Decreasing terrestrial water storage limited the increase of woody plant structure resulting from afforestation in the Loess Plateau, China.

Changes in terrestrial water storage in the Three-North region of China over 2003–2021: Assessing the roles of climate and vegetation restoration

Monitoring spatio-temporal variations of terrestrial water storage changes and their potential influencing factors in a humid subtropical climate region of Southeast China

In recent decades, terrestrial water storage anomaly (TWSA) has experienced systematic shifts. Despite these observations, debates continue regarding the hotspots where terrestrial water storage changes dramatically and their causes. This study aims to address these controversies. Utilizing four TWSA products, this research analyzes TWSA's changing patterns and identifies hotspots of significant shifts from 1982 to 2019. The study employed the Bayesian Three‐Cornered Hat method to synthesize the best‐quality TWSA from original four TWSA products and the trends consistent method to identify regions with highly consistent trends. Subsequently, the elasticity coefficient method was used to reveal the causes of TWSA's dramatic changes in hotspots. Results show that TWSA has a declining trend over 66.1% global terrestrial areas during 1982–2019, with an average rate of −0.5 mm/y. The study identified six regions where marked changes in TWSA occurred, including Northern China, Southern Canada, Northern India, Central‐Southern Europe, Southwestern Africa, and Northeastern South America. Attribution analysis reveals that the leaf area index is the predominant factor affecting TWSA changes, dominating in 40.3% of global regions. Potential evapotranspiration (PET) follows closely, dominating in 39.8% of global regions. Meanwhile, only 13.1% and 6.8% of global regions are primarily influenced by precipitation and cropland density respectively. The dominant factor varies in different latitudes. Vegetation greening primarily controls TWSA changes in the high‐latitude regions of the Northern Hemisphere. This study identified hotspots of TWSA changes and investigated the causes of these variations. Those results will offer direction for prioritizing areas in future water resource management.

Satellite observations of Earth's gravity field from the Gravity Recovery and Climate Experiment (GRACE) mission offer a unique dataset for analysing terrestrial water storage and effectively closing the continental water balance. Comparing terrestrial water storage (TWS) anomalies from GRACE with in-situ groundwater level measurements is crucial for understanding how groundwater contributes to the overall water retention. In this study, monthly changes in TWS anomalies (ΔTWS) from GRACE are compared with changes in groundwater storage (ΔGWS) obtained from in-situ measurements. The analyses are performed for various aquifer systems and different hydrodynamic zones across Poland.The analysis of correlations between GRACE-based ΔTWS and in-situ ΔGWS indicates that, while the magnitude of ΔTWS is greater than that of in-situ ΔGWS, a strong relationship exists between these two quantities in alluvial aquifers and in systems with rapid water exchange, such as fractured and karst aquifers. In contrast, the porous reservoirs in postglacial formations with a thick vadose zone - typical for a large part of the area of Poland - exhibit a weak correlation between ΔTWS and in-situ ΔGWS. This indicates that the standard method of calculating ΔGWS as the difference between ΔTWS-GRACE and ΔTWS from the GLDAS (Global Land Data Assimilation System) model should be revised to account for the complexities of these aquifer systems.Since ΔSWS-GLDAS (Soil Water Storage) effectively captures changes in water content in the vadose zone (ΔVZ), and ΔVZ is a major component of ΔGWS in alluvial aquifers, calculating ΔGWS in these areas as a difference between ΔTWS-GRACE and ΔSWS-GLDAS might be inaccurate. Our results indicate that ΔGWS for alluvial systems should be estimated based solely on ΔTWS-GRACE data. In turn, for aquifer systems with a thick vadose zone (above approximately 2 meters), the ΔGWS should be estimated using the difference between ΔTWS-GRACE and ΔSWS-GLDAS.We also use a balance approach to determine ΔTWS, employing precipitation data (P) from the European E-OBS database, evapotranspiration (ET) from the Simplified Surface Energy Balance database, and river discharge (Q) from in-situ measurements, by calculating ΔTWS = P − ET − Q. The results demonstrate that from 2009 to 2023, there has been a general decrease in GWS in Poland, with an average rate of -1.4 mm/year. The highest downward trend is observed in the lowland area in central Poland and in the southeast part of the country, with a rate of -1.7 to -2.2 mm/year. In the coastal zone and parts of northeastern Poland, the GWS decline is smallest (from 0 to -1 mm/year). The results of this study suggest that for deeper aquifer systems, GWS accounts for about 76% of the total TWS, whereas for alluvial systems, it is nearly 100%.Satellite gravimetry complements in-situ observations and model data by independently measuring changes in GWS and offering a continuous spatial view of these variations. Combining remote sensing data with the water balance method promises high-resolution estimates of GWS changes, essential for effective groundwater resource management.Key words: GRACE, groundwater, terrestrial water storage

