This study aims to assess hydrologic dynamics and the water resource potential of the Rift Valley Lake Basin (RVLB) in Ethiopia using the Water Evaluation and Planning System (WEAP) model. The surface water of the RVLB comprises streamflow and an open water system. The model was configured with 13 catchments and a monthly time series of approximately 43 years (1981–2022) to address the spatial variability of rainfall-runoff interaction. Statistical performance indicators were used to evaluate the accuracy of the model in simulating streamflows, and the results showed that the coefficient of determination (R2) ranges from 0.82–0.93, Nash-Sutcliffe coefficient of efficiency (NSE) ranges from 0.68–0.86, percentage of bias (PBIAS) ranges from –9.45 to –1.85, standard deviation ratio (RSR) ranges from 0.35 to 0.59 and index of agreement (IA) ranges from 0.62 to 0.84. The available surface water for abstraction is estimated to be 358 million cubic meters (MCM) available as lake water abstraction, and 6,534 MCM as streamflow water, making it a total surface water flow of 6,892 MCM. Considering the temporal distribution of the surface water sources, 67.5% is available in the rainy season, June–October, and 32.5% during the dry period, November–May, in the basin.

  • It solves the knowledge gap regarding the potential water resource availability of the basin with a comprehensive assessment.

  • The findings of this research can be used for planning water resources.

  • The WEAP model is used with appropriate data input for calibration and validation which make thoroughness and validity of the research.

  • It can be used as a literature for studies.

  • It indicated research gaps for further studies

The Rift Valley Lakes Basin (RVLB) is one of the 12 major river basins in Ethiopia. The RVLB is considered a high priority because it is an area of significant ecological and environmental interest, with a system of lakes and wildlife parks and reserves, having substantial areas of productive rainfed agricultural land and good rangelands (RVLBA 2020; Hulluka et al. 2023).

The RVLB is endowed with several rivers and lakes of all sizes. Although the basin has a significant amount of water resources, little has been developed for drinking water supply, hydropower, agriculture and other purposes (Ermias 2019). Over 80% of Ethiopia's sizable freshwater lakes are found in the RVLB and for more than 15 million people, this basin provides a versatile source of water (Abraham et al. 2021). The hydrological stability of most of the Ethiopian rift valley lakes is sensitive to climate variability (Belete et al. 2015).

The RVLB has 5.3–7.83 BCM water resource potential (Halcraw & GIRDC 2009; JICA 2012; MoWIE 2018; Ermias 2019). All the inconsistent reports generated regarding the actual current water resource potential of the RVLB are mainly due to the data, selected hydrological models and methodology used. This will impact the sustainable and efficient utilization and planning of water resources. Besides the discrepancies in the reports, the establishment of effective water resource management plans is hampered by the fact that the majority of the catchments that replenish these lakes are ungauged and have poorly measured water balances (Abraham et al. 2021).

Understanding the availability and use of water in a basin, the condition of the waterways, the effects of human activity on these ecosystems, effective management of water resources, and the potential effects of climate change on freshwater resources all depend on the assessment of water resources at the river basin scale. It also gives regulators the assurance they need to make wise decisions about the distribution of water, which promotes the efficient use of water resources and avoids conflicts over water use (ACIL Allen 2014; VSG 2020; Zy Harifidy et al. 2022).

Considering the climate change and variability and existing and future demand for water in the sectors, developing an optimized water resource development system and improving the water resource system performance is also a must. Hence, determining the hydrological dynamics and water resource potential of the basin helps with water resource planning, sustainable and efficient utilization of water and land resources and optimizing water allocation. This study assesses hydrological dynamics and water resource potential of the basin using the Water Evaluation And Planning (WEAP) model with the soil moisture balance method approach and it will help to optimize water allocation which will be conducted to the competitive sectors based on their demand, efficiency, equity and environmental sustainability. The outcomes of this study are useful for the RVLB authority, in managing the water resource and understanding its temporal and spatial distribution, and it is useful for the policymakers and stakeholders in the basin attempting efficient and sustainable utilization of the available water resource in the basin.

Study area

The RVLB is one of the 12 major river basins in Ethiopia with a total area of about 53,000 km2. The basin is characterized by a chain of lakes varying in size and in hydrological and hydrogeological settings. It constitutes seven lakes: Lake Ziway, Lake Langano, Lake Abiyata, Lake Shalla, Lake Hawassa, Lake Abaya and Lake Chamo (Figure 1) and all are located south and southwest of the Ethiopian capital, Addis Ababa. The RVLB is shared administratively between four regional states, Oromia Sidama, Central and South Ethiopia regions.
Figure 1

Location map of the RVLB.

Figure 1

Location map of the RVLB.

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Climate and hydrology

The principal feature of the RVLB is that it is a graben, a block fault geological structure in which the floor of the valley has become vertically displaced for the valley sides. As in all areas, the temperature and rainfall of the RVLB tend to vary as a function of elevation and, in consequence, so do relative humidity and potential evapotranspiration. The top of the rift valley on the east and west sides is, therefore, cooler, wetter and with lower evapotranspiration rates and hotter, drier and with higher evapotranspiration in the central lowlands. On the valley bottom, average annual rainfall varies from approximately 400 mm at Chew Bahir in the extreme south of the basin, to approximately 700 mm near the northern lakes. In contrast, average annual rainfall approaches 2,000 mm near Geese, west of Lake Chamo and also at Yirga Chefe, east of Lake Abaya, both on the higher edges of the basin. Two principal patterns of rainfall season are apparent in the RVLB. North of Lake Abaya, the main rains occur July through September, with a secondary peak in March or April. South of Lake Abaya, the main rains occur earlier in the year, between March and May. Annual average potential evapotranspiration varies from approximately 1,200 mm in the northeast of the basin to approximately 1,900 mm at Chew Bahir. Throughout the rift valley floor, from Lake Ziway to Lake Chamo, the average values are typically on the order of 1,550 mm. Annual average temperatures vary from approximately 27 °C on the valley floor near Chew Bahir to a low of approximately 13 °C at higher elevations, particularly in the northeast of the basin (Halcraw & GIRDC 2009).

WEAP model for water resource system analysis

Model description

The WEAP system river basin model was developed by the Stockholm Environment Institute (SEI) in Boston, USA (SEI 2015) and further upgraded with several improvements and input system arrangements with the work of different stakeholders. WEAP is comprehensive, straightforward and easy to use and attempts to assist rather than substitute for skilled planners. The data structure and level of detail may be customized to meet the requirements of a particular analysis and reflect the limits imposed by restricted data. A graphical interface facilitates the visualization of the physical features of the system and its layout within the catchment and it operates on the basic principle of water balance accounting. Therefore, WEAP was selected to simulate the water resources of the RVLB.

Hydrological model selection

The hydrological water balance models, such as WEAP, SWAT, MIKE HYDRO Basin and WatBal, are the commonly used models to study integrated water resource management and planning analysis. In this study, the WEAP model is applied to estimate the values of hydrological water balance elements. The soil moisture hydrological method in WEAP is one of the best soil moisture accounting hydrological methods that help easily separate the surface runoff, interflow, base flow parts of the streamflows analysis (Romilly 2011) and the volume and water levels of the lakes. It is a tool for integrated water resources planning that attempts to assist the planners and decision-making processes in providing a comprehensive, flexible and user-friendly framework for planning and policy analysis. It also gives the sub-catchment scale and stream scale water balance, which helps to separate the causes of changes, as natural and manmade, on the quantity and quality of water in the sub-basin. Therefore, this model will be used due to its appropriateness for the envisaged assignment that will help to implement the water allocation plan to improve the currently unregulated and excessive water abstractions in the sub-basin.

WEAP has four optional methods for analyzing the hydrological system in the basin unit. These methods are (1) the simplified coefficient of rainfall–runoff, (2) irrigation demand versions of the FAO crop water requirements approach, (3) the soil moisture method and (4) the MABIA method. We selected the soil moisture method because it accounts for the climatic, land use and soil type effects of the basin to compute the streamflow with its three mean types (i.e., surface runoff, interflow and base flow).

The hydrological system in the basin unit can be analyzed using four different approaches in the WEAP software. The soil moisture technique was selected for this study because it considers the basin's effects on climate, land use and soil type while computing the streamflow with its three main categories (surface runoff, interflow and base flow).

The soil moisture method is a one-dimensional, two-compartment (or ‘bucket’) empirical function that describes evapotranspiration, surface runoff, subsurface runoff (i.e., interflow and base flow) and deep percolation in a watershed unit. This method allows for the characteristics of land use and soil type impacts on processes. The deep percolation within the watershed unit can be transmitted to a surface water body as a base flow or directly to groundwater storage if an appropriate link is made between the watershed unit node and a groundwater node (Figure 2).
Figure 2

Conceptual diagram and equations incorporated in the soil moisture model.

Figure 2

Conceptual diagram and equations incorporated in the soil moisture model.

