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Estimating the permanent loss of groundwater storage in the southern <scp>S</scp>an <scp>J</scp>oaquin <scp>V</scp>alley, <scp>C</scp>alifornia
156
Citations
26
References
2017
Year
Groundwater QualityPermanent LossEngineeringHydrologic EngineeringHydrogeologic SystemEarth ScienceGeophysicsCrustal DeformationGeoenvironmental EngineeringSpatial DistributionDrought ForecastingSan Joaquin ValleyHydrometeorologyHydrogeologySubsurface HydrologyGeographyHydrologyGroundwater StorageWater ResourcesDroughtRecent DroughtsLand SubsidenceGroundwater Management
Abstract In the San Joaquin Valley, California, recent droughts starting in 2007 have increased the pumping of groundwater, leading to widespread subsidence. In the southern portion of the San Joaquin Valley, vertical subsidence as high as 85 cm has been observed between June 2007 and December 2010 using Interferometric Synthetic Aperture Radar (InSAR). This study seeks to map regions where inelastic (not recoverable) deformation occurred during the study period, resulting in permanent compaction and loss of groundwater storage. We estimated the amount of permanent compaction by incorporating multiple data sets: the total deformation derived from InSAR, estimated skeletal‐specific storage and hydraulic parameters, geologic information, and measured water levels during our study period. We used two approaches, one that we consider to provide an estimate of the lowest possible amount of inelastic deformation, and one that provides a more reasonable estimate. These two approaches resulted in a spatial distribution of values for the percentage of the total deformation that was inelastic, with the former estimating a spatially averaged value of 54%, and the latter a spatially averaged value of 98%. The former corresponds to the permanent loss of m 3 of groundwater storage, or roughly 5% of the volume of groundwater used over the study time period; the latter corresponds to the loss of m 3 of groundwater storage, or roughly 9% of the volume of groundwater used. This study demonstrates that a data‐driven approach can be used effectively to estimate the permanent loss of groundwater storage.
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