The Central Valley in California (USA) covers about 52,000 km 2 and is one of the most productive agricultural regions in the world. This agriculture relies heavily on surface-water diversions and groundwater pumpage to meet irrigation water demand. Because the valley is semi-arid and surface-water availability varies substantially, agriculture relies heavily on local groundwater. In the southern two thirds of the valley, the San Joaquin Valley, historic and recent groundwater pumpage has caused significant and extensive drawdowns, aquifer-system compaction and subsidence. During recent drought periods (2007-2009 and 2012-present), groundwater pumping has increased owing to a combination of decreased surface-water availability and land-use changes. Declining groundwater levels, approaching or surpassing historical low levels, have caused accelerated and renewed compaction and subsidence that likely is mostly permanent. The subsidence has caused operational, maintenance, and construction-design problems for water-delivery and floodcontrol canals in the San Joaquin Valley. Planning for the effects of continued subsidence in the area is important for water agencies. As land use, managed aquifer recharge, and surface-water availability continue to vary, long-term groundwater-level and subsidence monitoring and modelling are critical to understanding the dynamics of historical and continued groundwater use resulting in additional water-level and groundwater storage declines, and associated subsidence. Modeling tools such as the Central Valley Hydrologic Model, can be used in the evaluation of management strategies to mitigate adverse impacts due to subsidence while also optimizing water availability. This knowledge will be critical for successful implementation of recent legislation aimed toward sustainable groundwater use.

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.

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Abstract. The dependency of surface-and groundwater flows and aquifer hydraulic properties on deformation induced by changes in aquifer head is not accounted for in the standard version of MODFLOW. A new USGS integrated hydrologic model, MODFLOW-OWHM, incorporates this dependency by linking subsidence and mesh deformation with changes in aquifer transmissivity and storage coefficient, and with flows that also depend on aquifer characteristics and land-surface geometry. This new deformation-dependent approach is being used for the further development of the integrated Central Valley hydrologic model (CVHM) in California. Preliminary results from this application and from hypothetical test cases of similar systems show that changes in canal flows, stream seepage, and evapotranspiration from groundwater (ET gw ) are sensitive to deformation. Deformation feedback has been shown to also have an indirect effect on conjunctive surface-and groundwater use components with increased stream seepage and streamflows influencing surface-water deliveries and return flows. In the Central Valley model, land subsidence may significantly degrade the ability of the major canals to deliver surface water from the Delta to the San Joaquin and Tulare basins. Subsidence can also affect irrigation demand and ET gw , which, along with altered surface-water supplies, causes a feedback response resulting in changed estimates of groundwater pumping for irrigation. This modeling feature also may improve the impact assessment of dewatering-induced land subsidence/uplift (following irrigation pumping or coal-seam gas extraction) on surface receptors, inter-basin transfers, and surface infrastructure integrity.