Groundwater Storage Anomalies Research Articles

Analyzing groundwater storage anomalies in data‐scarce areas of Ethiopia's Rift Valley Basin using artificial neural network

AbstractGroundwater storage anomalies (GWSA) study requires the use of hydro‐climatic models coupled with Gravity Recovery and Climate Experiment climate (GRACE) and GRACE Follow‐On (GRACE‐FO) Terrestrial Water Storage experiments. Understanding groundwater storage anomalies in the research area is difficult due to the restricted hydro‐meteorological data availability, especially, groundwater levels, changes in groundwater storage, and budgetary and technical constraints. No such investigation of GWS anomalies prior to this study in this Basin was conducted. The research focuses on GWSA investigations in the basin employing Global Land Data Assimilation Systems and Artificial Neural Networks‐Back Propagation Algorithm techniques are required. The ultimate objective of this study was to reduce the GRACE GWS from 1° to 0.25° and close the discontinuity between GRACE/GRACE‐FO observations. The results illustrated that GRACE/GRACE‐FO GWS throughout the basin were overall declining. The GWS anomaly significantly decreased between 2011 and 2013, with the highest monthly depletion in March 2011 (−158 mm). In contrast, from mid‐June to mid‐October, the maximum positive value varied from 20.80 mm in the southernmost basin to 39.49 mm in the central‐southeastern basin. The basin's eastern, central‐southeastern, northern, and southern areas displayed positive monthly GWS trends. The entire dry season has been influenced by negative anomalies. Hence, the identification of GWS variations and a comprehensive understanding of their effects provided valuable scientific insights. Identifying and analyzing anomalies in groundwater storage (GWS) can provide valuable information. This data can be efficiently used to develop appropriate regulatory frameworks, which may contribute to the long‐term viability of the availability of groundwater.

Characteristics of groundwater drought and its correlation with meteorological and agricultural drought over the North China Plain based on GRACE

Groundwater drought is distinctive comparing with other droughts, featuring high concealment, long duration, and obvious hysteresis. It may have significant negative effects on agriculture, eco-environment, and social economy sectors. Therefore, investigating the temporal and spatial characteristics of groundwater drought and its driving factors is meaningful for monitoring and assessing the risks of groundwater shortage. In this study, a groundwater drought index based on groundwater storage anomalies (GWSAs) derived from GRACE satellites was developed to detect and analyze drought events. The spatiotemporal variations and trends of historical groundwater droughts from 2002 to 2021 in the North China Plain were evaluated. In addition, the correlations between groundwater drought and meteorological and agricultural drought were analyzed by the detrended cross-correlation analysis. The results indicated that (1) the standardized groundwater storage anomaly index (SGSAI) could better identify and characterize groundwater drought by eliminating the spatial heterogeneity of GWSAs; (2) from 2002 to 2021, the intensity, frequency, duration, and area of groundwater drought showed an increasing trend; (3) groundwater drought had an obvious hysteresis (>9 months) to meteorological drought and the correlation between groundwater drought and meteorological drought increased, while the relationship with agricultural drought decreased; (4) long-term overexploitation of groundwater resources might be the main driving factor for the exacerbating groundwater drought in this area.

Characterizing the drought events in Yangtze River basin via the insight view of its sub-basins water storage variations

Yangtze River Basin (YRB) is the most important strategic base of water resources in China, which has the characteristics of complex terrain and unbalanced water distribution in spatial and temporal domain. Drought events have occurred frequently in this basin due to the influence of global climate change and human activities. In this context, it is critical to formulate drought policies based on the characteristics of drought events in different sub-basins. Based on the latest HUST-Grace2020 model released by Huazhong University of Science and Technology, we calculate the terrestrial water storage anomaly (TWSAs) in YRB during January 2003 to July 2016, and combine water storage deficit index (WSDI) with a modified run theory to extract precisely drought events in terms of sub-basins. On this basis, the characteristics of drought events are identified in different sub-basins and different seasons. Subsequently, the links between drought events and topography as well as climate factors are explored in the YRB. The results indicate that: (1) Soil moisture storage anomaly (SMSA) is the uppermost component (41%) of TWSA but the largest contributor (59%) to TWSA trend is groundwater storage anomaly (GWSA). Precipitation is the main factor affecting the composition and trend of TWSC. (2) Eight drought events (one severe level, four moderate level and three mild level) are extracted across YRB and the most severe event can be monitored in each sub-basin which begins from May 2006 to August 2007. (3) The frequency of drought events is higher in the lower region of YRB, but serious drought events are more likely to occur in the source region of YRB. (4) Droughts are more frequent in summer and winter, while severe and extreme situations mainly occur in autumn. (5) Drought severity in the YRB is positively correlated with topography, and drought events are influenced by El Niño, especially in the downstream YRB. This study sheds a theoretical basis for drought early warning of different levels in YRB across the various sub-basins.

