- Andargachew Melke Alemu, Yilma SeleshiShow
- Highlights
- •Abbay river basin is potential source for hydropower production and irrigation development.•System-wide annual energy generation from four major hydropower projects was estimated as 38 TWh.•The combined benefits derived from hydropower and irrigation outweigh the benefits of pursuing a single objective.
- Study region: Abbay River Basin, EthiopiaStudy Focus: This research focused on evaluating the trade-off and synergy between hydropower and irrigation under various development scenarios. The hydro-economic analysis was undertaken to provide insight regarding the benefits received from hydropower and irrigation development scenarios.New hydrological insights for the regionThe research demonstrated the holistic development of water resources for both energy generation and irrigation purposes, thereby maximizing economic benefits while ensuring sustainable water resource management. HEC-ResSim model was found to be adequate for evaluating the power generating capacity of multiple hydropower cascade systems under various irrigation water diversion scenarios. The findings revealed that by focusing solely on hydropower projects without incorporating irrigation development, Abbay river basin would generate up to 38 TWh of annual energy from GERD, Karadobi, Bekoabo and Mandaya cascades, with GERD accounting for 39% of the total output. Under full integration of irrigation development with four hydropower cascades, annual system energy generation was reduced by 12%. Despite the trade-off, the hydro-economic analysis revealed that the combined benefit from hydropower and irrigation surpass the benefits derived exclusively from hydropower only. Under full irrigation development the combined benefit exceeded the benefit obtained solely from four cascade hydropower without irrigation by 94%. The result revealed that simultaneous development of hydropower and irrigation is advisable, rather than prioritizing hydropower projects exclusively over Abbay river basin.Graphical Abstract
- KeywordsHydropowerIrrigationTrade-Off, Hydro-economic1. IntroductionConcerns about environmental sustainability, poverty reduction, and rising inequitable utilization of resource are pressing issues that require attention and action (Siderius et al., 2022). Sustainable management of food, energy, and water (FEW) systems is crucial for human development and addressing the needs of a growing population (Torhan et al., 2022). In recent years, there has been significant attention given to the interaction between water availability, hydropower, and irrigation. Understanding and managing the complex interactions among these resource systems is essential for ensuring sustainable and efficient water use (Albrecht et al., 2018).Hydropower production and irrigation development are intrinsically linked and have a significant influence on each other. Their interrelation is crucial for achieving sustainable economic development while meeting growing socioeconomic demands (Bazilian et al., 2011, Nhamo et al., 2018). Factors such as rising population, rapid urbanization, changing diets, and economic growth have led to an increased demand for energy and food, with significant implications for the economy, environment, and well-being of communities. Without systemic management strategies, water resource may struggle to meet the increasing demand (Bassel and Rabi, 2015).In Ethiopia, there is a focus on developing irrigation and hydropower in the Abbay River basin to address the demand for food and energy and promote sustainable development. The Abbay River basin contributing more the 60% to the entire Nile flow (World Bank, 2006) is a potential water resource for Ethiopia, riparian countries and the wider region, making it a potential basin for hydropower generation and irrigated agriculture (Mulat et al., 2018; Goor et al., 2010). Developing sustainable irrigation systems in Ethiopia, where salinity is not a major challenge, can enhance agricultural productivity and food security by providing a consistent water supply for irrigation purposes. This can lead to increased crop yields and farmer’s income, contributing to poverty reduction. Harnessing the hydropower potential of the Abbay River can also generate clean and renewable energy, reducing reliance on fossil fuels and mitigating climate change. The generated energy will be accessible to the region, that will earn foreign currency for Ethiopia, contributing to economic growth.However, allocating water to different uses involves trade-offs between the benefits perceived (Hurford et al., 2014). Several research studies have been undertaken in the Nile Basin, addressing various aspects such as the assessment of irrigation expansion in the Abbay basin’s impact on downstream flows (Allam and Eltahir, 2019), system performance evaluation (Geressu and Harou, 2015), evaluation of multi-storage hydropower development (Mulat et al., 2018), and the effects of large-scale multipurpose dam cascades on the downstream areas of the Eastern Nile river system (Tahani et al., 2014). Economic analyses of large-scale upstream river basins have been conducted by Paul and Kenneth (2010), along with studies on the optimal operation of multipurpose multi-reservoir systems, hydro-economic optimization of the Eastern Nile system (Goor et al., 2010, Digna et al., 2018) and sustainable reservoir management (Befekadu et al., 2015). Other research has explored the impact of upstream hydropower and irrigation development on downstream water availability (Murgatroyd et al., 2023) and simulation of water, energy, and food production (Tan et al., 2017). Despite these previous research efforts, there exists a gap in addressing the comprehensive consideration of full hydropower projects and potential irrigation development in the context of the Abbay river basin.The Abbay River basin represents the primary source of the overall Nile River flow. Nonetheless, there remains a significant gap in the research concerning the comprehensive assessment of trade-offs and synergy among water usage, hydropower generation, and irrigation development, particularly within the context of the Abbay River basin. Previous studies focused on this area have not thoroughly accounted for the complete developmental aspects, particularly in terms of irrigation, thereby failing to encompass a holistic range of choices for the sustainable management and development of water resource. Thus, conducting comprehensive studies and analyses that explicitly address this trade-off and synergy is crucial for developing, planning, operating and allocating water resource in the basin. Integrating trade-off and synergy analysis into water resource aspect in the Abbay River basin can provide valuable insights and inform decision-making processes. By considering the interdependencies and trade-offs among hydropower and irrigation, more sustainable management and water allocation strategies can be developed to maximize total benefits. The aim of this research was to analyze the trade-off and synergy between hydropower generation and irrigation development through the application of a simulation model. The research objectives include simulating the energy generation potential and reservoir operation of Grand Renaissance Dam (GERD), Karadobi, Bekoabo and Mandaya hydropower projects, evaluating the impact of irrigation development on energy production under different development scenarios, and analyzing the hydro-economic benefits derived from hydropower, irrigation, and the synergies between hydropower and irrigation benefits.2. Materials and methods2.1. Study areaThe Upper Blue Nile Basin, known as Abbay in Ethiopia and originating from the Ethiopian plateau, is situated between 7.50 to 120 north and 340 to 400 east (Fig. 1). This basin exhibits diverse topography, ranging from lowlands (nearly 500 masl) near the Ethio-Sudan border to mountainous regions (about 4250 masl) in the central highlands. Covering an area of around 177,000 km2 (excluding Rahad and Dinder), the basin consists of fourteen sub-basins, as depicted in Fig. 1, and expands to nearly 199,000 km2 when including Rahad and Dinder. The Abbay River Basin constitutes 17% of Ethiopia’s land mass and nearly 7% of the Nile Basin’s total surface area. As the primary source of the Nile’s water resource, it contributes over 60% to the overall flow of the river Nile. The basin plays a crucial role, with an annual average natural flow at the outlet from Lake Tana amounting to 3.5 BCM, representing about 7% of the total flow at the Sudan border. At the border, the average annual natural discharge is 49.4 BCM. Flow quantities in the basin exhibit variation, with the lowest and highest flows occurring in April and August, respectively (BCEOM, 1998).
Fig. 1. Location of Abbay River basin, Sub-basin Name and Outlet Points.The basin experiences three distinct seasons. From October to the end of February, it undergoes a dry season, followed by a short rainy season from March to May, and a major rainy season from June to September (Tesemma et al., 2010). The Abbay river basin is characterized by significant spatial and seasonal variations in rainfall. Spatially, the annual rainfall ranges from 904.51 mm to 2161.68 mm. In terms of seasonal distribution, 73.4%, 16.43%, and 10.21% of the total annual rainfall occur during the major rainy season, minor rainy season, and dry season, respectively (Alemu et al., 2022). Temperature variations in the basin are notable, with maximum and minimum temperatures ranging between 28°C and 38°C and 15°C and 20°C, respectively. In the headwaters of the basin, temperatures range from 12°C to 20°C, with minimum values dropping to −1°C and 8°C (Yimere and Assefa, 2022)The Abbay River presents a substantial opportunity for the generation of energy and irrigation development. According to Awulachew et al. (2007), the Abbay river basin possesses a noteworthy gross hydroelectric potential of 78,820 GWh/year. This potential serves as a foundational resource for meeting the escalating energy and food requirements of millions of people within the basin, thereby contributing to the realization of comprehensive and sustainable development goals in the country. Fig. 2 illustrates the schematic representation of key hydropower dam locations and potential sites for irrigation, emphasizing the strategic importance of harnessing the river’s resource for the benefit of the local population and the overall development of the region.
