Evaluating the impact and uncertainty of reservoir ...

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and concentrate precipitation (Oki and Kanae 2006). The 2007 IPCC report predicted small increases in precipitation up to 10 % in eastern Africa (Giannini et al.
Climatic Change DOI 10.1007/s10584-016-1639-8

Evaluating the impact and uncertainty of reservoir operation for malaria control as the climate changes in Ethiopia Julia Reis 1 & Teresa B. Culver 2 & Paul J. Block 3 & Matthew P. McCartney 4

Received: 10 July 2015 / Accepted: 21 February 2016 # Springer Science+Business Media Dordrecht 2016

Abstract Promising environmental mechanisms to control malaria are presently underutilized. Water level fluctuations to interrupt larval development have recently been studied and proposed as a low-impact malaria intervention in Ethiopia. One impediment to implementing such new environmental policies is the uncertain impact of climate change on water resources, which could upend reservoir operation policies. Here we quantified the potential impact of the malaria management under future climate states. Simulated timeseries were constructed by resampling historical precipitation, temperature, and evaporation data (1994–2002), imposing a 2 °C temperature increase and precipitation changes with a range of ±20 %. Runoff was generated for each climate scenario using the model GR4J. The runoff was used as input into a calibrated HEC ResSim model of reservoir operations. The malaria operation management increased the baseline scenario median energy generation by 18.2 GWh y−1 and decreased the energy generation at the 0.5 percentile (during dry conditions) by 7.3 GWh y−1. In scenarios with −20 % precipitation, malaria control increased average annual energy generation by 1.3 GWh y−1 but only decreased the lowest 0.5 percentile of energy by 0.2 GWh y−1; the irrigation demand was not met on 8.5 more days, on average, Electronic supplementary material The online version of this article (doi:10.1007/s10584-016-1639-8) contains supplementary material, which is available to authorized users.

* Julia Reis [email protected]

1

Mailman School of Public Health, Columbia University, 722 West 168th Street, New York, NY 10032, USA

2

Civil and Environmental Engineering, University of Virginia, Thornton Hall, P.O. Box 400742, Charlottesville, VA 22904, USA

3

Civil and Environmental Engineering, University of Wisconsin, 1415 Engineering Drive, Madison, WI 53706, USA

4

International Water Management Institute, P. O. Box 4199, Vientiane, Lao People’s Democratic Republic

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per year. Applying the malaria control rule to scenarios with +20 % precipitation decreased the likelihood of flooding by an average of 1.0 day per year. While the malaria control would divert some water away from other reservoir operational goals, the intervention requires 3.3– 3.7 % of the annual precipitation budget, which is much less than reduction from potential droughts.

1 Introduction Climate change increases variability of precipitation and flow in rivers (Schwarz 1977; Nemec and Schaake 1982; Karl and Trenberth 2011), as warmer temperatures accelerate evaporation and concentrate precipitation (Oki and Kanae 2006). The 2007 IPCC report predicted small increases in precipitation up to 10 % in eastern Africa (Giannini et al. 2008), while the 2014 IPCC report is more agnostic regarding potential climate-driven changes to hydrology in Ethiopia (IPCC 2014), in part due to its vast geography, marked climatic variation, and relative paucity of monitoring data. Despite the high degree of variance among climate model predictions over the 21st century, a 0.4 mm mo−1y−1 decrease in precipitation has been observed in southern Ethiopia during the summer and spring (1948–2006), coupled with a 0.03 °C y−1 increase in temperature over the same period (Jury and Funk 2013; see also Viste et al. 2013). Williams and Funk (2011) noted an increasing tendency toward drought in the past two decades in eastern tropical Africa. In the Upper Nile Basin, forecasts predict a greater likelihood of drought (Kim et al. 2008). Conway and Schipper (2011) note that a decrease in precipitation could reduce crop production and instigate famine, depending on sociopolitical factors. Variability in water resources associated with climate change also complicates the management of dams (Gleick and Shiklomanov 1989; Burn and Simonovic 1996; Raje and Mujumdar 2010). Unpredictable flows make the decisions about water releases more difficult and unexpected events, such as floods, could require the development of more cautious management policies (Simonovic and Li 2004). In regions experiencing extended droughts, the ability to satisfy hydropower demands and human and ecological needs for water supply will be inhibited (Hurd et al. 2004; Christensen et al. 2004). Trends in precipitation tend to be intensified in runoff, so that decreased precipitation causes very low flows and conversely wet regions receiving modest increases in precipitation could flood unexpectedly from surges in inflow (de Wit and Stankiewicz 2006). Based on the predicted doubling of greenhouse gases over the twenty-first century (IPCC 2007), Hailemariam (1999) simulated a temperature increase of 2 °C in the Awash Basin, and found −20 % and −10 % precipitation corresponded to a decrease in runoff by 41 % and 25 %, respectively. Due to the uncertain future hydrology when simulating climate change, stochastic methods provide greater range and reliability than can be derived from the historical record (Block et al. 2009). Researchers continue to tease apart the geographical, climatological, and hydrological factors that lead to malaria transmission (e.g. Parham and Michael (2010)). Some research indicates that climate change will contribute to an increase in malaria (Tanser et al. 2003), while other studies regard shifts in socio-economic conditions as more important factors than climate change (Reiter et al. 2004; Sutherst 2004; Gething et al. 2010; Chaves and Koenraadt 2010). Rising temperatures may have contributed to an increase of malaria in highland regions where previously cooler climates inhibited transmission (e.g. Indonesia, Papua New Guinea, Madagascar, and Afghanistan) (Chaves and Koenraadt 2010). Studies of the east African

