HydroForecast: A Deep Learning-Based Probabilistic Flood Forecasting Model

Floods pose substantial risks to human society and ecosystems, making accurate flood forecasting essential for disaster mitigation and water resources management. However, reliable prediction remains challenging especially in data-scarce regions, where process-based models rely heavily on site-specific calibration and exhibit limited transferability. Here we present a unified global flood forecasting framework that combines systematic catchment attributes screening with a generative deep-learning-based probabilistic hydrological forecasting model, HydroForecast. Through importance ranking and stepwise forward feature selection, the framework first identifies a representative and non-redundant set of catchment attributes. Leveraging these attributes together with meteorological forcings, we construct the HydroForecast model, which directly learns the underlying discharge distribution and generates ensemble predictions without relying on restrictive parametric prior assumptions. Evaluated across more than 3,000 basins worldwide, HydroForecast consistently outperforms a Skewed Laplace–based LSTM benchmark, delivering more accurate flood peak prediction, improved event detection, and reliable uncertainty quantification. Additional analyses demonstrate that our model maintains stable performance in reservoir-regulated basins, while exhibiting pronounced performance differences across climate regimes that reflect the varying degrees of predictive difficulty associated with distinct hydro-climatic conditions. Together, these results highlight the strong potential and reliability of HydroForecast for large-sample flood forecasting and for improving predictive capability in ungauged regions.