Examining the Influence of Wildlife Population and Fecal Coliform Density Variability on Virginia Bacterial TMDL Development

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Virginia Tech

Pathogens are the most common cause of water quality impairment in Virginia. Bacteria TMDLs (Total Maximum Daily Loads) for watersheds are typically created using a modeling approach. These models require characterization of all residential, agricultural, and wildlife sources of bacteria. Wildlife bacteria source characterization is typically conducted with estimates of population and fecal coliform production. A sensitivity analysis was performed on the bacteria TMDL development process and the HSPF (Hydrological Simulation Program-FORTRAN) model to determine how wildlife population and fecal coliform density variability impacts simulated in-stream bacteria loads. The population and fecal coliform density values for seven wildlife species were sequentially varied and run through the TMDL model to analyze the changes in bacteria loads. For population density, high, median, and low values were tested, and for fecal coliform density, high and low values were tested. The analysis was conducted on three watersheds (Abrams Creek, Upper Opequon Creek, and Happy Creek), each with a different dominant land use. The results revealed that all watersheds were sensitive to the high fecal coliform densities of deer, muskrats, and raccoons. However, Happy Creek, the watershed with majority forested land use, was additionally sensitive to the high fecal coliform densities of ducks and the high population density for all species. Using the three watersheds as surrogates for comparing different land uses, the study showed the TMDL modeling process is most sensitive to changes in wildlife in watersheds dominated by forested land use. The results also demonstrated that TMDL calibration is more efficient when adjusting wildlife fecal coliform density rather than population density to match the modeled watershed with the observed water quality data.

TMDL, bacteria impairment, wildlife, Water quality, Modeling