Water resources implications of integrating malaria controlinto the operation of an Ethiopian dam

Julia Reis,1 Teresa B. Culver,1 Matthew McCartney,2 Jonathan Lautze,3

and Solomon Kibret4

Received 1 November 2010; revised 26 July 2011; accepted 15 August 2011; published 28 September 2011.

[1] This paper investigates the water resources implications of using a method ofhydrological control to reduce malaria around the Koka reservoir in central Ethiopia. Thismethod is based on recent findings that malaria is transmitted from the shoreline of theKoka reservoir, and on a similar method that was used to control malaria some 80 yr ago inthe United States. To assess the feasibility of implementing hydrological control at Koka,we considered the potential impact of the modified management regime on the benefitsderived from current uses of the reservoir water (i.e., hydropower, irrigation, flood control,water supply, and downstream environmental flows). We used the HEC-ResSim model tosimulate lowering the reservoir by a rate designed to disrupt larval development, which isexpected to reduce the abundance of adult mosquito vectors and therefore reduce malariatransmission during the season in which transmission of the disease peaks. A comparisonwas made of major reservoir uses with and without the malaria control measure. In the26-yr simulation, application of the malaria control measure increased total average annualelectricity generation from 87.6 GWh � y�1 to 92.2 GWh � y�1 (i.e., a 5.3% increase) butresulted in a small decline in firm power generation (i.e., guaranteed at 99.5% reliability)from 4.16 MW to 4.15 MW (i.e., a 0.2% decrease). Application of the malaria controlmeasure did not impact the ability of the reservoir to meet downstream irrigation demandand reduced the number of days of downstream flooding from 28 to 24 d. These resultsindicate that targeted use of hydrological control for malaria vector management could beundertaken without sacrificing the key benefits of reservoir operation.

Citation: Reis, J., T. B. Culver, M. McCartney, J. Lautze, and S. Kibret (2011), Water resources implications of integrating malaria

control into the operation of an Ethiopian dam, Water Resour. Res., 47, W09530, doi:10.1029/2010WR010166.

1. Introduction[2] An abundance of literature has demonstrated that

dams have increased malaria in surrounding communities[Atangana et al., 1979; Oomen et al., 1979; Jobin, 1999;Keiser et al., 2005b; Yewhalaw et al., 2009]. In Ethiopia,Kibret et al. [2009] found a 20-fold increase in the inci-dence of malaria in the vicinity of the Koka reservoir.Although malaria reduction is a global priority (e.g., theUN Millennium Development Goals, United Nations[2008], there are few efforts to offset the elevated burdenof vector-borne diseases around reservoirs [Keiser et al.,2005b]. The World Commission on Dams summarized 17reviews of dams but allocated just two out of 400 pages toa discussion of health impacts [Sleigh and Jackson, 2001;Keiser et al., 2005b]. Developing tools to reduce diseasesfor communities located close to water storage infrastructure

is particularly important given the current drive to buildhydropower capacity in Africa [e.g., Foster and Briceño-Garmendia, 2010].

[3] After epidemiologists of the early 20th century iden-tified the key environmental determinants of malaria trans-mission, many environmental controls were devised[Keiser et al., 2005a]. Although not very widely applied,the modification of dam operation to disrupt mosquito-breeding habitat was successfully applied in a few places.For example, to disrupt disease cycles, the Tennessee Val-ley Authority (TVA) modified their reservoir operations toraise or lower water levels by 0.3 m over about a week[Keiser, 2005b; Porter, 1938]. While the development ofchemicals such as DDT fostered a transition away fromenvironmental control measures in the 1950s, a growingnumber of public health experts have begun to recommendreintegrating environmental methods into disease controlstrategies [Batterman et al., 2009; Utzinger, 2001; WorldHealth Organization (WHO), 2004], and some engineershave begun developing environmental control methods[Bomblies et al., 2008; Gianotti et al., 2009]. However,there has been very little contemporary consideration ofmodifying dam operation to contribute to malaria control.The general presumption is that such alterations will dis-rupt optimal operation and the consequent loss of benefitswill make it economically nonviable. Though this is a

1Civil and Environmental Engineering, University of Virginia, Charlot-tesville, Virginia, USA.

