Testing a knowledge-based approach for inferring spatio-temporal characteristics of groundwater in the Australian State of Victoria.

Groundwater spatio-temporal characteristics are important information for groundwater development and management. However, such information is usually insufficient or even unavailable in many regions around the world due to insufficient or even lack of in-situ data such as from boreholes. Recently, a knowledge-based approach was proposed to infer 'where' and 'when' to find groundwater using Lake Victoria Basin (LVB) as an example for data-deficient regions. In this knowledge-based approach, groundwater model and inversion analysis of groundwater impact factors are used to infer groundwater storage potential and recharge timing. In the LVB's case, only 10 borehole data were used to test the spatio-temporal behaviours of groundwater, which are insufficient. In this contribution, therefore, using the Australian State of Victoria as an example, with over 15,000 boreholes data, the performance of the same knowledge-based approach is further tested in a well-controlled area. The results indicate that the knowledge-based approach is able to correctly infer regions with large groundwater storage potential suitable for extraction. The recharge timing of groundwater is also correctly indicated as the results show consistency with the borehole data. This provides further evidence of the reliability of the knowledge-based approach for inferring spatio-temporal characteristics of groundwater.

Inference of the spatio-temporal variability and storage potential of groundwater in data-deficient regions through groundwater models and inversion of impact factors on groundwater, as exemplified by the Lake Victoria Basin.

Groundwater is an important resource for supporting domestic water use for people's livelihoods and for maintaining ecosystems. Borehole observations provide the first-hand data that characterise the fluctuation, depth, and aquifer conditions of the groundwater. Unfortunately, such observations are not available or are insufficient for scientific use in many regions. Taking the Lake Victoria Basin (LVB) as an example of data-deficient regions, this study proposes a simple knowledge-based approach that uses the Global Land Data Assimilation System (GLDAS) Catchment Land Surface Model (CLSM) for the main data, with rainfall, hydrological, topographical and geological datasets as supports, by which to infer the spatio-temporal variability and storage potential of groundwater. The method is based on analysis and inversion of impact factors on groundwater, and the feasibility of such a method is proven by showing that the groundwater results from GLDAS CLSM can correctly indicate the seasonality, as well as the link to topographical and geological features. For example, both results from the water balance equation (WBE) and GLDAS CLSM indicate that there are two groundwater recharge seasons in the basin, e.g., March to May and September to November. Compared to the eastern side of the LVB, the western side has mountains blocking surface runoff, and thus, reasonably, has larger storage potential estimates in GLDAS CLSM. Due to the low degree of weathering of the basement rocks, it is expected that there is only small storage potential and variation of groundwater in the southeastern parts of the LVB. GLDAS CLSM also correctly reflects this behaviour. Additionally, the largest groundwater storage potential over the LVB is found in regions near the Kagera River and the western shoreline, since it associates with unconsolidated rocks and behaviours of large groundwater recharge from GLDAS CSLM during the wet year of 2006. The major limitation of this knowledge-based method is that the uncertainty in terms of magnitude on GLDAS CLSM groundwater changes cannot be assessed, in addition to the fact that the reliability of the results cannot be quantified in terms of specific numbers. Therefore, the results and interpretation of groundwater behaviours using such methods can only be a guide for 'where' and 'when' to find groundwater.

Impacts of extreme climate on Australia's green cover (2003-2018): A MODIS and mascon probe.

Australia as a continent represents a semi-arid environment that is generally water-limited. Changes in rainfall pattern will inevitably occur due to rising temperatures caused by climate change, which has a direct impact on the distribution of Australia's vegetation (green cover). As variability in rainfall continues to increase, i.e., in frequency and/or magnitude, due to climate change, extreme climate events such as droughts are predicted to become more pervasive and severe that will have an adverse effect on vegetation. This study investigates the effects of extreme climate on Australia's green cover during 2003-2018 for the end of rainy seasons of April and October in the northern and southern parts, respectively, to (i) determine the state of vegetation and its changes, (ii) identify "hotspots", i.e., regions that constantly experienced statistically significant decrease in NDVI, and (iii), relate changes in the identified hotspots to GRACE-hydrological changes. These are achieved through the exploitation of the statistical tools of Principal Component Analysis (PCA) and Mann-Kendel Test on Gravity Recovery and Climate Experiment (GRACE) hydrological products on the one hand, and the utilization of Australia's rainfall product and Moderate Resolution Imaging Spectroradiometer Normalized Difference Vegetation Index (MODIS-NDVI) used here with its native spatial resolution of 0.002413∘ × 0.002413∘ on the other hand. Differences between 3-year intervals from 2003 to 2018 for both April and October datasets are used to quantify vegetation variations. Through area change analysis, the vegetation differences (2003-2018) indicate that April exhibited larger increase (13.77% of total vegetation area) than decrease (7.83%) compared to October, which experienced slightly larger decrease (9.41%) than increase (8.71%). South Australia and Western Australia emerge as "hotspots" in which vegetation statistically decreased in October, with no noticeable change in April. GRACE-based hydrological changes in both hotspots reflect a decreasing trend (2003-2009) and increasing trend (2009-2012) that peaks in 2011, which then transitions towards a gradually decreasing trend after 2012. Australia-wide climate variability (ENSO and IOD) influenced vegetation variations during the data period 2003 to 2018.

