Dynamic parameter sensitivity in numerical modelling of cyclone-induced waves: a multi-look approach using advanced meta-modelling techniques

Rohmer, Jérémy; Lecacheux, Sophie; Pedreros, Rodrigo; Quetelard, Hubert; Bonnardot, François; Idier, Déborah

doi:10.1007/s11069-016-2513-8

Cited by 21 publications

(13 citation statements)

References 43 publications

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“…It should be noted that statistical downscaling models aiming at predicting cyclone-induced wave conditions rely on the cyclone characteristics rather than on spatio-temporal meteorological fields (see e.g. Jia and Taflanidis, 2013;Rohmer et al 2016).…”

Section: Characterization and Variability Of Offshore Wave Conditionsmentioning

confidence: 99%

The Contribution of Wind-Generated Waves to Coastal Sea-Level Changes

et al. 2019

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Surface gravity waves generated by winds are ubiquitous on our oceans and play a primordial role in the dynamics of the ocean-land-atmosphere interfaces. In particular, wind-generated waves cause fluctuations of the sea level at the coast over timescales from a few seconds (individual wave runup) to a few hours (wave-induced setup). These wave-induced processes are of major importance for coastal management as they add up to tides and atmospheric surges during storm events and enhance coastal flooding and erosion. Changes in the atmospheric circulation associated with natural climate cycles or caused by increasing greenhouse gas emissions affect the wave conditions worldwide, which may drive significant changes in the wave-induced coastal hydrodynamics. Since sea-level rise represents a major challenge for sustainable coastal management, particularly in low-lying coastal areas and/or along densely urbanized coastlines, understanding the contribution of wind-generated waves to the long-term budget of coastal sea-level changes is therefore of major importance. In this review, we describe the physical processes by which sea states may affect coastal sea level at several timescales, we present the methods currently used to estimate the wave contribution to coastal sea-level changes, we describe past and future wave climate variability, we discuss the contribution of wave to coastal sea-level changes, and we discuss the limitations and perspectives of this research field.

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Section: Characterization and Variability Of Offshore Wave Conditionsmentioning

confidence: 99%

The Contribution of Wind-Generated Waves to Coastal Sea-Level Changes

et al. 2019

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“…In principle, many approaches can be utilised to set up a metamodel (for an overview, see [40]), including GPs, neural networks, or support vector regression. For the present work, we select GP metamodels because they have shown very high predictive capabilities in previous applications for coastal flood assessments (among others, see [44], for applications to overflow-induced marine flooding; see [19,45], for applications to hurricanes; and see [46], for an application to San Francisco Bay). In particular, high performance was shown by the extensive comparison exercise conducted by [47].…”

Section: Metamodelling Techniquementioning

confidence: 99%

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

Idier

Aurouet

Bachoc

et al. 2021

JMSE

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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.

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“…To overcome the computational complexity of coastal flooding models, data-driven surrogates have been widely explored (Sacks et al, 1989;Rohmer et al, 2016;Liu and Guillas, 2017;Rueda et al, 2019). The latter models are arXiv:2007.14052v1 [stat.ML] 28 Jul 2020…”

Section: Introductionmentioning

confidence: 99%