Terrestrial Water Storage Variations Research Articles

Mass Variations in Terrestrial Water Storage over the Nile River Basin and Mega Aquifer System as Deduced from GRACE-FO Level-2 Products and Precipitation Patterns from GPCP Data

Changes in the terrestrial total water storage (TWS) have been estimated at both global and river basin scales from the Gravity Recovery and Climate Experiment (GRACE) mission and are still being detected from its GRACE Follow-On (GRACE-FO) mission. In this contribution, the sixth release of GRACE-FO (RL06) level-2 products applying DDK5 (decorrelation filter) were used to detect water mass variations for the Nile River Basin (NRB) in Africa and the Mega Aquifer System (MAS) in Asia. The following approach was implemented to detect the mass variation over the NRB and MAS: (1) TWS mass (June 2018–June 2021) was estimated by converting the spherical harmonic coefficients from the decorrelation filter DDK 5 of the GRACE-FO Level-2 RL06 products into equivalent water heights, where the TWS had been re-produced after removing the mean temporal signal (2) Precipitation data from Global Precipitation Climatology Project was used to investigate the pattern of change over the study area. Our findings include: (1) during the GRACE-FO period, the mass variations extracted from the RL06-DDK5 solutions from the three official centers—CSR, JPL, and GFZ—were found to be consistent with each other, (2) The NRB showed substantial temporal TWS variations, given a basin average of about 6 cm in 2019 and about 12 cm in 2020 between September and November and a lower basin average of about −9 cm in 2019 and −6 cm in 2020 in the wet seasons between March and May, while mass variations for the MAS had a relatively weaker temporal TWS magnitude, (3) the observed seasonal signal over the NRB was attributed to the high intensity of the precipitation events over the NRB (AAP: 1000–1800 mm yr−1), whereas the lack of the seasonal TWS signal over the MAS was due to the low intensity of the precipitation events over the MAS (AAP: 180–500 mm yr−1).

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.

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<0.01) over the Tibetan Plateau from 1980–2020, while evapotranspiration trended upward (12.9 mm/decade, p<0.01), which is slower than precipitation (15 mm/decade, p<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.

An efficient low-pass-filtering algorithm to de-noise global GRACE data

The global monthly equivalent water height (EWH) anomaly (ΔEWH), derived from the Gravity Recovery and Climate Experiment (GRACE) level-2 data, helps understand the terrestrial water storage variation. Unfortunately, the ΔEWH data consist of stripes or noise. Numerous de-noising approaches have been studied. However, the following issues exist, possible overly smoothed results, two-step (de-striping and Gaussian-based smoothing) procedures introducing additional uncertainty and error propagation, requiring multi-temporal datasets or prior knowledge, and possible computational inefficiency. Thus, after analyzing the spectrum of the ΔEWH data, a novel low-pass-filtering algorithm is proposed to remove the noise and resolve the issues. Furthermore, without the global in situ measurements, an alternative assessment method is studied based on the additive characteristics of the signal and noise in the GRACE data. The method consists of the residual analysis and root mean square (RMS) value of the de-noised signal. Then, the proposed algorithm was applied to de-noising the ΔEWH datasets between 2002 and 2015. De-noised results are satisfactory qualitatively and quantitatively. Compared with well-known two-step de-noising methods, data blurring does not occur after the proposed algorithm. The algorithm removes noise the most assessed by the residual analysis and preserves the signal the most evaluated by large RMS values.

