Evaluating water storage changes and addressing drought challenges in areas like the Tana sub-basin in Ethiopia is difficult due to limited data availability. The aim of this study was to evaluate the dynamics of terrestrial water anomaly and drought incidences by employing multiple data source. The Gravity Recovery and Climate Experiment (GRACE) and Global Land Data Assimilation System (GLDAS) datasets were used to assess the long-term water storage dynamics and drought incidences using the weighted water storage deficit index (WWSDI). WWSDI was used to identify drought periods, which ranged from severe to extreme drought. Despite the overall increase in average annual total water storage anomaly (TWSA) by 0.43 cm/year and a net gain of 50.68 cm equivalent water height from 2003 to 2022, there were instances of terrestrial water storage deficits, particularly in 2005, 2006, and 2009, during historical drought periods. The TWSA exhibited a strong correlation with Lake Tana water storage and precipitation anomalies after adjusting lag times. WWSDI displayed a high correlation with WSDI but a weak correlation with SPI and SPEI. Therefore, utilization of GRACE and GLDAS data is promising for evaluating terrestrial water storage and monitoring drought in data-deficient regions like the Tana sub-basin in Ethiopia.

  • The long-term dynamics of terrestrial water storage in the studied region showed a noticeable rising tendency.

  • During periods of earlier dry spells, deficiencies in terrestrial water storage were noted.

  • The long-term variation of total water storage anomaly was well expressed by weighted water storage deficit index (WWSDI) in the study area.

  • WWSDI exhibited a strong association with the water storage deficit index, but displayed a weak correlation with the traditional standard precipitation index and standard potential evapotranspiration index.

Ethiopia is referred to as the ‘water tower of Africa’ (MoWR 2011; Khadim et al. 2020; Woldegebriel et al. 2022) since many of its rivers originate from the central plateau and flow toward neighboring countries. The Blue Nile River is one of the major tributaries of the Nile River, which originates from Lake Tana (the largest lake in Ethiopia) and flows through Ethiopia and Sudan before joining the White Nile at Khartoum to form the Nile River.

The Tana sub-basin, located in the Upper Blue Nile Basin, has ample reserves of both groundwater and surface water resources (Ayenew et al. 2008; Abiy & Melesse 2017; Kindie et al. 2019). However, the sustainability of these resources has become questionable due to the increasing demand for agricultural, domestic, and industrial uses (Abiy et al. 2016a, 2016b; Abera et al. 2021). Overexploitation of these resources without considering sustainability may worsen the effects of human activities and climate extremes such as drought (Abiy et al. 2016a, 2016b; Nigate et al. 2016; Wakode et al. 2018; Abera et al. 2021). The stresses on groundwater and surface water resources are interrelated, and their depletion can negatively impact soil moisture as well (Abiy et al. 2016a, 2016b; Mamo et al. 2016; Walker et al. 2019; Tigabu et al. 2020). Therefore, proper management of terrestrial water storage (TWS), which integrates mainly surface water, groundwater, and soil moisture, is indispensable for sustainable development and proper utilization of water resources of the sub-basin. Improved water resources management requires monitoring systems that timely and accurately estimating the status of total or terrestrial water sources (Strassberg et al. 2009). Conventional methods for monitoring water resources and climate extremes (drought) involve taking hydrological and meteorological measurements at specific points, which may not be available or adequate in developing countries. Moreover, these methods are time consuming and expensive for large regions (Strassberg et al. 2009; Ochsner et al. 2013; Bi et al. 2014; Sun et al. 2021). Therefore, utilizing remotely sensed (RS) data and integrating it with traditional ground-based methods is essential to gaining a thorough understanding of the quantitative fluctuations of water resources in a given area.

Gravity Recovery and Climate Experiment (GRACE) (March 2002 to October 2017) and its successor GRACE-FO (GRACE-follow on) (since May 2018 to present) provide high-accuracy monthly gravitational field measurements of the water resources (Wang & Zhang 2017). Hereafter, GRACE is used to designate both GRACE and its follower GRACE-FO for convenience. GRACE has two identical satellites orbiting together at 220 km apart in a near-circular orbit and are connected by a highly accurate K-band microwave used to measure the distance variations between them due to gravity variations, including an accelerometer to remove nongravitational forces such as atmospheric effects (Tapley et al. 2004). GRACE is the recent remote sensing technology that paves a new way to assess the dynamics of terrestrial water storage anomalies (TWSAs). TWSA involves surface water storage anomaly (SWSA), snow water equivalent storage anomaly (SWESA), soil moisture storage anomaly (SMSA), and groundwater storage anomaly (GWSA) (Sun et al. 2021; Zhang et al. 2022).

Global Land Data Assimilation System (GLDAS) and land surface models (LSMs) are also used to estimate TWSA, which can be employed to compare with the GRACE-derived TWSA (Syed et al. 2008; Mohamed 2020; Liu et al. 2023). Numerous researchers have employed GRACE and GLDAS for different applications including assessing the dynamics of TWSA (Seyoum 2018; Xiong et al. 2021; Djessou et al. 2022), monitoring drought (Chen et al. 2018; Elameen et al. 2023; Getahun et al. 2023), and conducting groundwater change studies (Barbosa et al. 2022; Rusli et al. 2023) are a few examples.

