Multi-site watershed model calibration for evaluating best management practice effectiveness in reducing fecal pollution

References

• Abouali M, Nejadhashemi AP, Daneshvar F, Herman MR, Adhikari U, Calappi TJ, Selegean JP. 2018. Evaluation of the effectiveness of conservation practices under implementation site uncertainty. J Environ Manage. 228:197–204.
   PubMed Web of Science ®Google Scholar
• Ahmadisharaf E, Camacho RA, Zhang HX, Hantush MM, Mohamoud YM. 2019. Calibration and validation of watershed models and advances in uncertainty analysis in TMDL studies. J Hydrol Eng. 24(7):03119001.
   Web of Science ®Google Scholar
• Arnold JG, Kiniry JR, Srinivasan R. 2012. Soil & water assessment tool: input/output documentaion; [accessed 2019 Oct 17]. https://swat.tamu.edu/media/69296/swat-io-documentation-2012.pdf.
   Google Scholar
• Arnold JG, Srinivasan R, Muttiah RS, Williams JR. 1998. Large area hydrologic modeling and assesment Part I: model development. J Am Water Resour Assoc. 34(1):73–89.
   Web of Science ®Google Scholar
• [AVMA] American Veterinary Medical Association. 2007. US pet ownership and demographics sourcebook. 2007 ed. Schaumburg (IL): American Veterinary Medical Association.
   Google Scholar
• Baffaut C, Sadeghi A. 2010. Bacteria modeling with SWAT for assessment and remediation studies. A review. Trans ASABE. 53:1585–1594.
   Web of Science ®Google Scholar
• Bai J, Shen Z, Yan T. 2016. Effectiveness of vegetative filter strips in abating fecal coliform based on modified soil and water assessment tool. Int J Environ Sci Technol. 13(7):1723–1730.
   Web of Science ®Google Scholar
• Bicknell BR, Imhoff JC, Kittle JL, Donigian AS ,Johanson RC. 1996. Hydrological Simulation Program - Fortran user’s manual for release11. Athens (GA): US Environmental Protection Agency.
   Google Scholar
• Borah DK, Ahmadisharaf E, Padmanabhan G, Imen S ,Mohamoud YM. 2019. Watershed models for development and implementation of total maximum daily loads. J Hydrol Eng. 24.
   PubMed Web of Science ®Google Scholar
• Borel K, Swaminathan V, Vance C, Roberts G ,Srinivasan R ,Karthikeyan R. 2017. Modeling the dispersion of E. coli in waterbodies due to urban sources: a spatial approach. Water (Switzerland). 9:665.
   Google Scholar
• Bradshaw JK, Snyder BJ, Oladeinde A, Spidle D, Berrang ME, Meinersmann RJ, Oakley B, Sidle RC, Sullivan K, Molina M, et al. 2016. Characterizing relationships among fecal indicator bacteria, microbial source tracking markers, and associated waterborne pathogen occurrence in stream water and sediments in a mixed land use watershed. Water Res. 101:498–509.
   PubMed Web of Science ®Google Scholar
• Camacho RA, Martin JL, Wool T, Singh VP. 2018. A framework for uncertainty and risk analysis in Total Maximum Daily Load applications. Environ Model Softw. 101:218–235.
   PubMed Web of Science ®Google Scholar
• Chaudhary A, Hantush MM. 2017. Bayesian Monte Carlo and maximum likelihood approach for uncertainty estimation and risk management: application to lake oxygen recovery model. Water Res. 108:301–311.
   PubMed Web of Science ®Google Scholar
• Cho KH, Pachepsky YA, Kim M, Pyo J, Park M-H, Kim YM, Kim J-W, Kim JH. 2016. Modeling seasonal variability of fecal coliform in natural surface waters using the modified SWAT. J. Hydrol. 535:377–385.
   Web of Science ®Google Scholar
• Cho KH, Pachepsky YA, Oliver DM, Muirhead RW, Park Y, Quilliam RS, Shelton DR. 2016. Modeling fate and transport of fecally-derived microorganisms at the watershed scale: state of the science and future opportunities. Water Res. 100:38–56.
