Impacts of climate change on hydropower production in Blue Nile sub-basin.
Climate change is one of the greatest environmental, social and economic threats facing our planet today. However, it is only since the beginning of the industrial revolution that the impact of human activities has begun to extend to a global scale. In its Fourth Assessment Report (AR4), published in February 2007, the Intergovernmental Panel on Climate Change  indicates that, without further action to reduce greenhouse gas emissions, the global average surface temperature is likely to rise by a further 1.8-4.0[degrees]C in 21st century.
Water resource planning based on the concept of a stationary climate is increasingly considered inadequate for sustainable water resources management unless regional climate studies are included at river basin level .
Most parts of the Nile Basin are found to be sensitive to climatic variations [3,4]. The Blue Nile has been shown by several authors to be sensitive to changes in both precipitation and PET [5,6]. Both studies indicated that a 10% change in precipitation could lead to more than 30% change in flow. Sensitivity to PET was shown to be smaller.
Conway and Hulmes; , applied hydrological models of the Blue Nile and Lake Victoria sub-basins. The models were calibrated by simulating historical observation runoff and then driven with the temperature and precipitation changes from the three GCM climate scenarios. A wet case, dry case, and composite case produced +15 (+12), -9 (+9), +1 (+7), percent changes in mean annual Blue Nile (Lake Victoria) runoff for 2025, respectively.
Sayed  reported that a 10 % increase in rainfall over Blue Nile Basin would result in a 34 % increase in its outflows. A rainfall reduction of 10 % would result in outflow reductions of 24 % for the Blue Nile subbasin.
A number of studies have been conducted on the Nile River; however, very few studies have investigated the impact of climate change on Eastern Nile Nile River Sub Basin especially with regard to hydropower production.
In the light of the significant role of hydropower, the assessment of possible impacts of climate changes on regional discharge regimes and hydropower generation is very important for management of water resources in power generation. As per from general predictions for climate change a reduction in hydropower resource potential will occur with the exception of East Africa .
Hamududu and Killingtveit  have carried out a study on the sensitivity of hydropower generation to climate change using the output of GCM simulations with 12 different models. They found that by year 2050, the hydropower generation would be affected differently in various regions of the world. There are regions where hydropower generation will increase and there are also regions where hydropower generation will decrease.
2. Problem statement:
The region under study is one of the most vulnerable areas to climate change and climate variability, the situation aggravated by the interaction of 'multiple stresses', occurring at various levels, and with low adaptive capacity. Therefore adaptation to climate change tools, scenarios and approaches are very crucial to be addressed and disseminated. An interesting question will be how the vast hydropower and irrigation potential will be used to their optimum within the next decades.
Many development projects are planned in upper Nile basin which will affect the hydropower generation form different hydropower plants along the river.
3. Study Objectives:
The main objective of this research is to develop a methodology for assessing the impacts of climate change and its variability on water resource availability for Eastern Nile hydropower production, as well as building new water resources management scenarios based on future conditions using The Nile Basin Decision Support System (NB-DSS). The specific objectives of the study are to:
1--Collecting historical data for the study area gauges including climatology data such as precipitation, evaporation and temperature.
2--Generate stream flow of study area using NAM model.
3--Generate a schematization for the study area including all the existing and the potential projects.
4--Suggest different scenarios for sustainable development and climate change.
5--Analyze the result.
6--Conclusion and recommendations.
4. Study Area:
The Eastern Nile Basin includes the Abbay (upper Blue Nile) sub-basin, the Baro - Akobo sub-basin and Atbara sub-basin. The Blue Nile river where the research focuses is one of the two major tributaries of the Nile with a total length of 1,450 kilometers, of which 800 km are inside Ethiopia and the rest is inside Sudan. The Blue Nile flows generally south from Lake Tana and then west across Ethiopia and northwest into Sudan. The Blue Nile river basin has a drainage area of about 310,000 [km.sup.2] just upstream of Khartoum (Sudan), where it eventually joins the White Nile. This area covers most of Ethiopia and part of Sudan between longitude 32[degrees]E and 40oE and latitude 9oN and 16oN, shown in Figure 1 .It provides 62% of the flow reaching Aswan .
