Search Results (47)

Based on GRACE RL06 data, this study reconstructs a monthly Terrestrial Water Storage Anomaly (TWSA) series in Shandong Province (2003–2024) using Singular Spectrum Analysis (SSA) and derives Groundwater Storage Anomaly (GWSA) via the water balance equation. The spatiotemporal evolution characteristics of GWSA were systematically examined, and the relative contributions of climatic factors and human activities to groundwater storage changes were quantitatively assessed, with the aim of contributing to the development, utilization, and protection of groundwater in Shandong Province. The results indicate that temporally, GWSA in Shandong Province exhibited a statistically significant decreasing trend at a rate of −8.45 mm/a (p < 0.01). The maximum GWSA value of 17.15 mm was recorded in 2006, while the Mann–Kendall abrupt change-point analysis identified 2013 as a significant transition point. Following this abrupt change, GWSA demonstrated a persistent decline, reaching the minimum annual average of −225.78 mm in 2020. Although moderate recovery was observed after 2020, GWSA values remained substantially lower than those in the pre-abrupt change period. Seasonal analysis revealed a distinct “higher in autumn and lower in spring” pattern, with the most pronounced fluctuations occurring in summer and the most stable conditions in winter. Spatially, approximately 99.1% of the study area showed significant decreasing trends, displaying a clear east–west gradient with more severe depletion in inland regions compared to relatively stable coastal areas. Crucially, human activities emerged as the dominant driving factor, with an average contribution rate of 86.11% during 2003–2024. The areal proportion where human activities served as the decisive factor (contribution rate > 80%) increased dramatically to 99.58%. Furthermore, the impact of human activities demonstrated bidirectional characteristics, transitioning from negative influences during the depletion phase to positive contributions promoting groundwater recovery in recent years. At present, the GWSA in Shandong Province is expected to continue declining in the future, with an overall downward trend. Countermeasures must be implemented promptly. Full article

Groundwater constitutes a vital freshwater resource essential for sustaining agricultural productivity, industrial processes, and domestic water supply. Quantifying spatiotemporal dynamics of Groundwater Storage (GWS) across China provides a critical scientific basis for sustainable water resource management and conservation. Employing a unified methodology combining Gravity Recovery and Climate Experiment (GRACE) observations and global hydrological models (GLDAS, WGHM), this study investigates spatiotemporal variations in Groundwater Storage Anomalies (GWSA) across China and its nine major river basins from February 2003 to December 2019. The results indicate an overall declining trend in China’s GWSA at −2.27 to −0.38 mm/yr. Significant depletion hotspots are identified in northern Xinjiang, southeastern Tibet, and the Haihe River Basin. Conversely, statistically significant increasing trends are detected in the Endorheic Basin of the Tibetan Plateau and the middle reaches of the Yangtze River Basin. Although GWSA inversions derived from different Global Land Data Assimilation System (GLDAS) models show general consistency, there are still pronounced regional heterogeneities in model performance. The findings offer critical scientific foundations for water resources managers and policymakers to formulate sustainable groundwater management strategies in China. Full article

The Hailiutu River Basin in northern China represents a semi-arid area where groundwater-dependent ecosystems (GDEs) play a critical role in maintaining regional vegetation structure and ecological stability. This study investigated the spatiotemporal dynamics of GDEs and their relationship with water conditions using trend analysis, partial correlation, and Random Forest models over the period of 2002–2022. The results show that vegetation activity (NDVI) increased at a rate of 0.0052/yr in GDEs. Precipitation exhibited a basin-wide upward trend of 0.735 mm/yr, while SPEI increased at 0.0207/yr. In contrast, groundwater storage declined markedly at −11.19 mm/yr, highlighting a persistent reduction in water availability that poses a significant risk to the stability of GDEs. Both partial correlation analysis and the random forest model consistently showed strong ecohydrological interactions between vegetation and groundwater. Vegetation dynamics are primarily driven by groundwater availability, especially in groundwater-dependent ecosystems. Conversely, groundwater variations are most strongly influenced by vegetation. The results indicate that precipitation and the standardized precipitation–evapotranspiration index (SPEI) are the primary positive drivers of interannual NDVI variability, whereas groundwater plays a critical role in sustaining GDEs. Field observations of key species confirm the dependence of GDEs on groundwater, and vegetation dynamics are regulated by climate and groundwater; however, ongoing groundwater decline may threaten ecosystem stability. These findings demonstrate that vegetation transpiration exerts the dominant influence on groundwater variations, while groundwater simultaneously constrains vegetation growth, particularly in areas where declining groundwater storage anomalies (GWSAs) coincide with reduced NDVI. The results emphasize that continuous groundwater depletion threatens vegetation–groundwater sustainability, highlighting the need for balanced groundwater and vegetation management in arid regions. Full article

