Abstract Limitations in the temporal resolution of contemporary gravity satellite missions hinder the precise monitoring of rapid Earth surface mass changes. By the early 2030s, unprecedented high‐temporal monitoring of Earth's dynamic mass redistribution will be available using the temporal gravity field derived from the Hybrid Gravity Satellite Ensemble (referred to as the “HGSE” in this study), which contains GRACE‐FO, ChiGaM, TIANQIN‐2, GRACE‐C, and NGGM. This paper proposes a Hybrid‐Augmented Resolution Dealiasing (HARD) algorithm that utilizes a sliding window technique to co‐estimate 3‐day low‐degree and daily high‐degree spherical harmonic coefficients. The HARD algorithm reduces temporal aliasing errors by 18.4%–30.7% compared to conventional processing strategies. Based on predefined noise levels for each satellite, closed‐loop simulations demonstrate that the HGSE yields daily gravity field solutions (with a maximum degree and order of 60) that can effectively reduce noise by approximately 76.2% in long‐term trends and 39.3% in annual amplitudes compared to classical monthly solutions. Applications in terrestrial water storage (TWS) change, glacier mass change, and co‐seismic deformation reveal significant improvements: 39.4% enhanced TWS signal recovery in large river basins, 21.2% higher accuracy in monitoring Tibetan Plateau glacier mass variation, and 69.4% superior co‐seismic signal recovery for megathrust earthquakes. These findings underscore the potential of HGSE to advance high‐frequency gravity field monitoring, offering critical references for the performance analysis of future gravity satellite missions monitoring the Earth's dynamic system processes on a daily scale.

High Mountain Asia (HMA), the largest extra-polar repository of glaciers, critically regulates water resources for nearly two billion people and regional climate systems. Understanding climate impacts and regional water security requires quantifying regional mass changes at fine spatial scales, but the coarse resolution of GRACE(-FO) satellite data impedes this quantification. To address these limitations, this study proposes an XGBoost down-scaling method integrated with mass-conservation correction. After obtaining preliminary downscaling results with low uncertainty and RMSE using the XGBoost model, the weighted forward modeling approach is further applied to enforce mass-conservation correction, thereby enhancing physical consistency. This correction reduces the RMSE of the regional mean time series from 0.85 cm to 0.08 cm, demonstrating that mass conservation effectively improves the downscaled solution. The downscaled solution was validated against the public ASTER-derived global digital elevation model, yielding a mean absolute trend error of only 0.53 Gt/yr, and outperforms existing Mascon solutions. Analysis identifies a pronounced atmospheric oscillation over the Caspian-Black Sea region, which induces interannual variations in moisture transport along the northern branch of the westerlies. This modulation of moisture pathways leads to oscillatory precipitation variability across Central Asia, ultimately giving rise to a distinct 6-7 year interannual mass oscillation in the Tien Shan-Pamir region. The strong correlation (greater than 0.7) between this mass variability and detrended cumulative precipitation highlights the dominant role of large-scale atmospheric circulation in shaping fine-scale hydrometeorological-induced mass changes across HMA.

Abstract Gravity Recovery and Climate Experiment (GRACE) observation data processed by various institutions yields somewhat different spherical harmonic solutions, which are further used to derive terrestrial water storage (TWS) changes. Combining TWS solutions from different institutions helps to refine the effective signal while removing noise. This study investigates regularization constraints in the context of TWS fusion to enhance the resulting estimates. The considered constraints are Tikhonov regularization of different orders, as well as minimization of month‐to‐month year‐to‐year double differences (MYDD), and triple differences (MYTD). Different accuracy and signal evaluation approaches are implemented for both individual and combined solutions. Compared to individual solutions and unregularized combinations, the regularized TWS combined solutions demonstrate lower noise levels. Among them, the second‐order Tikhonov regularization performs slightly better than other constraints, providing lower noise levels. This study offers a novel perspective for exploring GRACE‐based TWS combination methodologies.

