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

Déborah Idier,Jérémy Rohmer,Rodrigo Pedreros,Audrey Baills,Andrés F López-Lopera,Fabrice Gamboa,José Betancourt,François Bachoc,Axel Aurouet,Thierry Klein,Alexandre Thibault

doi:10.3390/jmse9111191

Abstract

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.

Highlights

600 million people live in coastal plain areas, which are broadly recognised as “at-risk” territories that could be subjected to significant hazards in the future, with the increasing risk of coastal flooding due to sea-level rise
As a preliminary analysis of the numerical results, for the scalar input grid experiments Sim1 and Sim2 defined in Section 2.5.2, numerical modelling indicates that floods occur in 105 (155) scenarios out of the total number of scenarios, i.e., 144 (174)
We establish an forecast and early warning system (FEWS) prototype to predict local coastal flooding using the metamodelling approach to tackle the excessive computation time required by numerical models

Summary

Introduction

600 million people live in coastal plain areas (i.e., coastal regions less than 10 m above mean sea level; [1]), which are broadly recognised as “at-risk” territories that could be subjected to significant hazards in the future, with the increasing risk of coastal flooding due to sea-level rise. As highlighted by [3], effectively adapting to this rising flood risk requires a diversified approach of interventions, which may include structural flood protection measures, risk-informed land planning, nature-based solutions, social protection, risk financing instruments, and forecast and early warning systems (FEWSs). Owing to the significant progress achieved in numerical hydrodynamic models (especially phase-resolving models, e.g., Simulating Waves till Shore (SWASH), [4]), it is possible to precisely reproduce floods, even in the case of wave overtopping (see, e.g., [5,6,7])

Methods

Results

Discussion

Conclusion