An assimilated deep learning approach to identify the influence of global climate on hydrological fluxes

The rapid acceleration of the global water cycle caused by changes in global climate trigger complex processes that make conventional machine learning techniques limited in assessing impacts of such changes on terrestrial water storage (TWS). This study introduces an assimilated deep learning neural network to improve the modeling of TWS dynamics. Key predictors and inputs to this deep learning framework include runoff, rainfall, soil moisture, evapotranspiration, global teleconnection patterns and sea surface temperatures (SSTs). The proposed back propagation model in this study capitalizes on the availability of remotely sensed observations and model datasets to predict monthly TWS, a quantity that is difficult to observe in the field, but important for the estimation of regional water budget balance, and water resource management for agricultural purposes. By integrating pre-processed outputs from the Principal Component Analysis (PCA) and Independent Component Analysis (ICA) into our network using a deep neural pattern, we synthesized TWS from 2002 to 2017, and also made future predictions using our trained models. Results from these analyses showed that the ICA-BPNN model has a higher predictive accuracy compared to the PCA-BPNN. These models (ICA-BPNN and PCA-BPNN) were used to fit the three dominant temporal patterns of Gravity Recovery and Climate Experiment (GRACE) – observed TWS over Africa. Our simulation results from the testing phase indicate that the fits for the prediction of the first three leading modes of TWS for both models when compared to the observed GRACE-TWS were PCA-BPNN1 (89%), PCA-BPNN2 (82%), PCA-BPNN3 (84%) and ICA-BPNN1 (93%), ICA-BPNN2 (88%), ICA-BPNN3 (82%). The simulation fit of the BPNN corresponding to multi-annual time series, which are captured in the second and third orthogonal modes and localized patterns of TWS were lower than those of annual signals in both the PCA- and ICA-BPNN models. This was attributed to the fact that the multi-annual time series in GRACE-hydrological signals of our test-bed are complex compared to the annual patterns of TWS. On the one hand, this exemplifies the superior performance of our predictive framework in modeling naturalized system (annual changes in TWS driven by only climatic factors). On the other hand, the complexity in modelling multi-annual variations in TWS suggests heavily disturbed naturalized systems evidenced in the presence of human water management operations among other anthropogenic activities.

Reconstructing the Historical Terrestrial Water Storage Variations in the Huang–Huai–Hai River Basin With Consideration of Water Withdrawals

The Huang-Huai-Hai River Basin in eastern China has suffered from severe water scarcity during recent decades due to the effects of climate change and human activities. Quantifying the changes in the amount of terrestrial freshwater available in this region and their driving factors is important for understanding hydrological processes and developing a sustainable water policy. This study proposed an ensemble learning model to reconstruct historical variations in the terrestrial water storage (TWS) of the Huang-Huai-Hai River Basin, China. The model was trained using the observations of the variations in TWS from the Gravity Recovery and Climate Experiment mission (GRACE) satellites, climatic driving, and human withdrawal datasets produced on a monthly scale. The variations in the reconstructed TWS were compared with the results of several land surface and hydrological models with a variety of in situ measurements of the soil water content. The contributions of the climate and human activity to the ensemble learning model were also quantified. The results show that the proposed approach generally outperforms the land surface and hydrological models examined in this study, matches the patterns in the GRACE solutions, and reconstructs past changes in TWS, which are consistent with the GRACE observations. Climatic variables are the most important in the ensemble learning model, with precipitation over the prior month being a critical factor. The model that includes human intervention tends to perform better than without it. Irrigation, industry, and domestic water withdrawals contribute equally to the model. This study provides a flexible and easily implementable model that can bridge the gap between GRACE observations and past changes in TWS. The model is applicable in areas with intense human activities, and the results have the potential to be assimilated into and enhance hydrological models.

