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Hydrologjeal Sciences - Journal - des Sciences Hydrolopques, 34,6, 12/1989
A water quality model for the Tigris River downstream of Sadam Dam, Iraq
M.ANK AL-LAYLA Saddam Dam Research Center, Mosul University, Iraq
HASAN M. AL-RIZZO Research and Development Center, Mishrak Sulphur State Enterprise, Iraq
Abstract The present work describes the development and calibration of a mathematical model for the Tigris River downstream of Sadam Dam. The river stretch studied is 75 km long extending from the Sadam Dam to Mosul city. The field work was conducted during the period from July to September 1986. Water samples were collected bimonthly from specified sampling points. The model simulates river assimilation capacity for a variety of water quality parameters by performing the numerical solution of a set of differential equations representing the aquatic system under steady state conditions. Generally, a noticeable increase in the concentrations of water quality parameters arising from water impoundment was observed. A good agreement was found between measured and simulated concentrations of water quality parameters. However, discrepancies noticed during model calibration were attributed to the assumptions adopted in the model formulation, to lack of field data, and to exclusion of some variables in model building.
Un modèle de qualité de l'eau pour le Tigre en aval du barrage de Saddam en Iraq
Résumé Cette étude décrit la mise au point et l'étalonnage d'un modèle mathématique pour le Tigre en aval du barrage de Saddam, au nord de Mossoul. La partie étudiée de ce fleuve depuis le barrage jusqu'à la ville de Mossoul a une longueur de 75 kms. Le travail sur le terrain a été effectué à partir du mois de juillet jusqu'au mois de septembre 1986. Les échantillons d'eau d'eau ont été collectés chaque quinzaine à des points d'échan-tillonage spécifiés. Le modèle a simulé la capacité d'assimilation du fleuve pour une série de paramètres de la qualité de l'eau. Ceci comprend la solution numérique d'une série d'équations différentielles qui représentent le système aquatique sous un état de conditions stables. Généralement, on a observé une augmentation sensible de la concentration des paramètres de qualité des eaux. Ceci est du au confinement notable des eaux dans ce projet. Un bon accord a été trouvé entre la concentration des
Open for discussion until 1 June 1990 687
M. AnisAl-Layla & Hasan M. Al-Rhzo 688
paramétres de qualité des eaux mesurée et celle-ci simulée. De toute façon les désaccords observés durant l'étalonnage ont été attribués aux hypothèses adoptées dans la formulation du modèle, le manque de données de terrain, et l'exclusion de quelques variables dans la construction du modèle.
INTRODUCTION
Discharge of pollutants to a water resource system from domestic sewers, storm water discharges, industrial wastes discharges, agricultural runoff and other sources, all of which may be untreated, can have significant effects of both short term and long term duration on the quality of a river system (Crabtree et al, 1986).
Modelling the quality of a river system is generally done in order to describe and predict the observed and future effects of a change in the river system (Nemerrow, 1974). Modelling can therefore be used as an aid to quality management decision making for resource allocation to achieve designated water quality objectives.
The objectives of this study were to develop a water quality mathematical model and to apply it to the stretch of the Tigris River downstream of the Sadam Dam in Iraq.
IJTERATURE REVIEW
Many of the basic mechanisms of stream self-purification have been described qualitatively (Banks, 1976) and several attempts have been made to formulate quantitative mathematical models for the prediction of water quality (Rutherford & Sullivan, 1974). The basic concepts behind models of a particular type are similar, and they differ primarily only in their approach to a specific problem. Only models of types considered essential for studying the water quality of streams will be reviewed here.
DOSAG I is a model which was developed to solve the integrated form of the Streeter-Phelps equation and is applicable to systems which can be simulated as one-dimensional and steady state with no dispersion (Texas Water Development Board (WDB), 1970). The original DOSAG I was modified for the US Environmental Protection Agency (EPA) (Water Resources Engineers, 1973) and the resulting product was called DOSAG III. DOSAG III is similar to DOSAG I except that it has the ability to simulate the steady state behaviour of more water quality parameters.
The model QUAL I was also developed by the Texas WDB using a finite difference solution to solve the one-dimensional dispersion-advection equation (Texas WDB, 1971). Unlike the previously described models, which utilize a reach as a minimum element for computation, and need only discharges at the beginning and end of the reach to be input, QUAL I uses a standard computational element of a set length through the system. Computational elements with similar hydrological and physical properties are grouped as reaches.
