{
  "abstract": "Study region: Luolingqiao (326 km²) and Wenjiashi (164 km²), two small-to-medium sub-basins of the Xiang River Basin in Hunan Province, China. Study focus: Reliable flash flood forecasting in small-to-medium basins (SMBs) requires both high temporal resolution and physically constrained long-lead prediction. This study develops a dual-constraint physics-informed framework that couples the Xin'anjiang (XAJ) model with an encoder-decoder LSTM to produce 10-minute runoff forecasts at lead times of up to 6 h, forced exclusively by quality-controlled X-band radar quantitative precipitation estimation (QPE). XAJ-derived physical trajectories are supplied to the LSTM decoder, and a dual-constraint loss penalizes deviations from both observed discharge and XAJ-simulated runoff. The framework is evaluated on 28 flood events under blocked chronological cross-validation, in hindcast mode with observed QPE over the forecast horizon. New hydrological insights for the region: At the 6-h lead time, the hybrid model significantly outperforms the standalone XAJ model: relative to XAJ, NSE increases by 20%, RMSE decreases by 25%, and peak error decreases by 46%. Continuous evaluation from 1 to 6 h reveals a crossover near 3 h: the LSTM dominates at shorter horizons, whereas the hybrid model becomes optimal beyond 3–4 h, where physical regularization suppresses recursive error accumulation. Coupling process-based constraints with deep learning at 10-minute resolution is therefore feasible and valuable for flash flood early warning in SMBs with concentration times shorter than 3 h.",
  "abstract_shown": true,
  "affiliations": [
    "School of Hydraulic and Ocean Engineering, Changsha University of Science and Technology, Changsha 410114, China; Key Laboratory of Dongting Lake Aquatic Eco-Environmental Control and Restoration of Hunan Province, Changsha 410114, China; Corresponding author at: School of Hydraulic and Ocean Engineering, Changsha University of Science & Technology, Changsha 410114, China.",
    "School of Hydraulic and Ocean Engineering, Changsha University of Science and Technology, Changsha 410114, China; Key Laboratory of Dongting Lake Aquatic Eco-Environmental Control and Restoration of Hunan Province, Changsha 410114, China",
    "Power China Zhongnan Engineering Corporation Limited, Changsha 410019, China"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 5,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Xinkui Wang"
    },
    {
      "affs": [
        1
      ],
      "name": "Changbo Jiang"
    },
    {
      "affs": [
        1
      ],
      "name": "Yuannan Long"
    },
    {
      "affs": [
        1
      ],
      "name": "Xinyi Song"
    },
    {
      "affs": [
        2
      ],
      "name": "Xingyi Zhang"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/87yfezk6jq9xc.bib",
  "cited_by": 0,
  "doi": "10.1016/j.ejrh.2026.104183",
  "fulltext_url": "http://www.sciencedirect.com/science/article/pii/S2214581826010839",
  "id": "87yfezk6jq9xc",
  "issns": [
    "2214-5818"
  ],
  "landing_url": "https://doi.org/10.1016/j.ejrh.2026.104183",
  "language": "en",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "lcc",
      "GB3-5030"
    ],
    [
      "lcc",
      "QE1-996.5"
    ],
    [
      "openalex",
      "2305|Hydrological Forecasting Using AI"
    ]
  ],
  "openalex": "W7225226976",
  "pdf_url": null,
  "pmcid": null,
  "pmid": null,
  "published": "2026-11-05",
  "publisher": "Elsevier",
  "ris": "https://research.draconitic.com/paper/87yfezk6jq9xc.ris",
  "sources": [
    {
      "first_seen": "2026-11-05T14:21:20Z",
      "native_id": "06e9442c7abc40ecb40aba83a147b1b2",
      "source": "doaj",
      "url": "https://doi.org/10.1016/j.ejrh.2026.104183"
    }
  ],
  "subjects": [
    {
      "field": "earth",
      "origin": "openalex:2305|Hydrological Forecasting Using AI",
      "subfield": "environmental-science"
    }
  ],
  "title": "Dual-constraint physics-informed LSTM-XAJ hybrid model for 10-minute flood forecasting in small-to-medium basins: A case of the Xiang River Basin, China",
  "title_text": "Dual-constraint physics-informed LSTM-XAJ hybrid model for 10-minute flood forecasting in small-to-medium basins: A case of the Xiang River Basin, China",
  "type": "peer-reviewed",
  "updated_at": "2026-11-07T14:20:33Z",
  "url": "https://research.draconitic.com/paper/87yfezk6jq9xc",
  "venue": "Journal of Hydrology: Regional Studies",
  "versions": []
}