Drought refers to a sustained TWS deficit caused mainly by climate variability, which severely influences socioeconomic development and environmental systems (Bayissa et al. 2018; Nigatu et al. 2021b; Getahun et al. 2023). Drought is becoming more frequent and intense in many parts of the world, including Ethiopia (Enyew et al. 2014; Nigatu et al. 2021b; Elameen et al. 2023; Getahun et al. 2023). Recurrent droughts occur in Ethiopia every 8–10 years or even 3–5 years, especially in the north (Enyew et al. 2014; Getahun et al. 2023). The Tana sub-basin in the Upper Blue Nile region has been the subject of several drought studies (Awange et al. 2014; Enyew et al. 2014; Legese et al. 2016; Bayissa et al. 2018; Seyoum 2018). Nevertheless, these studies have relied on traditional hydrological modeling that uses limited in situ meteorological and hydrological data. The use of RS data for investigating both TWS dynamics and drought monitoring is very limited in the region. Investigating the long-term TWS variations using multiple data sources and drought events using weighted water storage deficit index (WWSDI) is the first of its kind in Tana sub-basin. This study aims to (i) evaluate the long-term variations of TWSA employing different sources of TWSA, (ii) identify drought events and their interaction with TWSA dynamics, and (iii) test the performance of WWSDI in identifying drought events.

Description of the study area

Tana sub-basin is located in northwestern Ethiopia in the Upper Blue Nile Basin (Figure 1). The sub-basin has a total surface area of about 15,092 km2. This sub-basin consists of the largest lake in Ethiopia (Lake Tana), which covers approximately 20% of the sub-basin surface area. Lake Tana is fed by major rivers such as Gilgel Abbay, Ribb, Megech, and Gumara. Base flow in these rivers also contributes to the lake water balance (Mamo et al. 2016; Walker et al. 2019; Tigabu et al. 2020). The elevation of the sub-basin ranges from the lowest altitude of 1,785 m (outlet of Lake Tana) to the highest elevation of 4,032 m (Guna Mountain). The study region has a tropical climate with a mono-modal rainy season from June to September (Abebe et al. 2017; Duan et al. 2018). The maximum rainfall reaches 250–330 mm/month during July or August and the mean annual rainfall reaches up to 1,400 mm (Abebe et al. 2017; Kindie et al. 2019). The temperature of the study area shows slight seasonal variability with a 20 °C mean value (Setegn et al. 2010; Abebe et al. 2017).
Figure 1

Study area map. Note: BDRMS = Bahir Dar meteorological station, LTLGS = Lake Tana level gauge station, Met_station = meteorological station, GMS = Gondar meteorological station. DTMS = Debre Tabor meteorological station, and DMS = Dangila meteorological station.

Figure 1

Study area map. Note: BDRMS = Bahir Dar meteorological station, LTLGS = Lake Tana level gauge station, Met_station = meteorological station, GMS = Gondar meteorological station. DTMS = Debre Tabor meteorological station, and DMS = Dangila meteorological station.

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Dataset and analysis

GRACE data

GRACE enables one to track how water is transported and stored within the Earth's environment (Tapley et al. 2004; Wei et al. 2022). GRACE provides TWSA, which constitutes surface water storage (SWSA), SWESA, soil moisture storage (SMSA), and GWSA (Strassberg et al. 2009; Khorrami & Gunduz 2021; Djessou et al. 2022; Wang et al. 2023) (Equation (1)):
formula
(1)

The GRACE dataset has four processing (using different algorithms) and archive centers, namely, Center for Space Research (CSR), Jet Propulsion Laboratory (JPL), Goddard Space Flight Center (GSFC), and GeoForschungs Zentrum Potsdam (GFZ) (Wahr et al. 1998; Landerer & Swenson 2012; Khorrami & Gunduz 2021). These GRACE satellite datasets have two forms, mass concentration (Mascon) and spherical harmonics (SH) solutions. The GRACE Mascon solutions have a lesser leakage error than the SH (Save et al. 2016; Scanlon et al. 2016; Wei et al. 2022). In addition, Mascon solutions do not need postprocessing, filtering, and have less dependency on scale factors (Save et al. 2016; Li et al. 2019). Thus, the GRACE Mascon solutions retrieved from CSR (http://www2.csr.utexas.edu/grace), GSFC (https://earth.gsfc.nasa.gov/geo/data/grace-mascons), and JPL (https://podaac-tools.jpl.nasa.gov/drive/files/GeodeticsGravity/tellus/L3/mascon) were employed.

Global land data assimilation system

GLDAS consists of LSMs using satellite and in situ observations to estimate disintegrated TWS components such as SWES, SWS (summation of canopy water storage and surface runoff (Qs)), and SMS (Sun et al. 2021; Xiong et al. 2021). GLDAS LSMs comprise the community land model, the Mosaic, the variable infiltration capacity (VIC), the NOAH, and the catchment land surface (CLSM) models (Li et al. 2019). In the present study, three GLDAS version 2.1 LSMs (Noah, VIC, and CLSM) were selected to calculate the average TWSA and compared with GLDAS 2.2-derived and GRACE-derived TWSA.

