title: Source specific fecal bacteria modeling using soil and water assessment tool model.
abstract: Fecal bacteria can contaminate water and result in illness or death. It is often difficult to accurately determine sources of fecal bacteria contamination, but bacteria source tracking can help identify non-point sources of fecal bacteria such as livestock, humans and wildlife. The Soil and Water Assessment Tool (SWAT) microbial sub-model 2005 was used to evaluate source-specific fecal bacteria using three years (2004-2006) of observed modified deterministic probability of bacteria source tracking data, as well as measure hydrologic and water quality data. This study modeled source-specific bacteria using a model previously calibrated for flow, sediment and total fecal coliform bacteria (FCB) concentration. The SWAT model was calibrated at the Rock Creek sub-watershed, validated at the Deer Creek sub-watershed, and verified at the Auburn sub-watershed and then at the entire Upper Wakarusa watershed for predicting daily flow, sediment, nutrients, total fecal bacteria, and source-specific fecal bacteria. Watershed characteristics for livestock, humans, and wildlife fecal bacterial sources were first modeled together then with three separate sources and combinations of source-specific FCB concentration: livestock and human, livestock and wildlife and human and wildlife. Model results indicated both coefficient of determination (R(2)) and Nash-Sutcliffe Efficiency Index (E) parameters ranging from 0.52 to 0.84 for daily flow and 0.50-0.87 for sediment (good to very good agreement); 0.14-0.85 for total phosphorus (poor to very good agreement); -3.55 to 0.79 for total nitrogen (unsatisfactory to very good agreement) and -2.2 to 0.52 for total fecal bacteria (unsatisfactory to good agreement). Model results generally determined decreased agreement for each single source of bacteria (R(2) and E range from -5.03 to 0.39), potentially due to bacteria source tracking (BST) uncertainty and spatial variability. This study contributes to new knowledge in bacteria modeling and will help further understanding of uncertainty that exists in source-specific bacteria modeling.
authors.affiliation: Department of Agronomy, Kansas State University, 2011D Throckmorton Plant Sciences Center, Manhattan, KS 66506, USA. parajpb@ksu.edu
authors.forename: Prem B
authors.initials: PB
authors.lastname: Parajuli
authors.forename: Kyle R
authors.initials: KR
authors.lastname: Mankin
authors.forename: Philip L
authors.initials: PL
authors.lastname: Barnes
mesh_terms: D015169:Colony Count, Microbial / Q000379:methods*; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D004755:Enterobacteriaceae / Q000302:isolation & purification*; D004784:Environmental Monitoring / Q000379:methods*; D005243:Feces / Q000382:microbiology*; D008954:Models, Biological; D012212:Rheology / Q000379:methods; D012988:Soil Microbiology; D014871:Water Microbiology