The Blue Nile is a river originating at Lake Tana in Ethiopia. The river and its tributaries drain a large proportion of the central, western and south-western highlands of Ethiopia before dropping to the plains of Sudan. The confluence of the Blue Nile and the White Nile is at Khartoum. The basin is characterized by a highly rugged topography and considerable variation of altitude ranging from about 350 meters (m) at Khartoum to over 4,250 meters above sea level (masl) in the Ethiopian highlands.
The climate of the Blue Nile basin varies from humid in Ethiopian Plateau to semi-arid in the north of Sudan. Climate in the highlands is strongly influenced by the effects of elevation and is generally temperate at higher elevations and tropical at lower elevations. The southern parts in the highlands of Ethiopia experience heavy rain (more than 1520 mm during the summer). Tropical climate with well-distributed rainfall is found in parts of the Lake Tana region and southwestern Ethiopia.
About 70% of the annual precipitation falls during the period between June to September . In the Ethiopian Plateau, there is little variation in the mean daily temperature throughout the year, which ranges from 14[degrees]C to 27[degrees]C depending on locality and altitude. Annual mean daily relative humidity is about 68% on the average, which also varies with altitude.
Similar climatic conditions prevail in the Southern parts of Sudan, which receive around 1270 mm rain over nine-month period between March and November, with the maximum occurring in August . The annual free surface water evaporation varies from 3.0 mm/day for Lake Tana to 6.5 mm/day in Sennar and eventually to 7.8 mm/day around Khartoum.
[FIGURE 1 OMITTED]
--Using RegCM4 regional climate change modeling procedure for the window covering the eastern Nile basin to predict climate change parameter such as precipitation, temperature and evapotranspiration.
--The precipitation and evapotranspiration outputs from A1B emission scenario of ECHAM5 GCM model were used to generate future flow scenarios for the three sub-basins-White Nile, Blue Nile and Atbara subbasins using NAM model.
--The Decision support system model (NB-DSS) was implemented to assess the impact of climate change on hydropower production of the eastern Nile basin.
6. Climate Change Scenario:
The climate change scenario for Eastern Nile basin was developed using RegCM4 regional model using ECHAM (version 5) global climate change output as boundary condition. The emissions trajectory of the A1B Scenario of ECHAM output obtained from the International Centre for Theoretical Physics (ICTP) for use in RegCM4 was implemented to develop the climate scenario for this study.
6.1 RegCM4 Description:
The RCM used for this research is the ICTP (The Abdus Salam International Center for Theoretical Physics) Regional Climate Model, RegCM4 (RegCM4; F. 12 Giorgi, 2012). The model is available at http://www.ictp.it/~pubregcm.
RegCM4 is an updated version of the RegCM3. The model is a primitive equation, hydrostatic, compressible, limited-area model with sigma pressure vertical coordinate. The soil-vegetation atmosphere interaction processes are parameterized through BATS scheme. The radiative transfer scheme of NCAR CCM4 is used in RegCM4 that includes the effect of different greenhouse gases, cloud water, cloud ice and atmosphere.
6.2 Climate Change Parameters:
In the validation run of the nesting experiment, the raw precipitation outputs for the RegCM4 model reference period (1985-2000) with boundary conditions obtained from ECHAM5 were compared to the observed precipitation and the precipitation outputs. It was found that the nested RCM highly overestimated the rainfall during the SON season. Therefore, a bias correction scheme was used in order to produce closer outputs to the observed precipitation patterns. A monthly 2-d grid of spatially varying bias correction factors was generated based on the difference between the averages observed and average ECHAM simulated values. The comparison of the simulated and observational (CRU and GPCC) datasets following bias correction showed a better agreement for the rainy season.
The change in the main areal precipitation over the Blue Nile sub-basin is shown in figure (2) as we can see that the change in the average annual precipitation is estimate by (6%).