2International Water Management Institute, Addis Ababa, Ethiopia.3International Water Management Institute and USAID, Washington,

D.C., USA.4Addis Continental Institute of Public Health, Addis Ababa, Ethiopia.

Copyright 2011 by the American Geophysical Union.0043-1397/11/2010WR010166

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widely held perception, no recent studies have evaluatedthe water resources implications of modifying reservoiroperations (or water level management) to reduce malaria.

[4] Hence, when considering modifying dam operationto mitigate malaria, a key issue that must be addressed isthe impact that changes to dam operation will have on theexisting uses of reservoir water. This paper presents ahydrological simulation using HEC-ResSim 3.0 [Klipscheand Hurst, 2007] and assesses the water resource effects ofusing reservoir management to disrupt mosquito habitat inorder to reduce malaria transmission. This is believed to bethe first time that ResSim has been used to investigate pub-lic health concerns [McPherson, 2009].

2. Study Area[5] The Koka Reservoir is located in the Awash River

Basin in central Ethiopia (8�260N, 39�020E) (Figure 1). The1200 km-long Awash River, which has its headwaters inthe plateau near Addis Ababa at 2300 m (above meansea level), discharges below sea level into Lake Abbe inthe Danakil Desert. The Koka Reservoir is located 90 kmsouth of Addis Ababa at an elevation of 1600 m. It has asurface area of about 200 km2 and a capacity of 1650 Mm3

(Table 1). The dam is operated by the Ethiopian ElectricPower Corporation (EEPCo) but the Ethiopian Ministry ofWater Resources (MWR) has ultimate responsibility for thestation and makes recommendations on operation. The damwas constructed primarily for hydropower generation withan installed capacity of 43.2 MW from three turbines. Thisis �5% of the current total grid-based generating capacity ofEthiopia [Ethiopian Electric Power Corporation (EEPCo),2009]. Indeed, Koka’s energy contribution is increasinglydwarfed by much larger reservoirs recently built and nowunder construction.

[6] The average annual rainfall at the Koka Reservoir is880 mm, but with considerable variation from year to year[BookerTate and Metaferia Consulting Engineers, 2003].Typically most rain falls from June to September, but‘‘short rains’’ usually occur between March and May. Themean annual temperature is 24�C [Halcrow, 1989]. The ge-ology of the area is a fractured volcanic plain located alongthe central northern Ethiopian Rift [Mamo, 1995]. Thesouthwestern bank of the Koka Reservoir is formed bybasalt and other igneous rocks, while the northeastern bankis composed of alluvial and lacustrine sediments and hasa shallower slope than the igneous southwest bank. Surfi-cial deposits downstream from the dam, which are similarto soils on the northeastern bank in which larvae-filledpuddles form, has a permeability of 0.1–88.2 m � d�1

[Mamo, 1995].[7] The majority of large-scale irrigated land in Ethiopia

lies in the Awash River Basin, mainly below the KokaDam [Ejeta at al., 2009]. Some 60,000 ha of large sugarand fruit plantations lie downstream from the Koka Reser-voir. The Wonji sugar cane irrigation district (6000 ha),located �12 km downstream from the dam, is entirely

Figure 1. The Koka Reservoir is located in the southwestern part of the Awash River Basin in Ethiopia.The Awash River runs SW–NE through the upper, middle, and lower valleys.

Table 1. Physical Features of the Koka Reservoira

Koka DamDam crest 1593.2 mAverage tailwater 1551.2 mLength 426.0 m

HydrologyCatchment area 11,500 km2

Annual runoff 1610 Mm3

aData from Seleshi [2006] and Kibret et al. [2009].

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dependent on releases from the Koka Reservoir. Furtherdownstream, the Awash River supplies water for the goats,sheep, and cattle of Afar pastoralists [Gamaledinn, 1987].Minimum flow releases from the Koka Reservoir areneeded to ensure water delivery to the downstream pastorallands and to meet the ecological requirements of the AwashRiver. The Awash River below the Koka Reservoir alsosupplies drinking water to some 350,000 people of Nazaret(also known as Adama), the third largest city in Ethiopia.In addition, the dam is used for flood control.

