Do something for our planet, print this page only if needed. Even a small action can make an enormous difference when millions of people do it!
The Water Accounts Spatial Units dataset is an extraction of the European catchments and Rivers network system (ECRINS), aggregation catchments and reference layers - version 1, Jun. 2012. It contains 117 river basins extracted from the ECRINS functional river basin districts (EcrAgg). The Ecrins river basin districts are delineated according to the hydrological thresholds and do not necessarily follow administrative boundaries. The main purpose of the data set is to display the EEA water accounts outputs at the river basin level.
The WISE Water Accounts database contains monthly water accounts for the years 1990-2015 for 117 European river basins extracted from the ECRINS functional river basin districts. The water accounts data can be downloaded in two different formats: a spreadsheet that contains each accounting variable in a separate worksheet, and a database that contains all the variables of the asset and flow accounts to facilitate an integrated analysis. The WISE Water Accounts Spatial Units can also be downloaded from the EEA web site. Extensive clarifications on the development of the European water accounts can be found in the following reports published by the European Commission (DG ENV): "Guidance document on the application of water balances for supporting the implementation of the Water Framework Directive" (http://ec.europa.eu/environment/water/blueprint/balances.htm) and "Water ecosystem accounts reports" (http://ec.europa.eu/environment/water/blueprint/balances.htm)
The raster files are the annual above ground growing season length time-series and the derived linear trends for the period 2000-2016. The data set addresses trends in the season length of land surface vegetation derived from remote sensing observed time series of vegetation indices. The vegetation index used in the indicator is the Plant Phenology Index (PPI, Jin and Eklundh, 2014). PPI is based on the MODIS Nadir BRDF-Adjusted Reflectance product (MODIS MCD43 NBAR. The product provides reflectance data for the MODIS “land” bands (1 - 7) adjusted using a bi-directional reflectance distribution function. This function models values as if they were collected from a nadir-view to remove so called cross-track illumination effects. The Plant Phenology Index (PPI) is a new vegetation index optimized for efficient monitoring of vegetation phenology. It is derived from radiative transfer solution using reflectance in visible-red (RED) and near-infrared (NIR) spectral domains. PPI is defined to have a linear relationship to the canopy green leaf area index (LAI) and its temporal pattern is strongly similar to the temporal pattern of gross primary productivity (GPP) estimated by flux towers at ground reference stations. PPI is less affected by presence of snow compared to commonly used vegetation indices such as Normalized Difference Vegetation Index (NDVI) or Enhanced Vegetation Index (EVI). The product is distributed with 500 m pixel size (MODIS Sinusoidal Grid) with 8-days compositing period.
This maps show the linear trend in the annual maximum of daily river discharge over the period 1960-2010. Blue indicates increasing flood discharges and red denotes decreasing flood discharges (in per cent change of the mean annual flood discharge per decade).
These maps show the relative change in maximum 100-year daily river discharge for two scenarios of global warming(1.5 °C and 3 °C)
The datasets below correspond to a new version of the Effective Mesh Density (seff) 2016 dataset with improved input data, for the years 2009, 2012 and 2015. This time-series uses the Copernicus Imperviousness and the TomTom TeleAtlas datasets as fragmenting geometries. The Effective Mesh Density (seff) is a measure of the degree to which movement between different parts of the landscape is interrupted by a Fragmentation Geometry (FG). FGs are defined as the presence of impervious surfaces and traffic infrastructure, including medium sized roads. The more FGs fragment the landscape, the higher the effective mesh density hence the higher the fragmentation. An important consequence of landscape fragmentation is the increased isolation of ecosystem patches that breaks the structural connections and decreases resilience and ability of habitats to provide various ecosystem services. Fragmentation also influences human communities, agriculture, recreation and overall quality of life. Monitoring how fragmentation decreases landscape quality and changes the visual perception of landscapes provides information for policy measures that aim at improving ecosystem condition and restoration as well as maintaining the attractiveness of landscapes for recreational activities. The geographic coverage of the datasets is EEA39.
The figure includes all EEA member countries for which data are available on stringency of environmental policy. Countries are positioned according to their ranking in the WEF global competiveness index 2015 (x-axis) and the OECD stringency of environmental policy index (y-axis). The position of countries in the EU eco-innovation ranking are indicated using colours.
This figure presents the fuel efficiency and fuel consumption trends for private cars in the EU-28 in the period 1990 to 2015. The variables included are number of cars, average CO2 emissions of cars, average fuel consumption of cars, GDP, total distance travelled by cars and total energy consumptions of cars.
For visualisation purposes, the initial 100 m spatial resolution Corine Land Cover dataset was re-sampled to a 10 km2 grid. The observation periods can be visualised by activating the 'layers' icon and selecting the respective periods.
In 2015, on average, there were around 1.5 fragmented landscape elements per km 2 in the European Union [1] , a 3.7 % increase compared with 2009. Approximately 1.13 million km 2 , around 28 % of the area of the EU [1] , was strongly fragmented i n 2015 , a 0.7 % increase compared with 2009. There was less of an increase in fragmented landscape elements and in the area of strongly fragmented landscape between 2012 and 2015 than between 2009 and 2012 (1.4 and 0.18 percentage points, respectively). Arable lands and permanent croplands (around 42 .6 %) and pastures and farmland mosaics (around 40.2 %) were most affected by strong fragmentation pressure in 2015 in the EU. Between 2009 and 2015, however, the largest increase in the area of strongly fragmented landscape was in grasslands/pastures and in farmland mosaics. Luxembourg (91 %), Belgium (83 %) and Malta (70 %) had the largest proportions of strongly fragmented landscape in 2015 (as a proportion of their country area). The Baltic countries and Finland and Sweden were on average the least fragmented countries in the EU. Between 2009 and 2015, the area of strongly fragmented landscape increased most in Croatia, as well as in Greece, Hungary and Poland. [1] Romania is excluded because of the poor coverage of fragmentation geometry data in 2009.
This web map application uses the new version of the Effective Mesh Density (seff) 2016 dataset with improved input data, for the years 2009, 2012 and 2015. This new dataset uses the Copernicus Imperviousness and the TomTom TeleAtlas data sets as fragmenting geometries. The application shows the change in effective mesh density (seff), i.e. the number of landscape elements between 2009 and 2012 and between 2012 and 2015.
For references, please go to https://www.eea.europa.eu/data-and-maps/find/global or scan the QR code.
PDF generated on 25 Apr 2025, 07:36 AM
Engineered by: EEA Web Team
Software updated on 26 September 2023 08:13 from version 23.8.18
Software version: EEA Plone KGS 23.9.14