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This study utilized data from the NASA Gravity Recovery and Climate Experiment (GRACE) to examine the variability of the terrestrial water storage anomaly (TWSA) in Iraq between 2002 and 2019. The analysis focused on six grid cells representing the Iraqi territory. The season trend decomposition (SLT) method was used to decompose the signal time series of TWSA to reveal the seasonality, trend, and random noise for the six GRACE blocks. Results proved that Block01 in northwestern side of Iraq, experienced a significant reduction in TWS between 2002 and 2009, followed by a negative linear trend until 2015, and then a positive trend. Block02 which is located in the northeastern part of Iraq showed a decreasing trend in TWS until 2008, after which it had a positive trend. Block03 in the western central side of Iraq reveals a decrease in TWS from 2002 to 2008, followed by a negative linear trend until 2016, and then a positive trend. Block04 in the eastern Mesopotamian plain had a minor increase in TWS until 2006, followed by a substantial decrease until 2016, before stabilizing and showing a positive trend. Block05 in the southwestern side of Iraq demonstrated a continuous decline in TWSA. In contrast, TWS increased in block6 on the southeastern side from 2002 to 2007, then decreased from 2007 to 2009. TWSA levels remained stable from 2009 to 2016, then increased from 2016 to the end of 2019. The seasonal fluctuations in TWS varied across the blocks, but generally, surpluses were observed in winter and spring, while deficits were observed in summer and autumn. The shortage in TWS is attributed to drought and excessive use of groundwater for irrigation. The recent positive trends in TWS in some blocks suggest that the drought may be coming to an end, but further analysis is necessary to reach a definitive conclusion. [ABSTRACT FROM AUTHOR]

The Songhua River Basin is an important grain production base and ecological region in China, and studying its water supply and water quantity changes is of great significance for achieving scientific water allocation and ecological environment protection in the basin. This study employed the CSR RL06 Mascon data products derived from the GRACE and GRACE-FO gravity satellites from 2003 to 2020. For the missing data between GRACE and GRACE-FO, published reconstructed data were used to fill the gaps. The accuracy of the data in the study area was validated through comparison with GLDAS simulated water storage data. Based on this, the spatial and temporal variation characteristics of terrestrial water storage in the basin were analyzed using a combination of Theil-Sen and Mann-Kendall trend tests. Additionally, the Pearson correlation coefficient and geographical detector methods were utilized to delve into the influence of various factors, including precipitation (Pre), evapotranspiration (ET), population density (POP), normalized difference vegetation index (NDVI), ecological zones (Ez), and digital elevation model (DEM), on terrestrial water storage. Furthermore, aiming to address the issue of errors introduced by resampling when performing correlation analysis on remote sensing data of different resolutions, the traditional raster data resampling method was optimized. The research findings indicate the following: 1 The GRACE data products exhibit good accuracy in the study area, and the linear trend range of their changes exhibits a high correlation with GLDAS data. 2 The optimized resampling method is more suitable for spatial studies similar to Pearson correlation analysis, and it significantly improves the accuracy of most data compared to directly using raw data. 3 The water storage in the Songhua River Basin has shown an increasing trend during the study period, with a noticeable increase in the extreme value ratio. By the end of the study period, the spatial distribution of water storage in the region tended to stabilize, exhibiting a basic pattern of more water in the northeast and less in the southwest. 4 The primary individual factors causing changes in water storage in the basin are precipitation and evapotranspiration, and the combined effects of most factors manifest as nonlinear enhancement. [ABSTRACT FROM AUTHOR]

The product data based on GRACE gravity satellites provide important support for large-scale terrestrial water storage (TWS) research. However, these data cannot meet the needs of long-term sequence research because of the limited data length. Based on meteorological and hydrological observation data, a variable infiltration capability (VIC) model was constructed in the ten water resource zones in China. Based on the soil water and snow water storage output from the VIC model and meteorological observation data, an artificial neural network model based on a multilayer perceptron was developed to reconstruct a long-term (1980—2018), high-resolution (0.25° × 0.25°) monthly TWS anomaly (TWSA) dataset in China. The reconstructed TWSA data were evaluated using GRACE data from 2003 to 2018. The results demonstrate the following: ① The VIC model exhibits overall good simulation performance, with better performance in humid basins than in semi-arid basins. ② The reconstructed TWSA dataset is highly consistent with the GRACE data at the spatial scale and can effectively capture interannual variations and evolution trends of the TWSA in most basins, especially in humid basins. ③ From 1980 to 2018, the TWSA exhibited a significant downward trend (>5 mm/a) in the North China Plain, Eastern Liaoning, Southern Songhua, and Southwest and Northwest parts of China, while a significant upward trend (>20 mm/a) was mainly concentrated in some regions of Western China. The TWSA data constructed can provide data support for hydrological and meteorological research in China [ABSTRACT FROM AUTHOR]

Understanding mass (re‐)distribution within the Earth system, and addressing global challenges such as the impact of climate change on water resources requires global time‐variable terrestrial water storage (TWS) estimates along with reasonable uncertainty fields. The Gravity Recovery and Climate Experiment (GRACE) and GRACE‐FO satellite missions provide time‐variable gravity fields with full variance‐covariance information. A rigorous uncertainty propagation of these errors to TWS uncertainties is mathematically challenging and computationally inefficient. We propose a Monte Carlo Full Variance‐Covariance (MCFVC) error propagation approach to precisely compute TWS uncertainties. We also establish theoretical criteria to predict the actual convergence and accuracy of MCFVC, showing a convergence after 10,000 realizations with the relative error of 2.8% for variance and 4.7% for covariance at the confidence level of 95%. This can be achieved in few seconds using a single CPU to compute the uncertainties of each 1° resolution globally gridded TWS field. A validation against the rigorous error propagation method indicates relative differences of less than 0.8%. A global uncertainty assessment shows that neglecting the covariance of gravity coefficients can considerably bias the TWS uncertainties, that is, up to 60%, in some basins like Eyre. Flexibility of MCFVC allows the quantification of filtering impacts on the uncertainty of TWS fields, for example, up to 35% in the Tocantins River Basin. An empirical model is provided to reproduce GRACE‐like TWS uncertainty fields for hydrological studies. Finally, experiments of GRACE(‐FO) data assimilation for hydrological applications and sea‐level budget estimation are presented that indicate the importance of accounting for the full covariance information in these studies. Plain Language Summary: Terrestrial water storage (TWS) represents a vertical summation of all available water of the Earth's land surface including groundwater, soil layers, surface water, and snow. Therefore, understanding TWS changes is essential for hydrological assessments. Satellite gravity missions such as the Gravity Recovery and Climate Experiment (GRACE) and its Follow‐On mission (GRACE‐FO) provide a unique opportunity to estimate TWS globally. However, to use GRACE(‐FO)‐like TWS estimates in geophysical and hydrological studies, one needs to quantify their uncertainties reliably at desired spatial resolution. In this study, an efficient statistical Monte Carlo method is presented, which provides high flexibility of processing choices, and satisfies the accuracy needed to compute the uncertainties of GRACE(‐FO)‐like TWS products. The simplicity and generality of this method also allows it to be easily integrated in the operational data post‐processing chains. We demonstrate the importance of the full covariance estimation of TWS products in hydrological data assimilation and sea‐level budget estimation studies. Key Points: A Monte Carlo Full Variance‐Covariance method is proposed to propagate errors of GRACE(‐FO) level‐2 data, where the covariance is hardly reached by traditional methodsConsidering full variance‐covariance information and filtering methods have considerable impacts on the uncertainty of GRACE(‐FO)‐like terrestrial water storage (TWS) fieldsAn empirical model is provided to account for spatial‐temporal variation of GRACE(‐FO)‐like TWS uncertainty fields, and for hydrological studies [ABSTRACT FROM AUTHOR]

