Journal of Hydrology (2008) 353, 59– 75
available at www.sciencedirect.com
journal homepage: www.elsevier.com/locate/jhydrol
Hydrologic impacts of engineering projects on the
Tigris–Euphrates system and its marshlands
C. Jones a, M. Sultan a,*, E. Yan b, A. Milewski a, M. Hussein c,
A. Al-Dousari d, S. Al-Kaisy e, R. Becker a
a
Department of Geosciences, Western Michigan University, 1903 West Michigan Avenue, Kalamazoo, MI 49008, USA
Environmental Science Division, Argonne National Laboratory, Chicago, IL, USA
c
Iraq Reconstruction Management Office, US Embassy, Baghdad, Iraq
d
Kuwait Institute for Scientific Research, Kuwait City, Kuwait
e
Department of Applied Geology, University of Tikrit, Tikrit, Iraq
b
Received 25 June 2007; received in revised form 16 January 2008; accepted 22 January 2008
KEYWORDS
Tigris–Euphrates
watershed;
Continuous rainfall–
runoff model;
SWAT;
Engineering projects
Summary 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
extent (1966: 8000 km2; 2002: 750 km2) of the Mesopotamian Marshes downstream.
We constructed a catchment-based continuous (1964–1998) rainfall runoff model for
the watershed (area: 106 km2) using the Soil Water Assessment Tool (SWAT) model to
understand the dynamics of the natural flow system, and to investigate the impacts
of reduced overall flow and the related land cover and landuse change downstream
in the marshes. The model was calibrated (1964–1970) and validated (1971–1998)
against stream flow gauge data. Using the calibrated model we calculated the temporal
variations in the average monthly flow rate (AMFR), the average monthly peak flow rate
(AMPFR), and annual flow volume (AFV) of the Tigris and Euphrates into the marshes at
a location near Al-Basrah city (31N, 47.5E) throughout the modeled period. Model
results indicate that the AMPFR (6301 m3/s) and average annual flow volume (AAFV:
80 · 109 m3/yr) for period A (10/1/1965–09/30/1973), preceding the construction of
the major dams is progressively diminished in periods B1 (10/1/1973–09/30/1989; AMPFR: 3073 m3/s; AAFV: 55 · 109 m3/yr) and B2 (10/1/1989–09/30/1998; AMPFR, 2319 m3/s;
AAFV: 50 · 109 m3/yr) that witnessed the construction of the major dams (B1: Keban,
Tabqa, Hamrin, Haditha, Mosul, Karakaya; B2: Ataturk) due to the combined effects
* Corresponding author. Tel.: +1 269 387 5487.
E-mail address: [email protected] (M. Sultan).
0022-1694/$ - see front matter ª 2008 Elsevier B.V. All rights reserved.
doi:10.1016/j.jhydrol.2008.01.029
60
C. Jones et al.
of filling artificial lakes, evaporation and infiltration of impounded water and its utilization for irrigation purposes. To investigate the impacts of reduced flow on the areal
extent of the marshes, we examined the variation in marsh size extracted from temporal satellite data (1966, 1975, 1976, 1977, 1984, 1985, 1986, 1987) acquired around the
same approximate time period (July to September) of the year versus simulated AFV for
the period preceding the onset (1987) of major local engineering projects (e.g., Crown
of Battles River, Loyalty to the Leader Canal, Mother of Battles River) in and around
the investigated marshes. Results indicate that the areal extent of the Central and
Al-Hammar marshes (e.g., 1966: 7970 km2, 1977: 6680 km2, 1984: 5270 km2) decreases
with a decrease in AFV (e.g., 1966: 60.8 · 109 m3, 1977: 56.9 · 109 m3, 1984:
37.6 · 109 m3). Using a relationship that describes the impact of reduced AFV on the
areal extent of the marshes, we evaluated 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 few years. Upon completion of the ongoing
South Eastern Anatolia project, with projected reductions in AFV exceeding 5 · 109
m3/yr, the sustainable marshes in the Central and Al-Hammar area will be reduced
by at least an additional 550 km2.
ª 2008 Elsevier B.V. All rights reserved.
Introduction
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 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 programs by
Iraq and its neighbors have brought drastic modification
to the Tigris and Euphrates watershed (106 km2) and the
Mesopotamian Marshes downstream in southern Iraq
(Fig. 1). The marshlands that extended over an area of
8000 km2 in the early sixties declined to about 750 km2 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 of upstream projects include the Tabaqa dam (1975; storage
capacity (SC): 11.7 · 109 m3) and Tishrine dam (1999; SC:
1.9 · 109 m3) in Syria, the Ataturk dam (1992; SC:
48.7 · 109 m3) and Keban dam (1975; SC: 31 · 109 m3) in
Turkey and the Karkheh dam (2001; SC: 7.8 · 109 m3) in
Iran (Table 1). Examples of local engineering projects that
devastated the marshes include the diversion canals that
were largely constructed in the late 1980s and early
1990s (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. Fig. 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 (2002–2007), there has been a reversal in this
devastating trend, with the marshland’s area increasing
from 750 km2 in 2002–3980 km2 in 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). Fig. 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
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
cannot be as easily reverted.
We constructed a catchment-based continuous (1964–
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–
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 1998). 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 SWAT model was selected because (1) the model is an open source code giving
the user the flexibility for making modifications as needed,
Hydrologic impacts of engineering projects on the Tigris–Euphrates system and its marshlands
61
Figure 1 Location and extent (dark blue outline) of the Tigris–Euphrates watershed plotted on a color coded digital elevation map
derived from Shuttle Radar Topography Mission data (SRTM). Also shown in black contours are the annual precipitation amounts. The
lowest annual precipitation (<300 mm) occurs in the Mesopotamian plain and the highest precipitation (>600–1200 mm) falls over
the mountainous belt to the north (e.g., Taurus Mountains) and east (Zagros Mountains) of the plains.
(2) it is a continuous model, allowing rainfall–runoff and
groundwater-recharge estimates to be made over extended
periods of time, and (3) Geographic Information System
(GIS) data sets which were generated for the watershed
could be readily imported into the model.
