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>
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Complete Metadata
| @type | dcat:Dataset |
|---|---|
| accessLevel | public |
| bureauCode |
[ "010:12" ] |
| contactPoint |
{ "fn": "Farshid Rahmani", "@type": "vcard:Contact", "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> <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> |
| distribution |
[ { "@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" } ] |
| 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 |
{ "name": "U.S. Geological Survey", "@type": "org:Organization" } |
| 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 |