Monitoring Mass Variations in Iraq Using Time-Variable Gravity Data

Iraq is facing a water shortage due to water scarcity and anthropogenic activities. The recent advance in technologies in geophysical methods has made groundwater monitoring possible. Time-variable gravity data and outputs of the climatic model, as well as rainfall data, are integrated to investigate the spatio-temporal mass variations caused by groundwater changes over Iraq. The findings are: (1) For the entire study period (04/2002–12/2020), Period I (04/2002–12/2006), Period II (01/2007–12/2017), and Period III (01/2018–12/2020), the study region had an average annual precipitation rate of 223.4, 252.5, 194.2, and 311.6 mm/y, respectively. (2) The average Terrestrial Water Storage variations (ΔTWSs) varied from −5.79 ± 0.70 to −5.11 ± 0.70 mm/y based on the three different gravity solutions with a mean of −5.51 ± 0.68 mm/y for the entire investigated period. (3) For Periods I, II, and III, the average ΔTWS fluctuation was calculated to be +6.82 ± 1.92, −6.20 ± 1.17, and +28.58 ± 12.78 mm/y, respectively. (4) During the entire period, Periods I, II, and II, the groundwater fluctuation was averaged at −4.86 ± 0.68, +2.47 ± 2.20, −3.79 ± 1.20, and −4.63 ± 12.99 mm/y, respectively, after subtracting the non-groundwater components. (5) At the beginning of the 2007 drought during Period II, a decline in rainfall rate, and significant groundwater withdrawal during Period III all appear to have contributed to groundwater depletion. The Euphrates and Tigris Rivers, as well as the Mesopotamian plain, receive water from the running streams created by the ground relief. The area of the Mesopotamian plain, which has a thicker sedimentary sequence that can reach 9000 m, is found to have a positive TWS signal, indicating that its groundwater potential is higher. The integrated approach is informative and cost-effective.

Model Estimates of China's Terrestrial Water Storage Variation Due To Reservoir Operation

AbstractUnderstanding the role of reservoirs in the terrestrial water cycle is critical to support the sustainable management of water resources especially for China where reservoirs have been extensively built nationwide. However, this has been a scientific challenge due to the limited availability of continuous, long‐term reservoir operation records at large scales, and a process‐based modeling tool to accurately depict reservoirs as part of the terrestrial water cycle is still lacking. Here, we develop a continental‐scale land surface‐hydrologic model over the mainland China by explicitly representing 3,547 reservoirs in the model with a calibration‐free conceptual operation scheme for ungauged reservoirs and a hydrodynamically based two‐way coupled scheme. The model is spatially calibrated and then extensively validated against streamflow observations, reservoir storage observations and GRACE‐based terrestrial water storage anomalies. A 30‐year simulation is then performed to quantify the seasonal dynamics of China’s reservoir water storage (RWS) and its role in China's terrestrial water storage (TWS) over recent decades. We estimate that, over a seasonal cycle, China's RWS variation is 15%, 16%, and 25% of TWS variation during 1981–1990, 1991–2000, and 2001–2010, respectively, and one‐fifth of China’s reservoir capacity are effectively used annually. In most regions, reservoirs play a growing role in modulating the water cycle over time. Despite that, an estimated 80 million people have faced increasing water resources challenges in the past decades due to the significantly weakened reservoir regulation of the water cycle. Our approaches and findings could help the government better address the water security challenges under environmental changes.

Impact assessment of the seasonal hydrological loading on geodetic movement and seismicity in Nepal Himalaya using GRACE and GNSS measurements

The Himalayan terrain is an epitome of ongoing convergence and geodetic deformation where both tectonic and non-tectonic forces prevail. In this study, the Gravity Recovery and Climate Experiment (GRACE) and Global Positioning System (GPS) datasets are used to assess the impact of seasonal loading on deformation with seismicity in Nepal. The recorded GPS data from 21 Global Navigation Satellite System (GNSS) stations during 2017–2020 are processed with respect to ITRF14 and the Indian reference frame, and the Center for Space Research (CSR) mascon RL06 during 2002–2020 is adopted to estimate the terrestrial water storage (TWS) change over the Ganga-Brahmaputra River basin. The results indicate that the hydrological loading effect or TWS change shows high negative, high positive, and moderately positive values in pre-monsoon, co-monsoon, and post-monsoon months, respectively. The detrended GPS data of both horizontal and vertical components correlate with the seasonal TWS change using the Pearson correlation coefficient at each GNSS site. In addition, the correlation coefficient has been interpolated using inverse distance weighting to investigate the regional TWS influence on geodetic displacement. In the north component, the correlation coefficient ranges from −0.6 to 0.6. At the same time, the TWS is positively correlated with geodetic displacement (0.82) in the east component, and the correlation coefficient is negative (−0.69) in the vertical component. The negative correlation signifies an inverse relationship between seasonal TWS variation and geodetic displacements. The strain rate is estimated, which shows higher negative values in pre-monsoon than in post-monsoon. Similarly, the effect of seismicity is 47.90% for pre-monsoon, 15.97% for co-monsoon, and 17.56% for post-monsoon. Thus we can infer that the seismicity decreases with the increase of seasonal hydrological loading. Furthermore, the effect of strain is much higher in pre-monsoon than in post-monsoon since the impact of co-monsoon continues to persist on a small scale in the post-monsoon season.

