Terrestrial Water Storage Anomalies Research Articles

Reconstructed centennial precipitation-driven water storage anomalies in the Nile River Basin using RecNet and their suitability for studying ENSO and IOD impacts

While the Gravity Recovery And Climate Experiment (GRACE) and its Follow-On (GFO) missions have offered valuable observations for monitoring total water storage anomalies (TWSA), their short record constrains our ability to study the complete range and long-term variability of TWSA in the Nile River Basin (NRB). Previous studies reconstructing TWSA in this region either relied on specific hydrological models or did not consider spatial correlations among the TWSA grids. Here, we employ RecNet, a deep learning model capable of providing independent TWSA observations without relying on hydrological models while considering spatial correlations, to reconstruct precipitation-driven TWSA in the NRB from 1923 to 2022. The reconstructed data are validated by comparisons with the Global Land Data Assimilation System (GLDAS), the WaterGAP Global Hydrology Model (WGHM), the water balance budget, long-term runoff data, and GRACE-REC (i.e., a global reconstruction dataset freely available online). Subsequently, the suitability of the reconstructed data for studying El Niño Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD) impacts within the NRB is assessed. Dividing the NRB into four sub-regions, i.e., the Lake Victoria Basin (LVB), the Bahr el Jebel and Bahr el Ghazal basins (BJBG), Ethiopian Highlands region (EH), and the Lower Nile River Basin (LNRB), it is shown that RecNet successfully reconstructs precipitation-driven TWSA over BJBG and EH, achieving correlation coefficient (CC), normalized root mean square error (NRMSE), and Nash–Sutcliffeefficiency (NSE) of 0.94/0.11/0.88 and 0.96/0.09/0.91 during the testing period, respectively. Additionally, RecNet’s reconstruction shows better agreement with GLDAS and WGHM than GRACE-REC, correlating well with runoff and the water balance budget in these regions. The relatively poor performance in the LVB and LNRB regions could be attributed to the substantial influence of Lake Victoria and the arid climate, respectively. Correlation analysis and wavelet coherence analysis identify significant coherence between ENSO/IOD and the reconstructed TWSA in BJBG and EH, with CC values of −0.68/0.34 and −0.82/0.56, respectively. This study provides centennial reconstructed TWSA data that could be useful in climate change/variability studies and water resource management within the NRB.

Resilience and Resistance of Vegetation in Response to Droughts in a Subtropical Humid Region Dominated by Karst

The resilience and resistance of vegetation are important indicators of the vegetation’s response to droughts. Owing to the uniqueness of the environment in humid karst areas, results from studies on other climatic zones may not necessarily present the status of vegetation resilience and resistance in humid karst areas. Herein, We calculated vegetation resilience and resistance by autoregressive modeling using Enhanced Vegetation Index (EVI), Total Water Storage Anomaly (TWSA), temperature (TA), precipitation (PRE) data, An analysis of variance (ANOVA) was then conducted to compare the differences in resilience and resistance of different vegetation types in the study area, as well as the differences in resilience and resistance of vegetation in different sub-geomorphic zones. Finally, natural factors affecting vegetation resilience and resistance were quantified using partial least squares structural equation modeling (PLS-SEM). The results demonstrate the following points. First, vegetation resilience, total-water-storage anomaly resistance, and vegetation resistance against precipitation anomalies were lower in karst areas of the study area than in non-karst areas of the study area (except for vegetation resistance against temperature anomalies). Second, vegetation resilience was the lowest in some sub-geomorphic zones within karst areas, and it was still comparable to that in semiarid areas. Third, precipitation and temperature were important factors that affected the resilience and resistance of vegetation in karst areas, and the geochemical indicators (CaO, MgO, and SiO2) of soil parent material were major factors that affected the resistance and resilience of vegetation in non-karst areas. In summary, this study was undertaken to reveal the natural characteristics of vegetation resilience and resistance in humid karst regions. Our findings complement and expand the existing body of knowledge on vegetation resilience and resistance in other ecologically fragile zones limited by moisture.