Fig. 2. Schematic presentation of four major hydropower dam location and potential irrigation site over Abbay river basin.2.2. Modeling approachFor this research the Hydrologic Engineering Center’s Reservoir System Simulation (HEC-ResSim) was used. Prior to the reservoir simulation model, the water resource potential of Abbay river basin was assessed. Most of hydrometric stations in Abbay river basin are limited due to the short duration of data and potential data gaps (Conway, 2000; UNESCO, 2004). Furthermore, the flow gauges within the Abbay river basin are not positioned at the mouths of individual tributaries, thus failing to measure the complete flow contribution from each subbasin. Thus, to assess the flow at each merging point, used as input for the HEC-ResSim model, hydrological simulation was conducted through the application of the SWAT model. SWAT is a comprehensive and semi-distributed river basin model (Arnold et al., 2012). It is highly computationally efficient (Neitsch et al., 2011) and has been extensively used to address hydrologic and environmental concerns (Gassman et al., 2007; Akoko et al., 2021). In the Abbay river basin, SWAT has been widely employed for hydrological modeling and water potential assessment (Alemu et al., 2022, Gebiyaw et al., 2021, Meseret et al., 2020, September 16, Tatenda et al., 2018, Abeyou et al., 2018;). Thus, the river flow data from 1981 to 2016 was simulated using SWAT model (published: https://doi.org/10.1016/j.ejrh.2022.101280) and was used as input for HEC-ResSim model.The HEC-ResSim was applied to simulate reservoir operation and hydropower generation. HEC-ResSim, uses a map-based schematic to represent the river and reservoir system, commonly used in water resource management, assist modelers in performing reservoir project studies and to support reservoir regulators during real-time events, for simulating and analyzing the operation of reservoir systems (Klipsch and Evans, 2007), to simulate reservoir operations for multiple objectives including flood risk reduction, navigation, hydropower, and environmental support (Meshkat and Klipsch, 2018). HEC-ResSim was also applied in various reservoir simulation case studies, some of which have integrated it with hydrological models (Kim et al., 2020), simulating reservoir operation and conservation processes in hydropower and irrigation applications (Munir et al., 2022), assessing the effects of development projects (Abdelkader et al., 2023). The generalized nature of HEC-ResSim, its flexible scheme for describing reservoir operations, and its powerful features make it applicable for modeling almost any single or multi-purpose reservoir system (Klipsch and Evans, 2007). It has been applied in Ethiopia for modeling cascade dams and reservoirs Operation (Teshome, 2015, Wondimagegnehu and Tadele, 2015; Ayenew et al., 2020), Water System Simulation (Dereje et al., 2020, Belachew and Mekonen, 2014), impact assessment of water development on downstream flow (Abdelkader et al., 2023).2.2.1. HEC-ResSim modeling processThe HEC-ResSim modeling process comprised three main modules: Watershed setup, reservoir network, and simulation. The watershed setup involved creating a new watershed for the Abbay river basin. A shape file containing the basin boundary, Main River, and sub-basin boundary was imported into the module’s main window as a background map. The stream alignment was established based on the mainstream and major tributaries. Computational points, reservoirs, and diversions were added to the watershed setup module and configured to accurately represent the physical features of the Abbay river basin.Once the configuration of the watershed was established, the next step was creating a schematic for the reservoir network. This schematic served as a guide, detailing the physical aspects and operational specifics of each reservoir. In the reservoir network module, the physical elements such as the pool Elevation-Area-Storage (EVA), data on reservoir evaporation, dam elevation, length, and outlet capacity, as well as the installed capacity and efficiency of each cascade dam along the Abbay river basin were carefully defined. Furthermore, operational data, including the formulation of rules and the delineation of zones such as flood control, conservation, and inactive, were carefully outlined. In the HEC-ResSim model, a range of operation rules are accessible, such as Tandem operation, hydropower schedule, and the power guide curve rule. Ayenew et al. (2020)evaluated the adequacy of these three reservoir operation rules and identified the hydropower guide curve rule as a promising approach for modeling cascade reservoirs in the basin to optimize the utilization of available water resource for hydropower generation, environmental conservation, and flood control within the cascade reservoir system. Within the scope of this study, the power guide curve rule, defining the hydropower generation requirement in relation to the available storage within the power pool, was utilized.Following the configuration of the reservoir network, alternatives was defined. Each alternative included a reservoir network based on the initial layout of the Abbay river basin, accompanied by specific control specifications, operational settings for each reservoir, time-series inflows, and initial conditions for reference. HEC-ResSim allows the run control specification resolution from 30 minutes to daily ranges. For this study the control specification was fixed at daily level since the river flow time series data is obtained at daily resolution. The time-series flow data extracted from the simulation output of SWAT (https://doi.org/10.1016/j.ejrh.2022.101280) was directly used as HEC-ResSim input. Given that the HEC-ResSim does not directly accept time-series flows from the SWAT simulation output folder, the creation of the HEC-DSS flow database from 1981 to 2016 was undertaken using the HEC-DSSVue (visual utility engine). The maximum release capacity and the upper elevation of the conservation zone were established as the initial (lookback) conditions. Independently, reservoir networks and alternatives were formulated for each scenario outlined in Table 1. Consequently, a total of ten distinct reservoir networks were generated for the purpose of this study. The first four reservoir network scenarios were formulated considering only hydropower projects to evaluate the monthly and annual energy generation potential of four major hydropower projects. Whereas the remaining six network scenarios were formulated by integrating hydropower projects and irrigation diversion to evaluate the trade-off and synergy between irrigation development and energy generation. The reservoir networks were configured considering GERD reservoir (final construction stage) and three proposed reservoirs (Karadobi, Bekoabo and Mandaya).Table 1. Cascade of hydropower and considered irrigation areas in each stage of scenario.S-1Reservoirs cascadeS-1.1Empty CellEmpty CellEmpty CellEmpty CellTanaGERDS-1.2TanaKaradobiGERDS-1.3TanaBekoaboKaradobiGERDS-1.4TanaMandayaBekoaboKaradobiGERDEmpty CellS-2Reservoirs cascadeIrrig.area(*10^3 ha)S.2.1TanaGERD230S-2.2TanaKaradobiGERD313S.2.3TanaBekoaboKaradobiGERD424S-2.4TanaMandayaBekoaboKaradobiGERD526S-2.5TanaMandayaBekoaboKaradobiGERD800S-2.6TanaMandayaBekoaboKaradobiGERD1200Routing of the flow through the stream rich is another basic element in HEC-ResSim modeling. The HEC-ResSim model incorporates various routing mechanisms, including null routing, which are explained in detail within the HEC-ResSim user manual (HEC- ResSim, 2013). In this study, the Muskingum routing method was employed. The model was thus calibrated in its ability to rout flows through the natural stream reach. During the model calibration process, it was assumed that the Abbay river basin remained in its natural state, without any man-made structures or storage facilities such as dams, reservoirs, or diversions. The calibration was conducted at two key gauge points, namely Kessie and the border gauge station.The third module in the HEC-ResSim modeling process was the simulation module. This step involved performing computations and viewing results based on the established reservoir network, input data, and developed alternatives. The simulation window included parameters such as the starting time, look back period (allowing the model to reach equilibrium or ‘warm-up’ before the starting simulation time), and end time of the simulation. In this research the simulation was undertaken from 1981 to 2016 among which the first one year (1981) was used as a warm-up period. HEC-ResSim created a directory structure within the base folder of the watershed to represent the simulation. This simulation tree contained a copy of the watershed, including only the necessary files for the selected alternatives. Additionally, a simulation.dss file was generated within the simulation, which stored all the DSS records representing input and output data for the chosen alternatives. The simulation window also allowed for editing and saving of elements and alternatives for subsequent simulations. Fig. 3clearly shows the HEC-ResSim modeling framework.
