Tailoring seasonal climate forecasts for hydropower operations in Ethiopia ’ s upper Blue Nile basin

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Introduction
Seasonal climate forecasting capabilities continue to advance, attributable predominantly to enhanced observations, computing power, better physical understanding of the climate system, and experience (Barnston et al., 1994;Goddard et al., 2003;Barnston el al., 2005).Their principle goal is to reduce climate-related risks, providing advance information to potentially improve decision-making and increase societal benefits, especially over the long term.Currently, however, there exists little evidence of Figures explicit forecast use in operations, especially in water resources management, even in regions of scarcity.This is often ascribed to water managers tendency to act in a risk averse manner, "poor" forecast skill or scale, difficulty in integrating forecasts into existing decision support systems, lack of focus on specific user needs, anticipated shifts in the water sector, management and political disincentives, individual and institutional inflexibility, behavioral effects, and informational constraints (Pulwarty and Redmond, 1997;Hamlet et al., 2002;Ritchie et al., 2004;Rayner et al., 2005;Broad et al., 2007;Johnston et al., 2007;Lemos, 2008;Millner, 2009;Ziervogel et al., 2010).
The abundance of research and literature over the past decade identifying challenges and impediments should act as a stimulus for case studies evaluating potential economic benefits and improved reliability through forecast inclusion.These two determinants are powerful motivators for water resources managers and policy makers, and forecast-induced positive outcomes may provide incentive to address other barriers.Previous research studies have advocated for demonstrations of such effective forecast use (e.g., Pagano et al., 2001).Minimal applications within the water resources community, however, seek to quantify the actual monetary and reliability gains or losses of including a forecast in comparison to commonly accepted climatology-based operations, and most of those examples refer only to perfect forecasts, excepting a few (e.g., Yeh et al., 1982;Yao and Georgakakos, 2001;Hamlet et al., 2002;Chiew et al., 2002;Maurer and Lettenmaier, 2004;Axel and C éron, 2007;Sankarasubramanian et al., 2009).An absence in forecast adoption is unmistakable (Rayner et al., 2005), and further exaggerated in developing countries with limited hydrologic observations (Patt et al., 2007;Ziervogel, 2010).
This motivates the current research to demonstrate the improved economic value and reliability resulting from a flexible seasonal climate forecast -hydropower system, given biophysical, policy, and economic constraints, by mitigating losses and capitalizing on opportune conditions (Hellmuth et al., 2007).Gaining an understanding of expectations from a realistic, imperfect forecast imbedded in a dynamic operational system could prove enticing for water managers to adopt forecast inclusion, or justifi-Figures

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Full cation for rejecting.Appreciating benefits and reliability in a context of climate variability begins to address a number of the aforementioned impediments (summarized well in Ziervogel et al., 2010).This paper commences with a description of the application site, the Blue Nile basin in Ethiopia's highlands, followed by an outline of the coupled forecast-hydropower system model.The economic value and reliability produced from the seasonal climate forecast driven system are then compared with a non-forecast approach, ending with a discussion and conclusion.

