INTEGRATING TOOLS FOR SURFACE WATER QUALITY MANAGEMENT - Integrating GIS, Remote Sensing, and Mathematical Modelling for Surface Water Quality Management - page 52

As no model study can be more accurate than the information on which it is based, the importance
of adequate field data cannot be overemphasized.
The first steps in any model study is to specify the objectives; an assessment of the geophysical,
chemical, and biological factors involved; and collection of data essential to describe these factors.
Data collection and assessment should include the following remarks:
An identification of the various freshwater inflow sources, including their average, range, and
time distribution of flow.
Assessment of the tides and currents that are anticipated within the region.
Assessment of wind effects and other geophysical phenomena that may contribute to specific
influences on the area of study.
An identification of the sources which contributes to sedimentation and of sediment profile.
Identification of sources and the expected quantities and composition of industrial and
municipal effluents, non-point contaminants, and tributary constituent concentrations.
Identification of the aquatic biota of the region and the physical-chemical, and biological
factors which influence its behavior.
Identification of the available hydrographic and other geometric data pertinent to preparation
of the model.
These preliminary assessments have the purpose of ensuring the pertinent and availability of data to
provide a basis for the selection of the models needed and to provide a basis for planning field
sampling and data acquisition programs. The most satisfactory procedure is to plan the numerical
modeling and field data acquisition program together. If possible, the basic hydrodynamic model
should be operational during the period in which field data are being obtained. One major reason for
concurrent model simulation and data acquisition is that anomalies in field data frequently occur, and
the numerical model may be used to identify and resolve them.
Water Quality Models Classification
Water quality models are usually classified according to model complexity, type of water body or
water resource, and the water quality parameters (dissolved oxygen, nutrients, etc.) that the model can
predict. The more complex the model is, the more difficult and expensive will be its application to a
given situation. Model complexity could be a function of the following four factors;
The number and type of water quality indicators
In general, the more indicators that are included, the more complex the model will be. In addition,
some indicators are more complicated to predict than others; See Table (3-1) .
The level of spatial detail
As the number of pollution sources and water quality monitoring points increase, so do the data
required and the size of the model.
The level of temporal detail
It is much easier to predict long-term static averages than short term dynamic changes in water
quality. Point estimates of water quality parameters are usually simpler than stochastic predictions of
the probability distributions of those parameters.
The complexity of the water body under analysis
Small lakes that “mix” completely are less complex than moderate-size rivers, which are less complex
than large rivers, which are less complex than large lakes, estuaries, and coastal zones.