[8] Malaria poses a serious health challenge to commun-ities near the Koka Reservoir. Kibret et al. [2009] found upto a 20-fold greater incidence of malaria near the KokaReservoir, and Lautze et al. [2007] found greater preva-lence of Plasmodium falciparum (the most deadly malariaparasite) in communities adjacent to the reservoir andP. vivax in more distant villages. Typical of central Ethiopia,malaria is seasonally demarcated, with peak transmissionfollowing the long wet season, generally lasting from mid-September to mid-November. It appears that an increase inrainfall leads to an increase in breeding sites, leading togreater adult malaria vectors and greater malaria transmis-sion [Kibret et al., 2009]. Generally, the seasonal spike inmalaria follows from a spike in Anopheles mosquitoes,which are the vectors of transmission [Yohannes et al.,2005]. While the correlation between an increase in Anophe-les mosquitoes and an increase in malaria transmission maynot be universal, this dynamic appears common in areas ofseasonal, or unstable transmission [Ghebreyesus et al.,1996, 1999; Keiser et al., 2005b].

[9] Detailed entomological surveys found that the mainmalaria vectors in the vicinity of the reservoir are An.arabiensis and An. pharoensis. These mosquitoes primarilyhatch from puddles that form on the lakeshore during andafter the wet season. Because shoreline puddles dry out aswater levels drop, faster rates of reservoir drawdown havebeen shown to be correlated with reduced larval abundance[Kibret et al., 2009]. Following from the relationshipsexplained above, it has been hypothesized that more rapiddrawdown of reservoir water levels immediately after thewet season could disrupt larval development and that thiswould, in turn, reduce the abundance of adult vectors andhence less malaria in villages situated close to the reservoir[Kibret et al., 2009].

[10] While the goal of this paper is to determine thewater resources impacts of a hydrological approach to con-trol malaria and our work makes no attempt to predict themagnitude of malaria reduction resulting from the reservoirdrawdown measure, we do expect that appropriate manage-ment of the reservoir will result in some reduction inmalaria. This supposition was based on two key assump-tions. First, when the rate of change in the pool elevation isgreater or equal to 0.5 m per month, reservoir-amplifiedmalaria would decrease. This is presumed from the factthat mosquito larvae abundance has been shown todecrease significantly when the pool elevation declined atthis rate, implying a reduction in adult vectors; and the sea-sonal spike in malaria case-rates is highly correlated with aspike in abundance of adult Anopheles mosquitoes [Kibretet al., 2009]. Second, the puddles would drain and desic-cate if the reservoir pool was drawn down because theobserved soil permeability (estimated as 0.1–88.2 m � d�1

[Mamo, 1995]) was much greater than the proposed draw-down rate (0.5 m � mo�1).

3. Method[11] ResSim was used to simulate dam operation with

and without the malaria control measure. The results werethen compared to deduce the effect of the malaria controlmeasure on energy production, water for irrigation, floodcontrol, and environmental flows. The allocation of drink-ing water for Nazaret city is <0.3 Mm3 � mo�1, a require-ment easily met under any scenario, so it was not discussedin the results.

[12] Configuration of the Ressim model for the KokaReservoir involves a wide range of information, includingenvironmental data, historical flows, pool elevation infor-mation, and physical and operational data for the dam,which was entered into ResSim. The modeling was doneon a daily time step for the 26-yr period between 1980 and2005. Modeled water inputs into the Koka Reservoirincluded river flow and rainfall to the surface of the reser-voir. Modeled water outputs included evaporation from thesurface of the reservoir, seepage below the reservoir, leak-age around the dam, and releases from the reservoir.

[13] Inflow to the reservoir was calculated from gageslocated upstream on inflowing tributaries. Rainfall andpotential evaporation data were obtained from the NationalMeteorological Agency’s stations operated in the vicinityof the reservoir. A time series of the monthly net evapora-tion (mm) from the reservoir pool (i.e., evaporation minusprecipitation) was estimated [BookerTate and MetaferiaConsulting Engineers, 2003; Ethiopian Ministry of WaterResources (MWR), 2008]. The average annual net evapora-tion (1200 mm) represents a loss of 14% of the streaminflows into the Koka Reservoir. This monthly accumulatednet evaporation data was evenly distributed into a dailytime series for the simulation using DSSVue [CEIWR-HEC, 2009], a USACE data storage system that stores timeseries data and computes basic statistics (daily, monthly,and annual mean, min, max, standard deviation, etc.) andtime changes (e.g., averaging months into years).

