Articles | Volume 25, issue 2
https://doi.org/10.5194/hess-25-811-2021
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, Troy E. Gilmore, Natalie Nelson, Aaron Mittelstet, and John K. Böhlke

Data sets

Dutch Flats Groundwater Nitrate for Machine Learning Martin Wells and Troy E. Gilmore https://doi.org/10.32873/unl.dr.20200428

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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.