Articles | Volume 25, issue 2
Hydrol. Earth Syst. Sci., 25, 811–829, 2021
https://doi.org/10.5194/hess-25-811-2021
Hydrol. Earth Syst. Sci., 25, 811–829, 2021
https://doi.org/10.5194/hess-25-811-2021

Research article 19 Feb 2021

Research article | 19 Feb 2021

Determination of vadose zone and saturated zone nitrate lag times using long-term groundwater monitoring data and statistical machine learning

Martin J. Wells et al.

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Interactive discussion

Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision
ED: Reconsider after major revisions (further review by editor and referees) (22 Aug 2020) by Nunzio Romano
AR by Troy E. Gilmore on behalf of the Authors (09 Oct 2020)  Author's response    Manuscript
ED: Referee Nomination & Report Request started (14 Oct 2020) by Nunzio Romano
RR by Scott Gardner (14 Nov 2020)
RR by Pia Ebeling (26 Nov 2020)
ED: Publish subject to minor revisions (review by editor) (28 Nov 2020) by Nunzio Romano
AR by Troy E. Gilmore on behalf of the Authors (09 Dec 2020)  Author's response    Manuscript
ED: Publish as is (27 Dec 2020) by Nunzio Romano
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Short summary
Groundwater in many agricultural areas contains high levels of nitrate, which is a concern for drinking water supplies. The rate at which nitrate moves through the subsurface is a critical piece of information for predicting how quickly groundwater nitrate levels may improve after agricultural producers change their approach to managing crop water and fertilizers. In this study, we explored a new statistical modeling approach to determine rates at which nitrate moves into and through an aquifer.