{
  "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.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.06794",
  "author_total": 7,
  "authors": [
    {
      "name": "Wencong Yang"
    },
    {
      "name": "Leo Lonzarich"
    },
    {
      "name": "Yalan Song"
    },
    {
      "name": "Haoyu Ji"
    },
    {
      "name": "Ming Pan"
    },
    {
      "name": "Kathryn Lawson"
    },
    {
      "name": "Chaopeng Shen"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/aqammxtpgw6c1.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.06794",
  "fulltext_url": null,
  "id": "aqammxtpgw6c1",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.06794",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "physics.geo-ph"
    ],
    [
      "openalex",
      "2305|Hydrological Forecasting Using AI"
    ]
  ],
  "openalex": "W7212059382",
  "pdf_url": "https://arxiv.org/pdf/2609.06794",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-06",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/aqammxtpgw6c1.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:19:48Z",
      "native_id": "2609.06794",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.06794"
    }
  ],
  "subjects": [
    {
      "field": "earth",
      "origin": "arxiv:physics.geo-ph",
      "subfield": "geophysics"
    }
  ],
  "title": "Hourly U.S.-wide flood simulation beyond the limits of traditional and data-driven models",
  "title_text": "Hourly U.S.-wide flood simulation beyond the limits of traditional and data-driven models",
  "type": "preprint",
  "updated_at": "2026-10-30T12:19:48Z",
  "url": "https://research.draconitic.com/paper/aqammxtpgw6c1",
  "venue": "arXiv",
  "versions": []
}