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highlands that found no correlation between rising temperature and malaria transmission have been disputed (Hay et al. 2002; Shanks et al. 2002; Patz et al. 2002). Some researchers have contended that climate variability did initiate malaria epidemics (Zhou et al. 2004; Alsop 2007). In other regions, the range of malaria transmission may be shifting rather than expanding in geographic range (Lafferty 2009; Tonnang et al. 2010). Regardless of the potential for more cases of malaria due to climate change, currently malaria produces an estimated annual burden of 660,000 deaths, including the death of one child every minute, and 219,000,000 infections, mostly in sub-Saharan Africa (WHO 2013). While the global incidence of malaria decreased by 17 % and mortality declined by 26 %, in the decade following the establishment of the Millennium Development Goals campaign (United Nations 2013), current methods used for preventing malaria rely on chemical interventions. As mosquitoes continue to develop resistance to insecticides (Norris et al. 2015), environmental management for vector control is an attractive strategy to reduce malaria transmission (Utzinger et al. 2001; Lizzi et al. 2014). Globally, large dams and irrigation schemes increase the risk of malaria transmission by creating additional breeding grounds for malaria vector mosquitoes (Keiser et al. 2005). Water reservoirs have been found to increase the incidence of malaria in African countries both in the east (Oomen et al. 1979; Roggeri 1985; Ghebreyesus et al. 1999; Kibret et al. 2009) and in the west (Atangana et al. 1979; King 1996). The amplification of malaria by reservoirs is a concern in tropical regions, such as Ethiopia, where new reservoirs are being constructed. Ethiopian hydropower capacity will nearly double with the completion of the 6500 GWhy−1 Gibe III dam in 2016; the Grand Ethiopian Renaissance Dam, 45 % complete, will create over 15,000 GWhy−1 (EEPCo 2014). The new dams built and planned in the Awash Basin (Ejeta et al. 2009) and in other regions of Ethiopia may increase the local incidence of malaria. Kibret et al. (2009, 2012) found that puddles along the shores of the Koka Reservoir harbored Anopheles arabiensis and An. pharoensis mosquitoes. Further, Kibret et al. (2009) found a nearly 20-fold increase in the incidence of malaria less than one kilometer from the reservoir, as compared to locations beyond 5 km. This is consistent with the observed 2.5-km mean flying distance of An. gambiae, a species complex including An. arabiensis (Kaufmann and Briegel 2004). Moreover, Kibret et al. (2009) found a strong statistical relationship (p < 0.001) between annual case rates and distance to the reservoir. Subsequent work used historical hydrological data to calibrate a baseline model and found few negative consequences of including malaria control as an operational goal of the Koka Reservoir (Reis et al. 2011; McCartney et al. 2011). While altering reservoir operation policy has been established in the literature as a potentially effective means of targeting malaria transmission, implications for incorporating malaria control strategies into dam operations under climate change are unknown.

2 Background The Koka Reservoir is situated in the upper third of the Awash Basin at an elevation of 2300 m, about 90 km southeast of Addis Ababa, Ethiopia. The climate of the upper Awash Basin is semi-arid and has a rainy season during August and September (Girma and Awulachew 2007; Ejeta et al. 2009). Historically, the average precipitation ranges from 4.5 mm in December to 207.3 mm in July with an average annual precipitation of 880 mm (MWR 2008).