Abnormal depletion of terrestrial water storage and crustal uplift owing to the 2019 drought in Yunnan, China

SUMMARYDroughts are natural disasters that cause severe economic and social impacts. Analysing changes in terrestrial water storage (TWS) before and after typical drought events is important for understanding the principles of the regional terrestrial water transport law and drought response, and for providing scientific agricultural production guidance. Yunnan is a drought-prone region, and high temperatures and low rainfall in 2019 led to a severe drought. Precipitation data showed that the wet season in Yunnan started 1 month later than usual in 2019. Moreover, 52 per cent (205 000 km2) of the total area of Yunnan experienced a cumulative precipitation anomaly (PA; in percentage) of −30 per cent between January and May. In this study, we used data from the Gravity Recovery and Climate Experiment (GRACE) and Gravity Recovery and Climate Experiment Follow-On (GRACE-FO) missions to identify the spatial and temporal variations in TWS before and after the 2019 drought. Our results show that the drought led to an average reduction of 125 mm (equivalent water height) in the TWS, with the greatest loss in southern Yunnan, with a maximum loss of 272 mm. The reduced TWS caused by the delayed and anomalously weak southwest monsoon in 2019 gradually spread from the southwest to the northeast. By comparing with the drought in 2009–2010, we find that the southern Yunnan with abundant rainfall is more vulnerable to drought and has a relatively larger deficit in TWS. Furthermore, we used GRACE and GRACE-FO data to invert the crustal uplift caused by terrestrial water unloading in the 2019 drought, and found a maximum uplift of 8.4 mm. This showed a consistent spatial distribution with displacement variations recorded by 26 continuous Global Positioning System (GPS) stations in Yunnan during the same period, albeit slightly different in magnitude, indicating the potential of GPS for monitoring regional TWS changes.

Impacts of Climate Change, Glacier Mass Loss and Human Activities on Spatiotemporal Variations in Terrestrial Water Storage of the Qaidam Basin, China

Monitoring the variations in terrestrial water storage (TWS) is crucial for understanding the regional hydrological processes, which helps to allocate and manage basin-scale water resources efficiently. In this study, the impacts of climate change, glacier mass loss, and human activities on the variations in TWS of the Qaidam Basin over the period of 2002−2020 were investigated by using Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On (GRACE-FO) data, and other hydrological and meteorological data. The results indicate that TWS anomalies (TWSA) derived from five GRACE solutions experienced significant increasing trends over the study period, with the change rates ranging from 4.85 to 6.90 mm/year (1.37 to 1.95 km3/year). The GRACE TWSA averaged from different GRACE solutions exhibited an increase at a rate of 5.83 ± 0.12 mm/year (1.65 ± 0.03 km3/year). Trends in individual components of TWS indicate that the increase in soil moisture (7.65 mm/year) contributed the most to the variations in TWS. Through comprehensive analysis, it was found that the temporal variations in TWS of the Qaidam Basin were dominated by the variations in precipitation, and the spatial variations in TWS of the Qaidam Basin were mostly driven by the increase in glacier meltwater due to climate warming, particularly in the Narin Gol Basin. In addition, the water consumption associated with human activities had relatively fewer impacts.