Monitoring groundwater dynamics and basin-scale water budget closure is critical for sustainable water resource management, especially in regions facing climate stress and overexploitation. This study examines the temporal variability of total water storage and groundwater trends in Türkiye’s Kızılırmak River Basin by integrating GRACE/GRACE-FO satellite gravimetry, GLDAS-Noah land surface model outputs, ERA5-Land reanalysis products, and local meteorological observations. Groundwater storage anomalies (GWSAs) were derived from the difference between GRACE-based total water storage anomalies (TWSAs) and GLDAS-modeled surface storage components, revealing a long-term groundwater depletion trend of −9.55 ± 2.6 cm between 2002 and 2024. To investigate the hydrological drivers of these changes, lagged correlation analyses were performed between GRACE TWSA and ERA5-Land variables (precipitation, evapotranspiration, runoff, soil moisture, and temperature), showing time-shifted responses from −3 to +3 months. The strongest correlations were found with soil moisture (CC = 0.82 at lag −1), temperature (CC = −0.70 at lag −3), and runoff (CC = 0.71 at lag 0). A moderate correlation between GRACE TWSA and ERA5-based water storage closure (CC = 0.54) indicates partial alignment. These findings underscore the value of satellite gravimetry in tracking subsurface water changes and support its role in basin-scale hydrological assessments. Full article

Against the backdrop of global warming and intensified anthropogenic activities, groundwater reserves are rapidly depleting and facing unprecedented threats to their long-term sustainability. Consequently, investigating groundwater reserves is of critical importance for ensuring water security and promoting sustainable development. This study takes the Heilongjiang (Amur) River Basin as the research area. Groundwater storage was estimated using data from the Gravity Recovery and Climate Experiment (GRACE) satellite and the Global Land Data Assimilation System (GLDAS) covering the period from 2002 to 2024. A combination of Random Forest (RF), SHapley Additive exPlanation (SHAP) models, and Pearson partial correlation coefficients was employed to analyze the spatiotemporal evolution characteristics, driving mechanisms, and spatial linear correlations of the primary influencing factors. The results indicate that the basin’s groundwater storage anomaly (GWSA) exhibits an overall declining trend. GWSA is influenced by multiple factors, including climatic and anthropogenic drivers, with temperature (TEM) and precipitation (PRE) identified as the primary controlling variables. Spatiotemporal analysis reveals significant spatial heterogeneity in the relationship between GWSA evolution and its primary drivers. This study adopts a “retrieval–attribution–spatial analysis” framework to provide a scientific basis for enhancing regional groundwater security and supporting sustainable development goals. Full article

Leveraging multi-source remote sensing datasets and dynamic groundwater monitoring well observations, this study explores the multiscale spatiotemporal linkages of groundwater storage changes and land deformation in North China Plain (NCP) after the South-to-North Water Diversion Project (SNWDP). Firstly, we employed Gravity Recovery and Climate Experiment (GRACE) and interferometric synthetic aperture radar (InSAR) technology to estimate groundwater storage (GWS) and land deformation. Secondly and significantly, we proposed a novel GRACE statistical downscaling algorithm that integrates a weight allocation strategy and GWS estimation applied with InSAR technology. Finally, the downscaled results were employed to analyze spatial differences in land deformation across typical ground fissure areas. The results indicate that (1) between 2018 and 2021, groundwater storage in the NCP exhibited a declining trend, with an average reduction of −3.81 ± 0.53 km³/a and a maximum land deformation rate of −177 mm/a; (2) the downscaled groundwater storage anomalies (GWSA) showed high correlation with in situ measurements (R = 0.75, RMSE = 2.91 cm); and (3) in the Shunyi fissure area, groundwater storage on the northern side increased continuously, with a maximum growth rate of 28 mm/a, resulting in surface uplift exceeding 70 mm. Full article

