2020 article
Determination of vadose and saturated-zone nitrate lag times using long-term groundwater monitoring data and statistical machine learning
Wells, M. J., Gilmore, T. E., Nelson, N., Mittelstet, A., & Böhlke, J. K. (2020, May 5). (Vol. 5). Vol. 5.
TL;DR:
The statistical machine learning model results are consistent with previous observations of relatively high-water fluxes and short transit times for water and nitrate in the aquifer and indicate a sharp threshold where high groundwater nitrate concentrations are mostly associated with total travel times of seven years or less.
(via Semantic Scholar)
doi.org (repository)
UN Sustainable Development Goal Categories
6. Clean Water and Sanitation
(OpenAlex)
Source: ORCID
Added: July 7, 2020
Abstract. In this study, we explored the use of statistical machine learning and long-term groundwater nitrate monitoring data to estimate vadose-zone and saturated-zone lag times in an irrigated alluvial agricultural setting. Unlike most previous statistical machine learning studies that sought to predict groundwater nitrate concentrations within aquifers, the focus of this study was to leverage available groundwater nitrate concentrations and other environmental variable data to determine mean vertical velocities (transport rates) of water and solutes in the vadose zone and saturated zone (3.50 m/year and 3.75 m/year, respectively). Although a saturated-zone velocity that is greater than a vadose-zone velocity would be counterintuitive in most aquifer settings, the statistical machine learning results are consistent with two contrasting primary recharge processes in this aquifer: (1) diffuse recharge from irrigation and precipitation across the landscape, and (2) focused recharge from leaking irrigation conveyance canals. The vadose-zone mean velocity yielded a mean recharge rate (0.46 m/year) consistent with previous estimates from groundwater age-dating in shallow wells (0.38 m/year). The saturated zone mean velocity yielded a recharge rate (1.31 m/year) that was more consistent with focused recharge from leaky irrigation canals, as indicated by previous results of groundwater age-dating in intermediate-depth wells (1.22 m/year). Collectively, the statistical machine-learning model results are consistent with previous observations of relatively high-water fluxes and short transit times for water and nitrate in the aquifer. Partial dependence plots from the model indicate a sharp threshold where high groundwater nitrate concentrations are mostly associated with total travel times of seven years or less, possibly reflecting some combination of recent management practices and a tendency for nitrate concentrations to be higher in diffuse infiltration recharge than in canal leakage water. Limitations to the machine learning approach include potential non-uniqueness when comparing model performance for different transport rate combinations and highlight the need to corroborate statistical model results with a robust conceptual model and complementary information such as groundwater age.