Population of Ethiopia is growing fast, ever increasing the demand of energy for industries and domestic use. To save the environment, Ethiopia is looking for green energy polices, and thus they are properly planning hydropower exploitation. Large hydropower potential is present in the region, given orography, climate and large water availability (i.e., in the Blue Nile, the greatest tributary of Nile river [1
Ethiopia is presently building the biggest hydropower plant in Africa ever, i.e., the Great Ethiopian Renaissance Dam GERD, on the Blue Nile river. However, prospective operation of such dam increased the tensions between the governments of Ethiopia and Egypt [3
]. Other dams were built in the Omo river catchment, in South Ethiopia, including GIBE family, i.e., GIBE I, II, III, the last one to be operative in short, while GIBE I, II are operating fully (Figure 1
Water management strategies in these systems need to be pursued with an eye upon maximum production/profit, while possibly respecting downstream water requirements, be them for ecological flows (minimum in stream flows MIFs), irrigation, or other needs of downstream population, and environment.
GIBE I plant is used only for hydropower production, with a MIF evaluated from designers of 2 m3
according to the Ministry of Mines and the Energy Ethiopian Electric Light and Power Authority [4
]. GIBE II operates with downstream water from GIBE I, working in practice as a run of river ROR hydropower plant. GERD and GIBE III instead feature large storage capacity, and operation thereby is relevant for downstream water users. In the present literature, some information is available covering hydrology of the Nile and Omo rivers (e.g., [5
]), also giving information of climate in the area.
Even, some previous studies on the influence of climate change focused attention upon present changes in runoff (e.g., [9
Overall, the present literature shows that water resources of Ethiopia are highly sensitive to climate change, and interannual variability [15
], and water management requires a thorough assessment of the impact of climate change on stream flows [16
Climate change will impact the availability and seasonality of fresh water, because of the expectedly modified frequency of climate extremes, such as heat waves (e.g., by leading evapotranspiration patterns), meteorological and hydrological droughts, and changes in rainfall patterns [17
The uncertain availability of water resources may further affect agricultural production, challenging the socio-economic system and competing with energy production. The Intergovernmental Panel on Climate Change IPCC indicated Africa as a most vulnerable continent to climate change and climate variability [18
], particularly for countries with economy heavily dependent upon agricultural production, such as Ethiopia [19
]. Present, and prospective climate change may specifically affect reservoirs’ operation. Temperature, and possibly precipitation changes, and influence upon the hydrological regime thereby, may affect (either positively or negatively) hydropower production [20
Outside Africa, the impact of climate change on hydropower have been explored, e.g., by Li et al. [22
], examining the effect on the Pearl River, China. They highlight a greater vulnerability of the hydroelectric system, particularly in the drier years that are not compensated by the surplus in the more wet years, leading a general decrease in efficiency of the system. Tarroja et al. [23
] instead studied the hydropower generation under climate change in highly renewable electric grid configuration, and they found a possible future decrease on production of energy despite the increase in flow which, instead, causes spillage from the reservoirs.
The aim of this study is to describe potential changes in hydropower production for GERD and GIBE III dams, under future hydrological conditions until 2100.
First, based on a dependable and unique hydrological data set [24
] gathered during 1960–2019, we set up the model Poli-Hydro, able to mimic hydrological flow formation in several areas hitherto (see for further details e.g., [25
We used Poli-Hydro (see [25
]) to describe daily hydrological budget and flow routing of upstream catchments and Poli-Power to mimic optimal daily hydropower production from the dams.
Poli-Hydro was used to simulate hydrological behavior during a calibration period (CP) 1965–1988. Then, we simulated stream flows and potential hydropower production during a control run (CR) period 2010–2019. Hydropower production is optimized using a tool called Poli-Power [26
], which maximizes the revenues for plants’ manager under given hydrological regimes.
We considered here two operation scenarios, namely, (i) constrained, i.e., with proper release downstream, to supply irrigation and Turkana lake evaporation (mostly laid in Kenya) and (ii) unconstrained, i.e., with no release downstream besides MIF.
