|Shirmohammadi, A - UNIV OF MARYLAND|
|Chaubey, I - UNIV OF ARKANSAS|
|Munoz-Carpena, R - UNIV OF FLORIDA|
|Dharmasri, C - SYNGENTA CROP PROTECTION|
|Sexton, A - UNIV OF MARYLAND|
|Arabi, M - PURDUE UNIV|
|Wolfe, M - VIRGINIA TECH|
|Frankenberger, J - PURDUE UNIV|
|Sohrabi, T - UNIV OF TEHRAN, IRAN|
Submitted to: Proceedings of the American Society of Agricultural and Biological Engineers International (ASABE)
Publication Type: Proceedings
Publication Acceptance Date: July 19, 2006
Publication Date: July 19, 2006
Citation: Shirmohammadi, A., Chaubey, I., Harmel, R.D., Bosch, D.D., Munoz-Carpena, R., Dharmasri, C., Sexton, A., Arabi, M., Wolfe, M., Frankenberger, J., Graff, C.D., Sohrabi, T. 2006. Uncertainty in TMDL models. In: Proceedings of the American Society of Agricultural and Biological Engineers International (ASABE) July 9-12, 2006, Portland, OR. Interpretive Summary: The uncertainty of the model results used to develop Total Maximum Daily Loads (TMDLs) is a major concern for those that use TMDL reports to establish policy. This extended abstract presents a brief overview of sources of uncertainty, methods of uncertainty evaluation, and strategies for communicating uncertainty in TMDL models. This study concludes that the best method to account for uncertainty would be to develop uncertainty probability distribution functions and transfer such uncertainties to TMDL load allocation through the margin of safety component, which is currently selected arbitrarily. The results have significant implications for action agencies involved with TMDL development and implementation.
Technical Abstract: Although the U.S. Congress established the Total Maximum Daily Load (TMDL) program in the original Clean Water Act of 1972, Section 303(d), it did not receive serious attention from the States until the 1990s. Currently, two methods are available for tracking pollution in the environment and assessing the effectiveness of the TMDL process: field monitoring and mathematical/computer modeling. This extended abstract presents a brief overview of an extensive review describing the collective experience of scientists/engineers in the assessment of uncertainty associated with TMDL models. The issue of model uncertainty has important policy, regulatory, and management implications. This extended abstract provides an overview of methods commonly used to estimate uncertainty associated with TMDLs developed using computer models. The paper provides guidance on obtaining more extensive information on the topic.