USING A SPATIAL EQUILIBRIUM MODEL TO QUANTIFY THE BENEFITS OF TURKEY’S WATER PROJECT

Working paper presented at the Association of American Geographers Annual Meeting, February 2012

J.M. Fisch

Woodrow Wilson School, Princeton University, Princeton, NJ

Columbia Law School, New York, NY

Author contact information: J.M. Fisch 504 West 110th Street New York, NY, 10025 Email: [email protected]

ACKNOWLEDGEMENT

The author is grateful to Ambassador Robert Finn and Professor John Waterbury for their help, advice, and support. Ambassador Finn, the author’s senior thesis advisor, possessed a depth of knowledge about Turkey and about the thesis-writing process that made him an essential resource. Professor Waterbury inspired the author’s interest in this topic, provided invaluable research guidance, and helped the author transform his thesis into publication format.

This paper is based on an undergraduate senior thesis submitted to the Woodrow Wilson

School at Princeton University. The author thanks the Princeton Environmental Institute Grand

Challenges Program and the Woodrow Wilson School for providing research funding. The text of the senior thesis is available at http://www.columbia.edu/~jmf2143/fisch_thesis.pdf. TABLE OF CONTENTS

Abstract 3

Introduction 4

Background 6

Approach 8

Estimating Runoff Modeling Tigris and Euphrates Flow Rates 12 Results of the Runoff Model 16

Estimating Benefits Modeling the Benefits of Project Components 17 Modifications to the Model 21 Limitations and Caveats 25 Results of the Benefit Model 26

Conclusions 31

Notes 33

Tables 35 Table Captions 40

Figures 42 Figure Captions 48

References 49

2 ABSTRACT

This paper proposes a novel method for estimating the benefits of the Southeast Anatolia

Project (GAP), a series of hydroelectric dams and irrigation networks under construction along the Tigris and Euphrates rivers in Southeastern Turkey. The analysis presented here is driven by a spatial equilibrium model that can be used to calculate the benefit generated by the components of the project (individual dams and irrigation networks) under various development scenarios.

The paper also presents a correlation between temperature and precipitation measurements in the basin and annual river runoff on the Tigris and Euphrates. This correlation is used to make rough predictions of future river flows. These flow predictions are then incorporated into the spatial equilibrium model to observe how climate change may affect the benefit derived from components of GAP.

The estimated value of the components completed as of 2009 vastly exceeds construction costs, suggesting that these components were a worthwhile investment. However, the estimated benefits of constructing one of the as-yet-uncompleted components of GAP, the Ilısu/Cizre project, fall much closer to estimated costs, especially when reductions in river flows are considered. Moreover, the benefit of constructing the remaining irrigation infrastructure falls well below estimated costs. While the lack of data and other limitations make reaching precise benefit estimates difficult, this study demonstrates the need for Turkey to reconsider planned water infrastructure investments in light of climate change predictions. Planners should reevaluate the project component-by-component rather than implement all of elements prescribed by the 1989 Master Plan. By customizing a spatial equilibrium model for the GAP region, this paper lays the groundwork for making the necessary reevaluations.

3 INTRODUCTION

The Atatürk Dam rises out of the mist. Sixteen stories tall and a mile wide (Kaplan 1994), with a price tag of $2.3 billion (Kolars 1994, 62),1 the dam has the capacity to produce 6% of

Turkey’s energy.2 The dam is the capstone of the Güneydoğu Anadolu Projesi (meaning

Southeast Anatolia Project and abbreviated GAP), a project that already accounts for 16% of

Turkey’s electric capacity3 and that calls for an expansion of Turkey’s irrigated area by nearly

60%.4 The Atatürk Dam and the ambitious project it is part of exemplify Turkey’s turbo-charged economy and aspirations for the coming century.

But as GAP’s originally predicted completion date of 2005 (GAP Master Plan 1989, vol.

2, page 3.5) has come and gone, and as aspects of the project have been held up by protests (see

Wildlife Extra 2008 and Schleifer 2008 for examples) and lack of financing (Hürriyet 2009b), some are asking whether the remaining components of GAP are worth the costs (Schleifer 2008).

This question is complicated beyond a simple cost-benefit calculation by a number of factors, including the proposed displacement of residents living in the area and the project’s potential impact on the environment and cultural sites.

This study makes progress toward an answer to this question by developing a method to estimate the most easily quantifiable benefits of the project, the monetary value of the energy and agricultural production generated by the project’s dams and irrigation networks. This study also investigates the effects of regional climate change on the project’s future benefits. A reduction in river flow in the basin has the potential to significantly alter the cost-benefit calculation, and this study estimates the magnitude of that change.5 While previous studies, such as the 1989 GAP

Master Plan, estimate benefits of the entire project, no previously published estimate isolates the benefits from specific components of the project6 or determines how the benefits will change

4 under forecasted climate change scenarios.

A method of quantifying these benefits brings us closer to determining whether certain components should be completed. If the monetary benefits of a component of the project outweigh projected construction costs, the component would be viable if social, environmental, and other costs are low. If monetary benefits generated by the project do not outweigh construction costs, however, the component is unlikely to be viable unless large unquantified benefits exist.

The most pressing question that this analysis addresses is the viability of the Ilısu/Cizre project. The biggest dam remaining to be completed, Ilısu is expected to generate 3833

GWh/year (DSİ 2009b, 5) and cost about $2.54 billion (Hürriyet 2009a). Cizre, which is downstream of Ilısu and cannot be completed without Ilısu, will produce 1208 GWh/year (DSİ

2009b, 5) and irrigate 70,000 ha of farmland (Bağiş 1989, 63). The project has sparked protests in the affected region, in part because the project will displace residents and submerge historic sites. Estimates for the number of residents that will be displaced by the project range from

55,000 (Ilısu Consortium 2006, 5) to 108,000 (Hasankeyf Girisimi 2009, 6). The project was repeatedly stalled due to lack of financing, most recently after European banks withdrew financing for the dam in July 2009 (Luxmore 2009). Most recently, a Turkish regional court with the power to stop the construction ordered an investigation into the damage that the project would cause to Hasankeyf, a 3,000-year-old town that would be flooded by Ilısu (Güsten 2011).

With the fate of the project up in the air and Prime Minister Recep Tayyip Erdoğan calling for completion of the Ilısu by 2014 (Letsch 2011), examining how climate change can affect the benefits of the project takes on new urgency.

Although the data utilized in this report are not detailed enough to produce a firm policy

5 recommendation, I describe and implement a novel application of a spatial equilibrium model to determine benefits of infrastructure investment. I demonstrate the importance of incorporating reductions in river flow into benefit predictions. I conclude that the benefits of the components of the project completed as of 2009 greatly outweigh measured costs, even when accounting for reductions in river flow, suggesting that those parts of the project were good investments.

However, I conclude that projected benefits from the Ilısu/Cizre project fall much closer to measured costs, and further evaluation of those projects is recommended before construction is completed. This study shows that a new benefit analysis of the remaining components of GAP is necessary and develops a method for conducting such an analysis.

BACKGROUND7

The GAP administration calls GAP “the most ambitious regional development initiative of the Republic of Turkey” (GAP 2001, 1). Proponents of the project argue that the project is a necessary boon to the depressed economy of the Southeast Anatolia region (Aydın 2000).

Planners of GAP envision an “integrated regional development project,” including expenditures on health, education, transportation, and other sectors alongside water infrastructure development (Ünver 1997, 459-460). Since this article is primarily concerned with the water- related components of the project, I do not evaluate the cost-effectiveness of these other aspects, except to view water infrastructure investment in the context of these goals.

The GAP Master Plan called for thirteen dams capable of generating hydroelectricity

(vol. 2, figures 5.2 and 5.3). The five hydroelectric dams planned for the Euphrates, Keban,

Karakaya, Atatürk, Biricik, and Karakamış, were completed as of 2009. On the Tigris, the

Kralkızı, Dicle, and Batman dams have been completed. The Garzan, Silvan, Kayser, Ilısu, and

Cizre dams are not yet built (DSİ 2009b, 8). The Ilısu and Cizre dams will be located

6 downstream on the main branch of the Tigris north of the Iraqi border. Plans call for the Silvan and Kayser dams to be built on Batman Suyu, and the Garzan Dam will be built on the Garzan tributary (Sofer 1999, 95).8 A list of dams is presented in table 1.

The Master Plan called for 1.6 million hectares of irrigated land (vol. 2, page 3.5), but by

2009 the number of hectares planned had expanded to 2.07 million (DSİ 2009b, 5).9 However, only 287,295 hectares were irrigated as of 2009 (DSİ 2009b, 9). A list of irrigated areas can be found in table 2. A list of urban demand nodes is provided in table 3.

The most comprehensive cost estimates from the project are available from the Master

Plan. The Master Plan reported outlays made from 1981-1987 and forecast expenditures through

2005 (vol. 3, table C.8). This time period captures nearly all expected expenditures, since the only dam finished before 1981 was the Keban Dam, and the planners expected most of the project to be finished by 2005 (vol. 2, page 3.5). Under the full development scenario for hydropower, outlays on energy infrastructure total $13.02 billion (vol. 3, table C.8). (This figure is scaled to 2009 dollars, as are all sums listed in this report. See footnote 1 for more information on how sums were scaled.) With 13 hydropower plants planned in the 1989 report, this comes out to a cost of about $1 billion per plant. Assuming full development of agricultural potential, the planners expected to spend $14.7 billion on irrigation. Since the plan calls for developing 1.6 million hectares (vol. 2, page 3.5), this translates to around a $9,000 per-hectare cost. Non-water- related aspects of the project are expected to cost around $18 billion (vol. 3, table C.8).

