1. Model code for model archive: Identifying structural priors in a hybrid differentiable model for stream water temperature modeling

<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> <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> <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>

                                            [
  "010:12"
]
                                        

                                            {
  "fn": "Farshid Rahmani",
  "@type": "vcard:Contact",
  "hasEmail": "mailto:fzr5082@psu.edu"
}
                                        

                                            [
  {
    "@type": "dcat:Distribution",
    "title": "Digital Data",
    "format": "XML",
    "accessURL": "https://doi.org/10.5066/P9UDDHVD",
    "mediaType": "application/http",
    "description": "Landing page for access to the data"
  },
  {
    "@type": "dcat:Distribution",
    "title": "Original Metadata",
    "format": "XML",
    "mediaType": "text/xml",
    "description": "The metadata original format",
    "downloadURL": "https://data.usgs.gov/datacatalog/metadata/USGS.648f9bbdd34ef77fcb001ffc.xml"
  }
]
                                        

                                            [
  "US",
  "USGS:648f9bbdd34ef77fcb001ffc",
  "United States",
  "deep learning",
  "environment",
  "inlandWaters",
  "machine learning",
  "modeling",
  "streams",
  "water resources",
  "water temperature"
]
                                        

                                            {
  "name": "U.S. Geological Survey",
  "@type": "org:Organization"
}
                                        

                                            [
  "Geospatial"
]