Water Storage Variations Research Articles

Transient hydrology-induced elastic deformation and land subsidence in Australia constrained by contemporary geodetic measurements

Earth's lithospheric deformation in response to geophysical and climatic processes manifests as a wide range of spatio-temporal elastic/viscoelastic deformation, including seismic and tectonic motion, mantle flow, and climate-induced hydrologic-cyrospheric-atmospheric mass transports. We use increasingly accurate contemporary geodetic measurements, including those from GPS, Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On (GFO) missions, to quantify the competing processes of the hydrological cycle and geodynamics within the Australian continental lithosphere over the last two decades, 2002 to 2020. Observed precipitation and temperature anomalies reveal that anthropogenic climate change may have exerted significant impact on the terrestrial water storage (TWS) evolution in Australia, as evidenced by anomalous fluctuation during the 2010-2012 El Niño Southern Oscillation (ENSO) events. In particular, enhanced TWS inferred from the geodetic observations is shown to be highly correlated with increased precipitation during the 2010-2012 La Niña event. The GPS observed vertical land motion cannot be completely explained by the elastic deformation induced by surface water storage variations across Australia, nor by the Glacial Isostatic Adjustment (GIA) geophysical process. We suggest that the geodetic-observed land subsidence of Australia at high spatial scales could be related to anthropogenic aquifer groundwater depletion, while the long wavelength signals may be associated with reference frame error and then tectonic deformation.

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.

Longterm multisource satellite data fusion reveals dynamic expansion of lake water area and storage in a hyperarid basin of China

Lakes play an important role in global hydrologically and provide substantial water resources in hyperarid regions, and they are becoming even more significant as climate change impacts hydrological processes, especially in these sensitive regions. This study proposes a lake-monitoring framework that uses multisource satellite data to automatically estimate the spatiotemporal variations from 1987 to 2020 of lake area, lake water level, and lake water storage (LWS) in the Qaidam Basin (QB), which is a typical hyperarid basin in northwest China. The global surface water dataset provides long-time-series data for lake areas. Multisource satellite images (Landsat 7, Landsat 8, Sentinel 2) and altimetry data (CryoSat 2, ICESat, ICESat 2, Sentinel 3) are used to build hypsometric curves (the fit to the curve of lake area vs lake water level) to reconstruct lake water level before the 21st century. In contrast with traditional methods, we propose to verify the construction of a hypsometric curve to select a reasonable curve and thereby improve the accuracy of the extrapolation of lake water level. On this basis, we estimated the variation of LWS from 1987 to 2020 and to use products of the gravity recovery and climate experiment and of land surface model simulations to estimate the variation in total LWS in the QB for comparative analysis. The results show that the total LWS in the QB has increased by 82.63% over the past 33 years. Compared with the mean value from 2004 to 2009, the total LWS in 2020 increased by 4.41 km3, which is equivalent to 12.53%–19.49% of the increase in terrestrial water storage. This ratio cannot be ignored. The analysis of spatiotemporal variations in lakes shows that large shallow lakes are disappearing, leading to ecological deterioration in some areas. Conversely, there is increased water storage in small deep lakes, which may be beneficial for water conservation in hyperarid basins. In conclusion, the proposed automated processing program should facilitate the rapid monitoring of lake dynamics around the world. We emphasize that the variation of lake water storage in hyperarid basins cannot be ignored, which should stimulate more relevant research.

Spatio-temporal analysis of water storage variation and temporal correlations in the East Africa lake basins

Study regionEast Africa (EA), lake basins (Tana, Abaya-Chamo, Turkana, Victoria, and Tanganyika), 6472,404 km2 of total area. Study focusTotal water storage change (TWSC) exposes serious challenges in sustainable water resources management and ecosystem sustenance. While TWS changes have been increasingly studied based on Gravity Recovery and Climate Experiment (GRACE) derived TWS dataset, spatio-temporal changes in TWS and the driving factors remain unexplored. Herein, Mann–Kendall test, Theil-Sen's slope estimator, empirical orthogonal functions (EOFs), component contribution ratio (CCR), and Soil and Water Assessment Tool (SWAT) models were employed. We found a slight spatial fluctuation of TWS, and groundwater could affect TWS variability in the EA region. New hydrological insightsHigh annual TWSC was observed in the southwestern and northern regions, reaching values of 220 and − 100 mm/yr, respectively. Among lake basins, the maximum and minimum TWSC values attained 97.5 ± 28.2 mm/yr and 24.8 ± 7.7 mm/yr, in Victoria and Tana basins, respectively. The regional level groundwater contribution was about 42%, while 48%, 53%, and 46% were in Abaya-Chamo, Turkana, and Victoria basins, respectively. However, surface runoff contribution was 52% and 35% in Tana and Tanganyika basins, respectively. The seasonal TWS changes show substantial linkage with the lake basins' seasonal precipitation distribution. Our findings highlight the driving factors of TWS variability, which could help guiding water management policies, and promote satellite remote sensing datasets.