Towards hybrid modeling of the global hydrological cycle

Abstract. State-of-the-art global hydrological models (GHMs) exhibit large uncertainties in hydrological simulations due to the complexity, diversity, and heterogeneity of the land surface and subsurface processes, as well as the scale dependency of these processes and associated parameters. Recent progress in machine learning, fueled by relevant Earth observation data streams, may help overcome these challenges. But machine learning methods are not bound by physical laws, and their interpretability is limited by design. In this study, we exemplify a hybrid approach to global hydrological modeling that exploits the data adaptivity of neural networks for representing uncertain processes within a model structure based on physical principles (e.g., mass conservation) that form the basis of GHMs. This combination of machine learning and physical knowledge can potentially lead to data-driven, yet physically consistent and partially interpretable hybrid models. The hybrid hydrological model (H2M), extended from Kraft et al. (2020), simulates the dynamics of snow, soil moisture, and groundwater storage globally at 1∘ spatial resolution and daily time step. Water fluxes are simulated by an embedded recurrent neural network. We trained the model simultaneously against observational products of terrestrial water storage variations (TWS), grid cell runoff (Q), evapotranspiration (ET), and snow water equivalent (SWE) with a multi-task learning approach. We find that the H2M is capable of reproducing key patterns of global water cycle components, with model performances being at least on par with four state-of-the-art GHMs which provide a necessary benchmark for H2M. The neural-network-learned hydrological responses of evapotranspiration and grid cell runoff to antecedent soil moisture states are qualitatively consistent with our understanding and theory. The simulated contributions of groundwater, soil moisture, and snowpack variability to TWS variations are plausible and within the ranges of traditional GHMs. H2M identifies a somewhat stronger role of soil moisture for TWS variations in transitional and tropical regions compared to GHMs. With the findings and analysis, we conclude that H2M provides a new data-driven perspective on modeling the global hydrological cycle and physical responses with machine-learned parameters that is consistent with and complementary to existing global modeling frameworks. The hybrid modeling approaches have a large potential to better leverage ever-increasing Earth observation data streams to advance our understandings of the Earth system and capabilities to monitor and model it.

The importance of vegetation in understanding terrestrial water storage variations

Abstract. So far, various studies have aimed at decomposing the integrated terrestrial water storage variations observed by satellite gravimetry (GRACE, GRACE-FO) with the help of large-scale hydrological models. While the results of the storage decomposition depend on model structure, little attention has been given to the impact of the way that vegetation is represented in these models. Although vegetation structure and activity represent the crucial link between water, carbon, and energy cycles, their representation in large-scale hydrological models remains a major source of uncertainty. At the same time, the increasing availability and quality of Earth-observation-based vegetation data provide valuable information with good prospects for improving model simulations and gaining better insights into the role of vegetation within the global water cycle. In this study, we use observation-based vegetation information such as vegetation indices and rooting depths for spatializing the parameters of a simple global hydrological model to define infiltration, root water uptake, and transpiration processes. The parameters are further constrained by considering observations of terrestrial water storage anomalies (TWS), soil moisture, evapotranspiration (ET) and gridded runoff (Q) estimates in a multi-criteria calibration approach. We assess the implications of including varying vegetation characteristics on the simulation results, with a particular focus on the partitioning between water storage components. To isolate the effect of vegetation, we compare a model experiment in which vegetation parameters vary in space and time to a baseline experiment in which all parameters are calibrated as static, globally uniform values. Both experiments show good overall performance, but explicitly including varying vegetation data leads to even better performance and more physically plausible parameter values. The largest improvements regarding TWS and ET are seen in supply-limited (semi-arid) regions and in the tropics, whereas Q simulations improve mainly in northern latitudes. While the total fluxes and storages are similar, accounting for vegetation substantially changes the contributions of different soil water storage components to the TWS variations. This suggests an important role of the representation of vegetation in hydrological models for interpreting TWS variations. Our simulations further indicate a major effect of deeper moisture storages and groundwater–soil moisture–vegetation interactions as a key to understanding TWS variations. We highlight the need for further observations to identify the adequate model structure rather than only model parameters for a reasonable representation and interpretation of vegetation–water interactions.