Abstract Net primary productivity (NPP) is an important parameter reflecting vegetation growth, and water is one of the necessary factors for vegetation growth. Investigating the mutual influence between NPP and water is significant for ensuring the stable development of the ecological environment. This study focuses on the Yangtze River Basin (YRB) as the research area, and based on medium-resolution imaging spectrometer (MODIS) data, climate data, and gravity recovery and climate experiment (GRACE) data, the spatiotemporal evolution characteristics of vegetation NPP and terrestrial water storage (TWS) in the YRB from 2000 to 2022 are explored and analyzes the mutual influence of NPP with climate factors and TWS. The results show that vegetation NPP (4.10 gC·m−2·a−1) and TWS (0.55 mm) in the YRB have exhibited an increasing trend from 2000 to 2022, with a strong correlation between the two, which is related to recent environmental policies. Analysis of the impact of climate factors on NPP reveals that temperature and TWS significantly positively impact NPP changes. Furthermore, comparisons between NPP and TWS indicate that changes in TWS substantially promote plant growth. In addition, the comparison between NPP and TWS indicates that changes in TWS have an important promoting effect on plant growth. Surface water (SWS) and soil water (SM) have a significant promoting effect on plant growth, but with a strong lag, while the consumption of groundwater (GWS) has been promoting plant growth without significant lag.

Extreme hydrological events such as droughts and floods have been increasingly influenced by abnormal atmospheric disturbances caused by human activity and global warming. The Dongting Lake Basin (DLB) has experienced challenging circumstances over the past 20 years due to complex climatic variations, leading to extreme flooding and drought. This study aims to investigate the spatiotemporal variation in terrestrial water storage anomalies (TWSA) over the DLB using data from the GRACE/GRACE-FO and GLDAS spanning the latest two decades. A significant decline trend in TWSA is unveiled from July 2019 to May 2023, with the rate of change determined as −1.94 cm/year and −1.99 cm/year based on the GRACE/GRACE-FO and GLDAS, respectively. The GRACE- Drought Severity Index (DSI) is employed to identify and evaluate the severity and spatiotemporal evolution of the 2022 drought event in the DLB. The results accurately capture the drought event, which began in July 2022 and continued until March 2023, with the most severe conditions occurring in October 2022, when the GRACE-DSI value stood at −2.06 and the TWSA decreased by 15.24 cm and 33.51 cm relative to the same month in 2021 and 2020, respectively. Additionally, the daily water level variation at the Chenglingji hydrological gauging station in 2022 broke previous records, reaching a minimum of only 19 m. Comparing the 2022 drought event with the drought events in 2006 and 2011, the impact of drought on vegetation growth conditions was relatively small, but there was still significant vegetation degradation across the DLB.

Terrestrial water storage (TWS) variations are associated with water mass movements, which may cause the deformation displacements of the Global Navigation Satellite System (GNSS) stations. This study investigates the spatio-temporal TWS variations and addresses the relationship between deformation variations observed in the Huang-Huai-Hai River Basin (HHHRB) and local hydrological features. Results indicate that the vertical velocities at the GNSS stations induced by TWS changes are relatively small, and the impacts of the terrestrial water storage changes are mainly reflected in the changes of seasonal characteristics. Although there is a downward TWS trend from 2011 to 2022 in most HHHRB areas, velocities from the vertical displacements of both Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On (GFO) and the GNSS reflect that the HHHRB is undergoing an uplift process, while the magnitude of the GRACE/GFO derived velocities is much smaller than that of the GNSS solutions. Common hydrological deformations estimated from GRACE/GFO and GNSS measurements reveal that the TWS-derived displacements can explain 54.5% of the GNSS seasonal variations, with the phases of terrestrial water storage advancing by about one month relative to GNSS common signal phases. Moreover, the decrease of the groundwater storage in the HHHRB has been accelerating since 2008. After reaching its lowest level around mid-2020, it began to rise rapidly, which might be closely related to the implementation of the South-North Water Transfer Central Project.

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In order to master the influence of the East Route of South-to-North Water Diversion Project to the regional terrestrial water storage in Shandong, based on GRACE gravity satellite data from January 2010 to December 2017, combined with the data set of major hydrological factors (precipitation and evapotranspiration) in the same period, the changes of terrestrial water storage and major hydrological factors in the study area were studied by time period and region, and by the operating time of the South-to-North Water Diversion Project as the boundary. The influence of the South-to-North Water Diversion Project to the change of terrestrial water storage in Shandong Province was calculated based on the water transfer data of the South-to-North Water Diversion Project. The results show that the spatial distribution of terrestrial water storage in the study area decreases from east to west over the years. The seasonal characteristics show that the terrestrial water storage is abundant in summer and autumn, but less in spring and winter. The results show that the terrestrial water storage decay rate is 1.45 cm/a and -0.36 cm/a before and after the operation of the South-to-North Water Diversion Project respectively. It is found that the South-to-North Water Diversion Project has a good positive impact on the central and southern parts of western Shandong Province and the southern parts of central Shandong Province. In summary, the South-to-North Water Diversion Project has alleviated the decline trend of 56.9% of terrestrial water storage in Shandong Province, and has a positive effect on water resources conditions and terrestrial water storage enrichment in the study area during the study period. [ABSTRACT FROM AUTHOR]

The monitoring of Poyang Lake water area and storage changes using remote sensing and satellite gravimetry techniques is valuable for maintaining regional water resource security and addressing the challenges of global climate change. In this study, remote sensing datasets from Landsat images (Landsat 5, 7, 8 and 9) and three Gravity Recovery and Climate Experiment (GRACE) and Gravity Follow-on (GRACE-FO) mascon solutions were jointly used to evaluate the water area and storage changes in response to global and regional climate changes. The results showed that seasonal characteristics existed in the terrestrial water storage (TWS) and water area changes of Poyang Lake, with nearly no significant long-term trend, for the period from April 2002 to December 2022. Poyang Lake exhibited the largest water area in June and July every year and then demonstrated a downward trend, with relatively smaller water areas in January and November, confirmed by the estimated TWS changes. For the flood (August 2010) and drought (September 2022) events, the water area changes are 3032 km2 and 813.18 km2, with those estimated TWS changes 17.37 cm and −17.46 cm, respectively. The maximum and minimum Poyang Lake area differences exceeded 2700 km2. The estimated terrestrial water storage changes in Poyang Lake derived from the three GRACE/GRACE-FO mascon solutions agreed well, with all correlation coefficients higher than 0.92. There was a significant positive correlation higher than 0.75 between the area and TWS changes derived from the two independent monitoring techniques. Therefore, it is reasonable to conclude that combined remote sensing with satellite gravimetric techniques can better interpret the response of Poyang Lake to climate change from the aspects of water area and TWS changes more efficiently. [ABSTRACT FROM AUTHOR]

