Improving Operational Extreme Flood Forecasting and Climate Change Impact Assessment with Physics-Embedded Differentiable Modeling

Flash flooding is one of the deadliest natural hazards worldwide, causing severe loss of life and infrastructure damage. Predicting extreme flood events remains highly challenging because they often fall outside the range of historical observations, involve small-scale storm processes that are poorly resolved by existing forecasting systems, and include nonlinear flood-generation mechanisms that are inadequately represented in current models. Although pure AI models, such as LSTMs, generally outperform traditional hydrologic models in simulation accuracy, they often fail to predict extreme streamflow beyond a certain threshold and tend to underestimate extremes due to structural limitations, such as bounded activation functions. Differentiable models (DMs), which jointly train neural networks with process-based models, can overcome these limitations through interpretable physical modules and physically consistent representations, thereby achieving improved accuracy in extreme-event prediction compared with LSTMs. Here, we will demonstrate (1) how DMs improve extreme-event predictions and how dynamic parameters contribute to this improvement; (2) the feasibility of high-resolution, hourly differentiable models for operational extreme flood forecasting by resolving short-lived, small-scale storms; (3) the importance of incorporating different nonlinear flood-generation mechanisms; and (4) the robustness of DMs for long-term climate change impact assessment.