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In the context of global warming, comprehending the dynamics of terrestrial water storage (TWS) and its responses to natural and anthropogenic factors is paramount for hydrological research and the management of water resources in China. This study utilized GRACE (Gravity Recovery and Climate Experiment)/GRACE-Follow On (GRACE-FO) satellite data to analyze terrestrial water storage across nine basins in China from 2005 to 2020 at multiple temporal and spatial scales. Subsequently, employing a Geographic detector model, potential influencing factors were identified, and an enhanced Geographically Weighted Regression (GWR) method was proposed for attributing changes in TWS in China. The findings reveal a consistent declining trend in TWS based on GRACE/GRACE-FO data across different temporal scales, with the most pronounced decreases observed in August and September. Geographic Detector analysis unveils significant interactions among various environmental factors, with climate variables playing a pivotal role in modulating hydrological characteristics of major river basins, where rising temperatures can exacerbate the severity of precipitation events, thus increasing the risk of floods and droughts. Moreover, analysis of the primary influencing factors indicates significant impacts of population density and topography on water resources in the southeastern and southwestern regions, particularly amidst increasing human activities and urbanization expansion. The results of this study are crucial for comprehending the dynamic changes and mechanisms of TWS in China, as well as for formulating water resource management strategies.

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Terrestrial Water Storage (TWS) plays a pivotal role in water resource management by providing a comprehensive measure of both surface water and groundwater availability. This study investigates changes in TWS driven by human activities from 2003 to 2023, and forecasts future TWS trends under various climate change and development scenarios. Our findings reveal a continuous decline in China's TWS since 2003, with an average annual decrease of approximately 1.36 mm. This reduction is primarily attributed to the combined effects of climate change and human activities, including irrigation, industrial water use, and domestic water consumption. Notably, TWS exhibits significant seasonal and annual fluctuations, with variations ranging ±10 mm. For the future period (2024-2030), we project greater disparities between water resource supply and demand in specific years for the Songliao, Southwest, and Yangtze basins. Consequently, future water resource management must prioritize water conservation during wet seasons, particularly in years when supply-demand conflicts for limited water resources intensify. This study is valuable for effective planning and sustainable utilization of water resources.

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The variation of solid Earth's hydrologic loading could cause the elastic vertical deformation of the crust, and the Global Navigation Satellite System (GNSS) could effectively monitor the vertical displacement of surface loads. However, the widely used Green's function method does not work well in areas where GNSS sites are sparse. Here, the vertical displacement time series of GNSS stations and the Slepian basis function method have been applied to convert displacement signals into spatial spectrum signals. The elastic mass load theory is used to study the changes in terrestrial water storage on the Northeastern Tibetan Plateau (NETP). The temporal and spatial characteristics of seasonal water changes are well-represented by the GNSS, the Gravity Recovery and Climate Experiment (GRACE), and the Global Land Data Assimilation System (GLDAS). Several data points suggest that the change in water storage shows a gradual increase from the northeast to the southwest. The greatest annual amplitude of water storage retrieved by GNSS is ∼159 mm, which is greater than the ∼47 mm and ∼44 mm obtained by GRACE and GLDAS. These results demonstrate that GNSS is capable of capturing small-scale hydrological changes in this region, whereas GRACE and GLDAS data tend to underestimate seasonal variations in water storage. We also used GNSS to describe the hydrological drought conditions in NETP, showing that GNSS could be used as an independent method to characterize hydrological drought events. The findings suggest it could observe water storage with high spatial and temporal resolution and aid in comprehending regional hydrological trends with a sparse GNSS station network.