Temporal and Spatial Variation Analysis of Groundwater Stocks in Xinjiang Based on GRACE Data

Situated in China’s arid and semi-arid zones, the Xinjiang region heavily relies on groundwater for its freshwater supply. This study utilizes data from the Gravity Recovery and Climate Experiment (GRACE) satellite mission, covering the years 2003 to 2021, to quantitatively evaluate the temporal and spatial changes in groundwater storage anomalies (GWSA) in the Xinjiang region. Furthermore, we incorporate the HydroSHEDS dataset to examine the spatial variations in groundwater storage anomalies across watersheds of varying scales. Based on our findings, the GWSA decreased during the study period at a mean rate of −0.381 mm/month, marked by a consistent trend and notable interannual variability. In addition, significant regional disparities are observed; while groundwater storage in the southeastern watersheds is on an upward trend, a general decline is noted in the northern and central regions. The most pronounced depletion is detected in the northwest, especially in the Ili River basin and along the western slopes of the Tianshan Mountains. These changes are intricately linked to anthropogenic factors, including population growth and escalating water demands. In response, the study advocates for the development and enforcement of more rigorous and scientifically informed groundwater management strategies to promote sustainable water use in Xinjiang.

Providing Enhanced Insights into Groundwater Exchange Patterns through Downscaled GRACE Data

The measurement of groundwater exchange between neighboring regions is a critical topic in water resource management and can usually be achieved through a combination of field investigations and the use of groundwater flow models. In this study, we employed the water balance and Darcy’s law methods, utilizing downscaled Gravity Recovery and Climate Experiment (GRACE) and GRACE-Follow On (GRACE-FO) data to assess groundwater exchange patterns in the Beijing-Tianjin-Hebei (BTH) region of China. Additionally, we determined the contributions of human activities and climate factors to the observed variations via residual analysis. The results revealed a consistent decrease in groundwater storage in the study area since 2008, especially in the spring and summer months. The groundwater exchange rates calculated by 1° and 0.05° groundwater storage anomalies (GWSAs) were basically consistent, and the downscaled GWSAs could better reflect the small-scale groundwater exchange characteristics. The groundwater exchange rate showed a decreasing trend from the Piedmont plain to the coastal areas. A notable trend of declining groundwater exchange between the Taihang Mountains and Piedmont plains was observed, and the downward trend gradually intensified from north to south between 2003 and 2007. After 2008, there was an increasing trend, and coastal areas exhibited the smallest amount of groundwater exchange. Human activities emerged as the predominant factor accounting for more than 90.9% of the overall reduction in groundwater storage, while climate change imposed a minimal influence on groundwater storage variations. The insights obtained in this study hold significant implications for groundwater resource planning and management in the region.

Long-term spatiotemporal dynamics of groundwater storage in the data-scarce region: Tana sub-basin, Ethiopia

Imprudent extraction of groundwater tends to undue stress and portends its sustainability. Spatiotemporal analysis of groundwater storage anomaly (GWSA) is imperative for the judicious use, management, and sustainable development of a region. This study aimed to examine the changes in groundwater storage over the past 20 years in the Tana sub-basin using Gravity Recovery and Climate Experiment (GRACE) assimilated into Global Land Data Assimilation Systems (GLDAS). Validation analysis was carried out to evaluate the accuracy of GWSA against anomalies of Lake Tana water level, precipitation, and in-situ groundwater level. Modified Mann-Kendal test and Sen's slope estimator were applied for trend analysis of the GWSA. The results exhibited that GWSA strongly correlated (Pearson's correlation coefficient, R ranges from 0.75 to 0.96) with the three validation above variables, which elucidated in general, credible GWSA estimation. The net annual GWSA curve showed a non-significant (p > 0.05) decreasing trend from 2003 to 2012. However, years including 2005, 2006, and 2009 were drought periods, which caused 0.49 billion cubic meters (BCM) groundwater loss. In the entire study period (2003–2022), on the other hand, the net annual GWSA revealed a significant increasing trend (p < 0.05) with a rate of 0.333 cm/year. Generally, the Tana sub-basin was nurtured with a net 4.87 BCM groundwater gain in the study period. The most sensitive parts of the study area to large fluctuations of groundwater storage were mainly the nearby southern and eastern directions of Lake Tana.