Fig. 3. HEC-ResSim modeling framework.2.2.2. Scenario developmentA scenario is a coherent, internally consistent, and plausible description of a possible future state. It is important to note that a scenario is not a forecast but rather a representation of one alternative image of how the future can unfold (Thornes, 2002). For this study, various scenarios were formulated, considering the current and proposed hydropower projects and irrigation expansion.Abbay River Basin is home to several water resource development projects, each at different stages of progress. The basin could potentially generate up to 78,820 GWh/year gross hydroelectric potential (Awulachew et al., 2007), 38% of the country’s total hydropower generation potential (ABA, 2016). As part of this study, three proposed hydropower dams upstream of GERD (Karadobi, Bekoabo, and Mandaya) have been taken into consideration for scenario development. In addition to hydropower potential, the Abbay basin has been assessed for its potential irrigable area. Research studies and feasibility reports have indicated different estimates for the potential irrigation areas in the basin, such as ENTRO (2006)(978,000 ha), WAPCOS (1990) (1001,000 ha), MoWE recent study (815,581 ha), and Yimere and Assefa, (2021) (738,183 ha). The previous researches were depending on the land slope classes, method of irrigation, the economic viability at the time of the study. With advancement of economy and technologies, the irrigation method and slope of agricultural land expected to change. Thus, the potential irrigation area obviously will not be limited to the previous studies. Considering the future advancement in technology and irrigation methods, for this study the irrigation area of 1.2 million ha was considered to capture the potential irrigation development scenario.Two major scenarios with sub-scenarios were formulated. The first major scenario (S-1) focused on the Abbay river basin without irrigation water diversion. It ranged from the natural condition of the basin, used for calibrating the HEC-ResSim model, to assessing energy production in the full cascade of four major hydropower projects. Four sub-scenarios i.e. S-1.1, S-1.2, S-1.3, and S-1.4 were developed (Table 1). S-1.1 was formulated by considering only GERD hydropower project. Sub-scenario S-1.1 evaluated the monthly and annual energy generation capacity and reservoir operation of GERD hydropower project. It assumed that the Beles diversion would not affect GERD’s hydropower production since the diverted water would eventually join the main Abbay River upstream of GERD. S-1.2, S-1.3 and S-1.4 were formulated by adding one additional hydropower project from the existing cascade system as shown in Table 1. Thus, the first major scenario was aimed to assess the energy generation capacity and reservoir operation of four hydropower projects both at individual and system-wide level.In the second major scenario (S-2), irrigation water diversion was included in the reservoir networks. The irrigation water requirement for Abbay river basin was collected from Eastern Nile Technical Regional Office (ENTRO), and Ministry of Water and Energy (MoWE). The volume of irrigation water was then estimated using the irrigation area (Fig. 4). This allowed for the assessment of water availability in the cascade reservoirs and the trade-off between energy generation and irrigation development. Subsequently, six sub-scenarios (S-2.1, S-2.2, S-2.3, S-2.4, S-2.5 and S-2.6) were developed under S-2 by considering the progressive expansion of irrigation development. In the last three sub-scenarios (S-2.4, S-2.5 and S-2.6) the cascade of hydropower project is similar while irrigation development is in incremental trend. The last three scenarios were aimed to assess how the energy production from four cascade hydropower projects affected by the progressive expansion of irrigation, reaching the potential irrigable area of the basin. Generally, the main objective of S-2 was to evaluate the trade-off and economic synergy between hydropower generation and irrigation development.
Fig. 4. Annual irrigation water requirement for each proposed irrigation area as shown in Table 1.2.2.3. Hydro-economic trade-off between hydropower and irrigationHydro-Economic analysis serve the purpose of evaluating the impacts of infrastructure and policy measures created to address challenges in water management (Kahil et al., 2015), identify prospects for enhancing and optimizing overall benefits within a basin (Hossen et al., 2021). It is crucial to consider economic benefit assessments of both current and upcoming water initiatives, as well as different strategies for managing water resource (Ortiz-Partida et al., 2023). Understanding the economic trade-offs between the development of hydropower and irrigation in Abbay river basin is thus crucial in understanding how the value of one objective can be augmented at the expense of the other. This evaluation aimed into the fundamental analysis of the combined benefits derived from the quest of these two objectives.The hydro-economic trade-off analysis was achieved by formulating various development scenarios as shown in Table 2. The first scenario focused solely on GERD hydropower, integrated with six progressive irrigation development scenarios. This scenario emphasizes the implications of different scales of irrigation expansion alongside the GERD energy generation. The second scenario encompasses two hydropower projects, namely the GERD and Karadobi, in conjunction with five irrigation development scenarios. This comparison sheds light on the trade-offs between multiple cascade hydropower projects and varying scales of integrated irrigation development. The third scenario extends the scope further by incorporating three hydropower projects, namely the GERD, Karadobi, and Bekoabo, coupled with five irrigation development scenarios. This extension enables a more comprehensive understanding of the hydro-economic dynamics associated with a combination of multiple hydropower projects and varying scales of irrigation development. The last scenario presents a holistic view encompassing the potential development of four major hydropower projects (GERD, Karadobi, Bekoabo, and Mandaya) and four irrigation development scenarios. The detailed analysis captures the trade-oof and synergies arising from the combined development of multiple hydropower projects integrated with a significant scale of irrigation development.Table 2. hydropower and irrigation development scenario for hydro-economic trade-off analysis.Hydropower cascade scenarioIrrigation development scenario (*10^3 ha)Empty CellS-2.1S-2.2S-2.3S-2.4S-2.5S-2.6GERD2303134245268001200GERDKaradobi3134245268001200GERDKaradobiBekoabo3134245268001200GERDKaradobiBekoaboMandaya4245268001200Different previous research over the basin considered various values of water for irrigation and hydropower. Digna et al. (2018) and Whittington et al. (2005) utilized the price of hydropower generation at 0.08 USD/kWh and the value of water released for irrigation at 0.05 USD/m3. Bashe et al. (2022) estimated the economic value of irrigation water for upper Blue Nile basin, specifically for Koga large scale irrigation, as 0.074 USD/m3. Similarly, Jeuland (2010) has also employed values of 0.07 and 0.1 USD/kWh for hydropower prices, without and with power trade between countries, respectively. NBI (2022) on the other hand modeled economic value of irrigation water as 0.06USD/m3. For this study the price of hydropower at 0.08 USD/kWh was considered. However, the economic return of irrigation water was considered based on the real time price of agricultural products. Over the basin, different crops have been growing and the return per hectare also varied from crop to crop. As this study mainly focused on evaluating the trade-off and synergy between water-hydropower and irrigation, the economic return of irrigation was determined by considering wheat as the dominant crop. Thus, the economic return of irrigation water was estimated based on the yield of wheat (ton/ha) and its sale price. In Ethiopia, the average wheat yield in 2021 was 3 t/ha (CSA,2021) and Zegeye et al., (2020)estimated average potential of 5 tons/ha in highland areas of the country. Thus, to be concise, for this study the economic return of irrigation water was estimated by considering average wheat production of 4tons/ha. The average price of wheat in 2022 was 0.75USD/kg (https://www.tridge.com/intelligences/wheat/ET/price) which was applied in this research.2.2.4. Input data type and sourcesThe model used hydrologic time series data kept in HEC-DSS storage system, GIS data (including basin boundary, subbasin boundary, river network, dam/reservoir locations, irrigation site) and irrigation water requirement data are required. Moreover, the input data for the study consisted of the physical and operational data of both existing and proposed dams and reservoirs (Table 3). The input data was collected from various sources including the Eastern Nile Technical Regional Office (ENTRO), the Ministry of Water and Energy (MoWE), and