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The Koka Reservoir has a surface area of about 200 km2, a volume of 1650 Mm3, and electricity generating capacity of 43.2 MW from three turbines, according to the Ethiopian Electric Power Corporation (EEPCo 2014). The Koka Dam production capacity is rated as 110 GWh per year, and produced a median energy generation of 89.6 GWh y−1 from 1989 to 2005 (EEPCo 2008, personal data request). EEPCo operates the reservoir based on the policies of the Ethiopian Ministry of Water Resources. As described above, the Koka plant contributes a small fraction of the hydropower soon to be available in Ethiopia (EEPCo 2014). While Koka’s hydroelectricity generating capacity had diminished—by 1999 sedimentation had reduced storage by 30 % (EEPCo 2002)—Lake Koka has since been dredged (Akele 2011). The majority of commercial irrigated land in Ethiopia lies along the Awash River, some of it downstream of the Koka reservoir (Ejeta et al. 2009). The economy of the Awash Basin is growing rapidly (United Nations Environment Programme 2008), with new dams coming online to supply irrigation demand, such as the Kessem Dam that will supply 20,000 ha of farmland (Getachew 2014). The Koka reservoir supplies irrigation water to 69,000 ha of large sugar and fruit plantations downstream of the dam (Halcrow 1989), and could sustainably supply over 2 times this area (Girma and Awulachew 2007). Reservoir management has been proposed as an intervention to limit reservoir-amplified malaria at the Koka Reservoir (Kibret et al. 2009; Lautze et al. 2007; McCartney et al. 2011), and application of hydrological model has demonstrated the potential feasibility of this strategy (Reis et al. 2011). The proposed intervention would lower the elevation of the reservoir pool during the peak malaria transmission season, to interrupt the arthropod-to-human vector cycle. Simulations were used to develop multi-purpose reservoir management, for the Koka Reservoir, to control malaria and flooding, whilst simultaneously maintaining releases for irrigation, environmental flows, and hydropower generation. The simulations showed that a malaria control strategy modifying the reservoir-operating regime was technically feasible and, though slightly reducing dry season power production, could actually increase total hydropower production (Reis et al. 2011). The malaria control rule releases more water following the wet season, leading to reduced losses from evaporation and leakage and seepage during the dry season. The water resource effects of coupling climate change and malaria strategies on power and other management objectives, however, are not yet well understood.

3 Methodology Our method, summarized in Fig. 1, consisted of a three-step process of generating climate scenarios, estimating the time series of runoff into the reservoir for the altered climate states, and simulating reservoir operation, while accounting for and preserving stochastic effects. Each step is detailed in the following paragraphs.

3.1 Resampling Based on the historical climate data at the Koka Dam (1994–2002; 8°25’N 39°01’E) (National Meteorological Agency 2002; EEPCo 2008), one-hundred 10-year baseline time series of daily precipitation, temperature, and evaporation were generated by resampling from the historical record (Lall and Sharma 1996). A daily transition matrix was formed for precipitation based on wet (W) and dry (D) days. Four categories and associated transition probabilities were created, including WW (wet day followed by a wet day), WD, DW, and DD for each

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Fig. 1 A schematic describing each step in our methodology of integrating climate change scenarios with reservoir simulation, while accounting for stochastic effects

month. If, for example, January 1 was a wet day, then a number is randomly generated, and a category is selected for January 2 (either W or D), based on the probabilities in the transition matrix (WW or WD). Using this category, a day was randomly selected from the historical daily January record, with the precipitation and temperature are retained. If it was the Dry category, precipitation is zero. The process continues as described, using a Markov chain process (Fosler-Lussier 1998). These simulations of precipitation are statistically similar to the historical record (Table S1), yet represent a wider range of possible climatic variability, in comparison to the historical record in chronological order, given the sequencing of dry or wet periods not observed in the historical record. The resampling technique was thus conditioned on precipitation only. We refer to these time series of resampled input data (no change in precipitation) as the baseline scenario (see Fig. 1). Climate change scenarios were also constructed from these baseline input data to represent the decade 2050–2059 by adding 2 °C to all days, evaporation was calculated from temperature using modified Hargreaves (Hargreaves and Allen 2003), and by varying precipitation increasing/decreasing daily values ranging from ±20 % in 5 % increments (see Fig. 1).

3.2 Rainfall-runoff model The daily time series of precipitation and evaporation were used as inputs in the model GR4J (Perrin et al. 2003) to generate inflow to the Koka Reservoir for each 10-year time series. GR4J is a lumped soil moisture accounting model that uses four parameters to generate daily runoff from rainfall. The model parameters are the maximum capacity of the soil to store water (mm), reference capacity of storage during routing (mm), groundwater exchange coefficient,

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which is the maximum amount of water that can be released or added to the reference capacity (mm), and the time of the peak unit hydrograph (days). We used the GR4J to calibrate the values of the four lumped parameters for the historical rainfall-runoff data, and then input resampled rainfall data into GR4J to generate synthetic runoff as input to the reservoir (see Fig. 1).

3.3 Reservoir simulation Inflow and evaporation were inputs to the reservoir simulation model ResSim 3.2 (Klipsche and Hurst 2007), which was used to simulate operation of the Koka Reservoir. The ResSim model was developed by the Hydrologic Engineering Center of the US Army Corps of Engineers to help engineers and decision-makers plan the operation of hydropower reservoirs and to show stakeholders the implications of different plans (e.g. Teasley and McKinney 2005). The ResSim ensemble feature, currently in development, allows users to compute a collection of time series per modeling run, facilitating stochastic analysis (Klipsche 2010). Details of the dam and model parameters can be found in Reis et al. (2011). A brief summary of the dam operation is provided in Table 1; Reis et al. (2011) provides the zones of operation by month, based on elevation of the reservoir pool.

