Idier, D.; Aurouet, A.; Bachoc, F.; Baills, A.; Betancourt, J.; Durand, J.; Mouche, R.; Rohmer, J.; Gamboa, F.; Klein T.; Lambert, J.; Le Cozannet, G.; Le Roy, S.; Louisor, J.; Pedreros, R., and Véron, A.L., 2020. Toward a user-based, robust and fast running method for coastal flooding forecast, early warning, and risk prevention. In: Malvárez, G. and Navas, F. (eds.), Global Coastal Issues of 2020. Journal of Coastal Research, Special Issue No. 95, pp. 1111–1116. Coconut Creek (Florida), ISSN 0749-0208.
Scientific progresses now allow properly modelling coastal flooding events. Such models are nevertheless very expensive in terms of computation time (>hours) which prevents any use for forecast and warning or even for estimating the coastal flood hazard return period together with uncertainties. In addition, there is a gap between model outputs and information actually needed by the decision makers. Within the RISCOPE project, we aim at developing a user-based method contributing to forecast, early-warning and prevention of coastal flooding risks. The method should be robust, fast and integrate the complexity of coastal flood processes. To build such coastal flooding models, the solution explored relies on meta-models, i.e. mathematical functions which estimate, with good precision and at a negligible computational cost (<minutes), the results obtained with the numerical model. The overall method is presented, as well as key results, meta-model skills to reproduce the complexity of the coastal flooding processes and products delivered by the Decision Support System prototype, on the study site of Gâvres (France).