Outside of the Master Plan, estimates of individual components are available. Hürriyet reports that the Ilısu Dam is expected to cost €1.8 billion, about $2.54 billion in 2009 dollars, including costs of relocating displaced residents (Hürriyet 2009a). The Keban Dam cost $370 million, while the Atatürk dam cost $2.3 billion (Kolars 1994, 62). According to DSİ, $21 billion

7 has been spent on the project so far (DSİ 2009a, 60). (This includes the money thus far spent on non-water aspects, so it is an overestimate for the cost of already-completed dams and irrigation.)

APPROACH

The analysis presented here requires two steps. First, I estimate future runoff in the

Tigris-Euphrates basin. I accomplish this with a simple statistical model correlating past observations of precipitation and temperature with observed river flow, and use this statistical relationship to forecast future river flow based on predictions of precipitation and temperature.

Next, I model the benefits of each component of the project, accounting for changes in runoff.

To determine future climactic conditions, I rely on data from general circulation models

(GCMs). I use five climate models: GFDL-CM 2.1 (developed by the Geophysical Fluid

Dynamics Laboratory in Princeton), CNRM-CM3 (France’s Centre National de Recherches

Météorologiques), HadCM3 (the Hadley Center for Climate Change in England), CGCM3.1 (the

Canadian Center for Climate Modeling and Analysis), and GISS-ER (NASA’s Goddard Institute of Space Studies). Maurer (2010) provides downscaled monthly temperature and precipitation predictions from each of these GCMs through the end of the 21st century. Coarse-resolution predictions were downscaled to half-degree grid-boxes. The downscaling method is described in

Maurer et. al. (2009, 184-185).10

Previous research, including Önol and Semazzi (2008) and Kimura et. al. (2007), discussed downscaled predictions of climate simulations to the GAP region. Both papers showed a reduction in precipitation, particularly in the winter months, but did not translate these precipitation predictions into flow rates. A model by the Turkish Water Foundation, currently in development, will include predictions of river runoff based on downscaled climate models for several basins in Turkey and the Middle East (Su Vakfı 2009). The most accurate mechanism for

8 predicting river runoff from climate variables would be a complex hydrological model that takes into account information such as land cover, vegetation, and soils as well as historical runoff measurements.11 However, detailed information on the region and comprehensive records on river flow are difficult to obtain. Turkey ceased to publish hydrological data for the basin shortly after commencing work on GAP (Cullen and deMenocal 2000, 857).

The flow record that is most useful for this study is from the GAP Master Plan, which presented annual data for river flow measured at Birecik, on the Euphrates near the Syrian border, and at Cizre, on the Tigris just before the river passes between Syria and Iraq (vol. 4, figure E.5). The record for the Euphrates lasted from 1938-1980, and the record for the Tigris lasted from 1946-1983. No major dams were built on the Tigris during this period. Construction of the Keban Dam on the Euphrates affected river flows past 1974, but the authors of the Master

Plan revised the data to correct any distortions that this caused (vol. 4, table E.6). I obtained historical data on precipitation and temperature from a data set developed by the University of

Delaware (Matsuura and Willmott 2009) and made available by Mitchell (2009) as well as from a data set created by Adam and Lettenmaier (2003) and made available by Maurer (2010). The

University of Delaware data set contained earlier records and was used for the 1938-1950 period.

I derived a correlation between temperature and precipitation in certain seasons and catchment regions with river flow measured at Birecik and Cizre. I used this statistical correlation alongside predictions of temperature and precipitation from each of the climate models to estimate future runoff.

The next task was to develop a model that measures the benefits of components of the project. The model optimizes the benefit of water consumption across a series of consumption points to determine the benefit generated by the system. Demand parameters quantify the value

9 of water for agricultural and urban use as well as the value of the energy produced by water flowing through the dams. On the supply side, the model estimates the cost of transporting water from dams to consumers. I estimate the net present value of a component of the project by comparing the benefit generated by the project under one scenario (for example, removing the

Ilısu and Cizre dams from the final plans) to the benefit generated under another scenario (for example, the entire project, including the Ilısu/Cizre component).

Models that measure benefit from water use have been put to work by researchers to solve a variety of problems. Vaux and Howitt (1984) use a spatial equilibrium model to determine the benefits of interregional water trade in California.12 The authors specify local demand and supply functions for water in a number of regions. The authors also specify costs for transporting water from one region to another. Maximizing producer and consumer surplus allows the authors to determine how the water would be used if utilized optimally and to measure the benefit of water transfer between California’s regions. Dinar et. al. (2007) develop a similar model to quantify the benefits of riparian cooperation on a shared water basin, but incorporate hydropower generation, balancing the gains of irrigation with the opportunity cost of foregone power generation. The authors assume that the demand nodes are located in different countries, using the results of the model to make conclusions for riparian politics.

Moving closer to the GAP region, Fisher et. al. (2005) create a model for the Jordan

Valley, the Water Allocation System (WAS). Like Dinar et. al., the authors’ primary goal is to estimate the benefits to the basin riparians of cooperation on use of water resources. The authors compare net benefit of the water under scenarios in which water use is optimized across the entire basin to the scenario in which each country optimizes water allocation internally (196-

218). The authors propose use of their model to evaluate infrastructure projects (35-38),

10 providing precedent for use of a spatial equilibrium model to determine the benefits of components of GAP.

To build the model utilized in this paper, I expanded upon a model developed by

Küçükmehmetoğlu (2002, 2009; Küçükmehmetoğlu and Guldmann 2004). Küçükmehmetoğlu’s

Euphrates-Tigris river basin model is the most complete interregional transfer model covering the GAP region available in the literature. Although Küçükmehmetoğlu built the model in order to estimate the benefits of cooperation among basin riparians, his model can be altered to estimate benefits from the GAP project. No authors have yet used a spatial equilibrium model to quantify the benefits of GAP or other large water projects.

Figure 1 shows a diagram of this paper’s model. This schematic shows all demand nodes

(irrigated land and urban areas) and supply nodes (dams) as planned for the full development scenario. Water flows into each supply node from groundwater and river tributaries as well as from upstream. Water can be released from each supply node either to a demand node or down the river. If the dam has hydroelectric capacity, releasing water downstream generates revenue.

Water released down the river can be used again at downstream supply nodes. Part of the water consumed at each demand node flows back into the system in the form of return flows. Water consumption at upstream demand nodes decreases potential revenues from downstream hydroelectric schemes and other demand nodes.

For simulations in which a dam is not constructed, I assume that water for nodes supplied

“by” the dam is drawn from the river or the water table around the river unless otherwise specified in the Master Plan or in the literature. For example, the pumped irrigation drawing from the Ilısu area depletes the river upstream of Ilısu but is not dependent on the dam; in fact, a portion of this irrigation was completed as of 2009 (DSİ 2009b, 9). On the other hand, Bağiş

11 (1989) writes that 70,000 ha of irrigated farmland proposed for the Nusaybin-Cizre-İdil region is dependent on the Cizre Dam (63).13

To simulate reduced inflow due to climate change, I decrease the inflows to each supply node proportionately as predicted by the model. For example, if the flow model predicts that between 2055 and 2075 flow rates on the Euphrates will average 58% of the Euphrates’ historical average rate, I decrease groundwater and tributary inflows on all Euphrates supply nodes to 58% of original inflows while running the simulation for these years. Since the model is based on historical data for flow rates at Cizre and Birecik, the results will be most accurate for projects close to these locations. Fortunately, the Ilısu/Cizre project is close to the gauging station at Cizre, but results are less reliable for dams further upstream.

ESTIMATING RUNOFF

Modeling Tigris and Euphrates Flow Rates

To predict runoff, I derived a correlation between precipitation and temperature in watershed areas of Tigris and Euphrates with the rivers’ flow rates. To determine watershed areas, I traced the tributaries on a map of the basin.14 I divided the Euphrates into four catchment areas. The two principal tributaries, the Murat and the Karasu, combine north of Keban. Since the Murat covers a large area, a separate region is established for the Upper Murat. The downstream part of the river is designated the Lower Euphrates region. A number of tributaries flow into the Tigris from the north and east while the main branch of the Tigris originates west of

Diyarbakır. The northern tributaries are combined into their own catchment area, named Batman

Suyu. The main eastern tributary is Bühtan Deresi, which flows out of the mountains south of

Lake Van. The main branch of the Tigirs is divided into two catchment regions, the Upper

Tigris, centered around Diyarbakır, and the Lower Tigris, which contains the lower reaches of

12 the river after the confluence of the tributaries.

In order to build a record of temperature and precipitation in each season for each region,

I read monthly temperature and precipitation files using MatLab and aggregated monthly data for each half-degree grid box in each of the catchment regions to arrive at an average monthly measurement for that region. I then merged the monthly data into seasons. Winter contained

January and February, along with the previous year’s December. (Snowfall in December may contribute to the measured flow once the snow melts in the spring.) March, April, and May formed spring, while summer lasted from June through September. The remaining months were considered fall.

The river flow measurements are extrapolated from a chart presented in the Master Plan

(vol. 4, figure E.5) and Bağiş 1989 (46).15 Table 4 shows a model correlating Euphrates runoff with winter precipitation in the Lower Euphrates region and spring precipitation in the Murat region, along with a number of temperature variables. All of the variables in this model, including the constant, are significant at the 95% level or higher. Each of these variables is logically explainable. The Murat region receives the largest share of the basin’s spring precipitation, so it is unsurprising that spring precipitation there is included in the model. Since

Birecik is located in the Lower Euphrates region, precipitation there can be expected to impact measured flow, and precipitation is highest in the lower Euphrates in the winter, so winter lower

Euphrates precipitation is a good candidate for the model. (See Fisch 2011a, 38 for monthly regional precipitation data.)