Estimating Reservoir Storage Variations by Combining Sentinel-2 and 3 Measurements in the Yliki Reservoir, Greece

Inland water resources are facing increasing quantitative and qualitative pressures, deriving from anthropogenic causes and the ongoing climate change. The monitoring of reservoirs is essential for sustainable management and preparation against water scarcity and extreme events, such as droughts. This research, relying on the Sentinel-2 and 3 missions, attempts to demonstrate the efficiency of combining remotely sensed water level and water area estimations, in order to estimate the water storage variation of Yliki reservoir. The case study is conducted in one of the few sufficiently monitored reservoirs in Greece, enabling a direct comparison of the proposed methodology results with in situ observations. Moreover, this research work proposes a weekly time interval for pairing level and area estimations, instead of shorter time intervals. The results strongly demonstrate the efficiency of remote sensing in the production of empirical level–area–storage (L–A–S) curves. Correlation to in situ monitored storage- and satellite-derived water level, area stand for 98.81% and 99.27% respectively. Water storage variation is estimated and compared to the observed time series, resulting in an RMSE of 1.28% of the reservoir capacity and a correlation of 96.14%. The empirical L–S relationship underestimates storage, while the A–S relationship overestimates storage when compared to the existing L–A–S curve.

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.

A novel ecohydrological model by capturing variations in climate change and vegetation coverage in a semi-arid region of China

Variations in vegetation are influenced by regional climate regimes and, in turn, control the water balance behavior in water-limited regions. Owing to its role in ecohydrological processes, vegetation is an essential link in modeling the relationships among climate conditions, vegetation patterns, and dynamic water balance behavior. However, previous ecohydrological models have been empirical and complex, without physically significant parameters. Here, we propose a novel ecohydrological model (a Budyko model–coupled vegetation model) that combines the impacts of climate change and vegetation variations, featuring simple and deterministic parameters. In addition to accounting for the fundamental water balance model and its factors, mean precipitation, potential evapotranspiration, runoff, and variations in water storage (δS), the model showed better performance when incorporating δS (RMSE = 2.72 mm yr−1) and its parameter ε -, which is mechanically and quantitively subject to the vegetation coverage (R2 = 0.95, p < 0.01). This was estimated as a function of vegetation potential canopy conductance, mean rainstorm depth, mean time between storms, and potential rate of evapotranspiration in a semi-arid watershed with impulsive precipitation in China (R2 = 0.80, p < 0.01). The model also found that vegetation growth was mainly controlled by soil water content and decoupled the impact of the total amount of precipitation on vegetation in the northeastern area of the watershed. Hence, our method presents a new tool for building an ecohydrological model that includes deterministic parameters of mechanical significance.

Variations in lake water storage over Inner Mongolia during recent three decades based on multi-mission satellites

Lake shrinkage is a frequent water resource problem in arid and semi-arid regions that can affect the hydrological cycle and damage the local aquatic environment. Since the 1990 s, many lakes in Inner Mongolia have shrunk significantly. However, the causes of the shrinkage and long-term storage trends of lakes in Inner Mongolia are still poorly understood. Using observations from multi-mission satellites, we analyzed water storage changes in six major lakes in Inner Mongolia over the past 30 years and explored the underlying causes. The results reveal divergent changes between the two lake categories. For each of the tectonic lakes (including Lake Hulun, Lake Dalinuoer, Lake Daihai, and Lake Hongjiannao), the water storage has continuously decreased with annual rates of 0.067 km3/yr, 0.012 km3/yr, 0.021 km3/yr, and 0.01 km3/yr, respectively. In contrast, for oxbow lakes (including Lake Hasuhai and Lake Wuliangsuhai), the storage trends were insignificant (0.001 km3/yr for both). Human activities were the most significant driving factors, especially coal mining, agricultural irrigation, and grazing, which explained more than 70% of the storage changes in Lake Hulun, Lake Dalinuoer, and Lake Hasuhai. Among the natural factors, evapotranspiration and runoff were secondary influences on these lake storage changes. This study provides a systematic analysis and attribution of water storage changes for major lakes in Inner Mongolia and provides scientific guidance for local water resource management and planning. Our method provides a useful example of using multi-mission satellite data for estimating water storage changes.

