Towards a Better Understanding of the Hydrology of the Tigris- Euphrates System and Its Marshlands

Western Michigan University ScholarWorks at WMU

Master's Theses Graduate College

6-2007

Towards A Better Understanding of the Hydrology of the Tigris- Euphrates System and Its Marshlands

Christopher K. Jones

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Recommended Citation Jones, Christopher K., "Towards A Better Understanding of the Hydrology of the Tigris- Euphrates System and Its Marshlands" (2007). Master's Theses. 3971. https://scholarworks.wmich.edu/masters_theses/3971

This Masters Thesis-Open Access is brought to you for free and open access by the Graduate College at ScholarWorks at WMU. It has been accepted for inclusion in Master's Theses by an authorized administrator of ScholarWorks at WMU. For more information, please contact [email protected]. TOWARDS A BETTER UNDERSTANDING OF THE HYDROLOGY OF THE TIGRIS- EUPHRATES SYSTEM AND ITS MARSHLANDS

Christopher K. Jones

A Thesis, Submitted to the Faculty of The Graduate College in partial fulfillmentof the requirements for the Degree of Master of Science Department of Geosciences

Western Michigan University Kalamazoo, Michigan June 2007 Copyright by Christopher K. Jones 2007 ACKNOWLEDGMENTS

I would like to thank my advisor, Dr. Mohamed Sultan. He guided me through the whole process and would not let me quit. A huge thank you also goes out to Adam

Milewski. He was the huge support behind me the whole time and my best friend. My other committee members, Dr. Duane Hampton, Dr. Alan Kehew and Eugene Yan, also need to be recognized. Their support and faith in me was always appreciated. I would not have been able to complete this paper if it were not for my parents. I thank them both for their continued love and care.

The National Science Foundation through OISE grant 0455896 provided the fundingfor this research. Thank you.

Christopher K. Jones

11 TOWARDS A BETTER UNDERSTANDING OF THE HYDROLOGY OF THE TIGRIS- EUPHRATES SYSTEM AND ITS MARSHLANDS

Christopher K. Jones, M.S.

Western Michigan University, 2007

Rising demands for fresh water supplies are leading to water management practices that are altering natural flow systems world-wide. One of the most devastated of these natural systems is the Tigris-Euphrates watershed that over the past three decades has witnessed the construction of over 60 engineering projects that eliminated seasonal flooding, reduced natural flow and dramatically reduced the areal 2 2 extent (1966: 11000 km ; 2002: 750 km ) of the Mesopotamian Marshes downstream. We constructed a continuous ( 1964 to 1998) catchment-based rainfall runoffmodel 2 for the entire Tigris-Euphrates watershed ( area: one million km ) using the SWAT model to understand the dynamics of the natural flow system, and to investigate the impacts of reduced overall flow and the related LCLUC downstream in the marshes.

Using the calibrated model we calculated the monthly average flow rate (MAFR) and annual flow volume (AFV) of the Tigris and Euphrates into the marshes at a location near Basra city (31°N, 47.5°E). Model results indicate that diminishing flows in both rivers over the decades due to major damming projects have had the most detrimental affects on the Mesopotaminan Marshlands. If current water useage schemes continue to develop to Mashlands will only continue to diminish. TABLE OF CONTENTS

ACKNOWLEDGMENTS ...... n

LIST OF TABLES ...... V

LIST OF FIGURES...... VI

CHAPTER

I. INTRODUCTION...... 1

1.1 Background and Description of Problem ...... 1

1.2 Site Description...... 4

II. MODEL CONSTRUCTION AND DAT A COLLECTION...... 6

2.1 Model Framework...... 6

2.2 Data Collection ...... 7

2.2.1 Digital Elevation - Watershed and Stream Delineation,

and Reservoir Storage...... 9

2.2.2 Climate Datasets - Temporal and Spatial Precipitation

over Watersheds...... 10

2.2.3 Landuse and Soil Type...... 12

2.2.4 Stream Flow Data ...... 15

2.2.5 Temporal Satellite Data ...... 15

2.3 Model Calibration and Validation ...... 17

2.3.1 Snow Pack/MeltParameters ...... 18

lll Table of Contents-continued

CHAPTER

2.3.2 Soil andGround Water Parameters...... 19

2.3.3 Channel Characteristics...... 20

2.3.4 Calibration Criteria and Model Evaluation...... 21

III. DISCUSSION...... 23

3.1 Model Results...... 23

3.2 Impacts on Marshes ...... 24

JV. CONCL USIONS...... 29

4.1 Summary...... 29

4.2 Predictions...... 31

BIB LI OGRAPHY...... 43

IV LIST OF TABLES

1. Dams in the Tigris- Euphrates Watershed...... 32

V LIST OF FIGURES

1. Location and Extent of the Tigris- Euphrates Watershed...... 33

2. Diversion and Drainage Projects...... 34

3. Distribution of Land Use Typesand Climate Data Stations...... 35

4. Distribution of Soil Types and Water Management Projects...... 36

5a. Graphs Comparing Simulated Flow...... 3 7

5b. Calibrated Regression Lines ...... 38

6. Calibration and Validation Results...... 39

7. Simulated Monthly Flow Rates...... 40

8. Variations in the Areal Extent of the Central and Al Hammar Marshlands...... 41

9. Spatial Extent of the Marshlands ...... 42

Vl CHAPTERI

INTRODUCTION

1.1 Background and Description of Problem

Water shortages in the arid parts of the world are affecting the human welfare, economic activity, and political stability of these areas. Faced with overpopulation problems and demand for development of new agricultural lands to support increasing

population, many of the countries of the arid world are adopting aggressive policies to develop new agricultural communities without careful analysis of the environmental and hydrologic impacts of these projects. These problems are exemplified in the

countries of the Middle East. Aggressive water management programsby Iraq and its neighbors have brought drastic modification to the Tigris and Euphrates watershed

6 2 (1.2 x 10 km ; Fig. 1) and the Mesopotamian Marshes (Fig. 1) downstream in southernIraq. The marshlands that extended over an area of 10,000 km2 in the early

2 seventies declined to about 750 km in the summer of 2002 (Jones et al., 2005).

These observed land cover and landuse changes (LCLUC) over the Mesopotamian

Marshes reflect the impacts of large engineering projects in upstream countries

(Turkey, Iran, and Syria) and local engineering projects in Iraq. Examples include the

9 3 Tabaqa dam (1975; storage capacity (SC): 11.7 billion 10 m ) and Tishrine dam

9 3 9 3 (1999; SC: 1.9 10 m ) in Syria, the Ataturk dam (1992; SC: 48.7 10 m ) and Keban

9 3 dam (1975; SC: 31 10 m ) in Turkey (Table 1), and the Karkheh dam (2001; SC: 7.8

9 3 10 m ) in Iran. Examples of local engineering projects that devastated the marshes 2 include the diversion canals that were largely constructed in the late 1980's and early l 990's (Jones et al., 2005) to drain the marshes (e.g., Prosperity River; Aybas Canal, and Dike; Fig. 2a), or to divert surface water in and around the marshlands ( e.g.,

Crown of Battles River [COB]; Loyalty to the Leader Canal [LTL]; Mother of Battles

River [MOB]; Fig. 2a), water that would have flowed under natural conditions into the marshlands. Figure 2a also shows the reduction in marshland area associated with the implementation of local projects and large upstream engineering projects between

1969 and 2002.