The relationship between temperature and runoff in a given season is complex. Warm temperatures may melt snow cover and contribute to river runoff (Tekeli et. al. 2005, 681).

Warm temperatures also increase the amount of moisture that can be held in the atmosphere.

13 Since eddies transport atmospheric moisture in the mid-latitudes poleward, higher atmospheric storage capacity will result in greater movement of water out of the region and less precipitation in the region (Held 1993, 235-238). An increase in temperature in the Lower Euphrates (where there is little snow cover) during the spring months is likely to decrease precipitation due to the latter phenomenon, so this variable is negatively correlated with runoff. A similar explanation can be made for the negative relationship between spring temperature in the Karasu region and

Euphrates flow. Snowmelt probably explains the rest of the temperature variables, though other factors may play a role. Spring Murat and winter Upper Murat temperatures are positively correlated with Euphrates flow. These regions have significant permanent snow cover that may melt in warm temperature. For example, glaciers exist in the Bingöl Mountains (Turkey For You

2011). Warm temperatures in the Karasu region over the fall will cause precipitation to fall as rain rather than snow and contribute to river flow that year rather than in the following year.

Colder temperatures in the winter (which includes the December of the previous year) will cause precipitation to be preserved from the previous year and flow to the Euphrates in the year of interest.

The Tigris model is restricted to the four variables presented in table 5. Precipitation variables from the Lower Tigris region, the Upper Tigris region and the Bühtan Deresi region are all significant. Winter precipitation is significant in the Lower and Upper Tigris because the most precipitation falls there during the winter. Meanwhile, Bühtan Deresi sees its highest precipitation in March and April. The Batman Suyu catchment does not remain in the model; it is possible that the catchment is simply not a very important contributor to the Tigris. Last, summer temperature in Bühtan Deresi area is negatively correlated with runoff. Since Bühtan Deresi is a significant tributary that currently has significant precipitation in the summer, transport of water

14 out of the region during the summer will impact flow. Precipitation in Bühtan Deresi is higher than in the Lower and Upper Tigris regions throughout the summer (Fisch 2011a, 38).

Figures 2 and 3 show measured runoff in the Tigris and the Euphrates along with runoff predicted by the model for the observed period. Due to the small number of years for which flow rates were observed, I do not have a separate set of temperature and precipitation measurements with which to validate the correlative model. Instead, I generate new temperature and precipitation measurements from hindcast GFDL data — “predictions” of temperature and precipitation generated by GFDL’s GCM for previous years. Since GCMs are not meant to provide accurate year-to-year temperature and precipitation forecasts but rather show a general trend over a period of time, average runoff over a long period obtained from the correlative model using GFDL-generated temperature and precipitation data should be close to the average measured runoff during that period. On the Euphrates, the average runoff obtained using the model and hindcast GFDL data over the whole period is 29.1 bcm/year and the average measured runoff was 30.3 bcm/year. On the Tigris, the average runoff obtained from the model and hindcast data over the whole period is 16.0 bcm/year and the average measured runoff was

16.8 bcm/year. In figures 4 and 5, I split measurements into two periods and compare the averages over the periods. None of the differences in means are statistically significant, providing support for the runoff model.

Despite this validation, there remains much uncertainty in the predictions. It is uncertain that the same relationship between precipitation, temperature, and runoff will persist after dam construction and throughout the next century. Additionally, estimated coefficients of each variable have confidence intervals determined by their standard errors, but I make predictions using only point estimates. Moreover, the GCM predictions carry their own uncertainty, which I

15 do not make an attempt to measure. Despite these caveats, I am still confident in the predictions of the model to yield a rough picture of river flow. The conclusions I draw in this report require only a sense of the likely magnitude of change rather than a precise measurement.

Results of the Runoff Model

Predictions for river flow using statistical models for the Tigris and Euphrates along with forecast monthly temperature and precipitation from GCMs can be found in tables 6 and 7. I studied GCM output for the A2, B1, and A1b climate change scenarios. (For more information on these scenarios, see IPCC 2007a, 44 and IPCC 2207b, 18.) The 2015-2075 period is most relevant, because revenue from dams and irrigation earlier in the century has a much greater effect on the present value of the project. During this period, runoff predictions from A2 scenario fall between estimates from the A1b and B1 scenarios. Because A2 is considered the “business as usual” scenario (National Academies 2008, 14), I make most use of A2 projections to generate benefit calculations.

The runoff predicted using GFDL A2 is presented in figures 6 and 7. The GFDL A2 scenario shows average flows on the Euphrates dropping to 87% of observed 1938-1980 levels for the 2015-2035 period. Flows during the 2035-2055 period are 73% of their previous size.

This proportion drops to 58% and 39% in the 2055-2075 period and the 2075-2099 period, respectively. Decreases on the Tigris were greater than on the Euphrates in percentage terms.

Flows on the Tigris were initially lower than on the Euphrates, so similar numerical declines translate into big percentage declines. Additionally, the regions feeding the Tigris dry out somewhat more than the regions feeding the Euphrates. Under A2, the Tigris is predicted to flow at 72% of its prior rate between 2015 and 2035, dropping to 45% of its flow rate between 2035 and 2055. After 2055, rates get dangerously low, dropping to 32% from 2055-2075 and 10%

16 after 2075.16

Tables 6 and 7 display the predicted runoff under all models and scenarios analyzed. The

GFDL A1b scenario exhibits greater declines early on but smaller declines at the end of the century. The GFDL B1 scenario predicts a smaller decline, with flows at 59% of current levels on the Euphrates and 33% of current levels on the Tigris by the 2075-2099 period.

Looking at A2 under other models on the Euphrates, all models start off by projecting flows for the 2015-2035 period that are lower than current flows. Projections for fall in the 75%-

89% range. Over the 2035-2055 period, the models differ. While the GFDL prediction continues the decline over the 2035-2055 period, the UKMO, GISS, and CCMA models project flows for that period that are higher than those of the first period. These models delay the drying trend until later in the century. Projections begin to align again after 2055, with all of the models projecting a steady decline lasting through 2099. The models differ on the magnitude of the decline — GFDL, GISS, and CNRM all predict flows in the 34-39% range, while UKMO and

CCMA predict that flows will be 63% of the current level.

Projections are more consistent across models on the Tigris. In all models, the 2015-2035 period shows a decline to between 61%-72% of initial levels. The variation in the next period is a bit wider, with some models projecting slightly higher runoff than in the 2015-2035 period.

Runoff in all models declines in the third period. By the last period, all of the models predict flows in the 9%-22% range.

ESTIMATING BENEFITS

Modeling the Benefits of Project Components

The model described in Küçükmehmetoğlu (2002, 2009) and Küçükmehmetoğlu and

Guldmann (2004) is a starting point for the estimation presented here. The authors use their

17 model to evaluate the benefits of basin-wide cooperation over development and use of water resources. In this section, I adapt their model to evaluate specific components of GAP. I begin with the objective function: 17

1 !!"#!!"#$#%&"!!"#"$%& =

!"#$%&'(&#!'!!"#$%!!!(!"!×(!"#"$ − !"#$%×!"!!)) + !"#$%!!"#$%!!!(!"!×(!"#$% −

!"!!×!"#$%)) + !"##$%!!"#$%!! !"#×!"!× !"##$%!!"#$%!!(!"!,!)

VALAG is the amount of revenue water can generate in agriculture. This value is explored further in the “modifications” section below. WTi is the amount of water consumed at node i, in mcm. This is a variable that needs to be optimized. DSDi is the distance between each demand node and the nearest supply node. Distances are obtained from Küçükmehmetoğlu

(2002, 109) and Google Maps, and are presented in tables 2 and 3. AGRTC is the cost per kilometer of transferring 1 mcm of water for agricultural purposes. Küçükmehmetoğlu provides a value of $850 per km, which is scaled up to $1059 per km in 2009 prices (2002, 114). VALUR is the value of water for urban use – this includes domestic and industrial use. Küçükmehmetoğlu

(2002, 111) provides a value of $150,000 per mcm (15¢/m3) for this. This is scaled up to

$217,140 (22¢/m3) in 2009 dollars. URBTC is the cost of transporting 1 mcm of water for urban use. Küçükmehmetoğlu (2002, 114) estimates this at $4,958 per km, or $6,117 in 2009 dollars.

EPR is the price of 1 MWh of energy. I estimate an average annual price of $117/MWh based on the pre-tax price of electricity for Turkish industry provided by the International Energy

Agency (2010, 274). Over the past four years, price controls on the Turkish energy market have been removed so that electricity prices now reflect the shadow value of electricity (Tommila

2010, xiv). Prices of electricity are assumed not to respond to changes in electricity production in the GAP region. The region supplies 10% of Turkey’s electricity and represents 16% of installed capacity (DSİ 2009a, 33), so a change in supply from the region would probably have some

18 effect on energy prices. The effect would be small, however, especially since the market for GAP region electricity also includes Syria and Iraq. Changes in the value of energy would have a significant effect on the benefit estimates generated by the model. Future energy prices will depend on the availability of natural resources, technological development, and economic growth. It is difficult to predict how these factors will interact.

EGj is the amount of electricity generated per mcm of water flowing through each dam j.

