AI models built on the same broad technique used to generate images are showing promise for predicting extreme river flows. In a 2025 study, a diffusion-based runoff model improved reported nowcasting skill for the top 0.1% of flows in 72.3% of the representative contiguous U.S. basins studied. Those are research results—not evidence that the model is already powering nationwide flash-flood warnings.
How can image-generation AI help predict floods?
Diffusion models are a type of generative AI. They learn patterns from complete examples and can generate plausible outcomes. In image generation, those outcomes are pictures; in hydrology, the models instead predict water-flow variables and represent uncertainty by producing probabilistic forecasts.
The connection is the modeling approach, not the output: the flood studies do not generate images. The 2025 DRUM study applies diffusion to runoff and extreme-flood forecasting, while a separate 2026 study introduces h-Diffusion for hourly streamflow prediction and data assimilation. The U.S. Department of Energy’s OSTI record for the DRUM study and the h-Diffusion study in Water Resources Research describe these distinct applications.
What did the DRUM flood-forecasting study find?
The 2025 DRUM research team reported improved nowcasting skill for the top 0.1% of flows in 72.3% of the representative contiguous U.S. basins it studied. The figure is a share of sampled basins meeting a study metric; it is not the percentage of U.S. floods correctly forecast, nor an operational success rate.
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The team also reported nearly a full day of added reliable lead time for 20- and 50-year floods under its operational-scenario evaluation. Results differed under an idealized evaluation using measured precipitation: recall improved by 0.3–0.4, and warning for 50-year floods came 2.3 days earlier. The study further reported lead-time gains of 3–7 days for precipitation-driven flood zones in the eastern and northwestern United States. These figures come from different evaluation conditions and should not be treated as directly interchangeable guarantees for an individual flood.
For the study’s methods and findings, see the 2025 Geophysical Research Letters paper or its OSTI record.
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What does the separate h-Diffusion study add?
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Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Are these models already used in NOAA flash-flood warnings?
The available sources do not establish that DRUM or h-Diffusion has been deployed in NOAA operations. NOAA’s National Severe Storms Laboratory describes FLASH as a continental-scale flash-flood forecasting project that uses Multi-Radar Multi-Sensor (MRMS) precipitation information with hydrologic models. NOAA says the framework can accommodate multiple precipitation inputs, model structures, and newer AI and machine-learning methods; that flexibility is not confirmation that either featured diffusion model is running in FLASH.
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NOAA NSSL states: “The primary goal of the FLASH project is to improve the accuracy, timing, and specificity of flash flood warnings in the US, thus saving lives and protecting infrastructure.” The statement appears on the NOAA NSSL FLASH project page. NOAA’s separate flooding research page provides broader context on its flood research.
NOAA’s FLASH project page describes product resolution of 1 km and 5 minutes. Those specifications belong to FLASH, not to DRUM or h-Diffusion, and do not describe the resolution or performance of the research models. See the FLASH – Flooded Locations And Simulated Hydrographs Project page.
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How to interpret the reported warning gains
- Check the target: DRUM focuses on runoff and extreme-flood forecasting; h-Diffusion predicts hourly streamflow and explores data assimilation.
- Check the evaluation conditions: DRUM’s results using measured precipitation are described as an ideal condition. Do not assume that lead time would be the same with operational precipitation inputs.
- Keep geography in view: The DRUM study reports regional lead-time gains for precipitation-driven flood zones in the eastern and northwestern United States, not a uniform result everywhere.
- Distinguish a study from a warning service: Performance in a research evaluation does not by itself show that a model is deployed, validated for operational use, or issuing public alerts.
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