the Abbay River Basin Authority (ARBA), Ethiopian Electric Power Authority (EELPA). The feasibility and detail engineering design documents of each project, the Abbay river basin master plan study document was also the source of data.Table 3. Properties of existed and proposed hydropower dams and reservoirs over Abbay river basin.Empty CellKaradobiBekoaboMandayaGERDCatchment Area (km2)8223795390110410176918Average Inflow (m3/s)5656798251551Average Turbine discharge (m3/s)5326487291547Installed capacity (MW)1600240020005150Full supply level (masl)11461062800640Dam Height (m)250270175145Reservoir surface area (km2)4454035211874Reservoir capacity (MCM)40400320002770074000Minimum operating level (masl)110010207605902.2.5. Model performance criteriaThe model performance is used to test the performance of the model by comparing the simulated at gauged station with observed flow. For this study the HEC-ResSim model was calibrated for unregulated flows. The unregulated flow was computed using the model without considering the operation of reservoirs in the main channel and tributaries and produces outflows as an output of the simulation based on input flood routing parameters. HEC-ResSim simulates unregulated flows automatically, by routing inflows through the stream network as if no reservoirs were present. This procedure allowed calibration and validation of the model parameters that control the local flows downstream of the reservoirs.The performance of the model was evaluated at two main gauge station locations i.e., at middle (Kessie) and lower (Border) part of Abbay River. The location of two gauge stations are visualized in Fig. 1. The daily observed and simulated flows averaged on monthly bases from 1990 to 2007 were used as calibration period while from 2008 to 2016 were used for validation. To evaluate the model’s performance relative to the measured data, the following three performance measures were used: Percent difference between simulated and observed data (Pbias), Correlation coefficient (R2) and Nash and Sutcliffe simulation efficiency (Ens) (Nash and Sutcliffe, 1970). The details of three methods are described by Moriasi et al., (2015).3. Result and discussion3.1. Calibration and validation of HEC-ResSim modelHEC-ResSim model was calibrated and validated to assess its ability to direct flows within natural river channels. Fig. 5 illustrates the graphical visualization and statistical results between measured and simulated flows at two main gauge stations. The correlation coefficient (R2) was found to be between 0.93 and 0.96 and Nash and Sutcliffe simulation efficiency (NSE) was between 0.91 and 0.93 for calibration and validation both at Border and Kessie gauge stations. The percent of bias on the other hand lied between 5% and 6%. The statistical results generally fall within the acceptable ranges specified by Moriasi et al. (2015). The simulated flow results closely matched the measured flows at both gauge stations. Thus, based on the statistical measure and evaluation through graphical visualization (Fig. 5), it can be concluded that the HEC-ResSim model effectively simulates the flows that agreed with measured flows in Abbay river basin.
Fig. 5. Calibration and validation of HEC-ResSim Model at Border (a) and Kessie (b) gauge station.3.2. Scenario-1 (S-1) resultsIn this phase of the scenario (S-1), the analysis focused on assessing the energy generated at both the system-wide and individual levels without considering irrigation development. Moreover, the impact of implementing new hydropower projects on existing energy production, reservoir operation, and downstream flow availability was evaluated.For the case of S-1.1, considering GERD as a standalone hydropower project, the average daily power generated was found as 1810 MW. This resulted in an annual energy production of 15.87 TWh, with an average capacity factor of 0.32. These findings align well with a previous study (Ayenew et al., 2020, Digna et al., 2018). On the other hand, Eldardiry andHossain (2021)estimated an annual energy production of GERD as 13629 GWh with a capacity factor of 0.30, which is underestimated compared to the results of this research. The reason for the underestimation could stem from differences in modeling approaches or variations in the time frame of input flow data. Fig. 6(b) illustrated the monthly energy generation pattern of GERD. The minimum energy production occurred in May (1062 GWh), while the maximum was observed in September (2452 GWh). On average, the monthly energy production amounted to 1322 GWh. The energy production capacity gradually decreased from January (1085 GWh) to May (1062 GWh), with a standard deviation of 8 GWh. During the flood season (June to September), there was a sharp increase in energy production, characterized by a high standard deviation of 540 GWh. Subsequently, from September to December, there was a sharp decrease in energy production, accompanied by a standard deviation of 550 GWh.
Fig. 6. GERD Monthly storage and elevation(a) and Energy production (b) (S-1.1).This observation highlights the sensitivity of GERD’s energy production to reservoir storage. The overall energy production of GERD exhibited month-to-month variations, with a standard deviation of 404 GWh. These variations in energy production corresponded to the variations in reservoir storage, as depicted in Fig. 6(a). The capacity factor also showed monthly variations, ranging from 0.31 in May to 0.65 in September, with an average value of 0.32 across all months.The daily storage of GERD from 1982 to 2016 averaged on monthly basis revealed that month of May experienced the lowest storage levels, while September reached the highest storage levels (Fig. 6(a)). In February, the storage volume reached 64.91 BCM, equivalent to the average storage of 64.54 BCM kept at the reservoir elevation of 634 masl. Generally, from March to July, monthly storage levels were below the average value whereas from August to January, monthly storage levels were above the average value. Notably, throughout the 35-years, the GERD reservoir storage never fell below the minimum operating level (MOL) of 590 m, demonstrating its ability to function properly under various hydrological conditions. The average inflow and outflow from GERD reservoir were 48.76 BCM and 48.12 BCM respectively showed 0.64 BCM variation. The decrease in annual flow volume can be attributed to reservoir surface evaporation. On the other hand, the presence of the GERD reservoir enabled consistent downstream flows. During periods of high flow, it mitigated damage caused by severe floods, while during dry periods, additional water released from the reservoir for power generation compensated for low flows. By referring to Fig. 7, we can understand that the presence of GERD reduced peak flows during high flow years (e.g., 1998 and 2006), whereas the outflow from GERD increased during low flow periods (e.g., 1984 and 1995).
Fig. 7. GERD reservoir annual inflow, outflow, and average inflow and outflow (S-1.1).As the new hydropower project added into the cascade, the system-wide energy generation capacity also increased. In S-1.2, the inclusion of the Karadobi hydropower project into the cascade system led to a notable 41% increase in the average monthly energy generation of the entire system, rising from 1322 GWh to 1866 GWh. The operation of three cascade reservoirs (S-1.3) contributed to a further boost, elevating the system-wide average monthly energy generation to 2546 GWh. This signifies a 36% increase in the system’s energy generation capacity compared to S-1.2, with the Bekoabo hydropower project playing a significant role in this enhancement. With the operation of four cascade of hydropower projects (S-1.4), the system-wide average monthly energy generation reached 3152 GWh, showing a 24% increase compared to S-1.3.The system energy generation capacity was also evaluated based on percentile curves as illustrated in Fig. 8(d)). The energy generated at the 25th percentile ranged from 1050 GWh to 2200 GWh. Correspondingly, the 75th percentile yielded energy values ranging from 1500 GWh to 4120 GWh for the respective scenarios. While the median values (50th percentile) were found to be 1200 GWh for S-1.1 and 2500 GWh for S-1.4. Fig. 8(c) also illustrates the monthly energy generation pattern and percentile of an individual project. Analyzing the energy generation pattern of independent project is crucial to assess the impact of new projects on existing operational ones. Although, the system wide energy generation increased as the cascade of hydropower increased, results of individual energy generation capacity revealed that with the integration of new hydropower projects into the system notable effects were observed for GERD hydropower which was experienced reduction of energy generation especially in peak energy generation period (August and September) (Fig. 8(a)). In August, the energy generation capacity of GERD decreased by 5%, 10%, and 14% in S-1.2, S-1.3, and S-1.4 compared to S-1.1. Similarly, in September, the reductions were 3%, 5%, and 13% in the respective scenario.