Recently (2002 to 2007) there has been a reversal in this devastating trend, with the marshland's area increasing from 745 km2 (2002) to 3983 km2 (2005) (Jones et al.,

2005) largely due to piecemeal interventions by locals and the coalition forces.

Drainage canals ( e.g., COB, LTL, Prosperity River, MOB) were dammed at the inflow point to allow the water to continue flowing as it naturally would into the marshes (Basgall, 2003; IMET, 2004). Figure 2b illustrates how a local engineering project, the MOB in this case, can devastate the marshes in a short time and illustrates how the revival of the marshes is attainable if these projects were reversed. Similarly, it could be demonstrated that engineering projects, in and around the marsh area, had dramatic, but reversible effects on the spatial extent of the marshes.

In this manuscript we show that although the major destruction of the marshes could be related to the local engineering projects in Iraq, it is the reduced flow due to 3 damming in neighboring countries that presents the largest and more permanent threat to the marshes. The local projects were damaging but their devastating impacts could be readily reverted if the named projects could be eliminated. The reduced flow due to damming on the other hand can not be as easily reverted.

We constructed a catchment-based continuous (l 964 to 1998) rainfall-runoff model for the entire watershed and calibrated the model against stream flow data in Turkey and in Iraq using the Soil and Water Assessment Tool (SWAT). The calibrated model was used to characterize the natural flow of the system for the time period

(1964 to 1970) preceding the construction of the major engineering projects and to investigate the hydrologic impacts that resulted from the implementation of the engineering projects inside and outside of Iraq in the following decades (from 1970 to present). Moreover, the calibrated model was used as a predictive tool to investigate the impacts of projected reductions in river flow with the construction of additional dams in neighboring countries. The SW AT model was selected because (1) the model is an open source code giving the user the flexibility formaking modifications as needed, (2) it is a continuous model, allowing rainfall-runoff and groundwater recharge estimates to be made over extended periods of time, (3) Geographic

Information System (GIS) data sets which were generated for the watershed could be readily imported into the model, and (4) the model provides capabilities for futuristic simulations of water management scenarios and agriculturalpractices. 4

1.2. Site Description

The Euphrates and Tigris Rivers originate from the highlands of Turkey, Iran, and

Syria, flow down the Mesopotamian plain, merge near the city of Al- Basrah and distribute greatly near this confluence feeding the Mesopotamian Marshlands (Fig. 1 ).

The Euphrates River originates in Turkey at the foothills of the Taurus Mountains, heads south through Syria into Iraq, into the Mesopotamian Marshlands (Al-Hammar

Marshes) before it reaches its final destination, the Arabian Gulf, via the Shatt-al

Arab (Fig. 1 ). The Tigris River originates in Turkey, near Lake Van ( elevation: 3000 m a. s.1.; Fig. 1), flows south along the base of the Zagros Mountains, across the

Mesopotamian plain, into the Central and Al-Haweizah marshes (Fig. 2a), and finally drains in the Arabian Gulf (Figs 1 and 2a). The highlands of Turkey provide 88-98% of the Euphrates total flow, modest contributions come from the Syrian highlands and only minimal additions occur inside the Iraqi borders (UNEP, 2001). The Tigris

River, on the other hand, receives only 32-50% of its flow from these highlands, whereas the remaining flow comes from numerous tributaries that derive their water from the Zagros Mountains that straddle the Iraq-Iran borders (UNEP, 2001).

Precipitation feeding both rivers is temporally distributed (from November to April), most of which falls as snow, and remains in the snow pack until the spring snow melting season. Because snow melt is the dominant source for the flow in the Tigris and Euphrates rivers, annual peak flows are observed to coincide with seasonal melting of snow in May on both rivers. By the month of October most of the snow on 5 the highlands would have melted and it is in this month that the lowest flow on both rivers is observed. 6 CHAPTER II

MODEL CONSTRUCTION AND DATA COLLECTION

2.1 Model Framework

The model for the Tigris-Euphrates watershed was constructed through the SWAT framework to simulate the hydrologic processes by using its physically-based formulations. SW AT is a semi-distributed continuous watershed simulator that computes long-term water flow using daily time steps at high levels of spatial detail over large basins. Major model components include: flow generation, stream routing, pond/reservoir routing, erosion/sedimentation, plant growth, nutrients, pesticides, and land management. The model has been validated for several watersheds (Arnold et al.,

1998). In SW AT, a large-scale watershed can be divided into a number of subbasins, which are further subdivided into small groups called hydrologic response units

(HRUs) that possess unique land cover, soil, and management attributes. The water balance of each HRU is calculated through four water storage bodies: snow, soil profile, shallow aquifer, and deep aquifer. Flows generated from each HRU in a subbasin are then summed and routed through channels, ponds and/or reservoirs to the outlets of the watershed. The detailed descriptions of formulation used m modeling hydrologic processes in HRUs/subbasins and routing can be found m

Arnold et al., 1998; DiLuzio et al., 2004; Neitsch et al., 2005; and Srinivasan et al.,

1998. In this study, a nonlinear areal depletion curve (Anderson et al., 1976) was used to estimate seasonal growth and recession of snow pack and to determine snowmelt. 7 A modified Soil Conservation Service (SCS) curve number method, (SCS, 1972) adjusted according to soil moisture conditions, was used to calculate direct surface runoff. The Penman-Monteith method (Monteith, 1981) was used to estimate evaporation on bare soils and evapotranspiration (ET) on vegetated areas. The variable storage routing method (a variation of the kinematic wave model) developed by Williams, 1969 was used for channel routing. The Arcview GIS interface for

SWAT2005, developed by DiLuzio et al., 2001, was utilized to provide the inputs for model construction.

2.2 Data Collection

A major component in model development was the construction of co-registered digital datasets for the entire watershed that serve as inputs to our hydrologic model.

