Terrestrial Water Storage Anomalies Research Articles

Spatiotemporal Analysis of Future Trends in Terrestrial Water Storage Anomalies at Different Climatic Zones of India Using GRACE/GRACE-FO

This work is a climatological evaluation of terrestrial water storage anomalies (TWSAs), which act as driving forces for sustainable development, in one of the most populous countries of the world. The objective of this work is to evaluate RL06 mascon data from the GRACE and GRACE-FO satellite missions over India to explore seasonal and interannual changes in terrestrial water storage, encompassing an area of ~3.29 million km2 with 285 grid points, from 2002 through to 2020. Several statistical tests are performed to check the homogeneity (i.e., Pettitt’s test, the BRT, the SNHT, and the VNRT). Most of the homogeneous data are found in winter, pre-monsoon, and post-monsoon, approximately above 42% to 47%, and the least are found in monsoons and annual with only 33%, at a 95% significance level. According to Pettitt’s test, the majority of the breakpoints are present in 2014 for winter, 2012 for pre-monsoon, 2011 for monsoons and post-monsoon, and 2008 as well as 2011 for annual. Furthermore, to detect trends and magnitudes we employed the nonparametric MK test, the MMK test, Sen’s slope estimator, and the parametric SLR test. According to the MK and MMK tests, the most significant negative and positive trends indicate the chances of droughts and floods, respectively. The Indo–Gangetic region shows the highest declination. According to Sen’s slope and the SLR test, the most declining magnitude is found in Delhi, Panjab, Uttrakhand, the northern part of Rajasthan, and Uttar Pradesh. Based on our findings, the average declining rate of yearly terrestrial water storage data from the MK, MMK, and SLR tests is −0.0075 m (−0.75 cm/year) from 2002 to 2020. Koppen–Geiger climate zones are also used to depict the seasonal and interannual descriptive statistics of TWSA trends. Interestingly, the annual means of arid desert cold (−0.1788 cm/year) and tropical savanna (−0.1936 cm/year) have the smallest declining trends when compared to other climatic zones. Northern Indian regions’ temperate dry winter, hot/warm summer, and dry arid steppe hot regions show the maximum declining future trend. This study could be useful in planning and managing water resources, agriculture, and the long-term growth of the country by using an intelligent water delivery system.

Drought Propagation in Brazilian Biomes Revealed by Remote Sensing

Drought events have been reported in all Brazilian regions every year, evolving slowly over time and large areas, and largely impacting agriculture, hydropower production, and water supplies. In the last two decades, major drought events have occurred over the country, such as the 2010 and 2015 events in the Amazon, the 2012 event in the Pampa, and the 2014 event in the Cerrado biome. This research aimed to understand drought propagation and patterns over these biomes through joint analysis of hydrological, climatic, and vegetation indices based on remote sensing data. To understand the drought cascade propagation patterns, we assessed precipitation, evapotranspiration, soil moisture (at surface and sub-surface), terrestrial water storage, land surface temperature, enhanced vegetation index, and gross primary productivity. Similar drought patterns were observed in the 2015 Amazon and 2012 Pampa droughts, with meteorological and agricultural droughts followed by a hydrological drought, while the 2014 event in the Cerrado was more associated with a hydrological drought. Moreover, the 2015 Amazon drought showed a different pattern than that of 2010, with higher anomalies in precipitation and lower anomalies in evapotranspiration. Thus, drought propagation behaves differently in distinct Brazilian biomes. Our results highlight that terrestrial water storage anomalies were able to represent the hydrological drought patterns over the country. Our findings reveal important aspects of drought propagation using remote sensing in a heterogenous country largely affected by such events.

Spatiotemporal correlation and multivariate analysis between vegetation health, terrestrial water storage and precipitation

Vegetation health is an essential indicator in the global hydrologic cycle as it is interrelated with the hydrological components. In tropical areas where vegetation dominates, analysing their correlation at a regional scale helps forecast the hydrologic cycle and understand vegetation’s response to climate change. However, the interactions between vegetation, terrestrial water storage and climate factors such as precipitation remain poorly understood in this region. Therefore, using Landsat and Gravity Recovery and Climate Experiment (GRACE) remote sensing and observed precipitation data, this study analysed the spatiotemporal correlation of Normalized Difference Vegetation Index (NDVI), Terrestrial Water Storage Anomaly (TWSA) and precipitation for the whole Peninsular Malaysia. The correlation coefficient (R) was used to assess the temporal variability of NDVI with TWSA and precipitation separately. Furthermore, a Geographically Weighted Regression (GWR) model was constructed to evaluate the spatial non-stationarity and heterogeneity relationships between the multi variables. The findings revealed complex interactions between the variables, where the strength of the correlations varied depending on the localised region and study period. The results suggest that downscaled GRACE-derived TWSA data would be helpful for detailed vegetation modelling and water resources management.

