Álvarez-Cuesta et al. (2026) Enhancing coastal flood hazard assessments through spatio-temporal event modelling at large scales

Identification

• Journal: Weather and Climate Extremes
• Year: 2026
• Date: 2026-08-31
• Authors: Moisés Álvarez-Cuesta, Jérémy Rohmer, Alexandra Toimil, I. J. Losada, Gonéri Le Cozannet
• DOI: 10.1016/j.wace.2026.100952

Research Groups

• IHCantabria-Instituto de Hidr´aulica Ambiental de la Universidad de Cantabria (Spain)
• BRGM, French Geological Survey (France)

Short Summary

This study presents a spatio-temporal statistical model that integrates wave and storm surge contributions to estimate water level footprints and their associated flooding over large spatial scales. The approach combines physics-based hydrodynamic modelling with machine learning to generate synthetic databases of extreme events.

Objective

• To develop an accurate method for assessing coastal flood hazards at large scales by considering the spatio-temporal evolution of combined waves and storm surges.
• To improve upon traditional site-by-site analyses that oversimplify spatial dependencies and neglect temporal evolution.

Study Configuration

• Spatial Scale: Large-scale coastal region (1,230 km) in the Bay of Biscay, covering northern Spain and western France.
• Temporal Scale: 500-year time frame with hourly resolution for wave and water level hindcast data.

Methodology and Data

• Models used:
  • WaveWatch III model for global wave reanalysis
  • ROMS model for storm surge data
  • SFINCS model for flood simulations
  • Machine learning techniques (random forests, gradient boosting) for emulator development
• Data sources:
  • ERA5 atmospheric fields
  • Copernicus EU-DEM GLO-30 digital elevation model
  • Astronomical tide hourly time series from TPXO9 model

Main Results

• The study generated a synthetic database of 286,000 extreme events with associated flooding over the large spatial scale.
• The results revealed notable differences between the event-based approach and traditional site-by-site analysis, particularly at larger spatial scales.
• Spatially uniform 100-year return levels were substantially less frequent when spatial consistency was enforced.

Contributions

• This study provides a novel approach to generating synthetic databases of extreme events that account for spatio-temporal evolution and dependencies.
• The method improves upon traditional flood hazard assessments by considering the combined effects of waves, storm surges, and astronomical tides.

Funding

• This research was funded by [project/program name] with reference code [reference code].

Citation

@article{ÁlvarezCuesta2026Enhancing,
  author = {Álvarez-Cuesta, Moisés and Rohmer, Jérémy and Toimil, Alexandra and Losada, I. J. and Cozannet, Gonéri Le},
  title = {Enhancing coastal flood hazard assessments through spatio-temporal event modelling at large scales},
  journal = {Weather and Climate Extremes},
  year = {2026},
  doi = {10.1016/j.wace.2026.100952},
  url = {https://doi.org/10.1016/j.wace.2026.100952}
}