Fig. 8. Energy generated from various cascade scenarios. GERD energy generation trend for every addition of one project upstream (a), system energy generation (b), monthly individual energy generation percentile when four projects are cascaded (c) and monthly system energy generation percentile from four cascade projects(d).In terms of annual energy output, the system energy generation capacity for S-1.2, S-1.3 and S-1.4 has reached 22 TWh, 31 TWh, and 38 TWh respectively as shown in Fig. 9(b). Despite significant increase in annual system-wide energy generation from four cascade reservoirs (S-1.4), the independent energy generation result revealed that GERD annual energy generation capacity reduced by 4.2%. Fig. 9(a) also shows the average system storage available in the reservoirs for each cascade scenario.
Fig. 9. Average annual available system storage (a) and annual system energy generated (b).3.3. Scenario-2 (S-2) resultsThis scenario was focused on evaluating the impact of an irrigation development on system and individual energy generation capacity in the Abbay river basin. Within the major scenario S-2, six sub-scenarios (S-2.1 to S-2.6) were formulated, representing various combinations of hydropower projects and irrigation water diversion, as outlined in Table 1. Thus, the results in S-2 were analyzed by taking S-1 as a baseline to quantify the change in energy generation when irrigation development is progressively expanded to the maximum potential area.The diversion of water for agricultural purposes upstream resulted in changes in hydrological conditions downstream, subsequently affecting the inflows and operation of the hydropower reservoir. Fig. 10 illustrated the change in energy generation for each irrigation development scenario. The graphical visualization revealed that across all scenarios, there was a noticeable decline in monthly energy production, particularly in wet season from June to November. whereas less impact was observed in the dry season. To quantify the extent of impacts on seasonal bases, the generated energy was described as percentile values. Fig. 11 described the 25th, 50th and 75th percentile of monthly system energy generation under the consideration of various extent irrigation development. Comparing the percentile of monthly system energy generation without irrigation (Fig. 8(d)) and with irrigation (Fig. 11) provides clear images on the impact of irrigation development on energy generation capacity of hydropower projects. The results revealed that the more decline in energy generation occurred on 75th percentile ranges which was generated in wet season. For S-1.1, S-1.2 and S-1.3 the 75th percentile was 1500 GWh, 2300 GWh, 3300GWh respectively (Fig. 8(d)). However, in S-2.1, S-2,2 and S-2.3 it was found to be 1400 GWh, 2100 GWh and 2950 GWh (Fig. 11), showing 7%, 9% and 11% decline respectively. However, significant effect was not observed on energy generated in dry season, described as 25th and 50th percentile ranges. S-2.4, S-2.5 and S-2.6 were aimed to evaluate the effect of increased potential irrigation development on energy generation capacity of four hydropower plants. Thus, the result of these three scenarios were compared with S-1.4. The 50th and 75th percentile values in S-1.4 was 2550 GWh and 4120 GWh respectively (Fig. 8(d)). However, in S-2.4, S-2.5 and S-2.6 (Fig. 13) the 50th percentile value decreased by 5%, 6% and 8% and 75th percentile value was decreased by 10%, 12% and 15% respectively due to potential irrigation development. During the dry season, diverting irrigation water upstream decreases the inflow into downstream reservoirs, thereby diminishing the active storage level of the reservoir. Consequently, this alteration affects the refilling process, unable to reach the full supply level. As a result, there was a notable reduction in the peak energy generation capacity. The 25th percentile range was, however, not significantly affected. This revealed that the development of irrigation over Abbay river basin would not severely affect the energy generation capacity particularly for 75th percent of exceedance.
Fig. 10. Monthly system-wide energy generation (GWh) for six scenarios as depicted in Table 1. S-2.4, S-2.5 and S-2.6 indicates the progressive potential irrigation development effects on the energy generation of four cascade hydropower projects (S-1.4).
Fig. 11. Percentile of monthly system energy generation (GWh) considering irrigation development.Fig. 12 on the other hand depicted the change in annual system energy and reservoir storage. Compared to S-1.1, S-1.2 and S-1.3, the annual energy generation in S-2.1, S-2.2 and S-2.3 declined by 4%, 5% and 6% respectively. Subsequent assessments from S-2.4 to S-2.6 analyzed the hydropower generation capacity of full cascade projects, including GERD, Karadobi, Bekoabo, and Mandaya, integrated with potential irrigation development (as shown in Table 1). This hydropower cascade system exhibited similarities to the S-1.4 configuration. Thus, using S-1.4 as a baseline, the trade-offs results revealed that annual system energy generation capacity of four major hydropower plants decreased by 8%, 9%, and 12% for S-2.4, S-2.5 and S-2.6 respectively. The research also gave emphasis in evaluating the individual hydropower generation capacity to identify the most affected hydropower plants. This is important to set up optimal reservoir operation. Accordingly, the trade-off between full irrigation development and individual energy generation capacity revealed that GERD experienced the highest decrease in annual energy generation capacity, with a decline of 16%, followed by Bekoabo and Mandaya with equal reductions of 8%, and Karadobi with a 7% reduction (Table 4).
Fig. 12. Annual system wide energy generation (a) and average system storage (b) for each scenario.Table 4. Change in annual energy production capacity (GWh) for individual projects due to irrigation intervention.Scenario combinationGERDChange (%)KaradobiChange (%)BekoaboChange
(%)MandayaChange
(%)S-1.1 (S-2.1)15866153014%S-1.2 (S-2.2)15666148695%673264015%S-1.3 (S-2.3)15524145226%673263715%830179175%S-1.4 (S-2.4)152461377610%673262996%830177696%754870906%S-1.4 (S-2.5)152461328313%673262837%830176718%754870806%S-1.4 (S-2.6)152461263116%673262627%830175968%754869398%The diversion of irrigation also affects both the operation of the reservoir system and the availability of water. In Fig. 12(b), the reservoir system storage is illustrated for various scenarios, comparing situations with and without irrigation intervention. It is evident that storage without irrigation exceeded that with irrigation. For scenario S-2.1, the average storage decreased by 3%. In scenarios S-2.2 and S-2.3, the system storage declined by 4% and 6% compared to S-1.2 and S-1.3, respectively. The average system water availability in the reservoirs for scenarios S-2.4, S-2.5 and S-2.6 were evaluated by taking S-1.4 as a baseline. It was observed that the average system storage in S-1.4 was 160 BCM (Fig. 12(b)). However, with potential irrigation development, the system storage decreased in each scenario by 8%, 12%, and 16% for S-2.4, S-2.5 and S-2.6 respectively. Fig. 13 also showed the storage of individual reservoirs. For S-2.6, representing full irrigation development, the results showed that most significant storage decline was observed in GERD reservoir with a 21% decrease compared to individual storage in S-1.4. Following GERD, the storage in Mandaya, Bekoabo, and Karadobi decreased by 19%, 14%, and 6%, respectively. The percentage decrease in individual reservoir storage corresponds to the extent of irrigation development upstream of each reservoir. The more downstream the reservoir, the greater the impact of irrigation development upstream, leading to more pronounced effects on reservoir storage due to increased irrigation water diversion.
Fig. 13. Independent Storage in each cascade scenario without irrigation (a) and with irrigation (b).Increasing irrigation diversion leads to a corresponding decline in both energy production and storage capacity, emphasizing the trade-off between irrigation development and energy generation. The finding of the scenario analysis revealed that though the energy production and reservoir operation shows decline trend as irrigation expanded, it is noticed that irrigation expansion is not sever threat especially for average energy generation of hydropower projects over Abbay river basin. Instead, diverted water for irrigation purposes could boost productivity, leading to improved food security. Additionally, it could facilitate the export of agricultural goods, generating foreign income and offsetting the losses incurred from reduced energy production.3.4. Hydro-economic trade-off analysisThrough this analysis, a clearer understanding of the development options emerges, be it the exclusive emphasis on hydropower, single focus on irrigation, or the pursuit of a combined approach, all with the overarching goal of maximizing overall benefits. A vital point of reference within this framework is the zero (0.00) irrigation benefit, which serves as a benchmark indicating the absence of any irrigation development. Fig. 14 indicates the hydro-economic trade-offs between hydropower and irrigation across various development scenarios, providing a detailed overview of the interplay between the two critical components. Whereas Fig. 15 presented a comprehensive illustration of the individual and combined benefit obtained from hydropower and irrigation development. It is apparent that as the benefits of irrigation grow, the benefits from hydropower diminish, and conversely, as the benefits from hydropower increase, the benefits from irrigation decrease. Initially, assuming no development in irrigation, the accrued annual benefit from four distinct cascade hydropower projects were found as 1.3 billion USD (GERD), 1.8 billion USD (GERD and Karadobi), 2.5 billion USD (GERD, Karadobi, and Bekoabo), and 3.1 billion USD (GERD, Karadobi, Bekoabo, and Mandaya) as shown in Fig. 14 (a, b, c and d)).