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Balancing water demand for socio-economic development and ecosystem stability presents a challenge for regional sustainable management, especially in drylands. Previous studies have indicated that large-scale ecological restoration projects (ERPs) lead to a decline in terrestrial water storage (TWS) in the Mu Us Sandyland (MUS). However, the effects of other human activities (e.g., cropland reclamation, coal mining) on water resources remain unclear, raising concerns regarding water crisis and human-natural system sustainability. Through the utilization of coal mine location data, we found that the impact of coal mass loss on the Gravity Recovery and Climate Experiment (GRACE) products cannot be ignored in MUS, especially in the coal-rich northeastern part. Combining these data with auxiliary datasets, we observed a significant (p < 0.05) decrease in TWS (-0.85 cm yr-1) and groundwater storage (GWS, -0.95 cm yr-1) in the MUS, with human activities accounting for 79.23 % of TWS and 90.45 % of GWS reductions, primarily due to increased agricultural and industrial water consumption. Agricultural water consumption increased 2.23 times from 2001 to 2020, attributed to enhanced water use intensity (62.6 %) and cropland expansion (37.4 %). Industrial water consumption in Shenmu, a representative coal county, experienced a 4.16-fold rise between 2001 and 2020. Despite these challenges, local governments have alleviated water stress, ensured food security, and increased household income by comprehensive management strategies, such as enhancing water-saving technology and enforcing stringent policies. Previous studies have overestimated the amount of water resources consumed by ERPs. However, ERPs has played a critical role in stabilizing the regional ecological environment and ensuring the region as a vital food and energy supplier. Our findings can guide for socio-economic development and water management policies in similar regions.

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Poyang Lake is the largest freshwater lake in China, serving as a natural reservoir and playing a paramount role in climate regulation, ecological environment, and water resource management. However, in recent years, Poyang Lake has approached desiccation multiple times, with severe droughts becoming increasingly common. Consequently, precise quantification and analysis of the terrestrial water storage anomalies (TWSA) and drought characteristics of the Poyang Lake basin (PLB) are of profound scientific and practical significance. This paper, for the first time, utilizes data for the period 2021-2022 from 77 newly-established GNSS observation stations in the PLB to precisely determine its vertical crustal displacement, invert daily and monthly TWSA, and investigate extreme hydrological drought. The results reveal the following: 1) The annual amplitude range of vertical surface displacements at GNSS stations in the Poyang Lake basin is from 7 to 14 mm, with the most substantial seasonal vertical displacements occurring during the months of June and July; 2) monthly GNSS-TWSA maintains a commendable consistency with TWSA data obtained from the Gravity Recovery and Climate Experiment (GRACE), the Global Land Data Assimilation System (GLDAS), and precipitation, with correlation coefficients of 0.67, 0.55, and 0.62, respectively; 3) at daily scale, the GNSS-derived Drought Severity Index (GNSS-DSI) accurately recorded the severity and intensity of eight drought events in the PLB during 2021-2022, in particular the period of extensive drought between October 2021 and February 2022, when drought intensity reaching a notable 1.03, which is classified as an extreme and prolonged drought event. Additionally, at local temporal scales, daily GNSS-DSI exhibits heightened sensitivity to drought signals. This study provides novel technological tools and datasets for multi-source satellite-based drought monitoring in the PLB.

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The destabilization of delta's worldwide due to climate change and human activities presents challenges in meeting the growing demands for freshwater and food. The Nile Delta in Egypt is a prime example of a vulnerable region facing various stressors. In order to preserve land and water resources, it is crucial to monitor the spatial and temporal changes in Land Use/Land Cover (LULC), shoreline, and Terrestrial Water Storage (TWS) in these vulnerable regions This study comprehensively investigates the dynamic changes in LULC and their associated water and soil responses in the Eastern Nile Delta under these combined impacts. To achieve this goal, a combination of remote sensing techniques utilizing Landsat (5, 8, and 9), and GRACE datasets, along with field observations and Geographic Information System (GIS) tools, was employed. Accordingly, shoreline changes show coastal erosion rates ranging from 5.28 to 34.92 m/year due to climate change-induced SLR, with continued inland movement predicted for the next 20 years. Moreover, the dynamic changes in urbanization and alterations in agricultural cover have considerable penalties for water demand. Analysis of GRACE data indicates a notable reduction in average TWS by 77.89 mm between 2002 and 2017, with an annual rate, estimated at -5.821 mm/year. Soil sampling in highly vulnerable areas confirms agricultural degradation attributed to elevated salinity levels, with EC values ranging from 3.60 to 190 ds/m. These finds provide valuable insights for stakeholders and policymakers, to make reliable strategies regarding water allocation, land use regulations, and climate change adaptation in the worldwide vulnerable deltas.