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 across 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.l.), flows south along the base of the Zagros
Mountains, across the Mesopotamian plain, into the Central
and Al-Haweizah marshes, 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 the highlands would have melted and it is
in this month that the lowest flow on both rivers is observed.
62
C. Jones et al.
Table 1
Dams in the Tigris–Euphrates watershed
Name
Completion
a
Country
Basin
b
Max. vol
109 m3
c
d
e
Dokan
Derbendikhan
Keban
Tabaqa
Hamrin
Mosul
Haditha
Karakaya
Ataturk
Kralkizi
Batman
Tishreen
1961
1962
1975
1975
1980
1985
1984
1987
1992
1997
1998
1999
IRR
IRR
HP
HP
IRR
HP,
HP,
HP
HP,
HP
HP,
HP
Iraq
Iraq
Turkey
Syria
Iraq
Iraq
Iraq
Turkey
Turkey
Turkey
Turkey
Syria
Tigris
Tigris
Euphrates
Euphrates
Tigris
Tigris
Euphrates
Euphrates
Euphrates
Tigris
Tigris
Euphrates
6.8
3
31
11.7
3.95
11.1
8.2
9.6
48.7
1.9
1.175
1.9
4.6
2
21.6
9.6
2.15
8
7
7.1
38.7
1.1
0.745
1.8
270
121
675
610
440
371
500
268
817
57.5
49.3
166
200
78
458
447
195
285
415
198
707
39.2
37.3
142
a
b
c
d
e
f
Use
IRR
IRR
IRR
IRR
Prin. vol 109 m3
Max. SA km2
Prin. SA
f
Res. K
1
1
2.5
2.5
2.5
2.5
4
1
2
2
2
2
IRR: irrigation, HP: hydroelectric power.
Maximum volume in billion cubic meters.
Principal volume in billion cubic meters.
Maximum surface area in km2.
Principal surface area in km2.
Reservoir hydraulic conductivity in mm/h.
Model construction and data collection
Model framework
The model for the Tigris–Euphrates watershed was constructed within the SWAT framework to simulate the hydrologic processes using its physically based formulations.
SWAT is a semi-distributed continuous watershed simulator
that computes long-term water flow over large basins using
daily time steps. Major model components include: flow
generation, stream routing, pond/reservoir routing, erosion/sedimentation, plant growth, nutrients, pesticides,
and land management. In SWAT, 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 in modeling hydrologic processes in HRUs/subbasins
and routing can be found in Arnold et al. (1998), DiLuzio
et al. (2004), Neitsch et al. (2005), and Srinivasan et al.
(1998). The model has been successfully validated over several watersheds (e.g. Arnold et al., 1998).
In this study, a non-linear areal depletion curve (Anderson et al., 1976) was used to estimate seasonal growth
and recession of snow pack and to determine snowmelt. A
Soil Conservation Service (SCS) curve number method,
(SCS, 1972) adjusted according to soil moisture conditions,
was used to calculate direct surface runoff and infiltration
in and through a soil profile (Arnold et al., 1993). The modified SCS method (SCS, 1985) introduces the antecedent
moisture condition (AMC) to account for the impacts of soil
moisture content on surface runoff. In SWAT model, the
curve number (CN) was estimated based on the retention
parameter (S) for the moisture content of the soil profile
on any given day (Neitsch et al., 2005). It varies non-linearly
from AMC I (dry) at wilting point to AMC III at field capacity,
and approaches 100 at saturation (Arnold et al., 1993). In
our case, an average soil moisture condition was found to
be the overwhelming condition throughout the calibration
period across the watershed. 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.
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) (spatial resolution:
85 m; vertical accuracy: +/ 15 m) data cells (NIMA,
1990, 1991a–c), 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 1964–1998 to extract
monthly precipitation and/or snow accumulation throughout the modeling period, and to constrain evapotranspiration, surface runoff, and ground water contributions. (3) A
mosaic for landuse types generated from various landuse
maps for Turkey, Iraq, Iran, and Syria (CIA, 1993a–d) and
a mosaic for reclassified (in SCS soil hydrologic groups) soil
types extracted from the United States Department of
Hydrologic impacts of engineering projects on the Tigris–Euphrates system and its marshlands
63
Figure 2 (a) Diversion and drainage projects in and around the marshes for the area covered by red box in Fig. 1 plotted over a
Landsat ETM+ band 2, 4, 7 false color image 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, respectively. White arrows indicate the direction of water
drainage to the named projects. (b) Temporal time series (1973 MSS, 2002 ETM+, and 2006 ETM+) for the area covered by the black
box in Fig. 2a showing the desiccation of a section in the Al-Hammar marsh in 2002 due to the implementation of the MOB River
project (outlined by yellow line with black hachuring) and the restoration of this area in 2006 after the project was reversed.
Agriculture (USDA) global soil data set (FAO-UNESCO, 2005);
the generated landuse and soil type mosaics were used to
construct digital 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) 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) Nine mosaics were generated from satellite imagery acquired around the same approximate time
period (July to September) over the marshes to investigate
the impacts of reduced flow on the areal extent of the
marshes for the period preceding the onset (1987) of major
local engineering projects in and around the investigated
marshes. Five of these mosaics are false color mosaics
(bands 2,4,7) generated from Landsat Thematic Mapper
(Landsat TM) images (spatial resolution: 28.5 m) acquired
in 1984, 1985, 1986, and 1987, one false color (bands
1,3,2) mosaic was generated from Landsat Multi Spectral
Scanner (Landsat MSS) images (spatial resolution: 70 m) acquired in 1977, and three black and white mosaics were generated from ARGON KH-9 (spatial resolution: 10 m) and
CORONA images (spatial resolution: 10 m) acquired in
1976, 1975, and 1966). To examine the overall disintegration and restoration of the marshlands from 1969 to 2005,
the nine mosaics together with additional 29 false color
Landsat TM and MSS mosaics and Moderate Resolution Imaging Spectrometer (MODIS) false color (bands 1,2,3 resampled to 250 m) images acquired from 1998 to 2005 were
generated. Tens of individual temporal Landsat (MSS and
TM) scenes were examined over specific areas of interest
64
(e.g., dams) to enable the extraction of reservoir storage
parameters (refer to Section ‘‘Temporal satellite data’’).