Description of application site
Ethiopia possess abundant water resources and hydropower potential, second only to the Democratic Republic of Congo in all of Africa, yet only 2% of this potential has been developed (World Energy Council, 2007).Currently, 83% of Ethiopia's population lacks access to electricity, with 94% still relying on fuel wood for daily cooking and heating (Tegenu, 2006).The Ethiopian government is therefore pursuing ambitious plans and programs to develop hydropower in an effort to substantially reduce poverty and create an atmosphere for social change.It has been shown that access to electricity, including rural electrification, is a key to poverty reduction in Ethiopia (MoFED, 2006).Implementation, however, is not trivial, especially due to the large financing and investment challenges, as well as required institutional capacity.
The Blue Nile headwaters emanate at the outlet of Lake Tana in the Ethiopian highlands, and is joined by many important tributaries, draining 180 000 km 2 in the Central and Southwestern Ethiopian highlands (Steenhuis et al., 2008), becoming a mighty river long before it reaches the lowlands and crosses into Sudan (Fig. 1).It Sudan presents the first streamflow record of sufficient length; monthly averages are illustrated in Fig. 2. The climate in the Blue Nile River basin varies between its inception in the wet, moderate highlands of Ethiopia and its confluence with the White Nile River in a drier, warmer region.Monthly precipitation records indicate a summer monsoon season, with highest totals in the June-September months (Block and Rajagopalan, 2007); seasonal precipitation averages in excess of 1000 mm in the highlands but only 500 mm near the Sudan border (Shahin, 1985;Sutcliffe and Parks, 1999), with significant interannual variability throughout as illustrated in Fig. 3a (solid line).Near the border, rains during this season account for nearly 90% of total annual precipitation, while in the highlands, approximately 75% of the annual precipitation falls during the monsoon season.The El Ni ño-Southern Oscillation (ENSO) phenomenon is a main driver of the interannual variability in seasonal precipitation in the basin, with El Ni ño (La Ni ña) events generally producing drier (wetter) than normal conditions (Block and Rajagopalan, 2007).Evaporation in the basin varies inversely with precipitation, favoring lesser annual rates in the highlands (∼1150 mm) compared with excessive rates (∼2500 mm) near the Sudan-Ethiopia border (Shahin, 1985;Sutcliffe and Parks, 1999).
In 1964, the United States Bureau of Reclamation (USBR), upon invitation from the Ethiopian government, performed a thorough investigation and study of the hydrology of the upper Blue Nile basin.Included in the USBR's study was an optimistic list of potential projects within Ethiopia, including preliminary designs of dams for irrigation and hydroelectric power along the main Blue Nile stem.The four major hydroelectric dams along the Blue Nile, as proposed by the USBR, are presented in Fig. 1.Operating in series, these four dams could impound a total of 73 billion cubic meters, which is equivalent to approximately 1.5 times the average annual runoff in the basin.
The total installed capacity at design head would be 5570 megawatts (MW) of power, about 2.5 times the potential of the Aswan High Dam in Egypt, and capable of providing electricity to millions of homes.This would be an impressive upgrade over the existing 529 MW of hydroelectric power within Ethiopia as of 2001 (Thomson Gale, Figures

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2006
).Initial construction costs range from $1.8-$2.2 billion per dam; annual costs (operation and maintenance, scheduled replacement, and insurance) begin in the first year post-construction and range from $12.5-$17.9 million (Bureau of Reclamation, 1964).While none of these dams have actually been constructed, chiefly due to financial constraints, the Ethiopian Government still has intentions for their full development.
For demonstration purposes, however, the dams are assumed online and functional, which is analogous to an operational-level planning study, ideally providing insight into additional expected benefits with forecast inclusion.

Coupled modeling system
To evaluate the expected benefits of forecast inclusion, in comparison to climatologybased operations, a coupled modeling system approach is adopted.This allows processing and transformation of the Kiremt (June-September) monthly precipitation into streamflow for hydropower optimization along the Blue Nile River.The framework is structured by linking previously developed, independent models.