The latest version of GLDAS is GLDAS-2.2, which renders integrated TWS including groundwater storage (GWS) daily and 0.25-degree spatial resolution data from February 2003 to the present. It is the product of ground-based and model combined data, and data assimilation with CSR-GRACE/GRACE-FO (Li et al. 2019; Hasan et al. 2021). The CSR mascon solution was selected in GLDAS 2.2 data assimilation because of its finer spatial resolution, no postprocessing requirement, and less leakage (Li et al. 2019). The data were retrieved from https://disc.gsfc.nasa.gov/datasets?keywords=GLDAS_CLSM025_DA1_D_2.2&page=1.

Unlike the earlier GLDAS versions and GRACE, GLDAS-2.2 is the best for providing finer spatiotemporal resolution of TWS and GWS products. It also avoids the data gaps in TWSA derived from GRACE/GRACE-FO created between the GRACE end and GRACE-FO launch. In the present study, anomalies for the 20-year study period (from 2003 to 2022) were computed using the same base time (2004–2009) as GRACE-derived TWSA. The GLDAS 2 documented by Rui et al. (2020) was inferred for further information for all the aforementioned GLDAS versions and products. The Climate Data Operators, Quantum Geographic Information System (QGIS), RStudio, and python were applied for the GRACE and GLDAS NetCDF data processing and analysis.

Satellite altimetry data

The satellite altimetry missions including Topex/Poseidon, jason2, jason3, Jason, gfo, Envisat, Saral, and sentinel3A have provided an accurate and continuous surface height of lakes and inland (Crétaux et al. 2011; Ni et al. 2017). Several researchers have used satellite altimetry to estimate the lakes' water level changes (Calmant et al. 2008; Zhang et al. 2011; Ni et al. 2017). In the present study, the satellite altimetry data derived from the aforementioned missions was downloaded from http://www.legos.obs-mip.fr/soa/hydrologie/hydroweb/StationsVirtuelles/SV_Lakes/Tana.html and used. These data were employed to surrogate the data gaps in the ground-based Lake Tana water level gauge station (LTLGS) installed near the Bahir Dar meteorological station (Figure 1) for TWSA comparisons. The Tana water level obtained from combining the in situ and satellite altimetry was converted to monthly anomalies by removing the mean of the same base time (2004–2009) as GRACE-derived TWSA data.

In situ data

Available data such as meteorological data (precipitation. temperature, wind speed, sunshine hours, and relative humidity) and daily water level of Lake Tana (at LTLGS) were acquired from the National Meteorological Agency (NMA) and the Ministry of Water and Energy (MoWE), respectively, for verification of GRACE- and GLDAS-simulated TWSA and drought indices.

Missing data imputation

Unlike GLDAS and altimetry data, GRACE data have 18 missing data from February 2003 to June 2017. There are also 11 consecutive months' data gaps (from July 2017 to May 2018) due to the gaps between GRACE ending and GRACE-FO launching periods. Besides, the August and September GRACE data were missed in 2018. First, the nonconsecutive missing data from February 2003 to June 2017 were filled using the linear interpolation method in the pandas-python data analysis library. Second, by using the previously filled data, the 11 consecutive months of missing GRACE data were filled by applying the seasonal auto-regressive integrated moving average (SARIMAX) forecasting model. Python package (statsmodels.tsa.statespace.sarimax) was employed for the imputation process (Barbosa et al. 2022).

TWSA decomposition

The TWSA time series data were disintegrated into its compartments (trend, seasonal or cyclic, and residual) using the season-trend decomposition by Loess (STL) method using statsmodels. tsa._stl python module (Seyoum 2018; Khorrami & Gunduz 2021). Disintegrating TWSA using the robust STL is imperative to identify a dominant component in time series analysis (Khorrami & Gunduz 2021). The STL technique was suggested by Cleveland et al. (1990) as expressed in Equation (2):
formula
(2)
where F(t) is the time series data which are TWSA in the present study, Tt is the trend (increasing or decreasing behavior), St is the seasonal (cyclic behavior), and Rt is the residual (random) component of TWSA.

Trend analysis

The Mann–Kendall (MK) trend test and Sen's slope estimator (Mann 1945; Kendall 1975) were employed to distinguish the existence of a trend and to estimate its slope magnitude (b) (Equation (8)), respectively (Getahun et al. 2023; Jiqin et al. 2023). However, serial correlation impacts the MK test negatively (Mirabbasi et al. 2020; Swain et al. 2022). The modified Mann-Kendal (MMK) overcomes this effect and offers the most reliable result (Mirabbasi et al. 2020; Swain et al. 2022). Therefore, MMK and Sen's slope estimator trend analysis methods were employed in the present study. The temporal time series data to be tested for the trend has a significant trend if Mackinnon's p-value < 0.05 and has no significant trend otherwise (Baig et al. 2021; Jiqin et al. 2023). The Sen's slope (b) sign implies a significant increasing trend (positive slope), and a significant decreasing trend (negative slope) when the absolute value of MMK's standardized test statistic (Zmmk) is greater than 1.96 (Banda et al. 2021; Swain et al. 2022; Aschale et al. 2023). Equations, which can be employed in MMK trend test analysis and Sen's slope estimator, are described from Equations (3) to (8) as suggested by several scholars (Banda et al. 2021; Swain et al. 2022; Aschale et al. 2023). The MK's test statistic, S, is computed as follows:
formula
(3)
where Li and Lj are the temporal data at consecutive i and j times, respectively; N is the number of data points in the long-term time series data, and the Sign() function is defined in Equation (4) as follows:
formula
(4)
The MK's test statistic variance (Vrmk (S)) multiplied by a correlation factor (Cf) provides the MMK's variance (Vrmmk) (Equation (5)):
formula
(5)
where N is the number of data points in the time series data, g is the number of grouped data points having the same values in the time series, and di is the data points number in the ith group.
The MMK's standardized test statistic (Zc) is determined by Equation (6) as follows:
formula
(6)
Sen's slope indicates the magnitude of the existing linear trend line (Equation (7)) in the temporal dataset:
formula
(7)
where F(t) is a function of temporal data; b is Sen's slope (Equation (8)), and c is constant:
formula
(8)
where Li and Lj with the subscripts are the same as defined in Equation (3). In the present study, however, the Pymannkendall python package was used to apply the aforementioned equations in the MK trend analysis.

