Exploring the exceptional performance of a deep learning stream temperature model and the value of streamflow data
<p>This data release provides all data and code used in Rahmani et al. (2020) to model stream temperature and assess results. Briefly, we used a subset of the USGS GAGES-II dataset as a test case for temperature prediction using deep learning methods. The associated manuscript explores the value of including stream discharge as a predictor in the temperature models, including the value of predicted discharge from a separate model when no discharge measurements are available.</p> <p>The data are organized into these items:</p> <ol> <li><a href="https://www.sciencebase.gov/catalog/item/5f908db182ce720ee2d0fef9">Spatial Information</a> - Locations of the 118 monitoring sites used in this study</li> <li><a href="https://www.sciencebase.gov/catalog/item/5f986594d34e198cb77ff084">Observations</a> - Water temperature observations for the 118 sites used in this study</li> <li><a href="https://www.sciencebase.gov/catalog/item/5f9865abd34e198cb77ff086">Model Inputs</a> - Model inputs, including basin attributes, weather drivers, and discharge</li> <li><a href="https://www.sciencebase.gov/catalog/item/5f9865cfd34e198cb77ff088">Models</a> - Code and configurations for the stream temperature models</li> <li><a href="https://www.sciencebase.gov/catalog/item/5f9865e5d34e198cb77ff08a">Model Predictions</a> - Predictions of stream water temperature</li> <li><a href="https://www.sciencebase.gov/catalog/item/5f9865fbd34e198cb77ff08c">Model Evaluation</a> - Performance metrics for each stream temperature model</li> </ol> <p>This research was funded by the Integrated Water Prediction Program at the US Geological Survey.</p>
Find Related Datasets
Search by Tags
Click any tag below to search for similar datasets
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 data release provides all data and code used in Rahmani et al. (2020) to model stream temperature and assess results. Briefly, we used a subset of the USGS GAGES-II dataset as a test case for temperature prediction using deep learning methods. The associated manuscript explores the value of including stream discharge as a predictor in the temperature models, including the value of predicted discharge from a separate model when no discharge measurements are available.</p> <p>The data are organized into these items:</p> <ol> <li><a href="https://www.sciencebase.gov/catalog/item/5f908db182ce720ee2d0fef9">Spatial Information</a> - Locations of the 118 monitoring sites used in this study</li> <li><a href="https://www.sciencebase.gov/catalog/item/5f986594d34e198cb77ff084">Observations</a> - Water temperature observations for the 118 sites used in this study</li> <li><a href="https://www.sciencebase.gov/catalog/item/5f9865abd34e198cb77ff086">Model Inputs</a> - Model inputs, including basin attributes, weather drivers, and discharge</li> <li><a href="https://www.sciencebase.gov/catalog/item/5f9865cfd34e198cb77ff088">Models</a> - Code and configurations for the stream temperature models</li> <li><a href="https://www.sciencebase.gov/catalog/item/5f9865e5d34e198cb77ff08a">Model Predictions</a> - Predictions of stream water temperature</li> <li><a href="https://www.sciencebase.gov/catalog/item/5f9865fbd34e198cb77ff08c">Model Evaluation</a> - Performance metrics for each stream temperature model</li> </ol> <p>This research was funded by the Integrated Water Prediction Program at the US Geological Survey.</p> |
| distribution |
[ { "@type": "dcat:Distribution", "title": "Digital Data", "format": "XML", "accessURL": "https://doi.org/10.5066/P97CGHZH", "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.5f908bae82ce720ee2d0fef2.xml" } ] |
| identifier | http://datainventory.doi.gov/id/dataset/USGS_5f908bae82ce720ee2d0fef2 |
| keyword |
[ "AL", "Alabama", "DE", "Delaware", "GA", "Georgia", "IA", "ID", "Idaho", "Iowa", "KS", "Kansas", "MA", "MD", "ME", "MI", "MS", "Maine", "Maryland", "Massachusetts", "Michigan", "Mississippi", "NC", "NJ", "NM", "NV", "NY", "Nevada", "New Jersey", "New Mexico", "New York", "North Carolina", "OH", "OK", "OR", "Ohio", "Oklahoma", "Oregon", "PA", "Pennsylvania", "RI", "Rhode Island", "SC", "South Carolina", "TN", "TX", "Tennessee", "Texas", "US", "USGS:5f908bae82ce720ee2d0fef2", "UT", "United States", "Utah", "VA", "Virginia", "WA", "WI", "WV", "WY", "Washington", "West Virginia", "Wisconsin", "Wyoming", "deep learning", "environment", "inlandWaters", "machine learning", "modeling", "streams", "water resources", "water temperature" ] |
| modified | 2020-12-09T00:00:00Z |
| publisher |
{ "name": "U.S. Geological Survey", "@type": "org:Organization" } |
| spatial | -123.32988684, 30.1454932, -70.97964444, 48.90595739 |
| theme |
[ "geospatial" ] |
| title | Exploring the exceptional performance of a deep learning stream temperature model and the value of streamflow data |