Filling the gap between GRACE and GRACE-FO data using a model integrating variational mode decomposition and long short-term memory: a case study of Northwest China

Terrestrial water storage anomalies (TWSA) play an important role in the regional and global hydrological cycle. The Gravity Recovery and Climate Experiment (GRACE) (from April 2002 to June 2017) and GRACE-Follow on (GRACE-FO) (from June 2018 to now) provide monthly global TWSA on a large scale, which contributes to the development of hydrology, but there is an 11-month data gap (i.e., from July 2017 to May 2018) between them due to the launch period. Therefore, based on the Center for Space Research (CSR) spherical harmonics (SH) and Mascons solution from April 2002 to June 2017, this study develops a method based on the Variational Mode Decomposition (VMD) with the Long Short-Term Memory (LSTM) model to fill the data gap of GRACE dataset and then tests over Northwest China (NWC). The result shows that integrated VMD–LSTM model achieve significantly higher prediction accuracy as a whole over NWC (NSE = 0.974, CC = 0.989, and RMSE = 0.808 cm) and exhibit higher reliability compared to the single LSTM models. The proposed method outperforms compared to previous similar studies. The current study offers an accurate and effective means of bridging the gap in GRACE-based TWSA data, especially in poor dataset regions like NWC, which may contribute to further understanding of the hydrological cycle and water resources management.

Monitoring the Catastrophic Flood With GRACE-FO and Near-Real-Time Precipitation Data in Northern Henan Province of China in July 2021

Zhengzhou and its surrounding areas, located in northern Henan Province, China, receive continuous extreme rainfall from July 17 to July 22, 2021. Northern Henan Province experiences extensive flash floods and urban floods, causing severe casualties and property damage. Understanding the variation of hydrologic features during this flood event could be valuable for future flood emergency response work and flood risk management. This study first demonstrates the rainstorm process based on near-real-time precipitation data from the China Meteorological Administration Land Data Assimilation System (CLDAS-V2.0). To meet the temporal resolution required for monitoring this short-term flood event, reconstructed daily terrestrial water storage anomalies (TWSAs) based on GRACE and GRACE-FO data and CLDAS-V2.0 datasets are first introduced. The spatial and temporal evolution of the reconstructed daily TWSA is analyzed in the study area during this heavy rainfall event. We further employ a wetness index based on the reconstructed daily TWSA for flood warnings. Furthermore, the modeled soil moisture data and daily runoff data are used for flood monitoring. Results show that the reconstructed daily TWSA increases by 437.7 mm in just six days (from July 17 to July 22, 2021), with a terrestrial water storage increment of 9.4 km <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> . Compared with ITSG-Grace2018, the reconstructed daily TWSA has better potential for near-real-time flood monitoring for short-term events in a small region. The wetness index derived from reconstructed daily TWSA is potential for flood early warning.

Global evaluation of the “dry gets drier, and wet gets wetter” paradigm from a terrestrial water storage change perspective

Abstract. The “dry gets drier, and wet gets wetter” (DDWW) paradigm has been widely used to summarize the expected trends of the global hydrologic cycle under climate change. However, the paradigm is largely conditioned by choice of different metrics and datasets used and is still comprehensively unexplored from the perspective of terrestrial water storage anomalies (TWSAs). Considering the essential role of TWSAs in wetting and drying of the land system, here we built upon a large ensemble of TWSA datasets, including satellite-based products, global hydrological models, land surface models, and global climate models to evaluate the DDWW hypothesis during the historical (1985–2014) and future (2071–2100) periods under various scenarios with a 0.05 significance level (for trend estimates). We find that 11.01 %–40.84 % (range by various datasets) of global land confirms the DDWW paradigm, while 10.21 %–35.43 % of the area shows the opposite pattern during the historical period. In the future, the DDWW paradigm is still challenged, with the percentage supporting the pattern lower than 18 % and both the DDWW-validated and DDWW-opposed proportion increasing along with the intensification of emission scenarios. We show that the different choices of data sources can reasonably influence the test results up to a 4-fold difference. Our findings will provide insights and implications for global wetting and drying trends from the perspective of TWSA under climate change.

