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 km³ 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 km³) in heavily exploited aquifers in southwestern and south‐central U.S. by ~2.4× (GRACE: −74 km³), 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.

Global assessment of the spatiotemporal variability in terrestrial total water storage anomalies (TWSA) in response to hydrologic extremes is critical for water resources management. Using TWSA derived from the gravity recovery and climate experiment (GRACE) satellites, this study systematically assessed the skill of the TWSA-climatology (TC) approach and breakpoint (BP) detection method for identifying large-scale hydrologic extremes. The TC approach calculates standardized anomalies by using the mean and standard deviation of the GRACE TWSA corresponding to each month. In the BP detection method, the empirical mode decomposition (EMD) is first applied to identify the mean return period of TWSA extremes, and then a statistical procedure is used to identify the actual occurrence times of abrupt changes (i.e., BPs) in TWSA. Both detection methods were demonstrated on basin-averaged TWSA time series for the world’s 35 largest river basins. A nonlinear event coincidence analysis measure was applied to cross-examine abrupt changes detected by these methods with those detected by the Standardized Precipitation Index (SPI). Results show that our EMD-assisted BP procedure is a promising tool for identifying hydrologic extremes using GRACE TWSA data. Abrupt changes detected by the BP method coincide well with those of the SPI anomalies and with documented hydrologic extreme events. Event timings obtained by the TC method were ambiguous for a number of river basins studied, probably because the GRACE data length is too short to derive long-term climatology at this time. The BP approach demonstrates a robust wet-dry anomaly detection capability, which will be important for applications with the upcoming GRACE Follow-On mission.

Many recent mass balance estimates using the Gravity Recovery and Climate Experiment (GRACE) and satellite altimetry (including two kinds of sensors of radar and laser) show that the ice mass of the Antarctic ice sheet (AIS) is in overall decline. However, there are still large differences among previously published estimates of the total mass change, even in the same observed periods. The considerable error sources mainly arise from the forward models (e.g., glacial isostatic adjustment [GIA] and firn compaction) that may be uncertain but indispensable to simulate some processes not directly measured or obtained by these observations. To minimize the use of these forward models, we estimate the mass change of ice sheet and present-day GIA using multi-geodetic observations, including GRACE and Ice, Cloud and land Elevation Satellite (ICESat), as well as Global Positioning System (GPS), by an improved method of joint inversion estimate (JIE), which enables us to solve simultaneously for the Antarctic GIA and ice mass trends. The GIA uplift rates generated from our JIE method show a good agreement with the elastic-corrected GPS uplift rates, and the total GIA-induced mass change estimate for the AIS is 54 ± 27 Gt/yr, which is in line with many recent GPS calibrated GIA estimates. Our GIA result displays the presence of significant uplift rates in the Amundsen Sea Embayment of West Antarctica, where strong uplift has been observed by GPS. Over the period February 2003 to October 2009, the entire AIS changed in mass by −84 ± 31 Gt/yr (West Antarctica: −69 ± 24, East Antarctica: 12 ± 16 and the Antarctic Peninsula: −27 ± 8), greater than the GRACE-only estimates obtained from three Mascon solutions (CSR: −50 ± 30, JPL: −71 ± 30, and GSFC: −51 ± 33 Gt/yr) for the same period. This may imply that single GRACE data tend to underestimate ice mass loss due to the signal leakage and attenuation errors of ice discharge are often worse than that of surface mass balance over the AIS.

Assessing spatiotemporal water storage variability in the Great Lakes Watershed (GLW) is critical given its transboundary status impacting both Canada and the United States. Here, we apply a novel inversion strategy to global positioning system (GPS) vertical movements to achieve high spatial resolution total water storage (TWS) variations in GLW through improved processing. The steps are composed of removing load changes driven by the lake water fluctuation by forward modeling, isolating the Great Lakes grids to solve the ill‐conditioned problem in inversion, and inverting the GPS residual series to estimate TWS variations on land (TWS_GPS). The results show that the regional dense continuous GPS observation network can successfully resolve TWS on land at monthly timescales with 30–45 km spatial resolution. We also could effectively capture fine‐scale TWS features than GRACE/GFO mascon products. GRACE/GFO satellites largely underestimate seasonal and long‐term TWS spatial fluctuations, but their temporal patterns coincide with those from GPS. The average annual amplitude of TWS_GPS on land reaches 82.0 mm, greatly exceeding estimates from GRACE/GFO (∼48.0 mm) and composite hydrological model outputs (∼62.0 mm). The seasonal groundwater fluctuations inferred from GPS have peak‐to‐peak amplitudes of ∼40 km³ with the maximum around September. This coincides with that from GRACE/GFO. However, the magnitudes and phases of groundwater storage from GPS vary markedly among the subbasins in GLW, and the different snow and soil moisture amounts measured in each subbasin cause discrepancies among these GPS estimates. This study shows the value of GPS data in spatially downscaling GRACE/GFO data and providing high‐resolution output at spatiotemporal scales with low latency.