Groundwater Storage Anomalies Research Articles

Enhancing groundwater management with GRACE-based groundwater estimates from GLDAS-2.2: a case study of the Almonte-Marismas aquifer, Spain

AbstractThe Almonte-Marismas aquifer, southwestern Spain, is a critical ecohydrogeological system that features extensive groundwater monitoring. This study investigates the utility of gravity recovery and climate experiment (GRACE) satellite data, specifically obtained from the global land data assimilation system (GLDAS) version 2.2, for assessing groundwater storage variations in the Almonte-Marismas aquifer. The presented research emphasizes the practical application of readily available GLDAS products that do not require data preprocessing. The study validates the GLDAS-2.2-based ready-to-use groundwater storage (GWS) time series by correlating it with precipitation and piezometric information, highlighting its effectiveness in medium-scale aquifers. The results reveal a strong agreement between GLDAS-2.2-derived GWS anomalies and in-situ measurements, confirming GLDAS-2.2’s potential for assessing aquifer depletion. The study discusses the consistency of seasonal variations in groundwater levels and GLDAS-2.2 data, emphasizing their close alignment with precipitation and pumping activities. Importantly, the study introduces GLDAS-2.2-derived volumetric groundwater storage (VGWS) as a valuable calibration parameter for numerical groundwater flow models, enhancing their accuracy over time. Moreover, the analysis reveals disparities in annual recharge values between GLDAS-2.2-derived data and the soil-water mass balance. These variations suggest the importance of additional inputs to precipitation, possibly related to subsurface or lateral connections. Overall, this study contributes to the ongoing discourse on the practical applications of GLDAS-2.2-derived GWS data in groundwater management, offering insights into its effectiveness in diverse hydrogeological settings, particularly in areas that lack monitoring infrastructure.

Machine learning downscaling of GRACE/GRACE-FO data to capture spatial-temporal drought effects on groundwater storage at a local scale under data-scarcity

The continued threat from climate change and human impacts on water resources demands high-resolution and continuous hydrological data accessibility for predicting trends and availability. This study proposes a novel threefold downscaling method based on machine learning (ML) which integrates: data normalization; interaction of hydrometeorological variables; and the application of a time series split for cross-validation that produces a high spatial resolution groundwater storage anomaly (GWSA) dataset from the Gravity Recovery and Climate Experiment (GRACE) and its successor mission, GRACE Follow-On (GRACE-FO). In the study, the relationship between the terrestrial water storage anomaly (TWSA) from GRACE and other land surface and hydrometeorological variables (e.g., vegetation coverage, land surface temperature, precipitation, and in situ groundwater level data) is leveraged to downscale the GWSA. The predicted downscaled GWSA datasets were tested using monthly in situ groundwater level observations, and the results showed that the model satisfactorily reproduced the spatial and temporal variations in the GWSA in the study area, with Nash-Sutcliffe efficiency (NSE) correlation coefficient values of 0.8674 (random forest) and 0.7909 (XGBoost), respectively. Evapotranspiration was the most influential predictor variable in the random forest model, whereas it was rainfall in the XGBoost model. In particular, the random forest model excelled in aligning closely with the observed groundwater storage patterns, as evidenced by its high positive correlations and lower error metrics (Mean Absolute Error (MAE) of 54.78 mm; R-squared (R²) of 0.8674). The downscaled 5 km GWSA data (based on random forest) showed a decreasing trend in storage associated with variability in the rainfall pattern. An increase in drought severity during El Niño lengthened the full recovery time of groundwater based on historical storage trends. Furthermore, the time lag between the occurrence of precipitation and recharge was likely controlled by the drought intensity and the spatial recharge characteristics of the aquifer. Projected increases in drought severity could further increase groundwater recovery times in response to droughts in a changing climate, resetting storage to a new tipping condition. Therefore, climate change adaptation strategies must recognise that less groundwater will be available to supplement the surface water supply during droughts.

CO2 Emissions Associated with Groundwater Storage Depletion in South Korea: Estimation and Vulnerability Assessment Using Satellite Data and Data-Driven Models

Groundwater is crucial in mediating the interactions between the carbon and water cycles. Recently, groundwater storage depletion has been identified as a significant source of carbon dioxide (CO2) emissions. Here, we developed two data-driven models—XGBoost and convolutional neural network–long short-term memory (CNN-LSTM)—based on multi-satellite and reanalysis data to monitor CO2 emissions resulting from groundwater storage depletion in South Korea. The data-driven models developed in this study provided reasonably accurate predictions compared with in situ groundwater storage anomaly (GWSA) observations, identifying relatively high groundwater storage depletion levels in several regions over the past decade. For each administrative region exhibiting a decreasing groundwater storage trend, the corresponding CO2 emissions were quantified based on the predicted GWSA and respective bicarbonate concentrations. For 2008–2019, XGBoost and CNN-LSTM estimated CO2 emissions to be 0.216 and 0.202 MMTCO2/year, respectively. Furthermore, groundwater storage depletion vulnerability was assessed using the entropy weight method and technique for order of preference by similarity to ideal solution (TOPSIS) to identify hotspots with a heightened potential risk of CO2 emissions. Western South Korean regions were particularly classified as high or very high regions and susceptible to groundwater storage depletion-associated CO2 emissions. This study provides a foundation for developing countermeasures to mitigate accelerating groundwater storage depletion and the consequent rise in CO2 emissions.

