Investigating metamodeling capability to predict sea levels and marine flooding maps for early-warning systems: application on the Arcachon Lagoon (France)

Abstract. Marine flooding events during storms are expected to occur more frequently due to sea level rise. Hence, early warning systems (EWS) dedicated to marine flooding are expected to develop in the coming years. In this study, we compare three data-driven methodologies to overcome the computational burden of numerical simulations. They are all based on the statistical analysis of pre-calculated databases, to downscale total sea levels and to predict marine flooding maps from offshore metocean operational forecasts. While the first one is a simple analog-based research from offshore metocean conditions, the next two both use a machine learning type metamodel to predict total sea levels at the coast, and either an analog or a deep-learning approach to predict marine flooding maps. The analysis, carried out with a cross-validation exercise and historical storms on the pilot site of Arcachon lagoon (Southwest of France), reveals that the analog-based approach is a valuable first step to explore the dataset and improve the understanding of flooding phenomena, but lack precision for operational forecast applications. On the other hand, the two metamodel-based approaches are more suitable for fast prediction with a lower prediction error of inland water heights for the deep-learning approach (on the order of 10 cm). Both approaches can then be complementary depending on the type of event, the required level of prediction accuracy to support operational decision making, and the forecast lead time. In this sense, the study also underlines the usefulness of precalculated databases to conduct a preparatory work with crisis managers to determine the type of information and the right level of complexity required to address operational needs.