Accurate estimation of terrestrial water storage (TWS) and understanding its driving factors are crucial for effective hydrological assessment and water resource management. The launches of the Gravity Recovery and Climate Experiment (GRACE) satellites and their successor, GRACE Follow-On (GRACE-FO), combined with deep learning algorithms, have opened new avenues for such investigations. In this study, we employed a long short-term memory (LSTM) neural network model to simulate TWS anomaly (TWSA) in the Pearl River Basin (PRB) from 2003 to 2020, using precipitation, temperature, runoff, evapotranspiration, and leaf area index (LAI) data. The performance of the LSTM model was rigorously evaluated, achieving a high average correlation coefficient (r) of 0.967 and an average Nash–Sutcliffe efficiency (NSE) coefficient of 0.912 on the testing set. To unravel the relative importance of each driving factor and assess the impact of different lead times, we employed the SHapley Additive exPlanations (SHAP) method. Our results revealed that precipitation exerted the most significant influence on TWSA in the PRB, with a one-month lead time exhibiting the greatest impact. Evapotranspiration, runoff, temperature, and LAI also played important roles, with interactive effects among these factors. Moreover, we observed an accumulation effect of precipitation and evapotranspiration on TWSA, particularly with shorter lead times. Overall, the SHAP method provides an alternative approach for the quantitative analysis of natural driving factors at the basin scale, shedding light on the natural dominant influences on TWSA in the PRB. The combination of satellite observations and deep learning techniques holds promise for advancing our understanding of TWS dynamics and enhancing water resource management strategies.

In this article, we explore the soil water effect on vegetation growth in west-central Africa using remotely sensed near-surface soil moisture content (nSMC), terrestrial water storage anomaly (TWSA), and leaf area index (LAI). TWSA is the only data to reflect deeper soil water used by perennial plants with their deep root systems, which microwave sensors cannot observe. However, deep groundwater complicates investigating the water availability of plants using TWSA. We confirmed that the TWSA’s slope during the dry season, which might be caused by vegetation transpiration, was steeper towards equatorial regions. Our results show that the relationship between LAI and the TWSA’s inclination during the dry season was negatively correlated in areas where the LAI was below 2 and the precipitation was less than 40 mm/month. This finding might enable us to investigate the water availability of perennial plants by focusing on the TWSA’s slope during the dry season.

Detecting hotspots of interactions between vegetation greenness and terrestrial water storage using satellite observations

Indonesia has experienced frequent fires due to the lowering of groundwater levels caused by drainage via extensive canal networks for agricultural development since the 1970s. However, the impact of canals on fire emissions is still poorly understood. Here we investigate canal impacts on smoke aerosol emissions for Indonesian peatland and non-peatland fires by quantifying the resulting changes of smoke aerosol emission coefficient (Ce) that represents total aerosol emissions released from per unit of fire radiative energy. First, we quantified the impacts of canal drainage and backfilling on water table depth (WTD) variations using field data and then expanded such impacts from field to regional scales by correlating field WTD to satellite terrestrial water storage (TWS) anomalies from Gravity Recovery and Climate Experiment. Second, we estimated Ce from fire radiative power and smoke-aerosol emission rates based on Moderate Resolution Imaging Spectroradiometer active fire and multi-angle implementation of atmospheric correction aerosol products. Finally, we evaluated the Ce variation with TWS anomalies. The results indicate: (a) Ce is larger in peatland fires than in non-peatland fires; (b) Ce increases significantly as TWS anomalies decrease for both peatland and non-peatland fires; and (c) Ce changes at nearly twice the rate in peatland for a given TWS anomaly range as in non-peatland. These phenomena likely result from the different fuel types and combustion phases prevalent under different moisture conditions. These findings support the Indonesian government’s recent peatland restoration policies and pave the way for improved estimation of tropical biomass burning emissions.

