Search Results (284)

Understanding how groundwater storage responds to extreme precipitation is essential for assessing aquifer resilience under climate variability. In this study, we developed a 1 km groundwater storage anomaly (GWSA) dataset for the Baiyangdian Watershed from 2002 to 2024 by downscaling GRACE observations with a Light Gradient Boosting Machine (LightGBM) model. The downscaled GWSA showed good consistency with independent hydrological datasets, including GLDAS and groundwater-level anomalies. Based on the downscaled product, we characterized long-term groundwater changes and quantified GWSA responses to extreme precipitation events (EPEs). Groundwater storage exhibited three distinct phases: rapid depletion before 2014 (−1.35 cm/yr), a slower decline during 2014–2019 (−1.04 cm/yr), and marked recovery after 2020 (+3.45 cm/yr). Spatially, GWSA generally increased from the southwest to the northeast of the watershed. Composite analysis of 11 EPEs revealed a delayed groundwater response, with the strongest signal occurring approximately two months after precipitation. Monthly effective precipitation was more closely associated with GWSA recovery than short-duration daily precipitation extremes, and the response was stronger in plains than in mountainous areas. These findings indicate that EPEs provide episodic recharge pulses, while sustained groundwater recovery depends on cumulative climatic inputs and human water-management influences. Full article

Spatial and temporal patterns of terrestrial water storage (TWS), and their relationship with groundwater levels, were investigated with the Gravity Recovery and Climate Experiment (GRACE) satellite data, the Global Land Data Assimilation System (GLDAS) land surface model results, and climate observations for the Murray–Darling Basin (MDB). The results show that: (1) TWS displays a clear temporal variability: a negative TWS anomaly with a declining trend during 2002–2009, a positive TWS anomaly with a decreasing trend during 2010–2017, and a period of mixed positive and negative TWS anomalies being accompanied by an increasing trend from 2018 to 2025; (2) five dominant cluster patterns were identified that explain the spatial variability of temporal TWS across the MDB; (3) overall, TWS temporal variability is strongly correlated with rainfall, although it is weak at certain locations; (4) TWS is also influenced by evaporation (both actual and potential evapotranspiration, AET and PET) and runoff, and a combined model significantly improves the overall performance in explaining TWS temporal variability; and (5) TWS-derived groundwater storage changes show both similarities and differences in comparison with groundwater level observation changes, reflecting complex hydrogeological processes and the influence of human activities such as groundwater extraction. These findings provide valuable insights to support improved groundwater resource management with GRACE satellite information and land surface models. Full article

As a critical alpine transition zone linking the Qinghai–Tibet Plateau and the Loess Plateau, Gannan Prefecture acts as an important water conservation area in the upper Yellow River basin of China. Based on GLDAS-2.1 surface soil moisture (SSM) datasets spanning 2000–2022 and interpretable machine learning tools (SHAP and ALE), this paper analyzes the spatiotemporal evolution, future trend sustainability, and nonlinear statistical associations between environmental predictors and SSM. The main results were as follows: (1) SSM exhibited a significant upward trend with an annual growth rate of 0.18 kg·m⁻²·a⁻¹ (p < 0.001), and an abrupt turning point occurred in 2017. The spatial pattern of high SSM in the southeast and low SSM in the northwest remained relatively stable, with the centroid migration distance being less than 1.81 km; most regions presented statistically significant moistening trends (p < 0.05). (2) Natural environmental predictors jointly carried 95.79% of the total statistical explanatory weight for modeled SSM variability. Precipitation possessed the highest explanatory proportion (37.93%), followed by temperature (27.30%), potential evapotranspiration (ET_p, 12.26%), elevation (10.44%), and fractional vegetation cover (FVC, 7.77%). One-dimensional ALE curves identified sample-limited statistical breakpoints: SSM gradually plateaued when precipitation reached 650–700 mm, while modeled SSM decreased substantially once ET_p exceeded 800 mm·a⁻¹. Two-dimensional ALE further characterized combined statistical correlations among precipitation, temperature, and ET_p. Model outputs also indicated that FVC above 0.45 corresponded to enhanced soil water retention within the observed sample range, which only reflects statistical patterns captured in this dataset rather than universal regulatory standards. This study offers quantitative statistical understanding of SSM variations across alpine transition zones. Full article