[FIGURE 2 OMITTED]
The spatial change of the daily temperature values is manipulated to produce the mean areal temperature for the daily values over climatologic months for the eastern Nile Basin, the results shown in figures (3), as it is clear there is an temperature increase .Although the result over the Blue Nile showed an average change for the dry months is vary from 2.0 C[degrees] to 4.5 C[degrees] however the change during wet months is vary from the 0.5 C[degrees] to 1.80 C[degrees]
The mean areal daily evapo- transpiration rate in mm/day is extracted from the model surface' outputs for both the reference and future predication runs. The result from comparing those values and the percentage of change is shown in figure (4), the figure showed that the change in the daily evapo- transpiration rates for the Eastern Nile; durig the wet months is approximate (3.0% to 6%) increase however during the dry season the change is not significant and estimate as (0.5 to 2.0%).
[FIGURE 4 OMITTED]
7. NAM Model:
The NAM / MIKE11 Rainfall Runoff (RR) model is a conceptual representation of the land phase of the hydrological cycle. The hydrological model simulates the rainfall-runoff processes occurring at the catchment scale. This rainfall-runoff module can either be applied independently or used to represent one or more contributing catchments that generate lateral inflows to the river network in a MIKE 11 Hydrodynamic (HD) model.
The basic data input requirements for the NAM / MIKE 11 RR model are the meteorological data such as precipitation time series, potential evapotranspiration time series, and temperature and radiation time series. On this basis, the model produces a time series of catchment runoff, a time series of subsurface flow contributions to the channel, and information about other elements of the land phase of the hydrological cycle, such as soil moisture content and groundwater recharge
7.1 NAM Results--Stream Flow:
To assess the reliability of the RegCM4 predictions for exploring eventual changes in stream flow, the NAM model was used. The simulated rainfall, temperature and evaporation were used as inputs in the NAM Rainfall-Runoff model to generate basin runoff and daily stream flows at key stations with reliable gauge measurements along the Eastern Nile basin.
7.1.1Blue Nile Sub-Basin:
The model was run at Diem station in Sudan, very close border to Ethiopia. The model better captures the seasonality, the peak and low flow regimes at the station. The Root Mean Square Error (RSE) is estimated to be (0.75). The correlation coefficient between observed and simulated flows is (0.87).
The inter-annual variation in flow over this period is fairly high, consistent with the observed river flows for the Upper Blue Nile. Figure (5) compares the monthly flows in the reference and future runs as well as the % change in the flow. The overall annual change in flow is about +1.5%, which shows an indication of flow increase due to future climate change. On average, early flood season flows in JJA increase by 10%, while dry season and late flood season flows decreased as shown in Figure 5.
[FIGURE 5 OMITTED]
7.1.2 Atbara Sub-Basin:
For the Atbara Sub-Basin the model is correctly captures the flow seasonality and simulates the peak and low flow profile at Atbara Km3 station. The Root Mean Square Error (RSE) is estimated to be (0.72). The correlation coefficient between observed and simulated flows is (0.84).
Figure (6) compares the monthly flows in the reference and future runs as well as the % change in the flow, which shows an indication of flow decrease due to future climate change where it has been the overall annual change in flow with about -12%.
[FIGURE 6 OMITTED]
7.1.3 White Nile Sub-Basin:
For the White Nile the model is correctly captures the flow seasonality and simulates the peak and low flow profile at Malakal station. The Root Mean Square Error (RSE) is estimated to be (0.68). The correlation coefficient between observed and simulated flows is (0.79).
Figure (7) compares the monthly flows in the reference and future runs, where the % change in the flow The overall annual change in flow is about -17%, which shows an indication of flow decrease due to future climate change.
[FIGURE 7 OMITTED]
8. Decision Support System Model (DSS):
Nile Basin Decision Support System (NB-DSS) is developed through Nile Basin Initiative (NBI) to support water resources planning and investment decisions in the Nile Basin, especially those with trans-boundary or basin level ramifications. It provides a framework for sharing knowledge, understanding river system behavior, evaluating alternative development and management strategies, and supporting informed decision making.