Dynamic mode decomposition of GRACE satellite data

Advancements in satellite technology yield environmental data with ever improving spatial coverage and temporal resolution. This necessitates the development of techniques to discern actionable information from large amounts of such data. We explore the potential of dynamic mode decomposition (DMD) to discover the dynamics of spatially correlated structures present in global-scale data, specifically in observations of total water storage anomalies provided by GRACE satellite missions. Our results demonstrate that DMD enables data compression and extrapolation from a reduced set of dominant spatiotemporal structures. The accuracy of its predictions of global system dynamics is preserved in its reconstruction of local time series. These findings suggest potential uses of DMD in analysis of remote-sensing data for hydrologic applications.

Methodological evaluation of river discharges derived from remote sensing and land surface models

This study assesses river discharges derived using remote sensing and hydrologic modeling approaches throughout the CONUS. The remote sensing methods rely on total water storage anomalies (TWSA) from the GRACE satellite mission and water surface elevations from altimetry satellites (JASON-2/3, Sentinel-3). Surface and subsurface runoff from two Land Surface Models (NOAH, CLSM) are routed using the Hillslope River Routing model to determine discharge. The LSMs are part of NASA’s Global Land Data Assimilation System (GLDAS). Differences in key physical processes represented in each model, model forcings, and use of data assimilation provide an intriguing basis for comparison. Evaluation is performed using the Kling Gupta Efficiency and USGS stream gauges. Results highlight the effectiveness of both satellite-derived discharge methods, with altimetry generally performing well over a range of discharges and TWSA capturing mean flows. LSM-derived discharge performance varies based on hydroclimatic conditions and drainage areas, with NOAH generally outperforming CLSM. CLSM-derived discharges may be impacted by the use of data assimilation (GLDAS v2.2). Low correlation and high variability contribute to lower KGE values. GLDAS models tend to perform poorly in snow dominated, semi-arid and water-regulated systems where both the timing and magnitude of the simulated results are early and overestimated.

Assessing long-term water storage dynamics in Afghanistan: An integrated approach using machine learning, hydrological models, and remote sensing

Assessment of Terrestrial Water Storage (TWS) components is crucial for understanding regional climate and water resources, particularly in arid and semi-arid regions like Afghanistan. Given the scarcity of ground-based data, this study leverages remote sensing datasets to quantify water storage changes. We integrated Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-on (GRACE-FO) data with WaterGap, Global Land Water Storage (GWLS), Catchment Land Surface Model (CLSM), and climate variables (precipitation, temperature, potential evapotranspiration) using artificial neural networks (ANN) and random forests (RF). Additionally, Ice, Cloud, and Land Elevation Satellite (ICESat-1,2) data were utilized to estimate glacier mass changes. Seasonal trend decomposition using Loess (STL) was applied to assess TWS changes from 2003 to 2022. Our methodology reveals a high correlation (R = 0.90–0.97) between reconstructed and observed TWS anomalies across Afghanistan's major basins. Glacier mass decreased by −0.59 and −1.17 Gt/year during 2003–2009 and 2018–2022, respectively, while overall TWS declined by −2.46 Gt/year. The HRB experienced the largest TWS loss (−1.47 Gt/year), primarily due to groundwater depletion (−1.18 Gt/year). These findings underscore the importance of our approach for assessing water resources, providing vital insights for sustainable management under a changing climate in a data-scarce country.

