Objectives of Lake Modeling

There are three main objectives for constructing and using lake models:

1. Improving the understanding of lake ecosystem function. Comparing results of simulations of a lake model with measured data provides a test of the hypotheses formulated in the model. Thus, lake models are ideal tools for quantitative testing of hypotheses about lake ecosystem theories. Furthermore, they provide a link from concentrations to fluxes and transformation rates that are much more difficult to measure than concentrations. The formulation of comprehensive lake models can also lead to the identification of knowledge gaps. Finally, performing model simulations and tests stimulates creative thinking about important mechanisms in lake ecosystems.

2. Summarizing and communicating knowledge about lake ecosystems. Lake models are perfect communication tools for exchanging quantitatively formulated knowledge about processes in lakes.

3. Supporting lake ecosystem management. Lake models can support lake management by predicting the consequences of suggested (alternative) measures. As both our knowledge and its representation in the models are incomplete, a considerable effort must be on quantifying prediction uncertainty if the models are applied for management purposes.

These objectives are essentially the same as in other fields of environmental modeling. However, lake models had a pioneering role in providing insight into the function of natural ecosystems and in model application for environmental management. The two most important reasons for this pioneering role are (1) the severe eutrophication problems many lakes with excessive nutrient input faced in the 1950s and 1960s, and (2) that already simple one- or two-box phosphorus mass-balance models were able to provide essential insights into these problems.

As in other fields of environmental modeling, the lake model to be used depends on the objective of the study. Typically, models for improving the understanding and communicating knowledge must have a higher structural resolution of model components and processes than models for lake management. For management purposes, getting the important mass fluxes correct is usually more important than providing a detailed insight into the substructures at the trophic levels of the food web. However, knowledge gained from more detailed research models often stimulates the development of simpler management models. Also the method of parameter estimation can depend on the objective of the study. For research purposes parameters are often estimated using frequentist techniques to avoid bias due to subjective prejudices. When the model is used for management purposes, there is usually not enough data available to perform a frequentist parameter estimation. In this case prior knowledge is best combined with empirical data using Bayesian techniques.

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