Groundwater (GW) overexploitation is a critical issue in North China with large GW level declines resulting in urban water scarcity, unsustainable agricultural production, and adverse ecological impacts. One approach to addressing GW depletion was to transport water from the humid south. However, impacts of water diversion on GW remained largely unknown. Here, we show impacts of the central South-to-North Water Diversion on GW storage recovery in Beijing within the context of climate variability and other policies. Water diverted to Beijing reduces cumulative GW depletion by~3.6 km 3 , accounting for 40% of total GW storage recovery during 2006-2018. Increased precipitation contributes similar volumes to GW storage recovery of~2.7 km 3 (30%) along with policies on reduced irrigation (~2.8 km 3 , 30%). This recovery is projected to continue in the coming decade. Engineering approaches, such as water diversions, will increasingly be required to move towards sustainable water management.

The Northwest India Aquifer (NWIA) has been shown to have the highest groundwater depletion (GWD) rate globally, threatening crop production and sustainability of groundwater resources. Gravity Recovery and Climate Experiment (GRACE) satellites have been emerging as a powerful tool to evaluate GWD with ancillary data. Accurate GWD estimation is, however, challenging because of uncertainties in GRACE data processing. We evaluated GWD rates over the NWIA using a variety of approaches, including newly developed constrained forward modeling resulting in a GWD rate of 3.1 ± 0.1 cm/a (or 14 ± 0.4 km3/a) for Jan 2005–Dec 2010, consistent with the GWD rate (2.8 cm/a or 12.3 km3/a) from groundwater-level monitoring data. Published studies (e.g., 4 ± 1 cm/a or 18 ± 4.4 km3/a) may overestimate GWD over this region. This study highlights uncertainties in GWD estimates and the importance of incorporating a priori information to refine spatial patterns of GRACE signals that could be more useful in groundwater resource management and need to be paid more attention in future studies.

Launched in May 2018, the Gravity Recovery and Climate Experiment Follow-On mission (GRACE-FO)-the successor of the erstwhile GRACE mission-monitors changes in total water storage, which is a critical state variable of the regional and global hydrologic cycles. However, the gap between data of the two missions is breaking the continuity of the observations and limiting its further application. In this study, we used three learning-based models, that is, deep neural network, multiple linear regression (MLR), and seasonal autoregressive integrated moving average with exogenous variables, and six GRACE solutions (i.e., Jet Propulsion Laboratory spherical harmonics (JPL-SH), Center for Space Research SH (CSR-SH), GeoforschungsZentrum Potsdam SH (GFZ-SH), JPL mass concentration blocks (mascons) (JPL-M), CSR mascons (CSR-M), and Goddard Space Flight Center mascons (GSFC-M)) to reconstruct the missing monthly data at a grid cell scale. Evaluation showed that the three learning-based models were reliable for the reconstruction of GRACE data in areas with humid and no/low human interventions. The deep neural network models slightly outperformed the seasonal autoregressive integrated moving average with exogenous variables models and significantly outperformed the multiple linear regression models in most of 60 basins studied. The three GRACE mascon data sets performed better than the SH data sets at the basin scale. The models with SH solutions showed similar performance, but the models with the mascon solutions varied markedly in some basins. Results of this study are expected to provide a reference for bridging the data gaps between the GRACE and GRACE-FO satellites and for selecting suitable GRACE solutions for regional hydrologic studies.

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