A large number of digital data sets were generated for modeling and calibration purposes: (1) a digital terrain mosaic (approximately 85-m resolution) constructed from 324 Digital Terrain Elevation Data (DTED) data cells (NIMA, 1990; NIMA,

1991a; NIMA, 1991b; NIMA, 1991c), each covering 1° latitude by 1° longitude was

constructed for stream delineation, and for computing reservoir storage-related parameters; (2) temporal and spatial distribution of precipitation, temperature, relative humidity, wind speed, and solar radiation collected from 36 stations over time period

of 1964 to 1998 to extract monthly precipitation and/or snow accumulation

throughout the modeling period, to constrain evapo-transpiration, surface runoff,

ground water contributions; (3) a mosaic for landuse types generated from various 8 landuse maps for Turkey, Iraq, Iran, and Syria (CIA, 1993a; 1993b; 1993c; I 993d) and a mosaic for reclassified (in SCS soil hydrologic groups)soil types extracted from the United States Department of Agriculture (USDA) global soil data set (FAO

UNESCO, 2005); the generated landuse and soil type mosaics were used to construct digital Curve Number (CN) maps for soil horizons (typically 3-7 horizons for each soil type) across the entire watershed; (4) Stream flow and relevant dam storage parameters (e.g., Principle and maximum storage and holding capacities) on the

Euphrates ( 4 gauges) and Tigris (7 gauges) in Iraq and Turkey for running and calibrating the model; (5) Two mosaics of the Landsat Thematic Mapper (Landsat

TM) scenes, one generated from scenes acquired in the late 1980s (Landsat Geocover

Dataset 1990; spatial resolution: 30 m; Tucker et al., 2004) and the other from scenes acquired in the late 1990s (Landsat GeoCover Orthorectified Thematic Mapper

Dataset 2000; spatial resolution: 15 m; Tucker et al., 2004) that were used to refine the DTED derived stream network and the landuse maps, derive dam-related storage parameters, and as a base map (Landsat GeoCover Dataset 2000); (6) Temporal satellite mosaics (twenty-nine mosaics) over the marshes to monitor the disintegration

(1969 to 2002) and restoration (year 2003 to 2006) of the marshes: Five false color

(bands 2,4, 7) Landsat TM mosaics (1984, 1990, 1992, 1998, 2002; spatial resolution:

28.5 m); five false color (bands 1,3,2) Landsat Multi Spectral Scanner (Landsat MSS) mosaics (two in 1973, three in 1977; spatial resolution: 70 m); one mosaic of

CORONA images (1969; spatial resolution: 1 Om); Sixteen Moderate Resolution

Imaging Spectroradiometer (MODIS) false color images (bands 1,2,3) from 1998- 9 2005 one in May and one in December of each year. All digital products were co registered using the TM global GeoCover dataset as a reference mosaic and brought to the same projection (UTM WGS84).

2.2.1 Digital Elevation - Watershed and Stream Delineation, and Reservoir Storage

The 3-arc-second (approximately 85-m) DTED mosaic covering the entire Tigris

Euphrates watershed was used to delineate the watershed. Due to computational restrictions, the dataset was reduced to a 540 m spatial resolution. In order to improve subbasin delineation, a stream network digitized from the Landsat TM mosaics with the resolution 15-30 m. was used as the reference surface water drainage system for the entire watershed by using a DEM "burning" method (DiLuzio et al., 2004). This method incorporates the surface drainage features into DEM data providing a more accurate delineation for the watershed, especially in areas experiencing gentle slopes

(e.g., < lcm/km in Mesopotamian plain) (Kolars and Mitchell, 1991).The subbasin delineation was performed by using the Topographic Parameterization (TOPAZ) program (Garbrecht and Martz, 1995) based on the DEM data and superimposed drainage features. The elevation of each cell in the watershed was compared to that of the neighboring cells, and the flow direction was assumed to be toward the cell with lowest elevation. The elevation data were smoothed to avoid generating false dams and pits by averaging elevations within individual cells. All cells draining into one outlet belong to the same watershed. As a result of basin delineation, the watershed is

2 subdivided into 149 subbasins, with sizes ranging from 2 to 10,000 km • 10

During the time period of 1971 to 1998, several large dams were constructed within or upstream of the watershed. To evaluate the impact of dams on flow in the watershed, the model was constructed to include reservoir storage and routing. The

DTED dataset was used to estimate the reservoir surface area and volumes for model construction

2.2.2 Climate Datasets -Temporal and Spatial Precipitation over Watersheds

The climate datasets compiled for model construction include the following data: (1) precipitation, temperature, relative humidity, and wind speed from 36 stations over the time period of 1964 to 1998, and (2) solar radiation from 4 gauging stations for the time period 1964 to 1998. Inspection of Figure 3 shows that climatic gauges are unevenly distributed across the watershed. Fortunately, the stations are concentrated in areas where the majority of precipitation occurs in the upper reaches of the watershed on the highlands surrounding the Mesopotamian plain. For modeling purposes, 119 subbasins were generated over the highlands to increase the accuracy of the SW AT-derived spatial distributions of precipitation in this area. SW AT assigns a subbasin the precipitation values of the gauge closest to the subbasin center. For small subbasins with multiple temperature gauges, the gauge that was most representative of the overall elevations of the subbasin was kept and others were discarded. Continuous daily precipitation data (1969-1998) was extracted from two sources: (1) global gauge data extracted from the Global Historical Climatology 11 Network (GHCN) (Earthlnfo, 1996-2003) dataset and (2) the historical global gridded precipitation (latitude cell: 2.5° x Longitude cell: 3.75°) data set (URL: gu23wld0098 dataset version 1, March 1999; (Hulme, 1992; Hulme, 1994; Hulme et al., 1998). The latter data set was only used when rain gauge data was absent and for these time

periods, the precipitation values of the enclosing cell were assigned to that particular gauge.

With very few exceptions ( 4 stations, Fig. 3), available climatic datasets are average monthly datasets ( e.g., monthly total precipitation, average monthly minimum temperature, average monthly maximum temperature, average monthly solar radiation, and average monthly wind speeds) that were extracted in large from the

GHCN global climatic data (Earthlnfo, 1996-2003). Daily precipitation was

computed throughout the entire modeling period by dividing total monthly precipitation by the number of days in each month. This is a reasonable

approximation, given the fact that the Tigris-Euphrates system is primarily driven by

contributions from melting snow and the majority of the precipitation is stored as snow pack in the winter months and released during the snow melting season in the spring. Monthly average values for wind speed, relative humidity, and solar radiation

were treated as daily estimates. With two exceptions (months of December and

March), the average monthly minimum and maximum values were treated as daily

minimum and maximum temperature values. 12 Inspection of the few daily temperature datasets (larger black cross, Fig. 3) in hand indicated that the first two weeks in March are colder than the last two weeks. The opposite trend was observed for the month of December. A first order approximation was applied to account for these temperature gradients. In computing the daily temperature minima and maxima from average monthly values for the month of

March, temperatures (minimums and maximums) were lowered for the first two weeks and increased for the third and fourth weeks in ways that preserve the reported average monthly temperatures. The opposite was done for the month of December.

Climatic data sets were imported to constrain spatial distribution of precipitation, to determine onset of snow pack growth and melting, and to compute evapo transpiration using the Penman-Monteith equation (Monteith, 1981) and to constrain surface runoff, ground water contributions.