Analysis of watershed terrestrial water storage anomalies by Bi-LSTM with X-11 time series prediction combined model

Analysis of watershed terrestrial water storage anomalies by Bi-LSTM with X-11 time series prediction combined model

Applicability analysis of comprehensive drought index based on GRACE data in ten major river basins in China

As hydroclimatic extremes, drought triggered by climate change and human activities are recurrent over China. Selecting an appropriate comprehensive drought index is an important measure to comprehensively monitor drought characteristics. Precipitation and Terrestrial Water Storage Anomalies (TWSA) are important variables for assessing drought triggered by water deficiency in the atmosphere and terrestrial systems. The study aims to compare and discuss the applicability of drought index based solely on TWSA (DSI) and based on the combined effects of precipitation and TWSA (CCDI) in monitoring comprehensive drought in China. The results reveal that precipitation anomalies do not align with TWSA in arid basins, while they are synchronized in humid basins. CCDI reveals more favorable spatiotemporal agreement with the traditional drought indices, whereas DSI shows more consistency with common drought indices in humid basins. CCDI and DSI are suitable for assessing comprehensive drought in arid and humid basins, respectively. Moreover, almost both DSI and CCDI successfully monitored major drought periods in humid and arid basins during 2002–2006 and 2013–2017, respectively. Overall, this study highlights the applicability of CCDI and DSI in comprehensive drought assessment in arid and humid basins, respectively, providing valuable support for optimizing water resources utilization, management, and drought resistance decision-making.

The spatial and temporal variation of the terrestrial water storage anomaly (TWSA) of Iraq for the period 2002-2019 based on GRACE gravity data

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

Benefits and Pitfalls of GRACE and Streamflow Assimilation for Improving the Streamflow Simulations of the WaterGAP Global Hydrology Model

AbstractDistribution and change of freshwater resources is often simulated with global hydrological models. However, owing to process representation limitations and forcing data uncertainties, these model simulations have shortcomings. Combining them with observations via data assimilation, for example, with data from the Gravity Recovery and Climate Experiment (GRACE) mission or streamflow measured at in situ stations is considered to improve the realism of the simulations. We assimilate gridded total water storage anomaly (TWSA) from GRACE into the WaterGAP Global Hydrology Model (WGHM) over the Mississippi River basin via an Ensemble Kalman Filter. Our results agree with previous studies where assimilating GRACE observations nudges TWSA simulations closer to the observations, reducing the root mean square error (RMSE) by 21% compared to an uncalibrated model. However, simulations of streamflow show degeneration at more than 90% of all gauge stations for metrics such as RMSE and correlations; only the annual phase of simulated streamflow improves at half the stations. Therefore, for the first time, we instead assimilated streamflow observations into the WGHM, which improved simulated streamflow at up to nearly 80% of the stations, with normalized RMSE showing improvements of up to 0.1, while TWSA was well‐simulated in all metrics. Combining both approaches, that is, jointly assimilating GRACE‐derived TWSA and streamflow observations, leads to a trade‐off between a good fit of both variables albeit skewed to the GRACE observations. Overall, we speculate that our findings point to limitations of process representation in WGHM hindering consistent flux simulation from the storage history, especially in dry regions.

How realistic are multi-decadal reconstructions of GRACE-like total water storage anomalies?

Reconstructions allow us to extend the Gravity Recovery And Climate Experiment (GRACE) data record into the past and bridge the one-year gap between GRACE and its successor, GRACE-FO (Follow on). Reconstructed total water storage anomalies (TWSA) are obtained by identifying relations between GRACE-derived TWSA and climate variables via statistical and machine learning techniques. However, a comparative analysis of the characteristics and realism of reconstructions is missing.In this contribution, we close this gap by comparing three global reconstructions by Humphrey and Gudmundsson (2019), Li et al. (2021) and Chandanpurkar et al. (2022) mutually and against output from the Water Global Analysis and Prognosis (WaterGAP) hydrological model from 1979 onwards, against large-scale mass-change derived from geodetic satellite laser ranging (SLR) from 1992 onwards, and finally against differing GRACE and GRACE-FO solutions from 2002 onwards. The reconstructions vary regarding design and trained GRACE solution.Reconstructions recover the TWSA signal for humid climate regions but disagree for arid climate regions, which is evident on the inter-annual timescales. At seasonal and sub-seasonal timescales, the reconstructions agree surprisingly well in many regions. Our comparison against independent SLR data reveals that reconstructions (only) partially succeed in representing anomalous TWSA for areas that are influenced by significant climate modes such as El Niño-Southern Oscillation (ENSO).