Downscaled GRACE/GRACE-FO observations for spatial and temporal monitoring of groundwater storage variations at the local scale using machine learning

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

Spatiotemporal distribution of groundwater drought using GRACE-based satellite estimates: a case study of Lower Gangetic Basin, India.

Droughts frequently occurring in India have significant societal, economic, and environmental effects. The lack of direct measurements of groundwater in location and time hinders quantitative methods to analyse the intricate nature of groundwater drought. This work used the datasets derived from the Gravity and Climate Experiment (GRACE and GRACE-FO) and Global Land Data Assimilation System (GLDAS) to extensively analyse Groundwater Storage changes in the Lower Gangetic Basin (LGB) using unique hydrological parameters between the years 2003 and 2022. The analysis highlights that the GRACE-derived terrestrial water storage anomaly in the LGB decreased significantly (-12.12 mm/yr), and the amount of Groundwater Storage Anomaly (GWSA) decreased similarly (-10.80 mm/yr), while in the GRACE-FO period, a positive trend has been noticed in TWSA (33.96 mm/yr) and GWSA (64.8 mm/yr) respectively. A drought indicator called the GRACE-derived groundwater drought index (GGDI) has been computed for the entire LGB region. A traditional drought study viz. Standardised Precipitation Index (SPI) was performed over LGB to justify the results of the GGDI. The results from GGDI study effectively matched the periods of significant drought occurrences with the 12-month SPI time series. From the GGDI, this study examined groundwater drought's spatial distribution, temporal evolution, and trend (Modified Mann Kendall trend) aspects. According to research findings, the LGB experienced three major drought periods between 2009-2010, 2019 (moderate), and 2015-2016 (severe). The study offers reliable quantitative data on the evolution of GRACE-derived groundwater drought, which may add a new perspective to additional drought research in the densely populated study area, which depends majorly on agriculture, livestock and less skilled water-intensive industries such as leather and textile industries in a sub-tropical climate. This paradigm incorporates changes in groundwater resources caused by human activities and climate change, paving the way for measuring progress towards sustainable use and water security.

Evaluating dynamics of land water storage and its response to climate variation in the Deccan Plateau, India

Inter-state river water disputes are frequent over the rain shadow (RS) region of the Western Ghats (WG), where the annual rainfall is much lower (75.2 cm) than that of overall India (118 cm). This study investigated the TWS (Terrestrial Water Storage) dynamics of the entire Deccan plateau (DP) and RS regions, located at the leeward side of the WG mountain range, with the help of Gravity Recovery and Climate Experiment (GRACE) based TWS anomalies (TWSA) data. Here, the latest TRMM (Tropical Rainfall Measuring Mission) rainfall product was evaluated at different time scales against gauge-based measurements to check its applicability as an alternative to gauge-based observed data. Trends in climate variables and TWSA were estimated to understand their variations during 2003–2016 in both regions. The response of GRACE-based TWSA and its constituent components to rainfall was determined using time-lagged correlation analysis. The contribution of constituent variables to TWSA was estimated using the component contribution ratio (CCR) method. It was observed that the TRMM-3B43 data captured the rainfall pattern over the entire DP with great accuracy. Results showed that the increasing trend of TWS in 2003–2009 had reversed in 2010–2016 due to the reduction of climate moistening in both regions. The warming trend of climate is more than twice in the RS region than that found in the DP, leading to a higher decreasing rate of TWS in this region than in DP. For both DP and RS regions, the time lags of groundwater storage anomalies (GWSA), total runoff anomalies (RA), canopy water storage anomalies (CWSA), and soil moisture storage anomalies (SMSA) to rainfall can be arranged as CWSA ≤ RA ≤ SMSA ≤ GWSA. SMSA and GWSA are the main contributors to the TWSA in both areas. However, SWSA also played a vital role in TWS variability, particularly in the RS region.