This is provided in table 1. PQj,l is a variable representing the amount of water flowing from upstream supply node j to downstream supply node l.

The objective function is constrained by a few equations. First of all, the amount of water flowing into each supply node needs to equal the amount of water flowing out:

2 !∀!!"##$%!!"#$%!!:! !!,! + (!"!,!) + !"#! !"#$%!!!"#$%!! !"##$%!!"#$%!!

= ! !"!!,!×!"! + !"! + !"!,! !"#$%!!!"#$%!! !"##$%!!"#$%!!

In this equation, Wi,j is the amount of water flowing to demand node i from each supply node j. Since the model assumes that each demand node is linked to only the closest supply node,

Wj,i = WTi if there is a link between the demand node and the supply node, and 0 if there is no link. RELj is the amount of water that evaporates from supply node j if a dam and reservoir exist there. Values for RELj are presented in table 1. RFRi,j is the proportion of water used in demand node i that flows into supply node j as return flows. Küçükmehmetoğlu (2002, 107) estimates return flows at 53% of water used in agricultural nodes and 80% of water used in urban nodes and provides destinations for return flows. TFj denotes the total inflows to each demand node j.

Values for TFj can be found in table 1. When I model the returns for the project over a number of years, I decrease TFj according to the predictions presented in the previous section.

19 As an additional set of constraints, I implement maximum and minimum water requirements for agricultural areas. These constraints lose their significance when I alter the model to account for diminishing returns to agricultural water use (below), but are necessary when the model assumes constant returns to agricultural water. The minimum requirement results from the assumption that project administrators, having built irrigated farmland, will provide a minimum amount of water necessary to grow some irrigated crops, regardless of transportation costs or farmers’ willingness to pay. I use Küçükmehmetoğlu’s suggested minimum of 1,070 m3 per hectare (2002, 112). The maximum requirement is a limit beyond which extra water delivered to agricultural land would no longer be useful. I use 34,000 m3/ha, about double the water requirement estimated by Demir et. al. (2009, 36) for fully planted land in the Siverek-Hilvan region.

I also impose minimum and maximum water requirements for urban areas. If I assume that urban areas require a minimum amount of water to satisfy basic demands, and I assume that the demand for water is inelastic when these demands are not satisfied but elastic when these demands are satisfied, the coupling of constant demand for urban water with a minimum urban water requirement is a good way to model urban water use. Küçükmehmetoğlu (2002, 113) estimates the minimum amount of water required by an urban population at 32.5 m3 per capita per year, the amount of non-agricultural water used by residents of the Gaza Strip. I set the maximum requirement at 168 m3 per capita, since this is Turkey’s overall urban per-capita rate

(European Environment Agency 2010). The sizes of each urban and agricultural node are determined from DSİ (2009b) and TurkStat (2009) and can be found in tables 2 and 3.

A final constraint that I incorporated into some simulations is that the minimum delivery on the Euphrates to Syria must be 15.8 bcm/year. This is consistent with the understanding

20 between Turkey and Syria requiring Turkey to deliver an average flow of 500 m3/sec (Carkoğlu and Eder 2001, 67). No such agreement exists on the Tigris.

Modifications to the Model

I modify the base model to develop better estimates for some aspects of the project. First,

I improve the way the model simulates the value of water used in agriculture, since irrigation is an important component of portion of the project that is not yet constructed. Küçükmehmetoğlu assumes constant returns to agricultural water. However, other studies, such as Vaux and Howitt

(1984), Booker and Young (1992), and Fisher et. al. (2005) all assume diminishing returns to agriculture. The model used by Fisher et. al. is the easiest to incorporate and appears to replicate conditions on the ground. Fisher et. al. model the relationship between water price and quantity of water available as P = βQα, where β is positive and α is negative and not equal to -1. (P and Q are positive.) The derivative of this function, αβQα-1, is negative across all values of Q, but has a much larger magnitude at low values of Q. As Q approaches 0, P approaches infinity and the derivative of P approaches negative infinity.

To determine values for β and α, I turn to an analysis by Tsur (2004, 108-111) of crops grown on the Harran Plains. Tsur uses a method developed by Howitt (1995) to derive the marginal benefit of water in the district at varying water constraints. The driving concept behind

Tsur’s method is that a region’s farmers grow crops that have high returns to water when water is scarce, and supplement those crops with less water-efficient crops as more water becomes available. For example, in Harran, peppers are the most water-efficient crop, followed by wheat.

Corn and cotton are less water-efficient. By observing the crop pattern in the district in a year in which there is no water scarcity, Tsur determines how much of each crop the region will plant in years with tighter water constraints. (Tighter water constraints eliminate the cotton crop first,

21 then diminish the corn crop, etc.) The marginal value of water at a given water constraint will be the value of the produce that could be grown in the region if the region were granted an additional unit of water. For a complete explanation of the “Positive Mathematical

Programming” method Tsur used to derive prices from observed patterns in Harran, see Howitt

(1995).

Tsur’s method was implemented in MatLab to derive the marginal value of water in

Harran at changing water constraints. The relationship between water price and water constraint derived from Tsur’s method is presented in figure 8. Fitting a function of the form P = βQα to the data, I obtain β = 89.96 and α = -0.37. (Q is measured in m3.) In place of the part of equation 1 that assumes constant returns to agriculture, I substitute the derived agricultural demand curve so the new objective function is:

3 !!"#!!"#$#%&"!!"#"$%& =

! ! !!! ( !)×(!"!×10 ) !! !"#$%&'(&#!'!!"#$%!!! ! + 1 − (!"!×!"#$%×!"!!) +

!"#$%!!"#$%!!!(!"!×(!"#$% − !"!!×!"#$%)) + !"##$%!!"#$%!! !"#×!"!× !"##$%!!"#$%!!(!"!,!)

M is the ratio of the size of node i to the size of the area that Tsur studied (136,514 hectares). In the absence of any data for districts other than Harran, I assume that the demand curve scales according to the size of the district. Larger districts grow proportionately more of each crop before switching to the next-most-water-efficient crop, and smaller districts grow less.

The model can also be modified to incorporate the opportunity costs to farmers of switching from rainfed agriculture to irrigated agriculture. Instead of assuming that farmers face a choice between buying irrigation water and not growing crops, I assume that irrigation replaces rainfed agriculture. The benefit of building irrigation infrastructure would then be defined as the benefit derived from irrigation water minus the benefit the farmers could have obtained by using

22 their land for rainfed agriculture. Based on pre-irrigation cropping patterns for each district found in the GAP Master Plan and profit per hectare of crop listed in the Master Plan and other sources,

I derive the pre-irrigation value of a hectare of rainfed land in each district. These values are presented in table 2, and more detail on their derivation is provided in Fisch (2011a, 96-97).

To implement this modification, I eliminate the minimum per hectare water requirement and assume that each 11,000 m3 of water received by the district irrigates an additional hectare of land. Kolars and Mitchell (1991, 204-205) list expected requirements of around 11,000 m3/hectare for many GAP districts. Instead only taking into account the cost of transporting water to an agricultural node, as I do in equations 1 and 3, I define the total cost of irrigating a node as:

!"#!$%! 4 !!"!× !"#$%×!"!! + 0.11

where OPCOSTi is the value per hectare of rainfed agriculture in the district containing node i.

This model specification would underestimate benefits from irrigation if irrigated agriculture does not always replace rainfed agriculture or if rainfed agriculture becomes less valuable as rainfall decreases.

Another possible change to the model is the incorporation of an intertemporal dimension.

Allowing the model to store water in dams during the first half of the year and release the water during the second half of the year accounts for an important benefit of dams — storing water from the winter rainy season to the summer dry season. Dam construction allows more water releases during the summer months, benefitting both Turkey and downstream countries

(Altınbilek 2004, 29). Küçükmehmetoğlu (2009, 3075-3077 and 3096-3097) implements changes to his 2002 model to simulate two seasons. A winter rainy season lasts from December through May, and a summer dry season lasts from June through November. Seventy percent of

23 the annual flow occurs in the winter, while the remaining 30% occurs in the summer. Water demand and reservoir evaporation is higher during the summer. Though Küçükmehmetoğlu assumes that demand for electricity is constant throughout the year, I rely on monthly data from

TEİAŞ (2009, 7-8) to estimate that electricity prices in Turkey over the summer are about 101% of the average annual price and that electricity prices over the winter are about 99% of the average price, assuming prices are only affected by the Turkish market. Demand for electricity in

Turkey has shifted toward the summer from the winter in recent years as winter heating has shifted to gas from electricity and as use of air conditioning has become more widespread (Tor

2011, 12). Reservoir capacities were obtained from Altınbilek (1997, 316-317).

The revised model determines optimal flows to each node during both seasons, as well as the amount of water that should be stored between seasons in each dam. Most of the modifications I made to the objective function and the constraints can be found in

Küçükmehmetoğlu (2009, 3075-3077). The reader is advised to refer to that paper and this paper’s source code for more detail (see note 16 for information on source code access).

Beyond the modifications described in Küçükmehmetoğlu’s 2009 paper, this upgrade required further revision to the derived agricultural demand curve in equation 3, which was not part of Küçükmehmetoğlu’s analysis, and the value of energy generated by the dams in each season. Since I have little information about the relative value of seasonal crops, I model benefits of water directed to agricultural uses in one season (season t) as:

! ! !!! ( !)×(!"!"×10 ) !!!! (5) − (!"!"×!"#$%×!"!!) !"#$%&'(&#!'!!"#$%!!! ! + 1

In this equation, p1 = .23 and p2 = .77. These ratios are from Küçükmehmetoğlu (2009, 3097).

