percentile) impact scenario. Based on 50 climate model projections from the CMIP5 (Taylor et al., 2012), for the RCP8.5 emissions scenario. The DEM Hydro1K was used to delineate river basins for each city. The 1 in 10 year high flow was estimated using a regression model based on gauge discharge data from the Global Runoff Data Centre (GRDC), and the European daily gridded dataset, E-OBS (Haylock et al., 2008). The cities included in the analysis (365) are those that have an upstream river basin above 500 km2

Description
The 10-year high flow corresponds with the 1 in 10-year return periods of annual maximum daily discharge. The changes are calculated as the projected 2051–2100 10-year high flow divided by 1951–2000 10-year high flow. They are shown for low (10th percentile) impact scenario and high (90th percentile) impact scenario. Based on 50 climate model projections from the CMIP5 (Taylor et al., 2012), for the RCP8.5 emissions scenario. The DEM Hydro1K was used to delineate river basins for each city. The 1 in 10 year high flow was estimated using a regression model based on gauge discharge data from the Global Runoff Data Centre (GRDC), and the European daily gridded dataset, E-OBS (Haylock et al., 2008). The cities included in the analysis (365) are those that have an upstream river basin above 500 km2
SRS 102100
Extent -1008515.8475000001,4187637.7871999964,3713940.0375999995,9627541.565899998
Layers High impact scenario (90th percentile),Medium impact scenario (50th percentile),Low impact scenario (10th percentile)
Map Name Changes in the 10-year high river flow for European cities with large river basins (ratio between 20
Category
Proportion of UMZ at risk of flooding (1961-1990) (MapServer)
Title RiverFloodsBaseline1961_1990
Author
Subject The map shows the proportion of the urban morphological zone (UMZ, i.e. densely built-up urban area) within core city administrative boundaries that is potentially exposed to river flooding at the return period of 1 in 100 years, taking into account baseline conditions (modelled for period 1961 - 1990). The extent of area that may be exposed to river flooding is an important indicator of flood ris
Keywords river flooding; flood; climate change; risk; disaster
Copyright Text Datasets: JRC (Lisflood model); Copernicus (Urban Atlas 2012); Eurostat (city boundaries); EEA (Urban Morphological Zone). Methodology: Rojas, R., Feyen, L., Bianchi, A. and Dosio, A., 2012, ‘Assessme
Registered first time 14 Feb 2020
Service Description

Datasets used in the analysis include: Urban Morphological Zone (UMZ) from Urban Atlas 2012. UMZ is the reference unit for the city morphology. They are regarded as the best approximation of the “real” city formand defined as a set of urban areas laying less than 200 m apart, within the core city administrative boundaries) and LISFLOOD model outputs from JRC. The discharge return levels were derived for every river pixel for return periods of 100 years. For time window of 30 years (1961–1990), a Gumbel distribution was fitted to the annual maximum discharges simulated by LISFLOOD in every grid cell of the modelled domain based on 12 models and the A1B scenario (Rojas et al.,2012; Rojas et al.,2013). The resultant modelled flood area was intersected with the Urban Morphological Zone extent, and the proportion of potentially flooded UMZ area was calculated for each city by dividing the potentially flooded area by the total UMZ area.

Importantly, the indicator is based on elevation and does not include flood protection measures like dams, dikes, etc., as data for these are not yet available. Areas shown here as potentially at risk of flood might in reality be protected by flood defences. However, since flood protection measures can fail in certain circumstances, the flood risk remains.

Description
The map shows the proportion of the city's Urban Morphological Zone (densely built-up urban area) potentially at risk of river flooding (1 in 100 years return period), modelled for the baseline period (1961 - 1990). This is based on the modelling of river discharge within Lisflood model (JRC).
SRS 102100
Extent -2412697.1213999987,3232574.361900009,3715702.892900005,10952085.098199999
Layers Percentage of UMZ potentially exposed to river flooding (1 in 100 years return period; 1961 - 1990)
Map Name Percentage of UMZ potentially exposed to river flooding (1 in 100 years return period; 1961 - 1990)
Category
Proportion of UMZ at risk of flooding (2071 - 2100) (MapServer)
Title RiverFloodsProjected2071_2100
Author
Subject The map shows the proportion of the urban morphological zone (UMZ, i.e. densely built-up urban area) within core city administrative boundaries that is potentially exposed to river flooding at the return period of 1 in 100 years, taking into account climate change (modelled for period 2071 - 2100). The extent of area that may be exposed to river flooding is an important indicator of flood risk and
Keywords river flooding; flood; climate change; risk; disaster
Copyright Text Datasets: JRC (Lisflood model); Copernicus (Urban Atlas 2012); Eurostat (city boundaries); EEA (Urban Morphological Zone).Methodology: Rojas, R., Feyen, L., Bianchi, A. and Dosio, A., 2012, ‘Assessmen
Registered first time 14 Feb 2020
Service Description

