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.

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While the physical processes governing groundwater flow are well understood, and the computational resources now exist for solving the governing equations in three dimensions over continental‐scale domains, there remains substantial uncertainty about the subsurface distribution of the properties that control groundwater flow and transport for much of the contiguous United States (CONUS). The transmissivity of the shallow subsurface is a key parameter for the simulation of water table position, shallow groundwater flow, and base‐flow discharge, but is not well‐characterized at large regional to continental scales. We used a process‐based inversion of CONUS‐extent groundwater information to generate national data sets of (a) the transmissivity of the shallow groundwater system, (b) the depth to the water table, (c) groundwater discharge as base‐flow, and (d) long‐term average water content in the unsaturated zone. CONUS‐extent coverage was developed in the form of 75 subdomain models, with the spatial distribution of long‐term average transmissivity for each subdomain model calibrated against water‐levels derived from U.S. Geological Survey (USGS) observation wells, NHDPlusV2 first‐order perennial streams, and National Wetlands Inventory (NWI) freshwater wetlands. Estimated transmissivities were lower in the western CONUS than the eastern CONUS, and across the CONUS both transmissivity and depth to water correlate with recharge, elevation, and topographic slope. These generated data sets provide spatially distributed, long‐term average estimates of subsurface properties and hydrological states that we anticipate will complement other environmental modeling efforts as explanatory variables, boundary conditions, or transport pathways.

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Numerous studies have documented the linkages between agricultural nitrogen loads and surface water degradation. In contrast, potential water quality improvements due to agricultural best management practices are difficult to detect because of the confounding effect of background nitrate removal rates, as well as the groundwater-driven delay between land surface action and stream response. To characterize background controls on nitrate removal in two agricultural catchments, we calibrated groundwater travel time distributions with subsurface environmental tracer data to quantify the lag time between historic agricultural inputs and measured baseflow nitrate. We then estimated spatially distributed loading to the water table from nitrate measurements at monitoring wells, using machine learning techniques to extrapolate the loading to unmonitored portions of the catchment to subsequently estimate catchment removal controls. Multiple models agree that in-stream processes remove as much as 75% of incoming loads for one subcatchment while removing <20% of incoming loads for the other. The use of a spatially variable loading field did not result in meaningfully different optimized parameter estimates or model performance when compared with spatially constant loading derived directly from a county-scale agricultural nitrogen budget. Although previous studies using individual well measurements have shown that subsurface denitrification due to contact with a reducing argillaceous confining unit plays an important role in nitrate removal, the catchment-scale contribution of this process is difficult to quantify given the available data. Nonetheless, the study provides a baseline characterization of nitrate transport timescales and removal mechanisms that will support future efforts to detect water quality benefits from ongoing best management practice implementation.Abbreviation: BFI, baseflow index; BMP, best management practice; TTD, travel time distribution; WSSE, weighted sum of square errors.