Understanding terrestrial water storage (TWS) dynamics and associated drivers (e.g., climate variability, vegetation change, and human activities) across climate zones is essential for designing water resources management strategies in a changing environment. This study estimated TWS anomalies (TWSAs) based on the corrected Gravity Recovery and Climate Experiment (GRACE) gravity satellite data and derived driving factors for 214 watersheds across six climate zones in China. We evaluated the long-term trends and stationarities of TWSAs from 2004 to 2014 using the Mann–Kendall trend test and Augmented Dickey-Fuller stationarity test, respectively, and identified the key driving factors for TWSAs using the partial correlation analysis. The results indicated that increased TWSAs were observed in watersheds in tropical and subtropical climate zones, while decreased TWSAs were found in alpine and warm temperate watersheds. For tropical watersheds, increases in TWS were caused by increasing water conservation capacity as a result of large-scale plantations and the implementation of natural forest protection programs. For subtropical watersheds, TWS increments were driven by increasing precipitation and forestation. The decreasing tendency in TWS in warm temperate watersheds was related to intensive human activities. In the cold temperate zone, increased precipitation and soil moisture resulting from accelerated and advanced melting of frozen soils outweigh the above-ground evapotranspiration losses, which consequently led to the upward tendency in TWS in some watersheds (e.g., Xiaoxing’anling mountains). In the alpine climate zone, significant declines in TWS were caused by declined precipitation and soil moisture and increased evapotranspiration and glacier retreats due to global warming, as well as increased agriculture activities. These findings can provide critical scientific evidence and guidance for policymakers to design adaptive strategies and plans for watershed-scale water resources and forest management in different climate zones.

Estimating Terrestrial Water Storage (TWS) not only helps to provide a comprehensive insight into water resource variability and the hydrological cycle but also for better water resource management. In the current research, Gravity Recovery And Climate Experiment (GRACE) data are combined with the available hydrological data to reconstruct a longer record of Terrestrial Water Storage Anomalies (TWSA) prior to 2003 of the Tarim River Basin (TRB), based on a Long Short-Term Memory (LSTM) model. We found that the TWSA generated by LSTM using soil moisture, evapotranspiration, precipitation, and temperature best matches the GRACE-derived TWSA, with a high correlation coefficient (r) of 0.922 and a Normalized Root Mean Square Error (NRMSE) of 0.107 during the period 2003–2012. These results show that the LSTM model is an available and feasible method to generate TWSA. Further, the TWSA reveals a significant fluctuating downward trend (p < 0.001), with an average decline rate of 0.03 mm/month during the period 1982–2016 in the TRB. Moreover, the TWSA amount in the north of the TRB was less than that in the south of the basin. Overall, our findings unveiled that the LSTM model and GRACE data can be combined effectively to analyze the long-term TWSA in large-scale basins with limited hydrological data.

Whether or not large-scale vegetation restoration will lead to a decrease in regional terrestrial water storage is a controversial topic. This study employed the Geodetector model, in conjunction with observed and satellite hydro-meteorological data, to detect the changes in terrestrial water storage anomaly (TWSA) and to identify the contributions of climate change and vegetation greening across China during the years 1982–2019. The results revealed that: (1) during the period of 1982–2019, TWSA showed a downward trend in about two thirds of the country, with significant declines in North China, southeast Tibet, and northwest Xinjiang, and an upward trend in the remaining third of the country, with significant increases mainly in the Qaidam Basin, the Yangtze River, and the Songhua River; (2) the positive correlation between normalized vegetation index (NDVI) and TWSA accounts for 48.64% of the total vegetation area across China. In addition, the response of vegetation greenness lags behind the TWSA and precipitation, and the lag time was shorter in arid and semi-arid regions dominated by grasslands, and longer in relatively humid regions dominated by forests and savannas; (3) furthermore, TWSAs decreased with the increase in NDVI and evapotranspiration (ET) in arid and semi-arid areas, and increased with the rise in NDVI and ET in the humid regions. The Geodetector model was used to detect the effects of climate, vegetation, and human factors on TWSA. It is worth mentioning that NDVI, precipitation, and ET were some of the main factors affecting TWSA. Therefore, it is essential to implement rational ecological engineering to mitigate climate change’s negative effects and maintain water resources’ sustainability in arid and semi-arid regions.