689 A water quality model for the Tigris River
QUAL II is a modification of QUAL I and is applicable to well-mixed dendritic streams. It can predict both the temporal and spatial variations of up to 13 water-quality constituents (Water Resources Engineers, 1973). QUAL II is perhaps the most comprehensive in a group of water quality models developed in the United States. It is readily adaptable to a wide variety of river quality modelling situations and has been modified for use on major European river systems. However, the efforts made in this field yield a great variety of water quality models ranging in increasing complexity from being relatively simple to being highly complex. The failure to accept and trust models stems from: (a) model application to a variable or process that is too complex for
formulation without quantifying the major parameters and assumptions; (b) application of sophisticated general case models without adequate
understanding of the particular river in question; (c) misapplication of model calibration and verification procedure; and (d) the use of poor data for interpretation, model calibration, and model
verification. To overcome these deficiencies, the selection of a particular model or
model configuration should be accompanied by a statement of limitations, predictive accuracy and suggested applications.
MODEL DESCRIPTION AND DEVELOPMENT
The mathematical model solves numerically the one-dimensional advection-dispersion equation for the water quality parameters of interest. The model routes water quality constituents by a one-dimensional mass balance analysis based on the equation:
(Aàx) = fc _ i ^ + (Aàc) TS ( 1 )
at ax ax dt
where: c = concentration of pollutant; x = distance; t = real time; A = cross-sectional area perpendicular to x ; DL = dispersion coefficient; U = water velocity perpendicular to x ; and S = source or sink of pollutant.
A solution of this equation would yield a relationship expressing the dependent variable C as a function of the independent variables x and t. The equation is solved by using a fully implicit finite difference procedure. The first order differential equations describing the variations in concentration for each constituent are shown in Annex 1. The mathematical model possesses the capability of routing the following water quality constituents:
M. Anis Al-Layla & Hasan M. Al-Rizzo 690
(a) non-conservative constituents: dissolved oxygen (DO); biological oxygen demand (BOD); nitrite-N; ammonia-N; organic-N; phosphate; and total coliform bacteria; and
(b) conservative constituents: total dissolved solids; total hardness (TH); sulphate; chloride; and sodium.
Model conceptualization
The model conceptualizes the stretch of river studied as a series of reaches. Reaches are assumed to represent portions of the river having uniform conditions (geometric, hydraulic and chemical/biological coefficients). Reaches are further subdivided into units called computational elements. Each computational element is modelled as a constant volume, completely mixed reactor with input, output and reaction terms.
Submodels
The mathematical model contains submodels covering the quality parameters, determination of coefficients, temperature calibration and a hydraulic submodel (Al Rizzo, 1987). These submodels interact together with the main model to simulate the water quality parameters of the river Tigris.
Hydraulic quality submodels Generally these submodels simulate each quality parameter by defining the nature of the source/sink, S, in the mass balance equation (1). The element consists of two parts. The first contains the forcing function inputs which enter directly into the mass balance equation. The second consists of the reactive components which are specific to the particular water quality variable being modelled (Annex 1).
Hydraulic submodel The hydraulic parameters were calculated via the Leopold & Maddock (1953) formulae. The exponents and coefficients of these formulae for meandering, braided and straight reaches were obtained from the previous work of Najib (1980) on the hydraulics of the river Tigris (Annex 2).
Coefficient Submodel In this submodel the coefficients of BOD, DO, nitrification, phosphate and coliform bacteria are calculated and calibrated according to the stream temperature by the general formula :
KT=K2Qx BT'20 (2)
where: KT = chemical reaction coefficient at temperature T; K2Q = chemical reaction coefficient at 20°C; and 9 = temperature correction factor.
691 A water quality model for the Tigris River
DESCRIPTION OF THE STUDY AREA
The river stretch studied is 75 m downstream of Sadam Dam. The catchment area is characterized by agricultural activities centred along both banks of the Tigris and around the villages distributed in this area. Most of these villages are located away from the Tigris banks except Wanah and Tel Hemian.
