3 Model Forcings: Deep learning approaches for improving prediction of daily stream temperature in data-scarce, unmonitored, and dammed basins
Dates
Publication Date
2021-09-27
Start Date
2010-10-01
End Date
2016-09-30
Citation
Rahmani, F., Shen, C., Oliver, S.K., Lawson, K., David Watkins, and Appling, A.P., 2021, Deep learning approaches for improving prediction of daily stream temperature in data-scarce, unmonitored, and dammed basins: U.S. Geological Survey data release, https://doi.org/10.5066/P9VHMO56.
Summary
This data release component contains model inputs including river basin attributes, weather forcing data, and simulated and observed river discharge.
Summary
This data release component contains model inputs including river basin attributes, weather forcing data, and simulated and observed river discharge.
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03_model_forcings.xml Original FGDC Metadata
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forcings.csv
58.05 MB
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Related External Resources
Type: Related Primary Publication
Rahmani, F., Shen, C., Oliver, S., Lawson, K. and Appling, A. (2021), Deep learning approaches for improving prediction of daily stream temperature in data-scarce, unmonitored, and dammed basins. Hydrological Processes. https://doi.org/10.1002/hyp.14400