The La Plata basin is one of the largest river basins in the world, draining about a fifth of the South American continent. It is a system of great hydrological significance, sensitive to extreme events, and also has the most economic development in the region. This work analyses the total water storage (TWS) changes over the La Plata basin from the GRACE (Gravity Recovery And Climate Experiment) mission and compares it with TRMM (Tropical Rainfall Measuring Mission) rainfall data. Subsequently, a study was conducted on the main sub-basins for the period 2003 to 2017 to identify regional-scale variations in TWS. Precipitation and GLDAS (Global Land Data Assimilation System) runoff data was evaluated in these areas, along with their connections to ENSO (El Niño Southern Oscillation) episodes. The results suggest that the majority of TWS variations are associated with extreme climatic events, such as the 2009 drought, and are linked to ENSO periods. The statistical analysis indicates a significant correlation between severe Niño/Niña episodes and changes in TWS in the sub-basins. [ABSTRACT FROM AUTHOR]

Regional sea level variability is linked to regional terrestrial water and the El Niño-Southern Oscillation (ENSO). This study assessed the relationships between the sea level variations in the South China Sea (SCS) and ENSO, the impact of terrestrial water storage (TWS) on non-steric sea level (NSSL), and the contributions of steric sea level (SSL) and NSSL to sea level anomaly (SLA), respectively. From 2003 to 2015, the SLAs exhibited a long-term trend of 6.65±0.78 mm/yr, which was primarily attributed to the SSLs. Additionally, during 2003–2015, ENSO events alternating with varying intensities might also be responsible for the unusually high SLA trend. Compared to the SSLs, the NSSLs contributed the seasonal signals to the SLAs, while the NSSLs changes were largely explained by the TWS in the Mekong River Basin at the seasonal scale and in the Pearl River Basin and Red River Basin at other time scales. In contrast to the TWS, the contributions of precipitation and evapotranspiration were relatively minor. A negative correlation between the sea level variations and ENSO was also found, with cross-correlation coefficients between the oceanic Niño index and SLAs/SSLs/NSSLs of −0.36/−0.37/−0.62 with lags of 2/3/2 months, respectively. These findings systematically reassessed the contributions of different components to the sea level variations. This study provided a benchmark for in-depth analysis of the impacts of terrestrial water and other potential causes on sea level rise in the SCS. [ABSTRACT FROM AUTHOR]

Changes in water resource storage are inevitable due to climate change and human activities, thus understanding alterations in water storage within a specific region is imperative for the planning and management of water resources. Data from the Gravity Recovery and Climate Experiment (GRACE) satellite mission are extensively employed to analyze large-scale total terrestrial water storage anomalies (TWSA). In this study, we derived a more reliable TWSA using different types of GRACE gravity models, which served as the basis for evaluating spatial and temporal variations in total terrestrial water storage and its hydrological components (soil moisture and groundwater) across the Loess Plateau. Additionally, we analyzed the impact of natural and anthropogenic influences on water storage in the Loess Plateau, categorizing them into primary and secondary influences, utilizing data on climate and human activities. The findings revealed a declining trend in the overall TWSA of the Loess Plateau, with a rate of decrease at - 0.65±0.05 cm/yr from 2003 to 2020 (P<0.01). As the direct factors affecting TWSA, soil moisture dominated the change of TWSA before 2009, and groundwater dominated the change of TWSA after 2009. Spatially, there was variability in the changes of TWSA in the Loess Plateau. More in-depth studies showed that soil moisture changes in the study area were primarily driven by evapotranspiration and temperature, with precipitation and vegetation cover status playing a secondary role. Human activities had a secondary effect on soil moisture in some sub-regions. Population change and agricultural development were major factors in altering groundwater storage in the study area. Other than that, groundwater was influenced by natural factors to a limited extent. These findings provided valuable insights for local governments to implement proactive water management policies. [ABSTRACT FROM AUTHOR]

GRACE (Gravity Recovery and Climate Experiment) has been widely used to evaluate terrestrial water storage (TWS) and groundwater storage (GWS). However, the coarse‐resolution of GRACE data has limited the ability to identify local vulnerabilities in water storage changes associated with climatic and anthropogenic stressors. This study employs high‐resolution (1 km2) GRACE data generated through machine learning (ML) based statistical downscaling to illuminate TWS and GWS dynamics across twenty sub‐regions in the Indus Basin. Monthly TWS and GWS anomalies obtained from a geographically weighted random forest (RFgw) model maintained good consistency with original GRACE data at the 25 km2 grid scale. The downscaled data at 1 km2 resolution illustrate the spatial heterogeneity of TWS and GWS depletion within each sub‐region. Comparison with in‐situ GWS from 2,200 monitoring wells shows that downscaling of GRACE data significantly improves agreement with in‐situ data, evidenced by higher Kling‐Gupta Efficiency (0.50–0.85) and correlation coefficients (0.60–0.95). Hotspots with the highest TWS and GWS decline rate between 2002 and 2023 were Dehli Doab (−442, −585 mm/year), BIST Doab (−367, −556 mm/year), Rajasthan (−242, −381 mm/year), and BARI (−188, −333 mm/year). Based on a general additive model, 47%–83% of the TWS decline was associated with anthropogenic stressors mainly due to increasing trends of crop sown area, water consumption, and human settlements. The decline rate of TWS and GWS anomalies was lower (i.e., −25 to −75 mm/year) in upstream sub‐regions (e.g., Yogo, Gilgit, Khurmong, Kabul) where climatic factors (downward shortwave radiations, air temperature, and sea surface temperature) explained 72%–91% of TWS/GWS changes. The relative influences of climatic and anthropogenic stressors varied across sub‐regions, underscoring the complex interplay of natural‐human activities in the basin. These findings inform place‐based water resource management in the Indus Basin by advancing the understanding of local vulnerabilities. Plain Language Summary: We used GRACE data to understand how water storage has changed over time across the Indus Basin at a resolution of 1 square kilometer. We generated the new high‐resolution data using machine learning techniques that implemented statistical methods. The new data for analyzing water storage matched well with the original data on a larger scale. Additionally, comparing this detailed data with measurements from 2,200 wells showed that our new method works well. The new high‐resolution data help us detect hotspots of water storage decline where water availability may face challenges in the future if status quo continues. Human activities like more farming, using more water, and building more areas for people to live are a major driver of the water storage decline. In upstream areas less influenced by human impacts, the decline is driven more by climatic factors. By improving understanding of local vulnerabilities, our study supports planning interventions for specific regions based on the need to reduce the impact of human activities or adapt to climate change. Key Points: Terrestrial water storage (TWS)/groundwater storage (GWS) derived from downscaled GRACE data show a declining trend across most sub‐regions of the Indus Basin between 2002 and 2023Anthropogenic stressors explain 47%–83% of TWS decline in the majority of sub‐regionsTWS/GWS changes in upstream sub‐regions, where shortwave radiations mainly control the TWS changes, are well explained by climatic factors [ABSTRACT FROM AUTHOR]

