1. Introduction
Recent studies focusing on the teleconnection between sea surface temperature (SST) conditions and regional–continental hydroclimatology show that interannual and interdecadal variability in exogenous climatic indices modulate both global- and regional-scale rainfall (Ropelewski and Halpert 1987) and streamflow patterns (e.g., Dettinger and Diaz 2000; Piechota and Dracup 1996). Advancements in understanding the linkages between exogenous climatic conditions such as tropical SST anomalies and local–regional hydroclimatology offer the scope of predicting season-ahead and long-lead-time (12–18 months) streamflow (Maurer and Lettenmaier 2003; Souza Filho and Lall 2003). Considerable improvement in the skill of seasonal climate forecasts over the last decade has also been achieved using the slowly evolving boundary conditions such as SSTs in the tropical oceans (Goddard et al. 2003). Seasonal forecasts of streamflow could also be utilized effectively for multipurpose water allocation and to prepare adequate contingency measures to mitigate hydroclimatic disasters (Voisin et al. 2006; Georgakakos and Graham 2008; Golembesky et al. 2009). Hence, the application of climate-based information for water management has been shown to result in improved benefits over the long term in comparison to the benefits that would be obtainable under no-forecast- (climatology) based operation. Still, application of climate forecasts for improving water management faces various challenges partly because the uncertainty in climate forecasts (Pagano et al. 2001, 2002) as well as because of the challenges in translating probabilistic forecasts for operational guidance (Sankarasubramanian et al. 2009).
Recent studies on operational streamflow forecast development show that seasonal streamflow forecasts downscaled from monthly updated climate forecasts are quite effective in reducing the uncertainty in intraseasonal water allocation (Sankarasubramanian et al. 2008, 2009). Efforts to reduce uncertainty in climate forecasts have also focused on combining climate forecasts from multiple climate models (Rajagopalan et al. 2002; Devineni and Sankarasubramanian 2010a,b). Recent studies based on a multimodel combination approach indicate better streamflow forecasting skill than any individual forecast models, as the skill of the multimodel ensembles is maximized by assigning optimal weights to each general circulation model (GCM; Robertson et al. 2004; Devineni and Sankarasubramanian 2010a,b). Studies have also shown the utility of multimodel streamflow forecasts derived from low-dimensional models in invoking restrictions and water conservation measures during drought years (Golembesky et al. 2009). Low-dimensional models primarily employ the dominant modes of variability in the predictors (e.g., precipitation forecasts from GCMs) to explain the variability in the predictand (e.g., precipitation–streamflow). For instance, Golembesky et al. (2009) utilized probabilistic multimodel streamflow forecasts to invoke water-use restrictions for improving the operation of Falls Lake Reservoir in the Neuse basin during below-normal inflow years. One important use of multimodel climate forecasts is in reducing the overconfidence of individual models, resulting in fewer false alarms and missed targets (Devineni and Sankarasubramanian 2010a; Weigel et al. 2008). This has important implications since multimodel climate forecasts can increase the confidence of stakeholders toward application of climate information for water management.
The main intent of this study is to evaluate the performance of probabilistic streamflow forecasts developed from single-GCM and from multimodel climate forecasts in improving the hydropower generation for the Tana River basin, Kenya. The Tana River basin accounts for about 57% of the total hydropower generated in Kenya and our analysis is focused on the Masinga Reservoir system, which accounts for about 67% of the total storage capacity in the Tana River basin. For developing the reservoir inflow forecasts, the study utilizes 3-month-ahead precipitation forecasts from the ECHAM4.5 GCM forced with constructed analog SST forecasts and the multimodel climate forecasts developed from the study of Devineni and Sankarasubramanian (2010a). The reservoir management model adopted here is a simplified version of the dynamic allocation framework reported by Sankarasubramanian et al. (2009).