This equation scales the agricultural demand curve for each half of the year by assuming that the

24 “winter” version of a district is 23% as large as the “whole year” version of the district and that the “summer” version of the district is 77% as large as the “whole year” version. I also model the value of the energy generated during each season using:

(6) !"#!×!"!× (!"!"#) !"##$%!!"#$%!! !"##$%!!"#$%!!

Here, EPR1 = $115.82/MWh and EPR2 = $118.11/MWh. These numbers are based on my estimations from the TEİAŞ report. I assume that the price of energy in either season is not significantly affected by changes in hydroelectric output in the GAP region. While I only model two seasons, the model could be expanded to cover more seasons. For example, if a drought year were forecast to follow a rainy year, the model could be used to determine optimal water use and reservoir storage best mitigate the effects of the drought. I expect the benefit of dams’ storage capacity to increase if the model allowed dams to retain water across a number of years.

A final modification corrects for the growth of urban nodes when the simulation is run for future years. Table 3 presents the size of each urban node in 2009 well as population growth rates. The populations of urban nodes are scaled up for future years according to these growth rates.

Limitations and Caveats

The model presented here has a number of limitations, involving both the structure of the model and the data used. Groundwater withdrawals are not incorporated into the model. Since I do not have enough information on the volume of groundwater withdrawals, I assume that any groundwater withdrawals simply lower the water table and lead to less water in the rivers. I do not account for loss of water during conveyance to demand nodes. It can be assumed that inefficiencies in conveyance will further lower the benefits of irrigated agriculture. DSİ (2011) estimates that open canal systems lose 10% of their water on the way to farms, though closed

25 systems lose less than that. The model assumes an identical agricultural demand curve across all agricultural nodes, scaled only based on the size of the node. Additionally, the value of water for urban use is assumed to be the same at each urban node. A more detailed model should assign a unique demand curve to each node. The model excludes benefits of the project outside of agriculture, electric production, and domestic use, such as jobs created, utility of shoppers who can now buy local vegetables, and increased credit supply in the region due to investment of profits. Similarly, the only costs measured are the costs of transporting the water to its destination. Other costs, such as environmental damages caused by water use, are not subtracted from the total benefit of the water. For a study that does try to incorporate these benefits and costs for the Atatürk Dam, see Tortajada (2000).

The model would benefit from more reliable data. For example, changes in energy prices will have a big effect on the outcome of the model. More information is needed to predict how energy prices will behave over the rest of the century. Decreases to TFj are determined according to the simple model in the previous section, but results of a more detailed model should be used.

Understanding these caveats, I recognize that the goal of this study is to establish a framework for measuring benefits and arrive at broad estimates rather than reach definite conclusions.

Results of the Benefit Model

Tables 8a-8d show the results of running the simulation using the objective function specified in equation 1 and modified in equation 3. The simulation assumes all planned components of the project are constructed. Urban nodes are fixed at 2009 populations. This scenario serves as a baseline from which to examine and modify the model. Table 8a shows the amount of water allocated to each irrigated area, as well as the benefit generated by that allocation. Table 8b shows volumes of water directed to each urban center. Some of the urban

26 centers that are far from the water source draw the minimum water requirement and lose money due to transportation costs. Table 8c shows the flows through each dam and the value of the electricity generated by those flows. In table 8d, total yearly benefit for this scenario is estimated at $4.51 billion. About 90% of yearly benefits are generated by energy component of the project.

The agricultural component generates only 8% of total benefits but is a large component of the part of the project that remains to be constructed. Urban use generates only 2% of revenues so it is not a focus of this investigation.

In order to check the results of the model to make sure they are reasonable, I compare them to the benefit estimates for the completed project presented in the Master Plan. Estimates for the completed project presented in the Master Plan should be in the same range as estimates obtained from this paper’s model when the model represents a similar situation. The Master Plan assumed that a certain amount of water would be directed to each irrigated node based on cropping patters there, regardless of transportation costs. To mimic this assumption, I temporarily alter the model to set a minimum requirement of 11,000 mcm at each irrigated node.

This generates a gross benefit from agriculture of $1.3 billion and a gross benefit from energy of

$3.3 billion (see table 9). The Master Plan estimates a gross benefit of $2.3 billion from agriculture (vol. 3, tables A.39-A.42) and $3.4 billion from energy (vol. 3, page B-22).18 This shows that the model’s estimate of benefits from electricity yields results similar to the Master

Plan. Agricultural estimates from the model are somewhat lower than estimates from the Master

Plan. It is possible that the values of the crops assumed by the Master Plan exceed actual crop values as observed by Tsur.

Table 10 provides an estimate of the benefits of the project as the project stood in 2009. I set the size of each irrigated area to the portion of the area that was completed as of 2009. (These

27 sizes are listed under the “Completed 2009” column in table 2.) I include only the dams that were completed by 2009 (listed in table 1 under the “Finished in 2009” column). The yearly benefit as determined by the model was $3.73 billion. Figure 9 shows annual benefits from the components completed by 2009 over the next 60 years, assuming that flows on the Tigris and Euphrates decrease as projected in figures 6 and 7. A 60-year timeframe is used because many dams in

Turkey have an estimated lifespan of around 50 years, although building dams in series prolongs the lifespan of downstream dams by decreasing the sedimentation rate (Berkun 2010, 693).

Population of urban areas is increased each year according to growth rates in table 3. The present value of the revenue stream in figure 9 is $63.12 billion. Since only $21 billion has been spent so far (DSİ 2009a, 60), benefits from the project on the ground still vastly exceed costs even if flows on the Tigris and Euphrates decrease. The project as completed thus far therefore appears to be a worthwhile investment.

Table 11 shows the value of the Ilısu/Cizre project under a number of different scenarios.

I calculate the value of building just Ilısu, building Ilısu and Cizre, and completing the Cizre irrigation in addition to both dams. Since the irrigation is dependent on Cizre and Cizre is dependent on Ilısu, the components of the project cannot be grouped any other way. Because estimates depend on whether the components are added to the project as it stood in 2009 or added to the full development scenario, I present both of these alternatives. I also present a scenario in which all of the planned irrigation is completed, but the remaining upstream dams on the Tigris are not completed. I present results for both the intertemporal model and for the basic model (only one period each year). I present two sets of intertemporal results: in one set I assume identical energy prices across both seasons, and in the other set I assume that energy prices are higher during the summer months.

28 The model shows that the annual benefit of the Ilısu/Cizre project and accompanying irrigation is about $400 million annually, depending on how the benefits are modeled. When the project is added to the infrastructure currently on the ground, it generates $444 million annually.

When the project is added to an otherwise developed basin, it generates $387 million annually.

The value of the project is lower under the full development scenario because irrigated agriculture upstream reduces the amount of water flowing through the dams.

Using the intertemporal model instead of the basic model does not alter the outcome of the simulation significantly unless seasonal energy demand is incorporated. Without the

Ilısu/Cizre project, upstream dams store more water from the winter to release in summer (see table 12). The intertemporal model does value the project slightly higher (by $10 million) than does the regular model if all planned irrigation is completed but the remaining upstream dams are not completed. In this case, Ilısu and Cizre are needed to store irrigation water for use during the summer season.

The intertemporal model that incorporates seasonal energy demand places a much lower value on the Ilısu/Cizre project than the regular model does ($234 million when added to the project on the ground). Since this result is very sensitive to the seasonal difference in energy prices, it should be treated cautiously. This result is obtained because the upstream dams do not have enough storage capacity to release the optimal amount of water during the more valuable but drier summer period, and the full energy potential of the dams is not realized. Table 12 shows that when power is more valuable during the summer than during the winter, all of the dams fill to capacity in the wet winter season to store water for release during the dry summer season. This makes upstream storage capacity very valuable, since the water that those dams store can be released over the summer to generate electricity at higher prices at a number of

29 dams. However, downstream dams become less valuable. In the absence of sufficient storage capacity to run enough of the total flow through the downstream dams during the summer, the value of the electricity generated by the dams decreases. Additionally, the water stored in downstream dams cannot be used downriver to generate power. Because Ilısu and Cizre are the most downstream dams on the Tigris system, the Ilısu/Cizre project’s value decreases under the seasonal energy demand model. (This only accounts for benefits to Turkey — if Iraq’s benefits were included, the value of the dams would increase.)

To see how the predicted decrease in river flows could affect the benefits of the project, I look at how the present value of the Ilısu/Cizre project, calculated in the context of full development of the rest of the basin, changes when runoff decreases as predicted in figure 7.

Table 11 shows that the yearly benefit in this scenario is $387 million, assuming no decrease in river runoff. The present value of this benefit over 60 years is $7.68 billion. Figure 10 shows that if the present value of the project from 2015 to 2075 is considered and decreases in river runoff are assumed, the present value decreases to $4.15 billion. As discussed in the background section,

Ilısu is expected to cost about $2.54 billion. There is no estimate available for the cost of Cizre or the associated irrigation, but assuming that Cizre costs $1 billion and irrigation costs $9,000 per hectare (the average cost of a hydropower dam and irrigated agriculture, as explained in the background section), the total cost of the project would be about $4.17 billion. While benefits assuming no decreases in river runoff are well above estimated costs, decreases in river runoff push benefits close to estimated costs. When other downsides are included, such as environmental costs and the social costs of population displacement, costs are likely to exceed benefits.