Drought/wetness indices

TWSA provides a better opportunity to detect climate extremes than the traditional drought or wetness measuring indices such as standard precipitation index (SPI) and standard potential evapotranspiration index (SPEI) (Xiao et al. 2023). In recent years, researchers have made use of the opportunity of TWSA derived from GRACE to estimate droughts using the terrestrial water storage deficit index (WSDI) (Hasan et al. 2021; Nigatu et al. 2021b; Getahun et al. 2023). However, WSDI uses only the integrated TWSA and does not consider its components, which cannot provide better drought/wetness index results (Elameen et al. 2023). To better estimate the drought/wetness index, a WWSDI, which considers the contribution of each component of TWSA (Wang et al. 2020; Elameen et al. 2023), was adopted in the present study.

Water storage deficit index

WSDI is a common drought monitoring method derived from TWSA and can be estimated by using Equation (9) (Nigatu et al. 2021b; Elameen et al. 2023; Getahun et al. 2023):
formula
(9)
where WSDij is the water storage deficit (Equation (10)) in the ith month and jth year, and and Sd are the mean and standard deviation of WSD time series data:
formula
(10)
where TWSAij and are TWSA in the ith month and jth year, and the average of the same ith month long-term TWSA in the study period.

Weighted water storage deficit index

WWSDI can be obtained by summing up all the TWSA components multiplied by their corresponding component ratios (Equation (11)) as suggested by early researchers (Zhang et al. 2019; Wang et al. 2020; Elameen et al. 2023):
formula
(11)
where CR1,n are the weight of the component ratios (Equation (12)) and S1,n are the components of the TWSA:
formula
(12)
where MADi is defined by Equation (13) as follows:
formula
(13)
where MADi is the mean absolute deviation of the ith month, Sij is the TWSA component in the ith month and jth year and is the average of the ith month long-term TWSA component throughout the study period.
Moreover, Vart in Equation (12) is the total variability of all the TWSA components expressed in Equation (14):
formula
(14)

SPI and SPEI

SPI and SPEI are the widely used traditional methods for drought monitoring (Seyoum 2018; Lotfirad et al. 2022; Getahun et al. 2023). SPI and SPEI are used to identify meteorological droughts for the short timescales, and hydrological and agricultural droughts for the medium and higher timescales (3 months and greater timescales) (Cammalleri et al. 2019; Getahun et al. 2023). These were employed here to compare the drought indices derived from TWSA. In the computation of SPI, monthly rainfall data were used as input, whereas in SPEI computation, monthly climatic-water balance, which is the rainfall after removal of potential evapotranspiration (PET), was employed. PET can be calculated using different available methods such as Hargreaves, Thronthwaite, Penman–Monteith, and FAO method procedures based on the available datasets (Beguería & Vicente-Serrano 2017). In the present study, Hargreaves and FAO methods were applied using SPI and SPEI functions in the SPEI RStudio package. The detailed procedures to calculate SPI and SPEI using spi and spei functions in the SPEI RStudio package are described in Beguería & Vicente-Serrano (2023).

Statistical measures for validation

The statistical indices were used to evaluate the relationship between observed and computed values from satellite data. The Pearson's correlation coefficient (r), Nash–Sutcliffe Efficiency (NSE), and root-mean-square error (RMSE) were used in the present study. Pearson's correlation coefficient (r) refers to the degree of linear relationship between the observed (v) and simulated (w) data. If the correlation coefficient, r in Equation (15) is close to one, it shows better model accuracy and vice versa:
formula
(15)
where and are the observed (Vi) and simulated (wi) mean variable values, respectively, and N is the total samples taken.
The NSE ranges between −1 and 1. The values of NSE between 0 and 1 indicate acceptable values whereas less than or equal to zero values portray unacceptable simulation results (Moriasi et al. 2007). The NSE was computed using Equation (16), and the variables are the same as defined in Equation (14):
formula
(16)
The RMSE is one of the widely used statistical measures to compare and estimate the difference between observed and simulated values (Karran et al. 2014; Alghafli et al. 2023). The lower value of RMSE reveals the best model and vice versa for the higher values:
formula
(17)