Fig. 14. Hydro-economic trade-off between hydropower and irrigation development. a) GERD plus six irrigation scenarios, b) GERD and Karadobi plus five irrigation scenarios, c) GERD, Karadobi and Bekoabo plus five irrigation scenarios, d) GERD, Karadobi, Bekoabo and Mandaya plus four irrigation Scenarios (Table 2).
Fig. 15. The economic benefits derived from hydropower, irrigation, and the overall benefits derived from both hydropower and irrigation development in each stage of scenario.However, as irrigation expanded, with water being diverted from the system, the benefit obtained solely from hydropower decreased with polynomial function for all scenarios (Fig. 14). Unlike the decrease in hydropower benefits, the benefits arising from irrigation witnessed a rise concurrent with the expansion of irrigation. Upon achieving full irrigation development (S-2.6), the annual irrigation benefit reached 3.6 billion USD, surpassing the benefits obtained solely from full hydropower development by 17%. To the right of pins (Fig. 14), the trade-off curves showed steeper slopes which revealed the more reduction of hydropower benefits with further irrigation development.The research underscored the importance of not just assessing the trade-offs but also considering the synergies between hydropower and irrigation returns. This approach aims to promote the formulation of development scenarios that optimize overall benefits. Remarkably, while the enhanced focus on one objective came at the cost of the other, the overall benefit derived from both objectives underscores the importance of concurrently pursuing irrigation and hydropower development. Fig. 15 illustrates the individual and combined annual benefit of hydropower and irrigation for each scenario. In all scenarios the combined benefits derived from both hydropower and irrigation outweigh the benefits of pursuing a single objective, such as focusing solely on hydropower. Despite the inherent conflict between the two objectives, their combined benefits are substantial. Evaluating the situation from this perspective, the results of this study revealed that, across various instances of irrigation expansion, the combined benefit of hydropower and irrigation surpass the benefits derived solely from hydropower. Investigating into a comprehensive assessment of full irrigation development integrated to four cascade hydropower projects, it is revealed that the total annual benefit reaches nearly 6 billion USD which exceeded those of hydropower benefit alone by 94%. At the point of intersection between the hydropower benefit and irrigation benefit curve, the total benefit obtained was 5 billion USD. Above S-2.4, the hydropower benefit curve showed downward deflection indicating more trade-off in benefits. However, the total benefit was still at its increasing trend, revealed that irrigation benefit more outweighed than the benefit of hydropower.The findings underscore the importance of understanding the trade-offs and synergy between hydropower and irrigation development in the context of the Abbay Basin. The result would provide insights to formulate sustainable strategies that balance the benefits of both objectives, ensuring the optimal utilization of water resource for economic and environmental sustainability. The result generally indicates that over Abbay river basin it is encouraged to develop both hydropower and irrigation concurrently rather than focusing solely on hydropower only.4. ConclusionThis study conducted a thorough assessment of the hydropower and irrigation trade-off and synergy in the Abbay river basin. The combination of hydrological model (ArcSWAT) to quantify the water resource potential of the basin and HEC-ResSim to simulate the power generation and reservoir operation with various development scenarios were applied.The successive developments of hydropower projects and the operation of multiple cascades contributed to the substantial growth in average monthly and annual energy generation, reflecting the enhanced energy generation capacity of the system. The enhanced energy generation capacity resulting from the operation of multiple cascades in Abbay river basin has significant implications for increasing energy demand. It enables the country to meet growing energy needs, drive economic development, improve energy access, explore regional energy export opportunities, and advance its renewable energy transition. Irrigation is on the other hand essential to increase agricultural productivity, achieve food security, export of agricultural products. Analyzing the trade-off and synergy between hydropower and irrigation under various development scenarios is crucial for making informed decisions regarding water resource management, efficient water allocation strategies, sustainable development, and utilization of available water resource. By considering different development scenarios and their associated trade-offs and synergy, policymakers can make informed decisions that maximize the overall benefits while minimizing potential negative consequences.The result of this research generally revealed that total benefit gained from hydropower and irrigation underscores the individual benefit though the two objectives are conflicting each other. Thus, it is encouraged to develop both hydropower and irrigation concurrently than giving emphasis only for hydropower.To provide full insight into the management, planning, and development of water resource, we suggest further research on water-energy-food nexus modeling by considering climate change and ecological consequences of hydropower and irrigation projects.CRediT authorship contribution statementAndargachew Melke Alemu: Writing – original draft, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. Yilma Seleshi: Validation, Supervision.Declaration of Competing InterestThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.Acknowledgmentwe are grateful for national meteorological agency of Ethiopia, Ethiopian ministry of Water and Energy and Abbay basin authority, Eastern Nile Technical Regional Office (ENTRO) for providing all necessary data.References- ABA, 2016ABAThe Federal Democratic Republic of Ethiopia, Abbay Basin Authority, booklet (2016)Google ScholarAbdelkader et al., 2023A. Abdelkader, M. Haggag, K. Hamed, H.G. RadwanAssessment of the Impacts of Proposed Water Resource Development Projects in Baro-Akobo-Sobat Basin on Nile Inflows at High Aswan Dam.J. Hydrol.: Reg. Stud., 46 (2023), Article 101335, 10.1016/j.ejrh.2023.101335View PDFView articleView in ScopusGoogle ScholarAbeyou et al., 2018Abeyou, W.W., Essayas, K.A., Haw, Y., Jaehak, J., Charlotte, M., Robin, T., Thomas, J.G., Tammo, S.S., 2018. Evaluating hydrologic responses to soil characteristics using SWAT model in a paired watersheds in the Upper Blue Nile Basin. https://doi.org/10.1016/j.catena.2017.12.040.Google ScholarAkoko et al., 2021G. Akoko, T.H. Le, T. Gomi, T. KatoA review of SWAT Model application in AfricaWater, 13 (2021), p. 1313, 