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.

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.