All digital products were co-registered using the TM global
GeoCover dataset as a reference mosaic and brought to
the same projection (UTM WGS84).
Digital elevation – watershed and stream delineation,
and reservoir storage
The DTED mosaic covering the entire Tigris–Euphrates watershed was used to delineate the watershed boundaries
and stream network using the Topographic Parameterization (TOPAZ) program (Garbrecht and Martz, 1995). The
spatial resolution (3-arc-second; approximately 85 m) of
the DTED data had to be reduced to 540 m due to computational restrictions. The TOPAZ method compares the elevation of each cell to that of the neighboring cells and
identifies the flow direction as being towards the cell with
lowest elevation. The watershed was subdivided into 149
subbasins, with sizes ranging from 200 to 10,000 km2. To account for the large disparities in physiogeographic characteristics of the examined watershed, a low Critical Source
Area (CSA) value of 970 km2 was selected to define the subbasins across the watershed. Manual adjustments were then
applied to subdivide these into smaller subbasins in areas of
varying physiogeographic characteristics, especially those
C. Jones et al.
found in higher altitudes. In contrast, adjacent subbasins
in the lowlands with similar physiographic characteristics
were re-grouped into relatively larger subbasins. Improvements in the definition of stream networks and subbasin
delineation in areas experiencing gentle slopes (e.g.,
<1 cm/km in Mesopotamian plain) (Kolars and Mitchell,
1991) were attained using a reference stream network extracted from the Landsat TM mosaics with high spatial resolution (15–30 m) and applying a DEM ‘‘burning’’ method
(DiLuzio et al., 2004). The DTED data was also used to extract dam storage parameters (e.g., reservoir surface area
and volume). Applying standard GIS techniques to DTED
data, we computed the emergency surface area, the area
enclosed by the contour having the surface elevation of
the emergency spillway. We then extracted the volume of
water subtended between the delineated surface and the
surface of the Earth portrayed in the DTED data, by summing all intervals between these two surfaces again using
standard GIS applications (ESRI, 2001).
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
Figure 3 Distribution of landuse types and locations of climate data stations across the Tigris–Euphrates watershed. Also shown
here are soil types and data (e.g., precipitation, relative humidity, temperature, solar radiation, and wind speed) from climate
stations that were used as inputs to the SWAT model.
Hydrologic impacts of engineering projects on the Tigris–Euphrates system and its marshlands
the time period of 1964–1998, and (2) solar radiation from 4
gauging stations for the time period 1964–1998. Inspection
of Fig. 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,
the largest concentration of subbasins (119 subbasins from
a total of 149 subbasins) was generated over the highlands
to increase the accuracy of the SWAT-derived spatial distributions of precipitation in this area. SWAT assigns a subbasin the precipitation value 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 Network
(GHCN) (EarthInfo, 1996–2003) dataset, and (2) the historical global gridded precipitation (latitude cell: 2.5 · longitude cell: 3.75) data set (URL: gu23wld0098 dataset version
1, March 1999; Hulme, 1992, 1994; Hulme et al., 1998). The
latter data set was only used when rain gauge data was absent for a particular gauge. For the time periods where such
data were absent, the precipitation values of the enclosing
cell were assigned to the enclosed gauge.
With very few exceptions (four 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 from the GHCN global climatic
data (EarthInfo, 1996–2003). We ran the model on daily
steps to take advantage of the daily data where available
and applied reasonable approximations to extract daily data
from monthly averages. Because no apparent trends were
identified in the available daily data for the climatic parameters, simple conversions were applied. 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 (77% 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, the months
of December and March, the average monthly minimum and
maximum values were treated as daily minimum and maximum temperature values.
Inspection of the few available daily temperature datasets (larger black cross, Fig. 3) 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
65
done for the month of December. Inspection of available
daily precipitation data for months other than March and
December did not reveal any obvious gradients and hence
approximations similar to ones reported above or applications of alternative spline functions for extracting daily values from monthly averages were not pursued. Similar
observations were noted for the remaining available daily
climatic datasets. Climatic data sets were imported to constrain spatial distribution of precipitation, to determine onset of snow pack growth and melting, and to compute
evapotranspiration using the Penman–Monteith equation
(Monteith, 1981) and to constrain surface runoff and ground
water contributions.
Landuse and soil type
A digital landuse mosaic was constructed from individual
landuse maps (CIA, 1993a–d) 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 lands’’, or ‘‘Irrigated farm land’’ in the River flood
plains. Also the soil type ‘‘Irrigated farm land’’ 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 SWAT’s
‘‘Desert Southwest Range’’. Similarly, the following
mapped units: ‘‘Mixed Forest’’, ‘‘Arable lands’’, ‘‘Wetlands’’, ‘‘Irrigated farm land’’, and ‘‘Pasture’’ were modeled as ‘‘Mixed forest’’, ‘‘Agricultural lands’’, ‘‘Mixed
wetlands’’, ‘‘Agricultural row crops’’, and ‘‘Pasture’’,
respectively, in SWAT. The description for the selected
landuse types in the SWAT landuse library closely resembles
the landuse classification produced for Iraq (CIA, 1993a–d).