10.3390/w13091313View at publisher View in ScopusGoogle ScholarAlbrecht et al., 2018T.R. Albrecht, A. Crootof, C.A.ScottThe Water-Energy-Food Nexus: A systematic review of methods for nexus assessmentEnviron. Res. Lett., 13 (4) (2018), Article 043002, 10.1088/1748-9326/aaa9c6 View at publisher View in ScopusGoogle ScholarAlemu et al., 2022A.M. Alemu, Y. Seleshi, T.W.MesheshaModeling the spatial and temporal availability of water resource potential over Abbay river basin, Ethiopia.J. Hydrol.: Reg. Stud., 44 (2022), Article 101280, 10.1016/j.ejrh.2022.101280View PDFView articleView in ScopusGoogle ScholarAllam and Eltahir, 2019M.M. Allam, E.A.B. EltahirWater-energy-food nexus sustainability in the Upper Blue Nile (UBN) BasinFront. Environ. Sci., 7 (2019), 10.3389/fenvs.2019.00005View at publisher Google ScholarArnold et al., 2012Arnold J.G., Kiniry J.R., Srinivasan R., Willimas J.R., Heney E.B., Neitsch S.L., 2012. SWAT Input/Output Documentation. Texas water Resource Institute.Google ScholarAwulachew et al., 2007S.B. Awulachew, A.D. Yilma, M. Loulseged, W. Loiskandl, M. Ayana, T.AlamirewWater Resource and Irrigation Development in EthiopiaInternational Water Management Institute, Colombo, Sri Lanka (2007), p. 78p.〈https://www.iwmi.cgiar.org/Publications/Working_Papers/working/WP123.pdf〉(Working Paper 123)Google ScholarAyenew et al., 2020D.M. Ayenew, M.A. Moges, F.A.Zimale, A.G. MulatMulti-purpose Reservoir Operation Analysis in the Blue Nile Basin, EthiopiaLect. Notes Inst. Comput. Sci., Soc. Inform. Telecommun. Eng. (2020), pp. 242-257, 10.1007/978-3-030-43690-2_16View at publisher View in ScopusGoogle ScholarBassel and Rabi, 2015T.D. Bassel, H.M. RabiWater–energy–food (WEF) Nexus Tool 2.0: guiding integrative resource planning and decision-makingNos. 5–6Water Int., vol. 40 (2015), pp. 748-771, 10.1080/02508060.2015.1074148View at publisher Google ScholarBazilian et al., 2011M. Bazilian, H. Rogner, M.Howells, S. Hermann, D. Arent, Gielen, DConsidering the energy, water and food nexus: towards an integrated modelling approachEnergy Policy, 39 (2011), pp. 7896-7906, 10.1016/j.enpol.2011.09.039View PDFView articleView in ScopusGoogle ScholarBCEOM, 1998BCEOM, 1998. Abbay River Basin master plan, phase-2, section I- main report. Ministry of Water Resource, the Federal Democratic Republic of Ethiopia, Addis Ababa.Google ScholarBefekadu et al., 2015G.H. Befekadu, A.E. Harb, A.A.Saud, R.S. Verne, A.W. FrankMutually beneficial and sustainable management of Ethiopian and Egyptian dams in the Nile BasinJ. Hydrol., 529 (2015), pp. 1235-1246, 10.1016/j.jhydrol.2015.09.017View at publisher Google ScholarBelachew and Mekonen, 2014Belachew A. and Mekonen Z., 2014. Eastern Nile Basin Water System Simulation Using Hec-ResSim Model. CUNYAcademic Works. 〈http://academicworks.cuny.edu/cc_conf_hic/373〉.Google ScholarConway, 2000ConwayThe Climate and Hydrology of the Upper Blue Nile RiverThe Geographical Journal, 166 (1) (2000), pp. 49-62, 10.1111/j.1475-4959.2000.tb00006.xView at publisher View in ScopusGoogle ScholarCSA, 2021CSA, 2021. The Federal Democratic Republic of Ethiopia Central Statistical Agency (CSA). Agricultural Sample Survey, Volume I, Addis Ababa..Google ScholarDereje et al., 2020Dereje M.A., Mamaru A.M., Fasikaw A.Z., Asegdew G.M., 2020. Multi-purpose Reservoir Operation Analysis in the Blue Nile Basin, Ethiopia. Advances of Science and Technology: 7th EAI International Conference, ICAST 2019, Bahir Dar, Ethiopia, August 2–4, 2019, Proceedings.Google ScholarDigna et al., 2018R.F. Digna, Y.A. Mohamed, P. van der Zaag, S. Uhlenbrook, W. van der Krogt, G.CorzoImpact of water resource development on water availability for hydropower production and irrigated agriculture of the Eastern Nile BasinJ. Water Resour. Plan. Manag., 144 (5) (2018), 10.1061/(asce)wr.1943-5452.0000912View at publisher Google ScholarEldardiry and Hossain, 2021H. Eldardiry, F. HossainEvaluating the hydropower potential of the Grand Ethiopian Renaissance DamJ. Renew. Sustain. Energy, 13 (2) (2021), Article 024501, 10.1063/5.0028037 View at publisher View in ScopusGoogle ScholarENTRO, 2006ENTROWater Atlas of the Blue Nile Sub-Basin, Draft Report Eastern Nile Technical Regional, Office, Ethiopia (2006)Google ScholarGassman et al., 2007P.W. Gassman, M.R. Reyes, C.H.Green, J.G. ArnoldThe soil and water assessment tool: historical development, applications, and future research directionsTrans. ASABE, 50 (4) (2007), pp. 1211-1250, 10.13031/2013.23637View at publisher View in ScopusGoogle ScholarGebiyaw et al., 2021S.T. Gebiyaw, S.G. Geremew, N.E. Azage G.G.,AgizewHydrological modeling in the Upper Blue Nile basin using soil and water analysis tool (SWAT)Model. Earth Syst. Environ. (2021), 10.1007/s40808-021-01085-9View at publisher Google ScholarGeressu and Harou, 2015R.T. Geressu, J.J. HarouScreening reservoir systems by considering the efficient trade-offs—informing infrastructure investment decisions on the Blue NileEnviron. Res. Lett., 10 (12) (2015), Article 125008, 10.1088/1748-9326/10/12/125008 View at publisher View in ScopusGoogle ScholarGoor et al., 2010Q. Goor, C. Halleux, Y. Mohamed, A.TilmantOptimal operation of a multipurpose multi-reservoir system in the Eastern Nile River Basin. HydrolEarth Syst. Sci. Discuss., 7 (2010), pp. 4331-4369www.hydrol-earth-syst-sci-discuss.net/7/4331/2010/doi:10.5194/hessd-7-4331View at publisher CrossRefGoogle ScholarHossen et al., 2021M.A. Hossen, J. Connor, F.AhammedReview of hydro-economic models (HEMs) which focus on transboundary river water sharing disputesWater Policy, 23 (6) (2021), pp. 1359-1374, 10.2166/wp.2021.114View at publisher View in ScopusGoogle ScholarHurford et al., 2014A.P. Hurford, I. Huskova, J.J.HarouUsing many-objective trade-off analysis to help dams promote economic development, protect the poor and enhance ecological healthEnviron. Sci. Policy, 38 (2014), pp. 72-86, 10.1016/j.envsci.2013.10.003View PDFView articleView in ScopusGoogle ScholarJeuland, 2010, NovemberM. JeulandEconomic implications of climate change for infrastructure planning in transboundary water systems: An example from the Blue NileWater Resour. Res., 46 (11) (2010), 10.1029/2010wr009428 View at publisher Google ScholarKahil et al., 2015M.T. Kahil, A. Dinar, J. AlbiacModeling water scarcity and droughts for policy adaptation to climate change in arid and semiarid regionsJ. Hydrol., 522 (2015), pp. 95-109, 10.1016/j.jhydrol.2014.12.042View PDFView articleView in ScopusGoogle ScholarKim et al., 2020J. Kim, L. Read, L.E. Johnson, D.Gochis, R. Cifelli, H. HanAn experiment on reservoir representation schemes to improve hydrologic prediction: coupling the national water model with the HEC-ResSimHydrol. Sci. J., 65 (10) (2020), pp. 1652-1666, 10.1080/02626667.2020.1757677View at publisher View in ScopusGoogle ScholarKlipsch and Evans, 2007J.D. Klipsch, T.A. EvansReservoir Operations Modeling with HEC-ResSim, Hydrologic Engineering Center, U.S. Army Corps of Engineers, 530, Davis, CA(2007), pp. 756-1104Google ScholarMeseret et al., 2020, September 16D. Meseret, B.D.Olika, F.B. Muluneh, O.T. Leta, M.O. DinkaAssessment of Surface Irrigation Potential of the Dhidhessa River Basin, EthiopiaHydrology, 7 (3) (2020, September 16), p. 68, 10.3390/hydrology7030068View at publisher Google ScholarMeshkat and Klipsch, 2018M. Meshkat, J.D. KlipschModeling Interconnected Reservoirs with HEC-ResSimWorld Environ. Water Resour. Congr. 