{"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"}