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

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.

funGp: An R Package for Gaussian Process Regression with Scalar and Functional Inputs

“…Here we apply the functionalities of funGp to a real case that is related to the problem of setting up a rapid FEWS for coastal flooding at Gâvres, France (Idier et al 2021). The modeling of coastal flooding at the proper resolution (metric) to predict floods in urban areas and account precisely for processes such as wave overtopping is so time consuming (i.e., the computation time is hardly smaller than the real time) that forecasting efforts are greatly hindered.…”

“…However, industrial and environmental applications often deal with complex inputs or outputs, which can be spatial, temporal, spatio-temporal i.e., functional in a more general way. As a motivating real case example, the coastal flood early warning system (FEWS) developed at Gâvres, France (Idier et al 2021) predicts scalar output indicators of flooding (like total flooded area, maximum volume of water entering the territory, maximum water depth at a given location, etc. ), based on inputs which are multiple scalar and time-varying maritime conditions (e.g., mean sea level, tide, atmospheric storm surge, and wave conditions), i.e., multiple time series (that are here sampled regularly over time).…”

Coastal Adaptation Pathways and Tipping Points for Typical Mediterranean Beaches Under Future Scenarios

“…Because NbS take a longer time to develop and deliver ecosystem services, adaptation pathways should include a monitoring and maintenance plan, supported by an early warning system (EWS) based on numerical models [28] or metamodels [29] that underpins proactive decisions. Risk is measured by the distance to TPs, which also determines the urgency to implement the proposed interventions within an adaptation plan that considers uncertainties and proposes limit dates for interventions, as a function of co-decided objectives, on risk reduction [30] according to stakeholder criteria.…”

Coastal Adaptation Pathways and Tipping Points for Typical Mediterranean Beaches under Future Scenarios

“…Because NbS take a longer time to develop and deliver ecosystem services, adaptation pathways with NbS should include a monitoring and maintenance plan supported by an early warning system (EWS) based on numerical models [31] or metamodels [32] that underpins proactive decisions. Risk is measured by the distance to TPs, which also determines the urgency to implement the proposed interventions within an adaptation plan that considers uncertainties and proposes limit dates for interventions as a function of co-decided objectives for risk reduction [33] according to stakeholder criteria.…”