The increasing temperature in the Yellow River Basin has led to a rapid rise in the melting level height, at a rate of 5.98 m yr-1 during the cold season, which further contributes to the transition from snowfall to rainfall patterns. Between 1979 and 2020, there has been a decrease in snowfall in the Yellow River Basin at a rate of -3.03 mm dec-1, while rainfall has been increasing at a rate of 1.00 mm dec-1. Consequently, the snowfall-to-rainfall ratio (SRR) has decreased. Snowfall directly replenishes terrestrial water storage (TWS) in solid form until it melts, while rainfall is rapidly lost through runoff and evaporation, in addition to infiltrating underground or remaining on the surface. Therefore, the decreasing SRR accelerates the depletion of water resources. According to the surface water balance equation, the reduction in precipitation and runoff, along with an increase in evaporation, results in a decrease in TWS during the cold season within the Yellow River Basin. In addition to climate change, human activities, considering the region's dense population and extensive agricultural land, also accelerate the decline of TWS. Notably, irrigation accounts for the largest proportion of water withdrawals in the Yellow River Basin (71.8%) and primarily occurs during the warm season (especially from June to August). The impact of human activities and climate change on the water cycle requires further in-depth research. [ABSTRACT FROM AUTHOR]

Study region: River basins in the Tibetan Plateau, including the Yarlung Zangbo River (YZR) basin, the Salween River (SWR) basin, the Lancang River (LCR) basin, the Yangtze River (YTR) basin, the Yellow River (YLR) basin, and the Tarim River (TRM) basin. Study focus: In recent years, the concept of seasonal catchment memory has emerged as a pivotal element in the field of hydrological science. Precipitation memory curve (PMC) is a good method to describe seasonal precipitation memory process, which is the key function connecting precipitation and terrestrial water storage change (TWSC). The model simulated TWSC with precipitation is proposed based on PMC. According to the performance of the model, this study investigates optimal curve functions to describe seasonal catchment memory in the Tibetan Plateau. New hydrological insights for the region: It is found that in most basins, the shape of PMC is consistent with the Boussinesq function or Maillet function. In the SWR basin, PMC varies between warm season and cold season. However, due to relatively less precipitation in the cold season compared to the warm season, the segmented-representation PMC has only a slight improvement on the TWSC simulation. In the TRM basin, the temperature-index method can improve the performance of the model. The PMC of the TRM basin during the warm season exhibits a short duration of precipitation memory of approximately three months.

The construction of dams for intercepting and storing water has altered surface water distributions, land-sea water exchanges, and the load response of the solid Earth. The lack of accurate estimation of reservoir properties through the land surface and hydrological models can lead to water storage simulation and extraction errors. This impact is particularly evident in many artificial reservoirs in China. The study aims to comprehensively assess the spatiotemporal distribution and trends of water storage in medium and large reservoirs (MLRs) in Chinese mainland during 1950–2016, and to investigate the gravity, displacement, and strain effects induced by the reservoir mass concentration using the load elasticity theory. In addition, the impoundment contributions of MLRs to the relative sea level changes were assessed using a sea-level equation. The results show impoundment increases in the MLRs during 1950–2016, particularly in the Yangtze River (Changjiang) and southern basins, causing significant elastic load effects in the surrounding areas of the reservoirs and increasing the relative sea level in China's offshore. However, long-term groundwater estimation trends are overestimated and underestimated in the Yangtze River and southwestern basins, respectively, due to the neglect of the MLRs impacts or the uncertainty of the hydrological model's output (e.g., soil moisture, etc.). The construction of MLRs may reduce the water mass input from land to the ocean, thus slowing global sea level rise. The results of the impact of human activities on the regional water cycle provide important references and data support for improving the integration of hydrological models, evaluating Earth's viscoelastic responses under long-term reservoir storage, enhancing in-situ and satellite geodetic measurements, and identifying the main factors driving sea level changes.

In the context of ongoing global climate warming, researchers have observed an intricate relationship between the variability of Terrestrial Water Storage (TWS) on the Tibetan Plateau (TP) and climate anomalies. In this study, we utilize a combination of Global Navigation Satellite System (GNSS), Gravity Recovery and Climate Experiment (GRACE), and precipitation data to investigate the response of TWS on the TP to extreme climate changes, with a focus on seasonal and interannual variations. Our findings reveal that the first Common Mode Component (1st CMC) extracted from GNSS coordinate time series exhibits strong interannual fluctuations. Additionally, both GNSS and GRACE-inferred TWS demonstrate noticeable anomalous changes during the 2015/2016 period. The estimated periods of the 1st CMC of GNSS and GRACE-inferred TWS are approximately 5 and 7 years, respectively. Both reach a minimum during the extreme climate event in 2015/2016. Furthermore, we investigate the spatial variation of summer precipitation before and after the extreme drought event on the TP. It reveals that the inflection point of TWS occurs approximately 1 year prior to the drought event, suggesting a consistent spatial propagation process between precipitation and TWS. This indicates that the changes of TWS during this drought event were primarily influenced by precipitation. Finally, we evaluate the response of 12 sub-basins on the TP to the drought event that occurred in 2015/2016. The regions experiencing significant abnormal fluctuations in TWS are mainly located in inner and northeastern plateau. These regions show rapid upward changes in the TWS and demonstrate a strong spatial consistency with summer precipitation.

Study region: The Tianshan region Study focus: Against the backdrop of ongoing global climate warming, the terrestrial water storage (TWS) changes are a complex dynamic process closely linked to climate change in the Tianshan. However, current researches in the Tianshan only focus on a specific component of TWS and are mainly analysed and discussed by using the Gravity Recovery and Climate Experiment (GRACE) observations, while the inversion results from Global Navigation Satellite System (GNSS) observations with high sensitivity and temporal resolution and their response analysis to extreme climate events are lacking. New hydrological insight for the region: The synthetic test show that (1) the GNSS stations in the Tianshan are able to recover the TWS changes in this region, and the recovery is better in the east Tianshan. The actual inversion results show that (2) the TWS changes inferred from various datasets exhibit good consistency in the spatiotemporal characteristics. Seasonal TWS changes all show a decreasing trend from western Tianshan to eastern Tianshan, but the GNSS observations show a larger magnitude than the GRACE and Global Land Data Assimilation System (GLDAS) observations. The annual phases in the southern Tianshan are about 2 months later than those in the northern Tianshan. Additionally, we also investigate the relationship between the interannual TWS changes and El Niño/Southern Oscillation (ENSO). The results show that (3) the interannual TWS changes inferred from the GNSS observations can more accurately detect hydrological anomalies induced by ENSO and better correspond to the abrupt years of spatial grid anomalies in the precipitation and atmospheric water vapor content, indicating that precipitation is a main driving factor of the TWS changes in the Tianshan. Our goal is to emphasize the significant potential of GNSS in estimating the TWS changes and to help monitor the impact of extreme climate changes on the hydrological processes.