In many developing regions, the lack of on-site weather data impedes the estimation of rainfall-driven processes, such as soil erosion. Satellite-based precipitation products (SPPs) can support hydrological modelling in gauge-sparse regions by providing continuous rainfall estimates. Accurate rainfall estimation is crucial to soil erosion modelling, particularly in data-scarce regions such as the TRB. In this study, seven satellite-based precipitation products—CHIRPS, IMERG, TRMM, ERA5, GLDAS, and PERSIANN-CDR, along with the IMD gridded dataset—were evaluated for their ability to represent rainfall patterns and support R-factor estimation in the RUSLE framework. This is the first comprehensive evaluation of multiple SPPs for RUSLE-based soil erosion modelling in the Tungabhadra river basin (TRB), providing insights for ungauged watersheds in India. CHIRPS and IMERG displayed relatively smooth and continuous patterns, while PERSIANN-CDR and TRMM exhibited fragmented rainfall zones. ERA5 and GLDAS demonstrated consistent but moderate values across the basin. IMD data served as the reference product for comparison. The findings reveal that the choice of precipitation dataset directly affects the accuracy of erosion estimation. Therefore, multi-dataset evaluation is recommended for reliable assessment of soil loss and watershed planning in ungauged or partially gauged catchments. Full article

Quantifying changes in groundwater storage (GWS) remains a fundamental challenge in hydrology, given the sparsity of long-term in situ monitoring networks and the inherent difficulty of direct subsurface observation. Although GRACE and GRACE-FO satellite missions provide a means of tracking total terrestrial water storage at the continental scale, their coarse spatial resolution (~300 km) and monthly temporal sampling limit their direct applicability to regional groundwater studies. Here, we present a spatio-temporal disaggregation and data fusion framework for reconstructing daily GWS anomalies (GWSAs) across the Korean Peninsula, integrating GRACE/GRACE-FO Mascon solutions with the GLDAS Catchment Land Surface Model (CLSM). The approach leverages satellite-derived mass variations to constrain the model’s long-term anomaly structure while retaining the high-frequency temporal dynamics of land-surface modeling. The framework is evaluated against in situ bedrock monitoring well records from five sites: Seoul, Cheongyang, Uiseong, Imsil, and Wonju. Raw time-series correlations range from R = 0.14 to 0.70; upon removal of the monthly climatology to isolate non-seasonal variability, R improves to 0.49–0.72 across all sites, reaching 0.718 in Seoul and 0.707 in Cheongyang, with Cheongyang’s RMSE declining from 8.847 to 7.574 cm. These results indicate that the GRACE-CLSM fusion framework captures genuine sub-monthly groundwater dynamics beyond the dominant seasonal cycle. To our knowledge, this represents the first reconstruction of daily GWS changes for the Korean Peninsula with explicit preservation of spatial mass conservation, and the resulting dataset has direct utility for operational groundwater monitoring in a region subject to hydroclimatic variability. Full article

Root zone soil moisture (RZSM) is critical for understanding hydrological processes and monitoring agricultural drought, yet its accurate representation remains challenging in topographically complex regions. Using 40 cm in situ SM observations from 19 ground stations in Yunnan Province, China, during 2008–2012 as the reference, this study systematically evaluated the performance of five widely used multi-source soil moisture (SM) products and their different depth layers, including ERA5-Land, GLDAS Noah, GLEAM, ASCAT H141, and CCI SM. A CCI-derived RZSM proxy generated by exponential filtering, hereafter CCI RZSM, was also included. Product performance was assessed using original and deseasonalized time series, and the effects of land-use type, long-term wetness background, and short-term dry conditions on product performance were explicitly examined. The results showed that the intermediate and deeper layers of ERA5-Land and ASCAT H141, especially the 7–28 cm layers, exhibited better performance in capturing RZSM dynamics, achieving a favorable balance among temporal correlation (r > 0.6), random error and systematic bias. Surface-layer products showed limited direct representativeness, and effective RZSM representativeness differed substantially among nominal product layers. Deseasonalization showed that original-series correlations were partly supported by the shared seasonal wet–dry cycle, whereas most products had weaker skill in tracking non-seasonal RZSM anomalies. Environmental background substantially modulated error structures: stronger positive Bias generally occurred at drier stations, Grassland showed higher positive Bias, Cropland showed greater dispersion, and Forest displayed relatively balanced performance. Under dry conditions, temporal correlations declined for nearly all products, whereas increases in random error were mainly concentrated in surface layers. Exponential filtering improved the temporal consistency of CCI SM in representing RZSM, but the filtering with a fixed characteristic time parameter (T) performed worse than filtering with station-optimized T, indicating limited generalizability in ungauged regions. Overall, RZSM representativeness in Yunnan is jointly controlled by product structure, environmental background, and wet–dry conditions. ERA5-Land and ASCAT H141 intermediate-to-deep layers are therefore more suitable for RZSM anomaly and drought applications in Yunnan Province. Full article