2.2.3 Landuse and Soil Type

A digital landuse mosaic was constructed from individual landuse maps (CIA, 1993a;

1993b; 1993c; 1993d) for Iraq, Turkey, Syria, and Iran. Six major landuse units were mapped throughout the watershed and surrounding domains: "Arable lands",

"Irrigated farm land", "Pasture", "Wetland", "Mixed Forest", and "Desert" (Fig. 3).

The highlands surrounding the Mesopotamian plain are largely composed of "Mixed

Forest" and "Pasture" landuse types, whereas the remaining lowlands are dominated by the "Desert" landuse type. There were few noted exceptions including small pockets of "Arable farm land", or "Irrigated farm land" in the River flood plains. 13 Also the soil type "Irrigated farmland" is widespread in the area bound by the Tigris and Euphrates rivers. Using the SWAT landuse library, we selected the landuse types that closely resemble the mapped units. For example, the mapped "Desert" landuse type was modeled as SW AT's "Desert Southwest Range". Similarly, the following mapped units: "Mixed Forest", "Arable lands", "Wetlands", "Irrigated farmlands", and "Pasture" were modeled as "Mixed forest", "Agricultural lands", "Mixed wetlands", "Agricultural row crops", and "Pasture", respectively, in SW AT. Landuse type distributions are used within the SW AT domain to estimate the spatial variations in evaporative demand and surface roughness coefficients using the Erosion

Productivity Impact Calculator (EPIC) model incorporated in SWAT (Williams and

Sharply, 1989)

A multi-step process was adopted to define soil types across the watershed. The

USDA global soil data set (FAO-UNESCO, 2005) was downloaded from the Natural

Resources Conservation Service (NRCS) web site (www.nrcs.usda.gov). Nine main soil types were identified in the watershed and surroundings: "rock", "sand",

"vertisols", "aridisols", ''ultisols", "mollisols", "alfisols", "inceptisols", and "entisols"

(Fig. 4). The rock units are located in the northern section of the watershed and are contiguous with the Zagros and Taurus mountains (Fig. I). The alfisol, entisols, and

vertisols are located along the river flood plain and in the Mesopotamian plain,

representing reworked river channels. The aridisols comprise much of the modeled area due to the high evaporation rates and the accumulation of evaporites. The 14 mollisols and ultisols occur m the higher elevations and are congruent with the locations of the "pasture" landuse. The sands are located in the southern section of the watershed in the hyper-arid area, primarily in Saudi Arabia (Fig. 4). For each of the nine soil types, and for every horizon within a particular soil type, particle size properties (percent clay; percent sand) were obtained using USDA Soil Taxonomy classifications (NRCS and USDA, 1999). Particle size properties (USDA soil texture triangles; (Rawls et al., 1992) were then used to estimate the saturated hydraulic conductivities for each of the investigated horizons (Rawls and Brakensiek, 1983).

Finally, the estimated conductivities were used to classify each of the soil horizons into SCS soil hydrologic groups: Group A: >0.76 cm/h; Group B: 0.38-0.76 cm/h;

Group C: 0.13-0.38 cm/h, and Group D soils: 0.0 - 0.13 cm/h (Rawls et al., 1992).

This procedure was applied to every soil horizon. The final shapefiles indicating distributions of soil hydrologic groups, together with the generated nine landuse digital maps (also shapefiles), were imported into the model. These two maps were used to define multiple HRUs for each of the 149 subbasins. With the threshold levels of 20% and 10% for landuse and soil, respectively, recommended by SWAT (DiLuzio et al., 2001), 1 to 7 HRU's for each subbasin were defined, resulting in a total of 401

HRUs forthe watershed. The integration of the information contained in the soil and landuse coverages allowed for the extraction of curve number (CN) distribution maps for each of the soil horizons across the entire watershed. 15 2.2.4 Stream Flow Data

We compiled daily (5 stations) and monthly stream flow data (6 stations) across the watershed (Fig. 4). With one exception (Keban station in Turkey) all our stream flow stations are in Iraq. Stream flow data served two purposes: (1) to calibrate the model, four stations were selected for calibration purposes: two (Keban, Hit; Fig. 4) on the

Euphrates and two on the Tigris (Mosul, Baghdad; Fig. 4), and (2) to constrain outflowfor dams upstream whenever this data was unavailable.

Daily flow data sets were converted to monthly flow averages and used for model calibration and validation throughout the simulation period. Daily outflows are available for each of the dams being modeled in Iraq (i.e., Saddam, Mosul, Dokan etc.; Fig. 4) but not for dams outside of Iraq (e.g., Keban and Tabqa reservoirs in

Turkey and Syria, respectively; Fig. 4). For the latter dams, stream flow data from the nearest gauges downstream were input into the model as as outflow values for the dams in question. For example, stream flow data from the Husabayh gauge was modeled as outflow for all dams located up gradient on the Euphrates. Similarly, the values from the Mosul gauge were used as inputs for the upstream dams on the Tigris

(Fig. 4).

2.2.5 Temporal Satellite Data

We generated temporal satellite mosaics (29 mosaics) over the marshes to monitor the disintegration (1969 to 2002) and restoration (2003 to 2006) of the marshes. False 16 color images were generated from Landsat TM bands 2 (blue), 4 (green), 7 (red)

Landsat MSS bands 1 (blue), 3 (green), 2 (red), and MODIS bands 1 (blue), 2 (green),

3 (red). In generating the mosaics, we minimized inter-scene spectral variations related to differences in acquisition time and atmospheric contributions. Digital numbers (DN) were converted to space reflectance values to reduce spectral variations related to differences in sun angle elevation and scenes were normalized

(using areas of overlap between adjacent scenes) to the one with the least atmospheric effects (Sultan et al., 1993; Sultan et al., 1992). In generating the false color composites, we selected the band combinations that discriminate vegetation from surrounding units taking advantage of one or more of the following: chlorophyll absorption feature in the 0.6 µm wavelength region (MSS, TM, and MODIS bands 2,

2, and 1, respectively), the relatively high ( compared to soils) reflectance of vegetation in the 0.69-0.83 µm wavelength region (TM band 4 and MODIS band 2) and relatively low reflectance of vegetation in the 0.4-0.6 µm wavelength region compared to the surrounding soil. Given the selected band and color schemes, vegetation appears in shades of greenon all mosaics, water as dark bodies, and soil in shades of other colors depending on their composition and texture (e.g., grain size)(Sultan et al., 1987). On these mosaics, the marshlands were mapped as contiguous areas of shades of green and black. The earliest multi-spectral satellite data we used is the 1973 Landsat. For earlier periods, only panachromatic images are available (CORONA satellite data; McDonald, 1995). Because these images are acquired in the visible (0.4-0.7 µm) wavelength region, the vegetation and the water 17 bodies inside the marshes appear as relatively dark areas compared to their surroundings.