Bridging the Data Gap between the GRACE Missions and Assessment of Groundwater Storage Variations for Telangana State, India

Because of changing climatic conditions, uneven distribution of rainfall occurs throughout India. As a result, dependence on groundwater for irrigation has increased tremendously for industrial and domestic purposes. In India approximately 89% of agricultural demands are met through groundwater. Due to increases in population, demand for groundwater and lack of effective utilization have resulted in rapid depletion of groundwater in most parts of the country. Therefore, quantifying groundwater resources is a serious concern in populated states of India, because it is now difficult to supply enough water to every citizen, and will remain so in the future. Because of difficulties in accessing observation data, researchers have begun to depend on satellite-based remote sensing information to deal with groundwater variations. The present study deals with filling the data gap between Gravity Recovery And Climate Experiment (GRACE) and GRACE Follow On (GRACE FO) missions using multilayer perceptron’s (MLPs) during 2017–2018 to obtain a continuous terrestrial water storage anomaly (TWSA) series from 2003 to 2020 for Telangana state, India. The MLP model performed well in predicting the TWSA, with a correlation coefficient of r = 0.96 between modeled TWSA and GRACE TWSA during the test period. Telangana state observed negative TWSAs (annual) in the years 2003, 2004, 2005, 2009, 2012, 2015, and 2016–19. This TWSA series (2003–2020) was then used to evaluate regional groundwater storage anomalies (GWSAs) in Telangana state, which is considered to be one of the water stress regions in India. The TWSAs were converted to GWSAs using Global Land Data Assimilation System (GLDAS) parameters. The Telangana state experienced decreasing GWSA in the years 2005, 2009, and 2012, and from 2015 to 2019, leading to severe droughts. Groundwater well measurements were obtained from the Central Groundwater Board (CGWB) and converted to GWSA at a seasonal scale. The GWSAs obtained from GRACE (GWSAGRACE) were converted to seasonal values and compared with GWSAs obtained from observation well data (GWSAobs). The performance metrics of r = 0.74, RMSE = 5.3, and NSE = 0.62 were obtained between (GWSAGRACE) and (GWSAobs), representing a good correlation among them. Over the past decade, Telangana state has significantly relied on groundwater resources for irrigation, domestic, and industrial purposes. As a result, evaluating groundwater storage variations at a regional scale may help policy makers and water resource researchers in the sustainable utilization and management of groundwater resources.

Drought monitoring by downscaling GRACE-derived terrestrial water storage anomalies: A deep learning approach

The current study proposes a new method to downscale the monthly GRACE-derived Terrestrial Water Storage Anomaly (TWSA) to 10 km spatial resolution over Iran using deep learning methods. First, the growing neural gas (GNG) method is utilized to cluster the TWSA data to find similar patterns. The Silhouette Chou Davies (SCD) validity measure, a combination of three robust cluster validity measures, is used to evaluate the performance of the GNG method. Then, the feasibility of deep learning Convolutional Long Short-Term Memory (ConvLSTM), shallow learning Feed Forward Neural Networks (FFNN), and Random Forest (RF) are examined in downscaling GRACE-derived TWSA using only remote sensing images. The results indicate that the deep learning method outperforms the RF and FFNN models by 7 % and 18 %, respectively. Then, the Ground Water Storage (GWS) data are isolated from TWSA, showing an agreement between the GRACE-derived GWS and ground-based measurements with R2 = 0.76. Additionally, the downscaled TWSA is used to generate annual drought frequency and change detection maps for the GWS from 2002 to 2016. Annual standardized precipitation index (SPI) drought frequency maps are also produced to gain deeper insight into the water resource scarcity of Iran. The results indicate that Iran has experienced water resource overexploitation since 2011 for agricultural activities, while meteorological drought is a trigger and intensifier for the water crisis in Iran. According to the results, Iran experienced exceptional drought in some regions, and the GWS has decreased all over the country. In addition, further analysis of Iran’s water resources is provided until 2022. The current study calls for integrated sustainable water resources management in Iran; otherwise, irreversible water and environmental problems with higher frequencies will be expected in the near future.