Close modal
A watershed unit can be divided into N fractional areas representing different land uses or soil types and a water balance is computed for each fractional area, j of N. Climate is assumed uniform over each catchment and the water balance is given as
formula
(1)
where Z1,j = [1,0] is the relative storage given as a fraction of the total effective storage of the root zone, Rdj (mm) for land cover fraction, j. The effective precipitation, Pe, includes snowmelt from accumulated snowpack in the catchment. Due to this empirical functionality, the method requires land use and climatic inputs. In the land use input section, the software takes the land use information of each catchment node as total area, percentage of area share for each land use and the Kc values of each land use type. The second major input is the climatic input, which requires areal rainfall, mean temperature, humidity and wind speed as a priority input.

Model input data

Major catchments
Based on the hydrological units, major water resource development and hydrological gauging stations, the basin was classified into 23 major watersheds. This watershed classification helps address the spatial variability of the input dataset as well as the calibrated parameters of the model (Figure 3). Accordingly, all the remaining input datasets of the model were organized based on these watersheds.
Figure 3

Major catchments and land use classification of the RVLB.

Figure 3

Major catchments and land use classification of the RVLB.

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Land use

After the acquisition high resolution images from the Sentinel-2 satellite, the images were processed to remove noises and artifacts using filtering. Feature extraction was the next step used for classification and then with semi-supervised classification the land use map was developed using GIS and finally outputs were evaluated to verify the accuracy. The land use map is developed as part of this study (Figure 3).

Further rearrangement of the land cover was done to facilitate the model to account for the irrigation and rainfed cultivation water use differently. The information for irrigated area mapping, which is conducted as part of this study using NDVI imagery analysis, Google Earth survey and ground truthing techniques was superimposed to the land use map and the share of the cropland divided into two as rainfed and irrigated cultivation.

When calculating evapotranspiration using the soil moisture balance method, the impacts of land use and cover were represented by the Kc value. The Kc value for the dominant crop type was considered in the model according to the crop calendar for each major catchment. The Kc values for each land use type were adopted from the FAO/EIAR reports and used as monthly data. Both the cropping calendar and Kc values differed between rainfed and irrigated areas.

Climatic data

Rainfall is a basic input in hydrological subsystem models. Climate data are present in each of the major catchments under consideration and are utilized as inputs to programs that predict the hydrological response. The monthly time series areal rainfall data for each major catchment were retrieved from a gridded CHRIPS precipitation product with a 4 km resolution from 1981 to 2022 because it requires monthly time series areal rainfall as the required input. Overlay analysis of Thiessen polygons surrounding each climatic grid point with a catchment layer was used to calculate areal precipitation over the catchment. Climatic datasets associated with each catchment, including temperature, wind and relative humidity monthly time series, were arranged as input files for the model.

Evaporation from lakes

Evaporation of water from a water surface – like an open reservoir, a swimming pool or similar – depends on water temperature, air temperature, air humidity and air velocity above the water surface.

Therefore, the formula used for estimating the evaporative loss of water from the Lakes in RVLB is (https://www.engineeringtoolbox.com/):
formula
(2)
where gs indicates the amount of evaporated water per second (kg/s); Θ = (25 + 19 v) = evaporation coefficient (kg/m2h); v indicates the velocity of air above the water surface (m/s); A indicates the water surface area (m2); xs indicates the maximum humidity ratio of saturated air at the same temperature as the water surface (kg/kg) (kg H2O in kg dry air); x indicates the humidity ratio of saturated air (kg/kg) (kg H2O in kg dry air).

Water resources potential assessment

System configuration and model set-up

The schematization of the model includes existing water resource systems, mainly lakes, reservoirs, and rivers. Major catchments were established by considering the availability of gauging stations. Therefore, the model schematizes the surface water resource systems of the basin using six catchment nodes and 17 river nodes. The natural lakes were schematized using reservoir nodes. Eleven selected hydrological gauging stations were used for the calibration and validation of the model (Figure 4).
Figure 4

Model schematized configuration.

Figure 4

Model schematized configuration.

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Calibration and validation of the model

The WEAP model was calibrated based on historical streamflows using manual methods. The calibration was achieved by comparing the historical pattern of streamflows and simulated flows. Calibration involves changing runoff parameters, altering demand priorities and modifying the operating rules of flow requirements to improve the fit between simulated and observed flows. Additionally, long-term gauge data of lake storage level and annual direct abstraction data from lakes (Lake Ziway: Pump inventory, discharge rates, irrigated area, irrigation water use data) are used for calibration. Even if all the assumptions made in the modeling were utterly correct, an exact fit between the simulation and observed flows is not to be expected. The three most sensitive runoff parameters, soil water capacity (Sw), runoff resistance factor (RRF) and deep conductivity (Kd), were adjusted to achieve calibration to streamflow. Other parameters, including crop coefficient (Kc), preferred flow direction (f), root zone conductivity (Ks), deepwater capacity (Dw), and initials (Z1 and Z2), were also used in calibration. The model is most sensitive to RRF. Thus, initial calibrations were focused on this parameter. Further refinements for base flow, the shape of the hydrograph and the timing of peak flows were adjusted using the remaining parameters.

Performance of the calibration and validation process was evaluated using statistical performance indicators: coefficient of determination (R2), Nash–Sutcliffe efficiency (NSE) and the percentage bias (PBIAS). The coefficient of determination (R2) assesses how strong the linear relationship between simulated and observed datasets and is heavily relied on by researchers when conducting trend analysis. It is used to check the fitness of simulated parameters with the observed ones. The NSE is used to evaluate how well the modeled streamflow matched the observed data. The NSE indicates how well a plot of observed versus simulated data fits into a 1:1 line. The PBIAS, on the other hand, calculates the relative volume difference between raw and observed rainfall volume. The index of agreement (IA) represents the ratio of the mean square error and the potential error. The standard deviation ratio (RSR) is calculated as the ratio of the root mean squared error (RMSE) and standard deviation of measured data. RSR varies from the optimal value of 0 to a large positive value. The formulas used for statistical performance are organized in Table 1 (Goshime et al. 2021a, 2021b).

Table 1

Formulas of statistical performance indicators

Performance indicatorEquationsBest fit values
Coefficient of determination  0.70 < R2 ≤ 1 Very good 
0.60 < R2 ≤ 0.70 good 
0.5 < R2 ≤ 0.60 Satisfactory 
Nash–Sutcliffe coefficient of efficiency   0.75 < NSE ≤1 Very good 
0.65 < NSE ≤ 0.75 good 
0.5 < NSE ≤ 0.75 Satisfactory 
Percentage of bias   PBIAS < ±10 Very good 
±10 ≤ PBIAS < ±15 Good 
±15 ≤ PBIAS < ±25 Satisfactory 
Index of agreement   0.75 < IA ≤1 Very good 
0.65 < IA ≤ 0.75 Good 
0.5 < AI ≤ 0.75 Satisfactory 
Standard deviation ratio   RSR < 0.5 Very good 
0.5 ≤ RSR ≤ 0.6 Good 
0.6 ≤ RSR ≤ 0.7 Satisfactory 
Performance indicatorEquationsBest fit values
Coefficient of determination  0.70 < R2 ≤ 1 Very good 
0.60 < R2 ≤ 0.70 good 
0.5 < R2 ≤ 0.60 Satisfactory 
Nash–Sutcliffe coefficient of efficiency   0.75 < NSE ≤1 Very good 
0.65 < NSE ≤ 0.75 good 
0.5 < NSE ≤ 0.75 Satisfactory 
Percentage of bias   PBIAS < ±10 Very good 
±10 ≤ PBIAS < ±15 Good 
±15 ≤ PBIAS < ±25 Satisfactory 
Index of agreement   0.75 < IA ≤1 Very good 
0.65 < IA ≤ 0.75 Good 
0.5 < AI ≤ 0.75 Satisfactory 
Standard deviation ratio   RSR < 0.5 Very good 
0.5 ≤ RSR ≤ 0.6 Good 
0.6 ≤ RSR ≤ 0.7 Satisfactory 

QS, simulated data; QO, observed data.