Deep learning-aided temporal downscaling of GRACE-derived terrestrial water storage anomalies across the Contiguous United States

The Gravity Recovery And Climate Experiment (GRACE) and GRACE-FollowOn (GRACE(−FO)) satellites have been monitoring Earth’s changes in terrestrial water storage (TWS) or surficial mass changes at monthly sampling and a spatial scale longer than ∼330 km (half wavelength) over the past two decades. At monthly sampling or revisit time, the use of satellite gravimetry is difficult to effectively monitor abrupt extreme weather events which are high-frequency, including the climate-induced hurricanes/cyclones, flash floods and droughts. The majority of the contemporary studies have focused on satellite gravimetry spatial downscaling, and not on reducing the temporal resolution of Earth’s mass change. Here, we developed a Deep Learning (DL) algorithm to downscale monthly GRACE/ GRACE(−FO) Mass Concentration (Mascon) TWS anomaly (TWSA) solutions to daily sampling over the Contiguous United States (CONUS), with the aim of monitoring rapidly evolving natural hazard episodes. The simulative performance of the DL algorithm is validated by comparing the modeling to an independent observation and the land hydrology model (LHM) predicted TWSA. To confirm that our daily and monthly simulations captured the climatic variations, we first compared our simulations with El Niño/La Niña Southern Oscillation (ENSO) circulation system index, which has a dominant climatological and socioeconomic impact across CONUS, and results reveal high correlations which are statistically significant. Next, we assessed the feasibilities to detect long- and short-term variations in the TWSA signals triggered by hydrological extremes, including the 2011 and 2019 Missouri River Floods, the August 2017 Atlantic Hurricane Harvey landfalls in Texas, the 2012–2017 drought in California, and the flash drought in the Northern Great Plains in 2017. Additional validation results using independent in situ observations reveal that our DL-aided gravimetry downscaled daily simulations are capable of elucidating hazards and water cycle evolutions at high temporal resolution.

Using hydrological modeling and satellite observations to elucidate subsurface and surface hydrological responses to the extreme drought

Climate change and anthropogenic activities have intensified extreme weather events globally. In the summer of 2022, the Yangtze River Basin (YRB) in China experienced an extreme drought, significantly impacting the ecosystems and society. However, the specific effects of this extreme drought on surface and subsurface hydrological dynamics remain unclear. Here we employed satellite-observed terrestrial water storage anomaly (TWSA) and a modified hydrological model with consideration of reservoir operation, human water consumption, and water diversion engineering to quantify how subsurface and surface water in YRB responded to such an extreme drought in 2022. Validation against a series of observations shows that the modified model has good performance in reproducing daily streamflow, reservoir water storage, lake water storage, and snow water equivalent. It achieved more precise GRACE TWSA estimates in the YRB with significant human intervention, and therefore it can fairly accurately quantify both surface and subsurface hydrological responses to the 2022 extreme drought. Compared to the same months (July-December) in 2015–2021, the drought in 2022 resulted in a decrease in precipitation and discharge of 373 km3 (36 %) and 324 km3 (50 %), respectively, while an increase in evapotranspiration of 156 km3 (29 %) in the YRB. In general, the surface water storage (SWS) is relatively low from July 2022, followed by subsurface water storage (SSWS) from August 2022, indicating an approximately one-month lag from the former to the latter. During the latter half year of 2022, the SWS and SSWS reduced by 48 km3 and 83 km3, respectively, suggesting the changes in the latter dominated the TWS variations. This study sheds light on the responses of surface and subsurface hydrology to extreme droughts, and the hydrological modeling framework with consideration of human activities proposed here holds applicability beyond the YRB.

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.