A New Spatiotemporal Estimator to Downscale GRACE Gravity Models for Terrestrial and Groundwater Storage Variations Estimation

This study proposes a new mathematical approach to downscale monthly terrestrial water storage anomalies (TWSA) from the Gravity Recovery and Climate Experiment (GRACE) and estimates groundwater storage anomalies (GWSA) at a daily temporal resolution and a spatial resolution of 0.25° × 0.25°, simultaneously. The method combines monthly 3° GRACE gravity models and daily 0.25° hydrological model outputs and their uncertainties in the spectral domain by minimizing the mean-square error (MSE) of their estimator to enhance the quality of both low and high frequency signals in the estimated TWSA and GWSA. The Global Land Data Assimilation System (GLDAS) was the hydrological model considered in this study. The estimator was tested over Alberta, Saskatchewan, and Manitoba (Canada), especially over the Province of Alberta, using data from 65 in-situ piezometric wells for 2003. Daily minimum and maximum GWS varied from 14 mm to 32 mm across the study area. A comparison of the estimated GWSA with the corresponding in-situ wells showed significant and consistent correlations in most cases, with r = 0.43–0.92 (mean r = 0.73). Correlations were &gt;0.70 for approximately 70% of the wells, with root mean square errors &lt;24 mm. These results provide evidence for using the proposed spectral combination estimator in downscaling GRACE data on a daily basis at a spatial scale of 0.25° × 0.25°.

Monitoring the extreme flood events in the Yangtze River basin based on GRACE and GRACE-FO satellite data

Abstract. Gravity Recovery and Climate Experiment (GRACE) and its successor GRACE Follow-on (GRACE-FO) satellite provide terrestrial water storage anomaly (TWSA) estimates globally that can be used to monitor flood in various regions at monthly intervals. However, the coarse temporal resolution of GRACE and GRACE-FO satellite data has been limiting their applications at finer temporal scales. In this study, TWSA estimates have been reconstructed and then temporally downscaled into daily values based on three different learning-based models, namely a multi-layer perceptron (MLP) model, a long-short term memory (LSTM) model and a multiple linear regression (MLR) model. Furthermore, a new index incorporating temporally downscaled TWSA estimates combined with daily average precipitation anomalies is proposed to monitor the severe flood events at sub-monthly timescales for the Yangtze River basin (YRB), China. The results indicated that (1) the MLP model shows the best performance in reconstructing the monthly TWSA with root mean square error (RMSE) = 10.9 mm per month and Nash–Sutcliffe efficiency (NSE) = 0.89 during the validation period; (2) the MLP model can be useful in temporally downscaling monthly TWSA estimates into daily values; (3) the proposed normalized daily flood potential index (NDFPI) facilitates robust and reliable characterization of severe flood events at sub-monthly timescales; (4) the flood events can be monitored by the proposed NDFPI earlier than traditional streamflow observations with respect to the YRB and its individual subbasins. All these findings can provide new opportunities for applying GRACE and GRACE-FO satellite data to investigations of sub-monthly signals and have important implications for flood hazard prevention and mitigation in the study region.

Modified flood potential index (MFPI) for flood monitoring in terrestrial water storage depletion basin using GRACE estimates

The Gravity Recovery and Climate Experiment (GRACE) and Follow-On (GRACE-FO) missions offer great potential for large-scale flood event monitoring. However, the effectiveness of current GRACE-based indices may be limited in areas with significant Terrestrial Water Storage (TWS) decline. In this study, a Modified Flood Potential Index (MFPI) was proposed to improve the monitoring capability for flood events in the Yarlung Tsangpo-Brahmaputra River Basin (YBRB). First, the GRACE/FO Terrestrial Water Storage Anomaly (TWSA) data gap was filled with a Multilayer Perceptron Artificial Neural Network Model (MLP-ANN). Based on the gap-filled data, the performance of the MFPI was compared with five selected indices: Flood Potential Index (FPI), Total Storage Deficit Index (TSDI), Modified Total Storage Deficit Index (MTSDI), Water Storage Deficit Index (WSDI), and Combined Climatologic Deviation Index (CCDI). Finally, the spatiotemporal monitoring ability of the MFPI was evaluated for typical and atypical flood events. We found that (1) MLP-ANN was able to predict the GRACE/FO data gap with a performance of ‘Very Good’; (2) the MFPI outperformed the FPI, TSDI, MTSDI, WSDI, and CCDI in capturing flood events, and could also reflect their spatiotemporal processes; and (3) the MFPI avoids long-term TWSA trend effects on flood event monitoring and is available for areas with significant TWSA trends. This study can provide a reference for using GRACE data to monitor large-scale flood events.