We then used climate change scenarios of IPCC from reports CMIP5/6 (3 models of each CMIP, with RCP and SSP scenarios, 21 scenarios overall, properly downscaled), until 2100 as inputs to the Poli-Hydro model, to generate daily future series of stream flows, to subsequently assess hydropower production under new regulation strategies.
The paper is organized as follows. In the section “Case study,” we present the areas of interest and main characteristics of the studied hydropower plants. In the section “Data Base,” we describe the available information used here for calibration of the models and, in general, for the study. In “Methods” section, we depict the main traits of our models. In the section “Results,” we report models’ performance, future flow regimes, and hydropower production. In the sections “Discussion” and “Conclusions,” we comment on our results against uncertain projections of future climate, we provide limitations and outlooks, and we summarize some conclusions of our study.
5.1. Hydrological Model
Here, we report the tuned values of the Poli-Hydro model parameters and goodness of fit statistics, against monthly discharge data as available. We used NOAA meteorological data, and the available stream flow data to tune the model. Given that there were not enough data for validation/calibration exercise, and considering our focus upon hydropower production rather than upon flow prediction, we only pursued model calibration. The Omo and Blue Nile regions display different hydrological behavior, so we calibrated the model separately. In the Table 4
and Table 5
, we report the results of model tuning. Goodness of fit of the model is reported in Table 6
. We thereby report monthly NSE (R2
), and mean volume error (Bias%
), as goodness of fit measures for the available river discharge stations. In Figure 3
, we report the observed monthly flows during the calibration period 1965–1988 and the modeled ones at the dam sites.
During the calibration period, the estimated mean annual discharge at dam site for the Blue Nile river was 3000 m3
and for the Omo river it was 605 m3
. The flood period in both rivers is between July and September. Therein, the Nile river would display (daily) discharge as high as 15,000 m3
. The ground water flow covers a smaller share of the total discharge in the case of Blue Nile, but it has more bearing in the Omo river, as seen in Figure 3
5.2. Hydropower Production
Historical hydropower production is not known here, so we built a reference scenario of hydropower production during 2010–2019 (CR). As reported above, we used two scenarios S1 and S2 for reservoirs’ operation. There were no historical measurements of stream flows during the control run CR (2010–2019), and weather information was sparsely available. Accordingly, we used stream flows as simulated from a mean climate scenario, taken from the IPCC simulations during the CR period. We report separately the results for GERD and GIBE III.
As seen in Figure 4
a for GERD plant, water release in option S2 has in practice little influence on, likely due to the very large size of the reservoir. Energy production, for GERD, follows closely stream flow dynamics, with a greater production during flood season, under S2, the energy production is smaller.
Analysis of the discharges in GERD provides on average (2010–2019), Qt = 2358 Qt = 2362 m3 s−1, for S1 and S2, respectively (with ca. +0.2% for S2, small value effect of little influence with respect to release policies). Yearly revenues (relative values) are consequently just little larger for S1 than for S2 (ca. +0.3%).
Instead in GIBE III, there is seemingly larger influence from the release conditions, with discharges homogeneously higher in time and with visibly changing stored volumes. If we consider the volumetric fluctuation in the reservoir, it reaches a maximum value of 3.4 Gm3, approximately, 34% of the total active volume.
Under the S2 scenario, energy production is smaller than under S1 scenario. This likely happens because Poli-Power model wants to maximize revenues, and thereby under the S1 scenario, it keeps the lake level high for more profitable periods. This can also be seen in the greater fluctuation of volumes in the S2 scenario, reaching a maximum value of 3.8 Gm3, 39% of the active volume.
Analysis of the hydropower discharges in GIBE III provides on average (2010–2019), Qt = 184 and Qt = 204 m3 s−1, for S1, and S2, respectively (with ca. +8% for S2). Yearly revenues (relative values) are larger for S1 than for S2 (ca. +6%).
