Spatially explicit pollutant load-integrated in-stream E. coli concentration modeling in a mixed land-use catchment

Area of Science:

• Environmental Science
• Water Quality Modeling
• Microbial Ecology

Background:

• Pathogen contamination, particularly fecal bacteria like Escherichia coli (E. coli), is a primary driver of surface water impairment in the U.S.
• Environmental models are crucial for assessing watershed management strategies and understanding microbial loading.
• The Upper Stroubles Creek in Virginia is designated as an impaired water body due to recent bacterial contamination.

Purpose of the Study:

• To assess E. coli contamination in the Upper Stroubles Creek, Virginia, using the Soil and Water Assessment Tool (SWAT).
• To evaluate the effectiveness of the Spatially Explicit Load Enrichment Calculation Tool (SELECT) for E. coli load estimations within the SWAT model.
• To investigate the role of sediment-bacteria interactions (resuspension-deposition) in E. coli transport and concentration.

Main Methods:

• Utilized the Soil and Water Assessment Tool (SWAT) for watershed modeling.
• Employed the Spatially Explicit Load Enrichment Calculation Tool (SELECT) for high-resolution E. coli load estimations at the Hydrological Response Unit (HRU) level.
• Simulated in-stream E. coli concentrations, incorporating sediment bacteria resuspension-deposition processes, and compared model outputs with measured data.

Main Results:

• Nash-Sutcliffe Efficiency (NSE) values for in-stream E. coli simulations ranged from -0.41 to 0.34 without sediment resuspension-deposition and -0.19 to 0.36 with it.
• The SWAT model demonstrated a tendency to overestimate E. coli concentrations at mid-range flows and underestimate at low flows.
• High-resolution E. coli loads and sediment processes indicated greater contamination in forested areas compared to urban and pasture lands.

Conclusions:

• Detailed E. coli load estimations and the inclusion of sediment bacteria resuspension-deposition are critical for accurate environmental modeling.
• Land use information significantly influences the distribution of E. coli in aquatic environments.
• Improved bacterial source characterization and modeling approaches are necessary for effective watershed management of impaired waters.