For example, the CIA landuse classifies ‘‘Arable Lands’’ as
land cultivated for crops that are replanted after each harvest (e.g. wheat, corn, and rice), whereas the description
for the equivalent unit, ‘‘Agricultural Lands’’ in SWAT, is
land that is annually harvested by plants such as wheat
and corn. Landuse type distributions are used within the
SWAT 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 (FAOUNESCO, 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 alfisol, entisols, and vertisols soil
types in the area are located along the river flood plain
and in the Mesopotamian plain; they are reworked river
channel deposits. Aridisols comprise much of the modeled
area, mollisols and ultisols occur in the highlands, and
sands are located in the southern hyper-arid section of the
66
C. Jones et al.
Figure 4 Distribution of soil types derived from the NRCS database and water management projects (dams: black squares; flow
barrages: pink squares) throughout the Tigris–Euphrates watershed. Also shown are locations of stream flow gauges (yellow circles)
used for calibration (Keban, Hit, Mosul, and Baghdad) and verification (Hit and Baghdad). Green circles denote gauge locations that
were used to define dam outflows upstream from the gauge locations.
watershed (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 used to estimate the saturated hydraulic
conductivities for each of the investigated horizons (Rawls
and Brakensiek, 1983) and the estimated conductivities
were then 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). The shapefiles
depicting distributions of soil hydrologic groups, together
with the generated landuse digital maps (also shapefiles),
were imported into the model and 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), 1–7 HRU’s were
defined for each subbasin, resulting in a total of 401 HRUs
for the watershed. The integration of the information con-
tained in the soil and landuse coverages allowed for the
extraction of a CN distribution map for soil types across
the entire watershed.
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 located in Iraq. Stream flow data served two purposes: (1) to calibrate the model and (2) to constrain
outflow for dams upstream whenever this data was unavailable. Four stations were selected for calibration purposes;
two stations (Keban and Hit) are located on the Euphrates
and the other two (Mosul and Baghdad) on the Tigris (Fig. 4).
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 such as
the Saddam, Mosul, and Dokan reservoirs but not for dams
outside of Iraq such as the Keban reservoir in Turkey and
Hydrologic impacts of engineering projects on the Tigris–Euphrates system and its marshlands
the Tabqa reservoir in Syria (Fig. 4). Stream flow data from
the nearest gauges downstream were treated in our model
as outflow values for the latter types of dams. For example,
stream flow data from the Husybah 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).
Temporal satellite data
We generated temporal satellite mosaics over the marshes
to monitor the disintegration (1969–2002) and restoration
(2003–2006) of the marshes. False 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., 1992, 1993). In generating these false color images,
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 lm wavelength region (MSS, TM, and MODIS bands
2, 2, and 1, respectively), the relatively high reflectance
of vegetation in the 0.69–0.83 lm wavelength region as
compared to soils (TM band 4 and MODIS band 2), and
the relatively low reflectance of vegetation in the 0.4–
0.6 lm wavelength region compared to the surrounding
soil. Given the selected band and color schemes, vegetation appears in shades of green on 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 utilized in this
study is a Landsat MSS 1973 scene. For time periods preceding 1973, only panchromatic satellite images are available (CORONA satellite data; McDonald, 1995). Because
these images are acquired in the visible (0.4–0.7 lm)
wavelength region, the vegetation and the water bodies
that define the areal extent of the marshes appear as relatively dark areas compared to their surroundings.
The digital mosaics were used to delineate the temporal
variations in marshland extent, and to extract reservoir
storage parameters. These parameters are required for
modeling reservoir storage and routing in SWAT. 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. We applied standard
GIS techniques to DTED data to compute the average areal
extent of the lake, the area enclosed by the contour having
the average surface elevation of the examined satellite
images. We then extracted the volume of water subtended
between the delineated surface and the surface of the Earth
portrayed in the DTED data, by summing all intervals
between these two surfaces again using standard GIS
applications (ESRI, 2001). Similar procedures were used to
67
delineate the emergency spillway surface area and volume
using DTED data and the known surface elevation of the
principal emergency spillway.
Model calibration and validation
The model was calibrated (1964–1970) by adjusting model
parameters of snowpack/snowmelt, soil, groundwater, and
channel characteristics to achieve the best possible match
to the stream flow data observed at two upstream (Mosul
and Keban) and two downstream (Hit and Baghdad)
(Fig. 4) stations on the Tigris and Euphrates Rivers, respectively. The calibration was conducted for the time period
preceding the onset of the construction of the major dams
across the watershed in the early seventies. An equilibration
period of one year was adopted to get the hydrologic cycle
fully operational (Neitsch et al., 2002).
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 slow
and gradual hydrologic process, compared to the rainfall–
runoff process. Model calibration, thus, was conducted via
a stepwise, iterative process by adjusting the following:
snow pack/melt parameters, followed by key soil/groundwater parameters, and finally channel routing parameters.
The calibration was conducted against the observed
monthly flow hydrographs for the upstream gauges followed
by the downstream gauging stations. Following the calibration period, dams were constructed on the Euphrates and
Tigris rivers. The calibrated model was validated (1971–
1998) by comparing the simulated stream flow rates to the
observed flow rates at the downstream stations (Hit and
Baghdad).
Snow pack/melt parameters
Several main snow pack/melt related parameters were adjusted to calibrate the simulated flow rates against observed flow rates at the Mosul and Keban gauging stations.
These two stations are proximal to the mountainous area
and receive the majority of the snow melt from the highlands further north. Keban is located in the Taurus Mountains, whereas Mosul is located near the base of the
Zagros Mountains. The calibration of these two stations is
essential for the calibration of the whole system. The SWAT
model classifies precipitation as snow or rain depending on
the snow fall temperature (SFTMP). For example, if the
SFTMP was set to 2 C and the maximum daily and minimum
daily temperatures were 10 C and 10 C, respectively, the
calculated average daily temperature (0 C) will be less than
the SFTMP and precipitation will be modeled by SWAT as
snow. 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 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
68
C. Jones et al.
(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. Seven parameters related to snow pack/melt
were adjusted one at a time following a general order from
a relatively sensitive to less sensitive parameters. The
adjustment were performed through trial-and-error within
a reasonable range for each parameter and the entire process was manually repeated a few times until a general
agreement between simulated and observed monthly hydrographs was reached.