Table 2 presents the value of the performance evaluation measures for eleven gauging stations used for calibration. Once the parameters of the catchment are configured, the performance of the model is further validated using the same gauging station streamflow data with different time series. Besides the statistical measures, the graphical representation of the monthly time series featuring simulated results versus observed data indicates the performance of the model fit with the existing conditions, as shown in Figures 5 and 6.
Table 2

Statistical performance measures of calibration

Gauging stationsCalibration
Validation
Time rangeR2NSEPBIASIARSRTime rangeR2NSEPBIASIARSR
Meki @Meki 1996–2005 0.89 0.78 4.77 0.78 0.47 2007–2011 0.83 0.59 16.11 0.59 0.64 
Katar @Fitte 1983–1993 0.87 0.76 2.13 0.76 0.49 1996–2000 0.89 0.66 −16.94 0.66 0.56 
Bilate @Tena 1999–2006 0.85 0.74 −4.29 0.67 0.58 1989–1993 0.82 0.67 11.76 0.84 0.4 
Dijo @CV 1983–1991 0.82 0.71 4.88 0.71 0.54 1998–2002 0.81 0.64 6.49 0.64 0.59 
Gidabo @Aposto 1990–2000 0.93 0.85 3.63 0.7 0.55 1983–1988 0.83 0.7 −7.39 0.85 0.39 
Kulfo @AM 1996–2003 0.88 0.84 −6.45 0.84 0.35 2004–2008 0.82 0.53 −15.17 0.53 0.61 
Gelana @Tore 1991–1998 0.92 0.84 1.8 0.67 0.58 1999–2003 0.82 0.67 3.24 0.84 0.37 
Hamessa @Wajefo 1992–1997 0.85 0.77 2.57 0.65 0.59 1984–1988 0.82 0.65 12.59 0.77 0.34 
Weito @Bridge 1993–2002 0.83 0.74 2.57 0.65 0.59 2003–2007 0.82 0.65 −13.37 0.74 0.34 
Segen @Konso 1997–2003 0.84 0.86 5.01 0.77 0.57 2004–2007 0.79 0.77 2.2 0.86 0.33 
Tikurwiha @Dato 1990–2000 0.85 0.68 9.45 0.62 0.77 2003–2007 0.77 0.62 −13.52 0.68 0.53 
L_Ziway Storage 2001–2010 0.89 0.63 10.05 0.63 0.56 2012–2015 0.81 0.59 7.05 0.53 0.55 
Gauging stationsCalibration
Validation
Time rangeR2NSEPBIASIARSRTime rangeR2NSEPBIASIARSR
Meki @Meki 1996–2005 0.89 0.78 4.77 0.78 0.47 2007–2011 0.83 0.59 16.11 0.59 0.64 
Katar @Fitte 1983–1993 0.87 0.76 2.13 0.76 0.49 1996–2000 0.89 0.66 −16.94 0.66 0.56 
Bilate @Tena 1999–2006 0.85 0.74 −4.29 0.67 0.58 1989–1993 0.82 0.67 11.76 0.84 0.4 
Dijo @CV 1983–1991 0.82 0.71 4.88 0.71 0.54 1998–2002 0.81 0.64 6.49 0.64 0.59 
Gidabo @Aposto 1990–2000 0.93 0.85 3.63 0.7 0.55 1983–1988 0.83 0.7 −7.39 0.85 0.39 
Kulfo @AM 1996–2003 0.88 0.84 −6.45 0.84 0.35 2004–2008 0.82 0.53 −15.17 0.53 0.61 
Gelana @Tore 1991–1998 0.92 0.84 1.8 0.67 0.58 1999–2003 0.82 0.67 3.24 0.84 0.37 
Hamessa @Wajefo 1992–1997 0.85 0.77 2.57 0.65 0.59 1984–1988 0.82 0.65 12.59 0.77 0.34 
Weito @Bridge 1993–2002 0.83 0.74 2.57 0.65 0.59 2003–2007 0.82 0.65 −13.37 0.74 0.34 
Segen @Konso 1997–2003 0.84 0.86 5.01 0.77 0.57 2004–2007 0.79 0.77 2.2 0.86 0.33 
Tikurwiha @Dato 1990–2000 0.85 0.68 9.45 0.62 0.77 2003–2007 0.77 0.62 −13.52 0.68 0.53 
L_Ziway Storage 2001–2010 0.89 0.63 10.05 0.63 0.56 2012–2015 0.81 0.59 7.05 0.53 0.55 
Figure 5

Graphical presentation of calibration

Figure 5

Graphical presentation of calibration

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Figure 6

Graphical presentation of validation.

Figure 6

Graphical presentation of validation.

Close modal

Water balance

Water balance is governed by the conservation of mass and the rate of water entering a specified domain is equal to the rate of water leaving the same domain with any differences as changes in storage. The link between surface water, groundwater, soil moisture content and the process of evapotranspiration has critical importance in this water balance study. The basin-level water balance study tries to quantify each element of the hydrological cycle. It evaluates the hydrological elements by considering elements as one system. Precipitation, incoming water to the basin from the atmosphere, is accounted as the inflow to the basin system. Evapotranspiration, surface runoff and subsurface flow (interflow, base flow and deep percolation) on the other side are counted as outflow from the basin system. The change between the soil moisture increments and reduction is considered a change in the storage of the system. Therefore, the balance between the right and left sides of the equation guarantees a correct annual balance of water in the basin.

Four interdependent hydrological basins, the Ziway-Shala basin (14,477 km2), the Hawassa basin (1,403 km2), the Abaya-Chamo basin (18,118 km2) and the Chew Bahir basin (19,029 km2) are widely thought to be the focal points of the RVLB's hydrology. The main rivers usually originate on the elevated east and west edges of the rift valley and flow south into the terminal lakes. The terminal lakes have no surface flow, making the RVLB a hydrologically closed system. Although there is not much information available right now to support either hypothesis, there may be groundwater outflows. Each subsystem of the RVLB undergoes a water balance study.

Ziway-Shalla sub-basin

The Ziway-Shalla sub-basin consists of four lakes, Ziway, Langano, Abijata and Shalla from which Abijata and Shalla are terminal lakes, as shown in (Table 3). The sub-basin obtains 12.27 BCM of annual rainfall from which 10.13 BCM is evapotranspiration loss and 2.15 BCM remains as surface runoff, baseflow, interflow and deep percolation. The balance analysis is done in not the hydrological year, hence the difference between inflow and outflow and the change in storage is non-zero.

Table 3

Annual water balance of the sub-basins

Sub-basinInflow (BMC)Outflow (BMC)=Change in storage (BMC)
PrecipitationEvapotranspiration + surface runoff + interflow + base flow + deep percolation=Increase in soil moisture–decrease in soil moisture
Ziway-Shall 12.27 – 10.13 + 1.24 + 0.40 + 0.25 4.13–3.56 
12.27  12.018  0.256 
  0.26  0.26a 
Hawassa 1.57 – 1.33 + 0.11 + 0.05 + 0.06 0.51–0.49 
1.569 – 1.553 0.016 
  0.02 0.02a 
Abaya_Chamo 18.49 – 15.04 + 2.03 + 0.62 + 0.42 5.20–4.81 
18.488 – 18.099 0.391 
  0.39 0.39a 
Chewbahir 17.7 – 14.90 + 1.24 + 0.68 + 0.47 4.66–4.24 
17.701 – 17.284 0.417 
  0.42 0.42a 
Sub-basinInflow (BMC)Outflow (BMC)=Change in storage (BMC)
PrecipitationEvapotranspiration + surface runoff + interflow + base flow + deep percolation=Increase in soil moisture–decrease in soil moisture
Ziway-Shall 12.27 – 10.13 + 1.24 + 0.40 + 0.25 4.13–3.56 
12.27  12.018  0.256 
  0.26  0.26a 
Hawassa 1.57 – 1.33 + 0.11 + 0.05 + 0.06 0.51–0.49 
1.569 – 1.553 0.016 
  0.02 0.02a 
Abaya_Chamo 18.49 – 15.04 + 2.03 + 0.62 + 0.42 5.20–4.81 
18.488 – 18.099 0.391 
  0.39 0.39a 
Chewbahir 17.7 – 14.90 + 1.24 + 0.68 + 0.47 4.66–4.24 
17.701 – 17.284 0.417 
  0.42 0.42a 

aThe balance analysis is not done in the hydrological year, hence the difference between inflow and outflow and the change in storage is non-zero.

Hawassa sub-basin

The Hawassa sub-basin consists of Hawassa Lakes, as shown in (Table 3), the sub-basin obtains 1.57 BCM of annual rainfall from which 1.33 BCM is evapotranspiration loss, and 0.24 BCM remains as surface runoff, baseflow, interflow and deep percolation. The hydrological analysis is not based on hydrological calendar hence the difference between inflow and outflow and the change in storage is non-zero.

Abaya-Chamo sub-basin

The Abaya-Chamo sub-basin consists of two major lakes, Abaya and Chamo with Lake Chamo being a terminal lake, as shown in (Table 3), the sub-basin obtains 18.49 BCM of annual rainfall from which 15.04 BCM is evapotranspiration loss and 3.45 BCM remains as surface runoff, baseflow, interflow and deep percolation.

Chewbahir sub-basin

The Chewbahir sub-basin has one lake and two major rivers, Weito and Segen, both flow into the lake. Variations in river discharge can result in significant area changes for Lake Chew Bahir. The lowest point in the northeast is always wet, yet it frequently dries out. All water entering the lake evaporates because there is no outlet for it. Chew Bahir has changed over the last century from a swamp to shallow open water that reaches a maximum depth of 7.5 m and has a surface area of up to 2,000 km2. Because it is so salty, the water in Chew Bahir cannot be used for agriculture or residential uses. As shown in Table 3, the sub-basin obtains 17.70 BCM of annual rainfall from which 14.90 BCM is evapotranspiration loss and 2.80 BCM remains as surface runoff, baseflow, interflow, and deep percolation.