Field survey has shown the absence of domestic and industrial waste water outfalls along the studied stretch. However, the main source of pollutants includes agricultural return flow, non-urban runoff and human and animal excrement. In general, three discharge levels can be distinguished in the annual cycle of the Tigris river water regime, viz. minimum, average, and maximum. The discharges at these levels are 151, 326 and 2219 m3 s"1
respectively. The rainfall is seasonal varying from 40 to 100 mm annually, occurring
mainly from November to April with 50% or more occurring between January and March.
The temperature change between winter and summer is as great as between day and night. July and August are the hottest months with the temperature in the shade as high as 50°.
EXPERIMENTAL BACKGROUND FOR MODEL CALIBRATION
Model calibration is the first operation, viz. tuning or fitting a model to a set of field data (Lombardo & Otto, 1974). The experimental part of this study consisted of collecting the necessary field data set, i.e. the sampling and analysis of the water quality parameters for the stretch of the Tigris extending from Sadam Dam to Mosul city.
The field work was conducted during the period from July to September 1986. The stretch was divided into twelve reaches (Fig. 1). Configuration of the stream channel, irregularity of cross-section, depth, bends and gradient were considered in defining these reaches. Water samples were collected bimonthly from these reaches during the field work period. The analysis of the water samples covered the conservative and nonconservative constituents. The physical, chemical and biological tests were conducted according to standard methods (APHA-AWWA-WPCF, 1975).
MODEL CALIBRATION: RESULTS AND DISCUSSIONS
To establish the validity of the model for use in the stretch of the Tigris downstream from Sadam Dam, coefficients were computed, data sets were entered and model output was obtained. The calibration process was based upon the field data obtained from the survey work done in the study area.
Usually, the discharge of the regulating reservoir at the Sadam Dam is varied according to a computerized discharge schedule. The average discharge from the Sadam Dam regulation reservoir was adopted as a basis for calibration calculations.
M. Anis Al-Layla & Hasan M. Al-Rizzo 692
Fig. 1 The area of model calibration at Mosul (Naom, 1985).
No attempts were made in measuring or modelling the water temperature of this stretch; the data necessary for heat-budget modelling were not available. Therefore, the temperature of the stream during the survey period was measured directly in the field.
The initial conditions for calibration were given by the water quality parameters of the regulating reservoir.
Hydraulics of the stretch
Table 1 displays the hydraulic parameters of the stretch studied. The stream velocity for different reaches varied from 1.23 to 1.61 m s"1, while the hydraulic radius varied from 2.04 to 3.14 m. However, the wetted perimeter varied from 126.4 to 215.5 m.
It is obvious that the variations in hydraulic parameters were not great, thus introducing the same initial conditions to self-purification processes within the stretch (Velz, 1970).
693 A water quality model for the Tigris River
Table 1 Hydraulic parameters for the stretch downstream of Sadam Dam
Section Distance (km)
Velocity (m sY)
Wetted perimeter (m)
Hydraulic radius (m)
1 2 3 4 5 6 7 8 9 10 11 12
4.20 5.85 4.43 5.05 6.20 7.20 6.50 9.70 8.00 8.40 5.90 3.70
1.61 1.56 1.61 1.56 1.23 1.47 1.42 1.49 1.57 1.36 1.25 1.43
212.2 176.1 156.5 173.2 215.2 160.2 173.5 165.2 126.4 185.1 192.2 132.2
2.98 2.23 3.05 3.22 2.97 3.22 2.43 2.98 2.04 2.22 2.08 3.15
BOD and DO profile
Observed and simulated BOD data from modelling for the stretch downstream of Sadam Dam are illustrated in Fig. 2. It is obvious that the BOD concentration decreases gradually during the river passage downstream of the dam. This phenomenon can be attributed to the natural self-purification of the river and the lack of outfalls along this stretch. Small discrepancies can be noticed between the observed and simulated concentrations of BOD. Generally, the simulated values of BOD are greater than those measured,
1.0
0.9
r - _ _ .
V-"\ x© ©~ - _
O.S t
l
:
0-7
• simulated
~~~~"~* 0 measured
\ \ \ \
\ to
^ ~«- " - .
\ - & • ' \
\ \ \
\ \ et
IS 21 27 33 39 45 51 57 S3 6S 75 Distance , km — * -
Fig. 2 BOD variation downstream of Sadam Dam.