Assessing the spatial and temporal variations of terrestrial water storage of Iraq using GRACE satellite data and reliability–resiliency–vulnerability indicators

Assessing the spatial and temporal variations of terrestrial water storage of Iraq using GRACE satellite data and reliability–resiliency–vulnerability indicators

Assessment of drought trends in the Senegal River Basin by a terrestrial water storage index (GRACE)

Droughts lead to significant environmental and economic consequences, especially in arid and semi-arid areas like the Sahel. While site-level assessments of drought in the Sahel are abundant, assessments at the scale of entire hydrological basins are less common. Here, we use a new drought index called the terrestrial water storage index (TWSI) to assess trends in drought throughout the Senegal River Basin. This area covers parts of Guinea, Mali, Senegal, and Mauritania, the study period is between 2003 and 2020. Over the entire period, water storage in the Senegal River Basin is increasing by 0.87 km3 y-1 on the total area of the basin. However, we observed two distinct phases within the time period: an overall water deficit between 2003 and 2012 and a surplus between 2013 and 2020. We also found variations in terrestrial water storage from highly negative at the end of the dry season (-12.47 cm in May 2003) to strongly positive at the end of the rainy season (15.30 cm in September 2020). Our study suggests that the TWSI can be a useful index for regional hydrological drought monitoring, especially for areas where meteo-hydrological observations are insufficient.

Tracking inter-annual terrestrial water storage variations over Lake Baikal basin from GRACE and GRACE Follow-On missions

The Gravity Recovery and Climate Experiment (GRACE) mission and its follow-on mission (GRACE Follow-On) can accurately track the Earth’s mass variations, which have been successfully used in various geoscience researches related to global and regional hydrological process. In this study, satellite gravimetry (GRACE and GRACR Follow-On) and hydrological model (GLDAS) are used to estimate the TWS variations over LBB in 2003–2020. As the world’s largest freshwater lake by volume, the terrestrial water storage (TWS) variations in Lake Baikal basin (LBB) significantly affect the surrounding ecological environment. However, the spatial scale of LBB is relatively small when compared to approximately 300 km spatial resolution of GRACE and GRACE Follow-On mission. Therefore, the forward modeling method is used to alleviate the leakage errors during satellite gravimetry data processing. To assess the performance of forward modeling method, a close-loop simulation study is designed in noise-contaminated condition. The simulation results demonstrate the feasibility of forward modeling method in accurately estimating TWS variations over LBB. After appropriate leakage correction, the inter-annual TWS from satellite gravimetry agrees well with GLDAS. The TWS variations over LBB demonstrate that (1) there are drastic variations and periodic characteristics around 4–5 years, and the drought events are observed in 2003, 2007, 2011 and 2015 as relatively smaller TWSs. (2) The soil moisture and groundwater are the major contributors to TWS variations (with the correlation coefficient of 0.88 and 0.89), and the surface water is the main contributor to the seasonal variations over LBB. (3) The TWS presents about 6 moths delay to precipitation variations, while the precipitation is still the major driver to affect the hydrological processes over this region.