GRACE based groundwater drought evaluation of Ganga Basin and analysis of drought propagation using wavelet based quantitative approach

The Ganga River Basin which is the home of almost half a billion people have plunged into groundwater drought due to anthropogenic activities. Hence groundwater drought assessment and its propagation from the meteorological drought is highly required for the Ganga River Basin. This paper focuses on the evaluation of historical groundwater drought using Gravity Recovery and Climate Experiment (GRACE) and Global Land Data Assimilation System (GLDAS) dataset, further to obtain the drought propagation times. Traditionally the drought propagation time is obtained from the correlation between groundwater drought time series and various scales of meteorological time series. However, the GRACE-derived groundwater drought index (GGDI) showed lesser correlation with the Standardized Precipitation Index (SPI) / Standardized Precipitation Evapotranspiration Index (SPEI) due to the presence of consistent trend in the GGDI series. Hence, a novel quantitative approach using Cross Wavelet Transform (XWT) is introduced to determine the drought propagation time which can devoid of the contribution of anthropogenic activities. Extracting the significant power area of XWT of GGDI with SPI/SPEI of different scales led to the determination of groundwater drought propagation time. The results showed Groundwater Storage Anomaly (GWSA) has a steep downtrend for Upper Gangetic Basin (UGB) (−26.2 mm/year) and Yamuna Chambal Basin (YCB) (−21.8 mm/year). It was observed that UGB and YCB faced groundwater drought from 2017 to 2022. The wavelet analysis showed that the drought propagation time of YCB is 14 months, UGB is 17 months, and the Lower Gangetic Basin (LGB) is 21 months. The frequency domain analysis of the drought signals suggested YCB had a faster response to the meteorological forcing, and LGB had the slowest response.

Quantifying groundwater depletion in Arabian Peninsula transboundary aquifer systems: Understanding natural and anthropogenic drivers

Groundwater is the primary source of freshwater for domestic, agricultural, and industrial usage in the Arabian Peninsula countries. It is increasingly becoming a limited resource due to human activities leading to excessive depletion and contamination. Thus, sustainable management of groundwater resources in this region is critical. The groundwater in the Arabian Peninsula countries is primarily found in transboundary systems such as the Wajid, Umm Er Radhuma, and Wasia Aquifers shared between Saudi Arabia, Iraq, Syria, Yemen, and Oman. These systems have no groundwater-sharing agreements, which leads to a lack of data sharing, unsustainable and uncoordinated development, rapid water depletion, water quality deterioration, and land subsidence. This study examines the Wajid, Umm Er Radhuma, and Wasia aquifer systems from April 2002 to May 2021 by analyzing monthly gravity field solutions from GRACE and GRACE-FO satellite data, other remote sensing observations, information from the Global Land Data Assimilation System (GLDAS), as well as field data to determine how regional water resources are changing over time and to identify the factors that influence these resources. The sharp decline in Total Water Storage Anomalies (TWSA) and the Groundwater Storage Anomalies (GWSA) across all three systems is caused by a combination of climatic and human factors. The observed decline in Total Water Storage can be partly attributed to a decrease in regional rainfall, whereas the depletion of Groundwater Storage has a strong correlation with the rise in groundwater extraction for irrigation purposes in the 2010s. The recent rise is groundwater depletion in specific areas of central Saudi Arabia may be attributed to agricultural irrigation and rapid urban development. The results are insightful for monitoring water storage in management plans and decision-making processes to preserve and efficiently use groundwater resources.

Improved Estimates of Sub‐Regional Groundwater Storage Anomaly Using Coordinated Forward Modeling

AbstractGroundwater storage anomaly (GWSA) can be estimated either at the large scale from the Gravity Recovery and Climate Experiment (GRACE) or at the local scale based on in situ observed groundwater level (GWL) and aquifer storage parameters. Yet, the accuracy of GRACE‐based estimate is affected by leakage errors, while that of local GWL‐based estimate requires the reliable specific yield (Sy) data that are usually not available. Here, we developed a novel approach, the coordinated forward modeling (CoFM), based on the iterative forward modeling to improve GWSA estimation at the sub‐regional scale smaller than the typical GRACE footprint. It is achieved by solving Sy through iterative comparisons between GRACE‐based and observation‐based GWSA at 0.5° grid scale, and then re‐calculating GWSA using the updated Sy and observed GWL. The utility of CoFM is explored by using the hypothetical experiments and a real case study in the Piedmont Plain (PP, ∼54,000 km2) and East‐central Plain (ECP, ∼86,000 km2) of North China Plain. Results show that CoFM can detect GWSA at 0.5° grid scale in the hypothetical experiments given the large spatial variability of GWL. While in the real case study, the CoFM distinguishes between the divergent unconfined GWSA trends (2005–2016) in PP (−41.80 ± 0.55 mm/yr) and ECP (−7.57 ± 0.60 mm/yr) caused by the differences in hydrogeological conditions and groundwater use. The improvement made by CoFM can be attributed to the use of the distributed GWL information to constrain GRACE leakage errors. This study highlights a practical important solution for improving sub‐regional GWSA estimation through the joint use of large‐scale GRACE data and local‐scale well observations.

