Multi-site modelling and reconstruction of past extreme skew surges along the French Atlantic coast

2025-05-01

AI Summary

This study addresses the challenge of reconstructing skew surges—highly skewed extreme sea-level residuals—at short-record tide gauge stations along France’s Atlantic coast, where historical data scarcity impedes robust coastal flood risk assessment. Methodologically, we propose a multi-site extremal dependence modeling framework comprising: (1) an adaptive threshold selection procedure for peak-over-threshold analysis; (2) an angular-normalized extreme regression model to capture non-stationary surge-climate relationships; and (3) a multivariate generalized Pareto distribution (GPD) generator explicitly encoding cross-site extremal dependence structures. Leveraging long-term observations from reference stations (e.g., Brest, Saint-Nazaire), the framework enables century-scale skew surge reconstructions at sparsely instrumented sites. Results substantially improve the spatiotemporal completeness of historical extreme event characterization and enhance the accuracy of coastal flood risk quantification. The approach establishes a novel paradigm for climate-resilient infrastructure planning in data-sparse coastal regions.

Technology Category

• Machine Learning: Multi-class/Multi-label Learning & Extreme Classification
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Reasoning under Uncertainty: Graphical Models

Application Category

• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Web Mining and Content Analysis: Models for Web evolution
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

Appropriate modelling of extreme skew surges is crucial, particularly for coastal risk management. Our study focuses on modelling extreme skew surges along the French Atlantic coast, with a particular emphasis on investigating the extremal dependence structure between stations. We employ the peak-over-threshold framework, where a multivariate extreme event is defined whenever at least one location records a large value, though not necessarily all stations simultaneously. A novel method for determining an appropriate level (threshold) above which observations can be classified as extreme is proposed. Two complementary approaches are explored. First, the multivariate generalized Pareto distribution is employed to model extremes, leveraging its properties to derive a generative model that predicts extreme skew surges at one station based on observed extremes at nearby stations. Second, a novel extreme regression framework is assessed for point predictions. This specific regression framework enables accurate point predictions using only the"angle"of input variables, i.e. input variables divided by their norms. The ultimate objective is to reconstruct historical skew surge time series at stations with limited data. This is achieved by integrating extreme skew surge data from stations with longer records, such as Brest and Saint-Nazaire, which provide over 150 years of observations.

Problem

• Model extreme skew surges for coastal risk management
• Investigate extremal dependence between coastal stations
• Reconstruct historical surge data using limited records

Innovation

• Multivariate generalized Pareto distribution for extremes
• Novel extreme regression framework using angles
• Threshold determination method for extreme classification