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The European Copernicus Coastal Flood Awareness System (ECFAS) project will contribute to the evolution of the Copernicus Emergency Monitoring Service by demonstrating the technical and operational feasibility of a European Coastal Flood Awareness System. Specifically, ECFAS will provide a much-needed solution to bolster coastal resilience to climate risk and reduce population and infrastructure exposure by monitoring and supporting disaster preparedness, two factors that are fundamental to damage prevention and recovery if a storm hits. The ECFAS Proof-of-Concept development will run from January 2021-December 2022. The ECFAS project is a collaboration between Istituto Universitario di Studi Superiori IUSS di Pavia (Italy, ECFAS Coordinator), Mercator Ocean International (France), Planetek Hellas (Greece), Collecte Localisation Satellites (France), Consorzio Futuro in Ricerca (Italy), Universitat Politecnica de Valencia (Spain), University of the Aegean (Greece), and EurOcean (Portugal), and is funded by the European Commission H2020 Framework Programme within the call LC-SPACE-18-EO-2020 - Copernicus evolution: research activities in support of the evolution of the Copernicus services. This project has received funding from the European Union’s Horizon 2020 programme The deliverables will have restrictedaccess at least until the end of ECFAS Description of the containing files. This guidelines of D3.1 accompanies the ECFAS coastal database (D3.1 - Coastal dataset including exposure and vulnerability layers) and provides information on the contents and structure of the coastal database. It also includes the description of the approach used for the preparation of the datasets, as well as an overview of the coastal datasets and their sources. The detailed characteristics, sources and URL links for each coastal dataset are given in the database. Disclaimer: ECFAS partners provide the data "as is" and "as available" without warranty of any kind. The ECFAS partners shall not be held liable resulting from the use of the information and data provided. This project has received funding from the Horizon 2020 research and innovation programme under grant agreement No. 101004211 |