Terrestrial water storage anomaly and dominant components

The terrestrial water storage anomaly (TWSA) derived from CSR (CSR-TWSA), JPL (JPL-TWSA), and GSFC (GSFC-TWSA) with missed values are depicted in Figure S1. Missed data in each GRACE-TWSA source were imputed, and then the GRACE-TWSA, the ensemble mean of the three sources of GRACE, data were computed (Figure S2). GRACE-TWSA data are strongly correlated (r = 0.99) with CSR-TWSA, JPL-TWSA, and GSFC-TWSA data. Nevertheless, GRACE-TWSA is differently associated (NSE = 0.97, RMSE = 2.11), (NSE = 0.91, RMSE = 3.58 cm), and (NSE = 0.96, RMSE = 2.25 cm) with CSR-TWSA, JPL-TWSA, and GSFC-TWSA data, respectively. It can be perceived here that CSR-TWSA has the best correlation with the GRACE-TWSA in the Tana sub-basin. The previous study by Abiy & Melesse (2017) took only the TWSA derived from the CSR mascon solution and showed a plausible result in the Tana sub-basin. Nigatu et al. (2021b) also employed and confirmed that CSR-mascons have lesser uncertainty than the GRACE SH in the Nile River basin, which includes the present study area.

The average GLDAS-derived TWSA was estimated using GLDAS 2.1 products obtained from NOAH, VIC, and CLSM. The TWSA components, namely, SWS anomaly (SWSA), SMS anomaly (SMSA), and SWESA, were derived from the mean of these three GLDAS LSMs. Rivers, lakes, and reservoirs are included in the SWSA but are not specifically modeled in GLDAS models. On the other hand, Qs anomaly (QsA) can substitute for SWSA (Bhanja et al. 2016; Shamsudduha & Taylor 2020), or QsA and CWSA can be summarized (Sun et al. 2021; Xiong et al. 2021). Here, the total of QsA and CWSA (CWSA + QsA) was used. Less water equivalent depth (ranging from −0.014 to 0.038 cm) was seen in the result, which was not included in the GLDAS-derived TWSA computation method (Figure S3).

The dominant components of the TWSA derived from GLDAS 2.2 (CLSM_CSR-TWSA) are soil moisture (CLSM_CSR-SMSA) and GSMAs (CLSM_CSR-GWSA) (Figure 2). The other components of CLSM_CSR-TWSA such as SWESA, CWSA, and QsA have the same values as obtained in the GLDAS 2.1 product. The long-term monthly CLSM_CSR-TSWA is highly correlated (r = 0.76) with GLDAS-TWSA despite the least amplitude that GLDAS-TWSA possesses (Figure 3). The least amplitude of GLDAS-TWSA is mainly linked to the lack of a GWSA component in it. The CLSM_CSR-TWSA, however, is very highly correlated (r = 0.93) with GRACE-TWSA and has comparable amplitude because of its GWSA component (Figure 3).
Figure 2

TWSA derived from GLDAS and GRACE assimilation in GLDAS2.2 and components.

Figure 2

TWSA derived from GLDAS and GRACE assimilation in GLDAS2.2 and components.

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Figure 3

Monthly long-term (2003–2022) TWSA of different datasets.

Figure 3

Monthly long-term (2003–2022) TWSA of different datasets.

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In general, the dominant components of TWSA derived from GLDAS 2.1 and GLDAS 2.2 are SMSA and GWSA, respectively. According to Wang et al. (2023), reducing the significance of SWSA in GRACE-TWSA decomposition by completely substituting it with the sum of QsA and CWSA might result in inaccurate findings. Twenty percent of the research area is made up of Lake Tana, and even in cases where reservoirs only make up one 1% of the watershed, their contribution to SWSA may not be disregarded (Longuevergne et al. 2013; Abiy & Melesse 2017). However, this study is not intended to address the process of translating Lake Tana level data to the real SWSA of the entire sub-basin (Djessou et al. 2022; Wang et al. 2023).

Long-term TWSA variations

The trend analysis result of the GRACE-TWSA showed a significant (p < 0.05) increasing trend of equivalent water thickness with 0.054 cm/month (Figure 3) and 0.604 cm/year (Figure 4). In the same fashion, the result of CLSM_CSR-TWSA trend analysis revealed a significant (p < 0.05) increasing trend with 0.042 cm/month and 0.431 cm/year equivalent water height. In the contrary, GLDAS-TWSA exhibited slightly decreasing behavior, which was not statistically significant (p > 0.05) (Figures 3 and 4). This was mainly attributed to the minor declining effects of GLDAS-SMSA data, derived from the three GLDAS 2.1 (VIC, NOAH, and CLSM (Figure S4)) data. Unlike the trivial decreasing trend of GLDAS-SMSA, CLSM_CSR-SMSA showed a significant (p < 0.05) increasing trend (Figure 2). This might be due to the superior performance of the new model of GLDAS 2.2 CLSM, which is assimilated with GRACE-CSR than the preceding version as evidenced in the earlier work (Khaki & Awange 2021).
Figure 4

Annual ensemble mean of TWSA for different sources.

Figure 4

Annual ensemble mean of TWSA for different sources.

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Despite some variations shown, both GRACE-TWSA and CLSM_CSR-TWSA recorded TWS deficit, which may be mainly attributed to the climatic extreme (drought), which occurred in 2005 and 2009 (Abiy & Melesse 2017; Getahun et al. 2023). The GRACE-TWSA and CLSM_CSR-TWSA, however, have a net gain of 70.24 and 50.68 cm equivalent water height, respectively. The TWSA monthly mean of the study period (Figure 5) revealed that the deficit (negative values of TWSA) and surplus (positive values) follow the dry and wet seasons or the rainfall pattern, respectively, as evidenced by the related previous work (Abiy & Melesse 2017).
Figure 5

TWSA monthly ensemble mean of the entire study period (2003–2022).