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The Paraná basin is the second largest river basin in South America and provides abundant water resources globally. However, current research lacks hydrological investigation of the region. The vertical crustal deformation recorded by the Global Navigation Satellite System (GNSS) can be used to accurately estimate regional-scale terrestrial water storage (TWS). Therefore, we utilized the daily vertical displacement time series data at 102 GNSS stations to recover the water storage variations in the Paraná basin from 2013 to 2020. To recognize primary spatiotemporal features of TWS changes, we applied the principal component analysis (PCA) method in the inversion strategy. Results indicate that the TWS variations inferred from GNSS generally align in spatiotemporal patterns with estimates from both the Gravity Recovery and Climate Experiment (GRACE) and the Global Land Data Assimilation System (GLDAS). However, some discrepancies are evident at local scales. The TWS changes derived from both GNSS and GRACE exhibited generally larger magnitude of oscillations than those estimated by GLDAS, while the GRACE results neglected the evident seasonal oscillation of the water mass in the southeast of the basin. Given the challenge of capturing large-scale runoff variations through in-situ observations, we innovatively applied GNSS and water budget closure method to provide a novel runoff estimate for the Paraná basin. The GNSS-inferred runoff exhibited a strong correlation (correlation coefficient of 0.72) with in-situ observations. Overall, our study fills the critical knowledge gap in geodesy-based hydrological investigation in the Paraná basin. We aim to highlight the immense potential of GNSS for hydrological parameter estimation and provide valuable reference data for regional hydrological research and for water resources management.

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To explore the responses of vegetation growth to change in terrestrial water storage in Southwest China, we analyzed the change trend and relationship between vegetation and terrestrial water storage anomaly (TWSA) in Southwest China from January 2003 to December 2021 by using TWSA data of Gravity Recovery and Climate Experi-ment (GRACE) satellite and normalized differential vegetation index (NDVI) data. The results showed that NDVI in Southwest China during the study period showed an overall upward trend. Meanwhile, TWSA showed a significant downward trend in central and southern Tibet, and a significant upward trend in northwest Tibet and southeast region of Southwest China. Results of Pearson correlation analysis showed that there were significant spatial differences in responses of NDVI to TWSA changes in Southwest China. NDVI had a significant negative response to TWSA changes in most regions of Tibet, but a significant positive response to TWSA changes in most regions of southeast region of Southwest China. Such differences were driven by climate change and topography.

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Land water storage plays a key role for the Earth's climate, natural ecosystems, and human activities. Since the launch of the first Gravity Recovery and Climate Experiment (GRACE) mission in 2002, spaceborne observations of changes in terrestrial water storage (TWS) have provided a unique, global perspective on natural and human-induced changes in freshwater resources. Even though they have become much used within the broader Earth system science community, space-based TWS datasets still incorporate important and case-specific limitations which may not always be clear to users not familiar with the underlying processing algorithms. Here, we provide an accessible and illustrated overview of the measurement concept, of the main available data products, and of some frequently encountered technical terms and concepts. We summarize concrete recommendations on how to use TWS data in combination with other hydrological or climatological datasets, and guidance on how to avoid possible pitfalls. Finally, we provide an overview of some of the main applications of GRACE TWS data in the fields of hydrology and climate science. This review is written with the intention of supporting future research and facilitating the use of satellite-based terrestrial water storage datasets in interdisciplinary contexts.