The manuscript is organized as follows: Section 2 provides baseline information on the Tana River basin and its linkage to El Niño–Southern Oscillation (ENSO) along with the seasonal streamflow forecasts developed from ECHAM4.5 and from multimodel climate forecasts. Following that, we present a brief description of the Masinga Reservoir simulation model and the retrospective reservoir analyses design. Section 4 compares the utility of streamflow forecasts derived from ECHAM4.5 and multiple climate models with climatology in improving the hydropower generation from the Masinga Reservoir. In section 5, we summarize the findings of the study and also give conclusions.
2. Hydroclimatology of the Tana basin and streamflow forecasts development
Kenya experienced major extreme climatic events in the recent past such as El Niño–related floods in 1997/98 and 2009/10 and La Niña–related droughts in 1999/2000 and 2008/09, which led to severe socioeconomic impacts in the country. Specifically, inadequate rainfall during the prolonged 1999/2000 drought led to severe water scarcity and shortage in electrical power supply causing serious power rationing throughout the country. In particular, the estimated losses in hydropower generation and industrial production due to water shortage during the 1999/2000 drought were over 2 billion U.S. dollars (Mogaka et al. 2006). Such enormous losses related to the extreme events underscores the need to translate the climate-based streamflow forecast information into planning, risk management, and decision making to minimize socioeconomic impacts and to meet increased energy demands in the near future.
Kenya is highly dependent on hydropower, which constitutes over 75% of the total electricity generated in the country. The bulk of this electricity is obtained from five generating plants along the upper Tana River basin (Fig. 1a), 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 (see Fig. 1a). Kenya Electricity Generating Company Limited is the leading electric-power-generation company in Kenya, producing about 80% of electricity from hydropower. The Masinga dam, the uppermost reservoir, controls the flow of water through a series of downstream hydroelectric reservoirs. The Masinga catchment area lies between 0°7′ and 1°15′S and 36°33′ and 37°46′E and has an area of about 7354 km2. The reservoir has a capacity of 1560 × 106 m3 at full supply level (FSL) with a surface area of 120 km2. The spillway for Masinga dam is 1056.5 m above mean sea level, which corresponds to the FSL. The minimum operating level is 1035.0 m above mean sea level. Tana River basin experiences bimodal precipitation pattern and accordingly dominant runoff seasons occur during April–June (AMJ) and October–December (OND). Observed inflows at the Masinga dam are available from 1940 until the present. Inflows during AMJ, which are heavily influenced by SST variations in the Indian Ocean (Mutai and Ward 2000), contribute more than 46% of the total annual inflows into the dam (Fig. 1b). Inflows during the OND season account for 26% of the annual flows and its interannual variations are significantly associated with ENSO variations (Mutai and Ward 2000). The correlation between OND flows and July–September (JAS) Niño-3.4, a commonly used index denoting ENSO conditions that indicates the average SSTs over 170°–120°W and 5°S–5°N, over the 1947–2005 period is 0.42. This strong association between SST and inflows indicates the potential in linking climate forecasts for developing season-ahead inflow forecasts for the Tana River basin.
(a) Location of the upper Tana River basin in Kenya with letters representing the following dams: A—Kiambere, B—Kindaruma, C—Gitaru, D—Kamburu, and E—Masinga. (b) Seasonal variation of the AMJ and OND total inflows into Masinga dam (1947–2005).
Citation: Journal of Applied Meteorology and Climatology 52, 11; 10.1175/JAMC-D-12-0300.1
Seasonal streamflow forecasts based on exogenous climate indices can be obtained using both dynamical and statistical modeling approaches. The dynamical modeling involves coupling of a hydrological model with a regional climate model that preserves the boundary conditions specified by the GCM by considering the topography of a region (e.g., Leung et al. 1999; Nijssen et al. 2001). However, uncertainty propagation from the coupling of these models (Kyriakidis et al. 2001) and converting the gridded streamflow–precipitation forecasts into reservoir inflow forecasts poses serious challenges in employing dynamical downscaling for water management applications. On the other hand, statistical modeling basically employs statistical models to downscale GCM outputs to develop streamflow forecasts at a desired location (Gangopadhyay et al. 2005). Studies have also related well-known climatic modes to observed streamflow in a given location using a variety of statistical models ranging from simple regression (e.g., Hamlet and Lettenmaier 1999) to complex methods such as linear discriminant analysis (Piechota et al. 2001), spatial pattern analysis (Sicard et al. 2002), and semiparametric resampling strategies (Souza Filho and Lall 2003). Although both approaches have their advantages and limitations, statistical modeling approach is the least data intensive and is very relevant in regions such as Kenya, where high-resolution spatial data to run regional climate and hydrologic models are not readily available.