Groundwater is a vital resource in arid regions, where it sustains agriculture, industry, and livelihoods. In northwestern China’s Shule River Basin, located in the Hexi Corridor, increasing water stress has raised concerns about the sustainability of groundwater use. However, the relative contributions of climate variability and human activities to groundwater depletion in this region remain poorly quantified. This study investigates long-term groundwater storage changes in the Shule River Basin from 2003 to 2023 using GRACE satellite data combined with GLDAS land surface models. A water balance approach was applied to isolate natural (climatic) and anthropogenic contributions to groundwater storage anomalies (GWSAs). In addition, land use transitions and socioeconomic indicators were incorporated to assess the impact of human development on subsurface water dynamics. The results show a persistent downward trend in GWSA, with an average annual loss rate of −0.31 cm·yr⁻¹. Spatially, the central and lower reaches of the basin exhibit the most significant depletion, driven by intensive irrigation and urban growth. Contribution analysis indicates that natural factors accounted for 61% of the groundwater loss across the study period, while anthropogenic drivers became increasingly dominant over time, particularly after 2016, accounting for over 40% of total depletion in recent years. Strong correlations were found between groundwater decline and the expansion of cropland, impervious surfaces, and GDP. These findings highlight the intensifying role of human activities in shaping groundwater trends in arid inland basins. This study provides a data-driven framework to support sustainable groundwater management and offers transferable insights for similar water-stressed regions globally. Full article

There are serious ecological and environmental risks associated with groundwater level decline, particularly in areas with little in situ monitoring. In order to monitor and assess the resilience and dependability of groundwater storage, this paper proposes a solid methodology that combines data from land surface models and satellite gravimetry. In particular, the GRACE Groundwater Drought Index (GGDI) is used to analyze the estimated groundwater storage anomalies (GWSA) from the Gravity Recovery and Climate Experiment (GRACE) and the Global Land Data Assimilation System (GLDAS). Aquifer resilience, or the likelihood of recovery after stress, and aquifer reliability, or the long-term probability of remaining in a satisfactory state, are calculated using the core method. The two main components of the methodology are (a) calculating GWSA by subtracting the surface and soil moisture components from GLDAS, total water storage from GRACE, and comparing the results to in situ groundwater level data; and (b) standardizing GWSA time series to calculate GGDI and then estimating aquifer resilience and reliability based on predetermined threshold criteria. Using this framework, we validate GRACE-derived GWSA with in situ observations in eight sub-basins of the Chao Phraya River (CPR) basin, obtaining Pearson correlation coefficients greater than 0.82. With all sub-basins displaying values below 35%, the results raise significant questions about resilience and dependability. This method offers a framework that can be applied to assessments of groundwater sustainability worldwide. Full article

Groundwater depletion has emerged as a pressing global challenge, yet the low spatial resolution (0.25°) of Gravity Recovery and Climate Experiment (GRACE) satellite data limits its application in regional groundwater monitoring. In this study, based on 0.25° spatial resolution groundwater storage anomalies (GWSAs) data derived from GRACE satellite observations and GLDAS hydrological model outputs, supplemented with hydrological data, humanities data, and other geographic parameters, we constructed a Stacking-based ensemble machine learning model that achieved a 1 km spatial resolution of GWSAs distribution data across the contiguous United States (CONUS) from 2010 to 2020. The ensemble model integrates eXtreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), and Categorical Boosting (CatBoost) models using an Attention-Based Dynamic Weight Allocation (ADWA) approach, along with a ridge regression model. The results indicate that our ensemble model outperforms individual machine learning (ML) models, achieving a coefficient of determination (R²) of 0.929, root mean square error (RMSE) of 25.232 mm, mean absolute error (MAE) of 19.125 mm, and Nash–Sutcliffe efficiency (NSE) of 0.936, validated by 10-fold cross-validation. In situ measurements indicate that, compared with the original data, approximately 61.7% of the monitoring wells (266 out of 431) exhibit a higher correlation after downscaling, with the overall correlation coefficient increasing by about 18.7%, which suggests that the downscaled product exhibits an appreciable improvement in accuracy. The ensemble model proposed in this study, by integrating the advantages of various ML algorithms, is better able to address the complexity and uncertainty of groundwater storage variations, thus providing scientific support for the sustainable management of groundwater resources. Full article