3.4 Malaria control rule Each of the climate change scenarios described above were considered with and without the malaria control rule. The malaria control measure simulated drawing down the water level during the malaria transmission season (mid September – mid November) at a rate (0.5 m mo−1) specified to desiccate puddles before larvae of vectors (Anopheles arabiensis and An. pharoensis) could hatch. Key results of the previous work simulating 26 years were as follows: the malaria control measure would result in an overall 5.3 % increase in electricity generation, a 0.2 % decline in the simulated 0.5th percentile of power generation, no impact on meeting the contemporary downstream irrigation demand, and modest reduction in flooding of 4 days over the 26 years (Reis et al. 2011). The positive results using historical time series as input

Table 1 Basis of dam operational policies for the main variables and demands Variable

Description

Source

Leakage/Seepage

Function of reservoir elevation

(Mamo 1995; Seleshi 2007)

Hydropower demand

32 m3s−1

(Halcrow 1989)

Irrigation demand

Supplies the three largest farms downstream of the Koka Dam. Ranges from 6.0 m3s−1 in August to 17.5 m3s−1 in June.

(Seleshi 2007)

Environmental Minimum Flows

Estimated using the South Africa-based program Desktop Reserve Model. Range from 2 m3s−1 to 18 m3s−1

(Hughes and Hannart 2003)

Flood control

The maximum release, when water is below the dam’s full supply level, is the maximum capacity of the downstream channel 500 m3s−1.

(MWR 2008)

Drought operation

Demands release as water level falls below the ideal operating zone.

(MWR 2008)

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motivated the present inquiry into future application of the malaria control rule for reservoir operation, given an uncertain climate.

4 Results In the baseline, the variability and median statistics of the observed daily data (1994–2002) appear to be preserved. Supplemental Fig. 1 illustrates net evaporation as boxplots, with whiskers as minimum and maximum values, excluding any outliers. The average precipitation for the available historical data is highest in July and August, and is about 200 mm less in June and 165 mm less in September. Figure 2 shows the monthly water level (masl) in the Koka Reservoir, modeled by ResSim, for the baseline (without malaria control) and malaria control. With malaria control operation, the water recession goal (lowering the water level by at least by 50 cm mo−1 between 15 Sep. – 15 Nov) is met on 70 % of all days during that period, which is 31.4 more days each year, on average, than without malaria control. During 9 % of all days during the transmission season, the water level was increasing by over 50 cm mo−1 (due to high inflows), fast enough to flood the larval puddles (as described in Juel (2013)).

4.1 Energy generation

Water Level (m)

With the malaria policy in place, the water level for all months is lower (Fig. 2), yet the energy production is greater overall. The malaria control rule increases the annual mean energy by 9 GWh y−1 for the baseline case, and by 7.6 GWh y−1 averaged over all precipitation levels (−20 % to +20 %). The average and 0.5 percentile energy generation, during the dry (Jan–Jun), wet (Jul–Sep), and malaria transmission seasons (Oct–Dec) are shown in Table S2. In most scenarios, the mean energy generated by the malaria control rule increased during the season of malaria transmission, and remained about the same during the dry and wet seasons. The energy generated at the 0.5 percentile decreased or remained the same throughout the climate scenarios during all seasons. This lowest energy generation was particularly low during the wet season, which follows the dry season when the water level can fall into drought zone, where prescribed releases are low. 1595 1590 1585 1580

Jan Feb Mar Apr May Jun

Jul

Water Level (m)

Month

1595

Aug Sep Oct Nov Dec Baseline Malaria Control

1590 1585 1580

2050 2051 2052 2053 2054 2055 2056 2057 2058 2059

Year

Fig. 2 The simulated water level (masl) in the Koka Reservoir for the baseline and malaria control models (100 runs each) by month (top plot) and year (bottom plot)

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The energy generation is shown in Fig S2 as the percent exceedence for the baseline and malaria control models, for the median run of the extreme precipitation changes (±20 %). The 5th and 95th percentiles are shown in faint dotted lines to present the range in the modeling runs. In each precipitation scenario, both the magnitude and duration of surplus is greater than that of the deficit energy generation. Figure 3 shows the annual energy generation for the baseline and malaria control models across all precipitation levels. The gain in energy generation as a result of malaria management decreases as precipitation declines. Within each scenario, wetter years had a greater percentage of days with increased in energy due to the malaria management than drier years.

4.2 Analysis of flows Irrigation supply, environmental demand, and flood control were analyzed relative to releases from the Koka Dam. Figure 4 shows the percent exceedance of releases from the Koka dam for the baseline, malaria control, the ±20 % precipitation scenarios. The difference between the −20 % precipitation scenario with and without malaria control is also shown. In the baseline and malaria control models, there are an average 10.4 days and 12.1 days, respectively, of unmet irrigation demand per year, because reservoir releases drop below the flow required for irrigation diversion in months August to June. As shown in the lower right subplot of Fig. 4, there is very little difference in the releases for the median flows of the 100 runs as a result of the malaria control rule. Over a third of the −20 % precipitation flows, however, cannot meet the irrigation demand. This is illustrated in Fig. 5, which shows a boxplot for the monthly releases with and without malaria control for −20 % precipitation scenario; the irrigation demand and minimum environmental flows are plotted for reference. The apparent reduction in releases caused by the malaria control rule in August is an artifact of a steep exponential rise in outflow in both models around the 75th percentile; the difference between a release of 16.2 m3s−1 and 5.6 m3s−1 occurs respectively at 72 % and 77 % for the −20 % P and −20 % P MC models. Most of the days of unmet irrigation demand come during August, with an average of 22.4 days and 23.8 days, respectively, of unmet irrigation demand out of 128.4 days and 136.9 180

