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

Abstract

Abstract. Getting a deep insight into the role of coastal flooding drivers is of great 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 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 and (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).

Coastal Flood at Gâvres (Brittany, France): A Simulated Dataset to Support Risk Management and Metamodels Development

“…Several difficulties exist, however, namely accounting for the dependence of forcings, randomly generating events of low probability, and rapidly predicting flood maps. Rohmer et al [22] provided an example of flooding probability using a scalar flooding indicator. More advanced metamodelling techniques like those of Lopez-Lopera et al [16], however, must be used to extend this analysis to maps of flooding probability (i.e., assess coastal flood hazard for risk prevention).…”

A Hybrid Framework for Rapidly Locating Transition Zones: A Comparison of Event‐ and Response‐Based Return Water Levels in the Suwannee River FL

“…Data‐driven surrogate models empirically approximate the relationship between the inputs (and parameters) and the outputs of a complex model without attempting to emulate any of its internal parts (Razavi et al., 2012). Past applications of surrogate models in fluvial and coastal flooding studies range from conceptually simple look‐up tables (Apel et al., 2008) and empirical formulations (van Ormondt et al., 2021) to more complex approaches including Gaussian process models (Malde et al., 2016; Parker et al., 2019; Rohmer et al., 2022), kriging (Parker et al., 2019; Rohmer & Idier, 2012), 3D scatter interpolation (Serafin et al., 2019), bilinear interpolation (Couasnon et al., 2022), radial basis functions (Camus, Mendez, Medina, et al., 2011; Gouldby et al., 2014; Medellín et al., 2016; Rueda et al., 2016), support vector regression (Bermúdez et al., 2019; Chen et al.,. 2020; Jhong et al., 2017), random forests (Zahura & Goodall, 2022; Zahura et al., 2020), and artificial neural networks (Bermúdez et al., 2018; Peters et al., 2006; Santos et al., 2019).…”

“…Camus, Mendez, and Medina (2011) found the MDA better represented the boundaries of a data set (which are often the conditions leading to the most extreme responses) than other clustering techniques. Surrogate models aided by the MDA have been successfully developed for physics‐based models of dune erosion (Santos et al., 2019), wave transformation (Gouldby et al., 2014; Malde et al., 2016; Rohmer et al., 2022; Rueda et al., 2016), wave run‐up (Medellín et al., 2016), flood inundation (Bermúdez et al., 2018), and fluid–structure interactions (Lara et al., 2019).…”