Spatial and temporal analysis of daily terrestrial water storage anomalies in China

Spatial and temporal analysis of daily terrestrial water storage anomalies in China

Deciphering the Role of Total Water Storage Anomalies in Mediating Regional Flooding

AbstractRegional floods result from various flood generation mechanisms. Traditional analyses mainly link flooding to extreme rainfall, with limited input from soil moisture. Total water storage (TWS) is a holistic measure of basin wetness, including additional storage components from surface water, snow, and groundwater. Utilizing a new 5‐day Gravity Recovery and Climate Experiment and its Follow On (GRACE(‐FO)) data set, we investigated the linkage between short‐term TWS anomaly (TWSA) and regional flooding. The 5‐day TWSA solutions revealed flood signals missed by monthly TWSA solutions. Global basins exhibit distinct storage‐discharge co‐evolution patterns, offering new insights into flood mechanisms and propensity. Our bivariate event analyses show the annual maximum river discharges co‐occur more often with the TWSA maxima than with precipitation in many basins. Further analyses revealed TWSA's time‐lagged effect on river discharge, particularly in basins susceptible to floods triggered by saturation‐excess runoff. The 5‐day TWSA provides a new source of information for enhancing global flood preparedness.

A Probabilistic Approach to Characterizing Drought Using Satellite Gravimetry

AbstractIn the recent past, the Gravity Recovery and Climate Experiment (GRACE) satellite mission and its successor GRACE Follow‐On (GRACE‐FO), have become invaluable tools for characterizing drought through measurements of Total Water Storage Anomaly (TWSA). However, the existing approaches have often overlooked the uncertainties in TWSA that stem from GRACE orbit configuration, background models, and intrinsic data errors. Here we introduce a fresh view on this problem which incorporates the uncertainties in the data: the Probabilistic Storage‐based Drought Index (PSDI). Our method leverages Monte Carlo simulations to yield realistic realizations for the stochastic process of the TWSA time series. These realizations depict a range of plausible drought scenarios that later on are used to characterize drought. This approach provides probability for each drought category instead of selecting a single final category at each epoch. We have compared PSDI with the deterministic approach (Storage‐based Drought Index, SDI) over major global basins. Our results show that the deterministic approach often leans toward an overestimation of storage‐based drought severity. Furthermore, we scrutinize the performance of PSDI across diverse hydrologic events, spanning continents from the United States to Europe, the Middle East, Southern Africa, South America, and Australia. In each case, PSDI emerges as a reliable indicator for characterizing drought conditions, providing a more comprehensive perspective than conventional deterministic indices. In contrast to the common deterministic view, our probabilistic approach provides a more realistic characterization of the TWS drought, making it more suited for adaptive strategies and realistic risk management.

Assessing terrestrial water storage variations in Afghanistan using GRACE and FLDAS-Central Asia data

Study regionAfghanistan, Central Asia. Study focusIn this study, we evaluated the terrestrial water storage dynamics in Afghanistan and its five major river basins using the terrestrial water storage anomalies (TWSA) from three Gravity Recovery and Climate Experiment (GRACE) mascons observations from JPL, CSR, and GSFC processing centers, and the Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System – Central Asia (FLDAS-CA) simulation. New hydrological insights for the regionsSince 2008, due to intense and prolonged drought conditions and groundwater overexploitation, TWS in Afghanistan has been decreasing at an alarming rate. The average slopes of the TWSA trend in Afghanistan for the GRACE period (2003–2016) of the three products range between − 3.6 to − 4.8 mm/year. The decrease in TWS is further exacerbated during the GRACE-FO period (2019–2022), ranging between − 20.4 to − 30 mm/year. Because groundwater is heavily relied on in the country but human-induced change (i.e., groundwater extraction) is not simulated in FLDAS-CA, a significant difference could be observed between GRACE observations and FLDAS-CA results, especially following after each severe drought event (e.g., 2008, 2018) when substantial groundwater extraction occurred. The assimilation of GRACE observations into the FLDAS-CA framework will undoubtedly have a positive impact for decision-makers and local stakeholders in preparing and mitigating the impacts of drought and groundwater overexploitation in Afghanistan and Central Asia.

