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CHAPTER 1. ENVIRONMENTAL ASPECTS OF EUTROPHICATION

1.4. Causes of Eutrophication

1.4.2. Modeling Approaches

A model, as an approximation of a real lake or reservoir, is expressed usually in graphical, statistical or mathematical terms. Models used for understanding eutrophication focus on nutrient loading from the watershed and on processes within the lake or reservoir. While these models have considerable differences in their complexity, in most situations, simpler approaches are sufficient and, often, the only practical option. Several processes relevant to these models are discussed in sections 1.2.1., 1.2.2., 1.2.3., and 1.2.4.

Many simple empirical models have been developed to predict the concentration of total phosphorus in a lake as a function of annual phosphours loading. Extensions of such models offer predictions of chlorophyll concentrations in phytoplankton, Secchi disk visibility or dissolved oxygen levels. The values predicted by these models can have uncertainties from as low as ± 30% to as high as ±300%, and usually require modification for different regions. R.A. Vollenweider is credited with formulating a widely used empirical relationship to discriminate among trophic status based on annual phosphorus loading and mean depth divided by hydraulic residence time (Figure 1.16.). Refinements and adaptations of Vollenweider's approach have improved correlation and added or substituted nitrogen loading for some regions. Further research is required to incorporate responses of aquatic macrophytes into these models.

Figure 1.16. Application of data from the USA to Vollenweider's model (from Rast and Lee, 1978)

Dynamic simulation models incorporate mathematical descriptions of physical, chemical and biological processes in lakes or reservoirs. If properly designed and calibrated, these models can assist with management decisions that require considering alternative scenarios. Moreover, they often offer sufficient spatial and temporal resolution to model algal blooms and other responses to eutrophication. Conversely, the data requirements and process-level understanding demanded by dynamic models can be formidable. While such models have been in existence for over two decades and continue to be developed, it is prudent to be skeptical of their predictive power and realism. If a model is to be used, it should be selected based on the information available about the lake or reservoir and the questions to be answered. The most complex model is seldom necessary.

A new predictive technique for remediation of aquatic environment, which comes from the field of Information Technology, was recently described. This technique, known as the "knowledge-based" (K-B) approaches the problem differently from the mathematical modeling. Prediction by the mathematical modeling is a common choice in countries, which have a rich, reliable data base, scientific capacity for the modeling, and experienced management. All these are usually not available in developing countries. On the other hand, the "knowledge-based" prediction focuses on the use of local and domain knowledge. As the use of mathematical models in developing countries usually requires a foreign expert, the use of the K-B approach builds a local expertise in predictive techniques. Details and advantages of the K-B technique were recently discussed by Ongley and Booty (1999).

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