Groundwater storage is vital for managing water resources, especially as global water scarcity intensifies. Estimating groundwater levels regionally is challenging due to natural heterogeneity. We employed a large groundwater observation sample, along with Global Land Data Assimilation System (GLDAS) and Gravity Recovery and Climate Experiments (GRACE) datasets, to develop a random forest model for predicting groundwater levels in China’s Yellow River Basin. The model showed robustness, achieving an R² of 0.95 in calibration and an R² of 0.91 ± 0.009 in 10-fold cross-validation with 100 repetitions. Temporal predictability was lower, with an R² of 0.72 for April–May 2023; however, the temporal prediction is preliminary and limited by the short validation period (April–May 2023), which should be interpreted with caution. Spatial maps revealed significant seasonal declines in fall and winter, particularly in the middle and lower reaches. This study highlights the potential of machine learning with extensive observations to estimate regional groundwater levels and supports groundwater analysis with robust data. Full article

We present and discuss time-lapse gravity variations recorded by a large-scale absolute gravity network operating in Central Italy. The network comprises four stations distributed across the Lazio, Umbria, and Abruzzo regions, areas affected by the significant seismic activity of 2009 and 2016–2017. From 2018 to 2023, six campaigns were carefully conducted using an FG5 absolute gravimeter. We detected significant gravity decreases around 2020 reaching between −15 and −20 μGal in three sites and approximately −37 μGal at the fourth. The Sentinel-1 time series of permanent scatterers (PS) allowed us to exclude significant contribution from vertical deformations to the observed gravity changes. We analyzed both ground-based data (rainfall gauges and well water levels) and satellite-based observations (the Gravity Recovery and Climate Experiment-Follow-On, GRACE-FO, mission) together with the Global Land Data Assimilation System (GLDAS) and precipitation models. The results reveal a significant decrease in the regional groundwater content from 2018 to the end of 2020, which coincides temporally with the observed gravity decrease. We show that the absolute gravity variation trends observed at all stations are consistent with regional-scale hydrological processes, pointing to a significant decrease in terrestrial water storage (TWS) during the same time interval. At L’Aquila (AQUI), the gravity anomaly is larger than expected from regional hydrological products alone, suggesting an additional local component possibly related to the hydrogeological response of the fractured karst system undergoing significant post-seismic activity. Full article

Accurate estimation of global terrestrial evapotranspiration (ET) is fundamental for understanding the Earth’s water and energy cycles, yet existing multi-source ET products inevitably contain uncertainties that require spatially explicit characterization for optimal data merging or data assimilation. While Quadruple Collocation Analysis (QCA) offers a robust and reference-free approach to quantify uncertainties, its reliability in the ET discipline remains underexplored, and algorithmic non-convergence frequently results in substantial spatial data gaps. To address these limitations, this study evaluated the accuracy of the QCA method using validation errors derived from high-quality FLUXNET sites (N = 55). Moreover, we employed a Random Forest (RF) framework that is driven by 17 environmental variables to generate spatially seamless error maps for four mainstream ET products, i.e., ERA5, GLDAS, GLEAM, and MERRA2, from 2000 to 2020. Results demonstrate that QCA-based errors strongly correlated with ground-based errors as Pearson’s correlation coefficient was >0.3 for all four ET products. Furthermore, the RF model successfully reconstructed the spatial gaps in QCA errors, achieving an exceptionally low mean prediction error of approximately 0.03 mm/day. Based on these seamless maps, the global mean ET error is estimated at roughly 0.3 mm/day, with pronounced high-error clusters emerging in regions such as central Canada and northern Argentina driven by underlying land cover heterogeneity. Ultimately, this seamless gap-filling redefined the global map of product with the lowest estimated collocation error. ERA5 emerged as the superior choice across approximately 45% of the land surface (predominantly in the tropics and mid-to-high latitudes). Meanwhile, before algorithmic gap-filling, GLEAM was optimal across approximately 28% of the valid land pixels; after spatial gap-filling, it proved most effective across approximately 30% of the globe, particularly within arid deserts and glaciated regions. Our work provides useful geographic guidance for optimizing multi-source data merging and land data assimilation frameworks in future global hydrological studies. Full article