Multimodel inflow forecast development using multimodel climate forecasts
The primary intent of this paper is to utilize inflow forecasts developed using multimodel climate forecasts and compare their performance with inflow forecasts developed using single GCMs and with climatological inflows. Recent studies on reducing the uncertainty of climate forecasts show that combining multiple models result in reduced false alarms and missed targets resulting in improved probabilistic climate forecasts (Rajagopalan et al. 2002; Devineni and Sankarasubramanian 2010b). In this study, we utilize the multimodel precipitation forecasts developed by Devineni and Sankarasubramanian (2010b) for developing multimodel inflow forecasts for the Masinga Reservoir. The multimodel precipitation forecasts for the AMJ and OND seasons are developed by combining five coupled GCMs (CGCMs) and climatology (i.e., observed precipitation) based on the methodology described in Devineni and Sankarasubramanian (2010b). The precipitation forecasts from multiple models along with the climatology are combined by analyzing the skill of the candidate models contingent on the Niño-3.4 state. The main advantage of combining multiple GCMs conditional on the predictors' state is that the approach assigns higher weights for climatology and lower weights for the CGCMs particularly if the skill of a candidate model is poor under ENSO conditions. For additional details and a complete discussion on the multimodel combination methodology, see Devineni and Sankarasubramanian (2010a,b).
Details of CGCMs considered from the ENSEMBLES project for developing multimodel forecasts for this study.
To compare the performance of multimodel climate forecasts, we also consider precipitation forecasts from a single GCM–ECHAM4.5 forced with constructed analog SSTs. Retrospective precipitation forecasts from ECHAM4.5 are available at the International Research Institute (IRI) for 7 months in advance for every month beginning January 1957 with a resolution of 2.8° × 2.8° (http://iridl.ldeo.columbia.edu/SOURCES/.IRI/.FD/.ECHAM4p5/.Forecast/ca_sst/.ensemble24/.MONTHLY/.prec/). To force the ECHAM4.5 with SST forecasts, retrospective monthly SST forecasts were developed based on the observed SST conditions in that month based on the constructed analog approach. For additional details on ECHAM4.5 precipitation forecasts, see Li and Goddard (2005; http://iri.columbia.edu/outreach/publication/report/05-02/report05-02.pdf). The ensemble mean, which is computed from 24 realizations of ECHAM4.5 precipitation forecasts obtained based on different initial conditions, was downloaded over the Masinga catchment area from the IRI data library for the period 1957–2005. We utilize the ensemble mean of precipitation forecasts issued at the beginning of two rainy seasons (AMJ and OND), 1 April and 1 October, along with the previous month's streamflow (March–September) as an additional predictor. Though this results in a comparison of precipitation forecasts from multimodels and ECHAM4.5 at two different lead times, from the perspective of water management the allocation decisions are usually done at the beginning of the season. Thus, in the context of application, the best single-model forecast available at the beginning of the season is used.
Principal components regression (PCR)
Using PCR, we developed single-model (SM) inflow forecasts and multimodel (MM) inflow forecasts to obtain the leave-one-out cross-validated mean seasonal (conditional mean) streamflow forecasts for the AMJ (OND) season. Using the point forecast error obtained from the PCR, we obtained the conditional variance of the seasonal streamflows to develop the probabilistic reservoir inflow forecasts. Residual analyses of the PCR based on the quantile plots and skewness test on the residuals showed that the normality assumption is valid. This indicates that the seasonal flows during the AMJ and OND season could be assumed as a lognormal distribution. Based on this assumption, we developed 500 ensembles of the seasonal streamflows in log space using the conditional mean and the point forecast error obtained from the PCR. These ensembles are eventually transformed back to the original space for developing the probabilistic inflow forecasts that could be forced with the Masinga Reservoir model.
