Predicting high‐frequency variation in stream solute concentrations with water quality sensors and machine learning-Reference-Cited by-同舟云学术

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Predicting high‐frequency variation in stream solute concentrations with water quality sensors and machine learning

Published:2020-12-15 Issue:1 Volume:35 Page:
ISSN:0885-6087
Container-title:Hydrological Processes
language:en
Short-container-title:Hydrological Processes

Author:

Green Mark B.¹²^ORCID,Pardo Linda H.²,Bailey Scott W.²^ORCID,Campbell John L.²^ORCID,McDowell William H.³^ORCID,Bernhardt Emily S.⁴,Rosi Emma J.⁵

Affiliation:

1. Department of Earth, Environmental, and Planetary Sciences Case Western Reserve University Cleveland Ohio

2. Northern Research Station, U.S. Forest Service North Woodstock New Hampshire

3. Department of Natural Resources and the Environment University of New Hampshire Durham New Hampshire

4. Department of Biology Duke University Durham North Carolina

5. Cary Institute of Ecosystem Studies Millbrook New York

Funder

Directorate for Biological Sciences

Office of Integrative Activities

Publisher

Wiley

Subject

Water Science and Technology

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/hyp.14000

Reference68 articles.

1. New Seasonal Shift in In-Stream Diurnal Nitrate Cycles Identified by Mining High-Frequency Data

2. Effects of climate‐related variability in storage on streamwater solute concentrations and fluxes in a small forested watershed in the Southeastern United States

3. Approaches to stream solute load estimation for solutes with varying dynamics from five diverse small watersheds

4. Hydrometeorological database for Hubbard Brook Experimental Forest: 1955-2000

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