Figure 5

TWSA monthly ensemble mean of the entire study period (2003–2022).

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Comparison of TWSA with Lake Tana storage and precipitation anomalies

The GRACE-TWSA, CLSM_CSR-TWSA, and GLDAS-TWSA were compared with Lake Tana water storage (LTWSA) and precipitation anomalies (PAs) (Figure 6) to verify the TWSA sources as practiced in other studies (Voss et al. 2013; Nigatu et al. 2021b; Liu et al. 2023). Moreover, Lake Tana is fed mainly by major rivers and groundwater via the base flow of these rivers, and in turn, aquifers by precipitation recharge (Abiy et al. 2016a, 2016b; Mamo et al. 2016; Walker et al. 2019; Tigabu 2020; Yenehun et al. 2022). Lake Tana water level is gauged near Bahir Dar station, which is located at the average topographic elevation of Tana Lake (Yasuda et al. 2022). There has been a very high correlation (r = 0.99) with altimetry satellite and TWSA data. Thus, the altimetry data were used to surrogate large missing data within the Lake Tana water level at Bahir Dar station.
Figure 6

Temporal variation of TWSA of different sources compared with LTWSA and PA.

Figure 6

Temporal variation of TWSA of different sources compared with LTWSA and PA.

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The GRACE-TWSA exhibits a good correlation (r = 0.88, NSE = 0.7, RMSE = 8.59 cm) with a month lag LTWSA and (r = 0.87, NSE = 0.73, RMSE = 7.06 cm) with 2-month lag PA. On the other hand, the CLSM_CSR-TWSA has the goodness of fit (r = 0.84, NSE = 0.57, RMSE = 10.15 cm) with a month lag LTWSA and (r = 0.88, NSE = 0.67, RMSE = 7.76 cm) with a 2-month lag PA. The GLDAS-TWSA has also a good relation with a month lag LTWSA (r = 0.64, NSE = 0.09, RMSE = 14.83 cm) and with 2-month lag PA (r = 0.79, NSE = 0.5, RMSE = 9.6 cm). The goodness-of-fit results revealed that GLDAS-TWSA has the least correlation with in situ LTWSA and PA. This is mainly attributed to the lack of the GWSA component. Therefore, it could not be taken for further analysis. The GRACE-TWSA has a bit better goodness of fit than CLSM_CSR-TWSA. This is because GRACE-TWSA has a lesser amplitude than that of CLSM_CSR-TWSA (Figure 6). Nevertheless, a lesser amplitude of GRACE-TWSA may be induced due to leakage errors in GRACE-TWSA (Wei et al. 2022). Moreover, unlike GRACE-TWSA, CLSM_CSR-TWSA has no missed values, and thus, no errors were induced during the missing data-filling process (Elameen et al. 2023). Therefore, CLSM_CSR-TWSA was selected for drought analysis.

Advanced drought/wetness index (WWSDI)

The dominant components of the CLSM_CSR-TWSA are GWSA and SMSA as mentioned earlier. The advanced drought/wetness index (WWSDI) was calculated using these two components following the procedures suggested by Elameen et al. (2023). The CLSM-CSR-TWSA decomposition revealed a trend, seasonal/cyclic, and residual components, which contribute 56, 12, and 32%, respectively. These components were also used to calculate drought/wetness indices to compare with WWSDI (Khorrami & Gunduz 2021).

The results of the drought/wetness index computed using WWSDI are depicted in Figure 7 along with other indices. Eleven drought events were identified using this advanced index (Table 1). The largest drought intensity (Dint = 2.564) appeared from February 2003 to June 2003, in which, an extreme drought (WWSDI = −3.52) condition was registered (Figure 7). There were two drought events having the same and greatest drought duration (Dt = 26) that occurred from November 2004 to December 2006 and from July 2008 to August 2010 (Table 1). In these drought events, normal to severe drought events occurred as portrayed in Figure 7. These drought periods identified here by WWSDI were also confirmed by the earlier studies in the Upper Blue Nile (Seyoum 2018) and the entire Blue Nile Basin (Nigatu et al. 2021b; Elameen et al. 2023). The longest wetness events occurred from September 2018 to May 2022, which ranged from normal (WWSDI = 0.033) to severe wet (WWSDI = 2.031).
Table 1