Under the influence of climate change and human activities, significant changes have occurred in the basin water cycle, making the situation for water resources protection increasingly severe. In response to the issues of water shortage in the Yellow River Basin and the difficulty in monitoring terrestrial water storage, this study based on GRACE (Gravity Recovery and Climate Experiment) data and the Variable Infiltration Capacity (VIC) model, estimated the future changes of terrestrial water storage in the Yellow River Basin and analyzed the impact mechanism of climate change on terrestrial water storage. The results show that: a) the changes of terrestrial water storage calculated by GRACE and VIC using the component summation method shows high consistency, effectively simulating the spatiotemporal evolution characteristics of terrestrial water storage in the Yellow River Basin. b) From 2030 to 2100, there is a relatively insignificant increasing trend in terrestrial water storage in the Yellow River Basin, and under the SSP585 scenario, the increasing trend in terrestrial water storage is more intense. c) During the forecast period, precipitation, temperature, and evapotranspiration in various regions of the Yellow River Basin are all projected to increase compared to historical periods. However, runoff in the Yellow River source area and near Lanzhou is expected to decrease compared to historical periods. Although the overall land water storage in the Yellow River Basin shows an increasing trend during the forecast period, only the terrestrial water storage anomaly in Inner Mongolia is greater than 0. [ABSTRACT FROM AUTHOR]

The nature of the heterogeneity of terrestrial water storage in the main river basins of Ethiopia is poorly understood. In river basins, measurements of terrestrial water storage changes are exceedingly rare in all temporal dimensions. This study investigates the spatiotemporal variation of water storage in the main river basins of Ethiopia using the Gravity Recovery and Climate Experiment (GRACE) with Global Land Data Assimilation System (GLDAS). The results revealed significant variation in liquid water equivalents from GRACE and terrestrial water storage from GLDAS for each river basin, following a seasonal pattern. I found that GRACE and GLDAS datasets can be combined effectively to evaluate the long-term water storage in large-scale basins with limited hydrological data, and the obtained result provides a reliable outcome that will be essential for the establishment of climate change adaptation pathways in the future for essential water resource management and optimization. [ABSTRACT FROM AUTHOR]

The response of dryland vegetation to climate change is particularly sensitive in the context of global climate change. This paper analyzes the characteristics of spatial and temporal dynamics of vegetation cover in the Tarim River Basin, China, and its driving factors in order to investigate the response of vegetation growth to water storage changes in the basin. The Enhanced Vegetation Index (EVI), the GRACE gravity satellite, and meteorological data from 2002 to 2022 are used to decipher the characteristics of the response of water storage changes to vegetation changes, which is of great significance to the realization of regional ecological development and sustainable development. The results of the study show the following: (1) The vegetation in the Tarim River Basin has an overall increasing trend, which is mainly distributed in the Aksu Basin and the Weigangkuche River Basin and is spatially distributed in the form of a ring. (2) Vegetation distribution greatly improved during the 20-year study period, dominated by high-cover vegetation, with a change rate of 200.36%. Additionally, vegetation changes are centered on the watersheds and expand to the surrounding area, with a clear increase in vegetation in the Kumukuri Basin. Areas with a vegetation Hurst index of <0.5 account for 63.27% of the study area, and the areas with a continuous decrease were mainly located in the outer contour area of the Tarim River and Kumu Kuri Basins. (3) There are obvious spatial differences in the correlation between EVI and temperature and precipitation elements. The proportion of areas with positive correlation with temperature within the study area is 64.67%. EVI tends to be consistent with the direction of migration of the center of gravity of the population and GDP, and the areas with positive correlation between vegetation and terrestrial water reserves are mainly distributed in the northern slopes of the Kunlun Mountains, with an area proportion of about 50.513%. The Kumukuli Basin also shows significantly positive correlation. [ABSTRACT FROM AUTHOR]

Mass changes in the hydrosphere represent an important contributor to polar motion (PM) variations, especially at seasonal time scales (i.e., annual and semiannual). Although well studied, hydrological angular momentum (HAM) remains a major source of uncertainty in estimating PM excitation. In this work, we use a large number of climate models from the sixth phase of the Coupled Model Intercomparison Project (CMIP6) to determine HAM series both from individual models and their combination, formed with a multimodel mean, a weighted mean, and a three-cornered hat (TCH) method. The CMIP6-based HAM series are analysed in several spectral bands and evaluated with a reference hydrological signal in geodetically observed PM excitation (GAO). HAM determined from CMIP6 were also compared to HAM calculated from Gravity Recovery and Climate Experiment (GRACE) measurements. We find that while climate models do not allow for reliable estimation of nonseasonal changes in HAM, they can help interpret seasonal variability. For annual prograde and semiannual retrograde oscillations, several combined CMIP6- based series exhibit higher amplitude and phase consistency with GAO than the corresponding series computed from GRACE data. Whether one uses a simple average of the models, a weighted average, or a combination of models from the TCH method has little impact on the resulting HAM series and their level of agreement with GAO. Our study advances the understanding of hydrological signal in Earth's rotation at seasonal time scales. [ABSTRACT FROM AUTHOR]

The spatio-temporal variability of groundwater storage cannot be well understood without proper groundwater monitoring schemes. Since 2002, the launch of the Gravity Recovery and Climate Experiment Satellite (GRACE) mission has served to monitor Groundwater Storage Anomaly (GWSA) and filled the observational data gap on a regional scale. This study aimed to estimate the spatio-temporal GWSA in Ethiopia using GRACE satellite data. GWSA was calculated by disaggregating GRACE estimation of Terrestrial Water Storage Anomaly (TWSA) using auxiliary soil moisture and surface runoff data obtained from the Global Land Data Assimilation System. GWSA was decomposed using the Seasonal-Trend decomposition method, LOESS (STL). The results depicted an increasing variability of TWSA and GWSA over various regions of the country. Ethiopia experienced an increase in TWSA (3.8 mm yr−1) and GWSA (4.6 mm yr−1) between the years 2003 and 2021, with GWSA contributing primarily to the TWSA. Greater contributions to the rise in groundwater storage come from the Rift Valley, Omo Gibe, Baro Akobo, and a portion of the Genale Dawa, Awash, and Wabi Shebelle Basins. Except for the lowlands (Northwestern, Northeastern and Southeastern), most regions showed an average increase in GWSA per annum at varying rates. Precipitation, temperature, and evapotranspiration have a significant influence on the spatial variability of GWSA. The impact of precipitation on GWSA reached its maximum after a 2-month lag (correlation coefficient (R) = 0.62). GRACE captured the seasonal GWSA of Ethiopia reasonably well and can be used as a guide for a more detailed evaluation of the groundwater potential. [ABSTRACT FROM AUTHOR]

In recent decades, the water cycle process in the Loess Plateau has undergone drastic changes under the influence of anthropogenic disturbance and climate variability. The Loess Plateau has been greatly affected by human activities and climate change, and the dynamics of water balance and its key influencing factors remain unknown in this region. To deepen our understanding of water resource status in the Loess Plateau, we analyzed the dynamic changes in the hydrological cycle components and terrestrial water balance via remote sensing during the 2001–2020 period. Moreover, we also discussed the dominant factors affecting the terrestrial water balance. The results indicate that precipitation and ET exhibited increasing trends, with upward rates of 2.56 mm/yr and 5.27 mm/yr, respectively. Spatially, the annual average precipitation and ET showed increasing trends from the north to south. Precipitation in 62.05% of the entire region presented a significant upward trend, with a change rate of 0~2 mm/yr, and the annual change rate of ET in a range of 0~30 mm/year accounted for 89.12%. The runoff exhibited a fluctuating and significantly upward trend during the 2000–2022 period, with a change rate of 3.92 × 108 m3/yr, and was relatively large in the middle reach of the Yellow River. The annual average water consumption in the upper reach of the YRB presented a significant decreasing trend, with a downward rate of −0.06 × 108 m3/yr from 2001 to 2020, and yet it displayed a significant increasing trend in the middle and lower reaches of the YRB with the upward rate of 0.07~0.11 × 108 m3/yr. Based on the principle of water balance, the recharge amount was calculated at less than the discharge amount during the 2001–2020 period, and the difference between recharge and discharge is gradually expanding, with a change rate of −3.72 × 108 m3/yr. The spatial distributions of TWSC revealed that the eastern region was in an imbalanced state, while the western region was in a relatively balanced state. Terrestrial water balance changes were mainly affected by climate factors and human disturbance, and land use/cover change was the dominant factor. The results will be of great significance for optimizing water resource management and formulating various water-saving strategies in the Loess Plateau. [ABSTRACT FROM AUTHOR]

