{"accessLevel": "public", "bureauCode": ["010:12"], "contactPoint": {"@type": "vcard:Contact", "fn": "Farshid Rahmani", "hasEmail": "mailto:fzr5082@psu.edu"}, "description": "&lt;p&gt;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.&lt;/p&gt; &lt;p&gt;The data are organized into these items:&lt;/p&gt; &lt;ol&gt; &lt;li&gt;&lt;a href=\"https://www.sciencebase.gov/catalog/item/5f908db182ce720ee2d0fef9\"&gt;Spatial Information&lt;/a&gt; - Locations of the 118 monitoring sites used in this study&lt;/li&gt; &lt;li&gt;&lt;a href=\"https://www.sciencebase.gov/catalog/item/5f986594d34e198cb77ff084\"&gt;Observations&lt;/a&gt; - Water temperature observations for the 118 sites used in this study&lt;/li&gt; &lt;li&gt;&lt;a href=\"https://www.sciencebase.gov/catalog/item/5f9865abd34e198cb77ff086\"&gt;Model Inputs&lt;/a&gt; - Model inputs, including basin attributes, weather drivers, and discharge&lt;/li&gt; &lt;li&gt;&lt;a href=\"https://www.sciencebase.gov/catalog/item/5f9865cfd34e198cb77ff088\"&gt;Models&lt;/a&gt; - Code and configurations for the stream temperature models&lt;/li&gt; &lt;li&gt;&lt;a href=\"https://www.sciencebase.gov/catalog/item/5f9865e5d34e198cb77ff08a\"&gt;Model Predictions&lt;/a&gt; - Predictions of stream water temperature&lt;/li&gt; &lt;li&gt;&lt;a href=\"https://www.sciencebase.gov/catalog/item/5f9865fbd34e198cb77ff08c\"&gt;Model Evaluation&lt;/a&gt; - Performance metrics for each stream temperature model&lt;/li&gt; &lt;/ol&gt; &lt;p&gt;This research was funded by the Integrated Water Prediction Program at the US Geological Survey.&lt;/p&gt;", "distribution": [{"@type": "dcat:Distribution", "accessURL": "https://doi.org/10.5066/P97CGHZH", "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.5f908bae82ce720ee2d0fef2.xml", "format": "XML", "mediaType": "text/xml", "title": "Original Metadata"}], "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": {"@type": "org:Organization", "name": "U.S. Geological Survey"}, "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"}