The dataset represents the introduction of non-indigenous species in European seas. Non-indigenous species are species that have been spread as a result of human activities to areas where they do not belong naturally. The main concern are the invasive species, which are defined as causing a significant negative impact on biodiversity as well as serious economic and social consequences. The dataset has been prepared first by individually mapping each aquatic invasive species that had a distinctive distribution area, which had been provided by several non-indigenous species online databases. The distribution of the species were then resampled into the EEA 10 km grid and summed together, showing the number of non-indigenous species per grid cell. The temporal reference of the dataset covers the last 30 years (1989 - 2018). This dataset has been prepared for the calculation of the combined effect index, produced for the ETC/ICM Report 4/2019 "Multiple pressures and their combined effects in Europe's seas" available on: https://www.eionet.europa.eu/etcs/etc-icm/etc-icm-report-4-2019-multiple-pressures-and-their-combined-effects-in-europes-seas-1.

Global annual river discharge for a 0.5 degree resolution digital river network (blended, km3/yr per grid cell). Blended river flow represents a composite of observed and modeled river flow. Annual river discharge - computed as flow accumulated long term average runoff along a 30-minute resolution digital river network and blended with observed discharge data where available (km3/yr).

The GEBCO_2022 Grid is a global continuous terrain model for ocean and land with a spatial resolution of 15 arc seconds. In regions outside of the Arctic Ocean area, the grid uses as a base Version 2.4 of the SRTM15_plus data set (Tozer, B. et al, 2019). This data set is a fusion of land topography with measured and estimated seafloor topography. Included on top of this base grid are gridded bathymetric data sets developed by the four Regional Centers of The Nippon Foundation-GEBCO Seabed 2030 Project. The GEBCO_2022 Grid represents all data within the 2022 compilation. The compilation of the GEBCO_2022 Grid was carried out at the Seabed 2030 Global Center, hosted at the National Oceanography Centre, UK, with the aim of producing a seamless global terrain model. Outside of Polar regions, the Regional Centers provide their data sets as sparse grids i.e. only grid cells that contain data are populated. These data sets were included on to the base using a remove-restore blending procedure. This is a two-stage process of computing the difference between the new data and the base grid and then gridding the difference and adding the difference back to the existing base grid. The aim is to achieve a smooth transition between the new and base data sets with the minimum of perturbation of the existing base data set. The data sets supplied in the form of complete grids (primarily areas north of 60N and south of 50S) were included using feather blending techniques from GlobalMapper software. The GEBCO_2022 Grid has been developed through the Nippon Foundation-GEBCO Seabed 2030 Project. This is a collaborative project between the Nippon Foundation of Japan and the General Bathymetric Chart of the Oceans (GEBCO). It aims to bring together all available bathymetric data to produce the definitive map of the world ocean floor by 2030 and make it available to all. Funded by the Nippon Foundation, the four Seabed 2030 Regional Centers include the Southern Ocean - hosted at the Alfred Wegener Institute, Germany; South and West Pacific Ocean - hosted at the National Institute of Water and Atmospheric Research, New Zealand; Atlantic and Indian Oceans - hosted at the Lamont-Doherty Earth Observatory, Columbia University, USA; Arctic and North Pacific Oceans - hosted at Stockholm University, Sweden and the Center for Coastal and Ocean Mapping at the University of New Hampshire, USA.

This product is a map of the uncertainty of available digital bathymetry measurements for the North Atlantic Ocean. This is done for a spatial resolution feasible for this large area (25km x 25km). It is designed to assess the quality of the bathymetry readings with a view to supporting assessments of future need. The product is formulated through a number of characteristics of the data including age of measurement and slope.

The raster dataset represents bycatch fishing intensity (kilowatt per fishing hour) from bottom touching mobile gears in the European seas. The dataset has been derived from Automatic Identification System (AIS) based demersal fishing intensity data received from the European Commission’s Joint Research Centre - Independent experts of the Scientific, Technical and Economic Committee for Fisheries (JRC STECF) as well as Vessel Monitoring System (VMS) and logbook based demersal fishing intensity data downloaded from from OSPAR and HELCOM Commissions. The temporal extent varies between the data sources (between 2014 and 2017). OSPAR and HELCOM data superseded the JRC STECF data where they overlapped spatially. The cell values have been transformed into a logarithmic scale (ln1). This dataset has been prepared for the calculation of the combined effect index, produced for the ETC/ICM Report 4/2019 "Multiple pressures and their combined effects in Europe's seas" available on: https://www.eionet.europa.eu/etcs/etc-icm/etc-icm-report-4-2019-multiple-pressures-and-their-combined-effects-in-europes-seas-1.

This raster dataset presents the number of different hydrographical pressures per grid cell along the European coastlines. Hydrographical pressures are human activities that cause changes in hydrological conditions, i.e. changes to freshwater input, salinity, seawater flows, waves, currents, and temperature. Examples of such activities include riverine or coastal dams, offshore infrastructure, and outflows from power plants. The layer has been created using the Water Framework Directive (WFD) reported data on hydrographical pressures joined with the water body polygon features for the reference year 2016. The dataset was then rasterized into the EEA 10 km grid, and the cell values assigned with the number of different hydrographical pressures in the area covered by the cell. This dataset has been prepared for the calculation of the combined effect index, produced for the ETC/ICM Report 4/2019 "Multiple pressures and their combined effects in Europe's seas" available on: https://www.eionet.europa.eu/etcs/etc-icm/etc-icm-report-4-2019-multiple-pressures-and-their-combined-effects-in-europes-seas-1.

This raster dataset represents physical disturbance to the seabed in the European seas. Several human activities disturb the seabed either directly or indirectly. Alteration of benthic living conditions as a result of increased sedimentation or attenuation of light penetration, abrasion of the seabed and exploitation of benthic biota, temporarily disturb the benthic habitat quality. The dataset is an aggregation of several different human activities that cause physical disturbance to the seabed: aquaculture, demersal fishing, dredging and dumping of dredged material, oil and gas rigs, offshore installations, ports, sand and gravel extraction, shellfish mariculture, shipping in shallow waters and windfarms. The resulting dataset is a raster (10km grid cell) derived from EMODnet, MED-IAMER, JRC-STECF, OSPAR, HELCOM and 4C Offshore datasets, and with reference temporal coverage from 2012 to 2017. This dataset has been prepared for the calculation of the combined effect index, produced for the ETC/ICM Report 4/2019 "Multiple pressures and their combined effects in Europe's seas" available on: https://www.eionet.europa.eu/etcs/etc-icm/etc-icm-report-4-2019-multiple-pressures-and-their-combined-effects-in-europes-seas-1.

This product is a map of the uncertainty of available digital bathymetry measurements for the North Atlantic Ocean. This is done for a spatial resolution feasible for this large area (25km x 25km). It is designed to assess the quality of the bathymetry readings with a view to supporting assessments of future need. The product is formulated through a number of characteristics of the data including age of measurement and slope.