Groundwater is a critical component of the terrestrial water system, and understanding the spatiotemporal evolution of national groundwater storage is essential for ensuring water security and ecological sustainability in China. Based on GRACE and GRACE-FO satellite gravity data from January 2005 to December 2024, combined with land surface water components derived from GLDAS, this study investigates the spatiotemporal variations and driving mechanisms of groundwater storage anomalies (GWSAs) across China using parametric additive decomposition, Theil–Sen trend estimation, and the Mann–Kendall significance test. The results indicate that: (1) groundwater storage in China has experienced a persistent decline at an average rate of −1.97 mm yr⁻¹, with a cumulative depletion of 38.55 mm. This decline exhibits distinct phases, characterized by a rapid decrease during 2005–2011, a moderated decline during 2012–2017, and a renewed acceleration with increased variability during 2018–2024; (2) spatially, GWSA shows a pronounced “increase in the south and decrease in the north” pattern, with extremely significant declines concentrated in the North China Plain and arid northwestern inland regions, while significant increases are mainly observed in southwestern China and southeastern coastal areas; (3) GWSA exhibits a stable seasonal cycle of “summer surplus and spring deficit,” with groundwater replenishment driven by summer precipitation and substantial depletion caused by agricultural water demand in spring; and (4) groundwater variations in northern China are primarily controlled by intensive agricultural irrigation, resulting in sustained depletion, whereas southern China is mainly influenced by natural precipitation, maintaining a relatively dynamic equilibrium. These findings provide valuable insights for region-specific groundwater management and the sustainable utilization of water resources in China. Full article

Climate change poses increasing threats to water security in Colombia, particularly in regions where groundwater monitoring remains limited. This study evaluated groundwater potential as an adaptation measure across six strategic regions covering 16.5% of Colombian territory: Tolima-Huila, Boyacá-Santander, Caribe Oriental, Caribe Occidental, Cauca-Nariño, and Meta-Vichada. We integrated a multi-source methodological approach combining conceptual hydrogeological characterization, potential recharge estimation using the Thornthwaite–Mather method, and groundwater storage anomaly analysis through GRACE satellite data coupled with the GLDAS model for the period 1992–2022. Potential recharge was evaluated for the historical period (1992–2022) and projected under SSP2-4.5 and SSP5-8.5 climate change scenarios (2040–2070) using global circulation models. Results revealed significant regional heterogeneity, with critical recharge reductions reaching 230 mm/month in Cauca-Nariño and 202 mm/month in Meta-Vichada under SSP5-8.5 compared to the historical baseline. Positive groundwater storage anomalies identified sectors with exploitation potential even in low-recharge areas, particularly in northern Tolima-Huila. Priority zones were defined for implementing adaptation measures, including artificial recharge, strategic use of fractured media, and strengthened water governance. The proposed methodology provides replicable technical foundations for climate-resilient planning at regional and local scales, which is particularly relevant for contexts with scarce hydrogeological information. Full article

In order to improve the robustness and internal consistency of evapotranspiration estimation in arid regions and to reveal the characteristics of water consumption structure within a river basin, this study focused on the Aksu River Basin. Multiple data sources, including the Penman–Monteith model, MODIS remote sensing products, GRACE terrestrial water storage change data, and the GLDAS–Noah model, were integrated to establish a Bayesian Model Averaging (BMA)-based framework for fusing actual evapotranspiration (ETa) estimates. The results indicate that the BMA integration effectively mitigated model-dependent biases and improved the consistency and robustness of basin-scale ETa estimates. During the period 2000–2020, ETa in the basin exhibited an overall increasing trend (approximately 4.04 mm/a), with a spatial distribution pattern characterized by higher values in the northwest and lower values in the southeast. In terms of water consumption effectiveness, low-effectiveness water consumption predominated in the basin (accounting for 61.24%), while high-effectiveness water consumption accounted for a relatively smaller proportion (26.01%). These results suggest that the current water consumption structure remains dominated by low-effectiveness components, indicating potential room for optimization in balancing irrigation activities and ecosystem water use. The multi-source data fusion and water consumption effectiveness evaluation framework proposed in this study provides a scientific basis for water resource management and ecological water security assessment in arid river basins. Full article