SWAT’s default values were adopted as our initial parameter values and the implemented adjustments were constrained by the ranges of parameter variation provided by
SWAT. The reported SWAT default and range values were
based on results from previous studies (Anderson, 1976; Huber and Dickinson, 1988; Sangrey et al., 1984; Smedema and
Roycroft, 1983; Lane, 1983; and Chow, 1959). Table 2 lists
for each of the seven snow parameters (this section) and
other parameters listed in Sections ‘‘Soil and ground water
parameters’’, ‘‘Channel characteristics’’, and ‘‘Calibration
criteria and model evaluation’’ the following: parameter
definition, adopted parameter value, SWAT’s parameter
Table 2
Parameter
SWAT default range (value)
a
Final value
Definition
2.0
150.0
Snowfall temperature (C)
Minimum snow water content that corresponds to 100% snow
cover, SNO100 (mm H2O)
Fraction of snow volume represented by SNOCOVMX that
corresponds to 50% snow cover
Snow pack temperature lag factor
Snow melt base temperature (C)
Melt factor for snow on June 21 (mm H2O/ C day)
Melt factor for snow on December 21 (mm H2O/ C day)
Available water capacity of the soil layer (mm/mm soil)
Soil evaporation compensation factor
Threshold depth of water in the shallow aquifer required for
return flow to occur (mm H2O)
Threshold depth of water in the shallow aquifer for ‘‘revap’’
to occur (mm H2O)
Groundwater ‘‘revap’’ coefficient
Groundwater delay time (days)
Baseflow alpha factor (days)
Deep aquifer percolation fraction
Effective hydraulic conductivity in tributary channel
alluvium (mm/h)
Effective hydraulic conductivity in main channel alluvium
(mm/h)
Manning’s ‘‘n’’ value for the tributary channels
Manning’s ‘‘n’’ value for the main channel
SFTMP
SNOCOVMX
( )5–5 (1.0)
0–500 (1.0)b
SNO50COV
0–1 (0.5)a
0.58
TIMP
SMTMP
SMFMX
SMFMN
SOL_AWC
ESCO
GWQMN
0–1 (1.0)a
( )5–5 (0.5)a
0–10 (4.5)c
0–10 (4.5)c
Variesa
0–1 (0.95)a
0–5000 (0)a
0.01
5.0
4.5
0.0
Varies (0.01–0.50)
0.1
2000 (Aridisol)
REVAPMN
0–500 (1.0)a
400.0
GW_REVAP
GW_DELAY
ALPHA_BF
RCHRG_DP
CH_K1
0.2–1.0 (0.2)a
0–500 (31)d
0–1 (.048)e
0–1 (0.05)a
0–150 (0.50)f
0.2
Varies (20–65)
Varies (0.1–0.9)
0.05
50.0
CH_K2
0–150 (0.0)f
0.0
CH_N1
CH_N2
0–0.3 (0.014)g
0–0.3 (0.014)g
Varies (0.08–0.12)
Varies (0.035–0.05)
a
c
d
e
f
g
Soil and ground water parameters
The second step in the calibration process aimed at
achieving a finer fitting for the higher and lower flows
by adjusting soil and groundwater parameters. The impacts of these adjustments were more pronounced on
the lower gauges since they are largely controlled by
the overall groundwater system, whereas the upper
gauges are more impacted by the runoff system. The
groundwater parameters include soil available water
capacity (SOL_AWC), soil evaporation compensation coefficient (ESCO), threshold water levels in shallow aquifer for
base flow (GWQMN) and for re-evaporation/deep aquifer
percolation
(REVAPMN),
re-evaporation
coefficient
(GW_REVAP), deep percolation fraction (RCHRG_DP), delay time for groundwater recharge (GW_DELAY), and baseflow recession constant (ALPHA_BF). These parameters
dictate the amount as well as the timing of water flow
Calibration parameters
a
b
default value, default range, and the source for SWAT’s
defaults. An iterative process was applied over the calibration period to provide a coarse fitting for the main peaks
that develop in May and June. The characteristics of the
simulated hydrographs were found to be more sensitive to
the SNOCOVMX, SNO50COV, SFTMP, SMTMP parameters.
Neitsch et al. (2002)
Anderson (1976).
Huber and Dickinson (1988).
Sangrey et al. (1984).
Smedema and Roycroft (1983).
Lane (1983).
Chow (1959).
Hydrologic impacts of engineering projects on the Tigris–Euphrates system and its marshlands
through the soil zone and underlying aquifer to the stream
channel.
A classification scheme for GW_DELAY response was tied
to the topographic expressions within the watershed. Soils
within subbasins in the highlands were assigned lower delays
factors (20–35) compared to areas in the plains downstream
(40–65). The baseflow recession constant (ALPHA_BF) and
the groundwater delay (GW_DELAY) were found to be the
major parameters affecting the shape of the high flows;
these parameter and others listed above (e.g., GWQMN,
REVAPMN) were optimized manually through trial-and-error, whereas default values were adopted for a number of
the less sensitive parameters in our model (e.g., RCHRG_DP,
GW_REVAP) (Table 2).
Manual calibration methods were performed instead of
automated methods due to the vast differences in physical
characteristics experienced throughout the modeled region.
Heuristic calibration scenarios for rainfall–runoff models
for such complex systems are difficult to implement using
automated methods. For example, and as described earlier,
for each of the major physiographic areas (e.g., mountains,
lowlands), each parameter had to be calibrated separately
to achieve the best results. Some 400 runs were completed
to optimize the model fit. As is the case with the previous
calibration step, this step focused on calibrations of simulated hydrographs against observations from the upper
gauging stations. The adjustment for all key parameters
was repeated until the fitting of the monthly hydrographs
was significantly improved compared to fittings achieved
in the previous step.
Channel characteristics
The third step of the calibration process extended the fitting process to simulate the observed hydrographs at the
two downstream gauging stations with main consideration
given to parameters related to channel routing (CH_N). In
the model, a variable storage routing method (a variation
of the kinematic wave model) developed by Williams
(1969) was adopted for channel routing calculations. The
key parameter, the manning roughness coefficient (n), was
initially assigned for all HRUs using the SWAT default value
(0.014). Adjustments were then made guided by classifications described in Chow (1959) to improve the time to peaks
and peak flows for the two lower gauging stations. The following coefficients were adopted: mountainous area 0.05;
foothills 0.04; and vegetated channel plain 0.035.
