Impacts of Climate Variability on Power Genera on within the 7-Forks Dams in the Tana River Basin

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1 OLUDHE 13 Impacts of Climate Variability on Power Genera on within the 7-Forks Dams in the Tana River Basin Christopher Oludhe University of Nairobi; ABSTRACT The 7-Forks Dams are located in the upper Tana River Basin and form extremely important components of the Tana River System. The Masinga Dam serves as a storage reservoir, controlling hydrology through a series of downstream hydro-electric reservoirs. The opera on of Masinga Dam is therefore crucial in mee ng the power demands for the country, thus contribu ng significantly to the country s economy. The prolonged droughts of - and - in the Tana River basin due to La Nina related condi ons resulted in power shortages and prolonged power ra oning in Kenya. The overall objec ve of this study is to assess the impacts of climate variability on the hydropower sector in Kenya and to demonstrate the poten al use of climate informa on and predic on products in the decision making process in reservoir opera ons in the 7 Forks Dams of Tana River Basin in Kenya. This paper examines the impacts of climate variability on the power genera on at the 7-Forks dams and provides a forecas ng model for the management of water in the Masinga Dam for power genera on. The data used for this study were monthly rainfall observa ons over the upper Tana River catchment over the period The data were obtained from Kenya Meteorological Department (KMD) while monthly Tana River streamflow discharge, Masinga Dam levels, 7-Forks power genera on and spill over the period July 1981 July were obtained from the Kenya Electricity Generating Company (KenGen). The study u lized the reservoir inflow forecasts downscaled from monthly updated precipita on forecasts from ECHAM4.5 forced with persisted SSTs to generate the probabilis c streamflow forecasts for the April-June (AMJ) and October-December (OND) seasons for the Masinga reservoir in Kenya. Principal component regression (PCR) was employed to obtain the mean monthly streamflow forecasts during the AMJ and OND seasons. The results from retrospec ve reservoir analysis showed that the opera ons of the reservoir u lizing the forecasted inflows can significantly reduce the overall spill by increasing water alloca on for hydropower during above-normal inflow years and reducing the spill during the below-normal inflow years. 1. Introduc on Hydropower, largely considered as a clean renewable energy source, has provided many economic and social benefits to many countries in the world, such as improving domes c energy supply, providing energy security and services, s mula ng na onal economic development, and increasing economic growth. Hydropower is currently the major renewable source contribu ng significantly to electricity power supply in Kenya, and its future contribu on is an cipated to increase significantly with increase in industrial and popula on growth. Currently, 82 percent of Kenya s power is supplied by hydropower. Other major energy sources include geothermal (8%), thermal (8.7%) and wind (0.01%). The installed hydropower capacity in Kenya stands at 677.3MW. Kenya is highly dependent on hydropower and the bulk of the electricity produced in the country is obtained from five genera ng plants along the Upper Tana River Basin, namely Masinga (40 MW), Kamburu (94.2 MW), Kindaruma (44 MW), Gitaru (225 MW) and Kiambere (156 MW), typically known as the Seven-Forks Dams (Figure 1). KenGen is the leading electric power genera on company in Kenya, producing about 80 percent of the electricity consumed in the country. However, hydropower genera on largely depends on availability of water, which in turn depends on the prevailing clima c condi ons. Fluctua ons in clima c parameters such as rainfall, temperature and wind speeds significantly affect the rate of evapotranspira on, which in turn affects water in reservoirs

