A nuanced quantile random forest approach for fast prediction of a stochastic marine flooding simulator applied to a macrotidal coastal site

Pedreros

Rohmer

et al. 2020

JMSE

Self Cite

Coastal floods are driven by many hydro-meteorological forcing factors, among which are mean sea levels, tides, atmospheric storm surges, and waves. Depending on these conditions, wave overtopping may occur and, in some cases, lead to a significant flood. In the present study, we investigate the effect of the stochastic character of waves on the flood itself using a phase-resolving wave model (SWASH). We focus on the macro-tidal site of Gâvres (France), consider two past flood events (both resulting from wave overtopping), and investigate how the effect of randomness of waves on the flood is changing with the forcing conditions and the time span (minutes to hours). We clearly show that the effect of waves’ stochasticity on the flood itself is far from being negligible and, especially on a short time scale (~15 min), generates an uncertainty comparable to that induced by the sea-level rise scenarios, as long as the still water level remains smaller than the critical level above which overflow occurs. This implies that lower confidence should be assigned to flood projection on sites where wave overtopping is the main process leading to flood.

Section: Discussionmentioning

confidence: 99%

The Effect of Stochasticity of Waves on Coastal Flood and Its Variations with Sea-level Rise

Pedreros

Rohmer

et al. 2020

JMSE

Self Cite

“…We start by selecting a limited number n = 100 of offshore meteo-oceanic scalar conditions X = (SWL, Hs, Tp, Dp, U, Du), as was performed by [22]. This selection (S1) is designed by applying the clustering procedure described by [42] to a large dataset of extreme conditions (here, ≈150,000).…”

Section: Design Of Experimentsmentioning

confidence: 99%

“…To better explore the potential of metamodelling techniques for coastal flood FEWSs, the ANR RISCOPE project was initiated in 2017 with the aim of establishing a user-centred FEWS by relying on metamodelling techniques. This project led to the development and exploration of metamodels and their ability to predict information regarding inland floods [22][23][24]. Within this project, Ref.…”

Section: Introductionmentioning

confidence: 99%

A User-Oriented Local Coastal Flooding Early Warning System Using Metamodelling Techniques

Aurouet

Bachoc

et al. 2021

JMSE

Self Cite

Given recent scientific advances, coastal flooding events can be properly modelled. Nevertheless, such models are computationally expensive (requiring many hours), which prevents their use for forecasting and warning. In addition, there is a gap between the model outputs and information actually needed by decision makers. The present work aims to develop and test a method capable of forecasting coastal flood information adapted to users’ needs. The method must be robust and fast and must integrate the complexity of coastal flood processes. The explored solution relies on metamodels, i.e., mathematical functions that precisely and efficiently (within minutes) estimate the results that would provide the numerical model. While the principle of relying on metamodel solutions is not new, the originality of the present work is to tackle and validate the entire process from the identification of user needs to the establishment and validation of the rapid forecast and early warning system (FEWS) while relying on numerical modelling, metamodelling, the development of indicators, and information technologies. The development and validation are performed at the study site of Gâvres (France). This site is subject to wave overtopping, so the numerical phase-resolving SWASH model is used to build the learning dataset required for the metamodel setup. Gaussian process- and random forest classifier-based metamodels are used and post-processed to estimate 14 indicators of interest for FEWS users. These metamodelling and post-processing schemes are implemented in an FEWS prototype, which is employed by local users and exhibits good warning skills during the validation period. Based on this experience, we provide recommendations for the improvement and/or application of this methodology and individual steps to other sites.

“…The reader can refer to Rohmer et al (2020) for further details on the implementation for the site of interest here. A hundred of forcing conditions were selected via this procedure.…”

Section: Gp Metamodel Training and Validationmentioning

confidence: 99%

“…To overcome the computational burden of the procedure, we adopt a metamodeling approach, i.e. we perform a statistical analysis of existing databases of pre-calculated high-fidelity simulations to construct a costless-to-evaluate statistical predictive model (named "metamodel" or "surrogate") to replace the long running hydrodynamic simulator; see e.g., Rohmer et al (2020).…”

Section: Introductionmentioning

confidence: 99%

Partitioning the uncertainty contributions of dependent offshore forcing conditions in the probabilistic assessment of future coastal flooding at a macrotidal site

Rohmer

Thiéblemont

et al. 2021

Preprint

Self Cite

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.