Structure and components
Three major modeling components are required: precipitation forecast, rainfall-runoff, and hydropower/water systems optimization.The forecast model (Block and Rajagopalan, 2007) predicts total seasonal (June-September) precipitation over the Blue Nile basin.One-season lead (March-May) predictors include sea level pressures, sea surface temperatures, geopotential height, air temperature, and the Palmer Drought Severity Index (PDSI), identified through correlation mapping with seasonal precipitation (e.g., Singhrattna et al., 2005;Grantz et al., 2007).The correlation patterns in sea surface temperatures and sea level pressures resemble ENSO features, yet are more skillful than common ENSO indices.The remaining three predictors capture regional characteristics, with PDSI acting as a soil moisture surrogate.A nonparametric forecast model based on local polynomial regression (Loader, 1999) is adopted to address various shortcomings common with linear regression, including artificially high skill scores stemming from limited data length and multicollinearity among predictors, regression coefficients being greatly influenced by a small number of outliers, often leading to a poor fit, and the inability to capture nonlinear relationships.In the nonparametric approach, estimation of the model function is performed "locally" at the point to be estimated; therefore no global mathematical function exists.This "local" estimation provides the ability to capture features (i.e.nonlinearities) that might be present locally, without granting outliers any undue influence in the overall fit.Optimal model parameters and predictors (the five previously mentioned) are selected via the generalized cross validation score function (Craven and Wahba, 1979).A detailed implementation algorithm is available in Block and Rajagopalan (2007).
Forecast ensembles are created by adding normal random deviates (mean zero and standard deviation of the global predictive error) to the predicted precipitation value (Helsel and Hirsch, 1995).Ensembles contain 500 members, capture a portion of the forecasting uncertainty, and are normally distributed.Ensemble medians demonstrate a correlation coefficient of 0.69 with observations, and a rank probability skill score (using the full distribution) of 0.39, a marked improvement over climatology alone.Figure 3 illustrates the observed and modeled time-series for 1961-2000.Seasonal precipitation forecasts are disaggregated into monthly forecasts through a proportion vector calibrated on historic data.
The rainfall-runoff model employed is a derivative of the Watbal model (Yates, 1996;Yates and Strzepek, 1998), specifically calibrated to the Blue Nile basin.It is a semidistributed, average-monthly model, with lumped soil and vegetation type and distributed climatic inputs, applied to gridded data (0.5 • ×0.5 • for this study).The model simulates changes in soil moisture and runoff, and is essentially an accounting scheme based on a conceptualized, one-dimensional bucket that lumps both the root and upper soil layer.The model comprises two elements: the first is a water balance component that describes water movement into and out of a conceptualized basin; the second Figures is the calculation of potential evapotranspiration, which is computed using the FAO Penman-Monteith approach (FAO, 1998).The water balance component of the model comprises three parameters: surface runoff, sub-surface runoff, and maximum catchment water-holding capacity.The simplified representation of soil moisture dynamics has been shown to adequately represent runoff changes due to climate fluctuations (Yates, 1996;Yates and Strzepek, 1998).A final module translates runoff into Blue Nile River streamflow for critical points throughout the basin.The hydropower model selected is IMPEND, the Investment Model for Planning Ethiopian Nile Development (Block and Strzepek, 2010).It is classified as a planning tool with operational-level detail to help define feasibility and expectations of project choice.IMPEND is a deterministic water resources system model requiring a single input file of monthly streamflow and net evaporation at the four proposed Ethiopian dam locations and at the existing Roseires dam in Southeastern Sudan (all in series).The model thus encompasses the Blue Nile River from its inception at Lake Tana to the Roseires dam, just beyond the Sudan-Ethiopian border.The current version values hydropower at 8-cents per kilowatt-hour; reservoir head represents the decision variable and net present benefits constitutes the objective value.Specific model equations and details are provided in the Appendix.
A key attribute of the model is its ability to accept monthly input data varying from year to year, which is critical for proper performance assessment.Analysis based solely on a climatological perspective may well misjudge long-term project benefits (Block et al., 2008).IMPEND is also capable of assessment over various interest (or discount) rates; for the purposes of this study, this rate has been restricted to 10%.This social rate of discounting has been used by others (e.g., Jabbar et al., 2000) and falls within the range of discount rates experienced by Ethiopia within the last five years (Central Intelligence Agency, 2006).A final noteworthy characteristic is the flexibility of downstream flow policies, modulated by the downstream flow constraint established at the entrance to Roseires dam.The policy employed here allows for up to 5% of the annual flow passing the border to be impounded within Ethiopia.Introduction

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Full IMPEND may be configured to account for transient (filling) conditions for dams coming online, however for this analysis, all reservoirs are assumed initially filled to design height to mimic operational aspects.Reservoir operations are not restricted to explicitly following established rule curves, but rather are flexible, optimizing operations based on expected future streamflow.Benefits attributable to climate forecasts in hydropower optimization are an aspect not often considered.For a climatology-based operation, however, for which future streamflows are simply historical monthly means, this approach essentially collapses to a process analogous to following a rule curve.