Drought incidences identified by WWSDI

Drought eventsDrought duration (Dt)Drought magnitude (Dm)Drought intensity (Dint)
5 (Feb 03 to Jun 03) −12.82 −2.564 
4 (Mar 04 to Jun 04) −2.079 −0.52 
26 (Nov 04 to Dec 06) −27.092 −1.042 
8 (Sep 07 to Apr 08) −6.614 −0.827 
26 (Jul 08 to Aug 10) −22.325 −0.859 
7 (Nov 10 to May 11) −2.646 −0.378 
13 (Jul 11 to Jul 12) −7.425 −0.571 
11 (Sep 12 to Jul 13) −4.977 −0.452 
4 (Jul 15 to Oct 15) −3.307 −0.827 
10 4 (Nov 16 to Feb 17) −1.591 −0.398 
11 5 (Jan 18 to May 18) −2.38 −0.476 
Drought eventsDrought duration (Dt)Drought magnitude (Dm)Drought intensity (Dint)
5 (Feb 03 to Jun 03) −12.82 −2.564 
4 (Mar 04 to Jun 04) −2.079 −0.52 
26 (Nov 04 to Dec 06) −27.092 −1.042 
8 (Sep 07 to Apr 08) −6.614 −0.827 
26 (Jul 08 to Aug 10) −22.325 −0.859 
7 (Nov 10 to May 11) −2.646 −0.378 
13 (Jul 11 to Jul 12) −7.425 −0.571 
11 (Sep 12 to Jul 13) −4.977 −0.452 
4 (Jul 15 to Oct 15) −3.307 −0.827 
10 4 (Nov 16 to Feb 17) −1.591 −0.398 
11 5 (Jan 18 to May 18) −2.38 −0.476 
Figure 7

Comparison between WWSDI and different indices.

Figure 7

Comparison between WWSDI and different indices.

Close modal

WWSDI correlation with other indices

The WWSDI was compared with other commonly used traditional drought/wetness indices such as SPI, SPEI, and WSDI (Figure 7). In addition, the water storage deficit indices derived from the deseasoned (WSDIds), detrended (WSDIdt), and residual (WSDIres) values of TWSA were also employed to compare with the WWSDI as suggested by Khorrami & Gunduz (2021).

The WWSDI has a very high correlation with the GWSA-derived drought index (WSDIgws) (r = 0.997, NSE = 0.99, RMSE = 0.08 cm) (Table 2) since it is the largest component of the TWSA. Similarly, the drought magnitude (Dm) and intensity (Dint) of WWSDI throughout the study period (February 2003 to September 2022) have approximately the same as that of WSDIgws. For example, in the prolonged drought period from November 2004 to December 2006, Dm and Dint identified by WWSDI were 27.093 and 1.042, respectively (Table 1), whereas Dm and Dint identified by WSDIgws were 26.796 and 1.031, respectively. Second, WWSDI showed a strong correlation (r = 0.99, NSE = 0.98, RMSE = 0.13 cm) with WSDI, which was validated and widely used drought/wetness index around the globe (Sinha et al. 2017; Cammalleri et al. 2019; Nigatu et al. 2021b; Getahun et al. 2023).

Table 2

Goodness of fit between WWSDI and WSDI with other indices

IndicesWSDIWSDIltwsaWSDIsmsWSDIgwsWSDIdsWSDIdtWSDIres
WWSDI r 0.992 0.565 0.913 0.997 0.977 0.570 0.555 
NSE 0.98 0.13 0.83 0.99 0.95 0.14 0.11 
RMSE (cm) 0.13 0.93 0.42 0.08 0.21 0.93 0.94 
WSDI r 1.000 0.552 0.947 0.993 0.979 0.601 0.580 
NSE 1.00 0.10 0.89 0.99 0.96 0.20 0.16 
RMSE (cm) 0.00 0.95 0.33 0.12 0.2 0.89 0.92 
IndicesWSDIWSDIltwsaWSDIsmsWSDIgwsWSDIdsWSDIdtWSDIres
WWSDI r 0.992 0.565 0.913 0.997 0.977 0.570 0.555 
NSE 0.98 0.13 0.83 0.99 0.95 0.14 0.11 
RMSE (cm) 0.13 0.93 0.42 0.08 0.21 0.93 0.94 
WSDI r 1.000 0.552 0.947 0.993 0.979 0.601 0.580 
NSE 1.00 0.10 0.89 0.99 0.96 0.20 0.16 
RMSE (cm) 0.00 0.95 0.33 0.12 0.2 0.89 0.92 

The third-best correlation (r = 0.98, NSE = 0.95, RMSE = 0.21 cm) (Table 2) was observed with WSDIds. This was linked mainly to the least contribution (12%) of seasonal/cyclic component to TWSA, and thus, removing it was not sensitive for drought dynamics derived from WSDIds. WWSDI has also a fair correlation (r = 0.565, NSE = 0.13, RMSE = 0.93 cm) (Table 2) with the drought/wetness index derived from LTWSA (WSDIltwsa), a bit better than WSDI, which revealed the enhanced performance of WWSDI over WSDI (Table 2). In the present study area, however, WWSDI and WSDI are strongly correlated and can alternatively be employed to identify drought indices. This is mainly associated with GWSA accounting for a higher weight component ratio (CR = 0.73), whereas SMSA accounted for a lesser component ratio (CR = 0.27) of the TWSA.

It is interesting to note that the WWSDI) is considered to be a superior drought/wetness index compared to the WSDI. Unlike the WSDI, the WWSDI takes into account the contribution of TWSA components. However, in the present study, both WWSDI and WSDI showed similar results in identifying droughts, including onset, duration, intensity, and magnitude. This can be attributed to the fact that the GWSA component has a dominant contribution (73%) to TWSA.