A number of estimates of the present value of completing the irrigation component are presented in table 13. Not accounting for opportunity costs of foregoing rainfed agriculture, the

30 value of the irrigated agriculture is around $4 billion. Assuming that all irrigated agriculture replaces rainfed agriculture, benefits drop to just over $1 billion. These benefits assume no decrease in river flows. Since not all irrigated agriculture replaces rainfed agriculture, the benefit from agriculture is probably closer to $4 billion. However, assuming a cost of $9,000 per hectare, installing the remainder of the irrigated agriculture will cost over $8 billion. Rough estimates of the benefits of irrigated agriculture therefore fall below estimated costs.

CONCLUSIONS

This paper has demonstrated a novel technique for evaluating the benefits of Turkey’s

Southeast Anatolia Project. While better data must be gathered and modeling techniques improved before precise benefit estimates are obtained, the rough estimates presented here do lead to a few conclusions. First, the estimated benefits of the components of the project completed to date vastly exceed costs, even considering reductions in river flow due to climate change. I estimated the present value of the project at $63 billion, but only $21 billion has been spent. This suggests that the project as constructed thus far was a very good investment. Since benefits were over three times greater than costs, this conclusion is likely to hold even if the model or data are improved.

Estimates of the benefit of the Ilısu/Cizre project show that projected benefits of that component are close to costs. When river flow is reduced according to climate change predictions, the present value of the project is estimated at just over $4 billion, roughly equal to the estimated construction costs. Changes to the model or data, especially an increase in energy prices or predicted runoff, may increase benefit estimates. However, many costs are not accounted for, including the planned displacement of residents (Ilısu Consortium 2006, 5 and

Hasankeyf Girisimi 2009, 6), submerging of culturally important sites (Güsten 2011), and

31 environmental damages such as erosion of the riverbank and threats to endangered species (Ilısu

Environment Group 2005, 4-31, 4-59 – 4-61). The impact on downstream riparians is mixed; while dams save water for much-needed summertime releases and may mitigate droughts, water evaporation and water consumption in irrigation associated with dams reduces the total amount of water flowing downstream. Downstream flows are also severely reduced during dam impoundment (Altınbilek 2004, 29-31; Altınbilek 1997, 323-328).19 The presence of these unquantified costs coupled with the fact that quantified benefits do not exceed quantified costs suggests that planners should reevaluate the viability of the project.

Estimates of the benefit of irrigated agriculture show that benefits from irrigation are also below costs. Even not considering the opportunity costs of foregoing rainfed agriculture, the present value of constructing the remaining irrigation is estimated at about $4 billion, while costs exceed $8 billion. Moreover, the expansion of irrigated agriculture may cause international conflict at the end of the century when rivers are predicted to dry, by reducing water delivered to downstream riparians. Nonetheless, construction of irrigation infrastructure may be beneficial because agricultural components provide perceptible benefits directly to the area’s residents.

Kolars (1994, 68) argues in favor of irrigation, writing that a project that only generated electricity “might benefit the nation but will fail to alleviate the poverty and its attendant unrest which are evident in the southeast.” Planners can think of investment in agriculture as an expenditure required to reap the benefits of profitable hydropower components. The benefits of irrigation may increase as irrigation technology evolves, enabling farmers to extract greater value out of smaller amounts of water. In short, though irrigated agriculture may be beneficial for a variety of reasons, this analysis suggests that the construction costs of irrigated agriculture are not justified by the economic benefits of the water use alone.

32 This report implemented a technique for estimating the benefits of GAP and other large water projects and demonstrated the necessity of incorporating changes in river runoff into benefit estimates. Planners can follow and improve on the steps described here, using more complete data, to reach new estimates for the benefits of uncompleted components of GAP or other water projects. The paper’s results drive home the point that time scale over which the building of a dam realizes its societal benefit is not small compared with the time scale over which climate change could impact the estimate of that benefit. This point has implications for water projects worldwide. Many countries are planning large dams that displace thousands or even millions of residents (Yardley 2007; Barrionuevo 2010). Reevaluating the expected benefits of these projects in light of predicted climate change may help planners make wise decisions.

NOTES 1 All figures in this paper are reported in 2009 dollars. Historical exchange rates between lira and dollars were obtained from Forex-History.net as well as the GAP Master Plan. Dollar amounts were converted to 2009 dollars using a calculator provided by the US Bureau of Labor Statistics. 2 The total installed capacity of Turkey is 42,359 MW (DSİ 2009a, 27). The installed capacity of the Atatürk Dam is 2400 MW (DSİ 2009b, 5). The dam therefore represents 6% of Turkey’s installed capacity. 3 DSİ 2009a, 33 lists electric capacity for all dams in the project currently in operation (5568 MW). Keban Dam is not included in this list since it was built before the project officially started, but since I include Keban in my analysis, I added the electric capacity for Keban into this figure. The capacity of Keban (1330 MW) can be found in DSİ 2009b, 1. 4 DSİ 2009a, 55 lists total area irrigated by the State Hydraulic Works (DSİ) in Turkey (3.05 million hectares). DSİ 2009b, 5 lists planned irrigation in GAP. DSİ 2009b, 8 lists already-completed irrigation. About 1.78 million hectares remain to be completed. This would expand Turkey’s already-irrigated land by 58%. 5 Studies of other hydroelectric projects showed that climate change could, under some scenarios, drive the projects into unviable territory. A study of the proposed Batoka Gorge Dam on the Zambezi River in Zambia found the reductions in rainfall could cause the project to become unprofitable (Harrison and Whittington 2002, 238). Mimikou and Baltas (1998) found that climate change raised the risk that the volume of the Polyfyto reservoir on the Aliakmon River in Greece would fall below levels required for hydropower production unless the size of the reservoir were expanded. (See Schaefli et. al. 2007 for a more detailed literature review.) 6 The Master Plan derives its estimates by assigning a given amount of water to each district based on the irrigation requirements of crops planners have decided will be planted there, and assumes a certain amount of water will travel through each dam. The method used in the Master Plan does not permit easy extrapolation of benefits generated from individual components of the project. 7 For further background, the reader is advised to refer to International Journal of Water Resources Development 13.4 (1997), an issue of the journal devoted to GAP.

33

8 A number of smaller dams were listed in a 2001 report (GAP 2001) but not included in the Master Plan or this study. These dams have not entered the “preliminary study” phase and little information is available about them. The capacity of the plants planned in the report but not included in this paper comprises only 2% of total planned capacity (GAP 2001, 4). 9 This number was obtained by adding the planned irrigated area for each location listed on DSİ 2009b, 5. The number listed for the “total” irrigated area in the Euphrates basin on that page is not the sum of all of the areas listed in the Euphrates column. This number is assumed to be a mistake and its correct value is computed by totaling the numbers in the Euphrates column. I arrive at 2.07 million hectares after adding the recalculated total for the Euphrates to the total for the Tigris (which was tabulated correctly). 10 Maurer requests the following acknowledgement be printed in its entirety alongside any use of the downscaled GCM predictions: “Global climate model output, from the World Climate Research Programme's (WCRP's) Coupled Model Intercomparison Project phase 3 (CMIP3) multi-model dataset (Meehl et al., 2007), were obtained from www.engr.scu.edu/ ~emaurer/global_data/. These data were downscaled as described by Maurer et al. (2009) using the bias- correction/spatial downscaling method (Wood et al., 2004) to a 0.5 degree grid, based on the 1950- 1999 gridded observations of Adam and Lettenmaier (2003).” 11 Kavvas et. al. (2010) derive a correlation between historical precipitation and observed river flow using a more detailed model that takes this information into account. The authors do not utilize their model to predict future river flow and do not present most of their data. The model in development by the Turkish Water Foundation will be the most comprehensive study yet completed. Work completed to date is available upon request from the İstanbul Municipality. 12 Referenced in Küçükmehmetoğlu 2002, page 76. Küçükmehmetoğlu (page 65) and Vaux and Howitt (page 786) cite Samuelson (1952) as a foundational example of computing the value of a good across a number of interconnected regions using a spatial equilibrium model. 13 I also assume that urban nodes draw directly from the river or from the water table around the river in the absence of the reservoir. For example, in Figure 1, Batman and Siirt draw from the not-yet-completed Ilısu, and Şırnak draws from Cizre. Batman’s water needs are supplied primarily by the Zilek springs (DSİ 2009a, 68), Siirt draws from the Kezer River (a Tigris tributrary), and Şırnak is supplied by Mijin springs (67). These withdrawals would all decrease the water behind the dams they “draw” from in the diagram, but the cities are not dependent on the dams’ construction for their water supply. 14 This map can be found on page 39 of Fisch 2011a or online at http://www.columbia.edu/~jmf2143/MapJPEG.jpg. 15 The basin was mostly undeveloped during the period presented in the Master Plan. Analysis yields a correlation between observed data and flow at Cizre and Birecik in the absence of regulation by dams. While actual values at Cizre and Birecik will be different from predicted values, the predicted values provide an estimate for water availability in the system as a whole. 16 Applying the statistical model to the GFDL and other scenarios did predict some negative river flows in the last period. It is likely that these results were obtained because temperatures in the Bühtan Deresi region became high enough that the predictions from the model are driven into negative territory. Predicted negative values are set to 0. I assume that the negative correlation between Bühtan Deresi summer temperature and Tigris runoff holds as long as there is still water in tributary streams. However, after all of the water disappears from the tributary due to increased water transport out of the region, this correlation ceases to have an effect. 17 Equations 1 and 2 are modified versions of equations found on pages 97-102 of Küçükmehmetoğlu (2002). All models described in this section were programmed in MatLab and solved using the fmincon function. Source code from is available from Mudd Library, Princeton University or online at http://www.columbia.edu/~jmf2143/Code/. 18 Values are converted to 2009 dollars. Benefits for electricity are determined using a price of $117/MWh. The Master Plan presents a number of alternative scenarios; this estimate was based on “Alternative B,” which emphasizes electricity generation and most closely resembles the project as it has been constructed. For more information on how this estimate was reached, see Fisch 2011a, pages 51-55. 19 For a more complete exposition of costs and benefits of the project that were not quantified in the model, see Fisch 2011a, 105-116, and Fisch 2011b, 3-11.