Figure 2a (Fig. 2b) shows the conditional mean of the SM and MM seasonal streamflow forecasts for the period 1991–2005 developed based on the ECHAM4.5 and multimodel precipitation forecasts for the AMJ (OND) seasons. All the forecasts for the single model (multimodel) in Fig. 2 are obtained in a leave-one-out cross-validated mode using the observed flows and the predictors for the period 1961–2005 (1961–2005). Since the multimodel climate forecasts from ENSEMBLES project are available only up to 2005, we have evaluated the skill of the multimodel inflow forecasts only up to 2005. The inset in Fig. 2 shows the verification statistics for the multimodel (single model) inflow forecasts based on correlation coefficient and root-mean-square error computed between the ensemble mean of the forecasted streamflow and the observed streamflow over the period 1961–2005 (1961–2005). From Fig. 2, we observe that the multimodel streamflow forecasts perform slightly better than the single-model forecasts in predicting the conditional mean. It is important to note that the single-model inflow forecasts for the AMJ and OND seasons were developed using 3-month-ahead ECHAM4.5 precipitation forecasts issued at the beginning of April and October, respectively. On the other hand, the multimodel precipitation forecasts issued at the beginning of 1 February and 1 August were employed in developing the AMJ and OND inflow forecasts, which results in a lead time of two months for both seasons. We use these leave-one-out cross-validated probabilistic streamflow forecasts available to the probabilistic reservoir simulation model over the period 1991–2005 for evaluating the utility of streamflow forecasts developed from single-model and multimodel precipitation forecasts in improving the water and energy management for the Masinga Reservoir.
Comparison between the observed and predicted inflows into Masinga dam using SM and MM for the (a) AMJ and (b) OND seasons.
Citation: Journal of Applied Meteorology and Climatology 52, 11; 10.1175/JAMC-D-12-0300.1
3. Masinga Reservoir simulation model
(a) Masinga operational rule curves, and (b) comparison between observed (Obs) and simulated (Sim) June end storage.
Citation: Journal of Applied Meteorology and Climatology 52, 11; 10.1175/JAMC-D-12-0300.1
Prior to performing the retrospective reservoir analyses using the streamflow forecasts, we performed model verification from 1991 to 2005 by comparing the reservoir model's ability to simulate the observed end of June storages. The simulations were performed by forcing the model with the observed flows during AMJ and initial storages in April to determine the end-of-June storages by allocating the reported releases for water hydropower generation. Figure 3b shows the observed and model predicted stages at the end of June—the end-of-season stage. The observed and modeled storages obtained from the reservoir model were converted into stages using the available stage–storage relationship for the Masinga Reservoir. From Fig. 3b, we understand that the developed model is quite reasonable in predicting the observed June storages upon simulation with observed flows and the reported hydropower and water supply releases. This gives us confidence in employing the simulation model presented here for further analyses that utilize the seasonal streamflow forecasts from two models for improving water and energy management.
In this study, we consider three inflow forecasting schemes: 1) streamflow developed using ECHAM4.5 precipitation forecasts, 2) multimodel precipitation forecasts obtained by combining five GCMs from the ENSEMBLES project, and 3) climatological ensemble. Each scheme provides 500 members–realizations for a given season indicating the conditional distribution of the inflows into the Masinga dam. The climatological ensemble for each season is obtained by leaving out the particular year's observation from the observed inflow (1940–2005) with the remaining 70 years having equal chances of getting selected in the ensemble. This is reasonable, since the lag-1 correlation on the seasonal flows is almost zero. For each of the forecasting schemes, we first obtain the ps. Based on the end-of-season target storage probabilities estimated from climatological forecasts (accepted climatological risks), we explore the possibilities of modifying the releases from current releases to increase the power generated during above-normal storage conditions and impose restrictions during below-normal storage conditions. For instance, if the climate-information-based forecasts (i.e., schemes 1 and 2) suggest lower (higher) probability of 
4. Results and analysis
This section presents the retrospective analyses for understanding the utility of single-model and multimodel inflow forecasts in improving the hydropower generation for the Masinga dam utilizing the three candidate forecasting schemes. Since the multimodel forecasts are available only up to 2005, all the results presented in this section consider the period 1991–2005 for multimodel forecasts, whereas results for single-model forecasts and climatological ensemble are presented for the period 1991–2005.
