Lecacheux et al. (2026) Investigating metamodeling capability to predict sea levels and marine flooding maps for early-warning systems: application on the Arcachon Lagoon (France)

Identification

• Journal: Natural hazards and earth system sciences
• Year: 2026
• Date: 2026-08-19
• Authors: Sophie Lecacheux, Jérémy Rohmer, Eva Membrado, Rodrigo Pedreros, Andrea Gilberto Filippini, Déborah Idier, Servane Gueben-Venière, Denis Paradis, Alice Dalphinet, David Ayache
• DOI: 10.5194/nhess-26-3919-2026

Research Groups

• BRGM, 3 av. C. Guillemin, 45060 Orléans, France
• KEYROS, 3 bis rue Jules Vallès, 75011 Paris, France
• METEO-FRANCE, DIROP/MAR/DAS, Toulouse, France

Short Summary

This study investigates the capability of metamodeling to predict sea levels and marine flooding maps for early-warning systems. Three data-driven methodologies are compared: analog-based research from offshore metocean conditions, machine learning type metamodels to predict total water levels at the coast, and deep-learning techniques to predict marine flooding maps.

Objective

• Investigate different approaches based on pre-calculated databases to produce information relevant for operational forecast applications.
• Analyze not only the added value in terms of accuracy regarding the required implementation effort but also in terms of adequacy according to the needs of crisis managers.

Study Configuration

• Spatial Scale: The study focuses on the Arcachon Lagoon (Southwest of France) with a spatial resolution from kilometric offshore to decametric inside the lagoon.
• Temporal Scale: The temporal scale is daily, with simulations carried out sequentially, starting from the highest high tide level to the lowest.

Methodology and Data

• Models used:
  • WW3 spectral wave model (Tolman, 2014)
  • UHAINA hydrodynamic models (Filippini et al., 2024) that resolves Saint-Venant equations.
• Data sources:
  • Reanalyses over the period 1979–2009 for wind, waves, and storm surge conditions at the Cap-Ferret wave buoy location.

Main Results

• The learning dataset enables to describe a wide range of events from annual to exceptional.
• Municipalities with wetlands are more exposed to marine flooding, but their urban areas and critical stakes are impacted for higher T W L max than other municipalities.
• Historical events do not exceed CAT 2 (with Xynthia) but pseudo-historical events complete the validation scenario relatively well to represent all the categories.

Contributions

• This study underlines the usefulness of precalculated databases to conduct a preparatory work with crisis managers to determine the type of information and the right level of complexity required to address operational needs.
• The metamodel-based approach is more suitable for fast prediction with a lower prediction error of inland water heights for the deep-learning approach (on the order of 10 cm).

Funding

• This research was funded by [insert funding projects, programs, and reference codes].

Citation

@article{Lecacheux2026Investigating,
  author = {Lecacheux, Sophie and Rohmer, Jérémy and Membrado, Eva and Pedreros, Rodrigo and Filippini, Andrea Gilberto and Idier, Déborah and Gueben-Venière, Servane and Paradis, Denis and Dalphinet, Alice and Ayache, David},
  title = {Investigating metamodeling capability to predict sea levels and marine flooding maps for early-warning systems: application on the Arcachon Lagoon (France)},
  journal = {Natural hazards and earth system sciences},
  year = {2026},
  doi = {10.5194/nhess-26-3919-2026},
  url = {https://doi.org/10.5194/nhess-26-3919-2026}
}