Geodetic methods can monitor changes in terrestrial water storage (TWS) across large regions in near real‐time. Here, we investigate the effect of assumed Earth structure on TWS estimates derived from Global Navigation Satellite System (GNSS) displacement time series. Through a series of synthetic tests, we systematically explore how the spatial wavelength of water load affects the error of TWS estimates. Large loads (e.g., >1,000 km) are well recovered regardless of the assumed Earth model. For small loads (e.g., <10 km), however, errors can exceed 75% when an incorrect model for the Earth is chosen. As a case study, we consider the sensitivity of seasonal TWS estimates within mountainous watersheds of the western U.S., finding estimates that differ by over 13% for a collection of common global and regional structural models. Errors in the recovered water load generally scale with the total weight of the load; thus, long‐term changes in storage can produce significant uplift (subsidence), enhancing errors. We demonstrate that regions experiencing systematic and large‐scale variations in water storage, such as the Greenland ice sheet, exhibit significant differences in predicted displacement (over 20 mm) depending on the choice of Earth model. Since the discrepancies exceed GNSS observational precision, an appropriate Earth model must be adopted when inverting GNSS observations for mass changes in these regions. Furthermore, regions with large‐scale mass changes that can be quantified using independent data (e.g., altimetry, gravity) present opportunities to use geodetic observations to refine structural properties of seismologically derived models for the Earth's interior structure. Plain Language Summary: In many regions of the Earth, water resources used for agriculture, domestic, and industrial purposes rely on stream flow and groundwater sourced from the melting of winter snowpack in adjacent mountains. Modern shifts in climate have resulted in increasingly variable precipitation patterns and temperatures during winter months. Coupled with a rising global population, there has been a growing need for accurate estimates of freshwater stored above and beneath the land surface. A relatively new interdisciplinary approach called hydrogeodesy allows for freshwater resources to be monitored by using satellite‐ and ground‐based sensors to accurately measure changes in the shape and gravitational field of the Earth produced by the redistribution of water between hydrologic reservoirs. As this approach becomes increasingly utilized to inform decision‐makers, however, we require a deeper understanding of the assumptions and uncertainties of the models used to translate between geodetic measurements and estimates of water storage. Here, we consider the impact of assumptions about the Earth's interior structure on the error of geodetic water storage estimates. We present a set of case studies that display the varied influence of assumed Earth structure on water storage estimates depending on the spatial scale and amplitude of water storage variations. Key Points: Estimates of water storage made at fine spatial scales are highly sensitive to the Earth model used to invert geodetic measurementsSensitivities to Earth structure produce uncertainties in estimates of water storage that scale with the total weight of the water loadPredictions of uplift produced by melting of the Earth's ice sheets over the past two decades can differ by over 20 mm between Earth models [ABSTRACT FROM AUTHOR]

Since the arid regions of Central Asia (ACA) are located in the interior of Eurasia, water resources play a vital role in the stability of its ecosystem and economic development. Based on the terrestrial water storage anomaly (TWSA) of the Gravity Recovery and Climate Experiment (GRACE), we analyze the observed characteristics of the TWSA over the ACA during 2003–2014. Results indicate that the terrestrial water storage (TWS) in the region showed an overall declining trend from 2003 to 2014, and the autumn TWS in this region is the smallest compared to other seasons and exhibits a strong decreasing trend at least −4.5 cm/decade. This means water resources over the ACA are scarcer and more vulnerable in autumn. The Distance between Indices of Simulation and Observation (DISO) method is employed to evaluate the performance of the sixth phase of the Coupled Model Intercomparison Project (CMIP6) models in simulating the autumn TWSA over the ACA. Compared with observational results, the autumn TWSA values captured by CMIP6 models are larger and the declining TWS trends are weaker. Using the optimal CMIP6 models, the statistical downscaling method constrains the projection results of autumn TWSA values over the ACA using the GRACE datasets. It shows autumn TWS will continue to decrease in most parts of the ACA in the future, and water scarcity will be the most severe in Tajikistan and southwestern Kazakhstan. Under SSP126, Tajikistan’s TWSA is projected to decrease by 11.0 cm in the long term. This study reveals the current situation and possible future changes in TWS over the ACA in autumn, providing references for water resource management and sustainable development policies in this area to avoid losses caused by water scarcity.

The widely distributed surface water bodies (e.g., lakes and reservoirs) are important components of water resources on Earth, their spatial–temporal dynamics can pose significant effects on regional water resources. Previous studies generally attributed variability in terrestrial water storage (TWS) to groundwater dynamics, hardly considering surface water changes, making the influence of the changing surface water storage on TWS unclear. Here, by taking Hulun Lake as an example, which occupies 94.0 % of the total water areas in the target basin, we quantitatively revealed the impacts of changes in lake water volume on TWS. Using cloud computing-based water body monitoring and altimetry satellite-based water level measurements, we found continuous lake shrinkage (25 km2/yr) before 2012 while rapid expansion after that. By coupling water areas with levels, annual lake volumes were estimated. We showed that lake volumes also significantly declined (−0.52 km3/yr) before 2012, and then rapidly restored (0.65 km3/yr) due to the increasing water recharge from rivers. The increasing rate of lake water volume was higher than that of TWS (0.15 km3/yr), indicating the great contribution of lake volume increases to TWS gains. Our study highlights the considerable impacts of the changing surface water bodies on regional water resources and it is essential to consider storage changes in surface water when attributing the drivers of TWS variability in regions experiencing dramatic lake changes. Our method proposed is also applicable to evaluating the impacts of surface water changes on TWS in other similar areas around the world.

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.

Terrestrial Water Storage (TWS) changes have been estimated at basin to continental scales from gravity variations using data from the Gravity Recovery and Climate Experiment (GRACE) satellites since 2002. The relatively low spatial resolution (∼300 km) of GRACE observations has been a main limitation in such studies. Various data processing strategies, including mascons, forward modeling, and constrained linear deconvolution (CLD), have been employed to address this limitation. Here we develop a revised CLD method to obtain a TWS estimate that combines GRACE observations with much higher spatial resolution land surface models. The revised CLD constrains model estimates to agree with GRACE TWS when smoothed. As an example, we apply the method to obtain a high spatial resolution TWS estimate in Australia. We assess the accuracy of the approach using synthetic GRACE data. Plain Language Summary: The estimation of terrestrial water storage (TWS) changes using gravity recovery and climate experiment (GRACE) satellites suffers from low spatial resolution, making it challenging to interpret local‐scale mass changes. In this study, we improved the sparse resolution of GRACE observations by incorporating high‐resolution land surface models (LSM) that provides detailed hydrological information. Through synthetic experiments, we confirmed the accuracy of our estimations in regional‐ and local‐scale. When applied to real GRACE data, our new TWS estimations show better spatial resolution compared to conventional GRACE products. Further, our estimations consistently yield reliable results although different LSM were used. Key Points: High‐resolution terrestrial water storage was estimated by combining gravity recovery and climate experiment and land surface modelsOur new estimates reduced both land‐ocean and inter‐basin leakages simultaneously [ABSTRACT FROM AUTHOR]