This study investigated and compared the spatiotemporal evolution and driving factors of groundwater storage anomalies (GWSAs) under the dual pressures of climate change and urban expansion in two contrasting river basins of China. Integrating GRACE and GLDAS data with multi-source remote sensing data and using attribution analysis, we reveal divergent urban GWSA dynamics between the humid Yangtze River Basin (YZB) and semi-arid Yellow River Basin (YRB). The GWSAs in YZB urban grids showed a marked increasing trend at 3.47 mm/yr (p < 0.05) during 2002–2020, aligning with the upward patterns observed in agricultural land types including dryland and paddy fields, rather than exhibiting the anticipated decline. Conversely, GWSAs in YRB urban grids experienced a pronounced decline (−5.59 mm/yr, p < 0.05), exceeding those observed in adjacent dryland regions (−5.00 mm/yr). The contrasting climatic regimes form the fundamental drivers. YZB’s humid climate (1074 mm/yr mean precipitation) with balanced seasonality amplified groundwater recharge through enhanced surface runoff (+6.1%) driven by precipitation increases (+7.4 mm/yr). In contrast, semi-arid YRB’s water deficit intensified, despite marginal precipitation gains (+3.5 mm/yr), as amplified evapotranspiration (+4.1 mm/yr) exacerbated moisture scarcity. Human interventions further differentiated trajectories: YZB’s urban clusters demonstrated GWSA growth across all city types, highlighting the synergistic effects of urban expansion under humid climates through optimized drainage infrastructure and reduced evapotranspiration from impervious surfaces. Conversely, YRB’s over-exploitation due to rapid urbanization coupled with irrigation intensification drove cross-sector GWSA depletion. Quantitative attribution revealed climate change dominated YZB’s GWSA dynamics (86% contribution), while anthropogenic pressures accounted for 72% of YRB’s depletion. These findings provide critical insights for developing basin-specific management strategies, emphasizing climate-adaptive urban planning in water-rich regions versus demand-side controls in water-stressed basins. Full article

The Bazhou yak, a major native meat yak breed in Xinjiang, China, is renowned for its fast growth rate, strong adaptability, and particularly high intramuscular fat (IMF) content. However, limited knowledge regarding its phylogenetic history and genomic composition has hindered its long-term conservation and utilization. This study evaluated the genetic diversity, population phylogenetics, and genome-wide selective sweep analysis (GWSA) of 100 newly obtained Bazhou yaks through genome resequencing, as well as 340 public yak genomes from nine other populations on the Qinghai–Tibet Plateau. The results revealed moderate diversity, lower genomic inbreeding levels, and rapid linkage disequilibrium (LD) decay in Bazhou yaks. Principal component analysis (PCA) and phylogenetic analysis showed a clear separation of Bazhou yaks from other yak populations, indicating the Bazhou yak as an independent genetic population. Furthermore, less genetic differentiation was found between the Bazhou yak and the Huanhu yak, while ADMIXTURE analysis revealed a common ancestral lineage between Bazhou yaks and Huanhu yaks, indicating an important genetic contribution of the Qinghai yak population to Bazhou yaks. The GWSA identified a total of 833 selected genes in Bazhou yaks using the top 5% interaction windows of both parameters (F_ST, Pi ratio, and XP-EHH). A significant number of these genes are related to fat synthesis and deposition, such as MTOR, APOA1, and GPAT4. In summary, this study sheds light on the phylogenetic status and distinctive genomic features of Bazhou yaks, which facilitates our understanding of the genetic basis of the IMF phenotype in Bazhou yaks. Full article