5.3. GERD Operation during Filling Phase
Our simulations of GERD operation did not take into account the filling period of the reservoirs (also given that the GERD dam is not operating yet). We indeed started the simulations with an initial condition, given by a reasonable amount of water in the reservoir, greater than the minimum. However, given the very large size of the reservoir, and the relatively fast filling time as expected (ca. 6 years), dam operation during filling period may largely impact stream flows downstream and operation thereby (i.e., along the Nile river, e.g., [3
To analyze the effect of filling, we tested reservoir operation during the filling period. Using as input flows the estimated values during 2010–2019, we simulated filling of the reservoir under both conditions S1/S2. We increased the filling period, from 1 to 10 years (i.e., over the whole period with available flow estimates), considering an approximately linear increase of pool volume until filling. After filling, we subsequently hypothesized stationary (on average) operation until the end of the simulation period. We so obtained 10 scenarios of operation during the filling phase (i.e., with filling operation from 1 to 10 years). We then calculated the performance of the reservoirs under the 20 so obtained scenarios (1–10 years filling, S1/S2 option), in terms of (i) power production and (ii) fulfillment of the downstream flow requirements. We compared such performance against a reference scenario, with stationary regulation during 2010–2019 (i.e., with reservoir filled already). In Figure 5
, we report the results of our analysis, for both S1/S2 scenarios. We provide the loss of energy and the failure to supply MIF (in terms of MIF volume loss), with respect to the reference scenario, as a function of the filling period duration (1–10 years). These losses are referred to the whole considered period (2010–2019), so the values are comparable.
After a period of relative stability or oscillation (i.e., from 1 to 5 years), one notices a decrease in the energy produced, and the longer the filling period, the larger the loss. Indeed, the longer the filling time, the smaller the volume inside the reservoir. Lower volumes result into lower pool level, and thus into smaller vertical jump for hydropower production. Accordingly, longer filling time results into lower energy production, and increasing energy loss. When one considers the decrease (loss) of downstream flow, this is clearly the highest under the S1 (no release) scenario. Clearly, in lack of constraints for downstream flow release, and in need of gathering water to fill the reservoir, the downstream flow release decreases the most. Under the S2 scenario (fair release), albeit rapid filling is necessary, still some water needs to be released downstream (and used for hydropower), and thereby the MIF loss is lower.
One can spot a clear minimum of energy loss for a period of 4–5 years for filling, under S1 scenario, and a less clear region of minimum for 1–5 years under S2 scenario. Losses in downstream flows seem less variable in the face of filling time.
Periods longer than 6–7 years, however, seemingly provide spoiling of the performance for energy and downstream release, under all scenarios.
5.4. Future Climate of the Area
Here, we report (Figure 6
and Figure 7
) changes in climatology of the area under IPCC scenarios (vs. the benchmark of CR period), in terms of mean behavior of climate in Ethiopia. We consider two reference decades, 2050–2059 (P1, Mid Century) and 2090–2099 (P2, End of Century). All projections show increased temperature (Figure 5
), both under RCPs and SSPs, and larger increases at the End of Century (unless some scenarios for RCP2.6, SSP1 2.6). RCP8.5 and SSP5 8.5 show the greatest temperature increase at the end of century, as expected. Precipitation projections (Figure 7
) are more sensitive to model setup. Mid Century decade P1 in CMIP5 models show increases and decreases of mean precipitation, while CMIP6 shows increased precipitation, except for MIP-ESM (ECHAM 6.3) model. In the End of Century P2 decade, except for MIP-ESM, on average precipitation would increase, possibly due to proximity to the equatorial zone, with more available energy for evaporation, and eventually more precipitation.
5.5. Future Hydrology of the Area
5.5.1. Future Hydrology at GERD Dam
Changes in precipitation and temperature clearly influence stream flows at the hydropower plants. Using the Poli-Hydro model, with inputs of future temperature and precipitation, we could provide scenarios of hydrological regimes for the P1/P2 decades (Figure 8
). The CMIP5 scenarios display oscillations in both periods, while CMIP6 projections provide different results, with mostly increasing discharges.
5.5.2. Future Hydrology at GIBE III Dam
CMIP5 projections at GIBE III (Figure 8
) provide again oscillating results, under different models in both P1 and P2. The results from EC-EARTH are those with larger at the end of century. CCSM4 displays the largest increase in both decades. Most recent projections (CMIP6) provide larger increases in P2 than in P1, with CESM2 and EC-EARTH3, in certain scenarios, reaching until +100% or more.