Baseline Malaria Control

Annual Energy (GWy-1)

160 140 120 100 80 60 40 20 0

Base

+20% +15% +10% +05% -05%

-10%

-15%

-20%

Precipitation levels Fig. 3 Annual energy for the baseline and malaria control models across all precipitation levels

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3 -1

Flow Out (m s )

3

Flow Out (m s-1)

600

2000 Baseline Malaria Control (MC)

400

+20% P +20% P MC

1500 1000

200

500

0 10 -1

10 0

10 1

0 10 -1

10 2

200

150

150

100

100

50

50

0

0 10 -1

10 0

10 1

Percentile

-50 10 -1

10 2

10 0

10 1

10 2

10 0

10 1

10 2

Percentile

Fig. 4 The flows out of the Koka Dam for the baseline, malaria control, and ±20 % precipitation (P) scenarios. Bottom right plot: The difference between the −20 % P scenario with and without malaria control

unmet days total. While the days of unmet irrigation demand increase, the loss of volume during the key months is minimal, as presented in Table S3. The malaria control rule makes little difference in meeting environmental flows. Out of the average of 69.1 days and 71.5 days per year for the −20 % P and −20 % P MC scenarios, respectively, that do not meet the environmental flows requirement, 29 days (both with and without MC) occur during August when the environmental flow requirement is high (18.3 m3s−1). With more water in the system, the wetter climate scenarios met the irrigation demand, environmental flow requirements, and energy targets, and the malaria control rule slightly reduced flooding. For the +20 % P scenario, malaria-control reservoir management reduced the number of floods from 74.4 days to 64.5 days in the ten-year period. There is less than a 50 45

Releases (m3s-1)

40

-20% P -20% P Malaria Control Irrigation Demand Environmental Flows

35 30 25 20 15 10 5 0

Jan Feb Mar Apr May Jun

Jul

Month

Aug Sep Oct Nov Dec

Fig. 5 Releases from the Koka Dam for the −20 % precipitation scenarios with and without malaria control. The irrigation demand and minimum environmental flows are also shown

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4.0 % and 3.5 % probability of exceeding 500 m3s−1 for +20 % P and +20 % P MC, respectively (Fig. 4).

4.3 Summary Table 2 summarizes how the malaria control rule for reservoir operation may impact irrigation supply, environmental flows (EF), and flood control for all the scenarios. The table shows the average annual impacts on mean energy generation (GWh y−1), unmet irrigation demand (day), flooding (day), and unmet demand for minimum environmental flows (day).

5 Discussion This study indicates that a drier future climate will negatively impact the reservoir’s ability to meet operational goals (supplying power, irrigation, environmental flows, and reducing floods), more so than altering the reservoir operation to reduce malaria transmission. For example, compared to the baseline, a 10 % decrease in precipitation increased the average unmet irrigation demand by nearly 1.5 months, compared to an increase of just 1.7 days of unmet demand from adding malaria management to the baseline (Table 2). As listed in Table 2, the main impacts of the malaria rule are increased annual energy generation, decrease in meeting the irrigation demand, and reduced flooding. The malaria control rule increases energy generation overall by releasing more water following the wet season, benefitting from the higher hydraulic head while reducing losses due to evaporation and leakage and seepage over the dry season. The lowest 0.5 percentile of energy generation was reduced by the malaria control measure in the dry scenario, but was reduced much more significantly by the decrease of 20 % in precipitation. Most days of unmet environmental flow demand occurred during the rainy season, when the flow demand simulated in this study was quite high (18 m3s−1) compared to the 1 m3s−1 assumed throughout the year by another study (Seleshi 2007). As there have not been any detailed studies of the ecology of the Awash River, the ecological flow requirements are uncertain. Flooding increased in the wet scenarios but decreased with the malaria management. Table 2 Summary of average annual impacts with precipitation change (%P) and malaria control (MC) Scenario/ Impact

Mean Energy (GWh•y−1)

Unmet Irrigation (day•y−1)

Unmet EF (day•y−1)

Flooding (day•y−1)

