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Groundwater depletion is adversely affecting Beijing's ecology and environment. However, the effective execution of the South-to-North Water Diversion Project's middle route (SNDWP-MR) is anticipated to mitigate Beijing's groundwater depletion. Here, we propose a robust hybrid statistical downscaling method aimed at enhancing the capability of the Gravity Recovery and Climate Experiment (GRACE) to detect the small-scale groundwater storage anomaly (GWSA) in Beijing. We used three deep learning (DL) methods to reconstruct the 0.5° × 0.5° terrestrial water storage anomaly (TWSA) between 2004 and 2021. Moreover, multiple processing strategies were used to downscale the GWSA to 0.25° from 2004 to 2021 by integrating wells and GRACE/GRACE follow-on data from the optimal DL model. Additionally, we analyzed the spatiotemporal evolution trends of GW in Beijing before and after the implementation of the SNDWP-MR. The results show that the long short-term memory model delivers optimal performance in the TWSA reconstruction of Beijing, with the correlation coefficient (CC), Nash–Sutcliffe coefficient (NSE), and root mean square error (RMSE) being 0.98, 0.96, and 10.19 mm, respectively. The GWSA before and after downscaling is basically consistent with wells data, but the CC and RMSE of downscaling the GWSA from 2004 to 2021 are improving by 34% and 31%, respectively. Before the SNDWP-MR (2004–2014), the trend of GWSA in Beijing was − 17.68 ± 4.46 mm/y, with a human contribution of 69.30%. After SNDWP-MR (2015–2021), GWSA gradually increased by 10.00 mm per year, with the SNDWP-MR accounting for 18.30%. This study delivers a technical innovation reference for dynamically monitoring a small-scale GWSA from GRACE/GRACE-FO data. [ABSTRACT FROM AUTHOR]

The Gravity Recovery and Climate Experiment (GRACE) mission has monitored global total water storage anomalies (TWSA) with an unprecedented accuracy since 2002. Yet, many applications require a longer record, that is, extending prior to the GRACE period. Here, we present a new global reconstruction of long‐term (1979–2020) TWSA fields by combining machine learning with time series decomposition and statistical decomposition techniques. We find that the long‐term TWSA reconstructed from GRACE fits well with the GRACE‐FO observation over most grids (0.5° resolution) of the global land and successfully reproduces the strong El Niño signal. Comparisons to Satellite Laser Ranging solutions and to observed global mean sea level change suggest our reconstruction (doi: https://doi.org/10.5061/dryad.z612jm6bt) is more reliable than previously published products. This study provides a viable approach for both reconstructing past TWSA and filling the GRACE data gap at the global scale. Plain Language Summary: Since 2002, temporal changes of vertically integrated water storage have been detected globally with unprecedented accuracy via satellite gravimetry, as first implemented with the Gravity Recovery and Climate Experiment (GRACE) satellites. However, no similar observation system has been in place before 2002, that could have used to measure the total water storage anomalies (TWSA) with an accuracy comparable to GRACE. Several approaches, such as machine learning, have been proposed in previous studies to reconstruct past TWSA based on their empirical relationships with the climatic/hydrological drivers (e.g., precipitation and soil moisture). Different from traditional approaches, we here combine machine learning with statistical decomposition techniques to reconstruct GRACE‐like TWSA (0.5° resolution) for the period 1979–2020 over the global land. We find that the reconstructed TWSA shows strong abnormal signals during El Niño years. In addition, we compare our reconstruction to recent TWSA data obtained using Satellite Laser Ranging analysis to five satellites, that is, Lageos‐1, Lageos‐2, Ajisai, Stella, and Starlette, in the period since 1993, and to the observed global mean sea level change as validations. The comparison suggests that our reconstruction is more accurate than previously published products. We suggest our method provides a viable alternative for reconstructing long‐term TWSA. Key Points: We reconstruct Gravity Recovery and Climate Experiment‐like (GRACE) total water storage anomaly (TWSA) fields over the global land surface for the period 1979–2020TWSA reconstructed from GRACE fit well with GRACE Follow‐On data and correctly reproduce strong El Niño signalsOutside the GRACE period, our reconstruction fits better with satellite laser ranging solutions and global mean sea level than previous reconstruction [ABSTRACT FROM AUTHOR]

