Effects of Agricultural Management, Land Use, and Watershed Scale on E. coli Concentrations in Runoff and Streamflow

Fecal contamination of surface waters is a critical water quality concern with serious human health implications. Many states use Escherichia coli as an indicator organism for fecal contamination and apply watershed models to develop and support bacterial Total Maximum Daily Loads (TMDLs); however, model applicability is greatly restricted due to the sparse availability of E. coli data for validation and calibration at various scales. Similarly, watershed‐scale information on the effects of management practices and land use on E. coli fate and transport is limited. Thus, this study was designed to measure E. coli concentrations in edge‐of‐field runoff and in streams with various agricultural management practices, land uses, and watershed scales. Results showed that application of dairy compost to pasture, cultivated, and mixed land use sites did not significantly affect E. coli concentrations in runoff at the field scale. In contrast, grazed sites had significantly higher runoff E. coli concentrations than cultivated sites, but the increase cannot be attributed solely to grazing cattle. No significant differences in E. coli concentrations were determined for presumably “impacted” and “unimpacted” rural streams with differing anthropogenic inputs, which highlights the challenges of managing and regulating bacterial contamination. The results also showed that E. coli concentrations consistently decreased as watershed scale increased from field to small watershed to river basin scale. Results from this study highlight the importance of considering all potential sources (including animal feeding operations, wastewater treatment plants and on‐site systems, cultivated and pasture fields, wildlife, and streambed resuspension) to properly assess E. coli contamination in rural watersheds. Results also demonstrated the need for an improved basic scientific understanding of fecal bacteria in the environment to reduce the substantial uncertainty associated with assessing, modeling, managing, and regulating bacterial contamination.