M. Anis Al-Lctyla & Hasan M. Al-Rizzo 694
except that at reach 7. The abnormality in BOD removal from the stream can be attributed to the biological extraction of organic matter (Nemerrow, 1974). The biological growth attached to the stream bed or biological floe extracts the organic matter from the flowing stream, thus reducing the concentration of BOD (Velz, 1970). Hence, the measured values are less than the simulated values since the model structure omitted the formulation of this phenomenon. Also the assumptions adopted in the model formulation, mainly the complete mixing and dispersion of pollutants within the computational element, means that the sedimentation of organic matter and dead zones found within the reach would introduce the observed deviations (Velz, 1970).
The DO results from the simulation are shown in Fig. 3 along with the observed values. Generally, the DO increases gradually downstream of Sadam Dam due to re-aeration by turbulence and self-purification (Nemerrow, 1974).
The discrepancies between the observed and simulated values of DO can be explained by the photosynthetic activity of aquatic plants, notably the green and blue-green algae (O'Connor & Toro, 1970), sediment activity, stream temperature and the assumptions adopted in formulation. However, the adoption of the Leopold & Maddock (1953) hydraulic formulae in determining stream velocity and depth have a considerable effect on the calculated reaeration coefficient, hence on the simulated level of DO in the stream (Nemerrow, 1974).
9.0
8.5
S.O
7.8
/
/ /
/ '
/' S ' — — ®~~ ~~ """
/'/ / / / / /
—..~— _̂ _̂ — _ — — . _ — ...
S 15 21 27 33 39 <5 St 57 S3 69 75 Distance, Km — * -
Fig. 3 DO variation downstream of Sadam Dam.
Nitrification
Figure 4 displays the variations in the simulated and observed concentration
695 A water quality model for the Tigris River
l a ,
• simulated
© measured
\ \
\
9 /5 2? 27 33 39 45 51 57 63 69 75 Distance, km—v-
Fig. 4 Ammonia-N variation downstream of Sadam Dam.
of ammonia-N downstream from Sadam Dam. The nitrification process causes a gradual decrease in ammonia-N concentration along the studied stretch (Banks, 1976). However, the measured concentrations illustrate a gradual increase, mainly in the downstream (Banks; 1976; Water Resources Engineers, 1973). This increase may be attributed to the impact of agricultural return flow from the agricultural lands distributed along these reaches. The impact of return flow on ammonia-N arises from the excessive use of ammonia fertilizers (Anderson & Darcup, 1976).
The discrepancies between the measured and simulated values can be attributed to the magnitude of nitrification and the extent of organic-N decaying to ammonia-N (Wild & Crockett, 1971). However, the assumption adopted to determine the quantity and quality of agricultural return flow is also one of the reasons for these discrepancies.
Figure 5 illustrates the measured and simulated values of nitrate-N along the Tigris river stretch downstream from Sadam Dam. Generally, the nitrate-N concentration increases gradually along the studied stretch. This can be related to the considerable amounts of nitrate provided through the agricultural return flow and the presence of nitrite-N which converts to nitrate, and because the nitrate-N does not decay to any significant extent under aerobic conditions (Lornezen & Chen., 1977).
The major factors describing the discrepancies noticed between the measured and simulated concentrations of nitrate-N are the rate of the nitrification process, the assimilation of nitrate-N by aquatic plants, the extent of ion exchange with benthic sediment, the agricultural return flow and the assumptions adopted in model formulation (O'Conner & Toro, 1970).
The simulated and measured concentrations of nitrite-N downstream of Sadam dam are illustrated in Fig. 6. The simulated concentrations display a gradual decrease along the stretch, caused by nitrite-N oxidation to nitrate
M. AnisAl-Layla & Hasan M. Al-Rizzo 696
-ef
/ /
/ y
• si m ui a ted
© measured
9 (5 2/ 27 33 39 45 51 57 S3 6S Distance ,km.—»-
Fig. 5 Nitrate-N variation downstream of Sadam Dam.
(Anderson & Darcup, 1976). However, the measured concentrations fluctuate along the stretch considerably. This is due to the magnitude of ammonia-N added by the agricultural return flow and its rate of decay to nitrite-N. However the temperature, stream hydraulics and discharge obscure these variations also (Anderson & Darcup, 1976).