   PubMed Web of Science ®Google Scholar
• Coffey R, Cummins E, Flaherty VO, Cormican M. 2010. Analysis of the soil and water assessment tool (SWAT) to model Cryptosporidium in surface water sources. Biosyst Eng. 106(3):303–314.
   Web of Science ®Google Scholar
• Coffey R, Dorai-Raj S, O'Flaherty V, Cormican M, Cummins E. 2013. Modeling of pathogen indicator organisms in a small-scale agricultural catchment using SWAT. Hum Ecol Risk Assess. 19(1):232–253.
   Web of Science ®Google Scholar
• Cooper J, Alexander C. 2006. Pathogen TMDL for East Branch Coon Creek near Armada, MI. Lansing (MI): Michigan Department of Environmental Quality, Water Resources Division.
   Google Scholar
• de Brauwere A, Ouattara NK, Servais P. 2014. Modeling fecal indicator bacteria concentrations in natural surface waters: a review. Crit Rev Environ Sci Technol. 44(21):2380–2453.
   Web of Science ®Google Scholar
• DeFlorio-Barker S, Wing C, Jones RM, Dorevitch S. 2018. Estimate of incidence and cost of recreational waterborne illness on United States surface waters. Environ Health. 17:1–10.
   PubMed Web of Science ®Google Scholar
• Devane ML, Weaver L, Singh SK, Gilpin BJ. 2018. Fecal source tracking methods to elucidate critical sources of pathogens and contaminant microbial transport through New Zealand agricultural watersheds – a review. J Environ Manage. 222:293–303.
   PubMed Web of Science ®Google Scholar
• Fonseca A, Boaventura RAR, Vilar V. 2018. Integrating water quality responses to best management practices in Portugal. Environ Sci Pollut Res. 25(2):1587–1596.
   PubMed Web of Science ®Google Scholar
• Frey SK, Topp E, Edge T, Fall C, Gannon V, Jokinen C, Marti R, Neumann N, Ruecker N, Wilkes G, et al. 2013. Using SWAT, bacteroidales microbial source tracking markers, and fecal indicator bacteria to predict waterborne pathogen occurrence in an agricultural watershed. Water Res. 47(16):6326–6337.
   PubMed Web of Science ®Google Scholar
• Gassman PW, Sadeghi AM, Srinivasan R. 2014. Applications of the SWAT model special section: overview and insights. J Environ Qual. 43(1):1–8.
   PubMed Web of Science ®Google Scholar
• Hantush MM, Chaudhary A. 2014. Bayesian framework for water quality model uncertainty estimation and risk management. J Hydrol Eng. 19:1–14.
   Web of Science ®Google Scholar
• Hyer KE, Mayer DL. 2004. Enhancing fecal coliform total maximum daily load models through bacterial source tracking. J Am Water Resour Assoc. 40(6):1511–1526.
   Web of Science ®Google Scholar
• Iqbal MS, Hofstra N. 2019. Modeling Escherichia coli fate and transport in the Kabul River basin using SWAT. Hum Ecol Risk Assess Int J. 25(5):1279–1297.
   Web of Science ®Google Scholar
• Jeong J, Wagner K, Flores JJ, Cawthon T, Her Y, Osorio J, Yen H. 2019. Linking watershed modeling and bacterial source tracking to better assess E. coli sources. Sci Total Environ. 648:164–175.
   PubMed Web of Science ®Google Scholar
• Jia Y, Culver TB. 2008. Uncertainty analysis for watershed modeling using generalized likelihood uncertainty estimation with multiple calibration measures. J Water Resour Plann Manage. 134(2):97–106.
   Web of Science ®Google Scholar
• Kafcas E, Payne T. 2005. Worksheet for establishing deer population goals – DMU 050, Macomb County, 2006–2010. Lansing (MI): Michigan Department of Natural Resources.
   Google Scholar
• Kim J-W, Pachepsky YA, Shelton DR, Coppock C. 2010. Effect of streambed bacteria release on E. coli concentrations: monitoring and modeling with the modified SWAT. Ecol Model. 221(12):1592–1604.
   Web of Science ®Google Scholar
• Kim K, Whelan G, Molina M, Parmar R, Wolfe K, Galvin M, Duda P, Zepp R, Kinzelman JL, Kleinheinz GT, et al. 2018. Using integrated environmental modeling to assess sources of microbial contamination in mixed-use watersheds. J Environ Qual. 47(5):1103.