8.1 Mike Basin Baseline Model Configuration:
8.1.1 Model Conceptualization:
The model includes Lake Tana and five of its main tributaries, the Jemma tributary downstream of Lake Tana, the Fincha'a and Amerti tributaries and the Didessa, Dabus and Beles tributaries downstream of Kessie. Existing reservoirs in the Blue Nile at Fincha'a, Roseires and Sennar as well as Merowe and High Aswan Dam on the Main Nile downstream of the confluence with the Blue Nile are included with hydropower. A summary of the reservoirs and hydropower characteristics in the Blue Nile is presented in Table 1.
[FIGURE 8 OMITTED]
Irrigation water requirements were calculated for the demands upstream of Sennar, the Gezira &Managil Schemes and for the demands downstream of Sennar, as well as for the irrigation water requirements on the Main Nile downstream of Khartoum at Tamaniat, Dongola and Aswan. Schematic of the baseline model configuration is shown in Figure 8.
8.1.2 Model Validation:
The validation of the Blue Nile and Main Nile rivers down to Aswan Dam based on water balances (mean annual flows) and flow patterns in the Blue Nile at Khartoum Soba, in the Main Nile at Dongola and against observed water levels at Lake Tana and Roseires dams.
188.8.131.52 Blue Nile at Khartoum Soba:
The simulated flows for the full model simulation period (1951-1990) in the Blue Nile River at Khartoum Soba, upstream of the confluence with the Main Nile, were compared to the observed flows. The validation results are presented graphically in Figure 9. Overall; there is a good agreement between the simulated flows and the observed flows at Khartoum Soba. The mean annual flows are 5% less than the observed with an R2 value of 68%. The peak flows are slightly over simulated and the standardized residuals originally indicated a high error in February 1983, which, after closer inspection, revealed an error in the observed flow value, which was re-patched.
[FIGURE 9 OMITTED]
184.108.40.206 Main Nile at Dongola:
Observed flows at Dongola were available for the period (1962-1997) from the Nile Encyclopaedia. The simulated flows for the period (1962-1990) in the Main Nile at Dongola were compared to the observed flows. The validation results are presented graphically in Figure 10. The statistics show that overall, there is a good agreement between the simulated flows and the observed flows at Dongola with only 4% difference in MAR and with an overall [R.sup.2] of 74%, which is reasonable and was accepted.
[FIGURE 10 OMITTED]
220.127.116.11 Lake Tana Water Levels:
The inflows into Lake Tana was simulated for the full model simulation period (1951-1990). The simulated water levels in Lake Tana were then compared to the observed water levels (Figure 11) and were found to have a good agreement.
[FIGURE 11 OMITTED]
18.104.22.168 Roseires Water Levels:
Finally, the simulated water levels in Roseires Dam, after its construction in 1966, were compared to the observed water level, based on simulated flows in the Blue Nile catchment.
A comparison of the observed and simulated water levels in Figure 12 shows an overall good agreement. The difference between the minimum simulated water levels and observed minimum water levels relate to the variable implementation of minimum drawdown levels in Roseires Dam during the simulation period.
[FIGURE 12 OMITTED]
9. DSS Scenario:
It was decided to evaluate a baseline and two other scenarios. The baseline scenario included the existing infrastructure in the study area, i.e. (High Aswan Dam, Merowe Dam, Sennar dam, heightening of Roseires Dam). The future scenarios were based on future development interventions on the Blue Nile River in Ethiopia, i.e. the construction of Grand Renaissance Dam(GERD) (Scenario 1). Scenario 2 constituted a climate change scenario. As part of Scenario 2, climate change induced rainfall-runoff impacts were imposed on the Scenario 2 model with rainfall, temperature and evaporation as the main drivers. Irrigation input climate data along the Blue Nile River was also replaced with climate change altered sequences for the relevant climate stations, while losses and gains to reservoirs and lakes in the Blue Nile basin (precipitation and evaporation) were adjusted based on climate change information.