The Gravity Recovery and Climate Experiment (GRACE) mission ended its operation in October 2017, and the GRACE Follow‐On mission was launched only in May 2018, leading to approximately 1 year of data gap. Given that GRACE‐type observations are exclusively providing direct estimates of total water storage change (TWSC), it would be very important to bridge the gap between these two missions. Furthermore, for many climate‐related applications, it is also desirable to reconstruct TWSC prior to the GRACE period. In this study, we aim at comparing different data‐driven methods and identifying the more robust alternatives for predicting GRACE‐like gridded TWSC during the gap and reconstructing them to 1992 using climate inputs. To this end, we first develop a methodological framework to compare different methods such as the multiple linear regression (MLR), artificial neural network (ANN), and autoregressive exogenous (ARX) approaches. Second, metrics are developed to measure the robustness of the predictions. Finally, gridded TWSC within 26 regions are predicted and reconstructed using the identified methods. Test computations suggest that the correlation of predicted TWSC maps with observed ones is more than 0.3 higher than TWSC simulated by hydrological models, at the grid scale of 1° resolution. Furthermore, the reconstructed TWSC correctly reproduce the El Nino‐Southern Oscillation (ENSO) signals. In general, while MLR does not perform best in the training process, it is more robust and could thus be a viable approach both for filling the GRACE gap and for reconstructing long‐period TWSC fields globally when combined with statistical decomposition techniques. Key Points: We develop a unified methodological framework to compare viable approaches for reconstructing and predicting globally gridded GRACE fieldsPredicted total water storage change fields fit better to the observations than those simulated by hydrological modelsReconstructed total water storage change correctly reproduces a strong abnormal signal in the tropical river basin during the El Niño period [ABSTRACT FROM AUTHOR]

The Gravity Recovery and Climate Experiment (GRACE) satellite provides time-varying gravity field models that can detect total water storage change (TWSC) from April 2002 to June 2017, and its second-generation satellite, GRACE Follow-On (GRACE-FO), provides models from June 2018, so there is a one year gap. Swarm satellites are equipped with Global Positioning System (GPS) receivers, which can be used to recover the Earth's time-varying gravitational field. Swarm's time-varying gravitational field models (from December 2013 to June 2018) were solved by the International Combination Service for Time-variable Gravity Field Solutions (COST-G) and the Astronomical Institute of the Czech Academy of Sciences (ASI). On a timely scale, Swarm has the potential to fill the gap between the two generations of GRACE satellites. In this paper, using 26 global watersheds as the study area, first, we explored the optimal data processing strategy for Swarm and then obtained the Swarm-TWSC of each watershed based on the optimal results. Second, we evaluated Swarm's accuracy in detecting regional water storage variations, analyzed the reasons for its superior and inferior performance in different regions, and systematically explored its potential in detecting terrestrial water storage changes in land areas. Finally, we constructed the time series of terrestrial water storage changes from 2002 to 2019 by combining GRACE, Swarm, and GRACE-FO for the Amazon, Volga, and Zambezi Basins. The results show that the optimal data processing strategy of Swarm is different from that of GRACE. The optimal results of Swarm-TWSC were explored in 26 watersheds worldwide; its accuracy is related to the area size, runoff volume, total annual mass change, and instantaneous mass change of the watershed itself, among which the latter is the main factor affecting Swarm-TWSC. Knowledge of the Swarm-TWSC of 26 basins constructed in this paper is important to study long-term water storage changes in basins. [ABSTRACT FROM AUTHOR]

The Gravity Recovery and Climate Experiment (GRACE) mission has measured total water storage change (TWSC) and interpreted drought patterns in an unparalleled way since 2002. Nevertheless, there are few sources that can be used to understand drought patterns prior to the GRACE era. In this study, we extended the gridded GRACE TWSC to 1993 by combining principal component analysis (PCA), least square (LS) fitting, and multiple linear regression (MLR) methods using climate variables as input drivers. We used the extended (climate-driven) TWSC to interpret drought patterns (1993–2019) over the Amazon basin. Results showed that, in the Amazon area with the resolution of 0.5°, GRACE, GRACE follow on, and Swarm had correlation coefficients of 0.95, 0.92, and 0.77 compared with climate-driven TWSCS, respectively. The drought patterns assessed by the climate-driven TWSC were consistent with those interpreted by the Palmer Drought Severity Index and GRACE TWSC. We also found that the 1998 and 2016 drought events in the Amazon, both induced by strong El Niño events, showed similar drought patterns. This study provides a new perspective for interpreting long-term drought patterns prior to the GRACE period. [ABSTRACT FROM AUTHOR]

The Danjiangkou Reservoir (DJKR) is the freshwater source for the Middle Route of the South-to-North Water Diversion Project in China, and its water level and storage changes are important for water resource management. To maximize the potential capacity of the Gravity Recovery and Climate Experiment (GRACE) mission, an improved Lagrange multiplier method (ILMM) is first proposed to detect terrestrial water storage anomalies (TWSA) in the small-scale basin (DJKR). Moreover, for the first time, water diversion fingerprints are proposed to analyze the spatiotemporal pattern of the TWSA in the DJKR. The results indicate that the increased water level and storage signals due to the DJKR impoundment in 2014 can be effectively detected by using the ILMM, and they agree well with the results from altimetry and in situ data. Additionally, the water diversion fingerprints due to the DJKR impoundment are inferred, and describe the progression of spatiotemporal variability in water storage. The results show that water storage decreased in the upper Hanjiang River and increased in the DJKR as well as to the east of it during the period 2013–2015. Our research provides a scientific decision-making basis for monitoring the water resources of the DJKR and managing the South-to-North Water Diversion Project. [ABSTRACT FROM AUTHOR]