Datasets used in the analysis include: 1) Urban Morphological Zone (UMZ) from Urban Atlas 2012. UMZ is the reference unit for the city morphology. They are regarded as the best approximation of the “real” city formand defined as a set of urban areas laying less than 200 m apart, within the core city administrative boundaries). 2) LISFLOOD model outputs from JRC. The discharge return levels were derived for every river pixel for return periods of 100 years. For time window of 30 years (2071–2100), a Gumbel distribution was fitted to the annual maximum discharges simulated by LISFLOOD in every grid cell of the modelled domain based on 12 models and the A1B scenario (Rojas et al.,2012; Rojas et al.,2013). The resultant modelled flood area was intersected with the Urban Morphological Zone extent, and the proportion of potentially flooded UMZ area was calculated for each city by dividing the potentially flooded area by the total UMZ area.

Importantly, the indicator is based on elevation and does not includecurrently existing or plannedflood protection measures like dams, dikes, etc., as data for these are not yet available. Areas shown here as potentially at risk of flood might in reality be protected by flood defences. However, since flood protection measures can fail in certain circumstances, the flood risk remains.

Description
The map shows the proportion of the city's Urban Morphological Zone (densely built-up urban area) potentially at risk of river flooding (1 in 100 years return period), modelled for the future (period of 2071 - 2100). This is based on the modelling of river discharge within Lisflood model (JRC).
SRS 102100
Extent -2412697.1213999987,3232574.361900009,3715702.892900005,10952085.098199999
Layers Percentage of UMZ potentially exposed to river flooding (1 in 100 years return period; 2071 - 2100)
Map Name Percentage of UMZ potentially exposed to river flooding (1 in 100 years return period; 2071 - 2100)
Category
Total area burnt in core cities between 2000 and 2018 (MapServer)
Title total_area_burnt_core_cities_2000_2018
Author
Subject The map shows the total area of core cities within areas burnt by forest fires between 2000 and 2018.The areas burnt between the beginning of 2000 and the end of 2018 , obtained from JRC's EFFIS.
Keywords forest fires; burnt areas; city; urban
Copyright Text JRC EFFIS https://effis.jrc.ec.europa.eu/about-effis/technical-background/rapid-damage-assessment/ Eurostat Urban Audit 2018 spatial units: https://ec.europa.eu/eurostat/web/gisco/geodata/reference-da
Registered first time 14 Feb 2020
Service Description

The map shows the total area of core cities within areas burnt by forest fires between 2000 and 2018.The areas burnt between the beginning of 2000 and the end of 2018 , obtained from JRC's EFFIS.