[14] Time series of releases from the turbines and elec-tricity generated were obtained from a consultant report[BookerTate and Metaferia Consulting Engineers, 2003]and EEPCo [2008]. Leakage under the dam and seepagebelow the reservoir were included in the model. Leakagevolumes were assumed to escape the pool and continuedownstream, while seepage volumes were assumed lost todeep groundwater. Based on earlier studies [Mamo, 1995;Seleshi, 2007], the seepage and leakage were simulated asfunctions of the pool elevation. At full supply level, theseepage and leakage were estimated to be 11 and 13 m3 s�1,respectively.

[15] The physical properties of the dam, including crestheight and length (Table 1), and its spillway and turbineswere entered into ResSim. The spillway on the dam has fourradial gates and was modeled using data from BookerTateand Metaferia Consulting Engineers [2003], Seleshi [2007],and the Ministry of Water Resources (MWR) [Halcrow,1989]. There are three turbine outlets, and these releaseswere based on the observed maximum turbine releases andinformation provided by the MWR [Halcrow, 1989].

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[16] Observed reservoir water levels were used to cali-brate the model. The Ethiopian Ministry of Water Resour-ces (MWR) provided daily levels for recent years [MWR,2008]. These were converted to a monthly interval usingDSSVue. Earlier years were recorded on a monthly timestep [BookerTate and Metaferia Consulting Engineers,2003]. Observed monthly turbine releases (available for theyears 1980–2000) [BookerTate and Metaferia ConsultingEngineers, 2003] and monthly energy production data(1988–2005) [EEPCo, 2008] were also used to calibrate themodel. The reservoir was modeled on a daily time step toallow more accurate simulations especially pertinent forflooding and malaria transmission. However, the outputwas converted to monthly values using DSSVue for com-parison with the historic records.

[17] ResSim dam operations are determined by differentmanagement zones on the basis of the elevation of the res-ervoir pool. Rules within each zone are prioritized to tailorthe management toward different priorities such as maxi-mum power production. When no rules have been set,ResSim calculates releases needed to keep the reservoirpool elevation on the boundary between zones. In thisstudy, the guide curve defined the top of the conservationzone. If the pool is on the guide curve, the reservoirreleases inflow minus losses from evaporation and leakageto keep the elevation of the reservoir pool on the zoneboundary. The goal of the simulations is not to match the

guide curve levels but to operate the reservoir to satisfydemands in each zone.

[18] In this study, operating rules were first defined tomimic the historical dam operation and create the baselinesimulation (i.e., without malaria control). For the baselinesimulation, rules were created for hydropower generation,to meet the irrigation demands, to satisfy environmentalflows, and to ensure flows downstream from the dam stayedwithin the maximum channel capacity.

[19] Typically, several zones are used to define opera-tional requirements. The zones and operation policies ofthe Koka Reservoir under baseline conditions were basedon the 1989 MWR report [Halcrow, 1989] and are shownin Figure 2. The goal of the flood control zone is to storeenough water to prevent flooding without damaging the in-tegrity of the dam structure. The top of the flood controlzone was set to 1592.2 m (0.8 m below the crest of thedam). The conservation zone manages water for the fulldemands of such functions as hydropower generation,water supply, and irrigation. Zones B–D serve as droughtbuffers by rationing releases to meet demands later in theyear. These zones have lower energy and irrigation require-ments. The inactive zone begins at the level below whichwater cannot be released.

[20] As specified by the Ethiopian Ministry of WaterResources [Halcrow, 1989], the energy rule specified aconstant turbine release of at least 32 m3 s�1 when the

Figure 2. The zones of operation, based on elevation of the reservoir pool, for the Koka Reservoir asmodeled in ResSim (figure adapted from [Halcrow, 1989]).