Inverted Algorithm of Groundwater Storage Anomalies by Combining the GNSS, GRACE/GRACE-FO, and GLDAS: A Case Study in the North China Plain

As the largest groundwater drainage region in China, the per capita water resources in the North China Plain (NCP) account for only one-seventh of the country’s available water resources. Currently, the NCP is experiencing a serious water shortage due to the overexploitation of groundwater resources and a subsequent series of natural disasters. Thus, accurate regional assessments and effective water resource management policies are of critical importance. To accomplish this phenomenon, the daily terrestrial water storage anomaly (TWSA) over the NCP is calculated from the combination of the GNSS vertical deformation sequences (seasonal items) and GRACE (trend items). The groundwater storage anomaly (GWSA) in the NCP is obtained by subtracting the canopy water, soil water, and snow water equivalent components from the TWSA. The inversion results of this study are verified by comparisons with the Global Land Data Assimilation System (GLDAS) data products. The elevated annual amplitude areas are located in Beijing and Tianjin, and the Pearson correlation coefficient (PCC), root mean square error (RMSE), and Nash–Sutcliffe efficiency (NSE) between the two GWSA results are 0.67, 4.01 cm, and 0.61, respectively. This indicates that the methods proposed in this study are reliable. Finally, the groundwater drought index was calculated for the period from 2011 to 2021, and the results showed that 2019 was the driest year, with a drought severity index value of −0.12, indicative of slightly moderate drought conditions. By calculating and analyzing the annual GWSA, this work shows that the South–North Water Transfer Project does provide some regional drought mitigation.

Global Terrestrial Water Storage Reconstruction Using Cyclostationary Empirical Orthogonal Functions (1979–2020)

Terrestrial water storage (TWS) anomalies derived from the Gravity Recovery and Climate Experiment (GRACE) mission have been useful for several earth science applications, ranging from global earth system science studies to regional water management. However, the relatively short record of GRACE has limited its use in understanding the climate-driven interannual-to-decadal variability in TWS. Targeting these timescales, we used the novel method of cyclostationary empirical orthogonal functions (CSEOFs) and the common modes of variability of TWS with precipitation and temperature to reconstruct the TWS record of 1979–2020. Using the same common modes of variability, we also provide a realistic, time-varying uncertainty estimate of the reconstructed TWS. The interannual variability in the resulting TWS record is consistent in space and time, and links the global variations in TWS to the regional ones. In particular, we highlight improvements in the representation of ENSO variability when compared to other available TWS reconstructions.

New spectro-spatial downscaling approach for terrestrial and groundwater storage variations estimated by GRACE models

The study proposes a new mathematical method, referred to as spectral combination, to downscale Gravity Recovery And Climate Experiment (GRACE) observations. The goal is to improve the spatial resolution of GRACE from 1̊ to 0.25̊, based upon available hydrological variables. First, a new approach based upon condition adjustment is proposed to estimate uncertainties related to hydrological variables. Second, a spectral-spatial estimator is developed to derive downscaled Total Water Storage Anomalies (TWSA) by optimally combining GRACE models and hydrological variables. Last, groundwater storage anomalies (GWSA) are derived from the downscaled TWSA. The proposed spectral combination approach was tested over the Canadian Prairies by considering GRACE data and required Global Land Data Assimilation System (GLDAS) variables for February 2003 to December 2016. The results reveal greater details in TWSA after spatial downscaling. Quantitatively, retrieved downscaled GWSA were validated using 75 unconfined in situ piezometric wells that were distributed across the Province of Alberta. A correlation of 0.80, with an RMSE of 11 mm, was obtained after downscaling with all wells over the validation area. These results are better than those obtained before downscaling (correlation of 0.42, with an RMSE of 21.4 mm), demonstrating that the proposed approach is successful.

