{"accessLevel": "public", "bureauCode": ["010:12"], "contactPoint": {"@type": "vcard:Contact", "fn": "Farshid Rahmani", "hasEmail": "mailto:fzr5082@psu.edu"}, "description": "<p>This section provides model code described by Rahmani et al. (2023b). This code accepts basin attributes and forcings and predicts stream temperatures using a differentiable model with neural network and process-based equation components. Code files are contained within code.zip. A description of each code file is given in the 01_code.xml metadata file and also in code_file_dictionary.csv. Instructions on how to run the code are given in code_readme.md.</p>\n<p>The <a href=\"https://www.sciencebase.gov/catalog/item/64888368d34ef77fcafe3936\">full model archive</a> is organized into these four child items: <li><a href=\"https://www.sciencebase.gov/catalog/item/648f9bbdd34ef77fcb001ffc\"> [THIS ITEM] 1. Model code </a>- Python files and README for reproducing model training and evaluation </li> <li><a href=\"https://www.sciencebase.gov/catalog/item/648f9c49d34ef77fcb001fff\"> 2. Inputs </a>- Basin attributes and shapefiles, forcing data, and stream temperature observations </li> <li><a href=\"https://www.sciencebase.gov/catalog/item/648f9caed34ef77fcb002001\"> 3. Simulations </a>- Simulation descriptions, configurations, and outputs </li> <li><a href=\"https://www.sciencebase.gov/catalog/item/6495df90d34ef77fcb01e285\"> 4. Figure code </a>- Jupyter notebook to recreate the figures in Rahmani et al. (2023b) </li> </p>\n<p>The publication associated with this model archive is: Rahmani, F., Appling, A.P., Feng, D., Lawson, K., and Shen, C. 2023b. Identifying structural priors in a hybrid differentiable model for stream water temperature modeling. Water Resources Research. <a href=https://doi.org/10.1029/2023WR034420>https://doi.org/10.1029/2023WR034420</a>.</p>", "distribution": [{"@type": "dcat:Distribution", "description": "The metadata original format", "downloadURL": "https://data.usgs.gov/datacatalog/metadata/USGS.648f9bbdd34ef77fcb001ffc.xml", "format": "xml", "mediaType": "text/xml", "title": "Original Metadata"}, {"@type": "dcat:Distribution", "accessURL": "https://doi.org/10.5066/P9UDDHVD", "description": "Landing page for access to the data", "format": "xml", "mediaType": "application/http", "title": "Digital Data"}], "identifier": "http://datainventory.doi.gov/id/dataset/usgs-648f9bbdd34ef77fcb001ffc", "keyword": ["Streams", "USGS:648f9bbdd34ef77fcb001ffc", "Water Temperature", "Environment", "deep learning", "US", "modeling", "United States", "Machine learning", "water resources", "inlandWaters"], "modified": "2023-11-28T00:00:00Z", "publisher": {"@type": "org:Organization", "name": "U.S. Geological Survey"}, "theme": ["geospatial"], "title": "1. Model code for model archive: Identifying structural priors in a hybrid differentiable model for stream water temperature modeling"}