With the increasing global need for groundwater resources to fulfill domestic, agricultural, and industrial demands, we face the threat of increasing concentrations of naturally occurring contaminants in water sources and a consequential need to improve our predictive capacity. Here, we combine machine learning and geochemical modeling to reveal the biogeochemical controls on regional groundwater uranium contamination within the Central Valley, California. We use 23 environmental parameters from a statewide groundwater geochemical database and publicly available maps of soil and aquifer physicochemical properties to predict groundwater uranium concentrations by random forest regression. We find that groundwater calcium, nitrate, and sulfate concentrations, soil pH, and clay content (weighted average between 0 and 2 m depths) are the most important predictors of groundwater uranium concentrations. By pairing multivariate partial dependence and accumulated local effect plots with modeled aqueous uranium speciation and surface complexation outputs, we show that regional groundwater uranium exceedances of drinking water standards, 30 μg L–1, are dependent on the formation of uranyl–calcium–carbonato species. The geochemical conditions leading to ternary uranyl complexes within the aquifer are, in part, created by infiltration through the vadose zone, illustrating the critical dependence of groundwater quality on recharge conditions.

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