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 on improving the quality of
impaired water bodies: 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 ecosystem 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, but understanding the source and magnitude of uncertainty and its impact on
TMDL assessment has not been studied in depth. This paper describes collective experience
of scientists/engineers in the assessment of uncertainty associated with TMDL models. It
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 strategy
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. First Order Error (FOE) analysis and Monte Carlo Simulation (MCS) or any modified
versions of these two basic methods may be used to assess uncertainty. 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 selected arbitrarily at the present time. 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.