Bslc: A Tool for Bacteria Source Characterization for Watershed Management

Zeckoski, Rebecca W.; Benham, Brian Leslie; Shah, Sanjay B.; Wolfe, Mary Leigh; Brannan, Kevin M.; Al-Smadi, M.; Dillaha, Theo A.; Mostaghimi, Saied; Heatwole, Conrad D.

doi:10.13031/2013.19716

Cited by 37 publications

(31 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, no definitive data currently exist to verify the frequency of deposition and quantities of manure deposited. A bacteria source load calculator developed by Zeckoski et al (2005) assumed that 30% of manure is deposited directly to streams when access is possible for bovines within a catchment. However, this assumption was considered excessive for the catchment location and was not utilized.…”

Section: Direct Depositionmentioning

confidence: 99%

Modeling of Pathogen Indicator Organisms in a Small-Scale Agricultural Catchment Using SWAT

Coffey

Dorai-Raj

O'Flaherty

et al. 2012

Human and Ecological Risk Assessment: An International Journal

View full text Add to dashboard Cite

Degradation of water quality from microbial contaminants associated with agricultural activities has significant implications for source protection of potable water. Novel environmental approaches must be adopted to assess risks from waterborne pathogenic microbes. The objective of this study was to evaluate applicability of the Soil and Water Assessment Tool (SWAT) to predict daily concentrations of E. coli in a small-scale agricultural catchment in Ireland. The study area is based on the Kilshanvey catchment located in the west of Ireland. E. coli data (n = 25) from June 2006 to June 2007 were utilized for comparison with the model's predictions. Statistical analysis indicates an unsatisfactory to fair level of correlation for the model's predictions (R 2 = 0.03-0.35, NSE = -0.42-0.29). A sensitivity analysis identified direct stream deposition and die-off rates for E. coli as having a significant impact on the model's predictions. Our results suggest that the model is adequate to assess the magnitude of various microbial sources within catchments but capability to replicate daily observations is uncertain. However, model outputs could provide adequate data to develop a human exposure assessment to pathogen indicator organisms in surface water and assist policy-makers in developing appropriate risk management strategies.

show abstract

Section: Direct Depositionmentioning

confidence: 99%

Modeling of Pathogen Indicator Organisms in a Small-Scale Agricultural Catchment Using SWAT

Coffey

Dorai-Raj

O'Flaherty

et al. 2012

Human and Ecological Risk Assessment: An International Journal

View full text Add to dashboard Cite

show abstract

“…Related tools, such as the Bacteria Indicator Tool (BIT) [21] and the Bacteria Source Load Calculator (BSLC) [22], have been developed to estimate the FIO sources and fluxes from catchments into rivers. However, the uncertainly level is often still high because of daily, monthly and seasonal changes in grazing activities [23], manure age [24] and land-use types, etc.…”

Section: Brief Review Of Methods and Modelsmentioning

confidence: 99%

Integrated River and Coastal Flow, Sediment and Escherichia coli Modelling for Bathing Water Quality

Huang

Falconer

Lin

2015

Water

View full text Add to dashboard Cite

Due to the increasing economic and cultural value of bathing waters and the shellfish industry in the UK and worldwide, water quality in estuarine and coastal waters has attracted considerable public attention in recent years. To obtain accurate predictions of the concentration distributions of faecal indicator organisms (FIOs) in coastal waters for better management of bathing water compliance, it is necessary to build an integrated modelling system to predict the complete diffuse and point source inputs from river and catchment basins. In the present paper, details are given of the development of such an integrated modelling system for simulating the transport and decay processes of FIOs, from catchment areas upstream from the coastal region, in which a distributed catchment module, a 1D river network module and a 2D estuarine and coastal module are linked dynamically by boundary inputs and outputs. Extensive measured data from the catchments, river networks and estuaries have been collated to determine the model parameters. Verification results of the distribution of water levels, flows and velocities, and suspended sediment and Escherichia coli concentrations, at controlled monitoring sites are presented, which show that the integrated model predictions generally agree well with the measurements, although locally appreciable errors can occur. The model results also highlight the importance of including the flux of FIOs via sediments being an important factor in terms of assessing the quality of bathing waters. The main factors influencing the relatively high concentration values in the bathing region are analysed, based on the model predictions and measured data, with four categories of FIO concentration levels being reviewed. OPEN ACCESSWater 2015, 7 4753

show abstract

“…Two tools are publically available for predictive calculation of bacterial loading from grazing cattle into streams. The Bacterial Indicator Tool (BIT) was designed by the USEPA and is executed through Excel (USEPA, 2000) and the Bacterial Source Load Calculator was developed at Virginia Polytechnic Institute and State University and runs via Visual Basic for Applications (VBA) in Excel (Zeckoski et al, 2005). Both tools are designed to simulate the bacterial contribution from wildlife and grazing livestock into water bodies.…”

mentioning

confidence: 99%

“…These tools, however, have a potentially significant limitation in that the amount of time livestock spend in the stream is predicted through an estimated ratio that is applied across an entire month. The ratio estimation falls upon the user because limited information is available regarding the spatial tendencies of cattle in or around streams (Zeckoski et al, 2005). Furthermore, the tools do not utilize the factors thought to influence cattle behavior including weather conditions, pasture-stream geometry, pasture shade, pasture vegetation, or management practices such as restricting pasture access or providing water outside the stream corridor (Sheffield et al, 1997;Haan et al, 2006;Haan et al, 2010;Schwarte et al, 2011;Bear et al, 2012).…”

mentioning

confidence: 99%

Predicting Time Cattle Spend in Streams to Quantify Direct Deposition of Manure

2014

Appl. Eng. Agric

View full text Add to dashboard Cite

Current methods to predict bacterial loads into streams resulting from direct deposition of manure by livestock do not consider factors that influence livestock behavior. Data from three studies that monitored spatial behavior of cattle through GPS were used to develop a new method with increased temporal resolution and consideration of environmental factors to predict the time that cattle spend in streams. Information on relative location of the cattle to the pasture stream was used to calculate the number of hours a cow spent in the stream, and from that the load of bacteria deposited directly into the stream. Ultimately, four empirical equations were developed based on the pasture geometry and shaded area, and each varied as a function of the daily minimum temperature. The models were applied to the Duck Creek watershed, Iowa, (at USGS Station 05422560) to demonstrate the variation in temporal resolution when compared to standard monthly load allocation methods. Three of four models estimated fewer days of E. coli load exceeding the water quality standard than days predicted using conventional methods. While the models do not capture the entire range of cattle spatial behavior, results suggest that the models can be used as a more detailed means of calculating bacterial loads. Daily load estimations averaged over a month can be used to populate current predictive tools as an alternate to the less representative estimation method on which the current modeling tools rely.

show abstract

Bslc: A Tool for Bacteria Source Characterization for Watershed Management

Cited by 37 publications

References 5 publications

Modeling of Pathogen Indicator Organisms in a Small-Scale Agricultural Catchment Using SWAT

Modeling of Pathogen Indicator Organisms in a Small-Scale Agricultural Catchment Using SWAT

Integrated River and Coastal Flow, Sediment and Escherichia coli Modelling for Bathing Water Quality

Predicting Time Cattle Spend in Streams to Quantify Direct Deposition of Manure

Contact Info

Product

Resources

About