{
  "accessLevel": "public",
  "bureauCode": [
    "020:00"
  ],
  "contactPoint": {
    "fn": "Rajbir Parmar",
    "hasEmail": "mailto:parmar.rajbir@epa.gov"
  },
  "description": "The study results and data used and produced in this study are available through the Texas Data Repository at https://doi.org/10.18738/T8/A9X5ET (Srinivasan et al., 2023). The data also includes the necessary information to reproduce the figures and tables presented in the study. \n\nThis dataset is associated with the following publication:\nBawa, A., K. Mendoza, R. Srinivasan, F. O'Donncha, D. Smith, K. Wolfe, R. Parmar, J. Johnston, and J. Corona. Enhancing Hydrological Modeling of Ungauged Watersheds through Machine Learning and Physical Similarity-based Regionalization of Calibration Parameters.   ENVIRONMENTAL MODELLING & SOFTWARE. Elsevier Science, New York, NY,   186: 106335, (2025).",
  "distribution": [
    {
      "accessURL": "https://doi.org/10.18738/T8/A9X5ET",
      "title": "https://doi.org/10.18738/T8/A9X5ET"
    }
  ],
  "identifier": "https://doi.org/10.23719/1531961",
  "keyword": [
    "AI",
    "HAWQS",
    "PUB",
    "SWAT",
    "calibration",
    "streamflow"
  ],
  "license": "https://pasteur.epa.gov/license/sciencehub-license-non-epa-generated.html",
  "modified": "2025-01-05",
  "programCode": [
    "020:000"
  ],
  "publisher": {
    "name": "U.S. EPA Office of Research and Development (ORD)",
    "subOrganizationOf": {
      "name": "U.S. Environmental Protection Agency",
      "subOrganizationOf": {
        "name": "U.S. Government"
      }
    }
  },
  "references": [
    "https://doi.org/10.1016/j.envsoft.2025.106335"
  ],
  "rights": null,
  "title": "Enhancing Hydrological Modeling of Ungauged Watersheds through Machine Learning and Physical Similarity-based Regionalization of Calibration Parameters"
}