Baseline MC

85.7 94.7

10.4 12.1

18.0 19.4

1.5 1.5

20 % P

107.7

0.9

5.6

7.4

20 % P MC

120.2

2.5

8.5

6.5

10 % P

95.9

3.5

10.7

3.9

10 % P MC

107.3

7.8

15.0

3.3

−10 % P

65.8

49.9

39.4

0.3

−10 % P MC

70.1

61.3

43.7

0.3

−20 % P −20 % P MC

47.3 48.6

128.4 136.9

69.1 71.5

0.0 0.0

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We do not suggest that malaria transmission has a direct or linear relationship with hydrological changes, nor that rainfall and reservoir management are the only important factors in regulating malaria transmission rates. Other important factors in the transmission of vector diseases include the nature of human habitation (e.g. added defense against mosquitoes from house screening and bed nets), timely access to health care, and air temperature, which speeds larval hatching times. This study targeted larval habitat, which is an important factor in the arthropod-vertebrate transmission cycle of malaria. Although precipitation and evaporation are altered in the scenarios modeled in this paper, the intervention of lowering the water level was not altered because it is inherently linked to precipitation and inflow. Our model suggests that operating for malaria control in the future would not necessarily impede the other operational goals of the Koka Reservoir. In the scenario of a much drier climate, malaria management would have to be weighed against conserving water in the reservoir. Any volume losses imposed by the malaria management would present an opportunity cost for future development of irrigated agriculture. This model indicates that volume of releases and number of days of both unmet irrigation demand and minimum environmental flows would be similar with or without malaria control. The lowest percentile of energy reduction from malaria control would only be significant in the very dry scenario. Coordinating the operation of the multiple reservoirs, has the potential to mitigate the energy loss, and, potentially, releases for malaria control following the rainy season could be extracted downstream (African Progress Report 2015). Future work could narrow the range of predicted climate scenarios to allow reservoir managers to plan for the most likely scenarios and adopt a robust decision-making approach accounting for multiple purposes including malaria control. In particular, reservoir management could be optimized as a cascade of reservoirs in the Awash Basin. Extensive modeling of reservoir operation under a broad range of climate scenarios has indicated that malaria may be managed without reducing power generation or supply for irrigation demand downstream. The most significant next step would be to implement the reservoir operation rule described in this paper and design a study quantifying the incidence in malaria and other epidemiological indicators in the transmission of malaria. Acknowledgments This research was initially supported by the fellowship Graduate Assistance in Areas of National Need and by the Consultative Group for International Agricultural Research’s Challenge Program for Water and Food. We thank the Ethiopian Electrical Corporation, Ministry of Water Resources, and National Meteorological Agency for providing climate and water resources data. While we are prohibited from releasing raw data provided at cost by the agencies listed above, please contact the corresponding author to access resampled precipitation, evaporation, and temperature data.

References Africa Progress Report (2015), Power, people, planet: seizing Africa’s energy and climate opportunities. Africa Progress Panel, 182 pp Akele, S.T. (2011), The practice and challenges of lake management in Ethiopia—the case of Lake Koka, master of science thesis in environmental science. Swedish University of Agricultural Sciences, Slu, Uppsala, Sweden Alsop Z (2007) Malaria returns to Kenya’s highlands as temperatures rise. Lancet 370(9591):925–926 Atangana S, Foumbi J, Charlois M, Ambroise-Thomas P, Ripert C (1979) Epidemiological study of onchocerciasis and malaria in Bamendjin dam area (Cameroon). Med Trop 39(5):537–543 Block PJ, Souza Filho FA, Sun L, Kwon H-H (2009) A streamflow forecasting framework using multiple climate and hydrological models. JAWRA J Am Water Resour Assoc 45(4):828–843

