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Conference Papers Year : 2022

Partitioning the contributions of dependent offshore forcing conditions in the probabilistic assessment of future coastal flooding

Abstract

In this communication, we aim at getting a deep insight into the role of coastal flooding drivers is of high interest for the planning of adaptation strategies for future climate conditions. Based on the methods described in Rohmer et al. (202), NHESS, we measure the contributions of the offshore forcing conditions (wave/wind characteristics, still water level and sea level rise (SLR) projected up to 2200) to the occurrence of a flooding event at Gâvres town on the French Atlantic coast in a macrotidal environment. This procedure faces, however, two major difficulties, namely (1) the high computational time costs of the hydrodynamic numerical simulations; (2) the statistical dependence between the forcing conditions. By applying a Monte-Carlo-based approach combined with multivariate extreme value analysis, our study proposes a procedure to overcome both difficulties by calculating sensitivity measures dedicated to dependent input variables (named Shapley effects) using Gaussian process (GP) metamodels. On this basis, our results show the increasing influence of SLR over time, and a small-to-moderate contribution of wave/wind characteristics, or even negligible importance in the very long term (beyond 2100). These results were discussed in relation to our modelling choices, in particular the climate change scenario, as well as the uncertainties of the estimation procedure (Monte Carlo sampling and GP error).
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Dates and versions

hal-03852984 , version 1 (15-11-2022)

Identifiers

  • HAL Id : hal-03852984 , version 1

Cite

Jeremy Rohmer, Déborah Idier, Rémi Thiéblemont, Gonéri Le Cozannet, François Bachoc. Partitioning the contributions of dependent offshore forcing conditions in the probabilistic assessment of future coastal flooding. Rencontres MEXICO (MÉTHODES POUR L'EXPLORATION INFORMATIQUE DES MODÈLES COMPLEXES) 2022, Nov 2022, Cestas Gazinet, France. ⟨hal-03852984⟩
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