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The two-dimensional steady-state Budyko framework, widely used to study water-energy dynamics in landscapes, primarily focused on the partitioning of precipitation into evapotranspiration (ET) and water yield. Though this framework has been extended by incorporating water storage changes into precipitation input for non-steady state conditions, the interactions among water-energy dynamics, vegetation covers, and ocean-atmosphere oscillations within the Budyko framework at finer spatial and temporal scales have been unexplored. This study aims to investigate the interactions of regional hydroclimatic conditions, vegetation, and climate teleconnections over the Indo-China Peninsula (ICP), a region highly vulnerable to climate change. To achieve the objective, we propose a three-dimensional Budyko framework that incorporates the ratio of Gravity Recovery and Climate Experiment (GRACE)-based terrestrial water storage (TWS) or its changes (TWSC) to precipitation (SI/SCI) as the third dimension alongside the traditional two-dimensional Budyko framework. Our findings reveal that TWS has a significant impact on the Budyko framework, particularly during the dry season. The dryness index (DI)/evaporative index (EI) and SI/SCI exhibit positive (strongly negative) linear relationships in the wet (dry) season, respectively. Vegetation covers strongly influence the three-dimensional Budyko framework, with poor performance observed in highly vegetated regions due to high ET demand. Through relative importance analysis, we identify the Silk Road Pattern (SRP) as the most influential climate teleconnection among nine different teleconnections, affecting hydroclimatic conditions over the ICP. Positive (negative) phases of SRP encourage water-limited (energy-limited) ET conditions. This demonstrates that the Budyko parameter is influenced not only by landscapes but also by climate teleconnections, offering potential benefits for Budyko parameter estimation. Furthermore, the linear relationships between DI/EI and SI/SCI in three-dimensional Budyko framework can provide a promising alternative method for evapotranspiration and groundwater estimation.

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The recent dynamics of terrestrial water storage (TWS) and groundwater storage (GWS) fluctuations were investigated based on the Gravity Recovery And Climate Experiment (GRACE) observations over 25 basins of Türkiye. Coarse-resolution GRACE estimates were downscaled based on the Random Forest algorithm. The impacts of precipitation (P) and evapotranspiration (ET) on the variations of water storage were also assessed. The findings demonstrated good performance for the RF model in simulating finer resolution estimates of TWS. The results indicated a diminishing trend of TWS and its hydrologic components over all the basins from 2003 to 2020. The Dogu Akdeniz Basin with the annually decreasing TWS and GWS of [Formula: see text] and [Formula: see text] was the most critical basin of Türkiye. The least storage loss was observed in the Bati Karadeniz Basin with the annual TWS and GWS loss of [Formula: see text] and [Formula: see text], respectively. Based on the results, Türkiye has lost, on average, an estimated [Formula: see text] and [Formula: see text] of its TWS and GWS, respectively, which are equivalent to the total storage loss of [Formula: see text] and [Formula: see text] of TWS and GWS during the last 18 years. The results also indicated that P and ET interact differently with the variations of TWS and GWS. The net water flux was revealed to be partially correlated with the total water storage fluctuations, suggesting the governing role of other deriving forces particularly the anthropogenic factors in the spatiotemporal variations of Türkiye's water storage; therefore, a sector-specific analysis of the water storage variations is crucial for the country, particularly by concentrating more on the dynamics of GWS.

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Terrestrial water storage anomaly (TWSA) from Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-on was first exacted by using the forward modeling (FM) method at three different scales over the Yangtze River basin (YRB): whole basin, three middle sub-basins, and eleven small sub-basins (total 15 basins). The spatiotemporal variability of eight hydroclimatic variables, snow water storage change (SnWS), canopy water storage change (CnWS), surface water storage anomaly (SWSA), soil moisture storage anomaly (SMSA), groundwater storage anomaly (GWSA), precipitation (P), evapotranspiration (ET), and runoff (R), and their contribution to TWSA were comprehensively investigated over the YRB. The results showed that the root mean square error of TWS change after FM improved by 17 %, as validated by in situ P, ET, and R data. The seasonal, inter-annual, and trend revealed that TWSA over the YRB increased during 2003-2018. The seasonal TWSA signal increased from the lower to the upper of YRB, but the trend, sub-seasonal, and inter-annual signals receded from the lower to the upper of YRB. The contribution of CnWS to TWSA was small over the YRB. The contribution of SnWS to TWSA occurs mainly in the upper of YRB. The main contributors to TWSA were SMSA (~36 %), SWSA (~33 %), and GWSA (~30 %). GWSA can be affected by TWSA, but other hydrological elements may have a slight impact on groundwater in the YRB. The primary driver of TWSA over the YRB was P (~46 %), followed by ET and R (both ~27 %). The contribution of SMSA, SWSA, and P to TWSA increased from the upper to the lower of YRB. R was the key driver of TWSA in the lower of YRB. The proposed approaches and results of this study can provide valuable new insights for water resource management in the YRB and can be applied globally.

