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Several reviews and evaluations of applications of the TMDL process over the past several years have identified the need for improvement in the modeling applications used to inform the process. Therefore, as part of a project funded by the Water Environment Research Foundation (WERF) to identify and make recommendations for improving the TMDL process, we have compiled a set of guiding principles for modeling used to support a TMDL. Presented below are some principles of good modeling practice relative to all of the steps in developing and applying a model for computing a TMDL. These steps include: problem definition and setting management objectives; data synthesis for use in modeling; model selection; model calibration and, if possible confirmation; model application; iterative modeling; and model post-audit. Since mathematical modeling of aquatic systems is not an exact science, it is essential that these steps be fully transparent to all TMDL stakeholders through comprehensive documentation of the entire process, including specification of all inputs and assumptions. The overriding consideration is that data richness and quality governs the level of model complexity that can be applied to a given system. The model should never be more complex than the data allow. Although the appropriate level of model complexity is difficult to quantify and generally requires experienced professional judgment, it is always desirable to begin with a simple (“back-of-theenvelope”) model and increase complexity only as the data, and other project constraints, allow and the problem definition (water quality endpoints and tolerable uncertainty) demand. Simple models can provide some very insightful diagnostic understanding of the system. Also, in applying a model, one should always attempt to quantify the uncertainty in predictions. In general, quantifying uncertainty is easier with simple models, which is another reason to begin with a simple framework.

Document Type: Research Article


Publication date: 2002-01-01

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