Renewable water resources of the basin

The total renewable water resource of the basin is the sum of internal renewable water resources and external renewable water resources. Internal renewable water resources are defined as the long-term average annual flow of rivers and recharge of groundwater generated from indigenous precipitation for a given basin or region. On the other side, the external renewable water resources are part of the annual renewable water resources that are not generated in the basin. This includes inflow from upstream basins or regions (groundwater and surface water) and part of the water of border lakes. Since the basin is not interconnected with another basin system, the total renewable water resources are simply computed from the internal sources. It is the volume of water computed by separating the total evapotranspiration (41,316.06 million cubic meters (MCM)) of the basin from the total precipitation (50,031.29 MCM) of the basin. Accordingly, the total renewable water resource of the basin is 8,637.19 MCM.

Surface water resources

Surface water is water within the hydrological system that includes all inland waters permanently or intermittently occurring on the Earth's surface in either liquid (rivers, lakes, reservoirs and swamps) or solid (glaciers, snow cover) conditions. The latter, however, was not considered since it has no significant contribution to tropical countries like Ethiopia. The surface water of the RVLB accounts for two systems: streamflow and open water system (including renewable water at the lake, wetland and floodplain). Although the model is configured with 23 catchments and a monthly time series of about 43 years (1981–2022) to address spatial variability of rainfall–runoff interaction, the results of the surface water sources are presented based on the two surface water categories with a focus on the major rivers in the basin and lake water system.

Streamflow-based surface water sources

Streamflow is the central part of the surface water potential, commonly calculated at the outlet of the given hydrologic boundary. Surface water sources, based on the flow of the RVLB stream, are defined with 17 major rivers, which feed the lakes in the basin. Meki and Katar are the main sources of fresh water for Lake Ziway. Huluka is the main river that flows into Lake Langano. These two lakes are further interconnected with Lake Abijata (one of the terminal lakes) in the basin through the Bulbula and Horakalo rivers, respectively. The other terminal lake of the basin, Lake Shalla, receives water from the Dijo and Dedeba rivers. Tikurwiha river is the main source of water for Lake Hawassa, while Bilate, Gidabo, Gelana, Wajifo, Ginna and Kulfo rivers are the main source of water for Lake Abaya. For Lake Chamo the main sources of water are Sile, Sego and Gidole rivers. Weito and Segen rivers flow into Chewbahir, where there is no outlet, all water entering the lake is evaporated. Over the last century, Chew Bahir has varied from swamp to shallow open water with a maximum depth of 7.5 m and a surface area of up to 2,000 km². Chew Bahir's water is so highly saline that it cannot be used for irrigation or domestic purposes. Figure 7 indicates the pattern and temporal distribution of the streamflow of gauged rivers in the basin.
Figure 7

Spatial and temporal flowrates at gauging stations.

Figure 7

Spatial and temporal flowrates at gauging stations.

Close modal

The RVLB has many rivers, which constitute the unique ecosystem of the basin. The rivers that feed these lakes provide vital water for agriculture, fishing and other industries. Rivers in the Ethiopian Rift Lake Basin play a vital role in supporting the region's ecosystem and economy. These rivers provide important water resources for the lake and surrounding communities and contribute to the area's biodiversity.

Ziway-Shalla sub-basin

Meki River is one of the major rivers in the sub-basin which starts at the northern tip of the RVLB, primarily originating from the Gurage highlands. It flows eastward for 106 km before reaching Lake Ziway. Analyzing the surface water, the Meki River generates a streamflow of 322.08 MCM from a catchment area of 2,292.83 km2. At the river's outlet, a gauge is used for model calibration and validation, as indicated in Table 4, 47.10 MCM is abstracted from the river along the course. Conversely, the Katar River begins at the Arsi highlands in the northwestern part of the RVLB and flows 106 km westward into Lake Ziway. It generates a streamflow of 533.28 MCM from a catchment area of 3,390 km2 and only 493.63 MCM is estimated flow due to abstraction on the upstream.

Table 4

Water balance of streamflows of the Ziway-Shalla sub-basin (MCM)

RiversInter flowBase flowSurface runoffTotal streamflowOutflow at the outletAbstractions
Meki 51.91 34.71 235.46 322.08 274.98 47.10 
katar 76.77 11.16 445.36 533.28 493.63 39.65 
Dijo 24.29 6.18 139.09 169.56 160.09 9.47 
Ejersa/Hulka 13.41 23.13 289.23 325.76 324.64 1.12 
Ziway_UG 20.36 0.52 57.64 78.52 71.28 7.24 
Dedeba/Bisho 48.92 0.70 130.38 180.00 164.98 15.02 
Bulbula/Harkelo 16.91 7.91 21.66 46.47 43.61 2.86 
    1,655.68 1,533.21  
RiversInter flowBase flowSurface runoffTotal streamflowOutflow at the outletAbstractions
Meki 51.91 34.71 235.46 322.08 274.98 47.10 
katar 76.77 11.16 445.36 533.28 493.63 39.65 
Dijo 24.29 6.18 139.09 169.56 160.09 9.47 
Ejersa/Hulka 13.41 23.13 289.23 325.76 324.64 1.12 
Ziway_UG 20.36 0.52 57.64 78.52 71.28 7.24 
Dedeba/Bisho 48.92 0.70 130.38 180.00 164.98 15.02 
Bulbula/Harkelo 16.91 7.91 21.66 46.47 43.61 2.86 
    1,655.68 1,533.21  

The major rivers that enter Lake Langano are Hulka, Ejersa, Gedemso and Lepis. Lepis River is the longest, spanning 66 km in the catchment area. The streamflow from this catchment area, which covers 2,020 km2, is 325.76 MCM and abstraction in the catchment is less than that of other rivers. On the other hand, the Dijo River originates from the northwestern escarpments of the RVLB and flows 96.5 km into Lake Shalla, with a total streamflow of 169.56 MCM. The only ungauged catchment, Ziway catchment has a small stream size that flows into Lake Ziway, according to the water analysis result; the 1,615 km2 catchment area has a yield of 78.52 MCM annual streamflow. Bulbula and Harkelo are rivers that supply Lake Abijata. The water of Bulbula river is the outflow of Lake Ziway while the water in Harkelo river is the outflow from Lake Langano; however, the discharge originated from the catchments as streamflow is also considered in the water balance analysis of the lake.

Temporally, the streamflow in the sub-basin follows the rainfall variability over the sub-basin. Most of the sub-basins have high flow in the rainy season. Due to these temporal and topographical characteristics of the river system, the dry season (November–May) flow in all catchments covers only 21% of the annual flow (Figure 8).
Figure 8

Seasonal variability of streamflows of sub-catchments in the Ziway-Shalla sub-basin.

Figure 8

Seasonal variability of streamflows of sub-catchments in the Ziway-Shalla sub-basin.

Close modal
Abaya-Chamo sub-basin

Bilate River, situated in the southern part of Ethiopia, is a vital water source used for both agriculture and daily household needs. Originating from the Gurage highlands, the river flows southwards for a distance of 203 km until it reaches Lake Abaya. With a catchment area of 5,546 km2, the river generates a stream of 909.66 MCM from which 131.05 MCM is abstracted on the upstream and 778.62 MCM flows into Lake Abaya. The Galena River begins in the Gedeo mountains in the eastern part of the RVLB. It flows west for 149 km until it reaches Lake Abaya. The river's catchment area covers 3,129 km2 and it is estimated that the streamflow is 541.5 MCM (Table 5). The Gidabo River, one of the longest rivers in the basin, originates in the Sidama Mountains in the eastern part of the RVLB. It flows for 108 km before reaching Lake Abaya. The catchment area for the Gidabo River is 3,552 km2 and the estimated streamflow is 675.08 MCM.

Table 5

Water balance of streamflows of the Abaya-Chamo sub-basin (MCM)

RiversInterflowBase flowSurface runoffTotal streamflowOutflow at the outletAbstractions
Bilate 201.85 95.30 612.51 909.66 778.62 131.05 
Hamessa 14.04 2.89 67.88 84.82 70.02 14.79 
Gina 22.837 18.9 185.07 226.88 149.74 77.14 
Kulfo 15.55 2.47 31.80 49.83 39.31 10.52 
Sile 62.20 5.36 216.05 283.61 227.52 56.09 
Gidabo 82.09 3.65 589.34 675.08 604.02 71.06 
Gelana 98.81 19.92 422.78 541.50  475.60  65.90 
    2,771.38 2,344.82  
RiversInterflowBase flowSurface runoffTotal streamflowOutflow at the outletAbstractions
Bilate 201.85 95.30 612.51 909.66 778.62 131.05 
Hamessa 14.04 2.89 67.88 84.82 70.02 14.79 
Gina 22.837 18.9 185.07 226.88 149.74 77.14 
Kulfo 15.55 2.47 31.80 49.83 39.31 10.52 
Sile 62.20 5.36 216.05 283.61 227.52 56.09 
Gidabo 82.09 3.65 589.34 675.08 604.02 71.06 
Gelana 98.81 19.92 422.78 541.50  475.60  65.90 
    2,771.38 2,344.82  

The Kulfo River originates in the southwestern part of the RVLB, specifically in the Gamo Mountains and it flows into Lake Abaya. With a length of 39 km, the Kulfo River contributes 49.83 MCM of streamflow to Lake Abaya. On the other hand, the Hamessa River originates from the Welayta Mountains and also flows into Lake Abaya, generating a streamflow of 58.82 MCM, covering a catchment area of 521 km2. Within the West Abaya catchment, there are rivers such as Harre, Shefe, Baso, Doshe and Uraya, with Harre being the longest at 30 km. This catchment has an area of 1,339 km2 and a total annual streamflow of 226.88 MCM. These rivers are known for their unstable courses and there are extensive banana farms along their banks and 77.14 MCM of water is abstracted from these rivers. For Lake Chamo, Sego and Sille are the major supply rivers originating at the western highlands of Lake Chamo, the streamflow generated is 283.61 MCM from 1,807 km2 and 56.09 MCM is abstracted mainly for irrigation.

