Accurate and detailed knowledge of California's groundwater is of paramount importance for statewide water resources planning and management, and to sustain a multi-billion-dollar agriculture industry during prolonged droughts. In this study, we use water supply and demand information from California's Department of Water Resources to develop an aggregate groundwater storage model for California's Central Valley. The model is evaluated against 34 years of historic estimates of changes in groundwater storage derived from the United States Geological Survey's Central Valley Hydrologic Model (USGS CVHM) and NASA's Gravity Recovery and Climate Experiment (NASA GRACE) satellites. The calibrated model is then applied to predict future changes in groundwater storage for the years 2015-2050 under various precipitation scenarios from downscaled climate projections. We also discuss and project potential management strategies across different annual supply and demand variables and how they affect changes in groundwater storage. All simulations support the need for collective statewide management intervention to prevent continued depletion of groundwater availability.

NASA’s Gravity Recovery and Climate Experiment (GRACE) has already proven to be a powerful data source for regional groundwater assessments in many areas around the world. However, the applicability of GRACE data products to more localized studies and their utility to water management authorities have been constrained by their limited spatial resolution (~200,000 km2). Researchers have begun to address these shortcomings with data assimilation approaches that integrate GRACE-derived total water storage estimates into complex regional models, producing higher-resolution estimates of hydrologic variables (~2500 km2). Here we take those approaches one step further by developing an empirically based model capable of downscaling GRACE data to a high-resolution (~16 km2) dataset of groundwater storage changes over a portion of California’s Central Valley. The model utilizes an artificial neural network to generate a series of high-resolution maps of groundwater storage change from 2002 to 2010 using GRACE estimates of variations in total water storage and a series of widely available hydrologic variables (PRISM precipitation and temperature data, digital elevation model (DEM)-derived slope, and Natural Resources Conservation Service (NRCS) soil type). The neural network downscaling model is able to accurately reproduce local groundwater behavior, with acceptable Nash-Sutcliffe efficiency (NSE) values for calibration and validation (ranging from 0.2445 to 0.9577 and 0.0391 to 0.7511, respectively). Ultimately, the model generates maps of local groundwater storage change at a 100-fold higher resolution than GRACE gridded data products without the use of computationally intensive physical models. The model’s simulated maps have the potential for application to local groundwater management initiatives in the region.

In California, new groundwater legislation—the 2014 Sustainable Groundwater Management Act (SGMA)—mandates that groundwater sustainability agencies (GSAs) employ the concept of sustainable yield as their primary management goal. However, SGMA’s current definition of sustainable yield does not offer clear guidance for new agencies and lacks grounding in physics. This study presents a novel hydrologically based framework for quantifying sustainable yield under SGMA that is derived from a synthesis of scientific inquiry and analysis. We introduce a flexible three-step approach that basin managers can rely on to quantify sustainable yield values, incorporate the impact of “undesirable results”, and analyze groundwater sustainability over SGMA’s implementation horizon. Our framework is illustrated through a case study of the South San Joaquin Irrigation District, a proposed GSA in one of California’s critically overdrafted groundwater basins. We calculate sustainable yield for three different management scenarios and assess the impact of each scenario on future groundwater sustainability by performing an annual water groundwater balance through 2040. Our sustainable yield framework can be used as a basis for the development of SGMA’s groundwater management plans throughout California.