Consistency of agricultural drought characterization over Upper Greater Horn of Africa (1982-2013): Topographical, gauge density, and model forcing influence.

The negative impact of Upper Greater Horn of Africa's (UGHA) complex topography on drought characterization exacerbated by gauge density and model forcing parameters has not been investigated. In order to fill this gap, this study employs a combination of remotely sensed, in situ, and model products (1982-2013); precipitation (CHIRPS, GPCC, and CHIRP), soil moisture (ERA-Interim, MERRA-2, CPC, GLDAS, and FLDAS), vegetation condition index (VCI), and total water storage products (GRACE and MERRA-2) to (i) characterize drought, (ii) explore the inconsistencies in areas under drought due to topographical variations, gauge density, and model forcing parameters, and (iii), assess the effectiveness of various drought indicators over Ethiopia (a selected UGHA region with unique topographical variation). A 3-month time scale that sufficiently captures agricultural drought is employed to provide an indirect link to food security situation in this rain-dependent region. The spatio-temporal drought patterns across all the products are found to be dependent on topography of the region, at the same time, the inconsistencies in characterizing drought is found to be mainly driven by topographical variability (directly) and gauge density (inversely) for precipitation products while for soil moisture products, precipitation forcing parameters plays a major role. In addition, the inconsistencies are found to be higher under extreme and moderate droughts than severe droughts. The mean differences in the percentage of areas under drought and different drought intensities over the region are on average 15.87% and 6.16% (from precipitation products) and 12.65% and 5.20% (from soil moisture products), respectively. On the effectiveness of various indicators, for the duration under study, the following were found to be most suitable over Ethiopia; VCI, GPCC, ERA, CPC, and FLDAS. These results are critical in putting into perspective drought analysis outcomes from various products.

GRACE-derived groundwater changes over Greater Horn of Africa: Temporal variability and the potential for irrigated agriculture.

Greater Horn of Africa (GHA) is projected to face negative impacts on per capita food production due to dwindling nature of water resources forced by climate change and rising population growth. The region has limited groundwater irrigated agriculture and also lacks groundwater monitoring infrastructure. This study (i) employs Independent Component Analysis (ICA) to localize Gravity Recovery and Climate Experiment (GRACE)-derived groundwater changes and analyses the corresponding temporal variabilities and their link to climate indices (Indian Ocean Dipole (IOD) and El Niño-Southern Oscillation (ENSO)), and (ii), explores the irrigation potentials of the localized groundwater. Monthly GRACE-derived groundwater changes showed similar temporal variability to WaterGap Hydrological Model (WGHM), i.e., a correlation of 0.7 (significant at 95% confidence level), highlighting GRACE's potential to provide GHA-wide changes in groundwater. Based on GHA aquifer location maps, the study associated the localized groundwater changes to nine major aquifers namely; Nubian sandstone, Karoo Carbonate, Upper Nile, Ethiopian highlands, Lake Tana region, Kenya-Somalia, Central Tanzania, Karoo sandstone, and Ruvuma. All temporal groundwater changes, except Nubian sandstone and Kenya-Somalia, showed an annual (cyclic) pattern indicating an annual (yearly) recharge cycle. Weak relationships with rainfall and both climate indices were noted. Maximum correlation occurred when rainfall preceded the temporal groundwater changes by several months. Based on water availability (from GRACE), water quality (indicated by the total dissolved substance) and dominant soil types, potential for groundwater irrigated agriculture results showed: low potentials for Nubian Sandstone and Kenya-Somalia areas; low to moderate potentials for Karoo Carbonate, Lake Tana region, central Tanzania, and Ruvuma; moderate to high potentials for Upper Nile and Karoo Sandstone; and high potential for Ethiopian highland. Even though the study has considered relatively short time period (10 years), these results are critical to the sustainable management of the region's groundwater resources and appropriate/informed policy formulation.

