FloodCastBench Dataset

FloodCastBench provides comprehensive low-fidelity and high-fidelity flood forecasting datasets specifically for machine learning-based spatiotemporal, cross-regional, and downscaled flood modeling and forecasting.

Data Generation

To use the model, execute the following steps:

1. Prepare the necessary input files and parameters, including topography data (DEM), rainfall data, land use and land cover data (Manning coefficients), as well as initial and boundary conditions.
2. Please change the 'paths of different input data and results' to your local paths.
3. Run Data_Generation_Code/main.py

Dataset Structure

FloodCastBench

• Low-Fidelity Flood Forecasting
  • 480 m
    • Pakistan flood
    • Mozambique flood
• High-Fidelity Flood Forecasting
  • 30 m
    • Australia flood
    • UK flood
  • 60 m
    • Australia flood
    • UK flood
• Relevant Data
  • DEM
  • Land use and land cover
  • Rainfall
    • Pakistan flood
    • Mozambique flood
    • Australia flood
    • UK flood
  • Georeferenced files
  • Initial conditions

How to contribute

Our vision for FloodCastBench is to establish it as a foundational, dynamically expanding community flood forecasting dataset. We aim for it to be accessible and augmentable by researchers in the hydrology and ML fields. At present, the spatial distribution of flood events in FloodCastBench is restricted to a few regions globally. We hope that users will contribute and share their data, allowing FloodCastBench to eventually cover a wide range of flood events worldwide.

Contact

If you have any questions/feedback regarding the FloodCastBench dataset/project, please contact Qingsong Xu qingsong.xu(at)tum.de