5.6. Future Hydropower Production
We report here potential changes of hydropower production, under two scenarios, namely, (i) S1 with no water release (hydropower only) and (ii) S2 with water release (GERD, agricultural demand, GIBE III, Turkana lake supply).
5.6.1. GERD No Release Scenario
Energy production at GERD (Figure 9
) increases at half century P1, with higher increase during flood periods, July–September. RCP8.5/SSP5 8.5 project largest increase yearly, and monthly, in particular, under the EC-EARTH3 model. At the end of century P2, production would be higher than in CR, but smaller than during P1. Seasonally, production would be similar to that in P1, higher at the end of summer, and lower during winter, and spring. Overall, the mean annual production for all scenarios in CMIP5 would be 23.5 and 24.6 TWh/year, respectively, during P1 and P2 (see Table 7
). Accordingly, there could be a slight increase in energy production against CR (19.3 TWh/year). The CMIP6 projections depict similar behavior, with even larger production in both decades. The overall mean annual production would be 27.1 TWh/year in P1 and 25.8 TWh/year in P2. In Figure S1 of the Supplementary Material
, we report changes of (monthly) pool volume of the GERD reservoir. One sees a general increase under the different scenarios, in particular, for those with a major increase of flows. In addition, this increase is more noticeable in fall and winter, when the reservoir reaches the maximum volume. This also leads to an increase in the use of the reservoir volume, reaching a fluctuation of 55–60% of the total active volume with a maximum of 88%.
5.6.2. GERD Environmental Release Scenario
Due to the large reservoir’s size, no noticeable difference of production was seen when considering the release scenario S2 in GERD, as reported. According to our simulations, this would also happen in the future, and the projected energy production in this case would be, in practice, the same as in the S1 scenario, as shown in Figure 9
. As for the energy production, no change in pool volume can be appreciated in the Table S1
/S2 comparison, as seen in Figure S1 of the Supplementary Material
5.6.3. GIBE III No Release Scenario
provides the projected monthly production at Mid and End of Century for GIBE III under No Release S1 management scenario. Therein, one has on average a lower energy production against CR, both for CMIP5 and CMIP6. In CMIP5, the model CCSM4 always displays the largest production, while the EC-EARTH3 always shows the smallest. Seasonally, the production increases between May and August, and then it decreases at the end of the year. The overall mean annual production would be 3.7 and 4.1 TWh/year respectively, in P1 and P2, much larger than now (2.8 TWh/year). The new projections from CMIP6 have similar behavior, with greatest energy production during the flood season. The overall mean annual production would be 3.0 and 3.4 TWh/year, respectively, in P1 and P2, with a slight increase in energy production at the end of century. In Figure S2 in the Supplementary Material
, we report the changes in reservoir’s volume for the S1 scenario. A change between the different scenarios and decades, with respect to the CR, is not evident here.
5.6.4. Gibe III Environmental Release Scenario
Under CMIP5 scenarios (Figure 10
), in the Environmental Release Scenario, energy production at GIBE III increases with respect to the CR period, slightly during winter and spring and largely in summer during the flood season. This happens for both reference decades. The overall annual production would be 3.5 and 3.4 TWh/year, respectively, in P1 and P2, slightly larger than now (2.8 TWh/year, see Table 8
The new projections of CMIP6 (Figure 10
) give similar results, with similar production patterns, and production mostly increasing against CR. However, the mean annual energy production would be smaller than under the CMIP5 scenarios, namely, 2.8 and 2.9 TWh/year (however, larger than CR). Under the Environmental Release Scenario S2, the pool volume dynamics (Figure S2, Table S2, Supplementary Material
) is different with respect to S1 scenario, and a clear decrease against the control run is visible both in P1 and P2 under most scenarios (SSP2,3,5). The change is most evident for the CMIP6 scenario, particularly at the end of century P2. The fluctuation inside the reservoir is also greater, reaching also value of 55%, larger than under the CR scenario.