Challenges in applying water budget framework for estimating groundwater storage changes from GRACE observations

The application of a water budget framework to isolate Gravity Recovery and Climate Experiment (GRACE) groundwater storage anomalies (GRACE-GWA) from GRACE terrestrial water storage anomalies (GRACE-TWSA) is hindered by the lack of direct observations of water budget components. In GRACE groundwater studies, water budget components are frequently applied to isolate changes in a storage component from various auxiliary methods (e.g., land surface or hydrology models, reanalysis, or remote sensing) and are used as a supplement to, or a substitute for, in-situ measurements when direct observations are sparse or unavailable. The contribution of select auxiliary datasets to isolate GRACE-GWA from GRACE-TWSA is an enduring quandary, attributed to the various assumptions applied to resemble hydrologic processes in model formulations. This study systematically allocates water budget components from assorted auxiliary sources to demonstrate bias and distortion of GRACE-GWA resulting simply from water storage component data selection. Whereas previous GRACE-GWA studies used combinations of water budget component datasets with uncertainty derived from the variance of the combined water budget components, this study applies single estimates for each component to measure bias and variability. An initial comparative analysis focused on three basins with suitable in-situ groundwater observations and complex hydrology stores (e.g., large lakes and seasonal snow cover), with correlation coefficients ranging from 0.32 to 0.89. Our systematic analysis was extended to 12 additional basins that capture a range of hydrologic characteristics to highlight the inconsistency in GRACE-GWA estimates. The variability evident in GRACE-GWA estimates highlights that the selection of water budget components carries the risk of misleading outcomes when disaggregating GRACE-TWSA into GRACE-GWA. Our water budget intercomparison provides an important assessment of the limitation in extraction of GRACE-GWA, highlighting the usefulness of comprehensive appraisal of water budget components for GRACE groundwater studies.

Downscaled‐GRACE Data Reveal Anthropogenic and Climate‐Induced Water Storage Decline Across the Indus Basin

AbstractGRACE (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.

Groundwater Storage Variations across Climate Zones from Southern Poland to Arctic Sweden: Comparing GRACE-GLDAS Models with Well Data

The aim of this paper is to assess the correlation of groundwater level changes (or groundwater level anomalies (GWLA)) obtained from direct measurements in wells with groundwater storage anomalies (GWSA) calculated using Gravity Recovery and Climate Experiment (GRACE) products and Global Land Data Assimilation Systems (GLDAS) models across different climate zones, from temperate Poland to Arctic Sweden. We recognize that such validation studies are needed to increase the understanding of the spatio-temporal limits of remote sensing model applicability, not least in data-scarce sub-Arctic and Arctic environments where processes are complex due to the impacts of snow and (perma) frost. Results for temperate climates in Poland and southern Sweden show that, whereas one of the models (JPL_NOAH_GWSA) failed due to water balance term overestimation, the other model (CSR_CLM_GWSA) produced excellent results of monthly groundwater dynamics when compared with the observations in 387 groundwater wells in the region during 2003–2022 (cross-correlation coefficient of 0.8). However, for the sub-Arctic and Arctic northern Sweden, the model suitable for other regions failed to reproduce typical northern groundwater regimes (of the region’s 85 wells), where winter levels decrease due to the blocking effect of ground frost on groundwater recharge. This suggests, more generally, that conventional methods for deriving GWSA and its seasonality ceases to be reliable in the presence of considerably infiltration-blocking ground frost and permafrost (whereas snow storage modules perform well), which hence need further attention in future research. Regarding long-term groundwater level trends, remote sensing results for southern Sweden show increasing levels, in contrast with observed unchanged to decreasing (~10 mm/a) levels, which may not necessarily be due to errors in the remote sensing model but may rather emphasize impacts of anthropogenic pressures, which are higher near the observation wells that are often located in eskers used for water supply. For sub-Arctic and Arctic Sweden, the (relatively uncertain) trend of the remote sensing results nevertheless agrees reasonably well with the groundwater well observations that show increasing groundwater levels of up to ~14 mm/a, which, e.g., is consistent with reported trends of large Siberian river basins.

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.

Analysis of Groundwater Storage Anomaly and multi-source influencing factors in the North China Plain

Analysis of Groundwater Storage Anomaly and multi-source influencing factors in the North China Plain

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.