Introducing of Ce-Qual-W2 Model and Its Application - Water Quality Management: Assessment and Interpretation - page 113

However, it may be questioned to what extent a one-dimensional model ade-
quately represents a natural system such as a river. From the point of view of the
hydrodynamics, provided that the river is extremely long, narrow and shallow, a
one-dimensional hydrodynamic approach is satisfactory (Orlob 1983 ). Gradients in
the horizontal directions are generally small when compared to the vertical gradi-
ents that exist for much of the year, and are rapidly annihilated by gravitational
adjustments. Simple force balances can be used to make a general con rmation of

this assumption and to verify its applicability in particular.
By contrast, water quality variables, for example nutrient concentrations, exert a
negligible effect on the density distribution, and therefore could potentially display
a two- or three-dimensional distribution despite a 1D density distribution. While
this is recognized as a shortcoming of a 1D model, it does not necessarily imply that
a multi-dimensional approach would produce a more correct picture. Indeed, given
the dif culty of setting realistic initial conditions for all water quality variables in a

multi-dimensional model and the dif uxes at the

spatial scale of the model, a multi-dimensional predictive capacity (as opposed to a
multi-dimensional veri

culty of knowing all of the input

cation capacity) is a highly uncertain outcome in many
natural systems. By explicitly recognizing that the output from a 1D model is a
horizontally averaged result, the 1D assumption provides a base level prediction
which can be achieved with a greater degree of certainty and from which inferences
about possible horizontal distributions can be drawn (Khodadadi Darban 2010 ).

6.4.2.2 Comment

A time-dependent, laterally averaged, one-dimensional hydrodynamic and water
quality model was applied to the Gotvand-Shooshtar Region of the K in River

system to simulate the effects of pollutants on dissolved oxygen and CBOD dis-
tributions. The water quality model, supplied with the information for physical
transport processes from the hydrodynamic model, provides real-time predictions of
water quality state variables. Hydrodynamic model calibration and veri

ā r ū

cation were
conducted with mean data range, time varying surface elevation and longitudinal
velocity, turbulent mixing, and salinity distribution in the K

in River system. The
model was updated with the geometric data for 2005 and recalibrated with

ā r ū

field data
for water surface elevation and velocity measured in the same year. The overall
performance of the model was in qualitative agreement with field data. The water

quality model was also recalibrated using field data collected in 2005. Considering

the random variability inherent to natural systems and the goal of consistency in
calibrated coef cients, the agreement between the model predictions and eld

observations is more than satisfactory.
The recalibrated model was used to perform sensitivity analyses. It is demon-
strated that the DO concentration in the river is very sensitive to the magnitude of
river

-
cantly raises the DO level. The model is then used to simulate various water quality
management strategies including river

flow, particularly during the flow flow period. Increasing river flow signifi-

flow management and wastewater loading