Barada et al. (2025) Combining moored observations and SAR images in validating compound flood models

⚠️ Warning: This summary was generated from the abstract only, as the full text was not available.

Identification

• Journal: Geomatics Natural Hazards and Risk
• Year: 2025
• Date: 2025-12-30
• Authors: Mirko Barada, Martin W. Skov, Matthew Lewis, Peter Robins
• DOI: 10.1080/19475705.2025.2608912

Research Groups

Not explicitly stated in the paper text.

Short Summary

This study calibrated and validated the LISFLOOD-FP hydrodynamic model for simulating estuarine compound flood events using a combination of in situ measurements and Sentinel-1 SAR data, demonstrating that this integrated approach provides robust spatiotemporal validation for flood inundation models.

Objective

• To calibrate and validate the LISFLOOD-FP hydrodynamic model for simulating estuarine compound flood events using a combination of in situ measurements and Sentinel-1 SAR data.

Study Configuration

• Spatial Scale: Estuarine system, with specific focus on the upper estuary.
• Temporal Scale: Two distinct flood events.

Methodology and Data

• Models used: LISFLOOD-FP hydrodynamic model.
• Data sources:
  • In situ measurements: Moored pressure sensors (for water levels).
  • Satellite: Sentinel-1 SAR data (for flood extent).
  • Ancillary: Land cover map (for bottom roughness), Digital Elevation Model (DEM).
  • Techniques: Image classification (un-supervised and supervised), overlapping indices for comparison.

Main Results

• The LISFLOOD-FP model's bottom roughness parameterization was calibrated using a land cover map.
• Modelled in-estuary water levels during two flood events showed good agreement with observations from moored pressure sensors.
• Un-supervised image classification performed better than supervised classification for isolating surface water from dry land in Sentinel-1 SAR images.
• Comparison of modelled flood extent with processed SAR images using eight scenarios showed good matching in the upper estuary, where compound flooding was most pronounced.
• Accuracy was lower in other locations, attributed to unresolved drainage channels on the Digital Elevation Model (DEM).
• The study demonstrated that processed SAR images, when combined with traditional in situ measurements, provide robust spatiotemporal validation for flood inundation models.

Contributions

• Provides a robust spatiotemporal validation framework for flood inundation models by integrating processed Sentinel-1 SAR images with traditional in situ measurements.
• Evaluates the performance of the LISFLOOD-FP hydrodynamic model in simulating estuarine compound flood events using multi-source observational data.
• Compares the effectiveness of supervised versus un-supervised image classification techniques for extracting flood extent from SAR data in an estuarine context.

Funding

Not explicitly stated in the paper text.

Citation

@article{Barada2025Combining,
  author = {Barada, Mirko and Skov, Martin W. and Lewis, Matthew and Robins, Peter},
  title = {Combining moored observations and SAR images in validating compound flood models},
  journal = {Geomatics Natural Hazards and Risk},
  year = {2025},
  doi = {10.1080/19475705.2025.2608912},
  url = {https://doi.org/10.1080/19475705.2025.2608912}
}