   PubMed Web of Science ®Google Scholar
• Kim SM, Benham BL, Brannan KM. 2007. Water quality calibration criteria for bacteria TMDL development. Appl Eng Agric. 23:171–176.
   Web of Science ®Google Scholar
• Lacher IL, Ahmadisharaf E, Fergus C, Akre T, Mcshea WJ, Benham BL, Kline KS. 2019. Scale-dependent impacts of urban and agricultural land use on nutrients, sediment, and runoff. Sci Total Environ. 652:611–622.
   PubMed Web of Science ®Google Scholar
• Liu Y, Guo T, Wang R, Engel BA, Flanagan DC, Li S, Pijanowski BC, Collingsworth PD, Lee JG, Wallace CW, et al. 2019a. A SWAT-based optimization tool for obtaining cost-effective strategies for agricultural conservation practice implementation at watershed scales. Sci Total Environ. 691:685–696.
   PubMed Web of Science ®Google Scholar
• Liu Y, Wang R, Guo T, Engel BA, Flanagan DC, Lee JG, Li S, Pijanowski BC, Collingsworth PD, Wallace CW, et al. 2019b. Evaluating efficiencies and cost-effectiveness of best management practices in improving agricultural water quality using integrated SWAT and cost evaluation tool. J Hydrol. 577:123965.
   Web of Science ®Google Scholar
• Luo Y, Zhang M. 2009. Management oriented sensitivity analysis for pesticide transport in watershed-scale water quality modeling using SWAT. Environ Pollut. 157(12):3370–3378.
   PubMed Web of Science ®Google Scholar
• [MDEQ] Michigan Department of Environmental Quality. 2011. Revised total maximum daily load for E. coli for East Branch Coon Creek, Macomb and St. Clair counties. Lansing (MI): Michigan Department of Environmental Quality.
   Google Scholar
• Mishra A, Ahmadisharaf E, Benham BL, Gallagher DL ,Reckhow KH ,Smith EP. 2019. Two-phase monte carlo simulation for partitioning the effects of epistemic and aleatory uncertainty in TMDL modeling. J Hydrol Eng. 24:1–14.
   Web of Science ®Google Scholar
• Mishra A, Ahmadisharaf E, Benham BL, Wolfe ML, Leman SC, Gallagher DL, Reckhow KH, Smith EP. 2018. Generalized likelihood uncertainty estimation and Markov chain Monte Carlo simulation to prioritize TMDL pollutant allocations. J Hydrol Eng. 23(12):05018025.
   Web of Science ®Google Scholar
• Moriasi DN, Arnold JG, Van Liew MW, Bingner RL ,Harmel RD ,Veith TL. 2007. Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. Trans ASABE. 50:885–900.
   Web of Science ®Google Scholar
• Moriasi DN, Gitau MW, Pai N, Daggupati P. 2015. Hydrologic and water quality models: performance measures and evaluation criteria. Trans ASABE. 58:1763–1785.
   Web of Science ®Google Scholar
• Naramngam S, Tong S. 2013. Environmental and economic implications of various conservative agricultural practices in the Upper Little Miami River basin. Agric Water Manage. 119:65–79.
   Web of Science ®Google Scholar
• Nash JE, Sutcliffe JV. 1970. River flow forecasting through conceptual models Part one: a discussion of principles. J Hydrol. 27:282–290.
   Google Scholar
• [NESC] National Environmental Services Center. 2018. [accessed 2019 Oct 17]. http://www.nesc.wvu.edu/.
   Google Scholar
• Niazi M, Obropta C, Miskewitz R. 2015. Pathogen transport and fate modeling in the Upper Salem River Watershed using SWAT model. J Environ Manag. 151:167–177.
   PubMed Web of Science ®Google Scholar
• [NOAA] National Oceanic and Atmospheric Administration. 2018. Data Tools | Climate Data Online (CDO) | National Climatic Data Center (NCDC); [accessed 2019 Oct 17]. https://www.ncdc.noaa.gov/cdo-web/datatools/.