Transmission losses through channels were calculated in
SWAT for two different channel types: tributary and main
channels. The key parameter for losses is the effective
hydraulic conductivity (CH_K). The main channels in the watershed receive continuous groundwater, and thus the
transmission losses for these channels are minimal. A zero
transmission loss for the main channels was simulated by
setting effective hydraulic conductivity (CH_K2) to zero.
Most tributary channels are ephemeral or intermittent
receiving little groundwater and are thus likely to lose water
to bank storage or to the underlying aquifer. These channels
were assigned the initial SWAT default hydraulic conductivity (CH_K1) values (0.5 mm/h) and adjustments (adopted
value: 50.0 mm/h) were conducted within reported ranges
(Lane, 1983) to improve hydrograph fitting for all four gauging stations.
69
Calibration criteria and model evaluation
Fine-tuning calibration was performed manually by
repeating the 3-step adjustment described above until a
best fit was achieved between modeled and observed flow
values. The values for the parameters that achieved the
optimum calibration are given in Table 2; all of the selected parameter values are within the reported SWAT default ranges. Two statistical measures, coefficient of
determination (r2) and coefficient of efficiency (E) (Nash
and Sutcliffe, 1970) were used to quantify the achieved
levels of calibration and to evaluate the overall performance of the model.
Fig. 5a and b compares the simulated flow rates (blue
curve) to the observed rates (pink curve) for the Keban,
Mosul, Hit, and Baghdad gauges over the calibration period
(1964–1970). Visual inspection of Fig. 5a shows a general
agreement between simulated and observed monthly flow
rates throughout the calibration period. The observed minor deviations between simulated and observed flow rates
on the Euphrates could be in part attributed to uncertainties introduced by the adopted approximations converting
average monthly climatic data into daily values. The more
significant deviations observed on the Tigris River are probably largely related to uncertainties introduced by the paucity of climatic stations in the highlands compared to the
Euphrates system. Only two climatic stations are located
in the northeast highlands of the Tigris subbasin. The stations are located at extreme elevations (>4000 m),
whereas the remaining highlands that are of relatively lower elevations (1200 m) lack any climatic stations. This situation could factiously lower the modeled temperatures
and overestimate the modeled snow accumulation at the
expense of runoff. This could explain why our simulated
flow on the Tigris Mosul station is lower than the observed
flow during the winter months (Fig. 5a). The model
characterizes some of the precipitation as snow and underestimates observed runoff. Adjustment of SFTMP was
found to improve the simulated hydrograph on the Tigris
River but the simulation for the Euphrates River was
compromised.
The final calibration was achieved on the basis of the
overall model performance. The coefficient of determination (r2) for simulated flows ranges from 0.6 to 0.8 and
the coefficient of efficiency (E) exceeds 0.6. Compared to
the other three stations, the relatively larger differences
between modeled and observed values at the Baghdad
gauge are largely related to the absence of quantitative
estimates for the amount of water diverted during peak flow
seasons from the Tigris River into surrounding natural
depressions. For example, the Sammarra Barrage System
was constructed (1954) to divert water from the Tigris River
into the Thartar Depression to protect Baghdad against
flooding (Manley and Robson, 1994; UNEP, 2001). In absence
of such data, we could not simulate the diversion of these
waters resulting in higher modeled simulated peak flows
compared to observed flows.
Fig. 6 provides a comparison between the simulated
monthly flow rates and the observed rates at the Hit and
Baghdad gauging stations for the time period 1971 through
1998, the validation period. The model simulations correlate reasonably well with the observed flow rates; the
coefficient of determination (r2) and coefficient of
70
C. Jones et al.
0
5000
2000
3000
Keban Gauge
4000
6000
3
Monthly Flow Rate (m /s)
2000
8000
1000
10000
4000
2000
Mosul Gauge
3000
4000
2000
6000
1000
8000
0
5/3/1964 5/3/1965 5/3/1966 5/3/1967 5/3/1968
0
12000
5/3/1964 5/3/1965 5/3/1966 5/3/1967 5/3/1968 5/3/1969
Simulated Flow
0
Observed Flow
7000
10000
Cumulative Snow Melt (mm)
4000
Cumulative Snow Melt (mm)
a
5/3/1969
Snow Melt
6000
6000
5000
5000
Hit Gauge
Baghdad Gauge
4000
4000
3000
3000
2000
2000
1000
1000
0
5/3/1964 5/3/1965 5/3/1966 5/3/1967 5/3/1968 5/3/1969
0
5/3/1964 5/3/1965 5/3/1966 5/3/1967 5/3/1968 5/3/1969
Time
b
Euphrates River
Tigris River
4000
4000
Simulated Monthly Flow Rate (m3/s)
Keban
Gauge
Keban
Gauge
3000
3000
2000
2000
1000
Mosul
Gauge
Mosul
Gauge
1000
R2 = 0.8053
0
R2 = 0.7161
0
0
1000
2000
3000
4000
0
1000
2000
3000
4000
6000
5000
Hit Gauge
Hit Guage
4000
Baghdad
Baghda
d Gauge
Gauge
5000
4000
3000
3000
2000
2000
R 2 = 0.7636
1000
1000
0
R2 = 0.6041
0
0
2000
4000
6000
0
1000
2000
3000
3
Observed Monthly Flow Rate (m /s)
Figure 5 (a) Calibration results comparing the simulated monthly flow rates (blue curve) to observed flow rates (pink curve) for
the upstream gauges (Keban and Mosul) and downstream gauges (Hit and Baghdad) throughout the calibration period (1964–1970).
Also shown is the simulated cumulative snow melt (green curve) from all subbasins contributing to flow at Keban and Mosul gauges.
(b) Observed versus simulated flow rates at the Keban, Hit, Mosul, and Baghdad gauges throughout the calibration period. Red lines
show the 1:1 correlation.
efficiency (E) throughout the validation period are 0.62
and 0.6, respectively. The figure shows a general trend
of decreasing flow rate with time. Inspection of precipitation data over the watershed shows that this trend is not
related to any variations in precipitation over the past 40
years indicating that climatic variations are not likely to
have caused the observed general trend of decreasing
flow rate with time. Next we show that the reduced flow
is primarily caused by the major engineering projects on
the Tigris–Euphrates System.