Response of Soil Water Storage to Meteorological Factors in Alpine Shrub Meadow on Northeastern Qinghai–Tibetan Plateau

The Qinghai–Tibet Plateau (QTP) has an important function in ensuring the water ecological security of China, even Asia, and the soil water storage of alpine grassland is an important part of the ecosystem water. Grassland degradation directly affects the soil water storage capacity. However, the impact of degradation on specific soil storage capacity, especially alpine shrubs, is rarely studied. Here, we chose two plots of alpine non-degraded shrub and degraded shrub, using the automatic soil moisture monitoring system to study the change process of soil moisture storage, and then adopted the boosted regression tree (BRT) model to quantitatively evaluate the relative influence of environmental variables on soil water storage. Our results show: (1) The soil water storage in the growing season (May–September) is higher than that in the non-growing season (January–April and October–December), and the soil water storage reaches its highest in mid-July. (2) During the growing season, the 100 cm soil temperature was the most important factor affecting the seasonal variation in soil water storage, accounting for 51% of the total variation. During the non-growing season, the 40 cm soil temperature was the most important factor affecting the variation in soil water storage, accounting for 80% of the total variation. (3) The soil water storage of non-degraded Potentilla fruticosa shrub meadow increased by 6–25%, compared with degraded grassland shrub meadow during growing-season. (4) Various meteorological factors have a weak impact on soil water storage.

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.

Bathymetric and Capacity Relationships Based on Sentinel-3 Mission Data for Aswan High Dam Lake, Egypt

Aswan High Dam Lake (AHDL) is one of the most relevant hot spots at both local and global levels after construction of the Grand Ethiopian Renaissance Dam (GERD) was announced. The management of AHDL is a vital task, which requires the input of reliable information such as the lake bathymetry, water level, and the water surface area. Traditional, bathymetric methods are still very expensive and difficult to operate. Nowadays, satellite data and remote sensing techniques are easily accessible. In particular, datasets produced by operational missions are freely and globally available, and may provide efficient and inexpensive solutions for the retrieval of quantitative parameters concerning strategic water bodies, such as AHDL. This work identifies the performance of Sentinel-3A optical imagery data in the visible and NIR bands from the two optical instruments SLSTR and OLCI, and proposes the integration with Sentinel-3A radar altimetry from SRAL instrument applied to AHDL. This preliminary and first study investigated the relationship between the reflectance data and in situ data for water depth after a bathymetric campaign in the deep-water region using statistical regression models. These statistical models showed promising results in terms of correlation value (R2 &gt; 0.8) and normalized root mean square errors (NRMSE &lt; 0.4). Also, Heron’s formula was applied to combine optical imagery and Sentinel-3 altimetry water level datasets to estimate water storage variations in AHDL. In addition, equations governing the relationship between water level, water surface area, and water volume were analyzed. The work is very useful for all authorities and stakeholders dealing with large water bodies.

Remote sensing reconstruction of long-term water level and storage variations of a poorly-gauged river in the Tibetan Plateau

Study regionThe upper Brahmaputra River, China. Study focusThe long-term water level and storage variations of the upper Brahmaputra River (UBR) are not yet clear due to the low accessibility of gauged data in high-altitude and harsh environments. The main objective of this study is to facilitate investigations of long-term river water level and storage variations over the UBR. Here we developed a hypsometric method to reconstruct temporally resolved long-term river water level observations based on remote sensing-derived accumulative water inundation area information and short-term altimetric water level data at 24 virtual stations (VSs) of the UBR. We further analyzed the intra-annual and inter-annual variations of the water level (storage) changes of the studied river reaches. New hydrological insightsThe results reveal that the reconstructed water levels of the UBR are quite consistent with the water levels from the Hydroweb with an average R2 of 0.79 and the reconstructed water levels are generally consistent with the variation of the gauged runoff. The ascending amplitudes of the annual water level cycle of VSs were observed from upstream to downstream of the UBR, varying from 0.58 m to 6.92 m. Additionally, we find that the seasonal change in river water storage approximately accounted for one-tenth of the net mass seasonal amplitude of the UBR basin, as revealed from the Gravity Recovery and Climate Experiment (GRACE) data.

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.

