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Intense demand for water in the Central Valley of California and related increases in groundwater nitrate concentration threaten the sustainability of the groundwater resource. To assess contamination risk in the region, we developed a hybrid, non-linear, machine learning model within a statistical learning framework to predict nitrate contamination of groundwater to depths of approximately 500m below ground surface. A database of 145 predictor variables representing well characteristics, historical and current field and landscape-scale nitrogen mass balances, historical and current land use, oxidation/reduction conditions, groundwater flow, climate, soil characteristics, depth to groundwater, and groundwater age were assigned to over 6000 private supply and public supply wells measured previously for nitrate and located throughout the study area. The boosted regression tree (BRT) method was used to screen and rank variables to predict nitrate concentration at the depths of domestic and public well supplies. The novel approach included as predictor variables outputs from existing physically based models of the Central Valley. The top five most important predictor variables included two oxidation/reduction variables (probability of manganese concentration to exceed 50ppb and probability of dissolved oxygen concentration to be below 0.5ppm), field-scale adjusted unsaturated zone nitrogen input for the 1975 time period, average difference between precipitation and evapotranspiration during the years 1971-2000, and 1992 total landscape nitrogen input. Twenty-five variables were selected for the final model for log-transformed nitrate. In general, increasing probability of anoxic conditions and increasing precipitation relative to potential evapotranspiration had a corresponding decrease in nitrate concentration predictions. Conversely, increasing 1975 unsaturated zone nitrogen leaching flux and 1992 total landscape nitrogen input had an increasing relative impact on nitrate predictions. Three-dimensional visualization indicates that nitrate predictions depend on the probability of anoxic conditions and other factors, and that nitrate predictions generally decreased with increasing groundwater age.

Uranium (U) concentrations in groundwater in several parts of the eastern San Joaquin Valley, California, have exceeded federal and state drinking water standards during the last 20 years. The San Joaquin Valley is located within the Central Valley of California and is one of the most productive agricultural areas in the world. Increased irrigation and pumping associated with agricultural and urban development during the last 100 years have changed the chemistry and magnitude of groundwater recharge, and increased the rate of downward groundwater movement. Strong correlations between U and bicarbonate suggest that U is leached from shallow sediments by high bicarbonate water, consistent with findings of previous work in Modesto, California. Summer irrigation of crops in agricultural areas and, to lesser extent, of landscape plants and grasses in urban areas, has increased P co 2 concentrations in the soil zone and caused higher temperature and salinity of groundwater recharge. Coupled with groundwater pumping, this process, as evidenced by increasing bicarbonate concentrations in groundwater over the last 100 years, has caused shallow, young groundwater with high U concentrations to migrate to deeper parts of the groundwater system that are tapped by public-supply wells. Continued downward migration of U-affected groundwater and expansion of urban centers into agricultural areas will likely be associated with increased U concentrations in public-supply wells. The results from this study illustrate the potential longterm effects of groundwater development and irrigation-supported agriculture on water quality in arid and semiarid regions around the world.

Total dissolved solids (TDS) concentrations in groundwater tapped for beneficial uses (drinking water, irrigation, freshwater industrial) have increased on average by about 100 mg/L over the last 100 years in the San Joaquin Valley, California (SJV). During this period land use in the SJV changed from natural vegetation and dryland agriculture to dominantly irrigated agriculture with growing urban areas. Century-scale salinity trends were evaluated by comparing TDS concentrations and major ion compositions of groundwater from wells sampled in 1910 (Historic) to data from wells sampled in 1993-2015 (Modern). TDS concentrations in subregions of the SJV, the southern (SSJV), western (WSJV), northeastern (NESJV), and southeastern (SESJV) were calculated using a cell-declustering method. TDS concentrations increased in all regions, with the greatest increases found in the SSJV and SESJV. Evaluation of the Modern data from the NESJV and SESJV found higher TDS concentrations in recently recharged (post-1950) groundwater from shallow (<50 m) wells surrounded predominantly by agricultural land uses, while premodern (pre-1950) groundwater from deeper wells, and recently recharged groundwater from wells surrounded by mainly urban, natural, and mixed land uses had lower TDS concentrations, approaching the TDS concentrations in the Historic groundwater. For the NESJV and SESJV, inverse geochemical modeling with PHREEQC indicated that weathering of primary silicate minerals accounted for the majority of the increase in TDS concentrations, contributing more than nitrate from fertilizers and sulfate from soil amendments combined. Bicarbonate showed the greatest increase among major ions, resulting from enhanced silicate weathering due to recharge of irrigation water enriched in CO during the growing season. The results of this study demonstrate that large anthropogenic changes to the hydrologic regime, like massive development of irrigated agriculture in semi-arid areas like the SJV, can cause large changes in groundwater quality on a regional scale.