Inverting terrestrial water storage change (TWSC) from GNSS observed deformation based on elastic loading theory represents a significant topic in current hydrogeodesy. Traditional inversions use equally weighted Laplacian constrained to stabilize the results. However, this can lead to signal oversmoothing and loss of local high-frequency details. To address the challenges posed by a sparse GNSS network in Yunnan, this study proposes a GNSS inversion method with spatiotemporally weighted Laplacian constraints (GNSS-constrained) from GRACE observations and GLDAS hydrological model. Synthetic tests and real-data inversions demonstrate that GNSS-constrained inversion effectively enhances inversion accuracy and improves the recovery of seasonal hydrological signals by constructing weight matrix for each calendar month, while showing stronger robustness against GNSS observation noise. Compared with traditional GNSS-only inversion, GNSS-constrained inversion accurately captures the spatiotemporal characteristics of precipitation-driven TWSC and reveals more details. At the basin scale, the GNSS-constrained inversions show higher consistency with precipitation and water balance components (P-ET-R), demonstrating superior capability in detecting small-scale hydrological dynamics. Comparative analysis reveals that both GRACE and GLDAS underestimate the annual amplitude of TWSC compared to GNSS and fail to effectively recover local high-frequency hydrological signals. The GNSS-constrained inversion framework developed in this study can flexibly incorporate suitable prior information according to regional hydrological characteristics, providing a reliable and adaptable solution for hydrological load monitoring under sparse GNSS networks.

This study quantitatively evaluates four candidate four-satellite constellations-Dual-pair (DP) GRACE-type, Bender-type, DP Cartwheel-type, and DP Pendulum-type-for recovering Subweekly to Weekly Earth time-variable gravity fields (TVGF). Using controlled numerical closed-loop simulations (identical altitude, satellite count, and subcycles), we conduct an equitable comparison of these constellations' performances in spectral and spatial domains under identical conditions. Comparative analyses under the optimal spatial resolution for each constellation configuration reveal that the DP Cartwheel-type and DP Pendulum-type configurations outperform the Bender-type configuration in recovering TVGF models at higher temporal resolutions. Spectral and spatial analyses confirm that the DP Cartwheel and DP Pendulum configurations surpass the Bender-type configuration, reducing the error by approximately 12%. In geoscience applications, the DP Cartwheel-type and DP Pendulum-type configurations slightly outperform the highly advocated Bender constellation design. Specifically, compared to the Bender-type, the DP Cartwheel-type and DP Pendulum-type configurations improve large-basin signal recovery accuracy by similar to 7.4%, Greenland Ice Sheet's southern sector mass balance signals by similar to 3.2%, and coseismic signals by similar to 13.2%. The simulation results indicate that the DP Cartwheel-type and DP Pendulum-type configurations show superior performance in gravity signal detection. However, due to current limitations in fuel consumption, laser pointing accuracy, and mission budget, maintaining these configurations remains technically and economically challenging. With future reductions in launch costs and advances in relevant technologies, these two configurations may become promising candidates for next-generation gravity missions.

The GRACE mission, which continuously collects large-scale gravity change data over long periods,provides a new tool for monitoring co-seismic deformation. GRACE ' s time-variable gravity field products areprimarily derived through spectral or spatial domain modeling. Spherical harmonic products, the most classicaland widely used, are based on spectral domain modeling. The most popular spatial domain products are Mascon(Mass concentration) products. Currently, 22 spherical harmonic products and 4 Mascon products are availableworldwide. Among them, spherical harmonic products employ various post-processing methods, includingfiltering. However, selecting the appropriate GRACE product for earthquake co-seismic deformation studies remains a question that needs to be resolved. This study uses a unified (stacking) method to extract co-seismicgravity changes from three M9 subduction earthquakes (the 2004 Sumatra, 2011 Tohoku, and 2010 Chileearthquakes). GRACE monitored these earthquakes with spherical harmonic and Mascon products from majorinternational agencies. Evaluations based on spherical dislocation theory show that any spherical harmonicproduct can be chosen for co-seismic deformation studies combined with P3M6 decorrelation and Gaussianfiltering. The Gaussian filter radius should be adjusted based on the earthquake signal strength: for more minorearthquakes with significant noise, a larger radius Gaussian filter is needed. Evaluation based on dislocationtheory shows that any spherical harmonic product can be used to study co-seismic deformation. Pair it withP3M6 decorrelation and Gaussian filtering for post-processing. The Gaussian filter radius should be adjustedbased on the signal strength. For more minor earthquakes with significant interference, use a larger radius. TheGSFC-M Mascon product, combined with a 300 km Gaussian filter, is the best choice for studying co-seismicdeformation in the three earthquakes. The unfiltered GSFC-M product demonstrates the most significantenhancement of co-seismic gravity signals among the three Mascon products, with the extracted signal intensityreaching 82% of the original theoretical model. This study compares various GRACE spherical harmonic andMascon products and offers more precise guidance for selecting the best product.

