Temporal series (annual mean values) of temperature for each river mouth.Temporal series (annual mean values) of temperature for each river mouth.

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

Ce jeu de données recense les communes du département de la Gironde suivies pour le Moustique Tigre.

Combined product of Water body dissolved oxygen concentration based on DIVA 4D 10-year analysis on five regions : Northeast Atlantic Ocean, North Sea, Baltic Sea, Black Sea, Mediterranean Sea. The boundaries and overlapping zones between these five regions were filtered to avoid any unrealistic spatial discontinuities. This combined water body dissolved oxygen concentration product is masked using the relative error threshold 0.5. Units: umol/l

The SeaDataNet aggregated datasets over the Atlantic Ocean are regional ODV historical collections of all temperature and salinity measurements contained within SeaDataNet database and covering 3 European sea basins: North Arctic Ocean, North Sea, North Atlantic Ocean. Two versions have been published during SeaDataNet 2 and they represent a snapshot of the SeaDataNet database content at two different times: • V1.1 January 2014 • V2 March 2015 Each of them is the result of the Quality Check Strategy (QCS) implemented during SeaDataNet 2 that contributed to highly improve the quality of temperature and salinity data. The QCS is made by four main phases: 1. data harvesting from the central CDI 2. file and parameter aggregation 3. quality check analysis at regional level 4. analysis and correction of data anomalies. The aggregated datasets have been prepared and quality checked using ODV software.

Data from a number of different sources have been integrated to provide new perspectives on fishing activities. Vessel Monitoring Systems (VMS) record and transmit the position and speed of fishing vessels at intervals of two hours or less. Fishing time can be calculated from the VMS data and combining this parameter with vessel logbook data, maps of fishing effort and intensity at different spatial and temporal scales can be calculated. The statistical software package “R” is used to extract the required information then re-interrogated to produce maps of fishing effort or intensity per month and year. The use of Automatic Identification System (AIS) data was not considered as combining AIS data with fisheries logbook data would pose issues namely; the ability of the AIS system to be switched off, only mandatory on vessels > 15 meters in length, cost involved to purchase data, and confidentiality.

Temporal series (annual mean values) and long term average (LTA) of temperature for each river mouth.

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 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)°

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