Integrating GRACE/GRACE Follow-On and Wells Data to Detect Groundwater Storage Recovery at a Small-Scale in Beijing Using Deep Learning

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

Characterization of groundwater storage changes in the Amazon River Basin based on downscaling of GRACE/GRACE-FO data with machine learning models

Groundwater storage changes in the Amazon River Basin (ARB) play an important role in the hydrological behavior of the region, with significant influence on climate variability and rainforest ecosystems. The GRACE and GRACE-FO satellite missions provide gravity anomalies from which it is possible to monitor changes in terrestrial water storage, albeit at low spatial resolution. This study downscaled GRACE and GRACE-FO data from machine learning models from 1° (110 km approx) to 0.25° (27.5 km approx). It estimated the spatiotemporal variability of terrestrial and groundwater storage anomalies between 2002 and 2021 for the Amazon River Basin. In parallel, the Random Forest and AdaBoost algorithms were compared and analyzed. The results reflected a good fit of the models with a very low error and a slight superiority in the predictions obtained by AdaBoost. On the predictions at 0.25°, spatial patterns associated with the strong influence on storage changes of some rivers and snow-capped mountains were identified, as well as an increase in the accuracy of the scaled data of the original ones. Positive long-term behavior was also obtained in terrestrial and groundwater storage of 14.26 ± 1.18 km3/yr and + 22.24 ± 1.18 km3/yr, respectively. Validation of the time series of groundwater anomalies to water levels in the monitoring wells obtained maximum correlation coefficients of 0.85 with confidence levels of 0.01. These results are promising for satellite information in water management, especially in regional monitoring of unconfined aquifers. The obtained data is stored in a dedicated repository (Satizábal-Alarcón et al., 2023).

The GWR model-based regional downscaling of GRACE/GRACE-FO derived groundwater storage to investigate local-scale variations in the North China Plain

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

Combined monthly GRACE-FO gravity fields for a Global Gravity-based Groundwater Product

SUMMARY The Combination Service for Time-variable Gravity fields (COST-G) operationally provides combinations of monthly Earth gravity field models derived from observations of the microwave ranging instrument of the GRACE Follow-on (GRACE-FO) satellite mission, applying the quality control and combination methodology originally developed by the Horizon 2020 project European Gravity Service for Improved Emergency Management for the data of the GRACE satellites. In the frame of the follow-up Horizon 2020 project Global Gravity-based Groundwater Product (G3P), the GRACE-FO combination is used to derive global grids of groundwater storage anomalies. To meet the user requirements and achieve optimal signal-to-noise ratio, the combination has been further developed and extended to incorporate: • new time-series based on the alternative accelerometer transplant product generated in the frame of the project by the Institute of Geodesy at the Graz University of Technology, which specifically improves the estimation of the C30 coefficient and also reduces the noise at medium to short wavelengths, and • the new time-series AIUB–GRACE-FO–RL02 of monthly GRACE-FO gravity fields, which is derived at the Astronomical Institute of the University of Bern by applying empirical noise modelling techniques. The COST-G quality control confirms the consistency of the contributing GRACE-FO time-series concerning the signal amplitude of seasonal hydrology in large river basins and the secular mass change in polar regions, but it also indicates rather diverse noise characteristics. The difference in the noise levels is taken into account in the combination process by relative weights derived by variance component estimation on the solution level. The weights are expected to be inverse proportional to the noise levels of the individual gravity field solutions. However, this expectation is violated when applying the weighting scheme as developed for the GRACE combination. The reason is found in the high-order coefficients of the gravity field, which are poorly determined from the low–low range-rate observations due to the observation geometry and suffer from aliasing due to the malfunctioning accelerometer onboard one of the GRACE-FO satellites. Hence, for the final G3P-combination a revised weighting scheme is applied where the gravity field coefficients beyond order 60 are excluded from the determination of the weights. The quality of the combined gravity fields is assessed by comparison of the noise content and the signal-to-noise ratio with the individual time-series. Independent validation is provided by the COST-G validation centre at the GFZ German Research Centre for Geosciences, where orbit fits of the low-flying Gravity and steady-state Ocean Circulation Explorer satellite are performed that confirm the high quality of the combined GRACE-FO gravity fields. By the end of the G3P project, the new combination scheme is implemented by COST-G as the new COST-G–GRACE-FO–RL02 and continued to be used for the operational GRACE-FO combination.

