{
  "abstract": "Hydrological modeling is essential for water resources management but often requires specialized expertise, creating a significant barrier to its broad application. To address this challenge, we developed HydroCraft, a web-based platform designed to democratize the modeling process. HydroCraft provides a comprehensive and integrated workflow for basin extraction, driving data generation, parameter calibration, and result visualization, enabling rapid modeling for any watershed globally. Key features of the platform include: an adaptive watershed delineation method that optimizes the distribution of computational units based on user-defined sub-basin area thresholds; the integration of three hydrological models (THREW, Xin’anjiang, and Miyun Hydrological Model) suitable for diverse climatic and geological conditions; and the incorporation of a Large Language Model (LLM) agent, which serves as a robust assistant to provide guidance and automated task execution. Users only need a device with a web browser and an internet connection to perform advanced hydrologic simulations. Two case studies (user-driven vs. LLM-agent-assisted) demonstrated the platform’s efficiency and intelligence. The platform advances hydrological modeling toward greater intelligence and accessibility, providing support for applications such as water resource management and flood forecasting",
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  "affiliations": [
    "State Key Laboratory of Hydroscience and Engineering, Tsinghua University, Beijing 100084, China"
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  "author_total": 3,
  "authors": [
    {
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        0
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      "name": "Mahmut Tudaji"
    },
    {
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        0
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      "name": "Fuqiang Tian",
      "orcid": "0000-0001-9414-7019"
    },
    {
      "affs": [
        0
      ],
      "name": "Xiannan Wu"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/erxcc50rjv35v.bib",
  "cited_by": 8,
  "doi": "10.1016/j.jhydrol.2026.135801",
  "fulltext_url": null,
  "id": "erxcc50rjv35v",
  "issns": [
    "0022-1694",
    "1879-2707"
  ],
  "landing_url": "https://doi.org/10.1016/j.jhydrol.2026.135801",
  "language": "en",
  "licence": "CC-BY-NC-ND",
  "licence_label": "CC BY-NC-ND",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": null,
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      "1907|Environmental Monitoring and Data Management"
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  "openalex": "W7163713586",
  "pdf_url": null,
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  "pmid": null,
  "published": "2026-06-05",
  "publisher": "Elsevier BV",
  "ris": "https://research.draconitic.com/paper/erxcc50rjv35v.ris",
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      "url": "https://doi.org/10.1016/j.jhydrol.2026.135801"
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  "subjects": [
    {
      "field": "earth",
      "origin": "openalex:2308|Environmental Monitoring and Data Management",
      "subfield": "environmental-science"
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    {
      "field": "earth",
      "origin": "openalex:1907|Environmental Monitoring and Data Management",
      "subfield": "geology-geochemistry"
    }
  ],
  "title": "HydroCraft: an efficient online platform for hydrological modeling integrated with a large language model agent",
  "title_text": "HydroCraft: an efficient online platform for hydrological modeling integrated with a large language model agent",
  "type": "peer-reviewed",
  "updated_at": "2026-11-08T13:20:54Z",
  "url": "https://research.draconitic.com/paper/erxcc50rjv35v",
  "venue": "Journal of Hydrology",
  "versions": []
}