6.1. Hydropower Production under Climate Change
Assessment of future expected energy production requires proper hydrological knowledge, complex given the complex topography of the catchments of interest, and sparseness of the data base in our knowledge. Accurate hydrological modeling is necessary, to quantify the nexus between climate, water fluxes, and hydropower production. Here, use of the Poli-Hydro model was well suited in this respect [25
]. In addition, we were able to use the Poli-Power model, to depict optimal reservoir’s operation under present, and future hydro-climatological conditions. We considered explicitly the variability of price/demand against climate, and economic growth, a complex dynamic often neglected in recent studies covering present, and future hydropower potential [26
The scenarios investigated in [26
] suggest that possible future climatic trends will not substantially impact hydropower production on a national level (therein in Italy, a Mediterranean country). Yet, at the local level, individual hydropower plants may be subject to precipitation variability (plus/minus) coming from climate change that could lead to larger/smaller hydropower production losses than on average nation-wide.
We explored here potential operation of GERD and GIBE III reservoirs under two possible operation scenarios, one including explicit consideration of the water needs of Egypt and Sudan downstream GERD, and the environmental volumes needed to replace evaporation from Turkana lake in (Ethiopia and) Kenya. Operation under such scenarios may dampen water-based tensions between Ethiopia, and its downstream neighbors.
For GERD reservoir, we showed that under the S1/S2 scenarios with optimal reservoir’s operation, the production and revenues for Ethiopia’s government would be the same in practice. This happens because the hydropower plant properties are so peculiar that the influence of a minimum value for turbine water (i.e., environmental flows under option S2, still used for hydropower) seems negligible. Optimizing the revenues, energy production could be different in time, and yet revenues for the two scenarios are the same, so no large economic loss would be seen. Apparently therefore, the GERD dam could be managed so as to avoid tensions between states. Eventually, we discovered that energy production could increase at half century, with only a slight decrease at the end (and yet higher than now).
Our assessment concerning (optimal) duration of the filling time, seemingly indicates a lowest loss in energy production, and a smallest penalization of the downstream flows (especially interesting in the S2 scenario, with fair flow release), for a 4–5 years period, consistently with recent findings (e.g., [56
At GIBE III, we demonstrated that under the environmental release scenario (downstream flow to Turkana), one could produce slightly less energy than under the sole hydropower release. In addition, here, energy production could increase at half century, with a slight decrease at the end of century, still higher than now. Accordingly, one may guess that, whenever such difference would be significant, in practice under S1, a slightly more rewarding use of water would be obtained (i.e., with −8% discharge used and −6% revenues). Such circumstance would indicate a (slight) loss of economic efficiency when environmental release (i.e., for Turkana lake) is considered. CMIP6 projections are slightly different from CMIP5 ones, and especially changes of precipitation are uncertain, which is widely known, and exploitation of more scenarios provides an array of possible energy pathways.
A change of the control run period may clearly provide different results in terms of expected variation in absolute terms, but the relative trend in future projections remains similar. Eventually, one could state that energy production may likely change during the century, with likely increase in the 2050s and subsequent slight decrease (but still larger than now) in the 2090s.
To better assess the dependence of energy production against weather patterns, we here explored the relationship between hydropower oscillations and the corresponding climate variables. We pursued a visual correspondence analysis between percentage variation (vs CR 2010–2019) of energy production ΔE, and of precipitation ΔP, likely to be mostly affecting hydropower dynamics. In Figure 11
, we provide a chart displaying mean ΔE against ΔP, for Mid Century and End of Century, in our 21 scenarios for both dams. For reference, we report changes of ΔE/ΔP for the other decades during 2020–2099 (126 dots for each scenario S1/S2).
Concerning the GERD dam, one clearly sees a visual correspondence between the two variables ΔE/ΔP. Namely, the increase in precipitation leads to a significant increase in energy (regression coefficient of 2.05 and R2 = 0.88 for No Release scenario S1 and regression coefficient of 2.04 and R2 = 0.89 for Release scenario S2). Notice as reported that no large difference is seen in practice between the S1/S2 scenarios.
In the case of GIBE III, albeit generally a change of ΔP results into a synchronous change of ΔE, the relationship is weaker. This may derive from the fact that production here is limited more by the capacity of the reservoir and by monthly and seasonal variations, than by the annual variation of the (rainfall) input. Indeed, in some scenarios, due to the temporal combination of precipitation, the reservoir volume limits are reached and fluctuations are therefore restricted, limiting the production of energy only in this particular combination.