The temporal distribution of the streamflow in the sub-basin follows the rainfall variability over the sub-basin. Most of the sub-basins have high flow in the rainy season. Due to these temporal and topographical characteristics of the river system, the dry season (November–April) flow in all catchments covers only 30% of the annual flow (Figure 9).
Figure 9

Seasonal variability of streamflows of sub-catchments in the Abaya-Chamo sub-basin.

Figure 9

Seasonal variability of streamflows of sub-catchments in the Abaya-Chamo sub-basin.

Close modal
Hawassa sub-basin

Tikurwiha is the major river that flows into Lake Hawassa. The river starts at the northeastern part of the RVLB, as shown in Table 6, and the total streamflow size of the river is 129.06 MCM from the total catchment of 1,430 km2 catchment area and 15.49 MCM is abstracted.

Table 6

Water balance of streamflows of the Hawassa sub-basin (MCM)

RiversInterflowBase flowSurface runoffTotal streamflowOutflow at the outletAbstractions
Tikurwiha 18.52 2.69 107.85 129.06 113.57 15.49 
RiversInterflowBase flowSurface runoffTotal streamflowOutflow at the outletAbstractions
Tikurwiha 18.52 2.69 107.85 129.06 113.57 15.49 

Temporally, the streamflow in the sub-basin follows the rainfall variability over the sub-basin. The catchment has a high flow in the rainy season. Due to these temporal and topographical characteristics of the river system, the dry season (November–May) flow in the catchment covers only 42% of the annual flow (Figure 10).
Figure 10

Seasonal variability of streamflows of sub-catchments in the Hawassa sub-basin.

Figure 10

Seasonal variability of streamflows of sub-catchments in the Hawassa sub-basin.

Close modal
Chewbahir sub-basin

The Segen River, originating at the Plateau of the Netch-Sar National Park on the eastern side of Lakes Abaya and Chamo, is among the longest rivers in the basin. It flows 171 km south to Lake Chewbahir. The river is gauged at Konso and it is used to calibrate the model. The total streamflow from the catchment area of 7,900 km2 is 468.72 MCM (Table 7). The Weito River, on the other hand, is the longest river in RVLB, stretching 222 km. It starts at the junction mountains of South Omo, Gofa and Gamo zones and flows south into Lake Chewbahir. The estimated streamflow from its catchment area of 4,404 km2 is 528.80 MCM.

Table 7

Water balance of streamflows of the Chewbahir sub-basin (MCM)

RiversInterflowBase flowSurface runoffTotal streamflowOutflow at the outletAbstractions
Weito 196.48 2.12 330.21 528.80 432.64 96.16 
Segen 122.51 31.12 315.08 468.72 394.17 74.55 
Serbela 124.16 204.29 594.47 922.91 785.74 137.17 
    1,920.44 1,612.55  
RiversInterflowBase flowSurface runoffTotal streamflowOutflow at the outletAbstractions
Weito 196.48 2.12 330.21 528.80 432.64 96.16 
Segen 122.51 31.12 315.08 468.72 394.17 74.55 
Serbela 124.16 204.29 594.47 922.91 785.74 137.17 
    1,920.44 1,612.55  

Temporally, the streamflow in the sub-basin follows the rainfall variability over the sub-basin. The catchments have a high flow in the rainy season. Due to these temporal and topographical characteristics of the river system, the dry season (June–July) and (November–February) flow in all catchments covers only 38% of the annual flow (Figure 11).
Figure 11

Seasonal variability of streamflows of sub-catchments in the Chewbahir sub-basin.

Figure 11

Seasonal variability of streamflows of sub-catchments in the Chewbahir sub-basin.

Close modal

Lake-based surface water resources

The rift valley lakes are a series of lakes in the rift valley extending from northeast to south in Ethiopia. These lakes include the largest lakes in terms of area and volume, as well as some of the oldest and deepest lakes. Many are freshwater biomes with high biodiversity, while others are alkaline biomes that harbor highly specialized organisms. Most of the lakes in the Ethiopian rift valley have no outlet and are mostly alkaline. These lakes are home to a wide variety of plants and animals, including fish, birds, reptiles and mammals. Some of the famous animals found in the rift valley lakes include hippos, crocodiles and flamingos. The rift valley lakes are very important to the Ethiopian economy. They provide drinking and irrigation water and are popular tourist destinations, attracting tourists from all over the world. The water resource of the lakes is analyzed using the water balance approach. Table 8 indicates the volume and geometric descriptions of the lakes.

Table 8

Lakes of the RVLB

LakesArea (km2)Volume (Bm3)Mean depth (m)Max. depth (m)
L_Ziway 430.19 1.026 2.38 
L_Abijata 87.30 0.615 7.04 76 
L_Langano 231.13 1.917 8.30 46 
L_Shalla 313.70 21.355 266.00 290 
L_Hawassa 129.0 1.033 11.82 29 
L_Abaya 1,154.19 9.084 7.87 86 
L_Chamo 325.95 1.933 5.93 103 
LakesArea (km2)Volume (Bm3)Mean depth (m)Max. depth (m)
L_Ziway 430.19 1.026 2.38 
L_Abijata 87.30 0.615 7.04 76 
L_Langano 231.13 1.917 8.30 46 
L_Shalla 313.70 21.355 266.00 290 
L_Hawassa 129.0 1.033 11.82 29 
L_Abaya 1,154.19 9.084 7.87 86 
L_Chamo 325.95 1.933 5.93 103 

Lake Ziway

Lake Ziway is one of the freshwater lakes in the basin, with an area of 430 km2, an average depth of 2.38 m and a maximum depth of 9 m. It is a habitat for important bird and hippo populations and is also used for fish production.

Lake Zwe is fed by two main rivers, flowing westward and southeastward, respectively, from the mountains on either side of the Great Rift Valley Mountains. It is the second largest lake in the basin and is home to many endemic birds and a variety of wildlife. It is also one of the main sources of commercial fish farming in Ethiopia. The total drainage area of Ziwei Lake is about 7,297 km2. The main source of water for the lake is the tributaries of the Maki and Qatar rivers. Small rivers on the west and east sides of the lake also have inflows. The average annual discharges of the Maki River, Ketar River and the Little River around the lake are 288, 501 and 78.5 MCM, respectively. In addition, the average annual direct precipitation in the lake area is 41.16 MCM and the average annual inflow is 931.55 MCM. The lake's evaporative losses, withdrawals and discharges into the Bulbula River were 529, 153 and 241 MCM, respectively, making the lake's positive water storage change 0.8 MCM, which is good for the lake to absorb and resist climate changes and the effects of overabsorption upstream of tributaries and maintain water levels. Desta et al. (2020) reported similar findings.

Lake langano

Lake Langano, which is also a freshwater lake, relatively experienced less seasonal and inter-annual water level variability. Since the level of Lake Abijata is only a few meters lower than that of Lake Langano, the lake flows toward Lake Abijata to the south through the Harakalo River. The upstream catchment area of Lake Langano is 2,020 km2 and contributes a mean annual inflow of 328.97 MCM. Rainfall in the lake area contributes a mean annual water of 17.68 MCM. The total inflow to the lake system thus becomes 346.65 MCM. The outflow of Langano Lake consists of evaporation from the lake surface and outflows through the Harakalo River. Their mean annual volume accounted for 228.62 and 70.85 MCM, respectively. Deduction of these mean annual outflows from the mean annual inflows gives a positive annual change in storage of 5.18 MCM. Thus, the lake water balance confirms that the water level is stable against the long-year climate variability. Since there is no significant abstraction from the lake, its water level has not been affected by human activities in the system (Table 9).