a. End-of-season target storage probabilities
To begin with, we first evaluate the ability of the three candidate forecasting schemes in estimating the probability of meeting the June and December storage for the reported seasonal releases from Masinga over the period 1991–2005 without constraining the releases being ps = 0.5. Given that most of the reservoirs can hold water for more than the seasonal demand, the entire demand could be met with 100% reliability. However, we can modify the reservoir releases by comparing the ability of the three forecasting schemes in estimating probability of meeting the end-of-season target storage [Prob
Figure 4 shows the estimates of Prob
Comparison between climatology- and inflow-forecast-based estimates of failure probabilities in meeting (a) June (Jun) end storage and (b) December (Dec) end storage for SM and MM. Open circles denote observed inflows during normal years (Obs), gray filled circles show inflows during below-normal years (Obs_BN, <33rd percentile), and black filled circles show inflows during above-normal years (Obs_AN, >67th percentile).
Citation: Journal of Applied Meteorology and Climatology 52, 11; 10.1175/JAMC-D-12-0300.1
Figures 4a and 4b show clearly that the estimates of Prob
Comparing the performance of multimodel inflow forecasts with inflow forecasts developed using ECHAM4.5 precipitation forecasts, we infer that multimodel forecasts perform more consistently in indicating below-normal inflow storage conditions. For instance, multimodel forecasts correctly estimate the Prob
b. Hydropower generation for Masinga Reservoir utilizing multimodel forecasts
Although the results shown in Fig. 4 did not ensure ps = 0.5 for each forecasting scheme, the estimates of Prob
Estimated differences in releases suggested by the climatological ensembles to the releases obtained based on SM and MM forecasts for improving the hydropower generation at Masinga dam during the (a) AMJ and (b) OND seasons. The circle legend is as in Fig. 4.
Citation: Journal of Applied Meteorology and Climatology 52, 11; 10.1175/JAMC-D-12-0300.1
Estimated change in electrical power generation at Masinga dam during the (a) AMJ and (b) OND season using SM and MM. The circle legend is as in Fig. 4.
Citation: Journal of Applied Meteorology and Climatology 52, 11; 10.1175/JAMC-D-12-0300.1
Comparison between the observed and predicted spill for the (a) AMJ and (b) OND seasons dam using SM and MM. The circle legend is as in Fig. 4.
Citation: Journal of Applied Meteorology and Climatology 52, 11; 10.1175/JAMC-D-12-0300.1
Comparison between the (a) June end and (b) December end storage for SM and MM. The circle legend is as in Fig. 4.