V. \ ^̂ \ ^
V\ *< \-« * \ \ X \ \^ \ ^ x
•
V'"\ ^ \ \ \
v 0
• Simula ted
Q measured
i \
/ \ \
/ \ ~~*\' x
/ "̂ ^_ V---^--* - —-
- ~ 0
—~*
15 21 27 33 39 45 51 57 63 69 75 Distance , km —>-
Fig. 6 Nitrite-N variation downstream of Sadam Dam.
697 A water quality model for the Tigris River
Figure 7 shows the simulated and measured concentrations of the organic-N along the studied stretch. The noticeable decrease in organic-N along the stretch can be associated with organic-N decaying to ammonia by sophrophtic bacteria (Lornezen & Chen, 1977). The discrepancies between the simulated and measured concentrations of organic-N can be attributed to the kinetics of organic-N decaying, the impact of agricultural return flow and synthesis of this compound by algae (Anderson & Darcup, 1976).
,,-Q • simulated
o measured
\ \
27 33 39 Distance , km *-
Fig. 7 Organic nitrogen variation downstream of Sadam Dam.
Phosphate profile
The measured and simulated concentrations of phosphate modelling are illustrated in Fig. 8. Usually phosphate comes from the wash-off of fertilizers from agricultural areas (Naom, 1985). The self-purification phenemenon, algal uptake, zooplankton uptake and sedimentation cause the gradual decrease in phosphate concentration along the stretch studied (Banks, 1976).
It is obvious that the simulated values of phosphate are less than those measured in the field. This can be related to the effect of agricultural return flow, zooplankton release, algal decay and benthic sediment release (Baca & Arnett, 1976).
Coliform bacteria profile
The major sources of coliform bacteria in this stretch of the Tigris include runoff discharge, agricultural return flow, swimming and animal bathing and excrements (Banks, 1976).
Figure 9 reflects the measured and simulated values of coliform bacteria
M. Anis Al-Layla & Hasan M. Al-Rizzo 698
-•
e
\ \
• simulated
omeasured
"°- ..
27 33 39 Distance , km *~
Fig. 8 Phosphate variation downstream of Sadam Dam.
along the stretch studied. The simulated concentration of coliform bacteria displays a gradual decrease downstream of Sadam Dam which is due to the microbial self-purification of the Tigris. The measured values of coliform bacteria in the reaches 1, 2, 7, 8, 11 and 12 are less than those simulated by the model. These reaches are shallow, thus affording greater biological contact opportunity for bacterial absorption, hence increasing the coliform death rate (Banks, 1976). Water temperature, variable nutrients, toxins, pH and competitive life are other
530
a - s
? 9 5 /0 B
ac ter
a 500
S ~o
=; \_<S0
S a
480
170
460
K. \ . \ s
v©
N ^ / &
/a. < , _ ; - • & - '
--.«
y ^ \
\ . \
\
'*? x - \
\ ^ - ^ ^
\
® - ~
~"~- _. • v
/
~~--</
• simulated
© measured
^£>
v ' 0 r " \ . ' x
- ^ 21 27 33 39 45 51 57 63
Distance, km. — 9 -
Fig. 9 Coliform bacteria variation downstream of Sadam Dam.
699 A water quality model for the Tigris River
factors obscuring the discrepancies between the simulated and measured values.
Conservative constituents
The major sources of conservative constituent loads in the stretch studied are agricultural return flow, non-urban runoff and dissolution of benthic sediments (Anderson & Darcup, 1976)
The concentration of conservative constituents increases gradually downstream of the Sadam Dam. This is due to the impact of agricultural return flow and dissolution of salty benthic sediments. Further, these constituents do no undergo significant chemical decay (Velz, 1970).
The simulated and measured concentration of chloride, sulphate, TDS, TH and sodium ion are shown in Figs 10, 11, 12, 13 and 14 respectively. These Figures reflect a gradual increase in concentration travelling downstream from Sadam Dam. The magnitudes of these increases are 35.5 to 47.0 mg F1 for chloride, 49.3 to 59.5 mg l"1 for sulphates, 234 to 251.6 mg l"1 for TH and 20.8 to 29.4 mg l-1 for sodium ion.
The discrepancies between the measured and simulated concentrations of these constituents may be attributed to agricultural return flow, dissolution of conservatives present in benthic sediments and the seepage of very hard groundwater to the river body (Banks, 1976; Anderson & Darcup, 1976).
