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Validation of flood inundation modelling using multi-source SAR imagery.

Dan Gao1, Wenting Wang1, Jie Yin2

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Summary

Climate change increases extreme precipitation, challenging flood management. This study validates hydraulic models using satellite radar data, offering a reliable method for data-scarce regions to improve flood prediction and response.

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FloodMap-HydroInundation2DModel validationMulti-source SAR imageryNanyangPluvial flooding

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Area of Science:

  • Environmental Science
  • Remote Sensing
  • Hydrology

Background:

  • Climate change is increasing extreme precipitation events, exacerbating flood risks.
  • Validating hydraulic models is difficult in data-scarce regions, hindering effective flood management.
  • Satellite Synthetic Aperture Radar (SAR) offers weather-independent flood monitoring capabilities.

Purpose of the Study:

  • To validate 2D hydraulic simulations using flood extents derived from multi-source SAR data.
  • To develop and apply an automated framework for extracting flood extents from SAR imagery.
  • To assess the performance of the FloodMap-HydroInundation2D model in a real-world flood event.

Main Methods:

  • Applied the FloodMap-HydroInundation2D model to simulate a 2024 flood event in Nanyang, China.
  • Developed an automated threshold-based framework to extract flood extents from GF-3 and Sentinel-1 SAR imagery.
  • Validated model simulations against SAR-derived flood maps and conducted a sensitivity analysis on model parameters.

Main Results:

  • The automated threshold-based method effectively extracted water extents from SAR data with >85% accuracy and Kappa >0.6.
  • Hydraulic model simulations showed strong agreement with SAR-derived flood maps (Hit Rate: 88.8%, Bias: 0.058, CSI: 72.7%).
  • Hydraulic conductivity significantly influenced simulation outcomes more than Manning's roughness coefficient.

Conclusions:

  • The study presents a robust and scalable framework for validating hydraulic flood models in data-scarce environments.
  • SAR data combined with automated extraction techniques provide a reliable method for flood model validation.
  • This approach supports improved flood disaster mitigation and emergency response planning.