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reservoir pool is in the conservation zone, 24 m3 s�1 (75%of the full demand) in zone B, 16 m3 s�1 (50%) in zone C,and 0 m3 s�1 in zone D. To ensure adequate power produc-tion, a monthly wattage demand was added on the basis ofthe power equation:

P ¼ eT ePQT�H ; ð1Þ

where P is the power generated in watts (W), eT is the tur-bine efficiency, eP is the plant efficiency, QT is the flowthrough the turbines (m3 s�1), � is the specific weight ofwater (about 9.81 KN m�3), and H is the effective head(m). To calculate the wattage requirement in addition to theminimum flow release, QT was set to the target flow of32 m3 s�1 [Halcrow, 1989], H was set to the average effec-tive head (34.5 m), the turbine efficiency (92.5%) was basedon BookerTate and Metaferia Consulting Engineers [2003],and the plant factor was assumed to be 90%, meaning thatthe plant was running 90% of the time. Using the powerequation (1), the full monthly energy target, when the reser-voir is in the conservation zone, is 6586 MWh (the annual tar-get in this zone would be 79 GWh). The corresponding goalsfor zones B and C are then 4940 and 3293 MWh � mo�1,respectively. Zone D does not use a hydropower rule asreleases are not made for electricity generation. For thisstudy, in addition to the broad energy demand, a constant sta-tion use of 1 m3 s�1 was assumed to be needed to power thehydroelectric facility itself.

[21] As stated above, the Awash Basin contains some60,000 ha of irrigated farmland, some of which are fed bythe Koka Reservoir [Seleshi, 2006]. The irrigation demandfrom 2004 for the closest three plantations: Wonji (area:6780 ha), Tibila, and Metahara (area: 11,058 ha) (Table 2)[BookerTate and Metaferia Consulting Engineers, 2003;Seleshi, 2006; Girma and Awulachew, 2007] was less thanthe energy flow requirement and so could be ignored, exceptin zone D, where there was no energy requirement. If thereservoir water level is in zone D, the irrigation demand isreduced to 75% of the full demand listed in Table 2.

[22] Minimum flows downstream from the Koka Reser-voir are required for basic benthic and ecological health ofthe Awash River. Although Seleshi [2006] modeled a con-stant minimum discharge of 1 m3 s�1, higher flows may berequired. The desktop reserve model (DRM) provides amethod of rapidly assessing environmental flow requirementswhen ecological data are lacking [Hughes and Hannart,2003]. Although developed in South Africa it has been usedin other Africa countries, including Ethiopia [McCartneyet al., 2007]. In this study, the South African environmenttype of ‘‘The Dolomites’’ was assumed within DRM toestimate ecological flows for Awash River, immediatelydownstream from the dam. The monthly minimum flow rec-ommendations ranged from 1.6 to 18.3 m3 s�1 (Table 2).

[23] To prevent downstream flooding, releases from thedam must be <300 m3 s�1 up to a pool elevation of 1590.7 m,which is 1.5 m below the maximum flood storage level of1592.2 m [Halcrow, 1989]. As outlined in the MWR report[1989], to prevent damage to the dam structure, above1590.7 m allowable releases increase to 500 m3 s�1, corre-sponding to the maximum capacity of the downstreamchannel. Above the dam crest of 1593.2 m, spillway gatescan be fully opened to release up to the maximum capacityof the gates (1400 m3 s�1).

[24] The fit of the model was evaluated on the basis of avisual comparison of the baseline simulation and the his-toric time series, as well as by using the average differenceand the root-mean-square error (RMSE). The RMSE wasused to find the statistical difference between the historicaldata (h) and modeled predictions (m) :

RMSE ¼

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiPni¼1ðmi � hiÞ2

n

vuuut; ð2Þ

where n is the number of data points.[25] For the malaria control model, this study adapted

Kibret et al.’s [2009] recommendation of a reservoir draw-down rate of 0.5 m per month during the peak transmissionseason. The minimum release, to ensure the requisite draw-down, was calculated at each daily time step as a functionof inflow (m3 s�1) and the reservoir surface area (ha). Themalaria control rule was then added to the baseline rulesand zones during the peak transmission season (i.e., from15 September to 15 November). The impacts of the malariacontrol rule were evaluated by comparing the pool eleva-tions, dam releases, and energy generation to the corre-sponding predictions from the baseline simulations overthe 26-yr modeling period.