Spatio-temporal groundwater variations associated with climatic and anthropogenic impacts in South-West Western Australia.

South-West Western Australia (SWWA) is a critical agricultural region that heavily relies on groundwater for domestic, agricultural and industrial use. However, the behaviours of groundwater associated with climate variability/change and anthropogenic impacts within this region are not well understood. This study investigates the spatio-temporal variability of groundwater in SWWA based on 2997 boreholes over the past 36 years (1980-2015). Results identify the decline in groundwater level (13 mm/month) located in the central coastal region of SWWA (i.e., north and south of Perth) to be caused by anthropogenic impacts (primary factor) and climate variability/change (secondary). In detail, anthropogenic impacts are mainly attributed to substantial groundwater abstraction, e.g., hotspots (identified by above 7 m/month groundwater level change) mostly occur in the central coastal region, as well as close to dams and mines. Impacts of climate variability/change indicate that coupled ENSO and positive IOD cause low-level rainfall in the coastal regions, subsequently, affecting groundwater recharge. In addition, correlation between groundwater and rainfall is significant at 0.748 over entire SWWA (at 95% confidence level). However, groundwater in northeastern mountainous regions hardly changes with rainfall because of very small amounts of rainfall (average 20-30 mm/month) in this region, potentially coupled with terrain and geological impacts. A marked division for groundwater bounded by the Darling and Gingin Scarps is found. This is likely due to the effects of the Darling fault, dams, central mountainous terrain and geology. For the region south of Perth and southern coastal regions, a hypothesis through multi-year analysis is postulated that rainfall of at least 60 and 65-70 mm/month, respectively, are required during the March-October rainfall period to recharge groundwater.

The newly merged satellite remotely sensed, gauge and reanalysis-based Multi-Source Weighted-Ensemble Precipitation: Evaluation over Australia and Africa (1981-2016).

The Australian and African continents, regions prone to hydroclimate extremes (e.g., droughts and floods), but with sparse distribution of rain-gauge that are limited in time, rely heavily on complementary satellite and reanalysis data to provide important crucial information necessary for informing policies and management. The problem, however, is that satellite products suffer from systematic biases while reanalysis products carry over uncertainties from their forcing parameters. Multi-Source Weighted-Ensemble Precipitation (MSWEP) is a new global rainfall-product that merges satellite, rain-gauge and re-analysis data to exploit their advantages and minimise their disadvantages. Although MSWEP has been validated globally, this product, together with its potential applications, e.g., in water storage fluxes, river discharge and climate impacts studies over Australia and Africa, regions with urgent need of reliable products, has however, not been verified. Using GRACE satellite products, GLDAS model data, GRDC runoff products, and ENSO/IOD climate indices; five rainfall products - FLUXNET, BoM, GPCC, CHIRPS, and AgCFSR; and a suite of statistical methods (Pearson, Kolmogorov-Smirnov, PCA and Three-Corner-Hat (TCH)), this study (i) evaluates monthly MSWEP-V2.1 data (1981-2016), and (ii), assesses its potential applications to water storage flux (within the water balance framework), river discharge analysis, and climate impacts studies. The results show good MSWEP correlations and cumulative distribution with BoM product over most of Australia except in regions with heavy monsoonal rainfall, e.g., northern and north-western Australia where it tends to underestimate. Over Africa, MSWEP has no obvious advantages compared to insitu-GPCC, satellite-CHIRPS or reanalysis-AgCFSR. Furthermore, it is unable to reflect on major hydro-climate extremes over west, east and southern Africa, where it underestimates compared to CHIRPS. Its potential applications to water storage flux, discharge and climate impacts over the two continents show better suitability for water storage flux in Africa, while no advantages are seen compared to other rainfall products on other aspects.

Assessing data assimilation frameworks for using multi-mission satellite products in a hydrological context.