Table 9

Annual water balance of the RVLB (MCM)

LakesWater balanceJanFebMarAprMayJunJulAugSepOctNovDecAnnual
Ziway Rainfall 0.76 1.16 2.24 3.40 3.46 4.15 6.64 5.84 3.85 2.03 0.45 0.21 34.17 
Inflow 27.31 19.17 32.03 42.53 43.34 52.61 128.63 257.41 147.71 94.39 27.07 18.19 890.40 
Evaporation 79.23 80.42 57.14 36.16 27.57 21.15 20.95 18.63 16.56 34.59 60.49 76.66 529.53 
Abstraction 10.62 9.68 10.62 13.71 14.17 13.71 14.17 14.17 13.71 14.17 13.71 10.62 153.05 
Outflow 6.77 4.44 4.72 3.40 2.58 3.47 20.41 84.35 56.49 41.20 8.47 4.89 241.18 
Changes in storage −68.56 −74.21 −38.21 −7.34 2.49 18.44 79.74 146.11 64.80 6.46 −55.15 −73.77 0.80 
Abijata Rainfall 0.19 0.21 0.47 0.77 0.71 0.89 1.28 1.13 0.84 0.38 0.07 0.04 6.97 
Inflow 0.29 0.32 2.78 2.86 4.60 7.65 8.82 9.68 5.69 5.52 6.96 6.24 61.39 
Inflow from Lake Ziway 6.77 4.44 4.72 3.40 2.58 3.47 20.41 84.35 56.49 41.20 8.47 4.89 241.18 
Inflows from Lake Langano 11.15 12.04 13.71 9.85 7.30 5.55 2.37 1.84 1.82 1.17 1.75 6.06 74.59 
Evaporation 56.84 51.60 44.26 27.81 22.40 19.78 22.94 20.71 15.07 22.40 33.13 47.57 384.50 
Changes in storage −38.45 −34.59 −22.58 −10.92 −7.21 −2.22 9.94 76.28 49.76 25.86 −15.88 −30.34 −0.36 
Langano Rainfall 0.22 0.23 1.55 1.79 1.78 1.81 2.29 2.21 1.97 1.62 1.15 1.06 17.68 
Inflow 10.38 10.32 19.03 26.93 28.50 31.08 52.65 55.36 47.80 31.70 9.64 5.60 328.97 
Evaporation 37.27 39.78 31.79 17.59 12.87 12.56 14.85 13.12 11.40 16.24 27.40 35.75 270.62 
Outflow 13.33 12.81 10.75 6.27 4.51 4.56 3.15 1.06 0.91 1.31 3.17 9.03 70.85 
Changes in storage −40.00 −42.04 −21.96 4.86 12.91 15.77 36.94 43.38 37.45 15.76 −19.78 −38.12 5.18 
Shalla Rainfall 0.75 1.27 2.39 3.38 3.44 3.30 4.43 4.20 3.67 1.43 0.43 0.32 29.00 
Inflow 6.92 12.18 17.50 29.66 39.24 34.25 47.11 62.38 52.44 26.43 8.22 6.24 342.56 
Evaporation 52.09 48.94 40.98 24.15 18.59 16.84 19.92 18.60 15.46 26.95 39.52 50.17 372.21 
Changes in storage −44.42 −35.49 −21.09 8.90 24.10 20.70 31.61 47.97 40.64 0.90 −30.87 −43.61 −0.65 
Hawassa Rainfall 0.32 0.27 0.69 1.21 1.21 0.94 1.30 1.10 1.17 1.17 0.35 0.17 9.90 
Inflow 8.88 6.65 5.62 6.75 7.83 8.84 10.17 11.49 13.34 23.56 18.55 9.84 131.53 
Evaporation 17.17 18.10 16.18 9.16 6.82 7.33 9.67 9.24 7.72 7.62 11.83 16.52 137.35 
Abstraction 0.35 0.36 0.34 0.38 0.47 0.35 0.33 0.32 0.38 0.30 0.43 0.43 4.42 
Changes in storage −7.97 −11.17 −9.87 −1.20 2.22 2.45 1.80 3.35 6.79 17.12 7.07 −6.52 −0.35 
Abaya Rainfall 3.68 4.34 9.07 21.56 21.24 10.12 11.01 10.30 12.02 18.15 7.01 2.66 131.14 
Inflow 68.25 57.01 74.09 144.89 223.74 159.26 215.46 278.01 391.91 449.68 184.29 92.16 2,338.76 
Evaporation 357.10 342.96 279.61 95.26 45.53 84.84 134.64 138.21 106.93 94.72 164.78 281.63 2,126.20 
Abstraction 0.00 0.00 0.00 39.18 39.13 39.11 39.15 0.00 0.00 0.00 0.00 0.00 156.57 
Outflow 0.54 0.18 0.41 1.35 7.19 6.97 10.58 24.15 44.85 57.60 18.82 1.36 174.00 
Changes in storage −285.16 −281.61 −196.46 71.20 199.45 84.54 91.83 150.09 297.00 373.11 26.52 −186.81 13.14 
Chamo Rainfall 1.10 1.61 2.76 6.16 5.30 2.33 1.98 2.15 2.47 5.41 2.27 1.11 34.66 
Inflow 14.24 16.15 12.27 19.88 23.52 19.00 15.66 28.47 36.08 55.55 33.27 15.52 289.61 
Evaporation 30.24 23.39 19.79 10.52 10.65 18.50 21.65 19.44 24.43 23.08 34.80 38.04 274.54 
Abstraction 0.74 0.77 0.38 0.97 2.87 2.12 0.62 4.26 6.56 15.48 8.19 1.06 44.01 
Changes in storage −15.64 −6.40 −5.14 14.55 15.30 0.70 −4.63 6.93 7.56 22.41 −7.44 −22.47 5.73 
LakesWater balanceJanFebMarAprMayJunJulAugSepOctNovDecAnnual
Ziway Rainfall 0.76 1.16 2.24 3.40 3.46 4.15 6.64 5.84 3.85 2.03 0.45 0.21 34.17 
Inflow 27.31 19.17 32.03 42.53 43.34 52.61 128.63 257.41 147.71 94.39 27.07 18.19 890.40 
Evaporation 79.23 80.42 57.14 36.16 27.57 21.15 20.95 18.63 16.56 34.59 60.49 76.66 529.53 
Abstraction 10.62 9.68 10.62 13.71 14.17 13.71 14.17 14.17 13.71 14.17 13.71 10.62 153.05 
Outflow 6.77 4.44 4.72 3.40 2.58 3.47 20.41 84.35 56.49 41.20 8.47 4.89 241.18 
Changes in storage −68.56 −74.21 −38.21 −7.34 2.49 18.44 79.74 146.11 64.80 6.46 −55.15 −73.77 0.80 
Abijata Rainfall 0.19 0.21 0.47 0.77 0.71 0.89 1.28 1.13 0.84 0.38 0.07 0.04 6.97 
Inflow 0.29 0.32 2.78 2.86 4.60 7.65 8.82 9.68 5.69 5.52 6.96 6.24 61.39 
Inflow from Lake Ziway 6.77 4.44 4.72 3.40 2.58 3.47 20.41 84.35 56.49 41.20 8.47 4.89 241.18 
Inflows from Lake Langano 11.15 12.04 13.71 9.85 7.30 5.55 2.37 1.84 1.82 1.17 1.75 6.06 74.59 
Evaporation 56.84 51.60 44.26 27.81 22.40 19.78 22.94 20.71 15.07 22.40 33.13 47.57 384.50 
Changes in storage −38.45 −34.59 −22.58 −10.92 −7.21 −2.22 9.94 76.28 49.76 25.86 −15.88 −30.34 −0.36 
Langano Rainfall 0.22 0.23 1.55 1.79 1.78 1.81 2.29 2.21 1.97 1.62 1.15 1.06 17.68 
Inflow 10.38 10.32 19.03 26.93 28.50 31.08 52.65 55.36 47.80 31.70 9.64 5.60 328.97 
Evaporation 37.27 39.78 31.79 17.59 12.87 12.56 14.85 13.12 11.40 16.24 27.40 35.75 270.62 
Outflow 13.33 12.81 10.75 6.27 4.51 4.56 3.15 1.06 0.91 1.31 3.17 9.03 70.85 
Changes in storage −40.00 −42.04 −21.96 4.86 12.91 15.77 36.94 43.38 37.45 15.76 −19.78 −38.12 5.18 
Shalla Rainfall 0.75 1.27 2.39 3.38 3.44 3.30 4.43 4.20 3.67 1.43 0.43 0.32 29.00 
Inflow 6.92 12.18 17.50 29.66 39.24 34.25 47.11 62.38 52.44 26.43 8.22 6.24 342.56 
Evaporation 52.09 48.94 40.98 24.15 18.59 16.84 19.92 18.60 15.46 26.95 39.52 50.17 372.21 
Changes in storage −44.42 −35.49 −21.09 8.90 24.10 20.70 31.61 47.97 40.64 0.90 −30.87 −43.61 −0.65 
Hawassa Rainfall 0.32 0.27 0.69 1.21 1.21 0.94 1.30 1.10 1.17 1.17 0.35 0.17 9.90 
Inflow 8.88 6.65 5.62 6.75 7.83 8.84 10.17 11.49 13.34 23.56 18.55 9.84 131.53 
Evaporation 17.17 18.10 16.18 9.16 6.82 7.33 9.67 9.24 7.72 7.62 11.83 16.52 137.35 
Abstraction 0.35 0.36 0.34 0.38 0.47 0.35 0.33 0.32 0.38 0.30 0.43 0.43 4.42 
Changes in storage −7.97 −11.17 −9.87 −1.20 2.22 2.45 1.80 3.35 6.79 17.12 7.07 −6.52 −0.35 
Abaya Rainfall 3.68 4.34 9.07 21.56 21.24 10.12 11.01 10.30 12.02 18.15 7.01 2.66 131.14 
Inflow 68.25 57.01 74.09 144.89 223.74 159.26 215.46 278.01 391.91 449.68 184.29 92.16 2,338.76 
Evaporation 357.10 342.96 279.61 95.26 45.53 84.84 134.64 138.21 106.93 94.72 164.78 281.63 2,126.20 
Abstraction 0.00 0.00 0.00 39.18 39.13 39.11 39.15 0.00 0.00 0.00 0.00 0.00 156.57 
Outflow 0.54 0.18 0.41 1.35 7.19 6.97 10.58 24.15 44.85 57.60 18.82 1.36 174.00 
Changes in storage −285.16 −281.61 −196.46 71.20 199.45 84.54 91.83 150.09 297.00 373.11 26.52 −186.81 13.14 
Chamo Rainfall 1.10 1.61 2.76 6.16 5.30 2.33 1.98 2.15 2.47 5.41 2.27 1.11 34.66 
Inflow 14.24 16.15 12.27 19.88 23.52 19.00 15.66 28.47 36.08 55.55 33.27 15.52 289.61 
Evaporation 30.24 23.39 19.79 10.52 10.65 18.50 21.65 19.44 24.43 23.08 34.80 38.04 274.54 
Abstraction 0.74 0.77 0.38 0.97 2.87 2.12 0.62 4.26 6.56 15.48 8.19 1.06 44.01 
Changes in storage −15.64 −6.40 −5.14 14.55 15.30 0.70 −4.63 6.93 7.56 22.41 −7.44 −22.47 5.73 