The surface water and ocean topography (SWOT) mission is currently operating in scientific orbit. The Ka-band radar interferometer (KaRIn) altimeter represents a significant departure from nadir altimeters, offering abundant observations that would greatly advance oceanographic research. However, the accuracy of altimetry data is crucial to the validity of research results and must be evaluated using crossover discrepancies to obtain prior estimations. Traditional methods are not fully suited to the wide-swath data and tend to be inefficient when processing massive amounts of data. In this study, we propose an improved latitude difference method for calculating crossover discrepancies. The reliability of this algorithm is verified using both along-track and across-track split data. The results indicate that the accuracy of crossover discrepancies for SWOT is comparable to those of conventional altimetry satellites, confirming the performance of the SWOT low-rate L2 KaRIn product. The standard deviation of crossover discrepancies between SWOT and other satellites in the South China Sea (SCS) is about 8 cm, around 6 cm in the Indian Ocean (southern) (IOS), and approximately 8 cm in the Gulf of Mexico (GOM), further proving the accuracy of the SWOT data. Analyzing the discrepancies beyond 50 km offshore, the results demonstrate that the KaRIn altimeter is influenced by the coastline, while its performance improves in the open ocean. By leveraging the vectorization algorithm, the efficiency has been improved significantly. The computation speed of crossover discrepancies using the improved latitude difference method is around 0.006 s per point, faster than the traditional method, demonstrating that the algorithm is efficient.

This paper reports on an innovative mass concentration (mascon) solution obtained with the short-arc approach, named "GCL-Mascon2024", for estimating spatially enhanced mass variations on the Earth's surface by analyzing K- and Ka-band ranging satellite-to-satellite tracking data collected by the Gravity Recovery And Climate Experiment (GRACE) mission. Compared to contemporary GRACE mascon solutions, this contribution has three notable and distinct features: first, this solution recovery process incorporates frequency-dependent data-weighting techniques to reduce the influence of low-frequency noise in observations. Second, this solution uses variably shaped mascon geometry with physical constraints such as coastline and basin boundary geometries to more accurately capture temporal gravity signals while minimizing signal leakage. Finally, we employ a solution regularization scheme that integrates climate factors and cryospheric elevation models to alleviate the ill-posed nature of the GRACE mascon inversion problem. Our research has led to the following conclusions: (a) GCL-Mascon2024 mass anomaly estimates from GRACE data show strong agreement with the (Release) RL06 versions of mascon solutions (GSFC, CSR, JPL) in both spatial and temporal domains; (b) in Greenland and global hydrologic basins, the correlation coefficients of estimated mass changes between GCL-Mascon2024 and other RL06 mascon solutions exceed 95.0 %, with comparable amplitudes, and, especially over non-humid river basins, the GCL-Mascon2024 suppresses random noise by 27.8 % compared to contemporary mascon products; and (c) in desert regions, the analysis of residuals calculated after removing the climatological components from the mass variations indicates that the GCL-Mascon2024 solution achieves noise reductions of over 29.3 % as compared to the GSFC and CSR RL06 mascon solutions.

The rheology of the lower crust and upper mantle influences Earth's plate tectonic style of mantle convection, yet its spatial variability is poorly resolved, particularly in continental interiors. Here we use satellite radar interferometry to map the delayed uplift resulting from the desiccation of the Aral Sea, which has lost similar to 1,000 km(3) of water since 1960. From this we constrain the rheology of the underlying upper mantle by elastic and viscoelastic modelling. We find a long-wavelength uplift of up to similar to 7 mm yr(-1) between 2016 and 2020 that decays radially from the Aral Sea. This uplift pattern is best explained by viscoelastic relaxation of the asthenosphere below a strong lithospheric mantle. We estimate that the asthenosphere has an effective viscosity of 4-7 x 10(19) Pa s below 130-190 km depth, slightly larger than the values inferred from post-seismic deformation at subduction zones, but 1-2 orders of magnitude smaller than estimates from glacial isostatic adjustment in other tectonically stable regions. Such uplift highlights the potential for human activities to influence deep-Earth dynamics and the interconnectedness of surface and mantle processes.

