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  "name": "High-Resolution Global Streamflow Dataset from 1980 - 2020 for 2.94 Million Rivers Using the Physics-Embedded \u03b4HBV2\u2013\u03b4MC2 Model",
  "description": "This repo provides a complete set of  global-scale streamflow simulations generated with the physics-embedded, high-resolution \u03b4HBV2\u2013\u03b4MC2 model. Due to storage limitations on Zenodo, the complete global streamflow simulations are archived in HydroShare. For citation purposes, please reference the Zenodo record: [10.5281/zenodo.17042358].\r\n\r\nThis dataset is a direct result of Ji et al., 2025 described below, which built upon the work in Song et al., 2025. Please cite these two papers if you find the data to be of use (* indicates MHPI group members):\r\n\r\nJi, Haoyu*, Yalan Song*, Tadd Bindas*, Chaopeng Shen*, Yuan Yang, Ming Pan, Jiangtao Liu*, Farshid Rahmani*, Ather Abbas, Hylke Beck, Kathryn Lawson* and Yoshihide Wada. Distinct hydrologic response patterns and trends worldwide revealed by physics-embedded learning. Nature Communications. https://doi.org/10.1038/s41467-025-64367-1 \r\n\r\nSong, Yalan*, Tadd Bindas*, Chaopeng Shen*, Haoyu Ji*, Wouter J. M. Knoben, Leo Lonzarich*, Martyn P. Clark, Jiangtao Liu*, Katie van Werkhoven, Sam Lemont, Matthew Denno, Ming Pan, Yuan Yang, Jeremy Rapp, Mukesh Kumar, Farshid Rahmani*, Cyril Th\u00e9bault, Richard Adkins, James Halgren, Trupesh Patel, Arpita Patel, Kamlesh Sawadekar*, and Kathryn Lawson* (2025). High-resolution national-scale water modeling is enhanced by multiscale differentiable physics-informed machine learning. Water Resources Research, doi: 10.1029/2024WR038928",
  "url": "http://www.hydroshare.org/resource/6c8191d3613c4477b717be41c81a4372",
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    "http://www.hydroshare.org/resource/6c8191d3613c4477b717be41c81a4372"
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  "creator": [
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      "type": "Person",
      "email": "hjj5218@psu.edu",
      "identifier": null,
      "affiliation": {
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        "address": null,
        "name": "Pennsylvania State University"
      },
      "name": "Ji, Haoyu"
    }
  ],
  "dateCreated": "2025-09-03 02:51:47.276542+00:00",
  "keywords": [
    "global streamflow datasets",
    "differentiable model",
    "hydrology"
  ],
  "license": {
    "type": "CreativeWork",
    "name": "This resource is shared under the Creative Commons Attribution CC BY.",
    "description": null,
    "url": "http://creativecommons.org/licenses/by/4.0/"
  },
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    "url": "https://www.hydroshare.org/",
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  "inLanguage": "eng",
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    "description": "The resource is publicly accessible and can be viewed or downloaded by anyone"
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  "dateModified": "2025-11-12 17:21:20.222485+00:00",
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  "temporalCoverage": {
    "endDate": "2020-12-31 00:00:00",
    "startDate": "1980-01-01 00:00:00"
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  "citation": [
    "Ji, H. (2025). High-Resolution Global Streamflow Dataset from 1980 - 2020 for 2.94 Million Rivers Using the Physics-Embedded \u03b4HBV2\u2013\u03b4MC2 Model, HydroShare, http://www.hydroshare.org/resource/6c8191d3613c4477b717be41c81a4372"
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  "review_started": "2025-11-05T04:24:47.540537Z",
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