Predicted warming and frequent, extreme weather events will trigger a decline in surface water sustainability and exacerbate aquifer use for drinking water and irrigation demands. Groundwater is already a critical resource for semi-arid agricultural regions, like California’s Central Valley, and as agriculture becomes more irrigation-driven in response to changing climate and production pressure, it is essential that we understand how water management decisions affect groundwater quality, particularly the levels of geogenic contaminants. In this work, we use a statewide groundwater geochemical database to (1) determine the probability of hexavalent chromium [Cr(VI)] contamination and (2) spatially resolve anthropogenic and natural influences governing Cr(VI) release in groundwater across the Central Valley. We apply multivariate statistical techniques, including multiple factor and cluster analyses, to discern groundwater signatures susceptible to elevated Cr(VI). The driving processes are revealed through factor loading interpretations and cluster analysis of the compositional data. Factor scores are spatially interpolated using ordinary kriging to create maps of such processes; geostatistical simulation provides spatial uncertainty quantification. Our data analysis shows the existence of two distinct clusters controlled by alkalinity, redox, geology and population density. Elevated Cr(VI) concentrations only occurred in the first cluster, characteristic of oxic conditions, and positively correlated with nitrate and other oxyanions (e.g. V, Se, U). Natural, agricultural and urban controls were clarified when the multivariate workflow was reiterated on the first cluster. In particular, three extracted clusters illustrated different Cr sources and possible release mechanisms, including irrigation return and subsurface wetting/drying cycles from water table fluctuations. The relationship between these clusters and predictor variables (land use, aquifer parameters, geology, hydrology) can then be exploited in logistic regression to build predictive models.

Predicting drivers of groundwater Cr(VI) contamination in the Central Valley, CA: Integrated multivariate statistical & geospatial approach

2019 Fall National Meeting

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