The objective of the project is to develop and validate a new DSS for the risk assessment and management for the prevention and/or reduction of the negative impacts caused by global change and human activities on the water/sediment/soil system at river bas in scale in fluvial ecosystems. The DSS will combine and integrate environmental and geo-physical data from earth observation systems, in-situ sensors and geo-referenced information, advanced computer simulation and graphical visualisation methods and artificial intelligence tools for generating knowledge contributing to the assessment of the ecological impact and the design of effective response actions maximising the integrity and safety of the ecosystem and human life.
The RAMWASS DSS will be the result of the development, integration and validation of the essential technologies provided by the project partners: - Technology for the transfer of high resolution data emanating from earth observation systems and in-situ sensors into classified and usable in formation to be ingested as input data for the WASS simulation system (CIMNE) - Advanced computational methods for the fast and accurate simulation of different WASS situations and for evaluating the effect of alternative response scenarios (UPC, CIMNE, CI SM, U.Hannover, U.LAneburg) - Innovative ICT tools for the 3D visualisation of the environment hazard simulations (CIMNE) - An artificial neural network (ANN) based decision model educated using innovative Monte Carlo simulation tools developed by CIMNE A crucial activity of the project will be the in-depth calibration, validation and assessment of the performance, scalability and effectiveness of the DSS in its application to at three relevant aquatic and wetland ecosystems adjacent to river basins in Europe: 1) The marsh area of the Doaana Park in Spain; 2) the biosphere reserve Elbe Riverland in the Elbe river valley in Germany and 3) the marshland and lagoons of the Po river delta in Italy.