Seasonal Amplitude of Water Storage Variations of the Yangtze–Huai Plain Lake Group: Implicaion for Floodwater Storage Capacity

Lakes are of significant importance in regulating floods and providing water sources. The seasonal water storage variations for the plain lake group in the Yangtze–Huai River Basin (YHRB) are significant for alleviating flood pressure and regulating runoff. However, to date, the seasonal amplitude of lake water storage variations and its capacity of buffering floodwater in the YHRB is not quantified well and remains to be investigated comprehensively. To advance the understanding of such a critical scientific issue, the water level data of the plain lake group (area&amp;gt;100 km2, 29 lakes) in the YHRB is collected from multi-source data between 1990 and 2020. Using lake inundation area obtained from Global Surface Water and water level variations, water storage dynamics for the plain lake group are quantified. Furthermore, this study also uses the Gravity Recovery and Climate Experiment (GRACE) products to analyze the terrestrial water storage anomalies (TWSA) in the whole basin. The results indicate that the seasonal amplitude of water level change and water storage variation of the plain lake group are 2.80 ± 0.71 m and 37.38 ± 14.19 Gt, respectively. Poyang and Dongting Lakes, two lakes that maintain the natural connection with the Yangtze River, have the most substantial seasonal amplitude in the hydrological situation. The amplitude in water level and water storage in Poyang Lake is 9.53 ± 2.02 m and 14.13 ± 5.54 Gt respectively, and that in Dongting Lake is 7.39 ± 1.29 m and 7.31 ± 3.42 Gt respectively. The contribution of seasonal variation of water storage for large plain lakes to TWSA in the YHRB is approximately 33.25%, fully reflecting these lake’s imperative position in the YHRB. This study is expected to enhance the scientific understanding of the seasonal hydrologic regime for the large lakes in the YHRB and contribute to the management of flood risks and water resources in East China.

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.

A Daily Water Balance Model Based on the Distribution Function Unifying Probability Distributed Model and the SCS Curve Number Method

A new daily water balance model is developed and tested in this paper. The new model has a similar model structure to the existing probability distributed rainfall runoff models (PDM), such as HyMOD. However, the model utilizes a new distribution function for soil water storage capacity, which leads to the SCS (Soil Conservation Service) curve number (CN) method when the initial soil water storage is set to zero. Therefore, the developed model is a unification of the PDM and CN methods and is called the PDM–CN model in this paper. Besides runoff modeling, the calculation of daily evaporation in the model is also dependent on the distribution function, since the spatial variability of soil water storage affects the catchment-scale evaporation. The generated runoff is partitioned into direct runoff and groundwater recharge, which are then routed through quick and slow storage tanks, respectively. Total discharge is the summation of quick flow from the quick storage tank and base flow from the slow storage tank. The new model with 5 parameters is applied to 92 catchments for simulating daily streamflow and evaporation and compared with AWMB, SACRAMENTO, and SIMHYD models. The performance of the model is slightly better than HyMOD but is not better compared with the 14-parameter model (SACRAMENTO) in the calibration, and does not perform as well in the validation period as the 7-parameter model (SIMHYD) in some areas, based on the NSE values. The linkage between the PDM–CN model and long-term water balance model is also presented, and a two-parameter mean annual water balance equation is derived from the proposed PDM–CN model.

Analysis of groundwater storage (GWS) dynamics and its temporal evolution for The Indo-Gangetic plain using GRACE data

The Indo-Gangetic plain in India is one of the heavily populated and water stressed region in the world. A major part of water demand i.e., 80% for agriculture and 60% for drinking, in this area is taken care by surface water and groundwater storage (GWS) by extracting water from ground via well through pumping. Because of the heavy demand, there is over-exploitation of the water resource which is creating alarming situation. In the North-West part of India, this condition is very severe as the precipitation is very low and can't recharge the ground water completely. The Gravity Recovery And Climate Experiment (GRACE) mission recorded the temporal variation in gravity which is mainly attributed to variation in water storage in the region. The GRACE data combined with the hydrological model (i.e., GLDAS) was used to estimate the change in groundwater for the region area. The Indo-Gangetic plain is an extensive study area, and hence the variation in seasonality over space is expected. With the assumption of seasonal variability, the study area was classified based on seasonality, and GWS analysis was done separately on each part. Further, each spatially classified region was analyzed separately for the trend estimation. Almost all previous studies were using conventional statistics for trend analysis on same or any other study area and have not used moving statistics. Moving statistics is statistical method in which rolling statistics is calculated by taking a subset of data and this method provides better understanding of temporal evolution of the process. Hence, assessment of GWS variance was performed by moving statistics to understand its time evolution, long term trend and seasonal behavior. This approach can further use for identifying extreme hydrological events like flood and drought in the study area. In the seasonal analysis, it was observed that the maximum depletion rate was for the monsoon season and that too for the area near the Himalayan belt. The estimated rate of depletion for the monsoon season for the three clusters were (0.30±0.02) cm/month, (0.21±0.02) cm/month and (0.19±0.02) cm/month respectively. With the moving statistics analysis, the Kashmir flood of year 2005 and the Assam flood of year 2012 were identified which was not detectable using conventional trend analysis.