The mosaics were used to delineate the temporal variations in marshland extent, and to extract reservoir storage parameters as well. To incorporate reservoir storage and routing into the model, dam storage parameters had to be estimated. The principal surface area for each dam was determined by delineating the average areal extent of the lake that is being impounded by the dam using as many satellite images available following the filling of the lake. The principal volume was then calculated by summing all intervals between lake level and the topographic lake bottom portrayed in the DTED data. Similar procedures were used to delineate the emergency spillway surface area and volume using DTED data and the known surface elevation of the principal emergency spillway.

2.3 Model Calibration and Validation

The model was calibrated (1964 to 1970) by adjusting model parameters of snowpack\snowmelt, soil, groundwater, and channel characteristics to achieve the best possible match to the stream flowdata observed at two upstream (Mosul, Keban) and two downstream (Hit and Baghdad) (Fig. 4) stations on the Tigris and Euphrates

Rivers, respectively. Our examination of the temporal and spatial distribution of precipitation, temperature and stream flow data, indicated that snowmelt is the dominant source for stream flows in the study area. Snowmelt-runoff is a relatively 18 slow and gradual hydrologic process, compared to the rainfall-runoffprocess. Model calibration, thus, was conducted via a stepwise, iterative process by adjusting snow pack/melt parameters first, followed by adjustment with more emphasis on key soil/groundwater parameters and finally channel routing. The adjustment against observed monthly flow hydrographs was carried from upstream to downstream gauging stations. Following the model calibration step, we validated our model by examining the simulated stream flow rates against the observed data at available downstream stations (Hit and Baghdad Fig. 7) during the time period from 1971 to

1998.

2.3.1 Snow Pack/Melt Parameters

Seven main snow pack/melt related parameters were adjusted initially to calibrate the flows observed at the Mosul and Keban gauging stations. These two stations are proximal to the mountainous area. Keban is located in the Taurus Mountains whereas

Mosul is located near the base of the Zagros Mountains and receive the majority of the snow melt from the highlands further north. In the SW AT model, precipitation is classified as snow or rain depending on the mean daily air temperature (SFTMP). The seasonal growth and recession of the snow pack is defined using an areal depletion curve (Anderson, 1976). The latter is determined by the threshold snow water content

(SNOCOVMX) that corresponds to 100% snow cover and a specified fraction of

SNOCOVMX at 50% snow coverage (SNO50COV). The areal depletion curve affects snowmelt if the snow water content is below SNOCOVMX. In addition to the 19 areal coverage of snow, snowmelt is also controlled by the snow pack temperature, which is influenced by a lagging factor (TIMP), threshold temperature for snow melt

(SMTMP), and melting rate estimated by maximum and minimum snowmelt factors

(SMFMX and SMFMN). The adjustments for the seven parameters (SFTMP,

SNOCOVMX, SNO50COV, TIMP, SMTMP, SMFMX, and SMFMN) were made heuristically to achieve a general agreement betweensimulated and observed monthly hydrographs. The characteristics of the simulated hydrographs were found to be more sensitive to the SNOCOVMX, SNO50COV, SFTMP, SMTMP parameters. An iterative process was applied over the calibration period to provide a coarse fitting for the main peaks that develop in May and June.

2.3.2 Soil and Ground Water Parameters

The second step of calibration was performed with emphasis on soil and groundwater

parameters to provide a finer fitting for the higher and lower flows. The key parameters for adjustment include soil available water capacity (SOL_A WC), soil evaporation compensation coefficient (ESCO), threshold water levels in shallow

aquifer for base flow (GWQMN) and for re-evaporation/deep aquifer percolation

(REV APMN), re-evaporation coefficient (GW _REV AP), deep percolation fraction

(RCHRG_DP), delay time for groundwater recharge (GW_DELAY), and baseflow

recession constant (ALPHA_ BF). These parameters dictate the amount of water flow

through the soil zone and underlying aquifer to the stream channel as well as the

timing. A classification scheme for GW_DELAY was developed on the basis of four 20 different soil response groups A, B, C, and D constructed in the model. The adjustment to these four soil types was applied to every subbasin in the watershed.

The baseflow recession constant (ALPHA_BF) is one of the major parameters affecting the shape of the high flows; this parameter was optimized manually through trial-and-error. As is the case with the previous calibration step, this step focused on calibrations of hydrographs against observations from the two upper gauging stations.

The adjustment for all key parameters was repeated until the fitting of the monthly hydrographsis significantlyimproved fromthe previous step.

2.3.3 Channel Characteristics

The third step of calibration extends the fitting process to hydrographs observed at two downstream gauging stations with main consideration given to parameters related to channel routing. In the model, a variable storage routing method (a variation of the kinematic wave model) developed by (Williams, 1969) was used for channel routing.

The key parameter, manning roughness coefficient (n), was initially assigned to each

HRU (mountainous area: 0.05; foothills: 0.04; vegetated channel plain: 0.035)(Chow,

1959). Adjustments were made to improve the time to peaks and peak flows for the two lower gauging stations.

Transmission losses through channels are calculated in SWAT for two different channel types: tributary and main channels. The key parameter for losses is effective hydraulic conductivity (CH_K). CH Kl was set to zero for all main channels. Most 21 of tributary channels, however, are ephemeral or intermittent rece1vmg little groundwater. The flow in these channels loses partially to bank storage or to the underlying aquifer. Tributary channels were assigned initial hydraulic conductivity

(CH_K.2) values that were based on the reported ranges for various stream bed materials (Lane, I 983). Adjustments were made (within the reported ranges) to the

CH_K2 to improve hydro graph fitting forall four gauging stations.

2.3.4 Calibration Criteria and Model Evaluation

A further fine-tuning calibration was performed manually by repeating the 3-step adjustment as described above until a best fit is reached. Two statistical measures,

2 coefficient of determination (r ) and coefficient of efficiency (Nash and Sutcliffe,

1970) were used to quantify the level of calibration achieved and to evaluate the performance of the model. The coefficient of determination is obtained from the regression of the simulated values versus the observed values. The Nash-Sutchliffe coefficient of efficiency is definedby:

L(S;-0;)2 E = 1.0 - �i=�I ____ 2 Icoj -0> i=I

Where Si and Oi are the simulated and observed monthly flows for each month i, 0 is the mean of the observed monthly flows, and n is the total number of months over the simulation period. 22

Figure 5a shows that the temporal trend and magnitude of flows simulated for all four gauges. Examination of Figure 5a shows a close agreement between simulated and observed monthly flow over the calibration period. The model accurately tracks most of the peak flows. The coefficient of determination (r2) for simulated flows ranges

from 0.6 to 0.8 over the calibration period indicating a good agreement with observed

flows. Coefficientof efficiencyalso reaches an acceptable level (> 0.6).