   Google Scholar
• [NRCS] Natural Resources Conservation Service. 2010. Statewide conservation practice typical installation cost information –2011. Michigan: Field Office Technical Guide.
   Google Scholar
• Nshimyimana JP, Martin SL, Flood M, Verhougstraete MP, Hyndman DW, Rose JB. 2018. Regional variations of bovine and porcine fecal pollution as a function of landscape, nutrient, and hydrological factors. J Environ Qual. 47(5):1024.
   PubMed Web of Science ®Google Scholar
• Oliver DM, Porter KDH, Pachepsky YA, Muirhead RW, Reaney SM, Coffey R, Kay D, Milledge DG, Hong E, Anthony SG, et al. 2016. Predicting microbial water quality with models: over-arching questions for managing risk in agricultural catchments. Sci Total Environ. 544:39–47.
   PubMed Web of Science ®Google Scholar
• Pandey PK, Kass PH, Soupir ML, Biswas S, Singh VP. 2014. Contamination of water resources by pathogenic bacteria. AMB Express. 4(1):1–16.
   PubMedGoogle Scholar
• Pandey PK, Soupir ML, Ikenberry CD, Rehmann CR. 2016. Predicting streambed sediment and water column Escherichia coli levels at watershed scale. J Am Water Resour Assoc. 52(1):184–197.
   Web of Science ®Google Scholar
• Parajuli PB, Mankin KR, Barnes PL. 2008. Applicability of targeting vegetative filter strips to abate fecal bacteria and sediment yield using SWAT. Agric Water Manage. 95(10):1189–1200.
   Web of Science ®Google Scholar
• Parajuli PB, Mankin KR, Barnes PL. 2009. Source specific fecal bacteria modeling using soil and water assessment tool model. Bioresour Technol. 100(2):953–963.
   PubMed Web of Science ®Google Scholar
• Petersen CM, Rifai HS, Stein R. 2009. Bacteria load estimator spreadsheet tool for modeling spatial Escherichia coli loads to an urban Bayou. J Environ Eng. 135(4):203–217.
   Web of Science ®Google Scholar
• Pyo J, Baek S-S, Kim M, Park S, Lee H, Ra J-S, Cho KH. 2017. Optimizing agricultural best management practices in a lake erie watershed. J Am Water Resour Assoc. 53(6):1281–1292. doi:10.1111/1752-1688.12571.
   Web of Science ®Google Scholar
• [PSC] Public Sector Consultants. 2018. An assessment of failing septic systems in the Saginaw Bay region. Lansing (MI): Public Sector Consultants.
   Google Scholar
• Reder K, Flörke M, Alcamo J. 2015. Modeling historical fecal coliform loadings to large European rivers and resulting in-stream concentrations. Environ Model Softw. 63:251–263.
   Web of Science ®Google Scholar
• Sadeghi AM, Arnold JG. 2002. A SWAT/microbial sub-model for predicting pathogen loadings in surface and groundwater at watershed and basin scales. In: ASAE, ed. Total Maximum Daily Load (TMDL): Environmental Regulations, Proceedings of 2002 Conference. Fort Worth, TX: American Society of Agricultural and Biological Engineers.
   Google Scholar
• Santo Domingo JW, Bambic DG, Edge TA, Wuertz S. 2007. Quo vadis source tracking? Towards a strategic framework for environmental monitoring of fecal pollution. Water Res. 41(16):3539–3552.
   PubMed Web of Science ®Google Scholar
• [SSURGO] Soil Survey Geographic Database. 2018. SSURGO Downloader; [accessed 2019 Oct 17]. http://www.arcgis.com/home/item.html?id=4dbfecc52f1442eeb368c435251591ec.
   Google Scholar
• Song F, Lupi F, Kaplowitz M. 2010. Valuing Great Lakes beaches. Presentation at: the Agricultural & Applied Economics Association 2010 AAEA, CAES, & WAEA Joint Annual Meeting; 2010 July 25–27; Denver, CO.
   Google Scholar
• Sowa SP, Herbert M, Mysorekar S, Annis GM, Hall K, Nejadhashemi AP, Woznicki SA, Wang L, Doran PJ. 2016. How much conservation is enough? Defining implementation goals for healthy fish communities in agricultural rivers. J Great Lakes Res. 42(6):1302–1321.
