SignificanceWe increasingly rely on global models to project impacts of humans and climate on water resources. How reliable are these models? While past model intercomparison projects focused on water fluxes, we provide here the first comprehensive comparison of land total water storage trends from seven global models to trends from Gravity Recovery and Climate Experiment (GRACE) satellites, which have been likened to giant weighing scales in the sky. The models underestimate the large decadal (2002–2014) trends in water storage relative to GRACE satellites, both decreasing trends related to human intervention and climate and increasing trends related primarily to climate variations. The poor agreement between models and GRACE underscores the challenges remaining for global models to capture human or climate impacts on global water storage trends.

Recent developments in mascon (mass concentration) solutions for GRACE (Gravity Recovery and Climate Experiment) satellite data have significantly increased the spatial localization and amplitude of recovered terrestrial Total Water Storage anomalies (TWSA); however, land hydrology applications have been limited. Here we compare TWSA from April 2002 through March 2015 from (1) newly released GRACE mascons from the Center for Space Research (CSR‐M) with (2) NASA JPL mascons (JPL‐M), and with (3) CSR Tellus gridded spherical harmonics rescaled (sf) (CSRT‐GSH.sf) in 176 river basins, ∼60% of the global land area. Time series in TWSA mascons (CSR‐M and JPL‐M) and spherical harmonics are highly correlated (rank correlation coefficients mostly >0.9). The signal from long‐term trends (up to ±20 mm/yr) is much less than that from seasonal amplitudes (up to 250 mm). Net long‐term trends, summed over all 176 basins, are similar for CSR and JPL mascons (66–69 km3/yr) but are lower for spherical harmonics (∼14 km3/yr). Long‐term TWSA declines are found mostly in irrigated basins (−41 to −69 km3/yr). Seasonal amplitudes agree among GRACE solutions, increasing confidence in GRACE‐based seasonal fluctuations. Rescaling spherical harmonics significantly increases agreement with mascons for seasonal fluctuations, but less for long‐term trends. Mascons provide advantages relative to spherical harmonics, including (1) reduced leakage from land to ocean increasing signal amplitude, and (2) application of geophysical data constraints during processing with little empirical postprocessing requirements, making it easier for nongeodetic users. Results of this product intercomparison should allow hydrologists to better select suitable GRACE solutions for hydrologic applications.

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Global hydrological and land surface models are increasingly used for tracking terrestrial total water storage (TWS) dynamics, but the utility of existing models is hampered by conceptual and/or data uncertainties related to various underrepresented and unrepresented processes, such as groundwater storage. The gravity recovery and climate experiment (GRACE) satellite mission provided a valuable independent data source for tracking TWS at regional and continental scales. Strong interests exist in fusing GRACE data into global hydrological models to improve their predictive performance. Here we develop and apply deep convolutional neural network (CNN) models to learn the spatiotemporal patterns of mismatch between TWS anomalies (TWSA) derived from GRACE and those simulated by NOAH, a widely used land surface model. Once trained, our CNN models can be used to correct the NOAH-simulated TWSA without requiring GRACE data, potentially filling the data gap between GRACE and its follow-on mission, GRACE-FO. Our methodology is demonstrated over India, which has experienced significant groundwater depletion in recent decades that is nevertheless not being captured by the NOAH model. Results show that the CNN models significantly improve the match with GRACE TWSA, achieving a country-average correlation coefficient of 0.94 and Nash-Sutcliff efficient of 0.87, or 14% and 52% improvement, respectively, over the original NOAH TWSA. At the local scale, the learned mismatch pattern correlates well with the observed in situ groundwater storage anomaly data for most parts of India, suggesting that deep learning models effectively compensate for the missing groundwater component in NOAH for this study region.Plain Language Summary Global hydrological models are increasingly being used to assess water availability and sea level rise. Deficiencies in the conceptualization and parameterization in these models may introduce significant uncertainty in model predictions. GRACE satellite senses total water storage at the regional/continental scales. In this study, we applied deep learning to learn the spatial and temporal patterns of mismatch or residual between model simulation and GRACE observations. This hybrid learning approach leverages strengths of data science and hypothesis-driven physical modeling. We show, through three different types of convolution neural network-based deep learning models, that deep learning is a viable approach for improving model-GRACE match. The method can also be used to fill in data gaps between GRACE missions. Key Points: • Strong interests exist in fusing GRACE satellite TWS data into global hydrological models to improve their predictive performance • We train CNN deep learning models to learn the mismatch between TWS anomalies simulated by a land surface model and that observed by GRACE • Results show deep learning models significantly improved the predictive skills of land surface model by compensating for missing components Supporting Information: • Supporting Information S1