The calibrated model is further validated for monthly flows at the most downstream

gauging station. Figure 6 demonstrates that the model simulations correlate

reasonably well with the actual flows. The coefficient of determination (r2) and

coefficient of efficiency for the simulated monthly flows during the validation period

are 0.62 and 0.6, respectively, indicating a high level of model performance and

adequacy to predict the monthly flows. 23 CHAPTER III

DISCUSSION

3.1 Model Results

The calibrated model is used in this section to investigate the impacts of the construction of dams inside and outside Iraq on the size and livelihood of the marshes. We first address this issue by examining the changes in the general patterns and magnitude of simulated inflow of the Tigris and Euphrates into the marshlands, changes that are associated with the construction of the major dams. The simulated inflows are computed at a location near the city of Al-Basrah (Fig. 1 ), the location where both rivers merge after traveling through the marshlands. It is noteworthy that the nearest available stream flow data is upstream from Al-Basrah, some 560 km to the Hit station on Euphrates and 350 km to the Baghdad station on Tigris. The simulated flow for the time period (1/1/1964 to 1/1/1970), that preceded the

9 3 construction of the major dams (storage capacity> 3 l0 m ; Fig. 7), hereafter referred to as period A, is shown in red lines, and in black lines for the time periods, B 1:

1/1/1973 to 1/1/1989, and B2: 1/1/1989 to 1/1/1997 that followed the construction of the major dams. The Keban, Tabqa, Haditha, and Karakaya dams on the Euphrates,

9 3 and the Mosul and Hamrin dams (a total storage capacity of approx. 76 10 m ) on the

Tigris were constructed in period B 1, whereas the Ataturk (storage capacity of

9 3 approx. 50 10 m ) was constructed in period B2. The monthly average peak flow

3 (6301 m /s) in period A is progressively diminished in periods Bl (monthly average 24 3 3 peak flow: 3073 m /s) and B2 (monthly average peak flow: 2319 m /s) due to the combined effects of evaporation and infiltration of the water impounded behind dams and the utilization of stored water for irrigation purposes.

3.2 Impacts on Marshes

The impacts of dam-related reduced flowon the livelihood of the marshes was further investigated by comparing the simulated inflow from Tigris and Euphrates (Fig. 7; pink line) to the inflow that would have occurred if no dams were constructed (Fig. 7; black line). The simulated flow under these two scenarios was then used to compute percent reduction in annual flow (Fig. 7; blue line) throughout the calibration and validation periods. Inspection of Figure 6 demonstrates two ways by which the dams reduce the flow to the marshlands. The first is a one-time reduction that is associated with the impoundment of water behind the newly constructed dams forming large artificial lakes with storage capacities ranging from a few billion cubic meters to tens of billions of cubic meters. The second is a permanent reduction in flow due to loss of impounded water through evaporation, infiltrationand irrigation.

The construction of each of these large dams led to the development of large water impoundments behind the dams and the abstraction of large volumes of water that would have otherwise reached the marshlands. For the period preceding the construction of large dams, a sharp decrease in flow is observed on the percent reduction curve mimicking the filling of the reservoir. For example, for the period 25 (1970 to 1973) preceding the completion of the Keban and Tabqa dams in 1974, the simulated annual flow volume was reduced by up to 30%. Similarly, during the filling of the Ataturk reservoir, the annual flow volume was reduced by yet another

31% (from its I 988 values) (Fig. 7) over the course of three years (1989 to 1992). The construction of the major dams represented in Figure 7 led to an abstraction of over

75 109m3 over the course of the calibration and validation periods. If we include the

9 smaller dams (<3 10 m3), the one time extraction would have reached over 100

9 10 m3.

Inspection of the percent reduction in annual flow volume shows that following the lake-filling period, (1-3 years on the average), flow volumes increase, but generally to levels less than pre-dam levels. For example, following the filling of the Keban and

Tabqa reservoirs, the simulated annual flow volume bounced back to approximately

85% of its original volume in 1969 and following the filling period (1989 to 1993) of the Ataturk reservoir, the annual flow volume reached 70% of the 1969 values. It is these permanent reductions in flow volumes that pose, we believe, the largest threat to the livelihood of the marshes. The percent reduction in annual flow volume for period Bl ranged from 12% to 43% and averaged 21%, for period B2 it ranged from

26% to 48% and averaged 35%.

Next we demonstrate that the areal extent of the marshlands is directly related to the reduction of the inflow fromthe Tigris and Euphrates. The higher the flow, the larger 26 marsh area and vice versa. We restrict our analysis to the time period 1969 to 1987

(before period C, Fig. 7), the time period preceding the construction of the large engineering projects (Fig. 7) within and around the marshes to separate the spatial variations in the marshlands related to changes in inflow from those related to implementation of engineering projects. We also make our comparisons m marshland extent using scenes acquired around the same approximate time period of the year (month of July - September) to avoid complications arising from marshland spatial variations related to seasonal flow variations.

We investigated the effects of reduced inflow arising from the construction of the

Keban and Tabqa dams on the Central and Al-Hammar marshes. These marshes are fed by the Euphrates and Tigris Rivers and we analyzed variations in the marshes' size by comparing the areal extent of theses marshes before and after the construction of these dams. The areal extent of the marshes for the time period preceding the dams was extracted from a mosaic of CORONA KH4-B images (acquired in September,

1966) and the areal extent of the marshes for the period following the construction of the dams was extracted from MSS scenes acquired in August, 1977 and in July 1984.

2 2 The areal extent of the marshes in 1966, 1977, and 1984 was 7971 km , 6678 km ,

2 5269 km , respectively (Fig. 8). These spatial variations are primarily observed along the western and northern boundary and to a much lesser extent along the eastern and southern boundaries, where the marshes are generally bound by steeper topographic boundaries (cliffs). Because most of the flow in the Tigris and Euphrates is 27 generated in the snow melting season, it could be demonstrated that to a first order the areal extent of the investigated marshes increases with the increase of the annual flow volume (Fig. 9). Naturally, one should not expect a 1: 1 relationship because other factors such as local topography, average annual temperature, amount of evaporation and infiltration can influence the size of the marshland as well. Nevertheless we could use this relationship and other similar relationship for other marshes to achieve a first order understanding of what would be the impact of additional reductions in flow that will result from the implementation of the planned engineering projects on the Tigris-Euphrates system over the next fewyears.

The ongoing South Eastern Anatoilia project calls foran increase in irrigated area of~

1,500,000 hectares by 2010. Currently only 213,924 hectares are being irrigated on both the Tigris and Euphrates Rivers. This will be achieved by the completion of a series of dams and canal systems to channel the reservoir water to new irrigated land.

Currently 9 of the 22 dams in the project have been completed. The new dams under

9 3 construction or planned equates to a combined additional storage of 15.5 10 m .

