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Exploring the exceptional performance of a deep learning stream temperature model and the value of streamflow data

Dates

Publication Date
Start Date
2010-10-01
End Date
2016-09-30

Citation

Rahmani, F., Lawson, K., Ouyang, W., Appling, A.P., Oliver, S.K., and Shen, C., 2020, Exploring the exceptional performance of a deep learning stream temperature model and the value of streamflow data: U.S. Geological Survey data release, https://doi.org/10.5066/P97CGHZH.

Summary

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. The data are organized into these items: Spatial Information - Locations of the 118 monitoring sites used in this study Observations - Water temperature observations for the 118 sites used in this study Model Inputs - Model inputs, including basin attributes, weather [...]

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Purpose

Decision support, water quality research, and advancement of machine learning in hydrology

Additional Information

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Type Scheme Key
DOI https://www.sciencebase.gov/vocab/category/item/identifier doi:10.5066/P97CGHZH

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