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Published by the American Society of Agricultural and Biological Engineers, St. Joseph, Michigan

Citation:  Transactions of the ASABE. Vol. 49(4): 1003-1022. (doi: 10.13031/2013.21743) @2006
Authors:   G. Vellidis, P. Barnes, D. D. Bosch, A. M. Cathey
Keywords:   Case study, Dissolved oxygen, EFDC, HSPF, QUAL2E, Simulation models, TMDLs, WASP, Watersheds

In many regions of the U.S., low dissolved oxygen (DO) is a common freshwater impairment. States, territories, and tribes of the U.S. are required by federal law to develop Total Maximum Daily Loads (TMDLs) for waters not meeting established DO standards. Regulators and other professionals are increasingly relying on mathematical simulation models to develop these TMDLs. Because of the wide variety of potential applications and the number of models in existence, consistent and comprehensive model evaluations are needed to ensure that TMDL developers are able to select appropriate models for their application. The goal of this article is to provide a guide to mathematical simulation models available for developing DO TMDLs. For this work, a model is defined as easily available software that can be used to simulate DO dynamics in lotic systems. Four commonly used DO simulation models (QUAL2E, HSPF, EFDC, and WASP) are described in detail, while the characteristics of several others are summarized in tabular form. A case study is used to illustrate the process of developing a DO TMDL. DO models continue to become more sophisticated and thus better able to simulate the natural environment. Despite advancements, many DO models are still not capable of simulating some of the most complex drivers of DO dynamics, partly because the scientific community does not yet fully understand these processes, and continue to require user-estimated inputs for these processes. Because these processes are complex and difficult to quantify, model users are forced to rely on the few published data, which may or may not be applicable to their conditions. To overcome these limitations, future research must focus on understanding these processes and creating comprehensive and easily accessible databases of DO parameters.

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