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Watershed Planning within a Quantitative Scenario Analysis Framework
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Large-scale flood modeling and forecasting with FloodCast.

Qingsong Xu1, Yilei Shi2, Jonathan L Bamber3

  • 1Data Science in Earth Observation, Technical University of Munich, Munich 80333, Germany.

Water Research
|August 10, 2024
PubMed
Summary
This summary is machine-generated.

FloodCast, a new framework, enhances flood prediction using multi-satellite data and a novel geometry-adaptive neural solver. This system provides fast, accurate, and large-scale flood forecasting, improving hazard warnings.

Keywords:
Flood modeling and forecastingFloodCastGeometry-adaptive physics-informed neural solverMulti-satellite observationPakistan flood in 2022

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Area of Science:

  • Hydrology and Water Resources
  • Geospatial Science and Remote Sensing
  • Computational Science and Engineering

Background:

  • Traditional large-scale hydrodynamic models face limitations due to fixed grids, high computational costs, and challenges in accurate flood crest forecasting and timely hazard warnings.
  • Accurate and efficient flood modeling is crucial for disaster preparedness and response, especially in the face of increasing climate variability and extreme weather events.

Purpose of the Study:

  • To develop a fast, stable, accurate, resolution-invariant, and geometry-adaptive flood modeling and forecasting framework for large-scale applications.
  • To integrate multi-satellite observations with advanced hydrodynamic modeling for improved flood prediction capabilities.
  • To enable real-time, high-precision flood hazard forecasting.

Main Methods:

  • Developed FloodCast framework with two modules: multi-satellite observation (unsupervised change detection, rainfall analysis) and hydrodynamic modeling.
  • Introduced geometry-adaptive physics-informed neural solver (GeoPINS) with Fourier neural operators for resolution-invariant and data-free modeling.
  • Employed a sequence-to-sequence GeoPINS model for long-term temporal series and extensive spatial domains, validated using 2022 Pakistan flood data and SAR imagery.

Main Results:

  • GeoPINS demonstrated strong performance on partial differential equations across various domains.
  • The sequence-to-sequence GeoPINS model achieved high agreement with traditional methods during high water levels.
  • Comparative analysis showed sequence-to-sequence GeoPINS outperformed traditional hydrodynamics in simulating flood depth, with an average MAPE of 14.93% and MAE of 0.0610 m for 14-day simulations.

Conclusions:

  • The FloodCast framework offers a significant advancement in large-scale flood modeling and forecasting.
  • The geometry-adaptive physics-informed neural solver (GeoPINS) provides a computationally efficient and accurate alternative to traditional methods.
  • The developed system enables high-precision, real-time flood hazard forecasting, crucial for effective disaster management.