4. Figure code for model archive: Identifying structural priors in a hybrid differentiable model for stream water temperature modeling
Description
<p>This section provides code for reproducing the figures in Rahmani et al. (2023b).</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"> 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"> [THIS ITEM] 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>
Resources
| Name |
Format |
Description |
Link |
|
55 |
Landing page for access to the data |
https://doi.org/10.5066/P9UDDHVD |
|
55 |
The metadata original format |
https://data.usgs.gov/datacatalog/metadata/USGS.6495df90d34ef77fcb01e285.xml |
Tags
- environment
- modeling
- deep-learning
- united-states
- us
- usgs-6495df90d34ef77fcb01e285
- inlandwaters
- water-resources
- water-temperature
- streams
- machine-learning