   Web of Science ®Google Scholar
• [STEPL] Spreadsheet Tool for Estimating Pollutant Load. 2006. STEPL: spreadsheet tool for estimating pollutant load v. 4.0. Fairfax (VA): Tetra Tech, Inc.
   Google Scholar
• Teague A, Karthikeyan R, Srinivasan R, Persyn RA. 2009. Spatially explicit load enrichment calculation tool to identify potential E. coli sources in watersheds. Trans ASABE. 52:1109–1120.
   Web of Science ®Google Scholar
• [USDA-NASS] US Department of Agriculture-National Agricultural Statistics Service. 2012. CropScape - NASS CDL Program; [accessed 2019 Oct 17]. https://nassgeodata.gmu.edu/CropScape/.
   Google Scholar
• [USDA-NRCS] US Department of Agriculture-Natural Resources Conservation Service. 2007. Reducing risk of E. coli O157:H7 contamination. National Nutrient Management Technical Note No. 7. Washington (DC): USDA-NRCS.
   Google Scholar
• [USEPA] US Environmental Protection Agency. 2000. Bacterial indicator tool: user’s guide. EPA-823-B-01e003. Washington (DC): Office of Water.
   Google Scholar
• [USEPA] US Environmental Protection Agency. 2012. Recreational water quality criteria. Report No. 820-F-12-058. Washington, D.C.: Office of Water.
   Google Scholar
• [USEPA] US Environmental Protection Agency. 2017. Water quality assessment and TMDL Information; [accessed 2019 Oct 17]. https://ofmpub.epa.gov/waters10/attains_nation_cy.control#imp_water_by_state.
   Google Scholar
• [USEPA] US Environmental Protection Agency. 2018. About Clinton River AOC; [accessed 2019 Oct 17]. https://www.epa.gov/great-lakes-aocs/about-clinton-river-aoc.
   Google Scholar
• [USGS] US Geological Survey. 2018a. The national map: elevation; [accessed 2019 Oct 17]. https://nationalmap.gov/elevation.html.
   Google Scholar
• [USGS] US Geological Survey. 2018b. National water information system: web interface; [accessed 2019 Oct 17]. https://waterdata.usgs.gov/nwis.
   Google Scholar
• Verhougstraete MP, Martin SL, Kendall AD, Hyndman DW, Rose JB. 2015. Linking fecal bacteria in rivers to landscape, geochemical, and hydrologic factors and sources at the basin scale. Proc Natl Acad Sci USA. 112(33):10419–10424.
   PubMed Web of Science ®Google Scholar
• Walters HM, Brody S, Highfield W. 2018. Examining the relationship between development patterns and total phosphorus in the Galveston Bay Estuary. Environ Sci Pol. 88:10–16.
   Web of Science ®Google Scholar
• Whelan G, Kim K, Parmar R, Laniak GF, Wolfe K, Galvin M, Molina M, Pachepsky YA, Duda P, Zepp R, et al. 2018. Capturing microbial sources distributed in a mixed-use watershed within an integrated environmental modeling workflow. Environ Model Softw. 99:126–146.
   PubMed Web of Science ®Google Scholar
• Whitehead PG, Leckie H, Rankinen K, Butterfield D, Futter MN, Bussi G. 2016. An INCA model for pathogens in rivers and catchments: model structure, sensitivity analysis and application to the River Thames catchment, UK. Sci Total Environ. 572:1601–1610.
   PubMed Web of Science ®Google Scholar
• Wu J. 2019. Linking landscape patterns to sources of water contamination: implications for tracking fecal contaminants with geospatial and Bayesian approaches. Sci Total Environ. 650:1149–1157.
   PubMed Web of Science ®Google Scholar
• Zeckoski RW, Benham BL, Shah SB, et al. 2005. BSLC: a tool for bacteria source characterization for watershed management. Appl. Eng. Agric. 21:879–889.
   Web of Science ®Google Scholar
• Zeckoski RW, Smolen MD, Moriasi D, et al. 2014. Terminology for hydrologic and water quality modeling: challenges and consequences. Paper presented at: the 2014 ASABE and CSBE/SCGAB Annual International Meeting, ASABE, St. Joseph, MI.
   Google Scholar