4. Results4.1. Baseline Simulation

[26] The modeled pool elevations and the energy produc-tion matched the historic observations well, as shown inFigures 3 and 4. For each, the average difference betweenthe historical and simulated values were less than 3% ofthe range of physically feasible values for the reservoir(e.g., the reservoir pool elevation can lie between the bot-tom outlet at 1580 and 1590 m, so it has a range of 10 m)(Table 3), while the RMSE was less than 13% of this range(Table 3). While the average and ranges of the pool eleva-tions and the average energy production are closely repli-cated, the range in baseline energy production is generallysmaller than the historic range from November throughJune and with a greater range in August and September.

Table 2. The Monthly Irrigation (1) and Minimum Environmental Flow (2) Demands for the Koka Reservoir (m3 s�1), Respectivelya

January February March April May June July August September October November December

1 15.0 14.7 14.7 15.6 16.7 17.5 11.7 6.0 7.4 14.6 16.3 15.82 1.6 1.7 1.6 1.9 1.9 2.6 8.3 18.3 15.0 6.5 2.9 1.9

aData adapted from BookerTate and Metaferia Consulting Engineers [2003] and estimated using DRM.

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Note that the historic monthly average energy generation of7.59 GWh is greater than the target level of 6.59 GWh.This is because in months of high flows, more water can bepassed through the turbines. For the turbine releases, theRMSE was especially high due to the large modeledreleases in September of 1993, August of 1996, andSeptember of 1998, which correspond to high reservoir poolelevations. Some error is expected. Flow variability is highduring the wet season (July–September), and the high inter-annual variability makes these months the most difficult tosimulate because this is when the dam operators will mostlikely be modifying operation based not specifically on therule curves specified by the MWR but largely on expecta-tions of how inflows will change and maybe even how otherhydropower stations elsewhere in the country are operating.

4.2. Malaria Control Measure[27] The malaria control measure lowered the reservoir

pool throughout the year (Figure 5). Compared to the

baseline, the average reservoir pool elevation was reducedby 0.53 m 6 0.10 m. A time series of reservoir pool eleva-tion (m), release (Mm3), and energy (GWh � mo�1) areshown in Figure 6.

[28] Environmental flows downstream from the irriga-tion projects (i.e., after the water is removed for irrigation)were met throughout the most of the malaria control simu-lation. The flows were not met during some years inAugust, when the environmental flow requirement wasquite high (18.3 m3 s�1), and during December of 1987,when there was a 1 m3 s�1 deficit. This degree to whichenvironmental flow requirements were satisfied in themalaria control model nonetheless compares favorablywith baseline conditions, when there was a deficit duringseveral years in August and during September and Octoberof 1987 of 6 and 2 m3 s�1, respectively.

[29] Simulation of the malaria control measure showedno impact on irrigation, as all demands were still met. Flowduration curves for December and January (the most diver-gent months) show that the malaria control model releasesdownstream from the irrigation projects (the irrigationwithdrawals were subtracted from total reservoir releases)were lower than baseline releases (Figure 7). These indicatethat in January the flows exceeded 85% and 95% of thetime (Q85 and Q95), and reduce by 10% and 62%, respec-tively, when the malaria control rule is used. Similarly, inDecember the Q85 and Q95 decline by 8% and 60%,respectively. With the malaria control measure, the lowestthree downstream flows after irrigation withdrawal were2.9 m3 s�1 in December, 5.4 m3 s�1 in January, and6.7 m3 s�1 in February. After the initial decline in down-stream flow following the malaria transmission season, thereleases nearly returned to baseline levels during the latterpart of the dry season (i.e., May and June).

[30] The malaria control measure resulted in more vari-able energy production, but over the total period of simula-tion resulted in an aggregate increase because of greaterreleases during the malaria transmission season. Comparedto the baseline, the malaria control simulation produced5.3% more energy overall (i.e., 92.2 GWh � y�1 comparedto 87.6 GWh � y�1). With malaria management, theenergy generation was reduced up to 2.1% during the dryseason (i.e., January to June), but was greater during thewet season (i.e., July to September) and the malaria trans-mission season (i.e., October to December, Table 4). Formost months, the malaria control measure reduced the aver-age energy generation, but more energy was produced dur-ing September, October, and November. Figure 8 showsthe energy exceedence curves for the months that were

Figure 3. The annual cycle of average elevation of theKoka reservoir water level (m). The vertical bars indicatemaximum and minimum water level elevations in eachmonth over the 26-yr period.