Data
For the forecast model, global atmospheric and oceanic variables, including sea surface temperature, sea level pressure, geopotential height, and air temperature, were obtained from the National Oceanic and Atmospheric Administration's (NOAA's) Climate Diagnostics Center (CDC), based on National Centers for Environmental Prediction-National Center for Atmospheric Research (NCEP-NCAR) reanalysis data (Kalnay et al., 1996).These are monthly average values on a 2.5 • ×2.5 • grid for 1949 to the present.PDSI values (Dai et al., 2004), also at monthly time scales and on a 2.5 • ×2.5 • grid, for 1870-2003, were provided by NCAR's Climate and Global Dynamics Division.Observed precipitation data (the predictand) are part of the Climatic Research Unit (CRU) time series 2.0 dataset, obtained from the University of East Anglia, Norwich, United Kingdom (Mitchell and Jones, 2005).
In addition to the June-September monthly precipitation produced by the forecast model, the rainfall-runoff model requires inputs of mean daily temperature and the diurnal temperature range, acquired from the CRU dataset (Mitchell and Jones, 2005).Precipitation for months other than June-September represents climatology based on the same dataset.Monthly streamflow and net evaporation outputs are produced monthly.
Physical, hydrologic, and climatic data required for building and running IMPEND were acquired from a number of sources.Dam, reservoir, and power characteristics are provided in the USBR preliminary study (Bureau of Reclamation, 1964).Historical 3773 Introduction

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Full streamflow records for calibration and validation in the upper Blue Nile basin are publicly available through numerous sources, including NCAR's ds552.1 dataset (Bodo, 2001).The rainfall-runoff model provides streamflow and net evaporation.IMPEND outputs include project benefits or net benefits (discounted back to the simulation start year), energy production, and reservoir levels at monthly intervals.

Forecast modes and evaluation
To quantify the value of a seasonal precipitation forecast carried through the coupled system, three types of forecasts are evaluated: perfect, actual (imperfect), and monitoring (based on climatology), with 12 month foresight.In the perfect forecast case, observed precipitation and temperature are fed into the rainfall-runoff model to generate a streamflow and net evaporation sequence.For each time-step in the sequence, the hydropower model is privy to 12 subsequent months of "observed" streamflow and net evaporation, optimizing operations in the current step reflective of expected future conditions, given constraints.Marching forward one month to the next, the hydropower model receives new information regarding the time-step 12 months out, and adjusts reservoir decisions accordingly to maximize benefits.The actual forecast approach follows a similar overall progression, excepting application of predicted precipitation for June-September, with climatological precipitation in other months, as inputs into the rainfall-runoff model.(Observed temperatures are also applied.Minimal difference in the final results were noted between substituting climatological temperature for observed temperature, therefore observations were chosen to isolate the pure value of a precipitation forecast).Using the streamflow and net evaporation sequence, the hydropower model optimizes over the subsequent 12 months, as in the perfect forecast case, however prior to May, when the forecast is issued observed conditions, is updated.Decision-making for the monitoring approach is simply based on reacting to current reservoir conditions and planning based on climatological expectations, the common approach for most water managers.It is nearly identical to the forecast approach devoid of predictions, except for the use of climatological streamflow and net evaporation values based on observed precipitation (as opposed to climatological precipitation.) Forecast modes are evaluated by comparing the sum of monthly hydropower benefits, aggregated to decadal totals, using the historical 1961-2000 record.The decadal time-series length allows for examination of compounding effects and tends to smooth out noisy monthly optimization behavior.For the actual forecast approach, initial comparisons use forecast medians, however to appreciate a range of feasible outcomes, sampling by random draws from the normal forecast distributions and random year sequencing are also assessed.Initial comparisons include approaches with all four proposed dams online; latter comparisons include only Karadobi, the furthest upstream dam site.
Two performance metrics, analogous to reliability and resilience, are created for further comparison between actual forecast and monitoring benefits.Reliability is represented as: where FB t represents hydropower benefits from the actual forecast model system at time-step t, MB are the monitoring approach system benefits, z is a counting scalar, and n equals the total number of time-steps.This comparative reliability may therefore vary from 0-1; values less than 0.5 infer higher overall reliability by the monitoring methodology, while values greater than 0.5 indicate higher overall reliability by the actual forecast approach.Resilience measures the ability of the actual forecast system to respond to years with benefits lower than the monitoring approach with greater benefits Introduction

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Full in the following year.
where y is a counting scalar, and m equals the total number of occurrences.Resilience varies from 0-1 with larger values signifying greater actual forecast system resilience.
Tailored seasonal precipitation forecasts are also assessed through the coupledmodel system.For the context of this paper, tailoring refers to restricting or dampening precipitation forecasts in response to identified sensitivities of the hydropower model (e.g.extreme wet or dry conditions), to address risk-averse behavior.

