Source specific fecal bacteria modeling using soil and water assessment tool model.

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.

[1]  Walter J. Rawls,et al.  Predicting runoff from Rangeland Catchments: A comparison of two models , 1990 .

[2]  D. Mackay,et al.  Impacts of input parameter spatial aggregation on an agricultural nonpoint source pollution model , 2000 .

[3]  K. R. Mankin,et al.  Watershed-lake water quality modeling: Verification and application , 2003 .

[4]  R. B. Reneau,et al.  Determining Sources of Fecal Pollution in a Rural Virginia Watershed with Antibiotic Resistance Patterns in Fecal Streptococci , 1999, Applied and Environmental Microbiology.

[5]  A. Graves.  Determining Sources of Fecal Pollution in Water for a Rural Virginia Community , 2000 .

[6]  V. Lopes,et al.  EFFECTS OF WATERSHED REPRESENTATION ON RUNOFF AND SEDIMENT YIELD MODELING 1 , 2004 .

[7]  Brian D. Wardlow,et al.  A State-Level Comparative Analysis of the GAP and NLCD Land-Cover Data Sets , 2003 .

[8]  Jeffrey G. Arnold,et al.  EFFECT OF WATERSHED SUBDIVISION ON SWAT FLOW, SEDIMENT, AND NUTRIENT PREDICTIONS 1 , 2002 .

[9]  M. B. Beck,et al.  Water quality modeling: A review of the analysis of uncertainty , 1987 .

[10]  Vladimir Novotny,et al.  Water quality : diffuse pollution and watershed management , 2003 .

[11]  V. Singh,et al.  Computer Models of Watershed Hydrology , 1995 .

[12]  W. H. Wischmeier,et al.  Predicting rainfall erosion losses : a guide to conservation planning , 1978 .

[13]  Indrajeet Chaubey,et al.  SENSITIVITY ANALYSIS, CALIBRATION, AND VALIDATIONS FOR A MULTISITE AND MULTIVARIABLE SWAT MODEL 1 , 2005 .

[14]  David R. Maidment,et al.  Handbook of Hydrology , 1993 .

[15]  Raghavan Srinivasan,et al.  Effect of watershed subdivision on simulation runoff and fine sediment yield , 1997 .

[16]  Robert J. Bower.  Source identification of fecal pollution in the Tillamook watershed : antibiotic discriminant analysis , 2000 .

[17]  Indrajeet Chaubey,et al.  UNCERTAINTY IN TMDL MODELS , 2006 .

[18]  Jeffrey G. Arnold,et al.  The Soil and Water Assessment Tool: Historical Development, Applications, and Future Research Directions , 2007 .

[19]  Sybil P. Parker,et al.  McGraw-Hill dictionary of scientific and technical terms , 1976 .

[20]  S. Lele,et al.  The association between extreme precipitation and waterborne disease outbreaks in the United States, 1948-1994. , 2001, American journal of public health.

[21]  Jeffrey G. Arnold,et al.  A SWAT/Microbial Sub-Model for Predicting Pathogen Loadings in Surface and Groundwater at Watershed and Basin Scales , 2002 .

[22]  Sunny C. Jiang,et al.  Application of enterococci antibiotic resistance patterns for contamination source identification at Huntington Beach, California. , 2003, Marine pollution bulletin.

[23]  Yakov A. Pachepsky,et al.  Modeling bacteria fate and transport in watersheds to support TMDLs , 2006 .

[24]  R. A. Conway,et al.  Use of Antibiotic Resistance Analysis To Identify Nonpoint Sources of Fecal Pollution , 1999, Applied and Environmental Microbiology.

[25]  A. Saleh,et al.  EVALUATION OF SWAT AND HSPF WITHIN BASINS PROGRAM FOR THE UPPER NORTH BOSQUE RIVER WATERSHED IN CENTRAL TEXAS , 2004 .

[26]  Jeffrey G. Arnold,et al.  CUMULATIVE UNCERTAINTY IN MEASURED STREAMFLOW AND WATER QUALITY DATA FOR SMALL WATERSHEDS , 2006 .

[27]  J. R. Miner,et al.  Modeling Bacteria Movement in Livestock Manure Systems , 1989 .

[28]  V. Singh,et al.  The EPIC model. , 1995 .

[29]  John R. Williams,et al.  LARGE AREA HYDROLOGIC MODELING AND ASSESSMENT PART I: MODEL DEVELOPMENT 1 , 1998 .

[30]  Margaret W. Gitau,et al.  Modeling phosphorus transport in agricultural watersheds: Processes and possibilities , 2002 .

[31]  G. Craun,et al.  Possible information bias in a waterborne outbreak investigation , 2002, International journal of environmental health research.

[32]  A. E. Greenberg,et al.  Standard methods for the examination of water and wastewater : supplement to the sixteenth edition , 1988 .

[33]  Assefa M. Melesse,et al.  Influences of Potential Evapotranspiration Estimation Methods on SWAT's Hydrologic Simulation in a Northwestern Minnesota Watershed , 2006 .

[34]  Michael J. Noto,et al.  Use of Antibiotic Resistance Analysis for Representativeness Testing of Multiwatershed Libraries , 2003, Applied and Environmental Microbiology.

[35]  Jeffrey G. Arnold,et al.  APPLICATION OF A WATERSHED MODEL TO EVALUATE MANAGEMENT EFFECTS ON POINT AND NONPOINT SOURCE POLLUTION , 2001 .

[36]  Günter Blöschl,et al.  Spatial Patterns of Catchment Hydrology: Observations and Modelling , 2000 .

[37]  A. Mazumder,et al.  Source tracking fecal bacteria in water: a critical review of current methods. , 2004, Journal of environmental management.

[38]  Jeffrey G. Arnold,et al.  HYDROLOGIC SIMULATION ON AGRICULTURAL WATERSHEDS: CHOOSING BETWEEN TWO MODELS , 2003 .

[39]  J. Nash,et al.  River flow forecasting through conceptual models part I — A discussion of principles☆ , 1970 .

[40]  C. Hagedorn,et al.  Sources of Fecal Pollution in Virginia's Blackwater River , 2003 .

[41]  V. Harwood,et al.  Classification of Antibiotic Resistance Patterns of Indicator Bacteria by Discriminant Analysis: Use in Predicting the Source of Fecal Contamination in Subtropical Waters , 2000, Applied and Environmental Microbiology.

[42]  Daniel Hillel,et al.  Soil and water , 1971 .

[43]  Jeffrey G. Arnold,et al.  Model Evaluation Guidelines for Systematic Quantification of Accuracy in Watershed Simulations , 2007 .

[44]  B. Haggard,et al.  Comparison of two methods for modeling monthly TP yield from a watershed , 2004 .