Abstract

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. Using global sensitivity analysis, we aim to 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 the flooding event (defined when the inland water volume exceeds a given threshold YC) 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 through the computation of sensitivity measures dedicated to dependent input variables (named Shapley effects) with the help of Gaussian process (GP) metamodels. On this basis, our results outline the key influence of SLR over time. Its contribution rapidly increases over time until 2100 where it almost exceeds the contributions of all other uncertainties (with Shapley effect > 40 % considering the representative concentration pathway RCP4.5 scenario). After 2100, it continues to linearly increase up to > 50 %. The SLR influence depends however on our modelling assumptions. Before 2100, it is strongly influenced by the digital elevation Model (DEM); with a DEM with lower topographic elevation (before the raise of dykes in some sectors), the SLR effect is smaller by ~40 %. This influence reduction goes in parallel with an increase in the importance of wave/wind characteristics, hence indicating how the relative effect of the flooding drivers strongly change when protective measures are adopted. By 2100, the joint role of RCP and of YC impacts the SLR influence, which is reduced by 20–30 % when the mode of the SLR probability distribution is high (for RCP8.5 in particular) and when YC is low (of 50 m3). Finally, by showing that these results are robust to the main uncertainties in the estimation procedure (Monte-Carlo sampling and GP error), the combined GP-Shapley effect approach proves to be a valuable tool to explore and characterize uncertainties related to compound coastal flooding under SLR.

Highlights

  • Coastal flooding is generally not caused by a unique physical driver, but by a combination of them, including mean sea-level 30 changes, atmospheric storm surges, tides, waves, river discharges, etc. (e.g., Chaumillon et al, 2017)

  • By applying a Monte-Carlo-based approach combined with multivariate extreme value analysis, our study 15 proposes a procedure to overcome both difficulties through the computation of sensitivity measures dedicated to dependent input variables with the help of Gaussian process (GP) metamodels

  • Its contribution rapidly increases over time until 2100 where it almost exceeds the contributions of all other uncertainties

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Summary

Introduction

Coastal flooding is generally not caused by a unique physical driver, but by a combination of them, including mean sea-level 30 changes, atmospheric storm surges, tides, waves, river discharges, etc. (e.g., Chaumillon et al, 2017). Flood severity are significantly increased by the cooccurrence of extreme waves and surges at a number of major tide gauge locations (Marcos et al, 2019), of high sea-level 35 and high river discharge in the majority of deltas and estuaries (Ward et al, 2018), of high sea-level and rainfall at major US cities (Wahl et al, 2015) This intensification of compound flooding is expected to be exacerbated under climate change (Bevacqua et al, 2020). 55 Unlike these previous studies, the application of GSA to our study site faces two main difficulties: (1) the physical processes related to flooding are modelled with numerical simulations that have an expensive computational time cost (i.e. larger than the simulated time) This hampers the Monte-Carlo-based procedure for estimating the sensitivity measures; (2) the offshore forcing conditions cannot be considered independent and the probabilistic assessment should necessarily account for their statistical dependence. This complicates the decomposition of the respective contributions of each physical drivers in GSA 60 (see a discussion by Do and Razavi, 2020)

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