The baseline and scenario models were registered in the NB-DSS Scenario Manager. Under each model in the Scenario Manager, scenarios were defined which represented specific development interventions and/or management options to be simulated with that particular model. For each scenario, model objects (nodes) and associated output time series were also specified as necessary. A simulation period of 1951 to 1990 was used for all scenarios and initial levels for dams were based on randomly selected storage levels.
Scenario 0: Baseline:
The development and implementation of the MIKE Basin model representing the baseline (current) Blue Nile system is detailed in Section 8.2.
Scenario]: Grand Renaissance Dam:
This Scenario considers the construction of Grand Renaissance Dam in addition to the current Blue Nile system. The characteristics for GERD used in this pilot study are based on a full supply level of 640 m. All excess water was assumed to spill (i.e. no spillway function was defined). The reduction factor applied for energy production due to friction losses or electrical efficiency losses assumed to be 0.86 (the default value in MIKE Basin).
The operating rules for GERD, and the other existing dams on the Blue Nile River, were dictated by the regulation of seasonal river flows for flood control and irrigation, maintaining minimum flow releases, maintaining operational head levels for hydropower generation, and maintaining storage capacity by flushing to control sedimentation.
The hydropower operating rule that was adopted for GERD assumed that the dam would be used as a "peak" dam in order to maximize energy production during some months of the year. This involved setting the target power equal to the installed capacity, combined with a relatively limited active storage (i.e. a high minimum operating level) in order to maximize head availability. This ensured very high energy production annually during the main rainfall season. A schematic of the MIKE Basin model configuration that was used to implement Blue Nile Scenario 1 is presented in Figure 13.
[FIGURE 13 OMITTED]
Operational levels as well as hydropower characteristics for GERD are summarized below.
Scenario 2: Climate Change Scenario with Grand Renaissance Dam:
Scenario 2 represents climate change as predicted by RegCM4, along with ECHAM5, in addition to the construction of Grand Renaissance Dam. For this scenario, climate change impacts were imposed on the Scenario 2 Blue Nile model in terms of rainfall and evaporation.
The NAM model was re-run for the selected climate change scenario using altered rainfall and evaporation inputs. The Irrigation input climate data along the Blue Nile River was also replaced with climate change altered sequences for the relevant climate stations for rainfall, temperature and evaporation variables. The losses and gains to reservoirs and lakes in the Blue Nile basin (precipitation and evaporation) were also updated with climate change information. The Mike Basin configuration was then re-run with the updated climate data.
Results And Analysis:
To assess the impact of climate change on hydropower production, a comparison has to be done between scenarios. The comparison will be done for hydropower generated of proposed and existing dams along Blue Nile and Main Nile.
The following figure (14) shows the results of the comparison between the averages monthly generated HP of GERD in different scenarios.
The generated HP from GERD may fluctuate between a maximum of 1719 GWH in the wettest historical flows and the minimum of 743 GWh. By implementing climate change impact scenario, the generated HP will be increases in an average value of 70 GWh but with the same pattern.
[FIGURE 14 OMITTED]
When the comparison applied between the averages monthly generated HP of Roseirs Dam in different scenarios as shown in figure (15), it has been noticed that the power generated is ranged from 52 GWh in low flow period to 207 GWh in high flow in Baseline scenario. After the implementation of GERD, the generated HP will increase by an average value of 22 GWh due to the rise of water level in Sudan dams. In climate change scenario the generated HP will increase by an average value of 28 GWh due to wetting trend of climate change.
[FIGURE 15 OMITTED]
For HP generated from Sennar Dam, The following figure (16) shows the results of the comparison between the averages monthly generated HP of Sennar Dam in different scenarios.
The generated HP from Sennar Dam may fluctuate between a maximum of 11GWH in the wettest historical flows and the minimum of 2 GWh in Baseline scenario. It has been noticed that there is a difference between the generated HP in the different scenarios. The generated HP will increase with the same pattern by an average value ranged from 0.5 to 4 GWh in GERD scenario, and from 1 to 5 GWh in climate change impact scenario.