Economic value and reliability of seasonal climate forecasts
The benefits of the coupled model system drawing on the three forecasting approaches, reliabilities between the actual forecasting and monitoring approaches, and application of a tailored actual forecast to address water manager's risk-aversion are presented in the following section.

Chronological historical analysis
Using the four decades from the historical record, decadal hydropower benefits of the coupled model system from the perfect, actual forecast (medians), and monitoring approaches are presented in Fig. 4. Dams are assumed to be online from the onset.Only the 4-reservoir scheme is displayed, as the single-reservoir (Karadobi only) scheme behaves quite similarly.As expected, the perfect forecast outperforms both the actual and 3776 Figures

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Full monitoring forecasts; with the exception of the first decade, the actual forecast system benefits are on par or surpass those of the monitoring system.Poor forecasts in the early years, especially 1962-1963, for which notably wetter than observed conditions are predicted, contribute to the slightly inferior actual forecast system performance of that decade.The third decade, for which the actual forecast approach benefits far exceed those of the monitoring approach, is a relatively dry period with two exceptionally dry years (1982,1987).The actual forecast model does predict drier than normal conditions for those years, but not to the extreme observed.From this preliminary analysis, it would appear there is value in using an actual seasonal forecast compared with climatology.

Sampling from the historical record
Clearly evaluating the historical record in chronological order only represents one plausible sequence.To augment the ensemble size of decades and better capture potential variability (i.e.climate and model uncertainty), random sampling from all available years  was performed to generate 100 decadal sequences.In addition, for each of the ten years within the 100 sequences, the actual precipitation forecast was randomly selected from that year's forecast distribution (as opposed to using forecasted means) to represent model uncertainty.Although minimal temporal structure is evident, no attempt was made to preserve interannual (or longer time-scale) autocorrelation at this stage.
Figure 5 is a comparison of decadal benefits between the actual forecast and monitoring system approaches for the 100 sequences.Both the 4-reservoir and singlereservoir (Karadobi only) schemes are displayed.Points above the 1-to-1 line represent sequences for which the actual forecast method's cumulative benefits surpass those of the monitoring method; similarly, points below the line favor the monitoring method.
For both the multi and single reservoir schemes, the majority of points are bundled around the 1-to-1 line at the higher benefit end, not clearly favoring either forecasting approach.For sequences resulting in lower benefits, the actual forecast method tends Introduction

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Full to fare better.Negative benefits are possible due to a penalty function applied to low reservoir levels/low outlet flows.Reliability for the single reservoir scheme, as computed by Eq. ( 2) and presented in Table 1, is 0.68 and 0.58 for decadal and annual (1000 years) series, respectively, indicating added value for actual forecast inclusion.
Resilience, based on Eq. ( 4) and reported in Table 1, is 0.48 for the annual assessment, indicating an immediate rebound for approximately half the occurrences.Lack of resilience may be explained by multiple sequential poor forecasts, or the inability of the system to respond timely to a poor forecast, even if the subsequent year's forecast is adequate.The multi-reservoir scheme reliability and resilience (not reported) is almost identical.
Although the overall results are generally positive, specific sequences, such as decades labeled A and B on Fig. 5, may be severe enough to dissuade managers from accepting an actual forecasting approach.Even though the likelihood of these events occurring is small, the risk may still be sufficient.Examining these specific sequences in detail is enlightening.Annual streamflow and benefits for decade A from the actual and monitoring forecast approaches, for the single-reservoir scheme, are illustrated in Fig. 6.Most notably from the streamflow series is the over-prediction by the actual forecast system in years 2-3 and 5.The ramifications of this are evident in the annual benefits figure: in years following a poor forecast, benefits drop noticeably in comparison to the monitoring approach, especially when forecasting greater than observed "wet" conditions.(The figure illustrates discounted benefits, so a general downward trend is not unexpected).This phenomenon is also apparent upon inspection of decade B, presented in Fig. 7, in which years 3 and 5-7 all represent forecasts greater than observed.Similar findings explain the poor performance of the actual forecast for decades A and B under the multi-reservoir scheme (not shown.)