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Satellite remote sensing provides a unique opportunity for calibrating land surface models due to their direct measurements of various hydrological variables as well as extensive spatial and temporal coverage. This study aims to apply terrestrial water storage (TWS) estimated from the gravity recovery and climate experiment (GRACE) mission as well as soil moisture products from advanced microwave scanning radiometer-earth observing system (AMSR-E) to calibrate a land surface model using multi-objective evolutionary algorithms. For this purpose, the non-dominated sorting genetic algorithm (NSGA) is used to improve the model's parameters. The calibration is carried out for the period of two years 2003 and 2010 (calibration period) in Australia, and the impact is further monitored over 2011 (forecasting period). A new combined objective function based on the observations' uncertainty is developed to efficiently improve the model parameters for a consistent and reliable forecasting skill. According to the evaluation of the results against independent measurements, it is found that the calibrated model parameters lead to better model simulations both in the calibration and forecasting period.

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The intensive agricultural expansion and rapid urban development in Abu Dhabi Emirate, United Arab Emirates (UAE) have resulted in a major decline in local and regional groundwater levels. By using the latest release (RL06) of Gravity Recovery and Climate Experiment (GRACE) satellite measurements and Global Land Data Assimilation System (GLDAS) products, the groundwater storage change was computed and compared with the time series of in-situ monitoring wells over the period of 2010-2016. The RL06 GRACE products from Jet Propulsion Laboratory (JPL), University of Texas Center for Space Research (CSR), German Research Center for Geosciences (GFZ), and JPL mass concentrations (MASCON) were assessed and have shown satisfactory agreements with the monitoring wells. The JPL MASCON reflected the in-situ groundwater storage change better than the other GRACE products (R = 0.5, lag =1 month, RMSE = 13 mm). The groundwater recharge is estimated for the study area and compared with the in-situ recharge method that considers multi recharge components from the rainfall, irrigation return flow and internal fluxes. The results show that the agreements between in-situ and GRACE-derived recharge estimates are highly agreeable (e.g., R2 = 0.91, RMSE = 1.5 Mm3 to 7.8 Mm3, and Nash-Sutcliff Efficiency = 0.7). Using the Mann-Kendall trend test and Sen's slope, the analyses of policies, number of wells, and farm areal expansion with groundwater time series derived from GRACE helped to validate GRACE and emphasize the importance of regulations for sustainable development of groundwater resources. The impacts of subsidy cuts after 2010 can be captured from the GRACE data in the eastern region of Abu Dhabi Emirate. The linear trend of groundwater storage anomaly obtained from GRACE over the period from 2003 to 2010 is -6.36 ± 0.6 mm/year while it showed a decline trend of -1.2 ± 0.6 mm/year after the subsidy cut. The proposed approach has a potential application for estimating groundwater recharge in other arid regions where in-situ monitoring wells are limited or absent.

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Hydrogeodesy, a relatively new field within the earth sciences, is the analysis of the distribution and movement of terrestrial water at Earth's surface using measurements of Earth's shape, orientation, and gravitational field. In this paper, we review the current state of hydrogeodesy with a specific focus on Global Navigation Satellite System (GNSS)/Global Positioning System measurements of hydrologic loading. As water cycles through the hydrosphere, GNSS stations anchored to Earth's crust measure the associated movement of the land surface under the weight of changing hydrologic loads. Recent advances in GNSS-based hydrogeodesy have led to exciting applications of hydrologic loading and subsequent terrestrial water storage (TWS) estimates. We describe how GNSS position time series respond to climatic drivers, can be used to estimate TWS across temporal scales, and can improve drought characterization. We aim to facilitate hydrologists' use of GNSS-observed surface deformation as an emerging tool for investigating and quantifying water resources, propose methods to further strengthen collaborative research and exchange between geodesists and hydrologists, and offer ideas about pressing questions in hydrology that GNSS may help to answer.

