Improving microbial fate and transport modeling to support TMDL development in an urban watershed

Abstract

Pathogen contamination, typically quantified by elevated levels of fecal indicator bacteria (FIB), remains the leading cause of surface water-quality impairments in the United States. Continuous watershed-scale models are typically employed to facilitate Total Maximum Daily Load (TMDL) restoration efforts. Due to limited understanding of microbial fate and transport, predictions of FIB concentrations are associated with considerable uncertainty relative to other water-quality contaminants. By focusing on a data-rich instrumented urban watershed, this study aims to improve understanding of microbial fate and transport processes. Weekly FIB concentrations in both the water column and streambed sediments were monitored for one year, and statistical correlations with hydrometeorological and physicochemical variables were identified. An intensive six storm intra-sampling campaign quantified and contrasted loading trends of both traditional regulatory FIB and emerging Microbial Source Tracking (MST) markers. Together, these intensive monitoring efforts facilitated evaluation of the impacts of bacteria-sediment interactions on the predictions of daily FIB concentrations in Hydrological Simulation Program-Fortran (HSPF) over multiple years. While superior overall model performance was demonstrated as compared to earlier efforts, the inclusion of bacteria-sediment interactions did not improve performance. Large wet-weather microbial loading appears to have dwarfed the effects of FIB release and resuspension from sediment. Although wet-weather loading is generally considered as a primary source of waterbody microbial loads, dry-weather periods are more directly associated with public health concern, which may be a more suitable area for future model-refinement efforts. Site evaluation is critical to determine whether the added model complexity and effort associated with partitioning phases of FIB can be sufficiently offset by gains in predictive capacity. Finally, a stochastic framework to translate simulated daily FIB concentrations into estimates of human illness risks is presented that can be can be readily integrated into existing TMDLs. As even small concentrations of FIB from human sources are associated with great risk, and monitoring efforts indicated moderate/high levels of human-associated MST marker in this watershed, remediation efforts to protect public health would be best directed toward infrastructure improvements. Uncertainty analysis indicates more site-specific knowledge of pathogen presence and densities would best improve the estimation of illness risks.