3 9 15 21 27 33 39 45 51 57 ~<5 ' 69 ' 75~ Distance , km —>~
Fig. 10 Chloride variation downstream of Sadam Dam.
CONCLUSIONS
The results obtained during the model calibration yield the following
M. Anis Al-Layla & Hasan M. Al-Rizzo 700
conclusions: (a) Model calibration gave a good agreement between the simulated and
measured concentrations of conservative and non-conservative constituents. However, the discrepancies arising between the measured and simulated concentrations may be attributed to the lack of field data, complexity of the model formulation and assumptions adopted in the model construction.
61
60
• simulated
Q measured 0
y •
/
y > 5 5
I
_-©-.
# — • — © -
15 21 27 33 39 45 51 57 83 69 75 Distance , km *_
260
250
240
234
Fig. 11 Sulphate variation downstream of Sadam Dam.
^o O''
* • ' . " •
^ ̂ * ̂ JB-^ « ^
~- "" -̂̂"̂ ^-S''~' .-*^ ^^ ^— _-o- ^ '
^ J * - - - ~ " ^ - " • ^•~®~"~~ ^ — - • * ' ^ '
-— " ~" . "-— ' » — — ^ _ _ . — • • - — • '
y y 0- - - y
,y^' // /S
3 9 15 21 27 33 39 45 51 57 63 69 75 Distança , km »_
Fig. 12 Total dissolved solids variation downstream of Sadam Dam.
701 A water quality model for the Tigris River
(b) The microbial self-purification of the non-conservative constituents reduced their concentration downstream from Sadam Dam notably. However, the extent of this process is insufficient to overcome the impact of the Sadam Dam project on the water quality of the Tigris river. This is due to the considerable increase in the concentrations of these constituents, the impact of agricultural return flow and insufficient travelling time available for self-purification in this stretch.
(c) Agricultural return flow resulting from inefficient management of
a > 220
/ /
y
-w
27 33 39 Distance , km —»-
Fig. 13 Total hardness variation downstream of Sadam Dam.
'
• ''<? / / y y
y y y ' y
— ~er
S> "^~ /
/ / _-•— — a' .— - — -
/ y / y / y
/ y
/y
,0 y
y
y '
y ' /
.-^' ^j m . • - — • " " " ' "
• simulated
0 measured
15 21 27 33 39 45 51 57 SJ 69 75 Distance , km —^.
Fig. 14 Sodium ion variation downstream of Sadam Dam.
M. Anis Al-Layla & Hasan M. Al-Rizzo 702
irrigation, lack of proper drainage and improper use of fertilizers caused a considerable increase in the concentrations of conservative constituents in the stretch downstream of the dam. However, the assumptions adopted in return flow calculations, lack of field data and improper scheduling of irrigation introduce notable discrepancies between measured and simulated concentrations of these constituents.
(d) The expected expansion in Sadam Dam activities, e.g. irrigation projects and industrial activities within the dam catchment area, will increase significantly the concentrations of conservative and non-conservative constituents in the Sadam Dam project and Tigris River. However, the impact of the aquatic life present within the main and regulating reservoirs will increase gradually as these reservoirs are enriched with nutrient, thus impeding the eutrophication and introduction of obnoxious problems of algal growth and decay.
REFERENCES
Al Rizzo, H. M. (1987) A simulation model of the pollution impacts on Tigris River and Sadam Dam. MSc Thesis, Univ. of Mosul, Iraq.
Anderson, D. D. & Darcup, J. A. (1976) Water quality modelling of deep reservoir. /. Wat. Pollut. Control Fed. 48 (1), 134-146.
APHA-AWWA-WPCF (1975) Standard Methods for the Examination of Water and Wastewater. 17th edition, American Public Health Assoc. Washington DC, USA.
Baca, R. C. & Arnett, R. C. A. (1976) A Limnological Model for Eutrophic Lakes and Impoundments. Batelle Pacific Northwest Laboratories, USA
Banks, R. B. (1976) Distribution of BOD and DO in rivers and lakes. /. Env. Engng. Div. ASCE 102 (EE 2), 265-280.
Crabtree, R. W., Cluckie, I. D. & Forster, C. F. (1986) A comparison of two quality models. Wat. Res. 53-61.