Figure 4. The annual cycle of average energy productionper month (GWh). The vertical bars show the maximum andminimum energy within the 200-month calibration period.

Table 3. Summary of the Overall Calibration Results

Elevation(m)

Turbine(Mm3 � mo�1)

Energy(MWh � mo�1)

Historic average 1586.98 92.82 7592Simulated average 1587.19 90.54 7297Range of feasible values 10 326 31,060RMSE 0.86 41 3242Percent of range 8.57% 12.63% 10.44%Average difference from

historic0.21 2 295

Percent of range 2.14% 0.70% 0.95%

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most negatively affected by the malaria control. These rela-tively low energy levels were produced when the reservoirpool was in zone D. In zone D, there is no power rule andthe minimum release is governed by the minimum irrigationflow requirement, which at the lowest dips to 4.5 m3 s�1 inAugust. Overall, the firm power generation declined at the99.5% reliability threshold from 4.16 to 4.15 MW (i.e., a0.2% decrease).

[31] Flooding is a concern as flows greater than 300 m3 s�1

can exceed the channel capacity and flows greater than500 m3 s�1 can disrupt agricultural activities. Althoughthe malaria measure releases more water at the tail end ofthe rainy season, the malaria control measure lowered the

reservoir pool on average and so over the 26-yr simula-tion resulted in 16 and 4 fewer d of flows over 300 and500 m3 s�1, respectively.

[32] For the malaria control model, the turbine and totaldam releases are slightly greater than the baseline. This isbecause with a lower average pool level, the evaporation,spill, and seepage losses are reduced (Table 5).

5. Discussion[33] There are both costs and benefits to integrating

malaria control into reservoir management. In the malariacontrol model, there were no impacts on current irrigation

Figure 5. The annual cycle of the elevation of the reservoir water level (m). The vertical bars showeach month’s minimum and maximum water level over the 26-yr period.

Figure 6. The interannual monthly time series of Koka reservoir pool elevation (m), release from thedam (Mm3), and energy (GWh).

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or water supply, and there was very little impact on envi-ronmental flows. The simulation showed that the malariacontrol measure reduced electricity generation duringcertain months, especially December, January, July, andAugust. However, the malaria control model resulted inmore electricity generated in September, October, andNovember, and the overall average annual electricity gen-erated increased. The net increase in energy arose as morewater was released through the turbines because lossesfrom spill, seepage, and evaporation were reduced as a con-sequence of more rapid drawdown of the reservoir at theend of the wet season. The hydropower losses from themalaria measure during the dry season could be recuperatedif the Ethiopian reservoir hydropower network was man-aged as an integrated system (i.e., if periods of lower energyproduction at Koka were compensated for by increased pro-duction at other stations and vice-versa). New hydropowerstations are indeed under construction on the Awash River,downstream from Koka, and in the Omo/Gibe/Turkana andTekeze/Atbara (a sub-basin of the Nile River), which willprovide more flexibility in future station operation.

[34] One impact of integrating malaria control into theoperation of the reservoir with potential longer-term conse-quences is the reduced downstream flows in some dry sea-son months, most notably December and January. Whilethe reduced downstream flows may limit increases in irri-gated area, more analyses are required to conclusivelydetermine any impact on future planned increases in irri-gated areas. Furthermore, although flows on average remainabove the absolute minimum required for environmentalflows identified by the DRM, more detailed studies arerequired to investigate the ecological sensitivity of the riverand the possible socio-economic implications (particularlyin relation to likely future irrigation) of changes in dryseason river flows.

