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2 Observations: Deep learning approaches for improving prediction of daily stream temperature in data-scarce, unmonitored, and dammed basins

Dates

Publication Date
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 mean daily stream water temperature observations, retrieved from the USGS National Water Information System (NWIS) and used to train and validate all temperature models. The model training period was from 2010-10-01 to 2014-09-30, and the test period was from 2014-10-01 to 2016-09-30.

Contacts

Attached Files

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temperature_observations.csv 17.81 MB text/csv

Purpose

Water quality research, advancement of machine learning in hydrology, improve predictions of stream temperature in ungagged or dammed basins.

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