2 14 JOURNAL OF METEOROLOGY AND RELATED SCIENCES Fig. 1. Loca on of study area showing the 7-Forks Hydropower System (Source, KenGen, 2011 h p:// co.ke/index.php?page=business&subpage=powerplant). and dams. This makes hydropower highly dependent and sensi ve to clima c fluctua ons, especially the extremes such as droughts and floods. Droughts are known to be associated with low water levels in the major dams, while floods bring a lot of silt into the dams and can some mes lead to destruc on and damage to the turbines. It should also be noted that the impacts of such climate fluctua ons on the hydro energy resource directly or indirectly affect the welfare of the communi es and tend to enhance poverty. Kenya experienced major extreme clima c events in the recent past such as El-Niño related floods in / and / and La Niña related droughts in / and /, which led to severe socio-economic impacts in the country. Inadequate rainfall during the prolonged - drought, for example, led to severe water scarcity and shortage in electrical power supply, causing serious power ra oning throughout the country. Consequently, all economic sectors of the country suffered losses during the drought period. In par cular, the es mated losses in hydropower genera on and industrial produc on due to water shortage during the / drought were over 2 billion US dollars (World Bank, ). The enormous losses associated with these two events is a clear indica on that there is need to factor in climate informa on and predic on products into the planning and decision-making processes within the energy sector if sustainable development is to can be achieved. Climate change at the same me presents addi onal challenges to the water and hydropower sectors. The Intergovernmental Panel on Climate Change (IPCC) has indicated that warming of the climate system is unequivocal and that the globe is already experiencing the effects of climate change through changes in weather pa erns and ecosystems. Con nued GHG emissions at or above current rates would cause further warming that would threaten the development gains hard-earned by developing countries over the past decades and progress towards achieving the Millennium Development Goals (MDGs). The impacts of climate change include increased frequency and severity of droughts, floods and storms, water stress, decline in agricultural produc vity and food security, and further spread of water-related diseases, par cularly in tropical areas. 2. Objec ves of Study The overall objec ve of this study is to assess the impacts of climate variability on the hydropower sector in Kenya and to demonstrate the poten al use of climate informa on and predic on products in the decision making process in reservoir opera ons in the 7-Forks Dams of Tana River Basin in Kenya. The specific objec ves are: Determine the variability of rainfall, stream flow, dam levels and power genera on with in the 7-Forks Dam of the upper Tana River Basin in Kenya; Inves gate the rela onship between the downscaled ECHAM 4.5 precipi ta on pa erns over the Upper Tana River catchment with inflows into the Masinga Dam; Develop a seasonal predic on model for forecas ng inflows into the Masinga Dam for the major rainfall seasons over Kenya,

3 OLUDHE 15 Fig. 2. Seasonal Varia on of the AMJ and OND total inflows into Masinga Dam (1947 ) Masinga Levels (m.a.s.l) Generation (GWh) Jul-90 Jul-91 Jul-92 Jul-93 Jul-94 Jul-95 Jul-96 Jul-97 Jul-98 Jul-99 Jul-00 Jul-01 Jul-02 Jul-03 Jul-04 Jul-05 Jul-06 Jul-07 Jul-08 Jul-09 Jul-10 Months of the Year Masinga levels (m.a.s.l) FSL MOL Generation (GWh) Fig. 3. Monthly Varia on of Masinga Dam Levels and Power Genera on between July 1990 to July.

4 16 JOURNAL OF METEOROLOGY AND RELATED SCIENCES namely MAM and SOND seasons; Develop a risk based decision tool for the Masinga reservoir system that can be used for the op mum opera on of the dam water management that would increase the hydropower produc on and reduce water losses. Inflow (MCM) Literature Review The systems that control the space- me pa erns of rainfall over Kenya have been iden fied as the Inter-tropical Convergence Zone (ITCZ), Monsoonal Winds, African Sub-tropical An cyclones, Tropical cyclones, Easterly/Westerly Waves Perturba ons, Extratropical weather systems, Quasi-Biennial Oscilla on (QBO), Intraseasonal Waves, Thermally induced Mesoscale systems associated with complex topography and the large water bodies and Teleconnec ons with Indian Ocean Dipole (IOD), El-Niño/Southern Oscilla on (ENSO), Global Sea Surface Temperatures (SSTs) among others (Ogallo, 1989). Seasonal forecasts of streamflow can effec vely be u lized for mul purpose dam water management and to put in place adequate con ngency measures to mi gate extreme hydroclima c disasters (Le enmaier and Wood, : Yao and Georgakakos ; Hamlet et al., ; Maurer and Le enmaier, ; Voisin et al., ; Georgakakos and Graham, ; Golembesky et al., ). Hamlet et al. () have shown that streamflow forecasts for Columbia River can be effec vely u lized in opera ng reservoirs to obtain increased annual average hydropower genera- on. The next sec on briefly presents the data and methodology that was used in this study. 4. Data and Methodology The data used for this study were monthly rainfall observa ons over the upper Tana River catchment over the period The data were obtained from KMD while monthly Tana River streamflow discharge, AMJ_OBS AMJ_PRED Fig. 4a. Comparison between observed and predicted AMJ inflows into Masinga Dam. Inlfow(MCM) OND_OBS OND_PRED Fig. 4b. Comparison between the observed and predicted OND inflows into Masinga Dam. Masinga Dam levels, 7-Forks power genera on and spill over the period July 1981 July were obtained from KenGen. All the data were subjected to data quality and homogeneity tests using standard sta s cal tests. In this study, we u lize reservoir inflow forecasts downscaled from monthly updated precipita- on forecasts from ECHAM4.5 forced with persisted SSTs to improve the seasonal water alloca on during April-June (AMJ) and October-December (OND) seasons for the Masinga reservoir in Kenya. The probabilis c streamflow forecasts were developed for the AMJ and OND seasons using the monthly updated

