July 25, 2025Open Access

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

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Authors

SLSophie LecacheuxBureau de Recherches Géologiques et Minières JRJérémy RohmerUnited States Geological Survey EMEva MembradoBureau de Recherches Géologiques et Minières

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Implication

This analysis compares metamodeling approaches for predicting sea levels and marine flooding in Arcachon Lagoon, highlighting their implications for early warning systems.

Key Points

MAIN FINDING: The study reveals that metamodeling methods improve predictions of marine flooding events due to sea level rise.
KEY EVIDENCE: Deep-learning approaches show lower prediction errors of inland water heights, around 10 cm, compared to analog methods.
APPROACH: Three methodologies, including analog-based and machine learning techniques, were evaluated through historical storms at the Arcachon Lagoon.
SIGNIFICANCE: Findings emphasize the importance of pre-calculated databases for operational decision-making in marine flooding forecasts.

Cite This Study

Lecacheux et al. (2025) studied this question.

synapsesocial.com/papers/689a0627e6551bb0af8ce22f https://doi.org/10.5194/egusphere-2024-3615

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Implication

This analysis compares metamodeling approaches for predicting sea levels and marine flooding in Arcachon Lagoon, highlighting their implications for early warning systems.

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

Discussion

Member takes

Implication

Key Points

Cite This Study

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

Discussion

Member takes

Implication

Key Points

Cite This Study

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