The Volta Basin in West Africa plays a crucial role in supporting the livelihoods of millions of people, and effective management of its water resources is essential for climate change adaptation. This study utilized remote sensing technology, specifically the Gravity Recovery and Climate Experiment (GRACE), to assess terrestrial water storage (TWS) and its response to climate variability within the Volta Basin. The methodology involved integrating GRACE data with ground-based measurements, climate models, and other satellite observations to enhance the accuracy of TWS assessment. Despite numerous studies conducted within the basin, this research employed additional statistical techniques such as Independent Component Analysis (ICA) and El Niño Southern Oscillation (ENSO). It also utilized Climate Hazard Group Infrared Precipitation with Station (CHIRPS) to determine variations in TWS and climate variability observed within the Volta Basin. The results provide valuable insights into TWS dynamics, highlighting the complex interplay between precipitation patterns, groundwater storage, and surface water availability. Also, it was revealed that rainfall signals were strongest in the northernmost part of the basin, reaching a maximum value of 10 mm, while the lowest value of 5.5 mm was recorded in the southern part of the basin. Similarly, TWS signals were highest in the northern and lowest in the southern part of the basin, exhibiting values related to that of rainfall. Additionally, the highest TWS value of 250 mm was identified between 2010 and 2012. The increase in TWS during this period correlates with the occurrence of La Niña that happened between 2010 and 2012. This study offers essential information for water resource management, drought monitoring, flood forecasting, and climate change adaptation strategies not only within the Volta Basin but also in other basins across the globe. [ABSTRACT FROM AUTHOR]

The use of the global navigation satellite system (GNSS) for monitoring changes in terrestrial water storage (TWS) is growing. However, the density of GNSS stations is sparse in most areas, and the widely used Green’s function (GF) method cannot provide a satisfactory resolution for inversion problems. As the Slepian basis function (SBF) method has been successfully used for gravity inversions, GNSS geodesists have recently applied the SBF method to GNSS displacements. However, the evaluation of TWS differences inferred from GNSS using the SBF and the GF has been rarely assessed. In this study, we use both the GF and the SBF methods to investigate the TWS in Yunnan by using more than ten years (2010–2021) of GNSS observations. We observe a remarkable consistency between the two inversion methods employed for GNSS data, as well as a strong agreement with equivalent water heights (EWH) inferred from the Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On (GFO), hydrological model (GLDAS-NOAH), and precipitation data, despite variations in the amplitude. Furthermore, affected by monsoon climate and topography, Yunnan is prone to drought. We analyze the main nine drought events, evaluate the connection between GNSS-inverted TWS during 2010–2021, and find that almost all droughts in Yunnan occurred during the "trough period" within the GNSS-DSI. [ABSTRACT FROM AUTHOR]

Copyright of Arid Land Geography is the property of Chinese Academy of Sciences, Xinjiang Institute of Ecology & Geography and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Global Navigation Satellite System (GNSS) vertical displacements are widely used to infer terrestrial water storage (TWS) variations. The traditional Laplacian inversion requires dedicated efforts to determine the optimal parameters, which has an important effect on the spatial patterns. In this study, we develop a new GNSS inversion method with flexible spatial constraints. One major merit is that the new method only requires loose boundary conditions rather than optimal parameters. A closed-loop simulation shows that the inversion using spatial constraints is improved by 7–21% compared with the Laplacian constraints. We apply this method to 18 watersheds across the Contiguous United States (CONUS) to infer daily TWS variations from January 2018 to August 2022. The results show that the amplitudes of monthly TWS time series from the spatial and Laplacian constraints are comparable to the Gravity Recovery and Climate Experiment (GRACE) Follow-On (GFO) in 16 watersheds. Furthermore, the standard deviation between the spatial constraints and GFO is at the same level as that between the Laplacian constraints and GFO. We also extract the daily TWS variations caused by heavy precipitation events in California. Our results demonstrate that spatial constraint inversion supplements the existing constraint strategies of GNSS inversion in hydrogeodesy; therefore, spatial constraint inversion can be an alternative tool for GNSS inversion. [ABSTRACT FROM AUTHOR]

Copyright of China Rural Water & Hydropower is the property of China Rural Water & Hydropower Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Globally, a persistent decline of freshwater availability has been identified over a number of intensively irrigated agricultural regions. Large‐scale inter‐basin water transfer (IBWT) has been suggested as a key tool for stabilizing regional terrestrial water storage (TWS). However, IBWT projects are prohibitively expensive, and their large‐scale cost effectiveness remains unclear. Here we quantify the IBWT impacts on TWS trends in the North China Plain (NCP), a global hotspot for TWS depletion and IBWT. Based on in‐situ observations, remote sensing, and water balance principles, we provide a framework to disentangle complex climate and anthropogenic impacts on NCP TWS. Results show that the NCP TWS depletion rate was significantly attenuated in 2015–2021, which is primarily attributable to recently enhanced IBWT. Otherwise, the average NCP TWS would currently be 94.9 ± 4.9 mm (or 12.2 ± 0.6 km3) lower. However, the positive effect of IBWT is partly offset by increased crop water consumption (−24.1 ± 5.2 mm or −3.1 ± 0.7 km3). IBWT and agricultural management (i.e., reducing crop density) are both necessary for stabilizing future NCP TWS. Otherwise, a TWS declining trend exceeding 100 mm/year may occur under elevated CO2 conditions. As such, this study verifies the feasibility and effectiveness of IBWT for mitigating regional water shortages, as well as the crucial role of agricultural management in stabilizing regional TWS. Key Points: A new framework for reconstructing terrestrial water storage (TWS) is proposedThis framework physically distinguishes climate and human impacts on TWS anomaly variationsInter‐basin water transfer and agriculture management are critical for stabilizing TWS over the North China Plain [ABSTRACT FROM AUTHOR]

Mass changes in the hydrosphere represent an important contributor to polar motion (PM) variations, especially at seasonal time scales (i.e., annual and semiannual). Although well studied, hydrological angular momentum (HAM) remains a major source of uncertainty in estimating PM excitation. In this work, we use a large number of climate models from the sixth phase of the Coupled Model Intercomparison Project (CMIP6) to determine HAM series both from individual models and their combination, formed with a multi-model mean, a weighted mean, and a three-cornered hat (TCH) method. The CMIP6-based HAM series are analysed in several spectral bands and evaluated with a reference hydrological signal in geodetically observed PM excitation (GAO). HAM determined from CMIP6 were also compared to HAM calculated from Gravity Recovery and Climate Experiment (GRACE) measurements. We find that while climate models do not allow for reliable estimation of non-seasonal changes in HAM, they can help interpret seasonal variability. For annual prograde and semiannual retrograde oscillations, several combined CMIP6-based series exhibit higher amplitude and phase consistency with GAO than the corresponding series computed from GRACE data. Whether one uses a simple average of the models, a weighted average, or a combination of models from the TCH method has little impact on the resulting HAM series and their level of agreement with GAO. Our study advances the understanding of hydrological signal in Earth’s rotation at seasonal time scales.