Tailoring the seasonal forecast
Undeniably, water managers considering implementation of a seasonal forecast into operations would prefer the vast majority (perhaps all!) of the project benefits reside Introduction

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Full above the 1-to-1 line.While the results of the actual forecast system demonstrated thus far indicate potentially greater benefit value versus a monitoring approach, tailoring the forecast in response to hydropower model sensitivities is worth exploring.
Examining the actual forecast errors  by terciles (i.e.below normal, near normal, above normal) reveals approximately equal error means and standard deviations across each category, implying no inherent bias in the forecast.Hydropower response to forecast errors in the above and below terciles, however, is less even.A simple test using the historical record and prescribed errors to construct four ten-year time-series, demonstrates significantly higher hydropower model sensitivity to above normal versus below normal conditions.To test, two "observed" series are created: June-September precipitation for each year is set at the 75 percentile (25 percentile) of the historical record to represent consistently above (below) normal conditions; remaining months are set to climatology.To mimic a forecast error, two additional series are created by adding (subtracting) 25 mm of monthly precipitation from each of the June-September months to the above (below) normal "observed" series.Results are displayed in Table 2. Comparison of differences between "observed" and "observed with error" series clearly exhibits greater hydropower model sensitivity to above normal forecast errors.Thus, for this coupled model system, errantly predicting wetter than observed conditions in the above normal category appears to be more detrimental to hydropower operations and ensuing benefits than errantly predicting drier than observed conditions in the below normal category.This stems from the aggressive actions (significant release from storage) following a wet forecast as opposed to conservative actions (maintain storage) following a dry forecast.Therefore dampening "wet" forecasts and retaining "dry" forecasts is a reasonable option in light of this sensitivity.
To this end, the precipitation forecast is tailored such that all actual forecasts in the above normal and near normal terciles are modulated to reflect climatology.(Little is gained from a near normal forecast in comparison to climatology.)This procedure effectively eliminates prediction of wet forecasts; some opportunities are clearly lost, however damages due to poor wet forecasts deem them worthy of disregarding.Ac-Introduction

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Full tual forecasts in the below normal category remain unchanged.This modification has no effect on the perfect or monitoring forecast system approaches.Figure 8 illustrates the product of this tailored forecast for the single-reservoir scheme in comparison to the monitoring approach, updating Fig. 5a.The effect is quite drastic: decadal sequence benefits from the actual forecast approach nearly always outpace those of the monitoring approach, with the few actually favoring the monitoring approach in close proximity to the 1-to-1 line.The elimination of low or negative decadal benefits from the actual forecast system is promising, and may begin to entice managers to incorporate such methodologies into their practices.Decadal and annual reliability and resilience scores, presented in Table 1, indicate a marked improvement over the original actual forecast approach.The relatively low annual reliability may be deceiving, and is best understood in context.To take an example, in a dry year, benefits from the monitoring approach may outpace the actual forecast approach, as it prescribes the release of more water through the turbines that year, however repercussions to benefits in the following year are likely to be more severe for the monitoring case.The resilience metric addresses this issue, indicating a rebound by the actual forecast approach in the following year for more than two-thirds of the occurrences.
While tailoring the actual forecast to this stage is clearly beneficial, incentives to improve the forecast model to potentially draw even greater returns is evident through comparison with the perfect forecast output (Fig. 9).Attaining a perfect forecast may be unrealistic due to inherent climate uncertainty, however the potential for further advancement plainly exists.