Use of GRACE (Gravity Recovery and Climate Experiment) satellites for assessing global water resources is rapidly expanding. Here we advance application of GRACE satellites by reconstructing longterm total water storage (TWS) changes from ground-based monitoring and modeling data. We applied the approach to the Colorado River Basin which has experienced multiyear intense droughts at decadal intervals. Estimated TWS declined by 94 km 3 during 1986-1990 and by 102 km 3 during 1998-2004, similar to the TWS depletion recorded by GRACE (47 km 3 ) during 2010-2013. Our analysis indicates that TWS depletion is dominated by reductions in surface reservoir and soil moisture storage in the upper Colorado basin with additional reductions in groundwater storage in the lower basin. Groundwater storage changes are controlled mostly by natural responses to wet and dry cycles and irrigation pumping outside of Colorado River delivery zones based on ground-based water level and gravity data. Water storage changes are controlled primarily by variable water inputs in response to wet and dry cycles rather than increasing water use. Surface reservoir storage buffers supply variability with current reservoir storage representing 2.5 years of available water use. This study can be used as a template showing how to extend short-term GRACE TWS records and using all available data on storage components of TWS to interpret GRACE data, especially within the context of droughts.

GRACE satellite data are widely used to estimate groundwater storage (GWS) changes in aquifers globally; however, comparisons with GW monitoring and modeling data are limited. Here we compared GWS changes from GRACE over 15 yr (2002-2017) in 14 major U.S. aquifers with groundwater-level (GWL) monitoring data in~23,000 wells and with regional and global hydrologic and land surface models. Results show declining GWS trends from GRACE data in the six southwestern and south-central U.S. aquifers, totaling −90 km 3 over 15 yr, related to long-term (5-15 yr) droughts, and exceeding Lake Mead volume by~2.5×. GWS trends in most remaining aquifers were stable or slightly rising. GRACE-derived GWS changes agree with GWL monitoring data in most aquifers (correlation coefficients, R = 0.52-0.95), showing that GRACE satellites capture groundwater (GW) dynamics. Regional GW models (eight models) generally show similar or greater GWS trends than those from GRACE. Large discrepancies in the Mississippi Embayment aquifer, with modeled GWS decline approximately four times that of GRACE, may reflect uncertainties in model storage parameters, stream capture, pumpage, and/or recharge rates. Global hydrologic models (2003-2014), which include GW pumping, generally overestimate GRACE GWS depletion (total: approximately −172 to −186 km 3) in heavily exploited aquifers in southwestern and south-central U.S. by~2.4× (GRACE: −74 km 3), underscoring needed modeling improvements relative to anthropogenic impacts. Global land surface models tend to track GRACE GWS dynamics better than global hydrologic models. Intercomparing remote sensing, monitoring, and modeling data underscores the importance of considering all data sources to constrain GWS uncertainties. Plain Language Summary The major U.S. aquifers provide an ideal system to assess GRACE (Gravity Recovery and Climate Experiment) satellite data. We compared GRACE groundwater storage anomalies (GWSAs) with groundwater level anomalies (GWLAs) from~23,000 wells and with groundwater storage (GWS) from regional and global models in 14 major U.S. aquifers. Results show large GWS declines from GRACE in southwestern (Central Valley and Arizona Alluvial Basins) and south-central (Central and Southern High Plains and Texas) aquifers from multiyear droughts (5-15 yr). In contrast, GWS trends in aquifers throughout the rest of the U.S. showed mostly stable or rising values. Time series of GRACE GWSAs compared favorably with GWLAs from most aquifers, suggesting that GRACE data track groundwater (GW) dynamics. Regional GW models show similar or greater declines in GWS compared with GRACE data, with the largest discrepancy of a factor of four times greater modeled depletion in the ©2020. American Geophysical Union. All Rights Reserved.