[35] Discussion with senior water resource personnel andothers in Ethiopia has shown that most assume that intro-ducing a malaria control rule into the dam operation wouldsignificantly reduce its performance in terms of hydro-power production, irrigation, and flood control and that theconsequent economic costs of such a change would be pro-hibitive. The study reported here assessed the validity ofthis widely held assumption. The resulting comparison ofthe baseline and malaria control models provides an indica-tion of the feasibility of implementing a hydrologic malariareduction measure. The results of the simulation haveshown that the costs of introducing the malaria controlmeasure into the dam operation are much lower than manywater resource experts anticipated. Certainly in terms ofexisting uses, implementing the measure would have mini-mal consequences.

[36] Similar evaluations could be conducted aroundother reservoirs to support malaria reduction efforts there.With potential increases in standing water from proposeddams along rivers in Ethiopia and Africa [Foster andBriceño-Garmendia, 2010], mosquito habitat and diseaseinfection rates could increase. Climate change providesanother potential source of disease amplification [Zhouet al., 2004; Chaves and Koenraadt, 2010]. Regions that arecurrently either not malaria-endemic, or lie in several-yearcyclical epidemic malarial zones, may become seasonal or

Figure 7. The flow duration curves (m3 s�1) downstream from the irrigation during the dry season.

Table 4. The Average Energy (MWh � mo�1) Produced in theBaseline and Malaria Control Models

Month

Average Electricity Generated (MWh)

Baseline Malaria Control Difference

January 6770 6429 �341February 6328 6140 �188March 6553 6323 �230April 6364 6283 �81May 6418 6333 �85June 6256 6117 �139July 5611 5056 �555August 8897 7819 �1078September 13,442 18,077 þ4635October 7288 10,118 þ2830November 6767 6894 þ127December 6894 6594 �300

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endemic regions. The increasing risk and altered geographyof vector disease transmission calls for the identificationand implementation of effective control measures to offseta disease burden that is likely to grow.

[37] This study has shown that the costs of adaptingreservoir-based malaria control to a climate with one domi-nant rainy season, characteristic of many African and tropicalcountries, is not prohibitive. It is recognized that modifyingthe dam operation is unlikely to eliminate malaria in theregion on its own; other interventions are also needed. How-ever, the reservoir has been identified as a major source ofmalaria and so the study findings do call for testing the effector added value of this new measure as a component of abroader strategy of integrated malaria control. Ideally, such astrategy would include a number of measures, some of whichare already being used, such as education to reduce exposurerisks, bed net use, indoor residual spraying and, if possible,regulations that would reduce access to the reservoir shorefor both people and cattle.

6. Conclusion[38] Although the amplification of vector diseases near

hydrologic infrastructure has been identified, there are pres-ently few plans to reduce these diseases through reservoirmanagement. The model utilized in this paper evaluatedthe impacts of lowering the water level of the Koka Reser-voir after the wet season on the main functions of the reser-voir, which were to generate hydropower, prevent flooding,and provide releases for downstream irrigation and envi-ronmental flows. This study found that altering operation of

the Koka Reservoir to incorporate malaria managementcould generate more hydropower and release adequateflows for irrigation and ecological requirements. The poten-tial environmental implications and opportunity costs forpossible future downstream irrigation, arising from reduceddry season flows, require more detailed investigation. Still,the study has demonstrated that the costs of utilizing thismalaria reduction measure are potentially low and signifi-cantly lower than many would have anticipated. On thebasis of our simulations, we recommend implementing ahydrologic control for malaria vector management at thereservoir with accompanying monitoring to determine boththe impact on malaria transmission near the reservoir andon the region’s water resource system.

[39] Acknowledgments. This research was partially supported by thefellowship Graduate Assistance in Areas of National Need (GAANN) andfrom the Consultative Group for International Agricultural Research, Chal-lenge Program for Water and Food. We thank Paul Kirshen of Battelle Me-morial Institute for suggesting this modeling study. Beth Faber and othersat the US Army Corps of Engineers provided expert knowledge of reser-voir management and assistance in using their program HEC-ResSim. Wealso thank the Ethiopian Electric and Power Corporation for provision ofhydrological data.

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T. B. Culver and J. Reis, Civil and Environmental Engineering, Univer-sity of Virginia, Thornton Hall B228, 351 McCormick Rd., P.O. Box400742, Charlottesville, VA 22904-1000, USA. (tbc4e@virginia.edu)

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