The gravity recovery and climate experiment (GRACE) and its successor GRACE follow-on (GRACE-FO) provide critical insights into global terrestrial water storage anomalies (TWSAs) by precisely measuring changes in intersatellite distances. However, various uncertainty sources cause geographically correlated noise in the GRACE/GRACE-FO Level-2 data, which is manifested as north-south striping noise in TWSA grids and complicates hydrological investigations. To address these challenges, we introduce a novel deep learning framework, the Bayesian optimization convolutional neural network with spatial attention mechanism (BO-CNN-SAM), which directly mitigates systematic errors and recovers TWSA from Level-2 data. The results are as follows: 1) The BO-CNN-SAM model architecture demonstrates superior predictive accuracy and robustness, with the root mean squared error (RMSE) reducing by 142%, 120%, and 93% during training, and 136%, 89%, and 74% during validation, compared to other deep learning structures such as BO-CNN, CNN-SAM, and CNN, respectively. 2) In terms of recovered TWSA, BO-CNN-SAM outperforms other CNN-based models, achieving the lowest RMSE (4.25, 4.40, and 5.50 cm) and the highest Nash-Sutcliffe Efficiency coefficients of 0.78, 0.64, and 0.60 during the training, validation, and testing phase, respectively. 3) The BO-CNN-SAM framework is successfully used to estimate ice sheet mass balance in Greenland and to accurately quantify prolonged drought in the Yangtze River Basin during 2022. Looking ahead, BO-CNN-SAM has the potential to directly recover TWSA from raw GRACE/GRACE-FO Level-1 data to gridded Level-3 TWSA products. This capability opens new opportunities for producing TWSA fields with low latency and enhancing our understanding of hydrological processes.

Groundwater is a critical resource for sustainable development, particularly in arid regions facing water scarcity. The Gravity Recovery and Climate Experiment (GRACE) and its Follow-On, GRACE-FO, offer valuable data on groundwater storage anomalies (GWSA). However, while their coarse resolution has been improved using machine learning approaches such as the global random forest (RFG) model, the aspatial nature of the RFG model limits its ability to capture spatial heterogeneity when downscaling GRACE (-FO) data. Downscaling GWSA data to higher resolutions is crucial for assessing small-scale groundwater variations. To address this, a novel spatially weighted random forest (RFSW) model has been proposed to downscale GWSA to a high resolution (0.1°) across the North China Plain (NCP) from 2003 to 2023. We found that the RFSW model outperforms the RFG model, reducing RMSE by 44.44 - 17.08), Xingtai ( - 16.67), and Handan ( - 16.02 mm/yr), respectively. The winter wheat area doubling from 2.5 million to 5.8 million hectares, reducing GWSA from - 180 mm to - 480 mm. This improved downscaling technique enhances our understanding of local groundwater dynamics and their relationship to agricultural practices. This method’s high-resolution GWSA data can inform more targeted and effective water management strategies in water-stressed regions worldwide. The figure presents a detailed graphical representation of the methodology employed to downscale GRACE (-FO) derived GWSA data for evaluating groundwater storage distribution. The analysis commences with the GRACE (-FO) TWSA dataset, which exhibits gaps in monthly data. The Seasonal-Trend decomposition based on the Loess (STL) method is employed to reconstruct the missing values, resulting in a continuous TWSA dataset. The estimated continuous TWSA calculates GWSA by deducting SMSA and SWEA from TWSA. An RFsw model is utilized to downscale GWSA to a higher resolution of 0.1° by incorporating explanatory climatic (rainfall, LST, airT, ET) and hydrological (SMS, SWE, Qs, Qsb, Snowcov), along with additional NDVI. The validation of the downscaled GWSA against in situ data reveals a high correlation coefficient (CC = 0.85) and similar trends. Landsat-5/7/8 and Sentinel-2 multispectral imagery are utilized to map winter wheat, demonstrating the spatial distribution of wheat cultivation across the NCP region. The resulting distribution maps reveal significant spatial expansion of winter wheat, particularly in the groundwater-dependent areas. This workflow facilitates a more accurate comprehension of groundwater storage estimation and the downscaling of GWSA. The RFSW model’s predictions reduced RMSE and residuals by 44.44