Groundwater plays a crucial role in the formation of the Badain Jaran Desert-Sand Dune Lake System, which has been designated a UNESCO World Heritage Site in 2024. However, the region’s wetland ecosystem is significantly impacted by climate change and human activities. This study utilizes GRACE satellite data and in situ observation data to establish a groundwater storage anomaly (GWSA) time series for the Badain Jaran Desert and its surrounding areas (BJDCA) from 2003 to 2022. The analysis reveals the spatiotemporal patterns of groundwater drought and sustainability, as well as the underlying factors affecting regional groundwater sustainability. The results indicate that 99.2% of the study area exhibited a significant decline in GWSA (α ≤ 0.01), with the annual mean GRACE Groundwater Drought Index (GGDI) dropping from 1.44 to −1.54, reflecting a worsening groundwater drought. In 2022, the GGDI in the southeastern part of the BJDCA reached as low as −3.04, highlighting severe groundwater stress. Furthermore, the Sustainability Index (SI) of the study area declined markedly from 1.00 to 0.01, underscoring the critical impact of human activities on groundwater resources in the BJDCA. These findings provide valuable insights for formulating more effective groundwater resource management policies and promoting sustainable development in arid regions. Full article

The Gravity Recovery and Climate Experiment (GRACE) enables large-scale monitoring of terrestrial water storage changes, significantly contributing to hydrology and related fields. However, the coarse resolution of groundwater storage anomaly (GWSA) data limits local-scale research utilizing GRACE and GRACE-FO missions. In this study, we develop a regional downscaling model based on the linear regression relationship between GWSA and environmental variables, reducing the grid resolution of GWSA obtained from GRACE from approximately 25 km to 1 km. First, we estimate the missing values of monthly continuous terrestrial water storage anomaly (TWSA) for the period from 2003 to 2020 using interpolated multi-channel singular spectrum analysis (IMSSA). Next, we apply the water balance equation to separate GWSA from TWSA, which is provided jointly by the Global Land Data Assimilation System (GLDAS) and the distributed ecohydrological model ESSI-3. We then employ a partial least squares regression (PLSR) model to identify the most significant environmental variables related to GWSA. Precipitation (Prec), normalized difference vegetation index (NDVI), and actual evapotranspiration (AET), with variable importance in projection (VIP) values greater than 1.0, are recognized as effective variables for reconstructing long-term, high-resolution groundwater storage changes. Finally, we downscale and reconstruct the long-term (2003–2020), high-resolution (1 km × 1 km) monthly GWSA in the Songhua River Basin using fused and supplemented GRACE/GRACE-FO data, employing either geographically weighted regression (GWR) or random forest (RF) models. The results demonstrate superior performance of the GWR model (CC = 0.995, NSE = 0.989, RMSE = 2.505 mm) compared to the RF model in downscaling. The downscaled GWSA in the Songhua River Basin not only achieves high spatial resolution but also exhibits improved accuracy when compared to in situ groundwater observation records. This research enhances understanding of spatiotemporal variations in regional groundwater due to local agricultural and industrial water use, providing a scientific basis for regional water resource management. Full article

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. Full article

In central Sakha (Yakutia) Republic, groundwater icings, primarily formed by intrapermafrost water, are less prone to contamination and serve as a stable freshwater resource. The periodic growth of icings threatens infrastructure such as roads, railways, and bridges in permafrost areas. Therefore, research in this field has become urgently necessary. This study aims to analyze the impacts of various factors on the scale of icing formation using Landsat satellite data, Gravity Recovery and Climate Experiment (GRACE)/GRACE Follow-On (GRACE-FO) data, Global Land Data Assimilation System (GLDAS) data, and field observation results. The results showed that the surface area of icings in the study area showed an overall increasing trend from 2002 to 2022, with an average growth rate of 0.06 km²/year. Suprapermafrost water and intrapermafrost water are the main sources of icings in the study area. The total Groundwater Storage Anomaly (GWSA) values from October to April showed a strong correlation with the maximum icing areas. Icings fed by suprapermafrost water were influenced by precipitation in early autumn, while those fed by intrapermafrost water were more affected by talik size and distribution. Climate warming contributed to the degradation of the continuous permafrost covering an area of 166 km² to discontinuous permafrost, releasing additional groundwater. This may also be one of the reasons for the observed increasing trend in icing areas. This study can provide valuable insights into water resource management and infrastructure construction in permafrost regions. Full article