Development of Pathogen TMDLs within a Stochastic Framework

Section 303(d) of the Clean Water Act and EPA's Water Quality Planning and Management Regulations (40 CFR Part 130) require states to develop total maximum daily loads (TMDLs) for their water bodies which are not meeting designated uses under technology-based controls for pollution. The TMDL process establishes the allowable loadings of pollutants or other quantifiable parameters for a water body based on the relationship between pollution sources and in-stream water quality conditions. Currently, most TMDLs are developed using a continuous simulation approach that employs a traditional deterministic rainfall-runoff model (e.g. HSPF). Use of such an approach in developing pathogen TMDLs can be problematic due to the water mass-balance errors which normally remain following even the "best" hydrologic calibration effort, and due to the challenge of calibrating the predicted pathogen loads to the erratic pattern of most observed pathogen data. In the current study both flow and pathogen loadings are modeled using probability distributions that are evaluated using a system dynamics modeling environment (e.g. STELLA) and Monte-Carlo simulation. The proposed approach has the advantage of eliminating mass-balance errors by using observed stream flow as opposed to rainfall as well as a better way of characterizing the probability of success of associated management strategies.