Identifying potential hotspots of groundwater-climate interaction in the Great Artesian Basin, Australia

Groundwater aquifers around the world are experiencing stress due to the composite influence of climate change and excessive groundwater withdrawal. Despite this influence, there is still insufficient knowledge on climate-groundwater interaction dynamics in complex aquifers like Australia's Great Artesian Basin. This understanding can inform improved management, which can in turn, increase resilience of groundwater system to on-going groundwater withdrawal and climate change. The main aim of this research was to assess the impacts of climatic change on groundwater storage (GWS) changes over the Great Artesian Basin (GAB) using a range of multi-scale indicators, including Gravity Recovery and Climate Experiment (GRACE)-derived groundwater observations and standardized precipitation evapotranspiration indices (SPEI). Rotated principal component analysis (rPCA) was used to identify climatic hotspots (where GWS variation is particularly sensitive or vulnerable to the impacts of climate change), and correlation analyses were used to assess the relationship between these hotspots and changes in groundwater over space and time. The localized spatial pattern via rPCA was uncovered in the GAB's southeastern region, where annual variations in GWS exhibited conspicuous peaks in 2002, 2014, and 2015. Another finding was that the southeast region of the GAB experienced higher GWS variations as compared to other sub-basins during 2007–2008, which resulted in low groundwater recharge. These findings underscore the variations in GWS within the GAB, emphasizing its responsiveness to climatic variations (e.g., rainfall contributes to recharge of groundwater system and drought intensifies water extraction and restricts discharge). Moreover, this study found a substantial negative correlation (r = −0.7), between SPEI at 12-month scale and GWS variation in the southern GAB region. This relationship highlights the vulnerability of groundwater storage in the southern GAB region to prolonged drought conditions. Furthermore, this study identified positive correlations (r = 0.4 to 0.8), between key climate indices such as the El Niño-Southern Oscillation (ENSO), Pacific Decadal Oscillation (PDO) with GWS anomalies in the southeast and northern GAB regions. These correlations underscore the pivotal roles played by ENSO and PDO in shaping GWS variations in these areas, highlighting the need to incorporate an understanding of these climatic processes into water resource management strategies. This consideration is crucial for ensuring sustainable water management practices that can adapt to the influence of these climate phenomena. These findings provide essential insights into the dynamics of groundwater in the face of changing environmental conditions, with implications for sustainable water management.

Monitoring hydrological changes with satellite data: Spring thaw's effect on soil moisture and groundwater in seasonal Freezing-Thawing zones

Due to the dense vegetation coverage in mountainous areas and difficulties in field monitoring in mid-high-latitude freezing-thawing zones, satellite and remote sensing technologies can be used to monitor long-term global hydrological changes. This study uses multiple satellite data of snow water equivalent (SWE), soil moisture (SM), and groundwater storage anomaly (GWSA) to analyse their spatial–temporal changes in the seasonal freezing-thawing Tumen River Basin. Water transformation during the thawing period is determined through groundwater level fluctuations and correlation analysis methods; thus, the recharge effects of snowmelt and seasonally frozen soil thawing on soil moisture and groundwater are identified. The snow begins to melt in February, and snow water equivalent decreases gradually from upstream to downstream after reaching the peak. Furthermore, seasonally frozen soil melts gradually from March. Soil moisture downstream is higher than that in the upper and middle reaches. Additionally, the GWSA increases gradually in the thawing period, which is high in mountainous areas and low in plains. The Spearman correlation coefficient among ΔSWE, ΔSM, and ΔGWSA is more significant than 0.5 and dependent on altitude. With snow and frozen soil melting, vertical infiltration and meltwater percolation increase soil moisture and GWSA. This phenomenon is predominant at low altitudes (<600 m) and pore phreatic groundwater in the middle and lower reaches. However, snowmelt is the dominant factor in groundwater recharge at high-altitude upper reaches. For seasonal freezing-thawing zones, a close water transformation was observed between snow melting, soil water thawing, and groundwater in spring. Thus, the environmental and ecological impact of spring snowmelt and floods in freezing-thawing zones can be monitored using multi-source data.

