Book Description

Watershed models are used to represent the physical, chemical, and biological mechanisms that determine the fate and transport of pollutants in waterbodies (Daniel 2011). These models, in general, are used for exploratory, planning, and regulatory purposes (Harmel and others 2014). Watershed models have numerous applications; one such use is the development of total maximum daily load (TMDL). TMDL is the amount of pollution a waterbody can receive without becoming impaired. Because of the challenge of uncertainty associated with models and the TMDL development process, the United States Clean Water Act Section 303 (d)(1)(c) requires that a margin of safety (MOS) be specified to account for uncertainty in TMDLs. The question of how MOS is estimated in TMDL was identified as a problem by the National Research Council (NRC 2001). Since the identification of the problem about two decades ago, there have been very few inventories or audits of approved TMDL studies.This study describes a natural language processing and machine learning aided review of the MOS in approved TMDLs from 2002 to 2016. The study determined whether the MOS values incorporated followed a pattern and examined whether there exist a relationship between MOS values and some ecological conditions. Relatively few TMDLs were based on some form of calculation to estimate explicit MOS values; these TMDLs constituted only 16% of the reviewed sample. The remaining 84% used conventional values, but few of those studies provided reasons for their selected values. A statistical assessment of those MOS values revealed that the MOS depended on States (location of waterbody), USEPA regions, waterbody type, designated water use, TMDL model used, and dataavailability. The findings indicate that few TMDL developers are following the National Research Council’s suggestions of using a rigorous uncertainty estimation approach for rational choices for the MOS. An adaptive approach based on Bayes-Discrepancy was proposed for estimating an MOS for a TMDL. The approach is based on the Bayesian hierarchical framework of estimating uncertainty associated with watershed models. With this approach, TMDL developers can communicate the effects of their watershed model. The approach was applied to a Ferson Creek model of the Fox River watershed to access variability and uncertainty in the model results, and also estimate possible MOS values for two monitoring stations in the watershed. Results suggest that an MOS of 0.04 mg/L could lead to a 0.1 probability of violating the water quality standard for an underpredicting model. The Bayes-discrepancy estimation method will enable TMDL developers and watershed managers to strike a balance between implementation options and water quality concerns.