Assessing behavioral risk outcomes
The level of risk a water manager is willing to accept is intrinsically tied to institutional requirements, user demands, the flexibility of the system, and personal experiences, among other influences.This level implies consequent effects on system reliability and benefits.Two tendencies are addressed here through the use of a penalty function: one toward risk-taking (RT), one toward risk-aversion (RA).Figure 10  strates the simple linear functions adopted to impose a penalty (represented by energy loss but effectively financial loss) in the event of a predetermined energy threshold not being exceeded.To simulate RA (RT) behavior, a steep (moderate) sloped penalty function is employed.Lowering monthly energy production below the threshold results in larger penalties.
Assessment of the two risk levels for both the monitoring and forecast approaches is undertaken for four minimum energy thresholds, selected to span conceivably acceptable levels of reliability.The identical 100 decadal simulations from the prior evaluation are utilized.Figure 11a illustrates the monthly threshold -reliability relationship, and Fig. 11b the decadal threshold -benefits relationship.(Reliability here in the traditional sense refers to the number of months the threshold was exceeded over the 12 000 months evaluated.)Reliability and benefits substantially work in contrast to one another: higher (lower) reliability implies a reduction (increase) in benefits.Also, as the threshold level drops, the difference between levels of risk diminishes, becoming less of a factor when thresholds are easily surpassed.Of notable interest is the clear separation not simply between the monitoring and forecast approaches, but specifically between the RT monitoring and RA forecast.The RA forecast appears more stable, providing greater benefits and higher reliability over the course of thresholds evaluated.Even this conservative behavior produces superior performance when climate information is exploited, perhaps enticing mangers to consider forecast inclusion for improvements in reliability and benefits.

Discussion and conclusions
The modeling system is necessarily multi-disciplinary, linking climate, hydrology, and water management, an approach to valuing climate information that is often neglected due to its challenges and time consuming nature (Mjelde, 2002).The independent models themselves do not constitute new methodology; the uniqueness of the contribution comes in model integration, the exploitation of sensitivities between integrated Figures

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Full models, and ultimately a clear demonstration of economic value through actual forecast inclusion.Ritchie et al. (2004) assert that a forecast system may be considered useful if the forecast is statistically valid (verified) and demonstrates a positive value of information, both of which appear true for this study.The realization of added value and reliability through forecast inclusion, specifically addressed by dynamic management and decision-making through tailored climate information, is an important outcome.The retention of dry state forecasts adds quality information without subjecting the hydropower model to unreasonable levels of operational risk.Dry forecasts typically prescribe conservative reservoir action, and even if in error, will only forfeit minimal benefits (a higher than expected rainfall will still deliver streamflow to the reservoir for use in later months.)Although only exploiting a subset of the forecast range, and likely sacrificing benefits in wet years, a water manager may be inclined to adopt a mechanism that focuses more on reducing risk and potentially lost benefits than lost opportunities.This begins to address one historical impediment to forecast inclusion of not focusing sufficiently on user needs and applications (Ziervogel et al., 2010).Equally informative is the recognition of forecast benefit from a risk perspective.Riskaverse managers typically face constraints coercing conservative action, whereas risktakers have more latitude to absorb a low-output time-step in exchange for a substantial payoff later, typically leading toward greater aggregate benefits.Given the success of forecast inclusion demonstrated, it is rather expected that for a specified level of risk, utilizing a forecast produces benefits and reliability in excess of simply depending on climatology.More enlightening is how even conservative action bolstered by a forecast regularly outperforms a risk-taking approach conditioned on climatology, for equivalent energy threshold requirements.This addresses one of the cardinal impediments (risk-aversion) by theoretically allowing managers to remain risk-averse and realize considerable gains.
While the tailored approach demonstrated in this study is effective, it is errant to assume an identical procedure conducted for other regions or project types will ne-Introduction

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Locations susceptible to climate variability where forecast skill is apparent are ripe for investigation.As in this instance, it is also conceivable that decisions in other sectors (agriculture and food security, flood early warning, health, etc.) may likewise tend toward emphasis on one end of the forecast spectrum, although minimal evidence of demonstration currently exists.
In addition to climate, sensitivity to coincident drivers (policy, economics, demand, etc.) may also exhibit significant influence.A reassessment of hydropower model sensitivities subjected to different circumstances may be warranted.Other integrated and social factors may also be equally relevant and worthy of inclusion, beyond hydropower simulation, as advocated by the World Commission on Dams (WCD, 2000).The interrelationship of all these aspects is not trivial or currently well understood, however, potentially complicating decision-making (McCartney, 2007).
The forecast model presented here is generally founded on stationary climate principles.Multi-decadal effects or climate change are not explicitly addressed.A recent study by O'Connor et al. (2005) reveals the strongest determinant of forecast use is risk perception; if extreme climate conditions are anticipated in the near future, the uptake of a forecast is more likely.Climate change may pose such a perception.Integrating an adaptive forecast model into the coupled system to explore implications of changing precipitation trends and variability and increasing temperature warrants future attention.Introduction