Spatial and temporal downscaling schemes to reconstruct high-resolution GRACE data: A case study in the Tarim River Basin, Northwest China

Climate change and excessive exploitation of water resources exert pressure on groundwater supply and the ecosystem in drylands. Although The Gravity Recovery and Climate Experiment (GRACE) satellites has demonstrated the feasibility of quantifying global groundwater storage variations, monitoring regional-scale groundwater has been challenging due to the coarse resolution of GRACE. Previous GRACE downscaling studies focused on develop new algorithms based on the perspective of pixel spatial correlation to improve resolution, which cannot better capture the temporal evolution of GRACE data effectively. In this study, we employ the semi-supervised variational autoencoder (SSVAER) algorithm and the multi-scale geographically weighted regression (MGWR) model to establish two different downscaling schemes: pixel temporal continuity downscaling and pixel spatial correlation downscaling. These schemes achieve spatial resolution downscaling of GRACE-derived groundwater storage anomalies (GWSA) from 0.5° to 0.1°. Additionally, the applicability of the PCR-GLOBWB model in drylands is verified. Furtherly, the spatiotemporal distribution patterns of GWSA are analyzed. The results show that (1) Both the temporal and spatial downscaling methods produced consistent results, with data correlations ranged from 0.94 to 0.98 observed in over 80 % of the range before and after downscaling; (2) The groundwater storage change rate in the northern Tarim River Basin (TRB) is 25 times greater than the model results, while in other regions, the average deviation is 2.6 times; (3) The two schemes enhance the correlation (0.27) between GWSA and groundwater level anomaly (GWLA) to 0.59 and 0.52, respectively, with a three-month lag in GWSA relative to GWLA. The temporal downscaling approach exhibited higher CC and lower RMSE, outperforming the spatial downscaling approach. The high-resolution results in this study can well complement groundwater level prediction efforts in arid regions and provide quantitative information for local water resource management.

Downscaling inversion of GRACE-derived groundwater storage changes based on ensemble learning

ABSTRACT Gravity Recovery and Climate Experiment (GRACE) satellite data monitors changes in terrestrial water storage, including groundwater, at a regional scale. However, the coarse spatial resolution limits its applicability to small watershed areas. This study introduces a novel ensemble learning-based model using meteorological and topographical data to enhance spatial resolution. The effectiveness was evaluated using groundwater-level observation data from the Henan rainstorm-affected area in July 2021. The factors influencing Groundwater Storage Anomalies (GWSA) were explored using Permutation Importance (PI) and other methods. The results demonstrate that feature engineering and Blender ensemble learning improve downscaling accuracy; the Root Mean Square Error (RMSE) can be reduced by up to 18.95%. Furthermore, Blender ensemble learning decreased the RMSE by 3.58%, achieving an R-Square (R2) value of 0.7924. Restricting the downscaling inversion to June–August data greatly enhanced the accuracy, as evidenced by a holdout dataset test with an R2 value of 0.8247. The overall GWSA variation from January to August exhibited ‘slow rise, slow fall, sharp fall, and sharp rise.’ Additionally, heavy rain exhibits a lag effect on the groundwater supply. Meteorological and topographical factors drive fluctuations in GWSA values and changes in spatial distribution. Human activities also have a significant impact.

Spatio-temporal dynamics of water storage across Northwest China over the past four decades

Study regionThe Northwest inland basins of China (NWC). Study focusTerrestrial water resources, especially groundwater resources, are the main source of water for human activities and for maintaining the stability of the ecological environment in NWC. Excessive consumption of water resources will seriously affect the sustainable utilization of water resources and ecological security in this region. Therefore, it is urgent to clarify the long-term changes in water storage in this area in order to handle the pressure of future water resources and the natural environment. Using GRACE satellite datasets and global hydrological models (GHMs) products, this study analyzed spatiotemporal variations in terrestrial water storage anomalies (TWSA), groundwater storage anomalies (GWSA), soil moisture, snow water equivalent, and canopy interception combined anomalies (SSCA) in NWC through the application of the water balance, trend decomposition, and empirical orthogonal decomposition methods. Furthermore, the driving factors of water storage change and feasible water resource management strategies were discussed. New hydrological insights for the regionTWSA in the NWC has experienced a continuous decline over the past nearly 40 years, while SSCA has shown a weak increasing trend (0.03 cm yr-1). Since the availability of glacial retreat data (2003–2016), glacial water storage in the NWC has decreased by 0.09 cm per year, while TWSA, SSCA, and GWSA have changed at rates of − 0.25, 0.02, and − 0.18 cm yr-1, respectively. The North Tianshan Rivers Basin has become one of the areas with the most severe groundwater depletion in China. 2005–2010 was a turning period in the changes of TWSA, followed by widespread water loss across the NWC. Glacier and snow melt are the most important factors for the decline of TWSA in the Tianshan mountains area, and over-exploitation of groundwater by human activities is a secondary factor. For other regions, Groundwater losses remain the most significant contributor to TWSA losses. The massive loss of water storage in the Tianshan Mountains area, especially the accelerated retreat of glaciers, will affect the stable water supply to the middle and lower reaches of the oasis region, perhaps leading to increased groundwater extraction, which will threaten regional water security and sustainable development. Developing a water-saving society and implementing inter-basin water transfer are feasible ways to alleviate the water resource crisis. Conducting a comprehensive analysis of all inland rivers in China helps to facilitate horizontal comparisons between various basins, thereby providing more comprehensive insights of water storage fluctuations. The data on water storage changes, extending back to 1980, provide a longer-term perspective on water resource changes in the region, which can contribute to enhancing water resource security and ecological environmental protection.

