Hapi improves continental flood forecasts with fast multivariable AI model
Hapi: A Multivariable Land-Surface Transformer for Medium-Range Hydrological Forecasting at Continental Scale
Artificial Intelligence
Summary
Flood forecasting helps people prepare for heavy rains and floods days before they happen. The authors created Hapi, an AI model that quickly predicts water flow, runoff, snow, and soil wetness over the United States with fine details and wide context. It works faster and more accurately than current physics models and other AI models, especially detecting rare floods. The model runs quickly on a powerful GPU and could support better flood control and emergency responses.
What this means in practice
- •For emergency response teams: Generate more accurate short-term flood warnings across large regions to improve preparedness and reduce risks.
- •For water resource managers: Forecast runoff and soil moisture at high resolution to support better water allocation and reservoir operations.
Authors
Hong Zhang, John K. Hutchison, Rao Kotamarthi, Jeremy Feinstein, Haiwen Guan, Romit Maulik, Vijay P. Ramalingam, Jason Stock, Tom Wall
Abstract
Accurate flood forecasts several days in advance are essential for flood control, water-resource management, and emergency response. Producing them at high resolution over a continental domain calls for local hydrological detail together with spatial context extending from river basins to synoptic weather systems. We developed Hapi, a U-Net Swin Transformer that uses fine three-dimensional patches and hierarchical shifted-window attention to forecast discharge, surface runoff, snow water equivalent, and soil wetness across the contiguous United States. The model produces 24--72-hour forecasts at $0.05^{\circ}$ resolution, with learned Laplacian task weights adjusting each variable's contribution to training. On 2024 test data using reconstructed weather and land-surface inputs from ERA5-Land, Hapi outperformed an operational physics-based model and a state-of-the-art AI model in flood detection. Independent validation against 3,881 U.S. Geological Survey gauges and a Hurricane Helene case study supported its advantage over the physics-based model in reproducing daily discharge. Controlled experiments showed that learned task weighting strengthens rare-flood detection, which is particularly sensitive to changes in precipitation inputs. Hapi produced a four-variable, 72-hour forecast across the contiguous United States with an average inference time of 0.11 seconds on a single A100 GPU.