Lake Abijata

Lake Abijata is a terminal lake and the principal sources of water are Lakes, Ziway and Langano, through the Bulbula and Harakalo Rivers, respectively. The lake supports a wide variety of wildlife and migrant birds (Wagaw et al. 2019). Fluctuation of its principal water sources, its final position in the drainage area and its shallow depth make Lake Abijata more sensitive to climate and human interventions. Since Lake Ziway contributes about 80% of the inflow water to Lake Abijata, the abstraction from this lake and its feeder rivers, Meki and Ketar, will have a very significant effect on the water conditions of Lake Abijata.

The current lake water balance shows that the total volume of Lake Abijata is 384.14 MCM, where 241.18 MCM is overflow of Lake Ziway through the Bulbula River, 74.59 MCM is overflow of Lake Langano through the Harakalo River, 61.39 MCM stream flow from the catchment and 6.97 MCM from mean annual rainfall over the lake. Deduction of the mean annual lake evaporation 384.14 MCM and overflow in high water conditions gives a negative mean annual stored water change of 0.36 MCM, which indicates the continuous reduction of water level of the lake that improves only during high water flows in the flood season (Table 9). Due to the effect of uncontrolled abstraction issues over the lake area and feeder rivers, the lake is in serious natural water balance threat.

Lake Shalla

Lake Shalla is the deepest lake in the basin with a depth of about 266 m. It is separated from Abijata by a volcanic caldera rim. Lake Shalla, along with neighboring Lake Abijata, is part of the Lake Abijata–Shalla National Park, where more than 300 species of birds have been recorded. A hot spring situated in the lake's northeastern corner is a popular attraction. The lake is highly alkaline which makes direct water abstraction for irrigation and other uses impossible.

Lake Shalla does not have direct interconnection with other lakes with its surface water. However, since it is the lowest lake among the lakes in the basin, there is a greater probability of a subsurface water flow interaction. Although it is not interconnected with the rest of the lake catchment system, Lake Shalla has its river network that flows into it. The two main rivers, Dijo and Dedeba, feed this lake with mean annual inflows of 162.56 and 180 MCM, respectively. The rainfall over the lake surface having an area of 311 km2, accounts for 29 MCM of inflows, which makes the total mean annual inflows to the lake 372.56 MCM. The mean annual inflows and outflows of the lake balance with a negative 0.65 MCM mean annual changes of stored water (Table 9). Since Lake Shalla is deep, the effect of this recurrent mean annual reduction of 0.65 MCM did not show a change in the water level and lake surface area.

Lake Hawassa

Situated at the center of the RVLB, Lake Hawassa boasts an area of 87 km2. With a length of 16 km, a maximum width of 9 km and a maximum depth of 10 m, the lake rests at an elevation of 1,681 m above sea level. Lake Awasa's accessibility has made it one of the most studied lakes in the Ethiopian rift valley lake system by scientists. The main river, Tikurwiha, feeds Lake Hawassa with a mean annual inflow of 131.53 MCM. The rainfall over the lake surface, having an area of 87.43 km2, accounts for 9.90 MCM of inflows, which makes the total mean annual inflows to the lake 141.43 MCM. This makes the water balance analysis limited to carrying out the analysis of the difference between the inflows to the lake and evaporation plus abstraction from the lake according to which the mean annual water volume of the lake is 141.43 MCM. The mean annual inflows and outflows of the lake balance with a negative 0.35 MCM mean annual changes of stored water (Table 9).

Lake Abaya

Lake Abaya is positioned near Lake Chamo, which lies east of the Guge mountains, in the main Ethiopian Rift. The lake covers an area of 1,162 km2 and boasts a length and width of 60 and 20 km, respectively. Furthermore, it has a maximum depth of 13.1 m and is located at an elevation of 1,175 m above sea level. The lake is fed by three primary rivers: the Bilate, Gidabo and Gelana rivers. The lake and its surrounding savanna are home to a variety of birdlife and wildlife and they are also a vital fishing area for the local population. The only outflow of Lake Abaya is through the lower reaches of the Kulfo River, which is situated at an elevation of 1,190 m. The riverbed acts as a spillway during times of high lake levels, releasing excess water into Lake Chamo. The elevation difference between the two lakes is 62 m, with Lake Abaya being higher than Lake Chamo. The outflow from Lake Abaya is crucial for maintaining the hydrological balance of the Abaya-Chamo Lake basin. It helps regulate the water levels in the lakes and prevents them from becoming too saline. Additionally, the outflow provides a source of water for irrigation and drinking in the surrounding area.

Lake Abaya is the widest lake in the basin and the main source of water for the lake is the tributaries of the Bilate, Gidabo, Gelana, Hamessa and Kulfo rivers. Small rivers on the west sides of the lake also have inflows. As shown in Table 9, the total discharge that flows into Lake Abaya is 2,469.90 MCM. In addition, the average annual direct precipitation in the lake area is 131.14 MCM, and the lake's evaporative losses, withdrawals and discharges into the Lake Chamo were 2,126.20, 156.57 and 174 MCM, respectively, making the lake's positive water storage change 13.44 MCM, which is good for the lake to absorb and resist climate changes and the effects of overabsorption upstream of tributaries and maintain water levels. The results are consistent with previous studies. Findings are in line with previous study reports (Azeb 2009; Teffera et al. 2019; Abdi & Gebrekristos 2022).

Lake Chamo

Lake Chamo occupies an area of 3.17 km2. It has a maximum length and width of 32 and 13 km, respectively, reaches a maximum depth of 14 m and is situated at 1,110 m above sea level. Lake Chamo is primarily fed by the Kulfo River and the overflow from Lake Abaya.

The main source of water for Lake Chamo is the tributaries of the Sile, Sego and Gidole rivers. Small rivers on the southeast sides of the lake also have inflows. The total discharge that flows into the lake is 289.64 MCM. In addition, the average annual direct precipitation in the lake area is 34.66 MCM. The lake's evaporative losses, withdrawals and discharges into Lake Chamo were 274.54 and 44.01 MCM, respectively, making the lake's positive water storage change 5.73 MCM (Table 9), which is good for the lake to absorb and resist climate changes and the effects of overabsorption upstream of tributaries and maintain water levels. The results are consistent with previous studies (Hailemicael Mezgebe & Solomon 2011; Teffera et al. 2019; Nigussie 2021; Abdi & Gebrekristos 2022).

Other smaller lakes

The majority of studies and academics have not yet paid enough attention to the other minor lakes in the basin.

Lake Chitu is a small lake found adjacent to Lake Shalla. It is one of the crater lakes in RVLB located at on altitude of 1,540 masl. The lake has an area of 0.8 km2 and a maximum depth of 21 m. Unlike the other major lake, it is not possible to analyze the water balance for this lake since the required measured data are not available.

