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arXiv · 2609.06794

Hourly U.S.-wide flood simulation beyond the limits of traditional and data-driven models

Wencong Yang·Leo Lonzarich·Yalan Song·Haoyu Ji·Ming Pan·Kathryn Lawson·Chaopeng Shen
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Abstract

As increasingly-damaging floods can strike within hours of a storm and in ungauged reaches, hourly network-wide simulation has become critical societal infrastructure. Here we demonstrate a multi-timescale physics-embedded learning model which outperforms the United States' operational system and surpasses AI-based systems at flood peaks. Covering more than 800,000 river reaches of the conterminous U.S., the model elevates median hourly Nash-Sutcliffe efficiency at 2,831 gauges to 0.683 from 0.461 for the operational National Water Model v3.0, and narrows flood-peak timing errors from 7-8 hours to 4.5-6 hours. dHBV2.0MTS-MC captures 33% more >=50-year floods than NWM3.0 and 159% more than an operational LSTM baseline. Against recent AI models, overall hourly skill is comparable while rare-flood accuracy is distinctly higher, with relative peak-magnitude error reduced by 34% for >=100-year floods. It combines long-term hydrologic context, short-term shocks, and infiltration excess to resolve extraordinary hourly peaks not visible on a daily plot. Process-model parameters and hourly discharge are produced for every reach, seamlessly covering the continent at 7.2 km2 median resolution. This candidate for the next-generation National Water Model sets a new operational accuracy level for national-scale flood prediction.

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Wencong Yang, Leo Lonzarich, Yalan Song, Haoyu Ji, Ming Pan, Kathryn Lawson, Chaopeng Shen. 2026-09-06. Hourly U.S.-wide flood simulation beyond the limits of traditional and data-driven models. https://arxiv.org/abs/2609.06794

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