With a growing number of available datasets especially from satellite remote sensing, there is a great opportunity to improve our knowledge of the state of the hydrological processes via data assimilation. Observations can be assimilated into numerical models using dynamics and data-driven approaches. The present study aims to assess these assimilation frameworks for integrating different sets of satellite measurements in a hydrological context. To this end, we implement a traditional data assimilation system based on the Square Root Analysis (SQRA) filtering scheme and the newly developed data-driven Kalman-Takens technique to update the water components of a hydrological model with the Gravity Recovery And Climate Experiment (GRACE) terrestrial water storage (TWS), and soil moisture products from the Advanced Microwave Scanning Radiometer - Earth Observing System (AMSR-E) and Soil Moisture and Ocean Salinity (SMOS) in a 5-day temporal scale. While SQRA relies on a physical model for forecasting, the Kalman-Takens only requires a trajectory of the system based on past data. We are particularly interested in testing both methods for assimilating different combination of the satellite data. In most of the cases, simultaneous assimilation of the satellite data by either standard SQRA or Kalman-Takens achieves the largest improvements in the hydrological state, in terms of the agreement with independent in-situ measurements. Furthermore, the Kalman-Takens approach performs comparably well to dynamical method at a fraction of the computational cost.

The application of multi-mission satellite data assimilation for studying water storage changes over South America.

Constant monitoring of total water storage (TWS; surface, groundwater, and soil moisture) is essential for water management and policy decisions, especially due to the impacts of climate change and anthropogenic factors. Moreover, for most countries in Africa, Asia, and South America that depend on soil moisture and groundwater for agricultural productivity, monitoring of climate change and anthropogenic impacts on TWS becomes crucial. Hydrological models are widely being used to monitor water storage changes in various regions around the world. Such models, however, comes with uncertainties mainly due to data limitations that warrant enhancement from remotely sensed satellite products. In this study over South America, remotely sensed TWS from the Gravity Recovery And Climate Experiment (GRACE) satellite mission is used to constrain the World-Wide Water Resources Assessment (W3RA) model estimates in order to improve their reliabilities. To this end, GRACE-derived TWS and soil moisture observations from the Advanced Microwave Scanning Radiometer - Earth Observing System (AMSR-E) and Soil Moisture and Ocean Salinity (SMOS) are assimilated into W3RA using the Ensemble Square-Root Filter (EnSRF) in order to separately analyze groundwater and soil moisture changes for the period 2002-2013. Following the assimilation analysis, Tropical Rainfall Measuring Mission (TRMM)'s rainfall data over 15 major basins of South America and El Niño/Southern Oscillation (ENSO) data are employed to demonstrate the advantages gained by the model from the assimilation of GRACE TWS and satellite soil moisture products in studying climatically induced TWS changes. From the results, it can be seen that assimilating these observations improves the performance of W3RA hydrological model. Significant improvements are also achieved as seen from increased correlations between TWS products and both precipitation and ENSO over a majority of basins. The improved knowledge of sub-surface water storages, especially groundwater and soil moisture variations, can be largely helpful for agricultural productivity over South America.

Understanding the global hydrological droughts of 2003-2016 and their relationships with teleconnections.

Droughts often evolve gradually and cover large areas, and therefore, affect many people and activities. This motivates developing techniques to integrate different satellite observations, to cover large areas, and understand spatial and temporal variability of droughts. In this study, we apply probabilistic techniques to generate satellite derived meteorological, hydrological, and hydro-meteorological drought indices for the world's 156 major river basins covering 2003-2016. The data includes Terrestrial Water Storage (TWS) estimates from the Gravity Recovery And Climate Experiment (GRACE) mission, along with soil moisture, precipitation, and evapotranspiration reanalysis. Different drought characteristics of trends, occurrences, areal-extent, and frequencies corresponding to 3-, 6-, 12-, and 24-month timescales are extracted from these indices. Drought evolution within selected basins of Africa, America, and Asia is interpreted. Canonical Correlation Analysis (CCA) is then applied to find the relationship between global hydro-meteorological droughts and satellite derived Sea Surface Temperature (SST) changes. This relationship is then used to extract regions, where droughts and teleconnections are strongly interrelated. Our numerical results indicate that the 3- to 6-month hydrological droughts occur more frequently than the other timescales. Longer memory of water storage changes (than water fluxes) has found to be the reason of detecting extended hydrological droughts in regions such as the Middle East and Northern Africa. Through CCA, we show that the El Niño Southern Oscillation (ENSO) has major impact on the magnitude and evolution of hydrological droughts in regions such as the northern parts of Asia and most parts of the Australian continent between 2006 and 2011, as well as droughts in the Amazon basin, South Asia, and North Africa between 2010 and 2012. The Indian ocean Dipole (IOD) and North Atlantic Oscillation (NAO) are found to have regional influence on the evolution of hydrological droughts.

