DTM derived from a hydrographic survey (EMEPC LH1) performed within the scope of the Portuguese Continental Shelf Extension Project (UNCLOS). The data made available to the Emodnet project was sampled from the survey data with an aproximate 250m sampling interval, in N-S and E-W directions.

Satellite-derived intertidal bathymetry for the Tagus estuary near Lisbon Portugal

Satellite-derived intertidal bathymetry for the Danish Wadden on the West coast of Denmark

The Catchment Characterisation Model (CCM2) database covers the entire European continent, including the Atlantic islands, Iceland and Turkey. It includes a hierarchical set of river segments and catchments based on the Strahler order, a lake layer and structured hydrological feature codes based on the Pfafstetter system. It allows for analysis from the regional to the continental scale, corresponding to traditional mapping scales of up to 1:500,000. CCM2 covers an area of about 12,000,000 square kilometers and includes more than 2,000,000 primary catchments. These can be aggregated to drainage basins at different hierarchical levels, forming, for example, about 650 river basins of more than 1000 square kilometers. CCM2 further includes a coastline, fully congruent with the river basins, and some 70,000 lakes. The layers are generated from a 100 meters resolution digital terrestrial elevation model. The following layers are available: Seaoutlets: the major river basins, Main drains: the major rivers, Lakes: all surface water larger than 25x25 metres, Coastlines: coast line extracted from Image2000 imagery, River segments: Drainage channels from the primary catchments, Catchments: Primary catchments. This data-set refers to Lakes and River basins.

Annual time series of Water Discharge (Qw) [m3/s] from in situ and model data

The DTM is a compilation of multibeam echosounder surveys at Central Aegean Sea - around Santorini Island (Greece), acquired with R/V Aegaeo using Seabeam 2120 (20 kHz) echosounder during 2001, 2006, 2016 and 2017 cruises. Grid resolution is 1/32 arc-minutes processed for the purpose of the HR DTMs layer of EMODnet Bathymetry HRSM, October 2018.

This dataset was collected during a survey within GEOSED Project by means of the MBES system Reson SeaBat8111 in the area named Punta Licosa in the Tyrrhenian Sea, Central Mediterranean. The dataset was acquired on the continental shelf in the depth range 10-150 meters.The Multibeam has 101 beams 1°x1° and an operational frequency of 100 kHz. A LandStar 12 channel DGPS system was used for ship positioning and a motion sensor TSS-DMS02-05 was used to provide corrections for attitude data. A Sound Velocity Sensor was installed near the transducers thus providing realtime sound speed for beam steering. A sound velocity profiler Reson SVP20 was lowered through the water column every 6–8 h to get the velocity profile required for the depth computation. The bathymetric data were processed using the PDS2000 software. Data de-spiking was carried out without the application of automatic filters in order to preserve data accuracy and resolution

Annual time series of Total Nitrogen/Nitrates [mg/l] from in situ and model data

Monthly time series of Total Suspended Matter (TSM) from satellite data [mg/l]

The DTM was generated from two dataset acquired around Ischia and Procida Islands (Gulf of Naples, Italy). The dataset were collected within CARG (Geological CARtography) Project by means of the MBES system Reson SeaBat8125. The MBES was pole-mounted on a 6 meters long vessel. The MBES has 240 beams 1°x1°; the operational frequency was 455 kHz. A LandStar 12 channel DGPS system was used for ship positioning and a motion sensor Ixea Octans3000 was used to provide corrections for attitude data. A Sound Velocity Sensor was installed near the transducers thus providing realtime sound speed for beam steering. A sound velocity profiler Reson SVP15 was lowered through the water column every 6–8 h to get the velocity profile required for the depth computation. The bathymetric data were processed using the PDS2000 software. Data de-spiking was carried out without the application of automatic filters in order to preserve data accuracy and resolution.