6.2. Limitations and Outlooks
Our study aimed to produce credible scenarios of the future expected hydrological dynamics and of hydropower production in GERD and GIBE III power plants. Our hydrological model depicts reasonably well the hydrological processes in the area. The general lack (or sparseness) of data, and the introduction of lapse rates of precipitation, and temperature with altitude might have implied approximations, possibly adding noise.
The model performs quite well, even in complex terrains, as demonstrated by several former papers, provided some deal of information is available. The calibration parameters of Poli-Hydro are ground permeability and lag times, to be tuned against flow data. Ground permeability mostly affects water volumes, which were reasonably well tuned against the available hydrological data. Slight variations as tested during a preliminary sensitivity analysis, would not affect largely the results (and clearly, runoff water volumes would depend largely upon the precipitation inputs).
Lag times would modify slightly timing of flows (see [52
]), but again a preliminary sensitivity analysis demonstrated small changes in model performance. Most importantly, given the large size of the reservoirs, and the smoothing effects of water storage thereby, noise in discharge assessment did not result into visible changes in reservoir management, and hydropower production. In this sense, the model shows some robustness.
Here, the purpose was not to develop a (very) accurate hydrological model, but more to simulate potential for hydropower production, and possible changes in the future. Considering the size of the reservoirs as suggested above, noise in (daily) discharge assessment should not result into large error in reservoir management overall, and as well such noise should not disturb largely assessment of future production.
All GCMs, RCPs, and SSPs give future projections, so clearly embedding uncertainty and approximations, affecting the Poli-Hydro and Poli-Power models. Investigations of a larger array of possible scenarios (i.e., more GCMs/RCPs/SSPs) may add more insight.
Regarding RCPs and SSPs, in principle all of them can be taken as equally likely until the end of the century, so they represent equally possible evolutions of climate and of hydropower systems in cascade. However, recent findings [57
] indicates that recent global climate resembles more closely the scenarios as projected under RCP8.5, and possibly such scenarios (and SPP8.5) may be seen more likely for design.
Among others, Adera et al. [59
] studied the Tekeze hydropower plant located in the Tekeze river basin in the northern part of Ethiopia, another Nile river tributary. They used a combination of different regional climate model, and they showed potential increase in precipitation that my lead to an increase in energy production. Abera et al. [60
] in the same basin found also a similar pattern in precipitation and temperature increase, with consequent increase of discharge. They find a potential increase of the water volume stored in the reservoir, leading to an increase in production, similarly to what we found here for the GERD reservoir.
Further work may be devoted to obtaining data (whenever available) from hydrometric stations of the Ministry of Water Irrigation and Energy, owning, and operating streamflow stations in Ethiopia. Use of longer, more recent flow series may improve hydrological modeling. Finally, having access to energy production data from the plants may significantly improve the accuracy of energy production modeling, and projecting.
In the present study, we found that in the 21st century under climate change as expected, Ethiopia may count upon constant (if not increased) hydropower production from its largest dams GERD and GIBE III (and from GIBE I and GIBE II, however, small). Given the complexity of the nexus between climate, water flows, and hydropower production, accurate modelling is necessary to quantify present and future dynamics thereby. The Poli-Hydro, and Poli-Power models are suitable tools for the purpose of assessing optimal reservoir’s operation under present and future hydrological conditions, also considering explicitly the changes in economy, population growth, the presence of constrains, and agreements between nations.
Our results surely provide ground for discussion, for the assessment of future hydrology of the area of interest, and generally of Ethiopia, and subsequent impact upon operation, and production in the large dams GERD and GIBE III, fundamental for the future of Ethiopia energy-wise.
The projected patterns will have to be monitored, and updated on track. However, they are usable already now for an assessment of the potential for future evolutions, brainstorming of adaptation, and even planning of shared water management with bordering countries.
Our tool, exportable to other similar catchments/reservoirs, may be of use for scientists, policy makers, hydropower companies, and in general, for investigation of energy strategies for the future, in Ethiopia and elsewhere.