Groundwater is a critical strategic resource supporting agricultural production and ecological security in the transboundary river basins of Northeast China. However, intensified climate variability and rapid agricultural expansion over the past two decades have imposed increasing pressure on regional groundwater systems. In this study, we integrated GRACE-derived terrestrial water storage anomalies, GLDAS land surface data, meteorological datasets, land-use information, and agricultural statistics to construct a comprehensive assessment framework consisting of groundwater storage anomalies (ΔGWS), the GRACE Groundwater Drought Index (GGDI), and sustainability indicators—REL (Reliability), RES (Resilience), VUL (Vulnerability), and SI (Sustainability Index). By integrating GRACE-derived groundwater dynamics with sustainability indicators (REL, RES, VUL, and SI), enabling a basin-scale, long-term assessment of groundwater sustainability across Northeast China’s transboundary basins, and clarifying the relative roles of climatic variability and intensive human water use. We systematically examined the spatiotemporal evolution of groundwater conditions in the Heilongjiang, Suifen, Tumen, and Yalu River basins from 2002 to 2022, and quantified the relative roles of climatic and anthropogenic drivers. The results indicate that groundwater storage exhibited pronounced seasonal fluctuations alongside a persistent downward trend, with GGDI remaining predominantly negative after 2018, reflecting the development of structural groundwater drought. The SI declined markedly from 0.32 to 0.06, and areas with extremely low sustainability accounted for more than 90% of the study region in recent years. MIC-based dependence analysis showed that sown area (MIC = 0.98) and nighttime light intensity (MIC = 0.92) were the dominant drivers of groundwater degradation, exerting far greater influence than precipitation or potential evapotranspiration. These patterns highlight that policy-driven agricultural expansion and increased irrigation demand have surpassed natural recharge capacity, becoming the fundamental cause of long-term groundwater depletion. This study underscores the urgency of promoting agricultural green transformation, optimizing crop planting structures, improving irrigation efficiency, and enhancing ecological conservation to rebuild groundwater resilience. Moreover, coordinated cross-border groundwater monitoring and management will be essential for ensuring the sustainable use of water resources in Northeast Asia’s transboundary river basins. 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

Terrestrial water storage includes soil water storage, groundwater storage, surface water storage, snow water equivalent, plant canopy water storage, biological water storage, etc., which can comprehensively reflect the total change in water volume during processes such as precipitation, evapotranspiration, runoff, and human water use in the basin hydrological cycle. The Gravity Recovery and Climate Experiment (GRACE) satellite provides a powerful tool and a new approach for observing changes in terrestrial water storage and groundwater storage. The North China Plain (NCP) is a major agricultural region in the northern arid area of China, and long-term overexploitation of groundwater has led to increasingly prominent ecological vulnerability issues. This study uses GRACE and Global Land Data Assimilation System (GLDAS) hydrological model data to assess the spatiotemporal patterns of groundwater drought in the NCP and its various sub-regions from 2003 to 2022, identify the locations, occurrence probabilities, and confidence intervals of seasonal and trend mutation points, quantify the complex interactive effects of multiple climate factors on groundwater drought, and reveal the propagation time from groundwater drought to agricultural drought. The results show that: (1) from 2003 to 2022, the linear tendency rate of groundwater drought index (GDI) was −0.035 per 10 years, indicating that groundwater drought showed a gradually worsening trend during the study period; (2) on an annual scale, the most severe groundwater drought occurred in 2021 (GDI = −1.59). In that year, the monthly average GDI in the NCP ranged from −0.58 to −2.78, and the groundwater drought was most severe in July (GDI = −2.02); (3) based on partial wavelet coherence, the best univariate, bivariate for groundwater drought were soil moisture (PASC = 19.13%); and (4) in Beijing, Tianjin and Hebei, the propagation time was mainly concentrated in 1–5 months, with average lag times of 2.87, 3.20, and 2.92 months, respectively. This study can not only reduce and mitigate the harm of groundwater drought to agricultural production, social life, and ecosystems by monitoring changes in groundwater storage, but also provide a reference for the quantitative identification of the dominant factors of groundwater drought. Full article