34 TABLES

Table 1:

Node name Energy generation, Inflows, Evaporation, Capacity, Finished MWh/mcm mcm/yr mcm/yr mcm in 2009? Keban 377.2 19,015 900 17,000 Yes Karakaya 365.6 2,454 400 56,000 Yes Atatürk 361.8 3,067 1,600 19,300 Yes Birecik 123.8 920 109.67 700 Yes Karkamış 49.1 613.01 54.84 150 Yes Çamgazi 0 4,601.01 58.76 56.2 Yes Kralkızı 261.5 2,775 77.85 1,712 Yes Dicle 173.6 555 32.21 255 Yes Ilısu 167 2,775 577.72 7,460 No Cizre 51.8 0 37.21 222 No Devegeçidi 0 1,850 40.27 195 Yes Silvan 231.7 6,475 242.95 4,138 No Kayser 97.3 925 29.53 527 No Batman 165.5 925 72.48 737 Yes Garzan 170.3 1,850 25.50 435 No Silopi inflow — 370 — — —

Table 2:

Node name Also includes Planned Completed Dist. from Op. cost 2009 (ha) 2009 (ha) supply (km) ($/ha) Adiyaman Kahta Adiyaman Çamgazi, 81,130 8,788 4.69 606.58 Samsat Siverek-Hilvan Hacıhıdır, Derik 156,373 3,940 85.94 320.94 Bozova Pumping 45,488 9,749 17.19 320.94 Suruç Plain Yaylak Baziki 113,136 18,322 20.31 320.94 Şanlıurfa Harran 151,419 145,234 43.75 320.94 Mardin-Ceylanpınar Ceylanpınar YAS 336,469 9,000 101.56 505.66 Irriagation Adiyaman-Göksu- XX region works 71,598 4,939 43.75 895.51 Araban Kayacık-Kemlim 23,088 5,100 20.31 414.46 Şanlıurfa tunnel area Akçakale, Yukari Harran, 375,949 24,610 58 414.46 irrigation XV reg. works Gaziantep irrigation Hancağız irrigation 120,976 6,945 16 895.51 Dicle right bank Çınar-Göksu, Devegeçidi, 54,279 18,092 7.81 414.46 X reg. works Dicle pumped Kralkızı irrigation 75,880 6,692 15.63 414.46 Nusaybin-Cizre-İdil 89,000 8,600 28.13 380.63 Batman-Silvan 245,372 8,790 35.94 414.46 Project Batman Project 37,351 1,381 18.75 380.63 Garzan irrigation 60,000 3,973 10.94 380.63 Silopi plains 32,000 2,740 20.31 380.63

35

Table 3:

Urban center Population in 2009 Growth rt., 1990-2000 (%) Dist. from supply (km.) Elazığ (just city) 323,420 2.845 14.06 Adiyaman 588,475 1.998 10.60 Gaziantep 1,653,670 2.405 49.97 Mardin 737,852 2.334 165.63 Şanlıurfa 1,613,737 3.655 42.40 Batman 497,998 2.83 12.37 Diyarbakır 1,515,011 2.173 0.71 Siirt 303,622 0.798 7.07 Şırnak 430,424 2.986 28.27

Table 4:

Region Coefficient Standard Error Lower Euphrates winter precipitation*** .0535074 .0125669 Murat spring precipitation*** .043747 .0136432 Lower Euphrates spring temperature** -7.629347 2.940902 Karasu spring temperature*** -6.684933 2.029019 Karasu fall temperature** 5.478491 2.383286 Murat winter temperature** -3.815548 1.456539 Murat spring temperature*** 6.690687 2.04942 Upper Murat winter temperature*** 3.825566 1.374076 Constant*** 64.68695 23.37045 R2 = 0.6893 Prob > F = 0.0000 *Significant at 90% threshold ** Significant at 95% threshold *** Significant at 99% threshold

Table 5:

Region Coefficient Standard Error Lower Tigris winter precipitation** .0179915 .0079199 Upper Tigris winter precipitation*** .0317605 .0106753 Bühtan deresi spring precipitation*** .0370117 .006833 Bühtan deresi summer temperature** -1.312198 .5064916 Constant* 17.94308 10.17039 R2 = 0.8047 Prob > F = 0.0000 *Significant at 90% threshold ** Significant at 95% threshold *** Significant at 99% threshold

Table 6:

GFDL A2 GFDL A1b GFDL B1 CNRM HadCM3 CCCMA GISS-ER Year Bcm Pct. Bcm Pct. Bcm Pct. Bcm Pct. Bcm Pct. Bcm Pct. Bcm Pct. 2015- 26.9 87% 24.8 80% 25.9 84% 24.8 81% 25.0 81% 27.5 89% 23.2 75% 2035 2035- 22.6 73% 19.2 62% 26.0 85% 24.3 79% 25.9 84% 28.2 92% 25.0 81% 2055 2055- 18.0 58% 18.0 58% 22.9 74% 18.7 61% 23.5 76% 23.3 76% 19.8 64% 2075 2075- 12.1 39% 13.0 42% 18.0 59% 11.0 36% 19.2 63% 19.4 63% 10.6 34% 2099

36 Table 7:

GFDL A2 GFDL A1b GFDL B1 CNRM HadCM3 CCCMA GISS-ER Year Bcm Pct. Bcm Pct. Bcm Pct. Bcm Pct. Bcm Pct. Bcm Pct. Bcm Pct. 2015- 12.1 72% 11.4 68% 12.4 74% 12.1 72% 11.2 67% 11.4 68% 10.2 61% 2035 2035- 7.6 45% 5.9 35% 11.8 70% 10.7 64% 8.9 53% 10.9 65% 10.7 64% 2055 2055- 5.3 32% 4.0 24% 9.0 54% 3.4 20% 7.6 45% 7.0 42% 6.4 38% 2075 2075- 1.7 10% 2.9 17% 5.6 33% 1.7 10% 2.4 14% 3.7 22% 1.5 9% 2099

Table 8a:

Name Allocation (mcm/year) Value Adiyaman Kahta 838.71 $ 41.85 million Siverek-Hilvan 167.32 $ 6.19 million Bozova Pumping 196.58 $ 11.36 million Suruç Plain 239.74 $ 18.62 million Şanlıurfa Harran 162.02 $ 13.23 million Mardin-Ceylanpınar 360.02 $ 7.37 million Adiyaman-Göksu-Araban 201.90 $ 8.64 million Kayacık-Kemlim 156.76 $ 6.69 million Şanlıurfa tunnel 402.27 $ 26.79 million Gaziantep irrigation 1013.85 $ 42.98 million Dicle right bank 1845.49 $ 49.64 million Dicle pumped 1284.90 $ 37.36 million Nusaybin-Cizre-İdil 929.14 $ 23.11 million Batman-Silvan Project 262.55 $ 23.62 million Batman Project 132.50 $ 8.21 million Garzan irrigation 329.28 $ 19.07 million Silopi plains 370.00 $ 11.51 million

Table 8b: Urban center Allocation (mcm/year) Value/Cost Elazığ 54.33 $ 7.08 million Adiyaman 98.86 $ 14.99 million Batman 83.66 $ 11.77 million Diyarbakır 254.52 $ 54.15 million Gaziantep 53.74 $ -4.92 million Mardin 23.98 $-19.33 million Şanlıurfa 52.45 $ -2.35 million Siirt 51.01 $ 8.85 million Şırnak 72.31 $ 3.07 million

37 Table 8c:

From To Flow (mcm/year) Value Keban Karakaya 18082.40 $798.02 million Karakaya Atatürk 20158.13 $862.27 million Atatürk Birecik 24405.44 $1033.10 million Birecik Karakmış 23960.06 $347.05 million Karkamış Syria 24559.77 $141.09 million Çamgazi Atatürk 4542.25 — Kralkızı Dicle 2697.15 $ 82.52 million Dicle Ilısu 0 — Dicle Devegeçidi 2965.42 $ 60.23 million Ilısu Cizre 15141.94 $295.86 million Cizre Iraq 14349.50 $ 86.97 million Devegeçidi Ilısu 2929.66 — Silvan Batman 6864.97 $186.10 million Kayser Silvan 895.47 $ 10.19 million Kayser Batman 0 — Batman Ilısu 7585.00 $146.87 million Garzan Ilısu 1495.22 $ 29.79 million Silopi inflow Iraq 0 —

Table 8d:

Total Agriculture ($ million) Urban ($ million) Energy $4.51 billion Gross Net Gross Net $4.08 billion $573.83 $356.25 $161.74 $73.33

Table 9:

Total Agriculture ($ million) Urban ($ million) Energy $3.43 billion Gross Net Gross Net $3.27 billion $1267.73 $89.94 $161.74 $73.33

Table 10:

Total Agriculture ($ million) Urban ($ million) Energy $3.73 billion Gross Net Gross Net $3.59 billion $92.79 $59.48 $161.74 $73.33

