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United States Department of Agriculture

Agricultural Research Service

Title: Uncertainty in Tmdl Models

Authors
item Shirmohammadi, A - UNIV. OF MARYLAND
item Chaubey, I - UNIV. OF ARKANSAS
item Harmel, Daren
item Bosch, David
item Munoz-Carpena, R - UNIV. OF FLORIDA
item Dharmasri, C - SYNGETA CROP PROTEC.,INC.
item Sexton, A - UNIV. OF MARYLAND
item Arabi, M - PURDUE UNIV.
item Wolfe, M - VIRGINIA TECH. UNIV.
item Frankenberger, J - PURDUE UNIV.
item Graff, Carrie
item Sohrabi, T - UNIV. OF TEHRAN

Submitted to: Transactions of the ASABE
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: July 24, 2006
Publication Date: August 28, 2006
Citation: Shirmohammadi, A., Chaubey, I., Harmel, R.D., Bosch, D.D., Munoz-Carpena, R., Dharmasri, C., Sexton, A., Arabi, M., Wolfe, M.L., Frankenberger, J., Graff, C.D., Sohrabi, T.M. 2006. Uncertainty in TMDL Models. Transactions of the American Society of Agricultural and Biological Engineers. 49(4):1033-1049 (2006).

Interpretive Summary: Currently, two methods are available for tracking pollution in the environment and assessing the effectiveness of the Total Maximum Daily Load (TMDL) process: field monitoring and mathematical/computer modeling. Field monitoring may be the most appropriate method, but its use is limited due to high cost and extreme spatial and temporal variability. Mathematical models provide an alternative to field monitoring that can potentially save time, reduce cost, and minimize the need for testing management alternatives. However, the uncertainty of the model results is a major concern. This paper reviews sources of uncertainty, methods of uncertainty evaluation, and strategies for communicating uncertainty in TMDL models. Results indicate that uncertainty should be taken into consideration not only during the TMDL assessment phase, but also in the design of BMPs during the TMDL implementation phase. This collective 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 attention 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. Field monitoring may be the most appropriate method, but its use is limited due to high cost and extreme spatial and temporal variability. Mathematical models provide an alternative to field monitoring that can potentially save time, reduce cost, and minimize the need for testing management alternatives. However, the uncertainty of the model results is a major concern. The issue of uncertainty has important policy, regulatory, and management implications. This paper reviews sources of uncertainty (e.g., input variability, model algorithms, model calibration data, and scale), methods of uncertainty evaluation (e.g., First Order Approximation, Mean Value First Order Reliability Method, Monte Carlo, Latin Hypercube Sampling with Constrained Monte Carlo, and Generalized Likelihood Uncertainty Estimation), and strategies for communicating uncertainty in TMDL models to users. Four case studies are presented to highlight uncertainty quantification in TMDL models. Results indicate that uncertainty in TMDL models is a real issue and should be taken into consideration not only during the TMDL assessment phase, but also in the design of BMPs during the TMDL implementation phase. This collective 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. It is proposed that explicit quantification of uncertainty be made an integral part of the TMDL process. This will benefit private industry, scientific community, regulatory agencies, and action agencies involved with TMDL development and implementation.

Last Modified: 12/19/2014
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