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As an important component of terrestrial ecosystem, vegetation acts as a sensitive recorder of changes in hydroclimatic conditions. Long-term time series of remote sensing-based vegetation indices and their influencing environmental driving factors, such as human activities and climate change, have been widely discussed in the literature. Globally, however, little is known about the hydroclimatic processes controlling vegetation changes in mountainous regions, which are conceived as more sensitive to climate change than other landscapes. The present study aims to quantify the respective roles of two dominant hydroclimatic factors, namely, TWS (i.e., terrestrial water storage) and Tair (i.e., temperature), in the spatio-temporal changes of mountainous vegetation over global six contrasting climate zones (i.e., tropical, arid, subtropical, temperate, sub-frigid, and frigid zones) during the period 2003-2016 based on EVI (i.e., enhanced vegetation index), TWS, Tair, and elevation data. Results indicate that the mean EVI shows a larger increasing trend (+0.85 %/decade, p-value < 0.01) and a larger decreasing trend in TWS (-85 mm/decade, p-value < 0.01) across the global mountainous regions than other global regions combined together (+0.61 %/decade, p-value < 0.01), particularly over high latitudes. With the increasing latitudes, the positive effect of temperature more dominates mountainous vegetation growth than moisture, as evidenced by the increasing trends of EVI with warming. However, in certain low-latitude mountainous regions (e.g., East Africa, South Asia, the western Tibetan Plateau, Brazil Plateau, and the southern Rocky Mountains), mountainous vegetation may face degradation due to water deficit induced by increased snowmelt, especially among the high-elevation ecosystems. The water availability controls vegetation activities more than Tair in the mid- and low-latitude regions, including the tropical, arid, and subtropical climate zones. These findings indicate that the potential shifts in mountainous vegetation may occur under the notable interactions with hydroclimatic factors, as the high-latitudes are experiencing ongoing warming and the mid- and low-latitudes are getting dryer.

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Global Navigation Satellite System (GNSS) vertical displacements measuring the elastic response of Earth's crust to changes in hydrologic mass have been used to produce terrestrial water storage change (∆TWS) estimates for studying both annual ∆TWS as well as multi-year trends. However, these estimates require a high observation station density and minimal contamination by nonhydrologic deformation sources. The Gravity Recovery and Climate Experiment (GRACE) is another satellite-based measurement system that can be used to measure regional TWS fluctuations. The satellites provide highly accurate ∆TWS estimates with global coverage but have a low spatial resolution of ∼400 km. Here, we put forward the mathematical framework for a joint inversion of GNSS vertical displacement time series with GRACE ∆TWS to produce more accurate spatiotemporal maps of ∆TWS, accounting for the observation errors, data gaps, and nonhydrologic signals. We aim to utilize the regional sensitivity to ∆TWS provided by GRACE mascon solutions with higher spatial resolution provided by GNSS observations. Our approach utilizes a continuous wavelet transform to decompose signals into their building blocks and separately invert for long-term and short-term mass variations. This allows us to preserve trends, annual, interannual, and multi-year changes in TWS that were previously challenging to capture by satellite-based measurement systems or hydrological models, alone. We focus our study in California, USA, which has a dense GNSS network and where recurrent, intense droughts put pressure on freshwater supplies. We highlight the advantages of our joint inversion results for a tectonically active study region by comparing them against inversion results that use only GNSS vertical deformation as well as with maps of ∆TWS from hydrological models and other GRACE solutions. We find that our joint inversion framework results in a solution that is regionally consistent with the GRACE ∆TWS solutions at different temporal scales but has an increased spatial resolution that allows us to differentiate between regions of high and low mass change better than using GRACE alone.