[FIGURE 16 OMITTED]
The following figure (17) shows the results of the comparison between the averages monthly generated HP of Merowe Dam in different scenarios. By analyzing the figure, it has been noticed that the power generated is ranged from 275 GWh/month in low flow period to 910 GWh/month in high flow in Baseline scenario. The generated HP will increase by an average value of 106 and 117 GWh/month respectively due to the implementation of GERD, and the wetting trend of climate change.
[FIGURE 17 OMITTED]
11. Comparison of Hydropower Generation in different scenarios:
For hydropower generation, as shown in the Table 4, the annual energy generation in Ethiopia will increase from almost insignificant to 12460 GWh/year after the implementation of GERD, and to 13328 GWh/year due to the effect of climate change on Eastern Nile.
Mean monthly energy production of GERD varies depending on the seasons. According to the proposed rules, the energy may fluctuate between a maximum of 15000 GWh/year in the wettest historical flows and the minimum of 6000 GWh/year. That is, in the wet months (July to October) there is maximum energy generation, whereas in the dry months (February to June) energy production reduced to minimum.
Energy production in Sudan is from Roseires, Sennar and Merowe dams. As shown in Table 4, the combined energy production will increase in Sudan. It increases by at least 20% and 23% due to GERD implementation and climate change impact respectively.
The following figure (18) shows the results of the comparison between the total averages monthly generated HP of Blue Nile in different scenarios. The figure indicates the difference in energy production of Blue Nile between the current situation (before GERD), after GERD and after the impact of climate change. The Blue Nile energy will increase at least by 180% after the implementation of GERD, and by 194% due to the climate change impact on Eastern Nile.
[FIGURE 18 OMITTED]
12. Conclusion and Recommendations:
The research is aimed to improve the current understanding of climate change impacts on the hydropower on the Blue Nile Sub- Basin, due to the greater ability of RCMs to link changes in precipitation, temperature, and hydropower.
A model has been built for Blue Nile Sub-basin using DSS model to simulate the behavior of the basin under climate change scenario and the development project in Ethiopia (GERD). The simulation covered a period form 1951 till 1990.
Based on DSS model, the implementation of GERD shows an increase in the annual energy generation in Ethiopia from almost insignificant to 12460 GWH annually. Also it will cause a noticed increase of Sudan HP generation by about 1558 GWH annually.
The assessment of climate change impacts on the Blue Nile system, after construction of GERD showed that the power generated will increase at least by 1078 GWH annually.
Climate change impacts on HP generation from Blue Nile system shows an increase of HP generation from Ethiopia and Sudan by about 868 and 210 GWH annually respectively.
Future work should attempt to extend this analysis by:
1--Analysis of climate change prediction uncertainties, and develop methodology to mainstream these uncertainties into future water management aspects of the basin;
2--Compare the results of using RCM with the other downscaling techniques;
3--Exploring the relationship between climate and land use in the Blue Nile Sub-Basin, especially as it relates to watershed restoration projects or the construction of new storage reservoirs in Sudan and Ethiopia;
4--Further developing of the impact scenario for other hydropower stations such as Tekeze and AtbaraSetit hydropower in Ethiopia and incorporate it into the regional impact assessment model;
5--Perform a socio-economic impact assessment study to identify the impact of implementation of GERD f and recommend different mitigation measures;
6--More detailed information and longer time series can help in a better simulation.