38 Table 11:

Scenario Ilısu only Ilısu and Cizre Ilısu, Cizre, & irrigation On the ground in 2009 $328 million $429 million $444 million 2009, intertemporal $328 million $429 million $444 million 2009, it & seasonal energy $166 million $217 million $234 million Full development $283 million $372 million $387 million Full dev., intertemporal $283 million $372 million $387 million Full dev., it & seasonal en. $137 million $180 million $196 million Only irrigation is finished $275 million $361 million $376 million Only irrigation, intertemp. $282 million $371 million $386 million

Table 12:

2009 2009 + 2009 + Iısu/Cizre, Full dev, no Full dev, with Dam name: Iısu/Cizre seasonal energy Iısu/Cizre Iısu/Cizre Kralkızı 1456.92 893.44 1712.00 996.91 755.69 Dicle 237.98 18.92 255.00 253.56 252.81 Ilısu — 2204.24 7460.00 — 895.08 Cizre — 220.33 222.00 — 0.08 Devegeçidi 161.89 194.67 195.00 194.37 194.98 Silvan — — — 1076.20 1184.62 Kayser — — — 526.32 165.17 Batman 292.05 0.13 737.00 622.48 401.75 Garzan — — — 146.33 434.96 Total storage 2148.84 3531.72 10581.00 3816.16 4285.14

Table 13: Scenario irrigation is added to Annual val. of added irrigation Present value On the ground in 2009 $204 million $4.055 billion 2009, including opportunity cost $59.7 million $1.187 billion Full development $201.4 million $4.003 billion Full dev., including op. cost $52.1 million $1.036 billion

39 TABLE CAPTIONS

Table 1: Data for supply nodes. Dam names are obtained from DSİ (2009b, 6). The list of dams completed in 2009 is obtained from DSİ (2009b, 9). Energy generation, total inflows, and reservoir evaporation are obtained from Küçükmehmetoğlu (2002, 105). Reservoir capacity is obtained from Altınbilek (1997, 316-317). Capacity for Çamgazi is from Tosun et. al. (2007, 157).

Table 2: Data for agricultural demand nodes. Node names are obtained from DSİ (2009b, 5-9), the GAP Master Plan (1989, vol. 2, fig. 5.2-5.3), and Altınbilek (1997, 316-317). The sizes of the nodes in 2009 are from DSİ (2009b, 5). Currently completed sizes are from DSİ (2009b, 9). The distance from each node to the water supply is from Küçükmehmetoğlu (2002, 109) and estimated from Google Maps. The opportunity cost of irrigating each node (the value of the crops that could be grown at the node from rainfed agriculture) is calculated in Fisch (2011, 97).

Table 3: Data for urban demand nodes. The growth rate of the nodes is from TurkStat (2009, 33). Populations are from TurkStat (2009,43). Populations refer to provinces, except for Elazığ, for which only the city population is listed. (The Euphrates does not service the entire province). The population of Elazığ is from CityPopulation.de (2011). The distance from each node to the water supply is from Küçükmehmetoğlu (2002, 109).

Table 4: A model for Euphrates runoff involving 8 variables, with an R2 of 0.69.

Table 5: A model for Tigris runoff involving four variables, with an R2 of .80.

Table 6: Predicted Euphrates runoff for 2015-2099. We include the A2, A1b, and B1 scenarios for the GFDL model. For the other climate models, we show only the A2 scenario.

Table 7: Predicted Tigris runoff for 2015-2099, using a number of climae models.

Table 8: Water allocated to each irrigated (8a) and urban area (8b), as well as water flowing between dams (8c) under the full development scenario. Total yearly benefits from the project are shown in 8d. Assuming 2009 populations.

Table 9: Yearly benefits from the full development scenario, assuming each agricultural area receives at least 11,000 m3 of water per hectare. We make this assumption in order to compare the results of the model to estimates in the Master Plan.

Table 10: Yearly benefits from the project as completed in 2009.

Table 11: Yearly returns of the Ilısu Dam by itself, both the Ilısu and Cizre dams together, and the dams plus dependent irrigation, under various scenarios and models.

40 Table 12: Quantities of water stored (in mcm) in each dam on the Tigris under the intertemporal model, in various scenarios. Note that for some scenarios, more than one set of stored quantities may produce an optimal solution.

Table 13: Yearly returns of completing the irrigation component of the project under various scenarios and models, and present values of these returns over a 60-year timeframe.

41 Figure 1: Schematic of dams and demand nodes in full development scenario Sources: Küçükmehmetoğlu and Guldmann (2004) pg. 786, DSİ (2009b) pg. 5-9, GAP Master Plan (1989) vol. 2, fig. 5.2-5.3, Altınbilek (1997), pg. 316-317 Euphrates Tigris Irrigation Keban Kralkızı Kayser Urban use Batman- Silvan Elazığ Dicle Silvan Dam

Hydropower Devegeçidi Garzan Karakaya Diyarbakır Batman

Çamgazi Dicle right bank Batman Adiyaman Garzan Siverek/Hilvan Atatürk Adiyaman Batman Kahta Siirt Bozova Ilısu

Adiyaman Mardin Göksu Araban Dicle Birecik Şanlıurfa Pumped Tunnel Şanlıurfa Mardin Ceylanpınar Gaziantep Cizre Suruç Şırnak Karkamış Şanlıurfa Kayacık Harran Silopi inflow Kemlim Gaziantep irrigation Nusaybin Cizre İdil

Syria Iraq Silopi Plains

Art from hinkeltje.com and fundraw.com FIGURES

Figure 2:

Figure 3:

Tigris runoff, 1946-1983 101520253035Runoff1950196019701980YearMeasuredPredicted at Cizreby model ( bcm ) 10

Tigris runoff, 1946-1983 35 30 25 20 15 Runoff at Cizre ( bcm ) 10

1950 1960 1970 1980 Year

Measured Predicted by model

43

Figure 4:

Euphrates hindcast, 1951-1980 102030405060Runoff1950196019701980YearHindcastActualAverage runoffat of runoffBirecik hindcastactual (bcm) runoff runoff 10

Euphrates hindcast, 1951-1980 60 50 40 30 Runoff at Birecik (bcm) 20 10 1950 1960 1970 1980 Year

Hindcast runoff Actual runoff Average of hindcast runoff Average of actual runoff

Figure 5:

Tigris hindcast, 1951-1983 010203040Runoff1950196019701980YearHindcastActualAverage runoffat of runoffCizre hindcastactual (bcm) runoff runoff 0

Tigris hindcast, 1951-1983 40 30 20 10 Runoff at Cizre (bcm) 0

1950 1960 1970 1980 Year

Hindcast runoff Actual runoff Average of hindcast runoff Average of actual runoff

44 Figure 6:

Figure 7:

Tigris runoff, 2015-2099 (A2, GFDL model) 0102030Runoff20202040206020802100YearPredictedFitted20-year trendlineat averages Cizreby model (bcm) 0

Tigris runoff, 2015-2099 (A2, GFDL model) 30 20 10 Runoff at Cizre (bcm) 0

2020 2040 2060 2080 2100 Year

Predicted by model Fitted trendline 20-year averages

45 Figure 8:

Coefficient Value Standard error ln(β) 4.499355 (β = 89.96) .1405176 α -0.3735532 .0070327 R2 = 0.6908

Figure 9:

Annual value of project on the ground 22.533.54Value2009201920292039204920592069Year ($ billion) 2

Annual value of project on the ground 4 3.5 3 Value billion) ($ 2.5 2

2009 2019 2029 2039 2049 2059 2069 Year

46

Figure 10:

Value of Ilısu/Cizre project 2015-2075 02468Value2015202520352045205520652075Year ($ billions) AnnualPresent returns, value,value withcurrentreduced currentreduced currentreduced flows flowsflows flows flows: flows: $7.68 $4.15 billion billion Annual returns, current flows

Value of Ilısu/Cizre project 2015-2075 8 6 4 Value billions)($ 2 0

2015 2025 2035 2045 2055 2065 2075 Year

Annual returns, current flows Present value, current flows Annual returns, reduced flows Present value, reduced flows

Present value with current flows: $7.68 billion Present value with reduced flows: $4.15 billion

47 FIGURE CAPTIONS

Figure 2: The Euphrates runoff model plotted against the historical data used to create the model.

Figure 3: The Tigris runoff model plotted against the historical data used to create the model.

Figure 4: Hindcast Euphrates runoff using GFDL CM-2.1 data for 1951-1980. Hindcast and actual annual runoffs are shown for two periods.

Figure 5: Hindcast Tigris runoff using GFDL CM-2.1 data for 1951-1983. Hindcast and actual annual runoffs are shown for two periods.

Figure 6: Prediction for Euphrates runoff for 2015-2099, using the model in table 4 along with precipitation and temperature forecasts from GFDL CM-2.1, under the A2 scenario.

Figure 7: Prediction for Tigris runoff for 2015-2099, using the model in table 5 along with precipitation and temperature forecasts from GFDL CM-2.1, under the A2 scenario.

Figure 8: Demand curve for agricultural water, based on Tsur (2004). Tsur derived prices for water at different water constraints in the Harran Plains. We fit an equation of the form Price = βQα.

Figure 9: Annual value of the project as completed thus far, accounting for reductions in river runoff on the Tigris as predicted by the model in table 5. The total present value of the project in 2009 (discounting at a 5% rate over 60 years) was $63.12 billion.

Figure 10: Annual value and total present value of the Ilısu/Cizre component of the project under the full development scenario, from 2015 to 2075. The orange and green lines show the present value and annual returns if flows on the Tigris remain at historical levels, and the red and blue lines show the value if flows decrease according to the predictions of the GFDL model’s A2 scenario.

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