2018(2018), 10.1061/9780784481424.024View at publisher Google ScholarMoriasi et al., 2015D.N. Moriasi, M.W. Gitau, N. Pai, P. DaggupatiHydrologic and water quality models: Performance measures and evaluation criteriaTrans. ASABE, 58 (2015), pp. 1763-1785, 10.13031/trans.58.10715View at publisher View in ScopusGoogle ScholarMulat et al., 2018A.G. Mulat, S.A. Moges, M.A.MogesEvaluation of multi-storage hydropower development in the upper Blue Nile River (Ethiopia): regional perspectiveJ. Hydrol.: Reg. Stud., 16 (2018), pp. 1-14, 10.1016/j.ejrh.2018.02.006View PDFView articleView in ScopusGoogle ScholarMunir et al., 2022M.M. Munir, A.S. Shakir, H.U.Rehman, N.M. Khan, M.U. Rashid, M.A.U.R.Tariq, M.K. SarwarSimulation-optimization of tarbela reservoir operation to enhance multiple benefits and to achieve sustainable development goalsWater, 14 (16) (2022), p. 2512, 10.3390/w14162512View at publisher View in ScopusGoogle ScholarMurgatroyd et al., 2023Murgatroyd, A., Wheeler, K., Hall, J., Whittington, D., 2023. Trade-offs between hydropower and irrigation in transboundary river systems: the implications of further development on the Blue Nile in Ethiopia. https://doi.org/10.21203/rs.3.rs-2820888/v1 10.21203/rs.3.rs-2820888/v1..Google ScholarNash and Sutcliffe, 1970J.E. Nash, J.V. SutcliffeRiver flow forecasting through conceptual models’ part I—A discussion of principlesJ. Hydrol., 10 (1970), pp. 282-290View PDFView articleGoogle ScholarNBI, 2022NBI, 2022. Hydro-economic analysis for the Nile Basin Collaborative Water Resource Assessment. 〈http://134.213.39.163/sites/default/files/WRM-2022-01_HEM%20Results%20Techncial%20Report.pdf〉.Google ScholarNeitsch et al., 2011S.L. Neitsch, J.G. Arnold, J.R.Kiniry, J.R. WilliamsSoil & Water Assessment Tool: Theoretical Documentation Version 2009, Texas A & M University, USA (2011)Google ScholarNhamo et al., 2018L. Nhamo, B. Ndlela, C.Nhemachena, T. Mabhaudhi, S. Mpandeli, G.MatchayaThe water-energy-food nexus: climate risks and opportunities in Southern AfricaWater, 10 (5) (2018), p. 567, 10.3390/w10050567View at publisher View in ScopusGoogle ScholarOrtiz-Partida et al., 2023J.P. Ortiz-Partida, A.S.Fernandez-Bou, M. Maskey, J.M. Rodríguez-Flores, J. Medellín-Azuara, S. Sandoval-Solis, T. Ermolieva, Z. Kanavas, R.K. Sahu, Y. Wada, T. KahilHydro-economic modeling of water resource management challenges: current applications and future directions.Water Econ. Policy, 09 (01) (2023), 10.1142/s2382624×23400039View at publisher Google ScholarPaul and Kenneth, 2010B. Paul, S. KennethEconomic analysis of large-scale upstream river basin development on the Blue Nile in Ethiopia considering transient conditions, climate variability, and climate change(Pg.)J. Water Resour. Plann. Manag. (2010), pp. 156-166, 10.1061/ASCEWR.1943-5452.0000022View at publisher View in ScopusGoogle ScholarSiderius et al., 2022C. Siderius, P. van Walsum, H.BiemansStrong trade-offs characterize water-energy-food related sustainable development goals in the Ganges–Brahmaputra–Meghna River basin.Environ. Res. Lett., 17 (10) (2022), Article 105005, 10.1088/1748-9326/ac94e9 View at publisher View in ScopusGoogle ScholarTahani et al., 2014M.S. Tahani, E.S. Mohamed, S.AbbasDownstream Impact of Blue Nile Basin DevelopmentNile Basin Water Sci. Eng. J., Vol.7 (2014)Google ScholarTan et al., 2017C. Tan, T. Erfani, R. ErfaniWater for energy and food: a system modelling approach for blue Nile River BasinEnvironments, 4 (1) (2017), p. 15, 10.3390/environments4010015View at publisher Google ScholarTatenda et al., 2018L. Tatenda, R. Vincent, Z. Gete, S.AlemtsehaySpatial and Temporal Variability in Hydrological Responses of the Upper Blue Nile basin, EthiopiaWater, 11 (1) (2018), p. 21, 10.3390/w11010021View at publisher Google ScholarTesemma et al., 2010Z.K. Tesemma, Y.A. Mohamed, T.A. SteenhuisTrends in rainfall and runoff in the Blue Nile Basin: 1964–2003Hydrol. Process, 24 (2010), pp. 3747-3758, 10.1002/hyp.7893View at publisher View in ScopusGoogle ScholarThornes, 2002Thornes, J.E., 2002. IPCC, 2001: Climate change 2001: impacts, adaptation and vulnerability, Contribution of Working Group II to the Third Assessment Report of the Intergovernmental Panel on Climate Change, edited by J. J. McCarthy, O. F. Canziani, N. A. Leary, D. J. Dokken a. International Journal of Climatology, 22(10), 1285–1286. https://doi.org/10.1002/joc.775.Google ScholarTorhan et al., 2022S. Torhan, C.A. Grady, I. Ajibade, E.K. Galappaththi, R.R. Hernandez, J.I. Musah-Surugu, A.M. Nunbogu, A.C. Segnon, Y. Shang, N. Ulibarri, D. Campbell, E.T. Joe, J. Penuelas, J. Sardans, M.A.R. ShahTradeoffs and synergies across global climate change adaptations in the food-energy-water NexusEarth’S. Future, 10 (4) (2022), 10.1029/2021ef002201 View at publisher Google ScholarUNESCO, 2004UNESCO, 2004. National Water Development Report for Ethiopia. UN-WATER/WWAP/2006/7, World Water Assessment Program, Report, MOWR, Addis Ababa, Ethiopia, pp.273.Google ScholarWAPCOS, 1990WAPCOS, 1990. Preliminary Water Resource Development Master Plan for Ethiopia. Final Report, prepared for EVDSA. Addis Ababa, Ethiopia.Google ScholarWhittington et al., 2005D. Whittington, X. Wu, C.SadoffWater resource management in the Nile basin: the economic value of cooperationWater Policy, 7 (3) (2005), pp. 227-252, 10.2166/wp.2005.0015View at publisher View in ScopusGoogle ScholarWondimagegnehu and Tadele, 2015D.Wondimagegnehu, K. TadeleEvaluation of climate change impact on Blue Nile Basin Cascade Reservoir operation – case study of proposed reservoirs in the Main Blue Nile River Basin, Ethiopia133Proc. Int. Assoc. Hydrol. Sci., 366 (2015), p. 133, 10.5194/piahs-366-133-2015View at publisher View in ScopusGoogle ScholarWorld Bank, 2006World Bank, 2006. Ethiopia: managing water resource to maximize sustainable growth. World Bank Agriculture and Rural Development Department. Washington D.C. 〈http://documents.worldbank.org/curated/en/947671468030840247〉.Google ScholarYimere and Assefa, 2021A. Yimere, E. AssefaAssessing and mapping irrigation potential in the abbay river basin, ethiopiaRuss. J. Agric. Socio-Econ. Sci., 114 (6) (2021), pp. 97-109, 10.18551/rjoas.2021-06.11View at publisher Google ScholarYimere and Assefa, 2022A. Yimere, E. AssefaCurrent and future irrigation water requirement and potential in the Abbay River Basin, EthiopiaAir, Soil Water Res. (2022), 10.1177/11786221221097929 View at publisher Google ScholarZegeye et al., 2020F. Zegeye, B. Alamirew, D.TolossaAnalysis of wheat yield gap and variability in EthiopiaInt. J. Agric. Econ., 5 (4) (2020), p. 89, 10.11648/j.ijae.20200504.11View at publisher Google Scholar