List of abbreviations: BATS Biosphere-Atmosphere Transfer Scheme CRU Climate Research Unit, United Kingdom DSS Decision Support System ECHAM5 European Community-Hamburg ENJMP Eastern Nile Joint Multipurpose project GCM Global Climate Model GERD Grand Ethiopian Renaissance dam GPCC Global Precipitation Climate Centre GWH Giga Watt Hour ICTP International Center for Theoretical Physics JJA June- July- August MAR Mean Annual Runoff NAM lumped and conceptual catchment runoff model RegCM4 Regional Climate Model version 4 SON September-October-November
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 Giorgi, F., E. Coppola, F. Solmon, L. Mariotti, M. B. Sylla, X. Bi, N. Elguindi, G. T. Diro, V. Nair, G. Giuliani, U.U. Turuncoglu, S. Cozzini, I. Guttler, T.A. O'Brien, A.B. Tawfik, A. Shalaby, A.S. Zakey, A. L. Steiner, F. Stordal, L.C. Sloan, C. Brankovic, 2012. RegCM4: model description and preliminary tests over multiple CORDEX domains
(1) Enas Ahmed Abd El-Haliem, (2) Eman S.A. Soliman, (3) Nahla Aboul Atta
(1) Enas Ahmed Abd El-Haliem, NWS, MWRI, City, P. O Code 11471 Egypt.
(2) Eman S.A. Soliman, NWRC, MWRI, City, P. O Code12666, Egypt.
(3) Nahla Aboul Atta, Head of Hydraulics and Irrigation Department, Faculty of Engineering, Ain Shams University, Cairo, P.O 11566 Egypt.
Address For Correspondence:
Eman S.A. Soliman, NWRC, MWRI, City, P. O Code 12666 Egypt.
Received 12 June 2016; Accepted 28 July 2016; Available online 25 August 2016
Table 1: Reservoirs and hydropower generation characteristics in the Blue Nile Variable/rule Fincha'a Heightened Sennar Dam Dam Roseires Dam Dam crest level(masl) 2219 490 422 Flood control level(masl) 2219 485 (Oct-Jul) 421.8 417.2 (Aug-Sep) Top of dead storage(masl) 2213 467 415 Bottom level(masl) 2212 465 412 HP target power(MW) 70 280 15 HP installed capacity(MW) 130 415 15 Variable/rule Merowe HAD Dam Dam crest level(masl) 300 183 Flood control level(masl) 295 178 Top of dead storage(masl) 285 146 Bottom level(masl) 250 107 HP target power(MW) 625 720 HP installed capacity(MW) 1250 2100 Table 2: Blue Nile Scenarios Scenario Development Intervention Climate Change SC0 Baseline None SC1 (GERD) None SC2 (GERD) RegCM4 Table 3: Characteristic dam levels and HP parameters for GERD--Scenario 1 Variable/rule Units Value Dam crest level masl 645 Flood control level masl 640 Top of dead storage masl 580 Bottom level masl 500 HP target power MW 6000 HP installed capacity MW 6000 Table 4: Mean annual Energy generation (GWH) of each reservoir for different scenarios Ethiopia Sudan Scenarios GERD Roseires Sennar Merowe GWh/Yr Diff% GWh/Yr GWh/Yr GWh/Yr Baseline 1484.4 62.5 6241.4 GERD 12460 1744.4 86.8 7515 GERD+C.C 13328 7% 1818.4 88 7649.3 Sudan Blue Nile Scenarios total Diff% GWh/Yr Diff% Baseline 7788 7788 GERD 9346 20% 21806 180% GERD+C.C 9556 23% 22884 194% Fig. 3: The change in the daily temperature values for the climatologic months between the current condition and the future change up to year 2050; for Eastern Nile Basin. Jan Feb Mar Apr May Jun ECHAM RF 19.2 20.9 21.7 21.3 20.3 18.5 ECHAM FU 24 25.4 26.4 24.5 22.2 19 % change 25% 22% 22% 15% 9% 2% Jul Aug Sep Oct Nov Dec ECHAM RF 17.9 18.3 19 19.3 18.9 18.6 ECHAM FU 18.2 19 20.2 21.1 22 2.8 % change 2% 4% 6% 9% 16% 23%
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|Author:||El-Haliem, Enas Ahmed Abd; Soliman, Eman S.A.; Atta, Nahla Aboul|
|Publication:||Advances in Environmental Biology|
|Date:||Jul 1, 2016|
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