Citation: Journal of Applied Meteorology and Climatology 52, 11; 10.1175/JAMC-D-12-0300.1
Given that ps = 0.5 for each season in a given year, we utilize the three forecasting schemes to modify the reservoir releases to increase (reduce) hydropower generation if the inflow forecasts suggest above-normal (below normal) conditions. For instance in AMJ 1998 (above-normal inflow year), in Fig. 4, estimates of Prob
The main intent of this study is to understand the utility of multimodel streamflow forecasts in improving the water allocation for hydropower generation. For this purpose, the AMJ–OND multimodel inflow forecasts are utilized to modify the releases for hydropower generation over the 3-month period in the season during 1991–2005 by enforcing the end-of-season storage constraint to be equal to 0.5. We used the observed storage on 31 March (30 September) of each year during 1991–2005 as the initial storage (St−1) for the AMJ (OND) season. By combining the streamflow forecasts(qtk) issued in March (September) with the observed storage at the end of March (September), we obtain releases for hydropower use 
Figure 5 shows the estimated difference in the releases obtained using climatological ensemble (forecasting scheme 3) to the releases suggested by the single-model and multimodel forecasts for improving hydropower generation for the AMJ (Fig. 5a) and OND (Fig. 5b) seasons over the period 1991–2005. The releases for all the three forecasting schemes are obtained by ensuring ps = 0.5. The figure also shows the actual observed inflow during the period as below-normal, normal, or above-normal conditions on the secondary y axis. A positive (negative) change indicates that the model suggests a higher probability of not meeting the target storage, resulting in reduced (increased) release from the climatological ensembles predicted releases. From Fig. 5, we observe that single-model and multimodel forecasts suggest an increase (decrease) in releases compared to during above-normal (below normal) inflow years. Further, we can also see that the multimodel forecasts suggest more water release during above-normal years than do single-model forecasts. Similarly, during below-normal years, the multimodel forecasts suggest more reduction in release from the actual observed release than do SM forecasts.
Given that the Masinga Reservoir is primarily operated for hydropower generation, we also estimated the amount of hydropower (GW h) that results each year from operating the reservoir based on the seasonal forecasts. In other words, we combine the model determined releases with observed inflows to simulate to actual amount of hydropower that is generated based on the storage–elevation relationship of the reservoir. Figure 6 shows the estimated change in generated hydropower from the reservoir from both the forecasts. Analogous to Fig. 5, we can observe from Fig. 6 that the forecasts suggest an increase (decrease) in generated hydropower during above-normal (below normal) inflow years. It is important to note that the increase in hydropower generated during the above-normal years results from an increased allocation of water for power generation. This also in turn results in a reduced spill from the reservoir during above-normal inflow years. The estimated spill each year for both the seasons is shown in Fig. 7. We observe that for most of the years the spill obtained from the forecast models is less than the spill suggested by the climatological ensemble. This indicates that the model is actually releasing additional water for hydropower generation during above-normal years.
We can always increase the allocation for any use by allocating additional water. But such an increase should not come at the cost of failing to meet the target storage. To evaluate whether the changes in releases do not result in increased–decreased storage at the end of the season, we show the simulated end-of-season [June (Fig. 8a) and December (Fig. 8b)] storages from 1991 to 2005 by combining the forecast-suggested releases from both the models with the observed flows. We observe that during below-normal years the simulated end-of-season storage is less than the target storage
The retrospective reservoir analysis presented in this study can be utilized to determine the appropriate seasonal releases in conjunction with the future streamflow potential. If the forecasts suggest an above-normal inflow year, then the Prob
c. Discussion
Results from the multimodel climate forecasts improve the forecast skill by reducing the overconfidence of individual models (Weigel et al. 2008; Devineni and Sankarasubramanian 20010a,b). The intent of this study is to utilize them in applying them for improving reservoir management. For this purpose, we considered multimodel precipitation forecasts developed by Devineni and Sankarasubramanian (2010b) for developing seasonal inflow forecasts into Masinga Reservoir in the Tana River basin, Kenya. Inflow forecasts developed from multimodel and ECHAM4.5 clearly show that multimodel forecasts have improved skill in predicting the observed flows (Fig. 3). Utilizing analyses presented in Fig. 4 clearly shows that multimodel forecasts reduces the overconfidence of individual model forecasts and also reduces false alarms (e.g., year 1996 in Fig. 4a). Except for very few instances (OND 1991 in Fig. 4b), multimodel forecasts perform better than ECHAM4.5 model-based inflow forecasts in many years (e.g., OND 1995 in Fig. 4b) when compared with individual model forecasts. It is important to note that for both seasons, AMJ and OND, multimodel forecasts are developed two months (February for AMJ and August for OND) ahead of individual model forecasts, which are issued at the beginning of the season. Another advantage in using multiple models for analyzing the storage probabilities is during normal years. It is very clear from our analysis that the storage probabilities are around a smaller range indicating that a normal or business-as-usual operation could be pursued.
