{
  "accessLevel": "public",
  "bureauCode": [
    "010:12"
  ],
  "contactPoint": {
    "@type": "vcard:Contact",
    "fn": "Farshid Rahmani",
    "hasEmail": "mailto:fzr5082@psu.edu"
  },
  "description": "&lt;p&gt;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.&lt;/p&gt;\n&lt;p&gt;The &lt;a href=\"https://www.sciencebase.gov/catalog/item/64888368d34ef77fcafe3936\"&gt;full model archive&lt;/a&gt; is organized into these four child items: &lt;li&gt;&lt;a href=\"https://www.sciencebase.gov/catalog/item/648f9bbdd34ef77fcb001ffc\"&gt; [THIS ITEM] 1. Model code &lt;/a&gt;- Python files and README for reproducing model training and evaluation &lt;/li&gt; &lt;li&gt;&lt;a href=\"https://www.sciencebase.gov/catalog/item/648f9c49d34ef77fcb001fff\"&gt; 2. Inputs &lt;/a&gt;- Basin attributes and shapefiles, forcing data, and stream temperature observations &lt;/li&gt; &lt;li&gt;&lt;a href=\"https://www.sciencebase.gov/catalog/item/648f9caed34ef77fcb002001\"&gt; 3. Simulations &lt;/a&gt;- Simulation descriptions, configurations, and outputs &lt;/li&gt; &lt;li&gt;&lt;a href=\"https://www.sciencebase.gov/catalog/item/6495df90d34ef77fcb01e285\"&gt; 4. Figure code &lt;/a&gt;- Jupyter notebook to recreate the figures in Rahmani et al. (2023b) &lt;/li&gt; &lt;/p&gt;\n&lt;p&gt;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. &lt;a href=https://doi.org/10.1029/2023WR034420&gt;https://doi.org/10.1029/2023WR034420&lt;/a&gt;.&lt;/p&gt;",
  "distribution": [
    {
      "@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"
    },
    {
      "@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"
    }
  ],
  "identifier": "http://datainventory.doi.gov/id/dataset/USGS_648f9bbdd34ef77fcb001ffc",
  "keyword": [
    "US",
    "USGS:648f9bbdd34ef77fcb001ffc",
    "United States",
    "deep learning",
    "environment",
    "inlandWaters",
    "machine learning",
    "modeling",
    "streams",
    "water resources",
    "water temperature"
  ],
  "modified": "2023-11-28T00:00:00Z",
  "publisher": {
    "@type": "org:Organization",
    "name": "U.S. Geological Survey"
  },
  "spatial": "-124.138658984335, 29.1524975232233, -67.8714112090545, 49.0018341836332",
  "theme": [
    "geospatial"
  ],
  "title": "1. Model code for model archive: Identifying structural priors in a hybrid differentiable model for stream water temperature modeling"
}