Dynamic Changes of Terrestrial Water Cycle Components over Central Asia in the Last Two Decades from 2003 to 2020

The terrestrial water cycle is important for the arid regions of central Asia (CA). In this study, the spatiotemporal variations in the three climate variables [temperature (TMP), precipitation (PRE), and potential evapotranspiration (PET)] and terrestrial water cycle components [soil moisture (SM), snow water equivalent (SWE), runoff, terrestrial water storage (TWS), and groundwater storage (GWS)] of CA are comprehensively analyzed based on multiple datasets from 2003 to 2020. The major results are as follows: (1) Significant decreasing trends were observed for the TWS anomaly (TWSA) and GWS anomaly (GWSA) during 2003–2020, indicating serious water resource depletion. The annual linear trend values of TWSA and GWSA are −0.31 and −0.27 mm/a, respectively. The depletion centers are distributed over most areas of western and southern Kazakhstan (KAZ) and nearly all areas of Uzbekistan (UZB), Kyrgyzstan (KGZ), and Tajikistan (TJK). (2) TMP and PET have the largest significant negative impacts on SM and SWE. The PRE has a positive impact on terrestrial water variations. (3) During 1999–2019, water withdrawal did not significantly increase, whereas TWS showed a significant decreasing trend. Our results provide a comprehensive analysis of the basic TWS variation that plays a significant role in the water resource management of CA.

Spatiotemporal variability and controlling factors of groundwater depletion in endorheic basins of Northwest China

Recent global groundwater overpumping is threatening ecosystem stability and food security, particularly in arid basins. A solid investigation regarding the drivers of groundwater depletion is vital for groundwater restoration, hitherto, yet it remains largely unquantified. Here, a framework to quantify the contribution of natural forcing (NF) and anthropogenic perturbations (AP) to groundwater storage anomalies (GWSA) variability by separating the GWSA estimated by the Gravity Recovery and Climate Experiment (GRACE) satellite into natural- and human-induced GWSA was proposed in the northwest endorheic basin (NWEB) of China. Further, a multiple linear regression model was established for GWSA change prediction. Our results showed that, during the period 2003–2020, the GWSA depleted at a rate of 0.25 cm yr−1 in the entire NWEB. In addition, GWSA was found to decrease significantly (exceeding 1 cm yr−1) in the west of NWEB where there are heavily irrigated areas, and has become one of the regions with the most serious groundwater depletion in China. Whereas a significantly increasing trend (greater than 0.5 cm yr−1) was observed in the Qaidam basin and south part of the Tarim River basin, becoming a groundwater enrichment reservoir in NWEB. The negative contribution of AP to groundwater depletion has increased from 3% to 95% in the last decade, as determined by separating the effects of NF and AP on GWSA. The rapid expansion of the cropland area and the increase in water use due to population growth are investigated to be the main reasons for GWSA depletion, particularly in the North Tianshan Rivers, Turpan-Hami, and Tarim River basins. Therefore, we conclude that AP are dominating and accelerating groundwater depletion in the NWEB. The increase of GWSA in the Qaidam basin has been attributed to the increase in solid water melt and regional precipitation. The western route project of China’s south-north water diversion and water-saving irrigation are important ways to solve the problem of groundwater depletion in NWEB. Our results emphasize that a more feasible framework capable of reliably identifying the driving factors of groundwater storage change is a necessary tool for promoting the sustainable management of groundwater resources under both NF and AP in arid endorheic basins.