Temporarily impounded liquid water on the surface of the Greenland Ice Sheet (GrIS), prominently represented as supraglacial lakes (SGLs), may enhance ice flow and modulate surface meltwater runoff, serving as a dynamic indicator of the cryohydrologic cycle. Despite their importance in understanding glacier mass balance and regional climate change, a detailed description of SGLs and their intra-annual fluctuations across the entire GrIS remains understudied. Here, we present a deep learning-based approach to automatically map SGLs from passive optical satellite imagery across the entire GrIS during the melt seasons of 2017-2022. Approximately 150,000 Sentinel-2 and Landsat 8/9 images were utilized, each representing a 5-day average composite at a 10 km x 10 km grid resolution, with the Landsat images used as possible supplements. SGL predictions by the proposed method demonstrate high performance, achieving an F1-score of up to 0.959 compared to the independent test dataset. This high accuracy enables a detailed analysis of the key role SGLs play in enhancing surface ablation by absorbing solar radiation and delivering meltwater. The SGL-driven ablation effect was most pronounced in the South-West basin of the GrIS, where the peak lake area in July accounted for 44.9 % of the total GrIS-wide lake area. In contrast, the lowest magnitude (4.2 %) was observed in the South-East basin, despite similarly strong ablation in this region. Among all the generated SGL occurrence grids, peak SGL areas in certain grids (similar to 14 % of the total) were observed in May or September, rather than exclusively during the typical high-ablation months of June to August, reflecting regional and elevation-dependent variations. Grids further from the ice sheet margin generally showed peak SGL areas later in the melt season, which is evident in the western part of the GrIS. Monthly SGL peak areas shift dramatically from 253.18 +/- 123.94 km(2) to 5084.90 +/- 1043.26 km(2), with the lowest in May 2018 and the highest in August 2021. An extraordinary area spike occurred in September 2022 and was particularly monitored in the South-West basin, where abnormally intense rainfall and runoff simulated by the Mod & egrave;le Atmosph & eacute;rique R & eacute;gional (MAR) model were recorded. Our study highlights the significance of examining SGL area changes at short temporal intervals to understand the dynamics of cryospheric hydrology under future climate scenarios.

The Gravity Recovery and Climate Experiment (GRACE) and its successor, GRACE-Follow On, play an important role in monitoring mass transport across the Earth. Compared to spherical harmonic solutions, mass concentration (mascon) solutions offer less signal leakage and a "higher" spatial resolution. How the shapes, sizes, and positions of mascon are parameterized influences the accuracy of the solutions. In this study, we derive a variable-sized mascon solution that enhances spatial resolution in polar regions by considering orbital coverage of satellites. To this end, we present a numerical simulation aimed at evaluating the performance of different parameterizations in the mascon solutions. We demonstrate that using variable-sized mascons reduce parameterization error by up to 17% and improve goodness of fit by up to 34%. The accuracy of signal recovery improves by about 23%, 34%, and 42% for basin scales, respectively, in low-latitude, mid-latitude, and high-latitude zones. When applied to the GRACE (-FO) data, we see the optimized parameterization scheme reduces noise by up to 1.84 cm in the surface mass change time series. Additionally, the optimally parameterized mascon solution help to enhance signal recovery in mid-to-high latitude regions. We discuss and quantify benefits of variable-sized mason parameterizations for surface mass change recovery and suggest the optimal scheme based on the simulation and real data processing. Overall, the optimized parameterization scheme will benefit finer-scale mass change signal recovery for mascon solution.