Hydrologic impacts of engineering projects on the Tigris–Euphrates system and its marshlands
71
7000
7000
800
800
Hit Gauge
6000
6000
600
600
5000
5000
400
400
3000
3000
2000
2000
1000
1000
00
5/3/1964
5/3/1964
200
200
00
5/3/1968
5/3/1968
5/3/1972
5/3/1972
5/3/1976
5/3/1976
5/3/1980
5/3/1980
5/3/1984
5/3/1984
5/3/1988
5/3/1988
5/3/1992
5/3/1992
5/3/1996
5/3/1996
6000
6000
800
800
Baghdad Gauge
5000
5000
600
600
4000
4000
Precipitation (mm)
Monthly Flow Rate (m3/s)
4000
4000
400
400
3000
3000
2000
2000
200
200
1000
1000
0
5/3/1964
5/3/1964
5/3/1968
5/3/1968
5/3/1972
5/3/1972
5/3/1976
5/3/1976
5/3/1980
5/3/1980
5/3/1984
5/3/1984
5/3/1988
5/3/1988
5/3/1992
5/3/1992
0
0
5/3/1996
5/3/1996
Time
Time
Observed Flow
Observed Flow
Simulated Flow
Simulated Flow
No Data
No Data
Precipitation
Precipitation
Figure 6 Calibration and validation results for the downstream gauges (Hit and Baghdad). Simulated monthly flow rates (blue
curve) were calibrated (1964–1970) and validated (1971–1998) against observed flow rates (pink curve). Also shown are the
variations in total annual precipitation over the entire watershed (red curve).
Discussion
The calibrated model was used to investigate the impacts
of the construction of dams inside and outside Iraq on the
size and vitality of the marshes. We addressed 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 were
computed at a location near the city of Al-Basrah. It is
noteworthy that the nearest available stream flow data is
upstream from Al-Basrah, some 560 km from the Hit station on Euphrates and 350 km from the Baghdad station
on Tigris. The simulated flow for the time period (10/1/
1965 to 09/30/1973) that preceded the construction of
the major dams (SC: >3 · 109 m3), hereafter referred to
as period A, and the flow for the time periods, B1: 10/1/
1973 to 09/30/1989, and B2: 10/1/1989 to 09/30/1998
that witnessed the construction of the major dams are
shown in Fig. 7. The Keban, Tabqa, Haditha, and Karakaya
dams on the Euphrates, and the Mosul and Hamrin dams
(total SC: 76 · 109 m3) on the Tigris were constructed in
period B1, whereas the Ataturk (SC: 50 · 109 m3) was constructed in period B2. The AMPFR (6301 m3/s) in period A is
progressively diminished in periods B1 (AMPFR: 3073 m3/s)
and B2 (AMPFR: 2319 m3/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.
The impacts of dam related reduced flow on the vitality
of the marshes was further investigated by comparing the
simulated inflow from Tigris and Euphrates (Fig. 7; black
line) to the inflow that would have occurred if no dams were
constructed (Fig. 7; pink 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 Fig. 7 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, infiltration and 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
1970–1973 preceding the completion of the Keban and Tabqa dams in 1974, the simulated AAFV was reduced by up to
30%. Similarly, during the filling of the Ataturk reservoir, the
AAFV was reduced by yet another 31% from its 1988 values
(Fig. 7) over the course of three years (1989–1992). The
construction of the major dams represented in Fig. 7 led
to an abstraction of over 75 · 109 m3 over the course of
the calibration and validation periods. If we include the
smaller dams (<3 · 109 m3), the one time extraction would
have reached over 100 · 109 m3.
Inspection of the percent reduction in AFV shows that
following the lake-filling period, (1–3 years on the average),
flow volumes increase, but generally to levels less than predam levels. For example, following the filling of the Keban
and Tabqa reservoirs, the simulated AFV bounced back to
approximately 85% of its original volume in 1969 and following the filling period (1989–1993) of the Ataturk reservoir,
the AFV reached 70% of the 1969 values. It is these
72
C. Jones et al.
A
B1
B2
Keban (31)
Tabqa (11.7)
12000
Haditha (8.2)
Hamrin (3.95)
Karakaya (9.58)
Mosul (11.1)
?
? ?
-10
C
0
10
Ataturk (48.7)
20
10000
30
8000
40
50
6000
60
4000
70
80
2000
90
0
1/1/65
Percent Reduction Annual Flow Volume
(Dams /NoDams)
3
Average Monthly Flow Rate (m /s)
14000
100
1/1/69
1/1/73
Flow With Dams
1/1/77
1/1/81
1/1/85
Time
Flow Without Dams
1/1/89
1/1/93
1/1/97
Percent Reduction in Flow
Figure 7 Simulated monthly flow rates from 1965 to 1998 (black curve) are compared to flow rates that would have existed if no
dams were constructed (pink curve). Also shown are the percent reductions in AFV that resulted from the construction of dams (blue
curve). Three major time periods are represented on the figure: time period A (1965–1973) refers to the time where no major dams
were yet constructed; time period B1 (1973–1989) refers to the time span where the Keban, Tabqa, Hamrin, Haditha, Mosul, and
Karakaya dams were constructed, and time B2 (1989–1998) refers to the period where the Ataturk dam was completed. The pink
and black curves overlap in period A, the time period where no major dams were constructed, but progressively diverge in periods B1
and B2 as more dams are being constructed. Reported AFV values were calculated for each year starting in October 1st and ending in
September 30th in the following year.
permanent reductions in flow volumes that pose, we believe, the largest threat to the livelihood of the marshes.