Lake Tinishu Abaya is found at on altitude of 1,818 masl between 07°57.534′N and 038°21.129′E. The total area of the lake was about 12.5 km2. According to the information from the local people, the lake has become shallow with the shrinking of its area in recent years. Although there are several streams around the lake, many of them are overused for irrigation purposes and they hardly flow. There are irrigation farms all around the lake and the tributaries. Again, unlike for the other major lakes it is hardly possible to analyze the water balance of the lake since the required measured data are not available.

Water level fluctuations of the lakes are also analyzed with seasonal and annual variability of water balance components. Lake Ziway, the lake water level shows a 1 m change from the end of September, 1,636.2 masl, to the end of May, 1,635.8 masl. Annually, the lake water fluctuates from 1,635 masl in dry years to 1,636.8 m in wet years. It provides a 2 m active water depth in the lake when the frequency of occurrence of the lower and upper water levels for the last four decades (1981–2020) is analyzed (Figure 12). Lake Abijata's water level has reduced to a point where it cannot even satisfy the evaporation (environmental) requirement. The climate sensitivity is further pronounced when the long-term water level change is considered. The drop and recovery time vary based on the severity and magnitude of climatic variations. Seasonally, the Lake Langano water level shows a 1 m change from the end of September, 1,581.25 masl, to the end of May, 1,580.25 masl. Annually, the lake water fluctuates from 1,579 masl in dry years to 1,581 m in wet years. Lake Hawassa's water level fluctuates from 1,672 to 1,680 m. Moreover, Lake Abaya's water level fluctuated from 1,166 masl in December to 1,170.5 masl in September and Lake Chamo fluctuates between 1,102 and 1,109 masl. The climate sensitivity is further pronounced when the long-term water level change is considered. The water level drops during unfavorable climate scenarios and attempts to recover during years with above-average rainfall. The drop and recovery time vary based on the severity and magnitude of climatic variations.
Figure 12

Water level fluctuation of lakes.

Figure 12

Water level fluctuation of lakes.

Close modal

Total surface water resource potential of the RVLB

As indicated in Table 10, the basin has an annual renewable potential surface water resource of 6,534 and 358 MCM, which can be abstracted as streamflow from the river network system and lake water system, respectively. Lakes can serve as a storage facility to manage the time variability of the surface water resources. However, Abijata, Shalla and Chewbahir lakes have high concentrations of alkaline and fluoride, which limits the abstraction of water for domestic, irrigation and other related uses. Thus, storage facilities in the upper sections of the tributary rivers are highly required for efficient use of the surface water.

Table 10

Total surface water resource potential of the basin

Lake systemAvailable water from lake (MCM)Available water from streams (MCM)Total available surface water potential (MCM)
Lake Ziway 153.05 890.40 1,043.45 
Lake Langano  328.97 328.97 
Lake Abijata  377.16 377.16 
Lake Shalla  258.00 258.00 
Lake Hawassa 4.42 131.53 135.95 
Lake Abaya 156.57 2,338.76 2,495.33 
Lake Chamo 44.01 289.61 333.62 
Lake Chewbahir  1,920.44 1,920.44 
 358.05 6,534.86 6,892.91 
Lake systemAvailable water from lake (MCM)Available water from streams (MCM)Total available surface water potential (MCM)
Lake Ziway 153.05 890.40 1,043.45 
Lake Langano  328.97 328.97 
Lake Abijata  377.16 377.16 
Lake Shalla  258.00 258.00 
Lake Hawassa 4.42 131.53 135.95 
Lake Abaya 156.57 2,338.76 2,495.33 
Lake Chamo 44.01 289.61 333.62 
Lake Chewbahir  1,920.44 1,920.44 
 358.05 6,534.86 6,892.91 

The RVLB has many rivers, which constitute the unique ecosystem of the basin. The rivers that feed these lakes provide vital water for agriculture, fishing and other industries. Rivers in the Ethiopian Rift Lake Basin play a vital role in supporting the region's ecosystem and economy. These rivers provide important water resources for the lake and surrounding communities and contribute to the area's biodiversity.

The total surface water flow into the lakes of the basin is 6.48 BCM which is higher than the master plan study report of 5.3 BCM (Halcraw & GIRDC 2009) and 5.6 BCM reported by Ermias (2019) but lower than the 7.83 BCM reported by MoWIE (2018) and according to the OIDA (2018) report, the surface water resource potential of the 21,155 km2 catchment area which is part of the RVLB found in the Oromia state administrative boundary is 6.47 BCM, compared with the estimate of this study it is much higher.

The results were also compared with previous study reports which were conducted at major catchments level, for instance, Meki River has 10.2 m3/s average annual flow which is higher than 8.96 m3/s reported by Halcraw & GIRDC (2009), 8.73 m3/s reported by Halcraw & GIRDC (2009) and 8.86 m3/s reported by OIDA (2018) but, lower than 11.28 m3/s reported by Bunta & Abate (2021). For Katar Catchment the Katar River has estimated average annual streamflow of 16.9 m3/s, which is higher than the 12.47 m3/s reported by Halcraw & GIRDC (2009), 13.66 m3/s reported by Goshime et al. (2021a), and 12.88 m3/s reported by OIDA (2018). The Dijo River has an estimated 5.38 m3/s average annual flow which is comparable to the 5.72 m3/s reported by Halcraw & GIRDC (2009). The Bilate River has 28.58 m3/s flow which is comparable to the 27 m3/s reported by Megebo (2020), Tikurwiha has an estimated average annual flow of 4.09 m3/s which is also comparable to 3.64 m3/s reported by Ketema & Siddaramaiah (2020). The Segen River has 29.27 m3/s which is lower than the 38.3 m3/s reported by Kifle (2016), Kulfo and Hamessa Rivers have 1.58 and 2.69 m3/s average annual streamflow, respectively which is lower than the 2.82 and 3.06 m3/s streamflow reported by Halcraw & GIRDC (2009). Deviations are caused mainly due to the methodology, data used for calibration, model input data variations, different assumptions used, different study objectives and the time difference when the studies are conducted.

The WEAP is used for the hydrological analysis, calibrated and validated with time series stream gauge data obtained from MoW and RVLB authority office. The model performance was tested using statistical performance indicators, such as coefficient of determination, NSE and PBIAS, used to evaluate the model's accuracy in simulating streamflows and the results revealed that the RSQ ranges from 0.83 to 0.93, NSE ranges from 0.68 to 0.86, PBIAS ranges from 9.45 to −1.85, RSR ranges from 0.35 to 0.59 and IA ranges from 0.62 to 0.84. At basin-level water balance, the basin receives 50.03BCM total mean annual precipitation, of which 83% (41.32 BCM) evaporates back into the atmosphere. Since the basin water flows to the lakes Ziway, Langano, Abijata, Shall, Hawassa, Abaya, Chamo and Chewbahir, it does not have any water leaving the basin through the river as outflow. The remaining 8.64 CM becomes the renewable water source of the basin that can be accessed in the form of surface or subsurface water sources. The available surface water for abstraction was further evaluated based on type, space and time distribution. Accordingly, a total of 358 MCM is available as lake water abstraction and 6,534 MCM is available as streamflow water, making it a total surface water flow of 6,892 MCM available for any abstraction use in the basin. Considering the temporal distribution of the surface water sources, 79% is available in the rainy season, June–October, and 21% during the dry period, November–May, for Ziway-Shall sub-basin, 70% is available in the rainy season, May–October, and 70% during the dry period, November–April, for Abaya-Chamo sub-basin, 62% is available in the rainy season, March–May and August–October (because it has binomial rainfall regime), and 38% during the dry period, June–July and November–February, for Chewbahir sub-basin. The remaining Hawassa sub-basin receives 58% of the surface water sources during the rainy season, May–October. The water level and capacity of the lakes in the basin were characterized using a detailed water balance analysis. The positive change in storage value at Lake Ziway, Langano, Abaya and Chamo was an indication that there is water level stability and temporal increment in lake level. On the contrary, lake Abijata, Shalla and Hawassa show a negative change in storage, which leads to a continuous water volume drop from the lakes. Unlike Lake Abijata, the drop in water volume for Lake Shalla (which has a higher depth of about 260 m) was not significant enough for surface area shrinkage. The main lakes of the basin are climate-sensitive lakes. Especially, Lake Abijata, with its terminal, shallow and alkaline nature, is highly sensitive to climate change and over-abstraction on the two upstream lakes and their feeder rivers. Particularly, Meki and Ketar rivers’ supply is crucial since 82% of the inflows to Lake Abijata are contributed by the Lake Ziway subsystem which was fed by these rivers.

The results of this study support sustainable water resource management and decision-making in RVLB. Water resource assessment with spatial and temporal distribution is the basis to improve efficient and sustainable water use. Furthermore, to improve future attempts to regulate the water resource development in the RVLB, this study gives the spatial and temporal distribution of water resources available. The findings of this study, along with additional research on water resource assessment, can help to advise policymakers about optimized water allocation planning in the basin.

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

The authors declare there is no conflict.

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