5 OLUDHE 17 precipita on forecasts from ECHAM4.5 forced with persisted SSTs and seasonal streamflow as described in Sankarasubramanian et al., ). Principal components regression (PCR) was employed to obtain the mean monthly streamflow forecasts during the AMJ and OND seasons using the above predictors. The monthly reservoir inflow forecasts into Masinga Dam are then incorporated into a reservoir simula on model to allocate water for power genera on by ensuring high probability of mee ng the end of the season target storage that is required to meet the two seasonal water demands. The forecasts based alloca on is combined with observed inflows to es mate storages, spill and generated hydropower Storage Capacity (MCM) Obs_End of April_Storage Pred_End of April_Storage Fig. 5a. Comparison between observed and simulated end storage for the AMJ season. Storage Capacity (MCM) Obs_End of Oct_Storage Pred_End Of Oct_Storage Fig. 5b. Comparison between the observed and simulated end storage for the OND season. from the system. The performance of the reservoir is compared under two scenarios: forecasts issued at the beginning of the season and climatology. 5. Results and Discussions This sec on presents the results that were obtained in this study. Figure 2 presents the seasonal varia ons of the AMJ and OND inflows into Masinga Dam between It should be noted that April June (AMJ) and October December (OND) are the two dominant runoff seasons which accounts for most of the annual runoff in the study region. Computa ons were carried out in which it was found that the AMJ inflows contribute more than 50% of the total annual inflows into the dam. It can be seen from Figure 2 that the variability in streamflow is much higher during the AMJ period than OND period. Figure 3 gives the monthly varia on of the Masinga Dam levels and the corresponding power genera on between 1990 and. It can be seen from this chart that there were two cases when the dam levels were below the Minimum Opera ng Levels (MOL) which corresponded to the - and - La-Nina related droughts. The power genera on was similarly at their lowest during these years which resulted into load shedding across the country, thus impac ng nega- vely on the country s economy. The Masinga Dam operated at Full Supply Level (FSL) during high inflow years that corresponded mainly with El-Niño years (for example, /98). Figures 4a and 4b give a comparison between the observed and predicted AMJ and OND inflows into Masinga Dam between 1957 and respec vely. It can be seen from Figure 4a that the model was able to capture most of the peaks during the AMJ (r = 0.64) season while the OND season performed fairly well (r = 0.75). Figures 5a and 5b show the valida on of the reservoir simula on model that compares the observed monthly storages for