Description
The map shows thetotal area of core cities within areas burnt by forest fires between 2000 and 2018.The areas burnt between the beginning of 2000 and the end of 2018 , obtained from JRC's EFFIS.
SRS 102100
Extent -6794650.9515,-2384711.898600001,6178462.671300001,10952085.097999997
Layers Total area burnt by forest fires 2000 - 2018 (hectares)
Map Name Total area within core cities affected by forest fires 2000 - 2018
Category
Unemployment rate (% of economically active population) in Urban Audit cities (2014) (MapServer)
Title Unemployment_Rate
Author
Subject Unemployment status and rate are important indicators of socio-economic status, which in turn affects the vulnerability of the individual or population to climate-related hazards such as heatwaves or flooding. People of lower socio-economic stauts are more likely to be living in areas exposed to urban heat island or flooding, occupy poor-quality housing or have poor health. They are also less like
Keywords unemployment; socio-economic status; social vulnreability
Copyright Text Eurostat
Registered first time 14 Feb 2020
Service Description
The tabular data on unemployment rate was downloaded from Eurostat (indicator EC1020I in table "urb_clma"; http://appsso.eurostat.ec.europa.eu/nui/show.do?dataset=urb_clma&lang=en). It was then joined to the Urban Audit 2011-2014 cities' centroids (https://ec.europa.eu/eurostat/web/gisco/geodata/reference-data/administrative-units-statistical-units/urban-audit#ua11-14).
Description
Percentage of people who are unemployed in the city's working age population (2014)
SRS 102100
Extent -6794650.951511465,-2384711.8986121863,6178462.671310332,10952085.097966254
Layers Percentage of unemployed people in working age population (2014)
Map Name Unemployment rate (2014)
Category
Urban_Heat_Island_Intensity (MapServer)
Title Urban_Heat_Island_Intensity
Author
Subject
Keywords
Copyright Text Copernicus health contract for the C3s, VITO, De Ridder et al., 2015
Registered first time 14 Feb 2020
Service Description
Spatial P90 (90th percentile) urban heat island intensity of a given city is used. This indicator is calculated by subtracting the rural (non-water) spatial P10 (10th percentile) temperature value from the average, height-corrected (to exclude terrain effects), air temperature map
Description
The intensity of urban heat island (UHI) modelled for100 European cities by VITO based on UrbClim model (De Ridder, 2015) within Copernicus Health contract for C3S. The P90 indicator is calculated by subtracting the rural (non-water) spatial P10 (10th percentile) temperature value from the average, height-corrected (to exclude terrain effects), air temperature map.
SRS 102100
Extent -2430004.6303999983,4399767.607299998,3108867.6207000017,9380898.5216
Layers UHI intensity (90th percentile)
Map Name Urban Heat Island Intensity
Category
Modelled Urban heat Island for 100 cities (MapServer)
Title Urban_Heat_Island_modelling
Author
Subject The urban heat island modelling for 100 European cities shows the fine-scale (100m) temperature differences across cities, depedning on the land use, soil sealing, anthropogenic heat flux, vegetation index and climatic variables such as wind speed and incoming solar radiation.
Keywords Urban heat island; heat; climate change; adaptation; thermal comfort
Copyright Text Copernicus European Health contract for the Copernicus Climate Change Service (C3S), VITO
Registered first time 14 Feb 2020
Service Description
In the framework of the Copernicus European Health contract for the Copernicus Climate Change Service (C3S) , VITO provided 100m resolution hourly temperature data (2008-2017) for 100 European cities, based on simulations with the urban climate model UrbClim (De Ridder et al., 2015). As the cities vary in size, so do the model domains. They have been defined with the intention to have a more or less constant ratio of urban vs. non-urban pixels (as defined in the CORINE land use map), with a maximum of 400 by 400 pixels (due to computational restraints). The model data are published on the Climate Data Store of C3S and can be downloaded here: https://cds.climate.copernicus.eu/cdsapp#!/dataset/sis-urban-climate-cities?tab=form
Description
The urban heat island modelling for 100 European cities shows the fine-scale (100m) temperature differences across cities, depedning on the land use, soil sealing, anthropogenic heat flux, vegetation index and climatic variables such as wind speed and incoming solar radiation. In the framework of the Copernicus Health contract for the C3S data platform, VITO has provided 100m resolution hourly temperature data (2008-2017) for 100 European cities, based on simulations with the urban climate model UrbClim (De Ridder et al., 2015). As the cities vary in size, so do the model domains. They have been defined with the intention to have a more or less constant ratio of urban vs. non-urban pixels (as defined in the CORINE land use map), with a maximum of 400 by 400 pixels (due to computational restraints).
SRS 102100
Extent -2787968.533105738,4280801.7375052385,4580131.466894262,10255801.737505239
Layers Intensity of urban heat island (°C)
Map Name Modelled Urban Heat Island for 100 cities
Category
Urban Morphological Zones (2012) (MapServer)
Title Urban_Morphological_Zones_2012
Author
Subject The Urban Morphological Zone (UMZ) is a reference unit for city morphology (as the best approximation of the ‘real’ city form, which often does not correspond to the administrative delineation). UMZ more accurately reflects the physical shape of cities compared to the administrative boundaries.
Keywords urban; built-up
Copyright Text Copernicus; Eurostat
Registered first time 14 Feb 2020
Service Description
An UMZ is defined as ‘a set of urban areas laying less than 200 m apart’. Those urban areas are defined with land cover classes contributing to the urban tissue and function. UMZ are derived from the Copernicus Urban Atlas 2012 by using urban core classes (residential, industrial and commercial, green urban areas) and adding enlarged core classes if they fulfil certain neighbourhood conditions of the core classes. The UMZs are clipped by the city administrative boundaries (Urban Audit 2011-2014 polygons https://ec.europa.eu/eurostat/web/gisco/geodata/reference-data/administrative-units-statistical-units/urban-audit#ua11-14) to include only the areas within the city administrative area.
Description
Urban Morphological Zones, i.e. the extent of the densely built-up urban areas withn the city administrative area. Based on Copernicus Urban Atlas 2012.
SRS 102100
Extent -2456807.8905000016,3225413.596099999,3725267.9200999998,11081819.976300001
Layers UMZ_correct
Map Name Urban Morphological Zones (2012)
Category
Water consumption per capita in the city (MapServer)
Title Water_Consumption_Per_Capita
Author
Subject Many locations in Europe are already affected by droughts and water scarcity; for many of those, and other places, these phenomena will intensify and become more problematic in the future. Understanding the water demand in the city is important to comprehend the presence and scale of the water availability problem and adapt to it in the future.
Keywords water use; water scarcity
Copyright Text Eurostat; Tapia et al. (2017)
Registered first time 14 Feb 2020
Service Description
The indicator was obtained from Tapia et al. (2017). Total use of water from Eurostat ([urb_cenv] http://appsso.eurostat.ec.europa.eu/nui/submitViewTableAction.do) was divided by the city's total population number, also from Eurostat ([urb_cpop1]; http://appsso.eurostat.ec.europa.eu/nui/show.do?dataset=urb_cpop1&lang=en) in order to calculate the water consumption per capita. Various years were used in order to ensure the best spatial coverage.
Description
Total annual use of water (m3/capita)
SRS 102100
Extent -2412697.121283341,3372915.2592421174,3715702.8929676972,10952085.097966254
Layers Total annual use of water (m3 per capita)
Map Name Total annual use of water (m3 per capita)
Category
Water exploitation index plus for river basin disrtricts (Q3 2015) (MapServer)
Title WaterExploitationIndexPlus_2015_Q3
Author
Subject The water exploitation index plus (WEI+) aims to illustrate pressure on renewable water resources of a defined territory (river basin, sub-basin etc.) in a given period (e.g. seasonal, annual) as a consequence of water use for human activities. Values above 20 % indicate that water resources are under stress, and above 40 % indicate severe stress and a clearly unsustainable use of freshwater resou
Keywords water scarcity; drought; fresh water resources; water use; climate change
Copyright Text EEA; KNMI (E-OBS dataset); Eurostat; JRC (Lisflood)
Registered first time 14 Feb 2020
Service Description
The WEI+ has been estimated as the quarterly average per river basin district, for the years 1990-2015, as defined in the European catchments and rivers network system (ECRINS). The ECRINS delineation of river basin districts differs slightly from that defined by Member States under the Water Framework Directive. The Ecrins delineation is used instead of WFD because it contains geo-spatial information on Europe’s hydrographical systems with full topological information enabling flow estimation between upstream and downstream basins, as well as integration of economic data collected at NUTS or country level. In addition to using e WISE SoE – Water quantity database, a comprehensive manual data collection was performed by accessing all open sources (Eurostat, OECD, FAO) including national statistical offices of the countries. This was done because of the temporal and spatial gaps in the data on water abstraction. Moreover, a large part of the stream flow data from LISFLOOD has also been substantially updated by the Directorate-General Joint Research Centre. Similarly, a comprehensive update with climatic parameters has been performed by the EEA based on the E-OBS dataset.see https://www.eea.europa.eu/data-and-maps/indicators/use-of-freshwater-resources-2/assessment-3
Description
SRS 102100
Extent -2731050.0034000017,4105014.7386000007,4229008.7162999995,11466052.250799999
Layers Total use of water as % of renewable fresh water resources
Map Name Water exploitation index plus (WEI+) for river basin districts (Jul, Aug, Sep 2015)
Category
Water exploitation index plus for river basin disrtricts (Q3 2015) (MapServer)
Title WEI_Plus_2015_Q3
Author
Subject The water exploitation index plus (WEI+) aims to illustrate pressure on renewable water resources of a defined territory (river basin, sub-basin etc.) in a given period (e.g. seasonal, annual) as a consequence of water use for human activities. Values above 20 % indicate that water resources are under stress, and above 40 % indicate severe stress and a clearly unsustainable use of freshwater resou
Keywords water scarcity; drought; fresh water resources; water use; climate change
Copyright Text EEA; KNMi (E-OBS dataset); Eurostat; JRC (Lisflood)
Registered first time 14 Feb 2020
Service Description