Physical dynamics of Lake Victoria over the past 34 years (1984-2018): Is the lake dying?

Understanding changes in the physical dynamics of lakes (e.g., areas and shorelines) is important to inform policies, planning and management during climate extremes (e.g., floods and droughts). For Lake Victoria, the world's second largest freshwater lake, its physical dynamics and associated changes are not well understood as evidenced, e.g., from the citations of its area 66,400 - 69,485 km2, length 300 - 412 km, width 240 - 355 km, and shorelines 3300 - 4828 km. Its sheer size and lack of research resources commitment by regional governments hamper observations. This contribution employs a suite of remotely sensed products for the past 34 years (1984-2018); Landsat, Sentinel-2, MODIS, Google Earth Pro, CHIRPS, Multivariate El' Niño-Southern Oscillation Index and altimetry data together with the physical parameters from 37 publications (1969-2018) to (i) study the lake's dynamics and establish its current (2018) state, (ii) identify and analyse hotspots where significantly dynamic changes occur, and (iii), study the contributions of climate change and anthropogenic activities on these dynamics. Utilizing manual digitisation, MNDWI, NDVI and PCA methods, the study shows the lake's mean surface area to be 69,295 km2 (i.e., 812 km2 or 1.2% more than that of the 37 publications) and its 2018 value to be 69,216 km2 (i.e., ∼733 km2 (1.1%) more than that of the 37 publications). As to whether the lake is dying, it shrunk by 203 km2 (0.3%) compared to its 1984 value, a decrease noted mainly in four hotspot Gulfs (Birinzi 40%, Winam 20%, Emin Pasha 38% and Mwanza 55%). Correspondingly, the expansion of Nalubaale Dam (2002-2006) decreased the areas by 31%, 10%, 21% and 44%, respectively. Seasonal analysis shows an increase of 9 km2 in the lake's area during the heavy rainy season (March-May) while the ENSO enlarged the area by 0.23% (2007) and 0.45% (2010). It is evident, therefore, that both climate variability/change and anthropogenic activities are exerting a toll on the tropical's largest freshwater body thereby necessitating careful exploitation and management plans.

Improved remotely sensed satellite products for studying Lake Victoria's water storage changes.

Lake Victoria (LV), the world's second largest freshwater lake, supports a livelihood of more than 42 million people and modulates the regional climate. Studying its changes resulting from impacts of climate variation/change and anthropogenic is, therefore, vital for its sustainable use. Owing to its shear size, however, it is a daunting task to undertake such study relying solely on in-situ measurements, which are sparse, either missing, inconsistent or restricted by governmental red tapes. Remotely sensed products provide a valuable alternative but come with a penalty of being mostly incoherent with each other as they originate from different sources, have different underlying assumptions and models. This study pioneers a procedure that uses a Simple Weighting approach to merge LV's multi-mission satellite precipitation and evaporation data from various sources and then improves them through a Postprocessing Filtering (PF) scheme to provide coherent datasets of precipitation (p), evaporation (e), water storage changes (Δs), and discharge (q) that accounts for its water budget closure. Principal component analysis (PCA) is then applied to the merged-improved products to analyze LV's spatio-temporal changes resulting from impacts of climate variation/change. Compared to the original unmerged data (0.62 and 0.37 average correlation for two samples), the merged-improved products are largely in agreement (0.91 average correlation). Furthermore, smaller imbalances between the merged-improved products are obtained with precipitation (37%) and water storage changes (35%) being the largest contributors to LV's water budget. This data improvement scheme could be applicable to any inland lake of a size similar to LV.

Understanding the association between climate variability and the Nile's water level fluctuations and water storage changes during 1992-2016.

