Downscaling GRACE for groundwater management in California's Central Valley

Groundwater is a critical component of the local, regional and global water cycle. It constitutes an important storage of water, often relied upon in times of drought and in arid environments. Sustainable planning and management of groundwater resources requires accurate information about trends in groundwater water levels and quantities. In much of the globe, however, this data is limited. The 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 this data product to more localized studies and its utility to water management authorities has been constrained by its limited spatial resolution (~150,000 km2). Researchers have begun to address these shortcomings with data assimilation approaches that integrate GRACE total water storage estimates into complex regional models, producing higher-resolution hydrologic results (~4,000 km2). The present study takes these approaches one stepfurther by developing an empirically-based model capable of downscaling GRACE data to a high-resolution (~16 km2) dataset of groundwater storage changes. The model utilizes an artificial neural network (ANN) to generate a series of maps of groundwater level change over the GRACE time period (2002-present) using GRACE estimates of variations in total water storage and a series of publicly available hydrologic variables. The San Joaquin Valley Groundwater Basin in California’s Central Valley serves as the initial case study. Overall, the present study achieves two main goals: 1) it integrates robust numerical methods from the field of systems analysis with geodesy and hydrology; and most importantly 2) it also represents an important application of GRACE data to a local-scale water management study, illustrating how widely available remote sensing data can be utilized by management authorities.