Climatic Change Burn DH, Simonovic SP (1996) Sensitivity of reservoir operation performance to climatic change. Water Resour Manag 10(6):463–478 Chaves LF, Koenraadt CJM (2010) Climate change and highland malaria: fresh air for a hot debate. Q Rev Biol 85(1):27–55 Christensen NS, Wood AW, Voisin N, Lettenmaier DP, Palmer RN (2004) The effects of climate change on the hydrology and water resources of the Colorado River Basin. Clim Chang 62(1–3):337–363 Conway D, Schipper ELF (2011) Adaptation to climate change in Africa: challenges and opportunities identified from Ethiopia. Glob Environ Chang 21(1):227–237 de Wit M, Stankiewicz J (2006) Changes in surface water supply across Africa with predicted climate change. Sci 311(5769):1917–1921 EEPCo (2002) Koka Dam sedimentation study: recommendations report, Addis Ababa EEPCo (2014) Koka power station description; Facts about Hydro Electric Power, http://www.eepco.gov.et EEPCo (Ethiopian Electrical Power Corporation) (2008) Data was provided upon request, Addis Ababa Ejeta MZ, Biftu GF, Fanta DA (2009) Upper awash river system in Ethiopia. In: Mays LW (ed) Integrated urban water management in arid and semi-arid regions. UNESCO, Paris Fosler-Lussier E (1998) Markov models and hidden Markov Models: a brief tutorial. International Computer Science Institute, Berkeley Getachew, Z. (2014), Ethiopia: Kessem Irrigation Dam nears completion. All Africa; Ethiopian Radio and Television Agency, June 12, 2014 Gething PW, Smith DL, Patil AP, Tatem AJ, Snow RW, Hay SI (2010) Climate change and the global malaria recession. Nature Letters 465:342–346 Ghebreyesus TA, Haile M, Witten KH, Getachew A, Yohannes AM, Yohannes M, Teklehaimanot HD, Lindsay SW, Byass P (1999) Northern Ethiopia: community based incidence survey. Environ Manag 319:663–666 Giannini A, Biasutti M, Held IM, Sobel AH (2008) A global perspective on African climate. Clim Chang 90: 359–383 Girma, M. M. and S. B. Awulachew (2007), Irrigation Practices in Ethiopia: Characteristics of Selected Irrigation Schemes, Working Paper 124. Int. Water Management Institute, Colombo, Sri Lanka, 80 p Gleick, P. and I. A. Shiklomanov (1989), The impact of climate change for water resources, Second meeting of IPCC WG-2. World Meteorological Agency/United Nations Environment Programme, Geneva Hailemariam K (1999) Impact of climate change on the water resources of Awash River Basin, Ethiopia. Clim Res 12:91–96 Halcrow (1989) Master plan for the development of surface water resources in the Awash Basin. Ethiopian Valleys Development Authority, Ethiopia Hargreaves G, Allen R (2003) History and evaluation of Hargreaves Evapotranspiration Equation. J Irrig Drain Eng 129(1):53–63 Hay SI, Cox J, Rogers DJ, Randolph SE, Stern DI, Shanks GD, Myers MF, Snow RW (2002) Climate change and the resurgence of malaria in the East African highlands. Nature 415(6874):905–909 Hughes DA, Hannart P (2003) A desktop model used to provide an initial estimate of the ecological in-stream flow requirements of rivers in South Africa. J Hydrol 270(3–4):167–181 Hurd BH, Callaway M, Smith J, Kirshen P (2004) Climatic change and U.S. water resources: from modeled watershed impacts to national estimates. J Am Water Resour Assoc 40(1):129–148 (IPCC) Intergovernmental Panel on Climate Change (2007) Climate change, 2007: the physical science basis. Cambridge Univ. Press, Cambridge DIPCC] Intergovernmental Panel on Climate Change D2014] Climate change, 2007: the physical science basis. Cambridge Univ. Press, Cambridge http://www.ipcc.ch/pdf/assessment-report/ar5/wg1/WG1AR5_AnnexI_ FINAL.pdf Juel JS (2013) Mosquito larval source management by water level control. Outlooks on Pest Management 24(4): 173–175 Jury MR, Funk C (2013) Climatic trends over Ethiopia: regional signals and drivers. Int J Climatol 33(8):1924– 1935 Karl T, Trenberth KE (2011) Modern global climate change. Science 302(2003):1719–1723 Kaufmann C, Briegel H (2004) Flight performance of the malaria vectors Anopheles gambiae and Anopheles atroparvus. J Vector Ecol 29(1):140–153 Keiser J, de Castro MC, Maltese MF, Bos R, Tanner M, Singer BH, Utzinger J (2005) Effect of irrigation and large dams on the burden of malaria on a global and regional scale. Am J Trop Med Hyg 72(4): 392–406 Kibret, S., McCartney, M., J. Lautze, and G. Jayasinghe (2009), Malaria Transmission in the Vicinity of Impounded Water: Evidence from the Koka Reservoir, Ethiopia, Colombo, Sri Lanka: International Water Management Institute, (IWMI Research Report 143), 47 p