Leopold, L. B. & Maddock, T. J. (1953) The Hydraulic Geometry of Stream Channels and Some Implications. USGS Prof. Pap. 252.
Lombardo, P. M. & Otto, R. F. (1974) Water quality simulation and application. Wat. Resour. Bull. 10 (1), 15-21.
Lornezen, B. R. & Chen C. W. (1977) Discussion of nitrification in natural streams. /. Wat. Pollut. Control Fed. 49 (5), 615-617.
Najib, Y. E. (1980) Characteristics of Tigris River. MSc. Thesis, Univ. of Mosul, Iraq. Nemerrow, N. L, (1974) Scientific Stream Pollution Analysis 1st edn, McGraw-Hill, New
York, USA Naom, J. (1985) Effect of raw sewage from Mosul on Tigris River. MSc Thesis, College of
Engineering, Mosul University, Iraq. O'Conner, D. à Toro, D. M. (1970) Photosynthesis and oxygen balance in stream. /. Sank.
Engng Div. ASCE 96 (SAZ) 547-571. Rutherford, J. C. & Sullivan, M. J. (1974) Simulation of water quality in Tarawera River. /.
Env. Engng. Div. ASCE 100 (EE 2) 369-390. Texas Water Development Board (1970) Simulation of Water Quality in Streams and Canals:
DOSAG-I. Systems Engineering Division, Austin, Texas, USA. Texas Water Development Board (1971) Simulation of Water Quality in Streams and Canals
Report No. 128, Austin, Texas, USA. Velz, C. J. (1970) Applied Stream Sanitation, 1st edn John Wiley and Sons Inc., New York, USA, Water Resources Engineers (1973) Computer Program Documentation of the Stream Quality
Model QUAL-II. US Environmental Protection Agency, Washington DC, USA. Wild, H. E. & Crockett, C. P. (1971) Factors affecting nitrification kinetics. /. Wat. Pollut.
Control Fed. 43 (9) 516-525.
7 03 A water quality model for the Tigris River
Annex 1 Equations denning sources and sinks
Parameter Equation Coefficient Comments or variable
Carbonacious BOD
Dissolved oxygen
Ammonia
Nitrite
Nitrate
Organic-N
Phosphate
Coliform
Conservative Minerals
S — ~~iC L, KnL*
~%02+ h°S+ KAAt"h)
S = KNm NH4+ XkgNORG
* = % H 4 N H 4 - % 5 2 N 0 2
S = % Q 2 N 0 2 -KB(Af A2)
S = - i ^ g NORG
5 = - % > 4 1 P o 4 - ^ o 4 2 ^ 1 + *2>
S — "™-*C,n| C O L
S = 0.0
h h L
h
°S O
KA Al
^
% 0 2
N 0 3
KB
N 0 2
% H 4
NORG
Bioxidation coefficient for BOD
Coefficient of settling and scour effects
BOD concentrations
Re-aeration coefficient
Oxygen saturation point
DO concentration
DO from chlorophyll-A
Chlorophyll-A phytoplankton
Chlorophyll-A sessile algae.
Nitrite to nitrate rate
Nitrate concentration.
Nitrate uptake by algae
Nitrite concentration
Ammonia decay rate
Organic-N concentration
' ™ 4
^g
•^041
•%o42
P o 4
COL
^fcol
simmunui concentration Organic-N decay rate
Phosphate decay rate
Phosphate uptake by algae
Phosphate concentration
Coliform concentratiion
Coliform die-off rate
M. Anis Al-Layla & Hasan M. Al-Rizzo 704
Annex 2 Hydraulic submodel formulae (Leopold & Maddock, 1953):
W = a Qb
D = c Qf
V = K if1
Exponents and coefficients for different types of Tigris River reaches:
Velocity (V) Width (W) Depth (D) Type of reach
K m a b c f
Straight 0.700 0.156 28.74 0.307 0.050 0.535 Braided 0.560 0.051 2.45 0.680 0.433 0.258 Meandering 1.161 0.047 10.71 0.490 0.080 0.390
Symbols:
a, c, K = coefficients b, f, m = exponents W = wetted perimeter (m) D = hydraulic radius (m) V = stream velocity (m s~ )
Received 13 April 1988; accepted 21 March 1989