3 Assuming 3100 m of water are needed to irrigate a hectare of land in this area

(Gollehon and Quinby, 2006), we estimate the amount of water currently used at 1.3

9 3 I 0 m /yr and the required water allocation upon completion of the project to be 6.8

109m3/yr. This increase in irrigated farmland will further reduce the inflow of the

Tigris-Euphrates system into the marshes. At a minimum, the annual flow volume

9 3 will be reduced by an additional 5 10 m /yr. Reducing the average annual flow 28 9 3 2 volume in period B2 by 5 10 m /yr, we estimate that approximately 5700 km of marshes can be sustained in the Central and Al Hammar, approximately 5 % less than the area sustained by the current average annual flow (Fig. 8). 29 CHAPTER IV

CONCLUSIONS

4.1 Summary

We constructed a continuous (1964 to 1998) catchment-based rainfall runoffmodel for the entire Tigris-Euphrates watershed (area: approx. 1,000,000 knl) using the

SWAT model to understand the natural flow system, and to investigate the impacts of reduced overall flow and the related land cover and land use change downstream in the marshes. Precipitation was derived from daily and monthly rain gauge data (36 stations) from 1964-1990 and from the historical global gridded precipitation dataset

(1990 to 1998), landuse types ( 6) were derived from landuse map sheets and soil types (9) from global soil map coverages. The spatial extent of drainage basins, reservoir volume, and marshland volume were extracted from DTED data.

Evaporation on bare soils and ET on vegetated canopy was calculated using the

Penman-Monteith method, a Soil Conservation Service (SCS) curve number method was adopted to estimate direct runoffusing rainfall excess and channel routing was conducted using the variable storage method. The model was successfully calibrated against stream flowdata in Iraq (3 gauges) and Turkey (1 gauge).

The calibrated model was used to simulate the monthly flow rate and annual flow volume of the Tigris and Euphrates into the marshes at Al-Basrah. Model results were described for three time periods: ( 1) time period A (1/1/1964 to 1/1/1970), the time period that preceded the construction of the major dams, (2) time period B1 30 (1/1/1973 to 1/1/1989), and B2 (1/1/1989 to 1/1/1997), the time period that followed the construction of the major dams. The model shows that the monthly average peak

3 9 3 flow (6301 m /s) and average annual flow volume (80 10 m /yr) in period A is

3 progressively diminished in periods B 1 (monthly average peak flow: 3073 m /s;

9 3 average annual flow volume: 55 10 m /yr) and B2 (monthly average peak flow: 2319;

9 3 average annual flow volume: 50 10 m /yr) due to the combined effectsof evaporation and infiltration of the water impounded behind dams and the utilization of stored water for irrigation purposes.

We demonstrated that for time period C (1987 to 1998), the period preceding the construction of the major engineering projects in and around the investigated marshes, the areal extent of the marshes increases with an increase in annual flow volume and with an increase in average monthly flow rates. Comparisons in marshland extent were conducted using scenes acquired around the same approximate time period of the year (months of July-September) to avoid complications arising from marshland spatial variations related to seasonal flow variations. These relationships were then used to provide first order simulations of what would be the impact of additional reductions in flow that will result fromthe implementation of the planned engineering projects on the Tigris-Euphratessystem over the next few years. 31 4.2 Predictions

The ongoing South Eastern Anatolia project calls for an increase in irrigated area of~

1,500,000 hectares by 2010. Upon completion of the project, we estimate that an additional water allocation of 6.8 109m3 /yr will be needed to irrigate 1.5 million hectares, reducing the inflow of the Tigris-Euphrates system into the marshes by at

9 3 least 5 10 m /yr. This reduction in the annual flow volume would represent an approximate decrease in the Central and Al-Hammar marshes by another 5%. 32

Table 1. Dams in the Tigris-Euphrates Watershed

Name Completion Use" Country Basin Max. vol Prin. Max SA Prin. Res. 3B D E 109m vol km2 SA KF IQ9m3C Dokan 1961 IRR Iraq Tigris 6.8 4.6 270 200 I Derbendikhan 1962 IRR Iraq Tigris 3 2 121 78 I Keban 1975 HP Turkey Euphrates 31 21.6 675 458 2.5 Tabaqa 1975 HP Syria Euphrates 11.7 9.6 610 447 2.5 Hamrin 1980 IRR Iraq Tigris 3.95 2.15 440 195 2.5 Mosul 1985 HP, IRR Iraq Tigris 11.1 8 371 285 2.5 Haditha 1984 HP,IRR Iraq Euphrates 8.2 7 500 415 4 Karakaya 1987 HP Turkey Euphrates 9.6 7.1 268 198 I Ataturk 1992 HP, IRR Turkey Euphrates 48.7 38.7 817 707 2 Kralkizi 1997 HP Turkey Tigris 1.9 I. I 57.5 39.2 2 Batman 1998 HP, IRR Turkey Tigris 1.175 0.745 49.3 37.3 2 Tishreen 1999 HP Syria Euphrates 1.9 1.8 166 142 2

A IRR: Irrigation, HP: Hydroelectric Power 8 Maximum volume in billion cubic meters c Principal volume in billion cubic meters 0 Maximum surface area in km2 2 E Principal surface area in km F Reservoir hydraulic conductivity in mm/hr 33 Figure 1 : Location and Extent of the Tigris- Euphrates Watershed The location and extent (dark blue outline) of the Tigris- Euphrates watershed plotted on a color coded digital elevation map derived from SR TM data. Also shown in black contours are the annual precipitation Amounts. The lowest annual precipitation (<300mm) occurs in the Mesopotamian plain and the highest precipitation (>600-1200mm) falls over the mountainous belt to the north (e.g., Taurus mountains) and east (Zagros Mountains) of the plains.

45°0'0"E

35°0'0"

30°0'0" I \ - - '- Arabian G lf . .... \ � \'--� \ \ \ \ I ' 40•o·o"E 45°0'0"E 50°0'0"E N Elevation (m a.s.l.) 0 250 500 D -400 - 300 D 1300 - 2,000 Kilometers - 300-700 - 2,000 - 5,500 A D 100-1300 34

Figure 2: Diversion and Drainage Projects (A) Diversion and drainage projects in and around the marshes forthe area covered by red box in Figure 1 plotted over a Landsat ETM+ band 2,4,7 color composite acquired in 2002. The areas outlined in red and aqua represent the spatial extent of the marshes in 1969 and 2002, respectively. Diversion projects (Glory River, COB River, MOB River, LTL Canal) and drainage projects (Aybas Canal, Prosperity River, Dike) are highlighted in yellow and green. White arrows indicate the direction of water drainage to the named projects. (B) Temporal time series of the desiccation of a section in the Al- Hammar marsh. Area donated by this time series (1973 MSS, 2002 ETM+, and 2006 ETM+) is outlined by a black box on Figure 2a. The primary contributor to the desiccation and restoration of this area is the MOB River which is seen in Figure 2a as yellow with black hachuring.

N O 50 100 A --111k"',..lo•m -=e::;t=e=rs===i, 35 Figure 3: Distribution of Land Use Types and Climate Data Stations The accompanyingsymbols represent the locations of all climate datasets that were imported into the SWAT model for computation of surface runoff, ET, and snowmelt.