The percent reduction in AFV for period B1 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 from
the Tigris and Euphrates. The higher the flow, the larger the
marsh area becomes and vice versa. We restrict our analysis
to the time period 1966–1987 (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 in
marshland extent using scenes acquired around the same
approximate time period of the year, the months of July
through 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 and Landsat 5 scenes acquired in either July, August,
or September of 1975, 1976, 1977, 1984, 1985, 1986, and
1987. Analysis of these images showed that the areal extent of the marshes was reduced (1966: 7970 km2; 1977:
6680 km2; and 1984: 5270 km2) in response to the progressive construction of dams upstream (Fig. 8). Unfortunately,
satellite imagery over the examined area, season, and
time period are limited; to the best of our knowledge
there are only nine coverages between 1966 and 1987,
the ones reported here.
Spatial variations were primarily observed along the western and northern margins and to a much lesser extent along
the eastern and southern boundaries, where the marshes
are generally bound by steeper topographic boundaries
(e.g., cliffs). Because most of the flow in the Tigris and
Euphrates is generated in the snow melting season, it could
be demonstrated to a first order that the areal extent of the
investigated marshes decreases with the decline in AFV
(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 apply the same
procedures to extract similar relationships for marshes
other than the Central and Al-Hammar marshes within the
Tigris–Euphrates system. Such relationships could be used
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 few
years.
Hydrologic impacts of engineering projects on the Tigris–Euphrates system and its marshlands
73
needed to irrigate a hectare of land in this area (Gollehon
and Quinby, 2006), we estimate an additional water allocation of approximately 5.3 · 109 m3/yr will be required to
irrigate the new farmlands. This increase in irrigated farmland will further reduce the inflow of the Tigris–Euphrates
system into the marshes. At a minimum, the AFV will be reduced by more than 5 · 109 m3/yr not to mention losses to
evaporation and infiltration in reservoirs behind newly constructed dams. Fig. 9 shows that such a reduction in the
average AFV will result in an additional decline in the area
of the Central and Al-Hammar marshes amounting to
approximately 550 km2.
Summary
Figure 8 Spatial variations in the areal extent of the Central
and Al-Hammar marshlands extracted from temporal (1966–
1984) satellite data. The variations correspond to progressive
reduction in AFV caused by engineering projects (e.g., Keban,
Tabqa, Hamrin, and Haditha dams) outside of the marshes. The
maximum extent (September, 1966; black line) of the marshes
under natural flow conditions was reduced (August, 1977; blue
line) following the post filling of the Keban and Tabqa dams and
was further decreased (July, 1984; yellow line) following the
construction of additional dams (Hamrin and Haditha).
Annual Flow Volume (109 m3)
70
60
50
40
30
20
10
2
R = 0.730
0
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
Area: Al-Hammar and Central Marshes (km2)
Figure 9 Plot of spatial extent of the marshlands extracted
from temporal satellite images acquired in September 1966,
July 1975, August 1976, August 1977, July 1984, August 1985,
July 1986, September 1986, and August 1987 against the
simulated AFV for these nine time periods.
The ongoing South Eastern Anatolia project calls for an
increase in irrigated area of 1,700,000 hectares by 2010
(SPO, 1990). This will be achieved by the completion of a
series of dams and canal systems to channel the reservoir
water to new irrigated land. At present, nine of the 22 dams
in the project have been completed. The new dams under
construction or planned equate to a combined additional
storage of 15.5 · 109 m3. Assuming 3100 m3 of water are
We constructed a catchment-based continuous (1964–1998)
rainfall–runoff model for the entire Tigris–Euphrates watershed (area: 1,000,000 km2) 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 landuse change downstream in the marshes. Precipitation was derived from daily and monthly rain gauge data
(36 stations) from 1964 to 1990 and from the historical global gridded precipitation dataset (1990–1998). Six landuse
types were derived from landuse map sheets and nine soil
types were derived 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 SCS curve number
method was adopted to estimate direct runoff using rainfall
excess and channel routing was conducted using the variable storage method. The model was successfully calibrated
against stream flow data in Iraq (3 gauges) and Turkey
(1 gauge).
The calibrated model was used to simulate the AMFR,
AMPFR, and AFV of the Tigris and Euphrates into the
marshes at a location near Al-Basrah city for the following
time periods: time period A (10/1/1965 to 09/30/1973),
the time period that preceded the construction of the major
dams; and time periods B1 (10/1/1973 to 09/30/1989) and
B2 (10/1/1989 to 09/30/1998), the time periods that witnessed the construction of the major dams. The model
shows that the AMPFR (6301 m3/s) and AAFV (80 · 109
m3/yr) in period A is progressively diminished in periods
B1 (AMPFR: 3073 m3/s; AAFV: 55 · 109 m3/yr) and B2 (AMPFR: 2319; AAFV: 50 · 109 m3/yr) due to the combined effects of evaporation and infiltration of the water
impounded behind dams and the utilization of stored water
for irrigation purposes.
We demonstrated that for the time period preceding
the construction of the major engineering projects in and
around the investigated marshes, the areal extent of the
Central and Al-Hammar marshes extracted from temporal
satellite images acquired around the same approximate
time period of the year (months of July to September) decreased (e.g., marsh area 1966: 7970 km2; 1977: 6680 km2;
1984: 5270 km2) with reductions in AFV (e.g., AFV 1966:
60.8 · 109 m3; 1977: 56.9 · 109 m3; 1984: 37.6 · 109 m3). A
relationship that describes the impact of reduced flow on
the areal extent of the examined marshes was then used
to provide first order simulations of what would be the
74
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 few
years. The ongoing South Eastern Anatolia project calls
for an increase in irrigated area of 1,700,000 hectares
by 2010. Upon completion of the project, we estimate that
an additional water allocation of 5.3 · 109 m3/yr will be
needed. This together with additional losses to evaporation and infiltration behind dams will reduce the inflow
of the Tigris–Euphrates system into the marshes by at
least 5 · 109 m3/yr. This reduction in the AFV will result
in an additional decline in the area of the Central and
Al-Hammar marshes amounting to a minimum of an additional 550 km2.
Acknowledgements
Funding was provided by the National Science Foundation
through OISE Grant 0455896. We thank the reviewers of
the Journal of Hydrology for their constructive comments.
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