Ukasha, MuhammadRamirez, JorgeNiemann, Jeffrey2022-07-152022-07-152022https://hdl.handle.net/10217/235434http://dx.doi.org/10.25675/10217/235434Data in Figures 2 to 4.Department of Civil and Environmental EngineeringThe Gravity Recovery And Climate Experiments (GRACE) satellite mission has been instrumental in estimating large-scale groundwater storage changes across the globe. GRACE observations include significant errors, so pre-processing is normally required before the data are used. In particular, the regional observations of terrestrial water storage anomalies (TWSA) are usually filtered to reduce the effects of measurement errors and then rescaled to reduce the unintended impacts of the filtering. The rescaling is typically selected to maximize the Nash-Sutcliffe Efficiency (NSE) between the rescaled filtered TWSA and original TWSA from large-scale hydrologic models that represent an incomplete water budget. The objectives of this study are (1) to evaluate the use of NSE in the current GRACE rescaling methodology, (2) develop an improved methodology that incorporates a complete regional water budget, and (3) examine the impacts of the rescaling methodology on regional assessments of groundwater depletion. To evaluate the use of NSE as a performance metric, we implement an analytical solution to improve the relative variability between the filtered and original TWSA series. Improved relative variability produces more reliable estimates when comparing the results to TWSA estimates from global positioning systems (GPS) for the Sacramento and San Joaquin River basins (containing Central Valley) in California. Rescaling with the complete regional water budget based on observed hydrological fluxes results in a larger scale factor (3.18) than the scale factor from the large-scale hydrologic model outputs (1.97), and the new TWSA results are more consistent with those from GPS. The larger scale factor also suggests that regional groundwater depletion is more severe than previously estimated.PDFTXTengDatasetThe material is open access and distributed under the terms and conditions of the Creative Commons Public Domain "No rights reserved" (https://creativecommons.org/share-your-work/public-domain/cc0/).