The Greenland Ice Sheet is a major contributor to global sea-level rise, with accelerating mass loss due to climate change. Accurate estimation of Greenland Ice Sheet mass variations is critical for understanding ice sheet dynamics and predicting future sea-level changes. However, spherical harmonic coefficient solutions from GRACE/GRACE-FO suffer from high-frequency noise and signal leakage, particularly at Greenland Ice Sheet-ocean boundaries, limiting their reliability in regional mass balance studies. In this study, we propose an Improved Slepian Method to address these limitations. The Improved Slepian Method refines the inversion strategy by introducing satellite-altitude pseudo-observations to separate Greenland Ice Sheet and surrounding ocean/island signals, recovering leaked signals. It also uses satellite altimetry data as a regularization matrix to constrain spatial patterns that are critical to reduce signal leakage. Additionally, it weights the data with the error covariance matrix to suppress high-frequency noise, enabling the use of higher-degree (e.g., degree 96) time-varying gravity field models. Validated against Input–Output Method, the Improved Slepian Method improves accuracy by 25–58

The mass loss of the Greenland Ice Sheet (GrIS) has profound impacts on sea levels, the water cycle, and global climate variability. The Gravity Recovery and Climate Experiment (GRACE) and its follow‐on mission (GRACE‐FO) provide accurate but limited spatial resolution observations of GrIS mass changes. Therefore, we developed a novel multi‐time scale weighted forward modeling (WFM) approach that combines GRACE(‐FO) observations with satellite altimetry data to improve mass change estimations in the GrIS at high‐resolution (25 km × 25 km). The WFM solution effectively represents the glacier‐scale interannual mass variations, with an average correlation of 0.71 with the Input‐Output method, higher than Mascon products. The WFM solution reveals a spatial pattern of glacier mass change from 2020 to 2023, indicating that the slowdown in the GrIS glacier mass loss has shifted from the east to the west compared to 2013–2018; the mass loss rate decreased by 44.9 ± 1.04 Gt/yr in the western GrIS and increased by 42.3 ± 0.98 Gt/yr in the eastern GrIS. The most pronounced mass loss slowdown occurred at Jakobshavn Glacier (7.3 ± 0.07 Gt/yr). In this pattern, the trough of low‐pressure west of the GrIS moved westward, and a high‐pressure anomaly over the North Atlantic south of the GrIS intensified southwesterly winds over the GrIS. These winds transported warmer, moister air from the Atlantic toward the western GrIS, leading to increased snowfall and rainfall, thereby promoting glacier mass accumulation. If this pattern continues, it could benefit the preservation of the ice in the western GrIS.

We present an innovative global mass concentration (mascon) solution, "GCL-Mascon2024", derived using the short-arc approach to estimate spatially enhanced mass variations on the Earth's surface. This monthly solution is based on K-/Ka-band ranging (KBR) satellite-to-satellite tracking data from the Gravity Recovery and Climate Experiment (GRACE) mission. Compared to contemporary GRACE mascon solution computations, we introduce three key advancements: (1) a frequency-dependent data weighting strategy to mitigate low-frequency noise in the satellite observations; (2) a variable-shaped mascon geometry incorporating physical constraints such as coastlines and river basin boundaries to reduce signal leakage and better capture temporal gravity variations; and (3) a regularization scheme integrating climate factors and cryospheric elevation models to address the ill-posed nature of mascon estimation. Temporal signals from GCL-Mascon2024 show 7% to 20% lower residuals over continental regions compared to Release-06 (RL06) mascon solutions from GSFC, CSR, and JPL. For various river basins, GCL-Mascon2024 solutions reduce random noise over non-humid river basins by 37% relative to contemporary mascon products. In desert regions, residual analyses after removing climatological components reveal that GCL-Mascon2024 and JPL RL06 solutions are roughly equivalent in accuracy, with a 28% noise reduction compared to GSFC and CSR RL06 solutions. This study underscores the potential of GCL-Mascon2024 to enhance the accuracy of gravity field solutions, offering valuable insights into global mass variations, which is of importance for various geoscience applications.