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The threshold penalty function, Pen T , is only enacted for the final piece of analysis considering risk, and outlined in detail in the Assessing Behavioral Risk Outcomes section.For all other analysis, this penalty is zero.Electric energy is formulated around the head level in each reservoir.All operational aspects are nonlinear functions of head, including the reservoir storage, reservoir surface area for determining evaporative losses, the quantity of water released through the turbines, turbine efficiency, and reservoir spilling.These functions have been derived from either relationship curves in the preliminary USBR report, or typical relationships based on site-specific characteristics.Equations ( A4) and (A5) present the monthly reservoir storage balance and monthly energy production equations, respectively.area, m 2 , β=channel properties factor, CE=channel evaporation, m 3 /mo, Q P =flow for power, released through turbines, m 3 /mo, Q SP =flow over the spillway, m 3 /mo, H=reservoir head, m 3 , e=turbine and generator efficiency, α=conversion factor.
Rated (or installed) power, according to the USBR preliminary report, is assumed to be at design head, and increases linearly to the ultimate head (Bureau of Reclamation, 1976).The head level must be at the minimum operating level before power generation may commence.
The downstream flow constraint illustrating allowable annual flow based on the flow policy at Rosieres dam is presented in Eq. (A6).
to Roseires from basin runoff and upstream, m 3 /mo, FP=flow policy (fraction retained in Ethiopia).Net evaporation from the free water surface is computed monthly for the four reservoirs and channel lengths in-between.The NE value is multiplied by the dynamic reservoir area to determine losses or gains.For computation of net channel evaporation, which is comparably quite small, channel lengths and widths are assumed constant.Introduction

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Full  Full  Full Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | stretches nearly 850 km between Lake Tana and the Sudan-Ethiopia border, with a fall of 1300 m; the grades are steeper in the plateau region, and flatter along the low lands.Very few stream gauges exist along the Blue Nile River within Ethiopia, and those that do tend to have spotty or limited records, and are often not publicly available.Roseires dam in Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | , only climatological values are assumed.Thus in May of every year, streamflow and net evaporation for each of the subsequent 12 months of the sequence change from climatology to forecasted values.Following monthly forecast-based reservoir management operations, actual reservoir storage, based on the difference between forecasted and Introduction Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | where y=year, s=dam/reservoir site (Karadobi, Mabil, Mendaia, Border), NB=net benefit s − Pen T y,s × P HP × D − y s m × e s,m × α (A5) where: S=reservoir storage, m 3 , Q RO =inflow to reservoir from basin runoff, m 3 /mo, Q US =inflow to reservoir from upstream, =Q P +Q SP of u/s dam, m 3 /mo, NE=net evaporation (potential evapotranspiration minus effective precipitation), m 3 /mo, RA=reservoir Introduction Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | 1999.Tegenu, A.: Statement at the Fourteenth Commission on Sustainable Development, United Nations, New York, www.un.org/esa/sustdev/csd/csd14/statements/ethiopia 11may.pdf,2006.Thomson Gale, Encyclopedia of the Nations-Africa, Thomson Corporation, Farmington Hills, MIDiscussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper |

Fig. 10 .
Fig. 10.Penalty functions for risk-taking (RT) and risk-averse (RA) behaviors.T =energy threshold.Units for Energy Produced and Penalty are GW h/mo.

Fig. 11 .
Fig. 11.Comparison of monitoring and forecast approaches under risk-taking (RT) and riskaverse (RA) behavior for (a) threshold -reliability and (b) threshold -benefit relationships.

Table 1 .
Decadal and annual reliability and resilience for two forecasting approaches in comparison to monitoring (climatology).Single-reservoir scheme only.

Table 2 .
Results of hydropower sensitivity test to forecast errors in above and below normal terciles.Single-reservoir scheme only.