The interactive feedback mechanisms between terrestrial water storage and vegetation in the Tibetan Plateau

A component of terrestrial water storage, vegetation is also an influential driver of changes in terrestrial water storage. In the context of warming on the Tibetan Plateau, it is essential to explore the relationship between changes in terrestrial water storage and vegetation in this region to understand further the role of vegetation in the changes of water systems in alpine mountains. Our study combines terrestrial water storage anomalies data and vegetation indices to determine how their interact. The results indicate a warming rate of 0.44°C/decade (p&amp;lt;0.01) over the Tibetan Plateau from 1980–2020, while evapotranspiration trended upward (12.9 mm/decade, p&amp;lt;0.01), which is slower than precipitation (15 mm/decade, p&amp;lt;0.01). On the Tibetan Plateau, spatial-temporal differences in temperature, precipitation, and evapotranspiration dominate the variations in terrestrial water storage. The change in terrestrial water storage was relatively stable from 2003 to 2011, but decreased from 2012 to 2016. Terrestrial water storage increased in endorheic basins while decreasing in exorheic basins. Partial correlation analysis indicates a negative correlation between the terrestrial water storage anomaly and the temperature. It is found that terrestrial water storage and net precipitation are positively correlated in the Yangtze River Basin and the northeast of the endorheic basins. However, the Qaidam Basin and the north part of the Yellow River Basin are negatively correlated. Under the current climate change state (the increased rate of precipitation is faster than actual evapotranspiration), vegetation change has an insignificant impact on the changes in terrestrial water storage. In contrast, changes in terrestrial water storage (surplus/deficit) significantly affect vegetation changes (greening/browning) in parts of the Tibetan Plateau. The study contributes to a deeper understanding of the relationship between water system changes and vegetation on the Tibetan Plateau.

Water Storage Variation and Its Possible Causes Detected by GRACE in the Volta River Basin

This study applies Gravity Recovery and Climate Experiment (GRACE) data and the WaterGAP (Water Global Analysis and Prognosis) Global Hydrology Model (WGHM) to investigate the influence of the Bui reservoir operation on water storage variation within the Volta River Basin (VRB). Variation in groundwater storage anomalies (GWSA) was estimated by combining GRACE-derived terrestrial water storage anomalies (TWSA), radar altimetry records, imagery-derived reservoir (Lake Volta and Bui) surface water storage anomalies (SWSA), and Global Land Data Assimilation System (GLDAS)-simulated soil moisture storage anomalies (SMSA) from 2002 to 2016. Results showed that TWSA increased (1.30 ± 0.23 cm/year) and decreased (−0.82 ± 0.27 cm/year) during 2002–2011 and 2011–2016, respectively, within VRB, matching previous TWSA investigations in this area. It revealed that the multi-year averages of monthly GRACE-derived TWSA changes in 2011–2016 displayed an overall increasing trend, indicating storage increase in regional hydrology; while the Lake Volta water storage changes decreased. The GRACE-minus-WGHM residuals display an increasing trend in VRB water storage during the Bui reservoir impoundment during 2011–2016. The observed trend compares well with the estimated Bui reservoir SWSA, indicating that GRACE solutions can retrieve the true amplitude of large mass changes happening in a concentrated area, though Bui reservoir is much smaller than the resolution of GRACE global solutions. It also revealed that GWSA were almost stable from 2002 to 2006, before increasing and decreasing during 2006–2011 and 2012–2016 with rates of 2.67 ± 0.34 cm/year and −1.80 ± 0.32 cm/year, respectively. The observed trends in the GRACE-derived TWSA and GWSA changes are generally attributed to the hydro-meteorological conditions. This study shows that the effects of strong El-Niño Southern Oscillation events on the GWSA interannual variability within the VRB is short-term, with a lag of 6 months. This study specifically showed that the Bui reservoir operation significantly affects the TWSA changes and provides knowledge on groundwater storage changes within the VRB.

Spatial–temporal variation analysis of water storage and its impacts on ecology and environment in high‐intensity coal mining areas

AbstractIn common with other important coal bases in northwestern China, the hydrology and ecology and environment of Ordos, northern Shaanxi (Shanbei), and Shanxi Provinces have much. Terrestrial water storage anomaly (TWSA), soil moisture anomaly (SMA), and groundwater storage anomaly (GWSA), are important water storage qualities that play a critical role in the distribution and growth of vegetation. In this paper, multi‐source remote sensing data were used to analyze the spatial–temporal changes and relationships of water storage, ecology and environment from 2002 to 2020. The numerical results showed the TWSA and GWSA in the study area had a declining trend and the rates were −7.512 mm yr‐1 and −8.268 mm yr‐1, respectively. On the contrary, the SMS showed an increasing trend, and the rate was 0.840 mm yr‐1. By trend variation and correlation analysis, we found GWSA is the main part in the variation of TWSA. By further analysis, it was found that the climate factors are not the major factors causing the variation of GWSA, but coal mining and artificial irrigation activities, which are especially clear in the Taihang Mountains in eastern Shanxi Province. The ecology and environment have been improving, and water storage and climate factors have played a different role in different regions. The warm climate conditions and the increased 0–40 cm of soil water have promoted the growth of vegetation and the improvement of the ecology and environment. The research results are helpful to understand the impacts of water storage on the ecology and environment in high‐intensity coal mining areas.