Abstract. Understanding long-term terrestrial water storage (TWS) variations is vital for investigating hydrological extreme events, managing water resources and assessing climate change impacts. However, the limited data duration from the Gravity Recovery and Climate Experiment (GRACE) and its follow-on mission (GRACE-FO) poses challenges for comprehensive long-term analysis. In this study, we reconstruct TWS anomalies (TWSAs) for the period from January 1960 to December 2022, thereby filling data gaps between the GRACE and GRACE-FO missions and generating a complete dataset for the pre-GRACE era. The workflow involves identifying optimal predictors from land surface model (LSM) outputs, meteorological variables and climatic indices using a novel Bayesian network (BN) technique for raster-based TWSA simulations. Climate indices, like the Oceanic Niño Index and Dipole Mode Index, are selected as optimal predictors for a large number of grid cells globally, along with TWSAs from LSM outputs. The most effective machine learning (ML) algorithms among convolutional neural network (CNN), support vector regression (SVR), extra trees regressor (ETR) and stacking ensemble regression (SER) models are evaluated at each grid cell to achieve optimal reproducibility. Globally, ETR performs best for most of the grid cells; this is also noticed at the river basin scale, particularly for the Ganga–Brahmaputra–Meghna, Godavari, Krishna, Limpopo and Nile river basins. The simulated TWSAs (BNML_TWSA) outperformed the TWSAs from LSM outputs when evaluated against GRACE datasets. Improvements are particularly noted in river basins such as the Godavari, Krishna, Danube and Amazon, with median correlation coefficient, Nash–Sutcliffe efficiency, and RMSE values for all grid cells in the Godavari Basin, India, being 0.927, 0.839 and 63.7 mm, respectively. A comparison with TWSAs reconstructed in recent studies indicates that the proposed BNML_TWSA outperforms them globally as well as for all of the 11 major river basins examined. Furthermore, the uncertainty of BNML_TWSA is assessed for each grid cell in terms of the standard error. Results show smaller standard error magnitudes in grid cells in arid regions compared to other regions. The presented gridded dataset is published at https://doi.org/10.6084/m9.figshare.25376695 (Mandal et al., 2024), featuring a spatial resolution of 0.50° × 0.50° and offering global coverage.

Monitoring the spatiotemporal terrestrial water storage changes in the Yarlung Zangbo River Basin by applying the P-LSA and EOF methods to GRACE data

The interplay between terrestrial water storage and vegetation dynamics in arid regions is critical for understanding ecohydrological responses to climate change and human activities. This study examines the coupling between total water storage anomaly (TWSA) and vegetation greenness changes in the Hexi Corridor, an arid region in northwestern China consisting of three inland river basins—Shule, Heihe, and Shiyang—from 2002 to 2022. Utilizing TWSA data from GRACE/GRACE-FO satellites and MODIS Enhanced Vegetation Index (EVI) data, we applied a trend analysis and partial correlation statistical techniques to assess spatiotemporal patterns and their drivers across varying aridity gradients and land cover types. The results reveal a significant decline in TWSA across the Hexi Corridor (−0.10 cm/year, p < 0.01), despite a modest increase in precipitation (1.69 mm/year, p = 0.114). The spatial analysis shows that TWSA deficits are most pronounced in the northern Shiyang Basin (−600 to −300 cm cumulative TWSA), while the southern Qilian Mountain regions exhibit accumulation (0 to 800 cm). Vegetation greening is strongest in irrigated croplands, particularly in arid and hyper-arid regions of the study area. The partial correlation analysis highlights distinct drivers: in the wetter semi-humid and semi-arid regions, precipitation plays a dominant role in driving TWSA trends. Such a rainfall dominance gives way to temperature- and human-dominated vegetation greening in the arid and hyper-arid regions. The decoupling of TWSA and precipitation highlights the importance of human irrigation activities and the warming-induced atmospheric water demand in co-driving the TWSA dynamics in arid regions. These findings suggest that while irrigation expansion cause satellite-observed greening, it exacerbates water stress through increased evapotranspiration and groundwater depletion, particularly in most water-limited arid zones. This study reveals the complex ecohydrological dynamics in drylands, emphasizing the need for a holistic view of dryland greening in the context of global warming, the escalating human demand of freshwater resources, and the efforts in achieving sustainable development.

Extending GRACE terrestrial water storage anomalies by combining the random forest regression and a spatially moving window structure