Climatic Change Kibret S, Lautze J, Boelee E, McCartney M (2012) How does an Ethiopian dam increase malaria? Entomological determinants around the Koka reservoir. Tropical Med Int Health 17(11):1320–1328 Kim U, Kaluarachchi J, Smakhtin VU (2008) Generation of monthly precipitation under climate change for the upper blue Nile River Basin, Ethiopia. J Am Water Resour Assoc 44(5):1231–1247 King C (1996) The incorporation of health concerns into African River Basin planning. MIT Doctoral Thesis, Massachusetts Institute of Technology, Cambridge Klipsche, J. D. (2010), Utilizing Ensemble streamflow predictions To Incorporate uncertainty In Real-Time Reservoir Operations, in 2nd Joint Federal Interagency Conference, Las Vegas, NV Klipsche, J. D. and M. B. Hurst (2007), HEC-ResSim: Reservoir System Simulation User’s Manual, v.3.0. CPD 82, Davis: US Army Corps of Engineers Hydrologic Engineering Center, http://www.hec.usace.army.mil/ software/hec-ressim Lafferty KD (2009) The ecology of climate change and infectious diseases. Ecology 90(4):888–900 Lall U, Sharma A (1996) A nearest neighbor bootstrap for resampling hydrologic time series. Water Resour Res 32(3):679–693 Lautze J, McCartney M, Kirshen P, Olana D, Jayasinghe G, Spielman A (2007) Effect of a large dam on malaria risk: the Koka reservoir in Ethiopia. Tropical Med Int Health 12:982–989 Lizzi KM, Qualls WA, Brown SC, Beier JC (2014) Expanding Integrated Vector Management to promote healthy environments. Trends Parasitol 30(8):394–400 Mamo, S. (1995), Research study on the Koka Dam reservoir leakage paths. Ethiopia: Ethiopian Institute of Geological Surveys, report number 830–451-01. Addis Ababa McCartney, M. P., J. Reis, S. Kibret, T. B. Culver, and J. Lautze (2011), Manipulating dam operation for malaria control: an investigation of the Koka dam, Ethiopia, in HYDRO 2011 Conference Proceedings, Prague, Czech Republic, Aqua Media International Ltd, Wallington, UK MWR (Ethiopian Ministry of Water Resources) (2008) Personal data request National Meteorological Agency (2002). Data was provided upon request Nemec J, Schaake J (1982) Sensitivity of water resource systems to climate variation. Hydrol Sci J 27(3):327– 343 Norris LC, Main BJ, Lee Y, Collier TC, Fofana A, Cornel AJ, Lanzaro GC (2015) Adaptive introgression in an African malaria mosquito coincident with the increased usage of insecticide-treated bed nets. Proc Natl Acad Sci 201418892 Oki T, Kanae S (2006) Global hydrological cycles and world water resources. Science (New York, NY) 313(5790):1068–1072 Oomen GKK, Siongok ATK, Mutinga MJ (1979) Health and disease in the kamburu-gtaru dam area. Ecol Bull 29:105–150 Parham PE, Michael E (2010) Modeling the effects of weather and climate change on malaria transmission. Environ Health Perspect (Online) 118 5:620 Patz JA, Hulme M, Rosenzweig C, Mitchell TD, Goldberg RA, Githeko AK, Lele S, McMichael AJ, Le Sueur D D2002] Climate change: regional warming and malaria resurgence. Nature 420D6916]:627–628 discussion 628 Perrin C, Michel C, Andréassian V (2003) Improvement of a parsimonious model for streamflow simulation. J Hydrol 279(1–4):275–289 Raje D, Mujumdar PP (2010) Reservoir performance under uncertainty in hydrologic impacts of climate change. Adv Water Resour 33(3):312–326 Reis J, Culver TB, McCartney M, Lautze J, Kibret S (2011) Water resources implications of integrating malaria control into the operation of an Ethiopian dam. Water Resour Res 47(9) Reiter P, Thomas CJ, Atkinson PM, Hay SI, Randolph SE, Rogers DJ, Shanks GD, Snow RW, Spielman A (2004) Global warming and malaria: a call for accuracy. Lancet Infect Dis 4(6):323–324 Roggeri H (1985) African dams impacts in the environment. Environment Liaison Centre, Nairobi Schwarz HE (1977) Climatic change and water supply: how sensitive is the northeast? US NAS Climatic Change and Water Supply, Washington, D.C., pp. 111–120 Seleshi Y (2007) Koka Dam sedimentation study, prepared for EEPCo, XU0113/410/final report A02. Addis Ababa, Ethiopia Shanks GD, Hay SI, Stern DI, Biomndo K, Snow RW (2002) Meteorologic influences on plasmodium falciparum malaria in the Highland Tea Estates of Kericho, Western Kenya. Emerg Infect Dis 8(12):1404– 1408 Simonovic SP, Li L (2004) Sensitivity of the Red River Basin flood protection system to climate variability and change. Water Resour Manag 18:89–110 Sutherst RW (2004) Global change and human vulnerability to vector-borne diseases. Clin Microbiol Rev 17(1): 136–173

Climatic Change Tanser FC, Sharp B, le Sueur D (2003) Potential effect of climate change on malaria transmission in Africa. Lancet 362(9398):1792–1798 Teasley RL, McKinney DC (2005) Modeling the forgotten river segment of the Rio Grande/Bravo Basin, center for research in water resources (CRWR). Online Report 05-12, available online at https://repositories.lib. utexas.edu/bitstream/handle/2152/7007/crwr_onlinereport05-12.pdf. Accessed 7 Jan 2016 Tonnang HEZ, Kangalawe RYM, Yanda PZ (2010) Predicting and mapping malaria under climate change scenarios: the potential redistribution of malaria vectors in Africa. Malar J 9:111 United Nations (2013), Millennium Development Goals and Beyond 2015: Goal 6 Fact Sheet, Geneva, Switzerland United Nations Environment Programme (2008), 4.3 Awash River Basin. In Freshwater Threat: Africa. Vulnerability Assessment of Freshwater Resources to Environment Change, Nairobi, Kenya Utzinger J, Tozan Y, Singer BH (2001) Efficacy and cost-effectiveness of environmental management for malaria control. Trop Med Int Health 6(9):677–687 Viste E, Korecha D, Sorteberg A (2013) Recent drought and precipitation tendencies in Ethiopia. Theor Appl Climatol 112(3–4):535–551 Williams AP, Funk C (2011) A westward extension of the warm pool leads to a westward extension of the Walker circulation, drying Eastern Africa. Clim Dyn 37(11–12):2417–2435 World Health Organization (WHO) (2013), Malaria, Fact sheet N°94, Geneva Zhou G, Minakawa N, Githeko AK, Yan G (2004) Association between climate variability and malaria epidemics in the East African highlands. Proc Natl Acad Sci U S A 101(8):2375–2380