Analyses in Figs. 5–7 show that inflow forecasts from climate models could be adjusted to meet the climatological probability of meeting the target storage (ps = 0.5). However, our modeling framework facilitates target storage probability based on stakeholder's choice of interest. However, for such selected probabilities, inflow forecasts should be carefully analyzed to ensure the forecasts being well calibrated, indicating a good correspondence between forecast probabilities and their observed relative frequencies (Devineni et al. 2008). Such careful analyses on inflow forecasts based on user-selected target storage probabilities would reduce apprehensions on utilizing climate-information-based streamflow forecasts for improving water and energy management. Our analyses from Fig. 8 also show that forecast-based allocation ensures meeting the target storages in both seasons. Since Fig. 8 is obtained by combining forecast-based releases with the observed inflows, it is a validation of the performance of inflow forecasts in meeting the target storage as well as improving the hydropower generation. The lessons from this study also have potential applications for basins in the southeastern United States. This is primarily because both regions are semiarid and the river basins are predominantly belonging to rainfall–runoff regime. From hydroclimate perspectives too, the Southeast experiences dry and warm winter during La Niña conditions as like the Tana River basin. Our hydroclimatology research group in collaboration with the State Climate Office of North Carolina has developed an online portal (http://www.nc-climate.ncsu.edu/inflowforecast) for disseminating both the inflow forecasts from multiple models and the storage forecasts for the user-specified releases. Our hope is that as multiple climate models are analyzed in developing seasonal forecasts, providing online access to both inflow and storage forecast scenarios will result in real-time evaluation and application of climate-information-based streamflow forecasts for improving reservoir operations in regions that are significantly impacted by climate variability.
5. Summary and conclusions
A reservoir simulation model that uses ensembles of streamflow forecasts is presented and applied for improving the water allocation and thereby the energy management for the Masinga Reservoir in Tana River basin in Kenya. The Masinga Reservoir located in the upper Tana River basin is extremely important in supplying the power requirements of the country as well as in protecting the downstream ecology of the Tana River system. The dam serves as the primary storage reservoir, controlling streamflow through a series of downstream hydroelectric reservoirs. Prolonged droughts of 1999–2001 in the Tana River basin due to La Niña–related conditions resulted in power shortages and prolonged power rationing in Kenya. In this study, we utilize reservoir inflow forecasts downscaled from monthly updated precipitation forecasts from ECHAM4.5 forced with constructed analog SSTs and multimodel precipitation forecasts developed from the ENSEMBLES project to improve the seasonal water allocation during the April–June and October–December seasons for the Masinga Reservoir in Kenya. Three-month-ahead inflow forecasts developed from ECHAM4.5, multiple general circulation models, and climatological ensembles are forced into a reservoir simulation model to allocate water for power generation by ensuring climatological probability of meeting the end-of-season target storage that is required to meet the water demands during nonrainy seasons. The forecast-based releases are then combined with observed inflows to estimate storages, spill, and generated hydropower from the system. Retrospective reservoir analysis shows that inflow forecasts developed from a single GCM and multiple GCMs perform better than climatology reduce the spill considerably by increasing the allocation for hydropower during above-normal inflow years. Similarly, during below-normal inflow years, both these forecasts could be effectively utilized to meet the end-of-season target storage by restricting the releases of water for power-generation uses. Comparing the performance of inflow forecasts developed from multimodels with the inflow forecasts developed using ECHAM4.5 alone, we infer that the multimodel forecasts preserve the end-of-season target storage better in comparison with the single-model forecasts by reducing the overconfidence of individual model forecasts. Thus, considering multiple models for seasonal water allocation reduces the uncertainty related to a single model and provides the inflow forecasts with reduced model uncertainty for improving water and energy allocation.
Acknowledgments
We are thankful to NOAA for providing funding for this research through Grant NA09OAR4310146. We also appreciate the comments of three anonymous reviewers that have led to substantial improvements in the manuscript.
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