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.

Impacts of Climatic Fluctuations and Vegetation Greening on Regional Hydrological Processes: A Case Study in the Xiaoxinganling Mountains–Sanjiang Plain Region, Northeastern China

The Xiaoxinganling Mountains–Sanjiang Plain region represents a crucial ecological security barrier for the Northeast China Plain and serves as a vital region for national grain production. Over the past two decades, the region has undergone numerous ecological restoration projects. Nevertheless, the combined impact of enhanced vegetation greening and global climate change on the regional hydrological cycle remains inadequately understood. This study employed the distributed hydrological model ESSI-3, reanalysis datasets, and multi-source satellite remote sensing data to quantitatively evaluate the influences of climate change and vegetation dynamics on regional hydrological processes. The study period spans from 2000 to 2020, during which there were significant increases in regional precipitation and leaf area index (p < 0.05). The hydrological simulation results exhibited strong agreement with observed river discharge, evapotranspiration, and terrestrial water storage anomalies, thereby affirming the ESSI-3 model’s reliability in hydrological change assessment. By employing both a constant scenario that solely considered climate change and a dynamic scenario that integrated vegetation dynamics, the findings reveal that: (1) Regionally, climate change driven by increased precipitation significantly augmented runoff fluxes (0.4 mm/year) and water storage components (2.57 mm/year), while evapotranspiration trends downward, attributed primarily to reductions in solar radiation and wind speed; (2) Vegetation greening reversed the decreasing trend in evapotranspiration to an increasing trend, thus exerting a negative impact on runoff and water storage. However, long-term simulations demonstrated that regional runoff fluxes (0.38 mm/year) and water storage components (2.21 mm/year) continue to increase, mainly due to precipitation increments surpassing those of evapotranspiration; (3) Spatially, vegetation greening altered the surface soil moisture content trend in the eastern forested areas from an increase to a decrease. These findings suggested that sub-regional ecological restoration initiatives, such as afforestation, significantly influence the hydrological cycle, especially in areas with higher vegetation greening. Nevertheless, persistent increases in precipitation could effectively mitigate the moisture deficits induced by vegetation greening. The study’s outcomes provide a basis for alleviating concerns regarding potential water consumption risks associated with future ecological restoration and extensive vegetation greening projects, thereby offering scientific guidance for sustainable water resource management.

Spatial downscaling of GRACE-derived groundwater storage changes across diverse climates and human interventions with Random Forests

The Gravity Recovery and Climate Experiment (GRACE) satellites launched in 2002, which were followed by the GRACE Follow-On mission in 2018, represented a significant advancement in global groundwater storage monitoring. However, the inherent limitation of a relatively low spatial resolution confines its applicability primarily to large river basins and aquifers. This study introduces a novel statistical downscaling approach using Random Forests (RF) to enhance the spatial resolution from 3° × 3° to 0.25° × 0.25° for GRACE-derived groundwater storage (GWS) changes. Focusing on sub-Saharan Africa, the Middle East, and South Asia across diverse climates and human interventions, the analysis spans from February 2003 to December 2022. Predictors encompass hydrological fluxes and their accumulation, including IMERG precipitation, GLEAM evapotranspiration, and GLDAS runoff, alongside GLDAS soil moisture and MODIS NDVI. RF models show robust performance across 114 aquifers, with a Pearson’s correlation coefficient (CC) exceeding 0.92 for training sets and 0.85 for validation sets. Validation against in-situ groundwater level observations from wells in India reveals satisfactory performance, with positive Spearman’s CC values between in-situ groundwater levels and downscaled terrestrial water storage anomaly (TWSA) for a majority of dug and bore wells. This study concludes with the generation of monthly downscaled GWS changes (0.25° × 0.25°) spanning from February 2003 to December 2022. These datasets serve as a foundation for facilitating finer-scale hydrological studies and formulating local groundwater management strategies.