The monitoring of Poyang Lake water area and storage changes using remote sensing and satellite gravimetry techniques is valuable for maintaining regional water resource security and addressing the challenges of global climate change. In this study, remote sensing datasets from Landsat images (Landsat 5, 7, 8 and 9) and three Gravity Recovery and Climate Experiment (GRACE) and Gravity Follow-on (GRACE-FO) mascon solutions were jointly used to evaluate the water area and storage changes in response to global and regional climate changes. The results showed that seasonal characteristics existed in the terrestrial water storage (TWS) and water area changes of Poyang Lake, with nearly no significant long-term trend, for the period from April 2002 to December 2022. Poyang Lake exhibited the largest water area in June and July every year and then demonstrated a downward trend, with relatively smaller water areas in January and November, confirmed by the estimated TWS changes. For the flood (August 2010) and drought (September 2022) events, the water area changes are 3032 km2 and 813.18 km2, with those estimated TWS changes 17.37 cm and −17.46 cm, respectively. The maximum and minimum Poyang Lake area differences exceeded 2700 km2. The estimated terrestrial water storage changes in Poyang Lake derived from the three GRACE/GRACE-FO mascon solutions agreed well, with all correlation coefficients higher than 0.92. There was a significant positive correlation higher than 0.75 between the area and TWS changes derived from the two independent monitoring techniques. Therefore, it is reasonable to conclude that combined remote sensing with satellite gravimetric techniques can better interpret the response of Poyang Lake to climate change from the aspects of water area and TWS changes more efficiently.

Study Region: The Upper Blue Nile (UBN) basin, Ethiopia. Study Focus: In efforts to accurately close the water balance equation for the UBN basin using remote sensing products, river runoff is calculated as a residual from the water balance equation by incorporating Gravity Recovery and Climate Experiment (GRACE) terrestrial water storage (TWS) and remote sensing products for precipitation (P) and evapotranspiration (ET). The calculated river runoff is then compared to the gauge records located at the basin’s outlet. The best performing combination among the various combinations is chosen by aggregating rankings attributed to both error and linear fit metrics. The errors associated with each satellite product were assessed by forcing the In-Situ runoff to estimate the P, ET, and TWS. This methodology helps in addressing the uncertainty linked with each hydrological component. New Hydrological Insights for the Region: The best P, ET, and TWS combination performance products to estimate runoff are SM2RAIN-CCI, GLEAM, and GRACE Spherical Harmonic products, respectively. The statistical results for the six metrics are R2 = 0.7, slope = 1.6, y-intercept = - 0.5 cm, RMSE = 3 cm, MAE = 2.8 cm, and PBIAS = 36%. The uncertainty from each hydrological component was quantified and showed that improving the accuracy of P and ET estimation is a crucial step to successfully close the water balance.

Study region: North China Plain (NCP), China, a semi-arid region with intense groundwater withdrawals. Study focus: This paper developed a framework using meteorological data, model-simulated terrestrial water storage anomalies (TWSA), and additional in-situ (groundwater level, GL) data to improve the unsatisfactory GRACE-TWSA reconstruction in arid and semi-arid regions due to the intense anthropogenic influence on groundwater. The inconsistency between point-scale data (GL) and grid-scale data (GRACE-TWSA and predictors other than GL) is handled by feature extraction techniques. Moreover, to deal with temporal non-stationarity, the time series are separated into trend and detrended components, the patterns of which are further learned by linear and nonlinear machine learning models, respectively.New hydrological insights for the region: Multi-site GL observations in NCP can not only serve as validation data but also as predictors providing invaluable information on human effects for the reconstructed TWSA improvement (from 6.51 to 3.86 cm for Root Mean Square Error and from 0.56 to 0.82 for Nash-Sutcliffe Efficiency). Our results show that multi-site GL data in NCP are highly inter-correlated and can be represented by several principal components, demonstrating the strong hydraulic connectivity in NCP. We also find a significant one-month lag and linear relationship between the trends of GRACE-TWSA and GL changes in NCP. These deeper understandings of hydrologic processes have implications for enhancing the GRACE-TWSA estimations in other similar regions.

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. [ABSTRACT FROM AUTHOR]

Recent extreme events in the Middle‐Lower reaches of the Yangtze River basin (MLYRB) are proven to be possibly linked to the El Niño‐Southern Oscillation (ENSO) events as indicated by terrestrial water storage anomaly (TWSA). But the relatively short observation time of Gravity Recovery and Climate Experiment series missions (2002–2017; 2018–present) affects the robustness of the evaluation of TWSA. Here, the applicability of four long‐term TWSA data sets (since 1979) in the MLYRB is evaluated first using an evaluation framework including two completely independent tests. After selecting the optimal one, we investigate the effects of ENSO on TWSA in the MLYRB at the basin, subbasin, and grid cell scales, respectively. Results show that ENSO, especially the Eastern Pacific type ENSO has had a significant impact on TWSA variations in the MLYRB and its two subbasins (the Dongting Lake basin and the Poyang Lake basin) since 1979 with correlation coefficients at 0.56–0.65 and time lags at 5–6 months. However, TWSAs in the other two subbasins (the Hanjiang River basin and the Mainstream River basin) have almost no correlation with ENSO. Further analysis reveals that compared with human activity that has a limited impact on TWSA, precipitation is one of the key inducements for regional water storage changes in these two subbasins, and the no correlation between ENSO and TWSA is mainly caused by the weak link between ENSO and precipitation. Key Points: Applicability of four long‐term terrestrial water storage anomaly data sets is assessed using two completely independent testsAbnormally, terrestrial water storage anomalies of two subbasins have almost no correlation with El Niño‐Southern Oscillation (ENSO)This abnormality is dominated by regional precipitation's weak link with ENSO [ABSTRACT FROM AUTHOR]

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 Experiment (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. [ABSTRACT FROM AUTHOR]

Understanding the evolution of drought is of great significance to water resources management especially for the Yangtze River Basin that is home to 400 million people. However, this has been a scientific challenge due to the lack of a modelling framework to accurately reconstruct the drought regime and identify its driving factors in a comprehensive way. Here, we employ a coupled land surface-hydrologic model with explicit parameterizations of anthropogenic activities to investigate the drought characteristics of the Yangtze River Basin under climate change and human activities. Two numerical simulation experiments were first set up to reconstruct the long-term terrestrial water storage (TWS) variations of the Yangtze River Basin with and without human activities, respectively, and a water storage deficit index (WSDI) is then introduced to quantify the impact of human activities on the drought regime in the Yangtze River Basin. The results show that our TWS simulations taking account of human activities are in good agreement with the GRACE data for the Yangtze River Basin in both space and time. Reservoirs play a significant role in the TWS variations during the past decades. The terrestrial water storage drought is exacerbated by the human water withdrawal before 2009, and alleviated by the reservoirs after 2009. The correlations between Standardized Precipitation Index (SPI) and WSDI with taking account of human activities are smaller than that without taking account of human activities. The propagation time from meteorological drought to terrestrial water storage drought is shortened by the human water withdrawal and extended by the reservoirs. By reconstructing the terrestrial water storage anomaly (TWSA) in the Yangtze River Basin from 1981 to 2018, our findings could reveal the impact of human activities on terrestrial water storage anomaly and the attribution analysis of drought characteristics, providing support for the efficient utilization of water resources in the basin. [ABSTRACT FROM AUTHOR]