Deep learning approaches for improving prediction of daily stream temperature in data-scarce, unmonitored, and dammed basins
<p>This data release provides all data and code used in Rahmani et al. (2021b) to model stream temperature and assess results. Briefly, we modeled stream temperature at sites across the continental United States using deep learning methods. The associated manuscript explores the prediction challenges posed by reservoirs, the value of additional training sites when predicting in gaged vs ungaged sites, and the value of an ensemble of attribute subsets in improving prediction accuracy.</p> <p>The data are organized into these child items:</p> <ol> <li><a href="https://www.sciencebase.gov/catalog/item/606db85fd34e670a7d5f61f0">Site Information</a> - Attributes and spatial information about the monitoring sites and basins in this study</li> <li><a href="https://www.sciencebase.gov/catalog/item/6083384fd34efe46ec0a2333">Observations</a> - Water temperature observations for the sites used in this study</li> <li><a href="https://www.sciencebase.gov/catalog/item/6084cab2d34eadd49d31aeab">Model Inputs</a> - Model input, including meteorological drivers and discharge</li> <li><a href="https://www.sciencebase.gov/catalog/item/6084cb16d34eadd49d31aead">Model Code</a> - Model code, instructions, and configurations for running the stream temperature models</li> <li><a href="https://www.sciencebase.gov/catalog/item/6084cb2ed34eadd49d31aeaf">Model Predictions</a> - Predictions of stream water temperature</li> </ol> <p>This research was funded by the Integrated Water Prediction Program at the US Geological Survey.</p> <p>The publication associated with this data release is Rahmani, F., Shen, C., Oliver, S.K., Lawson, K., and Appling, A.P., 2021, Deep learning approaches for improving prediction of daily stream temperature in data-scarce, unmonitored, and dammed basins. Hydrologic Processes. DOI: XX.
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| description | <p>This data release provides all data and code used in Rahmani et al. (2021b) to model stream temperature and assess results. Briefly, we modeled stream temperature at sites across the continental United States using deep learning methods. The associated manuscript explores the prediction challenges posed by reservoirs, the value of additional training sites when predicting in gaged vs ungaged sites, and the value of an ensemble of attribute subsets in improving prediction accuracy.</p> <p>The data are organized into these child items:</p> <ol> <li><a href="https://www.sciencebase.gov/catalog/item/606db85fd34e670a7d5f61f0">Site Information</a> - Attributes and spatial information about the monitoring sites and basins in this study</li> <li><a href="https://www.sciencebase.gov/catalog/item/6083384fd34efe46ec0a2333">Observations</a> - Water temperature observations for the sites used in this study</li> <li><a href="https://www.sciencebase.gov/catalog/item/6084cab2d34eadd49d31aeab">Model Inputs</a> - Model input, including meteorological drivers and discharge</li> <li><a href="https://www.sciencebase.gov/catalog/item/6084cb16d34eadd49d31aead">Model Code</a> - Model code, instructions, and configurations for running the stream temperature models</li> <li><a href="https://www.sciencebase.gov/catalog/item/6084cb2ed34eadd49d31aeaf">Model Predictions</a> - Predictions of stream water temperature</li> </ol> <p>This research was funded by the Integrated Water Prediction Program at the US Geological Survey.</p> <p>The publication associated with this data release is Rahmani, F., Shen, C., Oliver, S.K., Lawson, K., and Appling, A.P., 2021, Deep learning approaches for improving prediction of daily stream temperature in data-scarce, unmonitored, and dammed basins. Hydrologic Processes. DOI: XX. |
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| modified | 2021-09-27T00:00:00Z |
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| title | Deep learning approaches for improving prediction of daily stream temperature in data-scarce, unmonitored, and dammed basins |