The SPI and SPEI drought/wetness indices (from 3 to 12 timescales) were also computed and compared with WWSDI and WSDI (Figure 8). The average precipitation, maximum, and minimum temperature data of the sub-basin from 2003 to 2018 were used due to data limitations. The site-specific SPI and SPEI calculations were also carried out at the three (Debre Tabor, Dangila, and Gondar) selected meteorological stations (Figure 1). The selection was based on the spatial distribution and their full meteorological data (wind speed, sunshine hours, and relative humidity) besides precipitation and temperature for the extended hydrological period from 2003 to 2021 (Figures S5–S7).
Figure 8

Temporal correlation of WWSD with different indices from 2003 to 2018.

Figure 8

Temporal correlation of WWSD with different indices from 2003 to 2018.

Close modal

WWSDI and WSDI have the best correlation with SPI9, SPI12, SPEI9, and SPEI12 within the sub-basin (Figure 8 and Table 3) and site-specific (Tables S1–S3) conditions. WWSDI and WSDI have relatively the highest association with SPI12 and/or SPEI12 of the Tana sub-basin and other site-specific except the Dangila meteorological station. The relatively highest correlation between WSDI and SPI12 was in agreement with the past study of drought using WSDI by Seyoum (2018). The rationale that WWSDI and WSDI have the highest correlation with longer timescales (9 and 12) was that GWSA is the dominant input of both WWSDI and WSDI, which has a good connection with slow hydrological responses (Cammalleri et al. 2019). More improved correlations have been observed between WWSDI/WSDI and SPEI indices than between WWSDI/WSDI and SPI indices (Table 3) due to PET effects. In general, the correlations between SPI indices and their corresponding SPEI indices are very high (ranging from r = 0.94 to r = 0.98). This portrays that the longer timescale hydrological droughts in the study area are mainly controlled by precipitation and less influenced by PET (Getahun et al. 2023).

Table 3

Correlation (r) between WWSDI/WSDI with SPI and SPEI of different timescales

IndicesSPI3SPI6SPI9SPI12SPEI3SPEI6SPEI9SPEI12
WSDI 0.275 0.212 0.279 0.299 0.309 0.237 0.293 0.305 
WWSDI 0.265 0.214 0.279 0.303 0.297 0.239 0.293 0.310 
IndicesSPI3SPI6SPI9SPI12SPEI3SPEI6SPEI9SPEI12
WSDI 0.275 0.212 0.279 0.299 0.309 0.237 0.293 0.305 
WWSDI 0.265 0.214 0.279 0.303 0.297 0.239 0.293 0.310 

In general, WWSDI and WSDI are reasonably related to SPI and SPEI indices, but have poor correlations (r ranged from 0.212 to 0. 435); details can be referred to Tables 1, S1, S2, and S3. The existence of these poor correlations was mainly attributed to the differences in hydrological responses between TWSA-derived indices (WWSDI and WSDI) and precipitation and PET-derived indices (SPI and SPEI) (Sinha et al. 2017).

In the present study, the long-term variations of GRACE-TWSA, CLSM-CSR-TWSA, and GLDAS-TWSA were employed and analyzed for the study period ranging from 2003 to 2022 in the Tana sub-basin. The GRACE-TWSA and CLSM_CSR-TWSA exhibited a very high correlation (r = 0.93) with each other. The TWSA obtained from the three aforementioned sources has also a strong temporal correlation with hydrological (Tana Lake water storage anomaly) and meteorological (precipitation anomaly) data. Unlike the GLDAS-TWSA, GRACE-TWSA and CLSM_CSR-TWSA showed a significant (p < 0.05) increasing (0.054 cm/month and 0.604 cm/year) and (0.042 cm/month and 0.431 cm/year) trend, respectively. CLSM_CSR-TWSA was chosen for drought analysis. The net annual CLSM-CSR-TWSA showed a terrestrial water storage deficit in 2005, 2006, and 2009 due to the climatic extremes (drought events) that occurred during those years. The drought events identified in those years and other years of a few months using WWSDI ranged from normal to extreme, and strongly influenced the dynamics of TWSA. However, the overall long-term trend analysis showed a significant (p < 0.05) increasing trend and 50.68 cm net equivalent water height gain in the study period. The WWSDI was compared with drought indices such as WSDIgws, WSDIds, WSDIltwsa, and traditionally used indices (including WSDI, SPI, and SPEI). In this particular study region, WWSDI has a very high correlation (r = 0.99) with the commonly used drought/wetness index (WSDI) and poor correlation with SPI and SPEI at different timescales. The WWSDI is an advanced drought/wetness index and is better than other indices since it considers all the TWS components. Although the overall TWSA showed a significant increasing trend, this study recommends different drought adaptation techniques including afforestation and water conservation to lessen water losses concomitant to droughts. This study may also be used as the basis for future researches focusing on spatiotemporal water storage and drought dynamics in the studied region. This study, in general, suggests that employing GRACE- and GLDAS-derived TWSA is vital for analyzing the long-term dynamics of total water storage and drought monitoring for sustainable water resources management, especially in data-scarce regions.

We are grateful to Arba Minch University, Water Resources Research Center who partially financed this study under a small grants scheme with a project code of GOV/AMU/TH28/AWTI/WRRC/03/2014. We also thank the Ethiopian Ministry of Water and Energy for providing the hydrological data and the Ethiopian National Meteorological Agency (ENMA) for providing the weather data. Finally, we greatly acknowledge Amhara Regional State Water Bureau and design offices for providing the field and historical water level data.

All relevant data are included in the paper or its Supplementary Information.

The authors declare there is no conflict.

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