Reconstruction of GRACE terrestrial water storage anomalies using Multi-Layer Perceptrons for South Indian River basins

The Gravity Recovery and Climate Experiment (GRACE) satellite mission began in 2002 and ended in June 2017. GRACE applications are limited in their ability to study long-term water cycle behavior because the data is limited to a short period, i.e., from 2002 to 2017. In this study, we aim to reconstruct (1960–2002) GRACE total water storage anomalies (TWSA) to obtain a continuous TWS time series from 1960 to 2016 over four river basins of South India, namely the Godavari, Krishna, Cauvery and Pennar River basins, using Multilayer Perceptrons (MLP). The Seasonal Trend Decomposition using Loess procedure (STL) method is used to decompose GRACE TWSA and forcing datasets into linear trend, interannual, seasonal, and residual parts. Only the de-seasoned (i.e., interannual and residual) components are reconstructed using the MLP method after the linear trend and seasonal components are removed. Seasonal component is added back after reconstruction of de-seasoned GRACE TWSA to obtain complete TWSA series from 1960 to 2016. The reconstructed GRACE TWSA are converted to groundwater storage anomalies (GWSA) and compared with nearly 2000 groundwater observation well networks. The results conclude that the MLP model performed well in reconstructing GRACE TWSA at basin scale across four river basins. Godavari (GRB) experienced the highest correlation (r = 0.96) between the modelled TWSA and GRACE TWSA, followed by Krishna (KRB) with r = 0.93, Cauvery (CRB) with r = 0.91, and Pennar (PCRB) with r = 0.92. The seasonal GWSA from GRACE (GWSAGRACE) correlated well with the GWSA from groundwater observation wells (GWSAOBS) from 2003 to 2016. KRB exhibited the highest correlation (r=0.85) followed by GRB (r=0.81), PCRB (r=0.81) and CRB (r=0.78). The established MPL technique could be used to reconstruct long-term TWSA. The reconstructed TWSA data could be useful for understanding long-term trends, as well as monitoring and forecasting droughts and floods over the study regions.

Effects of different types of drought on vegetation in Huang-Huai-Hai River Basin, China

Due to climate change and human activities, drought has become one of the most complex natural disasters in the Huang-Huai-Hai River basin, China. The purpose of the current research is to analyze the response of vegetation to drought in the Huang-Huai-Hai River basin by calculating the lag time correlation coefficient between the enhanced vegetation index (EVI) and drought indices. First, the water storage deficit index (WSDI) calculated from the total water storage anomalies (TWSA) was used as the hydrological drought index. The meteorological drought index we used was the self-calibrating Palmer drought severity index (scPDSI) provided by the Climate Research Unit (CRU). Then, the relationship between hydrological and meteorological drought was obtained based on the wavelet coherent change. Finally, the response of different vegetation types to drought was analyzed based on the lag time correlation coefficient. The results showed that: the average time of hydrological drought lagged meteorological drought in the three first-level basins of the Huang-Huai-Hai River basin, respectively, was 50 days (Yellow River basin), 43 days (Huaihe River basin), and 35 days (Haihe River basin); the lag time correlation coefficient of the different time scales (1–12 months) can reflect the sensitivity of vegetation to drought in different vegetation types and regions. The forest was the least sensitive to drought in the Huang-Huai-Hai River basin, followed by cropland, while grassland was the most sensitive to drought. It was discovered that hydrological drought had a huge influence on vegetation rather than meteorological drought by comparing the maximum lag time correlation coefficients of EVI with drought indices (scPDSI and SPEI). This study stresses the importance of taking into account the effects of various types of drought on the vegetation in the Huang-Huai-Hai River basin. This will help us better understand how drought affects vegetation.

Terrestrial Water Storage Dynamics: Different Roles of Climate Variability, Vegetation Change, and Human Activities across Climate Zones in China

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.