DOI: 10.5281/zenodo.7533336
title: CFMDG: a Coastal Flood Modelling Dataset in Gâvres (France) to support risk prevention and metamodels development

description: Along most of the coastal areas, detailed coastal flood observations (e.g. inland water depths) are scarce, and when they are available, this for a limited number of events. Given recent scientific advances, <strong>coastal flooding</strong> events can be properly modelled, even in complex environments and under the action of wave overtopping, and thus provide detailed information. However, such models are computationally expensive, which prevents their use for instance for forecasting and warning. At the same time, metamodelling techniques have been explored for coastal hydrodynamics and have shown promising results. Metamodels are functions that aim to reproduce the behaviour of a “true” model (e.g., a numerical hydrodynamic model) for given input variables (for instance, offshore conditions). Within the RISCOPE research project (https://perso.math.univ-toulouse.fr/riscope/) aiming at exploring to which extent such metamodelling techniques may allow to forecast coastal floods with a good accuracy, a <strong>simulated flood database</strong> has been built for the site of Gâvres (France), characterised by a significant effect of wave overtopping processes. The <strong>CFMDG dataset </strong>compiles a set of post-processed coastal flood simulations on the site of Gâvres. The dataset includes 250 scenarios. Each scenarios is defined by 6h time series centered on high tide, with one time series per forcing variables. The forcing variables (called X) are: local relative mean sea-level, tide, atmospheric storm surge, the offshore wave characteristics and the offshore wind. These scenarios combine past real (flood and no flood) events in the 1900-2021 time span with extreme statistics based events, and some complementary fictive events. The post-processed outputs (called Y) includes, for each scenario, the maximal flooded area (m²) and the maximal water depth (m) in each of the 64 618 inland model grid points. The modelling chain that allowed building this dataset relies on the joint use of a spectral wave model (WW3) to propagate the waves to the coast, and a non-hydrostatic wave-flow model (SWASH) to simulate the nearshore hydrodynamics and the flooding. The spatial and temporal resolution of the SWASH configuration validated on the Gâvres site are respectively 3 m and more than 10Hz. All the results are obtained for a Digital Elevation Model corresponding to the 2018 configuration of the site. Such type of dataset is of use for local knowledge, risk prevention, metamodel testing/training, and local coastal flood forecast. Part of this dataset has already been used in (Idier et al., 2021; López-Lopera et al., 2021; Betancourt et al., 2022), to develop metamodels and set up a coastal flood forecast and early warning prototype. We hope and expect that making this dataset accessible will trigger further developments/investigations for improving risk knowledge on the considered site as well as methodological developments on machine-learning/metamodel-based techniques to support flood forecast. The table below summarizes the variables contained in the dataset, for each scenario. <strong>Variable name</strong> <strong>Description and unit </strong> <strong>Comment</strong> Scenario n° Number of the scenario. <strong>INPUTS (X)</strong> NM Relative mean sea level, referenced to the French vertical datum (m, IGN69) Time series over 6h T Tidal water level (m), referenced to the relative mean sea level Time series over 6h S Atmospheric storm surge (m) Time series over 6h Hs Significant wave height (m) Time series over 6h Tp Wave peak period (s) Time series over 6h Dp Wave peak direction (° in nautical convention) Time series over 6h U Wind speed (m/s) Time series over 6h DU Wind direction (° in nautical convention) Time series over 6h t Relative time centered on the high tide of each event (min) Not Concerned High Tide date UTC date for scenarios corresponding to past real events Not Concerned <strong>OUTPUTS (Y)</strong> Smax Maximum flooded area during the event (m²) Post-processed scalar output Hmax Maximum water depth reached during the event (m), provided for each inland location Post-processed functional (map) output longitude Longitude (°, WGS84) For each inland location point latitude Latitude (°, WGS84) For each inland location point XL93 Longitude (m, Lambert 93) For each inland location point YL93 Latitude (m, Lambert 93) For each inland location point <br>
description: This dataset was produced in the framework of the RISCOPE project (Agence Nationale de la Recherche (ANR); grant no. ANR-16-CE04- 0011; https://perso.math.univ-toulouse.fr/riscope/). The dissemination of the data is done with the support of the ORACLES project (grant no. ANR-21-CE04-0012; https://oracles.brgm.fr/fr).
description: {"references": ["Idier, Aurouet et al (2021) A User-Oriented Local Coastal Flooding Early Warning System Using Metamodelling Techniques. J. Mar. Sci. Eng., doi:10.3390/jmse9111191, 2021.", "L\u00f3pez-Lopera, Idier et al. (2021) Multioutput Gaussian processes with functional data: A study on coastal flood hazard assessment. Reliab. Eng. Syst. Saf., 108139, doi:10.1016/j.ress.2021.108139.", "Betancourt, Bachoc et al. (2022) funGp: An R Package for Gaussian Process Regression with Scalar and Functional Inputs,. https://hal.archives-ouvertes.fr/hal-02536624"]}