Characterization of the hydro-geological regime of fractured aquifers in Benin (West-Africa) using multi-satellites and models

Study regionFractured aquifers in Benin have emerged in the past decades as a critical resource because of the increasing domestic water needs, agriculture, and livestock breeding. Hence this study was undertaken in the poorly gauged and complex basement aquifers beneath the 9th latitude. Study focusTo aid water resources management and sustainability, this study combined satellite remote sensing products with outputs from hydrological models and in situ measurement to assess the spatial and temporal patterns of water storage changes, including groundwater. The fractured aquifers’ hydrogeological regime is further analysed through monitoring wells’ series interpretation. New hydrological insights for the regionResults show considerable inter-annual variations in Terrestrial Water Storage (TWS) during the study period (2003–2019). Two uptrend intervals characterized by wet hydrological seasons from 2006 to 2013 and 2016–2019 with respectively, Sen’s slope values of 1.16 mm/yr and 4.47 mm/yr were observed. However, two downtrend intervals represented by dry hydrological seasons with Sen’s slopes of − 1.01 mm/yr (2003–2006) and − 1.19 mm/yr (2013–2016) were also observed. The inter-annual seasonality in land water storage components showed that unlike the 4 climatic seasons, the hydrogeological regime is affected by 3 seasons each year. The first dry season takes place from January to April-May (−29.1 ± 17.7 mm/yr) followed by the wet season (April-May to October-November: 41.6 ± 26.4 mm/yr) and the last dry period, which is stronger (November to December: −53.7 ± 31.6 mm/yr). Soil moisture (SM) and canopy water content (CW) declined during the 2008–2016 period, coinciding with decline in Tree cover areas, which dropped by 25.5% (3086.7 sqkm). Further from 2007 to 2014, the four monitoring wells have seen the groundwater level increased by, 1.44 m/yr, 2.0 m/yr, 3.2 m/yr, and 0.56 m/yr for the Affon, Zou, Okpara and Couffo sub-basins, respectively. The retrieved TWS and Water ‐ Global Assessment and Prognosis groundwater storage (WaterGap-GWS) sub-domains based on statistical decomposition was used to identify hidden patterns to improve knowledge on the climatic and hydrological conditions, which affected land water storage. Moreover, TWS, GWS and SM lost water 51%, 57% and 47% of the time, respectively, during the period and the storage potential is lower for the aquifers compared to the unsaturated zone. Precipitation is recognized as a primary driver of GWS (r = 0.69; α = 0.05) in the region and a change in GWS is more likely to drive the CW and affect the vegetation greenness. Moreover, the anthropogenic pressure exacerbates Tree cover loss. Additionally, the Extra Tree regressor performs best in the groundwater storage change ∆GWS prediction with a strong and positive Pearson Correlation Coefficient of 0.92. The basement aquifers assessment using the aforementioned approach has potential to support large-scale monitoring efforts for more reliable water resources management in the region.

Estimation of Terrestrial Water Storage Variations in Sichuan-Yunnan Region from GPS Observations Using Independent Component Analysis

GPS can be used to measure land motions induced by mass loading variations on the Earth’s surface. This paper presents an independent component analysis (ICA)-based inversion method that uses vertical GPS coordinate time series to estimate the change of terrestrial water storage (TWS) in the Sichuan-Yunnan region in China. The ICA method was applied to extract the hydrological deformation signals from the vertical coordinate time series of GPS stations in the Sichuan-Yunnan region from the Crustal Movement Observation Network of China (CMONC). These vertical deformation signals were then inverted to TWS variations. Comparative experiments were conducted based on Gravity Recovery and Climate Experiment (GRACE) data and a hydrological model for validation. The results demonstrate that the TWS changes estimated from GPS(ICA) deformations are highly correlated with the water variations derived from the GRACE data and hydrological model in Sichuan-Yunnan region. The TWS variations are overestimated by the vertical GPS observations the northwestern Sichuan-Yunnan region. The anomalies are likely caused by inaccurate atmospheric loading correction models or residual tropospheric errors in the region with high topographic variability and can be reduced by ICA preprocessing.