With the construction of the largest dam in Africa, the Grand Ethiopian Renaissance Dam (GERD) along the Blue Nile, the Nile is back in the news. This, combined with Bujagali Dam on the White Nile are expected to bring ramification to the downstream countries. A comprehensive analysis of the Nile's waters (surface, soil moisture and groundwater) is, therefore, essential to inform its management. Owing to its shear size, however, obtaining in-situ data from "boots on the ground" is practically impossible, paving way to the use of satellite remotely sensed and models' products. The present study employs multi-mission satellites and surface models' products to provide, for the first time, a comprehensive analysis of the changes in Nile's stored waters' compartments; surface, soil moisture and groundwater, and their association to climate variability (El Niño Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD)) over the period 1992-2016. In this regard, remotely sensed altimetry data from TOPEX/Poseidon (T/P), Jason-1, and Jason-2 satellites along with the Gravity Recovery And Climate Experiment (GRACE) mission, and the Tropical Rainfall Measuring Mission Project (TRMM) rainfall products are applied to analyze the compartmental changes over the Nile River Basin (NRB). This is achieved through the creation of 62 virtual gauge stations distributed throughout the Nile River that generate water levels, which are used to compute surface water storage changes. Using GRACE total water storage (TWS), soil moisture data from multi-models based on the Triple Collocation Analysis (TCA) method, and altimetry derived surface water storage, Nile basin's groundwater variations are estimated. The impacts of climate variability on the compartmental changes are examined using TRMM precipitation and large-scale ocean-atmosphere ENSO and IOD indices. The results indicate a strong correlation between the river level variations and precipitation changes in the central part of the basin (0.77 on average) in comparison to the northern (0.64 on average) and southern parts (0.72 on average). Larger water storages and rainfall variations are observed in the Upper Nile in contrast to the Lower Nile. A negative groundwater trend is also found over the Lower Nile, which could be attributed to a significantly lower amount of rainfall in the last decade and extensive irrigation over the region.

Understanding linkages between global climate indices and terrestrial water storage changes over Africa using GRACE products.

Africa, a continent endowed with huge water resources that sustain its agricultural activities is increasingly coming under threat from impacts of climate extremes (droughts and floods), which puts the very precious water resource into jeopardy. Understanding the relationship between climate variability and water storage over the continent, therefore, is paramount in order to inform future water management strategies. This study employs Gravity Recovery And Climate Experiment (GRACE) satellite data and the higher order (fourth order cumulant) statistical independent component analysis (ICA) method to study the relationship between terrestrial water storage (TWS) changes and five global climate-teleconnection indices; El Niño-Southern Oscillation (ENSO), North Atlantic Oscillation (NAO), Madden-Julian Oscillation (MJO), Quasi-Biennial Oscillation (QBO) and the Indian Ocean Dipole (IOD) over Africa for the period 2003-2014. Pearson correlation analysis is applied to extract the connections between these climate indices (CIs) and TWS, from which some known strong CI-rainfall relationships (e.g., over equatorial eastern Africa) are found. Results indicate unique linear-relationships and regions that exhibit strong linkages between CIs and TWS. Moreover, unique regions having strong CI-TWS connections that are completely different from the typical ENSO-rainfall connections over eastern and southern Africa are also identified. Furthermore, the results indicate that the first dominant independent components (IC) of the CIs are linked to NAO, and are characterized by significant reductions of TWS over southern Africa. The second dominant ICs are associated with IOD and are characterized by significant increases in TWS over equatorial eastern Africa, while the combined ENSO and MJO are apparently linked to the third ICs, which are also associated with significant increase in TWS changes over both southern Africa, as well as equatorial eastern Africa.

A study of Bangladesh's sub-surface water storages using satellite products and data assimilation scheme.

Climate change can significantly influence terrestrial water changes around the world particularly in places that have been proven to be more vulnerable such as Bangladesh. In the past few decades, climate impacts, together with those of excessive human water use have changed the country's water availability structure. In this study, we use multi-mission remotely sensed measurements along with a hydrological model to separately analyze groundwater and soil moisture variations for the period 2003-2013, and their interactions with rainfall in Bangladesh. To improve the model's estimates of water storages, terrestrial water storage (TWS) data obtained from the Gravity Recovery And Climate Experiment (GRACE) satellite mission are assimilated into the World-Wide Water Resources Assessment (W3RA) model using the ensemble-based sequential technique of the Square Root Analysis (SQRA) filter. We investigate the capability of the data assimilation approach to use a non-regional hydrological model for a regional case study. Based on these estimates, we investigate relationships between the model derived sub-surface water storage changes and remotely sensed precipitations, as well as altimetry-derived river level variations in Bangladesh by applying the empirical mode decomposition (EMD) method. A larger correlation is found between river level heights and rainfalls (78% on average) in comparison to groundwater storage variations and rainfalls (57% on average). The results indicate a significant decline in groundwater storage (∼32% reduction) for Bangladesh between 2003 and 2013, which is equivalent to an average rate of 8.73 ± 2.45mm/year.