Groundwater storage and depletion fluctuations in response to groundwater availability for irrigation require understanding on a local scale to ensure a reliable groundwater supply. However, the coarser spatial resolution and intermittent data gaps to estimate the regional groundwater storage anomalies (GWSA) prevent the Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On (GARCE-FO) mission from being applied at the local scale. To enhance the resolution of GWSA measurements using machine learning approaches, numerous recent efforts have been made. With a focus on the development of a new algorithm, this study enhanced the GWSA resolution estimates to 0.05 degrees by extensively investigating the continuous spatiotemporal variations of GWSA based on the regional downscaling approach using a regression algorithm known as the geographically weighted regression model (GWR). First, the modified seasonal decomposition LOESS method (STL) was used to estimate the continuous terrestrial water storage anomaly (TWSA). Secondly, to separate GWSA from TWSA, a water balance equation was used. Third, the continuous GWSA was downscaled to 0.05 degrees based on the GWR model. Finally, spatio-temporal properties of downscaled GWSA were investigated in the North China Plain (NCP), China's fastest-urbanizing area, from 2003 to 2022. The results of the downscaled GWSA were spatially compatible with GRACE-derived GWSA. The downscaled GWSA results are validated (R = 0.83) using in-situ groundwater level data. The total loss of GWSA in cities of the NCP fluctuated between 2003 and 2022, with the largest loss seen in Handan (-15.21 +/- 7.25 mm/yr), Xingtai (-14.98 +/- 7.25 mm/yr), and Shijiazhuang ( 14.58 +/- 7.25 mm/yr). The irrigated winter-wheat farming strategy is linked to greater groundwater depletion in several cities of NCP (e.g., Xingtai, Handan, Anyang, Hebi, Puyang, and Xinxiang). The study's high-resolution findings can help with understanding local groundwater depletion that takes agricultural water utilization and provide quantitative data for water management.

Groundwater utilization for several purposes such as irrigation in agriculture, industry, and domestic use substantially impacts water storage. Groundwater Storage Anomaly (GWSA) estimates have improved owing to the Gravity Recovery and Climate Experiment (GRACE) and GRACE -Follow On (GRACE -FO) advancements. However, the characterization of GWSA fluctuation hotspots has been hindered by the coarse resolution of GRACE data. To better measure groundwater storage and depletion variations throughout an area and identify GWSA variation hotspots, a fine spatial resolution of GWSA estimations is required. Therefore, due to the coarse resolution of GRACE measurements, the eXtreme Gradient Boosting (XGBoost) model was developed to simulate fine resolution 0.1 degrees GWSA combining climatic variables (soil moisture storage, evapotranspiration, temperature, surface runoff, and rainfall) from improved spatial high resolution FLDAS (Famine Early Warning Systems Network Land Data Assimilation System) model derived data and geospatial variables (elevation, slope, and aspect) extracted from Digital Elevation Model (DEM). A correlation of 0.98 demonstrated that the XGBoost model successfully simulated groundwater storage at a finer scale over the Upper Indus Plain Aquifer (UIPA). The findings suggested that the UIPA's groundwater storage has been depleted at an annual rate of 0.44 km3/yr which was 7.94 km3 in total between 2003 and 2020. According to the results, there seems to be consistency between the downscaled and original GWSA regarding temporal and spatial variability. The results were verified to show an improved correlation of 0.77 between the downscaled and the in -situ GWSA, compared to 0.75 between the GRACE -derived and the in -situ GWSA.

Based on its unique sensitivity to Earth's temporal gravity, and since 2002, the gravity recovery and climate experiment (GRACE) twin satellites, and its successor, GRACE follow-on (GRACE-FO) missions have accumulated a two-decade-long and continuing Earth's mass change climate data record. Additionally, the Chinese gravimetry mission was launched as the last polar-pair satellite formation in 2021. With the opportunity of two existing polar-pair gravity satellite formations (EPGF) in operations, we explore the ideal configuration to launch a third-pair satellite formation to construct the triple-pair gravity satellite constellation (TGSC). Here, we examine the selection of initial orbit parameters of the third satellite formation based on subcycles and orbit parameters of EPGF to augment TGSC. The simulation study explores the effectiveness of the monthly temporal gravity field from the TGSC in potential contributions to geosciences. Our study reveals that TGSC improves continental hydrological signal recovery by approximately 24% and 38% in large and small basins (above/below 106 km2), as compared with GRACE-FO, which would be the polar-pair gravity satellite in operations. TGSCs effectiveness varies across drainage systems of the Greenland ice sheet (GrIS) due to different ground track coverage. Compared with GRACE-FO, TGSC enhances GrIS mass balance recovery by 37%–56%. Simulations for six mega earthquakes (above Mw 7.7) reveal that TGSC outperforms GRACE-FO by approximately 46%–58% in extracting coseismic signals. Our study reveals the importance of incorporating existing on-orbit gravity missions into the design of future gravity satellite constellations. This strategy aims to not only accomplish the predefined objectives of gravity satellite missions but also potentially provide additional benefits to the field of geosciences.