The WEI+ has been estimated as the quarterly average per river basin district, for the years 1990-2015, as defined in the European catchments and rivers network system (ECRINS). The ECRINS delineation of river basin districts differs slightly from that defined by Member States under the Water Framework Directive. The Ecrins delineation is used instead of WFD because it contains geo-spatial information on Europe’s hydrographical systems with full topological information enabling flow estimation between upstream and downstream basins, as well as integration of economic data collected at NUTS or country level. In addition to using e WISE SoE – Water quantity database, a comprehensive manual data collection was performed by accessing all open sources (Eurostat, OECD, FAO) including national statistical offices of the countries. This was done because of the temporal and spatial gaps in the data on water abstraction. Moreover, a large part of the stream flow data from LISFLOOD has also been substantially updated by the Directorate-General Joint Research Centre. Similarly, a comprehensive update with climatic parameters has been performed by the EEA based on the E-OBS dataset.

see https://www.eea.europa.eu/data-and-maps/indicators/use-of-freshwater-resources-2/assessment-3

Description
SRS 102100
Extent -2731050.0034000017,4105014.7386000007,4229008.7162999995,11466052.250799999
Layers Total use of water as % of renewable fresh water resources
Map Name Water exploitation index plus (WEI+) for river basin districts (Jul, Aug, Sep 2015)
Category