6 18 JOURNAL OF METEOROLOGY AND RELATED SCIENCES Energy Output (GWh) Obs_OND_Ave_Pow Pred_OND_Ave_Pow Fig. 6. Comparison between the observed and simulated Power for the OND season. Spill (MCM) Obs_JUN_Spill Pred_JUN_Spill Fig. 7. Comparison between observed and predicted monthly spill for the AMJ season. management perspec ve, AMJ is the season in which the current prac ce results in increased spillage, owing to the increased inflows into the reservoir following the long rains of the MAM rainfall season. Figure 8 gives a comparison between climatology and forecast risk probabili es for end of AMJ season storages. Figure 8 also gives the forecast risk probabili es of not mee ng the end of season target storage for the given seasonal inflows over the valida on period. For example, the forecasted risk probability of not meeting the AMJ target storage for was low and indeed this was the case since the inflows for that par cular year was way above normal. The reverse is true for the year and. Figure 9 gives the es mated year to year change in water releases for power genera on at Masinga Dam during the AMJ season with the corresponding varia on of the addi onal or shor all of power genera on as a result of the changes in water releases (Figure 9). Figure 8 therefore suggests that less water releases should have been made in years with below normal inflows (to conserve water in the dam) while more water releases should have taken place during years with above normal inflows to reduce the overall spillage. AMJ and OND with the simulated storages over the valida on period -. It can be seen from both figures (5a and 5b) that the simula on model predicts the observed monthly storages fairly well over the valida on period. Similar results were obtained for simulated Power genera on (Figure 6). Figure 7 shows a comparison between the observed and predicted monthly spill for the AMJ season between and. It can be seen that both the observed and predicted were in close agreement over the 20-year period of valida on. From reservoir 6. Summary and Conclusions This study set out to assess the impacts of climate variability on the hydropower sector in Kenya and to demonstrate the poten al use of climate informa on and predic on products in the decision making process in reservoir opera ons in the 7 Forks Dams of Tana River Basin in Kenya. The data used for this study were monthly rainfall observa ons over the upper Tana River catchment over the period 1947 from KMD while monthly streamflow discharge, Masinga Dam levels, 7-Forks power genera on and spill over the period July 1981 July were obtained

7 OLUDHE Inflows (MCM) Probabilities AMJ_Inflow(MCM) BN AN Fcst_Probs Clim_Probs Fig. 8. Comparison between climatology and forecast risk probabili es for end of AMJ season storages Inflows (MCM) Change in Releases (MCM) -80 Change in_release AMJ_Inflow(MCM) BN AN Fig. 9. Es mated AMJ year to year change in water releases for power genera on at Masinga Dam.

8 20 JOURNAL OF METEOROLOGY AND RELATED SCIENCES Inflows (MCM) Change in Energy Output (GWh) Ave_Change_AMJ_Pow AMJ_Inflow(MCM) BN AN Fig. 10. Es mated year to year varia on of the AMJ addi onal or shor all of power genera on at Masinga Dam. from the KenGen. Retrospec ve reservoir analysis shows that opera on of the reservoir u lizing the forecasted inflows reduces the spill considerably by increasing the alloca on for hydropower during above-normal inflow years. Similarly, during below-normal inflow years, streamflow forecasts could effec vely be u lized to meet the end of the season target storage by restricting the releases of water. REFERENCES Georgakakos, A. P., Yao, H. M., Mullusky, M. G., and Georgakakos, K. P., : Impacts of climate variability on the opera onal forecast and management of the upper Des Moines River basin. Water Resour. Res., 34_4_, Georgakakos, K. P., and N. E. Graham, : Poten al benefits of seasonal inflow predic on uncertainty for reservoir release decisions, J. Appl. Meteorol. Climatol., 47, , Golembesky, K., A. Sankarasubramanian, and N. Devineni, : Improved drought management of Falls Lake Reservoir: Role of mul model streamflow forecasts in se ng up restric ons, J. Water Resour. Plann. Manag., 135(3), Hamlet, A. H., Huppert, D., and Le enmaier, D. P., : Economic value of long-lead streamflow forecasts for Columbia River hydropower. J. Water Resour. Plann. Manage., 128_2_, KenGen, : Kenya Electricity Genera ng Company Lim ited, Annual Report and Accounts,. Maurer, E. P., and D. P. Le enmaier (), Poten al effects of long-lead hydrologic predictability on Missouri River main-stem reservoirs, J. Clim., 17, , Ogallo, L.J., 1989: The spa al and temporal pa erns of the East African seasonal rainfall derived from Principal Component Analysis. J. Clim., 9, Sankarasubramanian, A., U. Lall, and S. Espinueva (), Role of retrospec ve forecasts of GCMs forced with persisted SST anomalies in opera onal streamflow forecasts development, J. Hydrometeorol., 9, Yao, H., and A. Georgakakos (), Assessment of Folsom Lake response to historical and poten al future climate scenarios: 2. Reservoir management, J. Hydrol., 249,

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