Climate Gauges □ DAILY PRECIPITATION + □ MONTHLY PRECIPITATION RELATIVE HUMIDITY + DAILY TEMPERATURE + MONTHLY TEMPERATURE X SOLAR RADIATION ... WIND SPEED

Land Use ' ' � ARABLE LANDS ' ' MIXED FOREST ' ' IRRIGATED FARM LAND ' ' � PASTURE 300 DESERT WETLAND Kilometers 36 Figure 4: Distribution of Soil Types and Water Management Projects Soil types were derived from NRCS database. Locations of engineering projects, namely dams and flow barrages, are denoted by blank and pink squares respectively, whereas locations of stream flow gauges are denoted by yellow circles for gauges used in calibration (Keban, Hit, Mosul, Baghdad) and verification (Hit, Baghdad). Green circles, on the other hand, denote gauge locations that were used to define dam outflows in the up gradient.

KRALKIZI 1997 DCLE 1997 BATMAN 1998 MOSUL 1985-2004 KEBAN 1975 MOSUL 1985 KEBAN 1964-1975 MOSUL 1985-2004 KARAKAYA 1987 MOSUL 1930-1997 ATATURK 1992 OOKAN 1961 BIRECIK 2000 --� OOKAN 195S-2000 TISHREEN 1999 TABAQA 1975 DBBIS 1965 CERBENDIKHAN 1962 CERBENDIKHAN 1962-2005 SAM ARRA BARRAGE 1954

HAMRIN HIRO HAMRIN 1QRf1..?nno. BAGHDAD 1930-2005

KU T BARRAGE1931 KARKHEH 1998 MEDINA 1993

SOILTYPE Engineering Projects -Al.FISOL ■ Reserviors ■ Flow Barrages Gauges Calibration • Reservior Outflow SAND N 0 500 ULTISOL -VERTISOL A Kilometers 37

Figure 5a: Graphs Comparing Simulated Flow Graphs comparing the simulated flow to the observed flow in the Keban and Hit gauges on the Euphrates River, and the Mosul and Baghdad gauges on the Tigris River. Traced in green on the figure is the cumulative snow melt occurring fromall subbasins that contribute to the flow at the Keban and Mosul gauges.

4000 0 e ,g KebanGauge 4000 2000 4000 ;; :I ;;! 3 3000 4000 2000 roJO C !0 "' C ., 2000 6000 "' 00'.)() Cl)> 1000 .. 10000 1000 8000 .!! 1 ::, ::, E 12000 10000a 5'l'1964 5'311966 5'311966 5'311967 5'311968 5'311900 51311964 51311965 51311966 51311967 51311968 51311969 + Simulated Flow + Observed Flow ... Snow Melt

1000�------�

6000

5000

4000 3000

2000

1000 0 +-"��-""-�--""..__�.:...._�....!L�---=i- 51'3/1964 51'311965 51'311966 51'311967 51'311968 51'311969 51311964 51311965 51311966 51311967 51311968 51311969

TIME 38

Figure 5b: Calibrated Regression Lines Regression lines associated with each of the calibration gauges for the calibration period.

Euphrates River Tigris River

4000 Keban Gauge 4000 ♦ Mosul Gauge ♦ 3000 3000 ♦ ♦ - 2000 2000 ♦ 1000 - 2 1000 2 R = 0.8053 R = 0.7161 - 0 0 0 1000 2000 3000 4000 0 1000 2000 3000 400 - ·-e 5000 6000 • r'1 4000 Hit Gauge • 5000 Baghdad Gauge • 3000 • 4000 3000 2000 2 2000 R = 0.7636 1000 • 1000 0 0 0 2000 4000 6000 0 1000 2000 3000

3 Observed Flow (m /s) 39

Figure 6: Calibration and Validation Results Calibration (Keban and Mosul) and validation (Hit and Baghdad) results. Simulated versus observed monthly flow.

7000 6000

5000 Hit Gauge 4000 3000

2000 --!!:! 1000 ], 0 513/1964 513/1968 513/1972 513/1976 5/3/1980 5/3/1984 5/3/1988 513/1992 5/3/1996 � s 6000 5000 Baghdad Gauge 4000 3000 2000

1000 0 513/1964 513/1968 513/1972 513/1976 513/1980 513/1984 513/1988 5/3/1992 513/19 Time

1-obseMd Flow � Simulated Flow No Data I 40

Figure 7: Simulated Monthly Flow Rates Simulated monthly flow rates from 1964-1998 (black curve) are compared to flowsthat would have existed if no dams were constructed (pink curve). Also shown are the percent reductions in annual flowvolumes that resulted from the construction of the dams. Three major time periods are represented in the figure: time period A (from 1964 to 1972) refers to the time when no major dams were yet constructed; time period B 1 refers to the time span span where Keban, Tabqa, Hamrin, Haditha, Mosul, and Karakaya dams were constructed, and time B2 refers to the period where the Ataturk dam was completed. Yellow stars represent the time intervals during which simulated flows were correlated with spatial extents of marshlands that were extracted from satellite data acquired during the same time period.

A Bl B2 14000 ---.------1�------,�==�======..r -10 Haditha(8.2) C 0 0 12000 I\ 10 E � I 0000 s,p,embe< 1966 20 5 30 � -�: 8000 "' 40 �1 - � 3 .... 50 EC o .2 6000 <( i 60 £ "::, � 4000 7 .,£> 0 i 80 C 2000 90 I 0 --+----�--��---�--�---�--��---�-� 100 1/1/65 1 /1 /69 1/1 /73 1 /1 /77 1/ 1/81 1 /1 /85 1 /1 /89 1 /1/93 1 /1 /97 Time - Flow With Dams - Flow Without Dams -- Precent Reduction in Flow 41

Figure 8: Variations in the Areal Extent of the Central and Al Hammar Marshlands Through the time period 1966-1984. The variations in the spatial extent correspond to the progressivereduction in annual flow volumes caused by engineering projects (Keban, Tabqa, Hamrin, and Haditha dams) outside the marshes. In September of 1966, the areal extent was at a maximum (black line) corresponding to natural flow period. In August of 1977 the areal extent was reduced (blue line) following the post filling of the Keban and Tabqa dams and in July of 1984, the areal extent (yellow line) was the most reduced due to the construction of additional dams (Harmrin and Haditha).

Marshland Extent - September 1966 -August 1977 July 1984

N 0___ 2<=s ===iso Kilometers 42

Figure 9: Spatial Extent of the Marshlands Plot of spatial extent of the marshlands (June 1969, June 1977, July 1984) against the average monthly flowand the total annual flow. Hollow triangles correspond to dashed linear trend line.

70.00 ...------,

.t:.Annual Flow Volume (Oct.-Sept.) � .! 60.00 "' .c 50.00 C: 0 � ... C: � o 40.00 ...... :; ..,-11' ·- ·;; :::, E tT (.) -.! <( 30.00

2 � R = 0.892 LL 20.00 Cl) C)

� 10.00 <(

0.00 ------.----.....-----...... --- - 0 2000 4000 6000 8000 1 0000 Area: Al-Hammar and Central Marshes (km2) 43 BIBLIOGRAPHY

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