Temporal/Spatial Variation of Terrestrial Water Storage and Groundwater Storage in Typical Inland River Basins of Central Asia

In this study, the Amu Darya river basin, Syr Darya river basin and Balkhash lake basin in Central Asia were selected as typical study areas. Temporal/spatial changes from 2002 to 2016 in the terrestrial water storage (TWS) and the groundwater storage (GWS) were analyzed, based on RL06 Mascon data from the Gravity Recovery and Climate Experiment (GRACE) satellite, and the sum of soil water content, snow water equivalent and canopy water data that were obtained from Global Land Data Assimilation System (GLDAS). Combing meteorological data and land use and cover change (LUCC) data, the joint impact of both human activities and climate change on the terrestrial water storage change (TWSC) and the groundwater storage change (GWSC) was evaluated by statistical analysis. The results revealed three findings: (1) The TWS retrieved by CSR (Center for Space Research) and the JPL (Jet Propulsion Laboratory) showed a decreasing trend in the three basins, and the variation of TWS showed a maximum surplus in spring (March–May) and a maximum deficit in autumn (September–November). (2) The decreasing rates of groundwater storage that were extracted, based on JPL and CSR Mascon data sets, were −2.17 mm/year and −3.90 mm/year, −3.72 mm/year and −4.96 mm/year, −1.74 mm/year and −3.36 mm/year in the Amu Darya river basin, Syr Darya river basin and Balkhash lake basin, respectively. (3) In the Amu Darya river basin, annual precipitation showed a decreasing trend, while the evapotranspiration rate showed an increasing trend due to an increasing temperature, and the TWS decreased from 2002 to 2016 in most areas of the basin. However, in the middle reaches of the Amu Darya river basin, the TWS increased due to the increase in cultivated land area, water income from flooded irrigation, and reservoir impoundment. In the upper reaches of the Syr Darya river basin, the increase in precipitation in alpine areas leads to an increase in glacier and snow meltwater, which is the reason for the increase in the TWS. In the middle and lower reaches of the Syr Darya river basin, the amount of evapotranspiration dissipation exceeds the amount of water replenished by agricultural irrigation, which leads to a decrease in TWS and GWS. The increase in precipitation in the northwest of the Balkhash lake basin, the increase in farmland irrigation water, and the topography (higher in the southeast and lower in the northwest) led to an increase in TWS and GWS in the northwest of the Balkhash lake basin. This study can provide useful information for water resources management in the inland river basins of Central Asia.

Reconstructing groundwater storage variations from GRACE observations using a new Gaussian-Han-Fan (GHF) smoothing approach

This study focuses upon development of a new filter, referred to as Gaussian-Han-Fan (GHF) filtering, and its application within a comprehensive procedure for estimating groundwater changes. The Canadian Prairies was the study area. Variations in groundwater were estimated by using 15 years of Gravity Recovery And Climate Experiment (GRACE) twin-satellite observations (April 2002 to June 2017). Surface water storage (sum of soil moisture, snow water equivalent, canopy water, and surface water bodies) was subtracted from reconstructed GRACE-based Terrestrial Water Storage (TWS) changes through GHF filtering. To estimate the required hydrological parameters, both the Global Land Data Assimilation System (GLDAS) and Water Global Hydrology Model (WGHM) were used and evaluated. Water level changes for major surface water bodies were estimated using satellite altimetry-based products. The monthly average of GWS variations over the Prairies ranged between −200 mm and +230 mm. A positive trend was found for both TWS and GWS variations, with the highest values in the region surrounding Hudson Bay, particularly in northern Manitoba (about 55 mm/year). Estimated GWS anomalies error was equivalent to about 10% of its absolute value, with a mean of 19 mm. GWS variations results were validated using 116 active in-situ groundwater level measurements in five different river basins (Peace-Athabasca, Churchill, North Saskatchewan, South Saskatchewan, Missouri), which were all located in Alberta (Canada). Good agreement was achieved in each river basin (correlation > r = |0.70|, P < 10−4, RMSE < 55 mm). Regardless of hydrological system (GLDAS or WGHM), better statistical metrics were found when the average of the five basins was considered (r > |0.90|, P < 10−4), with lowest errors (RMSE or UnRMSE < 30 mm).