Casting light on forcing and breaching scenarios that lead to marine inundation: Combining numerical simulations with a random-forest classification approach

Rohmer, Jérémy; Idier, Déborah; Paris, François; Pedreros, Rodrigo; Louisor, Jessie

doi:10.1016/j.envsoft.2018.03.003

Cited by 24 publications

(16 citation statements)

References 34 publications

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“…Otherwise, provided that the water level slightly increases and exceeds a specified threshold, overflow-induced inundation can occur and inland locations may be flooded. To tackle this effect, the following potential solutions should be explored: classification method in order to learn inputs where there is any inundation (see an example by [24]), or by adding constraints on the GP metamodels [16]. Secondly, although the usage of a functional basis aims at preserving spatial regularity, some flooded areas, in grey, are not always enough connected together in the predicted maps and consequently, connected to the sea.…”

Section: Discussionmentioning

confidence: 99%

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Functional principal component analysis for global sensitivity analysis of model with spatial output

Perrin

Roustant

Rohmer

et al. 2020

Preprint

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Motivated by risk assessment of coastal flooding, we consider timeconsuming simulators with a spatial output. The aim is to perform sensitivity analysis (SA), quantifying the influence of input parameters on the output. There are three main issues. First, due to computational time, standard SA techniques cannot be directly applied on the simulator. Second, the output is infinite dimensional, or at least high dimensional if the output is discretized. Third, the spatial output is non-stationary and exhibits strong local variations. We show that all these issues can be addressed all together by using functional PCA (FPCA). In contrast to the RKHS view of FPCA, we first specify a functional basis, such as wavelets or B-splines, designed to handle local variations. Secondly, FPCA reduces dimension by doing PCA on the basis coefficients with an ad-hoc metric. Finally, fast-to-evaluate metamodels are built on the few selected principal components. They provide a proxy on which SA can be done. As a by-product, we obtain analytical formulas for variance-based sensitivity indices, generalizing known formula assuming orthonormality of basis functions.

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Section: Discussionmentioning

confidence: 99%

“…Coastal flooding processes are simulated with the numerical code MARS [14], where adaptations were made by the BRGM to take into account specificities of local flooding processes (hydraulic processes around connections like nozzles, spillways, etc. and breaching phenomena) [24].…”

Section: Application On Coastal Flooding Model 61 Description Of the ...mentioning

confidence: 99%

Functional principal component analysis for global sensitivity analysis of model with spatial output

Perrin

Roustant

Rohmer

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Model emulation refers to a suite of approaches that replace a computationally intensive process model with an accurate alternative model structure that is much faster to execute (Castelletti, Galelli, Ratto, Soncini‐Sessa, & Young, 2012; Razavi, Tolson, & Burn, 2012; Rohmer, Idier, Paris, Pedreros, & Louisor, 2018). Although widely used in global climate modelling (Castruccio et al, 2014; Dueben & Bauer, 2018; Krasnopolsky & Fox‐Rabinovitz, 2006; Raper & Cubasch, 1996) and finding new applications in groundwater and other earth science domains (Bhattacharjya & Datta, 2005; Broad, Dandy, & Maier, 2005; Castelletti, Pianosi, Soncini‐Sessa, & Antenucci, 2010; Kumar, Sreenivasulu, & Ramakrishna Rao, 2010; Neelakantan & Pundarikanthan, 2000; Rogers & Dowla, 1994; Sreekanth & Datta, 2010; Yan & Minsker, 2003), emulation has not, to our knowledge, yet been applied to multiscale ecohydrological problems.…”

Section: Introductionmentioning

confidence: 99%

Sensitivity of dryland vegetation patterns to storm characteristics

Crompton

Thompson

2021

Ecohydrology

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Ecohydrological phenomena are o ften multiscale in nature, with behavioTur that emerges from the interaction of tightly coupled systems having characteristic timescales that differ by orders of magnitude. Models address these differences using timescale separation methods, where each system is held in psuedo‐steady state while the other evolves. When the computational demands of solving the ‘fast’ system are large, this strategy can become numerically intractable. Here, we use emulation modelling to accelerate the simulation of a computationally intensive ‘fast’ system: overland flow. We focus on dryland ecosystems in which storms generate overland flow, on timescales of 101 − 2 s. In these ecosystems, overland flow delivers crucial water inputs to vegetation, which grows and disperses ‘slowly’, on timescales of 107 − 9 s. Emulation allows for a physically realistic treatment of flow, advancing on phenomenological descriptions used in previous studies. Resolving the within‐storm processes reveals novel dynamics, including new transition pathways from patchy vegetation to desertification, that are specifically controlled by storm processes.

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“…For example, the years of 1953 and 1962 were marked by strong storm surge events, which, combined with tides, caused flooding over broad coastal areas in the south-western Netherlands and eastern England [1], and in northern Germany [2], respectively. In the last decade, the Xynthia (27 to 28 February 2010) storm severely impacted low-lying coastal areas located in the central part of the Bay of Biscay [3][4][5][6], causing 53 fatalities and material damage assessed at more than one billion euros. This event was a mid-latitude storm resulting in a storm surge of about 1.5 m, which coincided with a spring high tide.…”

Section: Introductionmentioning

confidence: 99%

Statistical Prediction of Extreme Storm Surges Based on a Fully Supervised Weather-Type Downscaling Model

Costa

Idier

Rohmer

et al. 2020

JMSE

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Increasing our capacity to predict extreme storm surges is one of the key issues in terms of coastal flood risk prevention and adaptation. Dynamically forecasting storm surges is computationally expensive. Here, we focus on an alternative data-driven approach and set up a weather-type statistical downscaling for daily maximum storm surge (SS) prediction, using atmospheric hindcasts (CFSR and CFSv2) and 15 years of tidal gauge station measurements. We focus on predicting the storm surge at La Rochelle–La Pallice tidal gauge station. First, based on a sensitivity analysis to the various parameters of the weather-type approach, we find that the model configuration providing the best performance in SS prediction relies on a fully supervised classification using minimum daily sea level pressure (SLP) and maximum SLP gradient, with 1° resolution in the northeast Atlantic domain as the predictor. Second, we compare the resulting optimal model with the inverse barometer approach and other statistical models (multi-linear regression; semi-supervised and unsupervised weather-types based approaches). The optimal configuration provides more accurate predictions for extreme storm surges, but also the capacity to identify unusual atmospheric storm patterns that can lead to extreme storm surges, as the Xynthia storm for instance (a decrease in the maximum absolute error of 50%).

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Casting light on forcing and breaching scenarios that lead to marine inundation: Combining numerical simulations with a random-forest classification approach

Cited by 24 publications

References 34 publications

Functional principal component analysis for global sensitivity analysis of model with spatial output

Functional principal component analysis for global sensitivity analysis of model with spatial output

Sensitivity of dryland vegetation patterns to storm characteristics

Statistical Prediction of Extreme Storm Surges Based on a Fully Supervised Weather-Type Downscaling Model

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