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The monthly high-resolution terrestrial water storage anomalies (TWSA) during the 11-months of gap between GRACE (Gravity Recovery And Climate Experiment) and its successor GRACE-FO (-Follow On) missions are missing. The continuity of the GRACE-like TWSA series with commensurate accuracy is of great importance for the improvement of hydrologic models both at global and regional scales. While previous efforts to bridge this gap, though without achieving GRACE-like spatial resolutions and/or accuracy have been performed, high-quality TWSA simulations at global scale are still lacking. Here, we use a suite of deep learning (DL) architectures, convolutional neural networks (CNN), deep convolutional autoencoders (DCAE), and Bayesian convolutional neural networks (BCNN), with training datasets including GRACE/-FO mascon and Swarm gravimetry, ECMWF Reanalysis-5 data, normalized time tag information to reconstruct global land TWSA maps, at a much higher resolution (100 km full wavelength) than that of GRACE/-FO, and effectively bridge the 11-month data gap globally. Contrary to previous studies, we applied no prior de-trending or de-seasoning to avoid biasing/aliasing the simulations induced by interannual or longer climate signals and extreme weather episodes. We show the contribution of Swarm and time inputs which significantly improved the TWSA simulations in particular for correct prediction of the trend component. Our results also show that external validation with independent data when filling large data gaps within spatio-temporal time series of geophysical signals is mandatory to maintain the robustness of the simulation results. The results and comparisons with previous studies and the adopted DL methods demonstrate the superior performance of DCAE. Validations of our DCAE-based TWSA simulations with independent datasets, including in situ groundwater level, Interferometric Synthetic Aperture Radar measured land subsidence rate (e.g. Central Valley), occurrence/timing of severe flash flood (e.g. South Asian Floods) and drought (e.g. Northern Great Plain, North America) events occurred within the gap, reveal excellent agreements.

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Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On (GRACE-FO), two successive satellite-based missions starting in 2002, have provided an unprecedented way of measuring global terrestrial water storage anomalies (TWSA). However, a temporal gap exists between GRACE and GRACE-FO products from July 2017 to May 2018, which introduces bias and uncertainties in TWSA calculations and modeling. Previous studies have incorporated hydroclimatic factors as predictors for filling the gap, but most of them utilized artificial intelligence or pure statistical models that generally de-trended TWSA and had no physical foundation. Thus, a physically-based reconstruction is required for increasing robustness. In this study, we bridge the temporal gap by developing an empirical hydrological model. The "abcd" model, a T-based snow component, and linear correction are utilized to represent runoff generation, snow dynamics, and long-term trends. The testing results indicate that our hydrological model can successfully reconstruct TWSA in tropical, temperature, and continental climates, although further improvement is needed for arid climates. Our reconstruction for the gap achieves high accuracy and robustness as shown by the evaluations against sea-level budget and GLDAS-derived TWSA. Compared to previous studies using artificial intelligence or statistical techniques, our hydrological model performs similarly in the gap filling but does not involve de-trended or de-seasonalized transformations, which will facilitate the combination of GRACE and GRACE-FO products and improve the physical understanding of global TWSA.

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Terrestrial water storage is a crucial component in water cycle and plays an important role in flood formations process, particularly in a changing environment. In this study, we aim to examine the future variation of terrestrial water storage anomaly (TWSA) and associated flood potential in one of the most flood-prone regions, the Yangtze River basin in China. Using the Gravity Recovery and Climate Experiment (GRACE) data, we perform bias correction for seven general circulation models (GCMs) from the Coupled Model Intercomparison Project Phase 6 under three Shared Socio-economic Pathway (SSP) scenarios: SSP126, SSP245, and SSP585. The spatiotemporal characteristics of changes in future Flood Potential Index are projected and compared between the near (2031-2060) and far (2071-2100) future with reference to the historical period (1985-2014). The results show that GCMs-simulated TWSA generally agrees well with the GRACE results after downscaling and bias correction with the average correlation coefficient of 0.86, Nash-Sutcliffe efficiency of 0.73 and the root mean square error of 21.68 mm. We found that the total variance of projected TWSA is mainly sourced from the internal variability and model uncertainties, while the uncertainties in scenarios contribute relatively less. Moreover, the flood potential is projected to decline during the near future under various scenarios and even lower during the far future under SSP585 scenario. Our findings provide implications for flood control and management under climate change over high flood risk regions worldwide.

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