Temporal series (annual mean values) and Long Term Average (LTA) of sediment load for each river mouth where in situ data is available. Different sources can be mixed if any.

The EEA coastline dataset is created for detailed analysis with a Minimum Mapping Unit of e.g. 1:100000, for geographical Europe. The coastline is a hybrid product obtained from satellite imagery from two projects: 1) EUHYDRO (Pan-European hydrographic and drainage database) [https://land.copernicus.eu/pan-european/satellite-derived-products/eu-hydro/view] and 2) GSHHG (A Global Self-consistent, Hierarchical, High-resolution Geography Database) [http://www.soest.hawaii.edu/pwessel/gshhg/]. The defining criteria was altitude level = 0 from EUDEM [https://land.copernicus.eu/pan-european/satellite-derived-products/eu-dem/view]. Outside the coverage of the EUDEM, the coastline from GSHHG was used without modifications. A few manual amendments to the dataset were necessary to meet requirements from EU Nature Directives, Water Framework Directive and Marine Strategy Framework Directive. In 2015, several corrections were made in the Kalogeroi Islands (coordinates 38.169, 25.287) and two other Greek little islets (coordinates 36.766264, 23.604318), as well as in the peninsula of Porkkala (around coordinates 59.99, 24.42). In this revision (v3, 2017), 2 big lagoons have been removed from Baltic region, because, according to HELCOM, are freshwater lagoons. This dataset is a polygon usable as a water-land mask.

Temporal series (annual mean values) with error of estimation and Long Term Average (LTA) with error of estimation of total phosphate load for each river mouth where in situ data is available. Different sources can be mixed if any.

The Global Sea Level Observing System (GLOSS) was established by the Intergovernmental Oceanographic Commission (IOC) of UNESCO in 1985 to establish a well-designed, high-quality in situ sea level observing network to support a broad research and operational user base. Various tide gauge networks have contributed to GLOSS, each with a different focus and each changing over time as research priorities evolve. The main component is the GLOSS Core Network (GCN), a global set of ~300 tide gauges that serves as the backbone of the global in situ sea level network. GCN gauges were allocated to each island or group of islands at intervals not closer than 500 km, and along continental coasts at intervals generally not less than 1000 km. Preference was given to islands in order to maximise exposure to the open ocean. Established in 1933, the Permanent Service for Mean Sea Level (PSMSL) is responsible for the collection, publication, analysis and interpretation of sea level data from the global network of tide gauges, including the GLOSS Core Network.

Temporal series (annual mean values) with error of estimation and Long Term Average (LTA) with error of estimation of total phosphate load for each river mouth where in situ data is available. Different sources can be mixed if any.

Maps of seasonal p90 (percentile 90) of Chla on the North Atlantic basin for the past ten years (2005-2014) using the Global Copernicus chla level 4 (L4) products (resolution of 4 km). Method as Gohin Francis, Saulquin Bertrand, Bryere Philippe (2010) Atlas de la Température, de la concentration en Chlorophylle et de la Turbidité de surface du plateau continental français et de ses abords de l’Ouest européen. Ifremer. http://archimer.ifremer.fr/doc/00057/16840/

We took inspiration from a “Matrix of marine activities” (appropriate for each IUCN management category) extracted from IUCN paper, to achieve the first objective by computing 1 product comprising the following 12 components: Product ATLANTIC_CH02_Product_1 / MPA Atlantic network classified in IUCN classification • Traditional fishing area • Sustainable fishing area (industrial) • Leisure fishing area • Leisure activity area (diving, surfing, tourist beaches) • Shipping area (shipping trajectory, aids navigation) • Scientific activity area • Renewable energy generation facility area (ocean energy facilities, wind farms) • Aquaculture area (finfish production, shellfish production) • Shipping infrastructure area (harbours, dredging area...) • Waste discharge area • Mining area (aggregate extraction, hydrocarbon extraction) • Habitation area (urban area) Each geographic information required for the components was compiled into a layer in grid format. These grids were intersected with the MPAs layer to assign each MPA a IUCN category according to the conditional matrix illustrated below : If the MPA area contains : Habitation area (urban area) The IUCN category is :V If the MPA area contains : Mining area (aggregate extraction, hydrocarbon extraction) The IUCN category is V If the MPA area contains : Waste discharge area The IUCN category is : V If the MPA area contains : Shipping infrastructure area (harbours, dredging area...) The IUCN category is IV If the MPA area contains : Aquaculture area (finfish production, shellfish production) The IUCN category is IV If the MPA area contains : Renewable energy generation facility area (ocean energy facilities, wind farms) The IUCN category is IV If the MPA area contains : Leisure fishing area The IUCN category is IV If the MPA area contains : Sustainable fishing area (industrial) The IUCN category is IV If the MPA area contains : Shipping area (shipping trajectory, aids navigation) The IUCN category is II If the MPA area contains : Leisure activity area (diving, surfing, tourist beaches) The IUCN category is Ib If the MPA area contains : Traditional fishing area The IUCN category is Ib If the MPA area contains : Scientific activity area The IUCN category is Ia

The International Council for the Exploration of the Sea (ICES), is a global organization that develops science and advice to support the sustainable use of the oceans. ICES is a network of more than 5,000 scientists from over 690 marine institutes in 20 member countries and beyond. 1,500 scientists participate in our activities annually. ICES has a well-established Data Centre, which manages a number of large dataset collections related to the marine environment. The majority of data – covering the Northeast Atlantic, Baltic Sea, Greenland Sea, and Norwegian Sea – originate from national institutes that are part of the ICES network. The ICES Data Centre provides marine data services to ICES member countries, expert groups, world data centres, regional seas conventions (HELCOM and OSPAR), the European Environment Agency (EEA), Eurostat, and various other European projects and biodiversity portals. ICES aims to provide all data collections online and according to the ICES Data policy, which enables open access to all data that are do not fall under specific commercial or personal privacy concerns.

The in-situ TAC integrates and quality control in a homogeneous manner in situ data from outside Copernicus Marine Environment Monitoring Service (CMEMS) data providers to fit the needs of internal and external users. It provides access to integrated datasets of core parameters for initialization, forcing, assimilation and validation of ocean numerical models which are used for forecasting, analysis and re-analysis of ocean physical and biogeochemical conditions. The in-situ TAC comprises a global in-situ centre and 6 regional in-situ centres (one for each EuroGOOS ROOSs). The focus of the CMEMS in-situ TAC is on parameters that are presently necessary for Copernicus Monitoring and Forecasting Centres namely temperature, salinity, sea level, current, waves, chlorophyll / fluorescence, oxygen and nutrients. The initial focus has been on observations from autonomous observatories at sea (e.g. floats, buoys, gliders, ferrybox, drifters, and ships of opportunity). The second objective was to integrate products over the past 25 to 50 years for re-analysis purposes... Gathering data from outsider organisations requires strong mutual agreements. Integrating data into ONE data base requires strong format standard definition and quality control procedures. The complexity of handling in situ observation depends not only on the wide range of sensors that have been used to acquire them but, in addition to that, the different operational behaviour of the platforms (i.e vessels allow on board human supervision, while the supervision of others should be put off until recovering or message/ping reception)°

Potentialités agronomiques par culture (blé, haricot, maïs, orge, tabac et tournesol).