SWAT Literature Database for Peer-Reviewed Journal Articles

Title:Evaluation of watershed-scale simulations of in-stream pesticide concentrations from off-target spray drift 
Authors:Winchell, M.F., N. Pai, B.H. Brayden, C. Stone, P. Whatling, J.P. Hanzas and J.J. Stryker 
Journal:Journal of Environmental Quality 
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URL (non-DOI journals): 
Broad Application Category:pollutant only 
Primary Application Category:pesticide fate and transport 
Secondary Application Category:input effects 
Watershed Description:164.6 km^2 Mill Creek and 53.7 km^2 Threemile Creek, located near the Dalles region in Oregon, U.S. 
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Abstract:The estimation of pesticide concentrations in surface water bodies is a critical component of the environmental risk assessment process required by regulatory agencies in North America, the European Union, and elsewhere. Pesticide transport to surface waters via deposition from off-field spray drift can be an important route of potential contamination. The spatial orientation of treated fields relative to receiving water bodies make prediction of off-target pesticide spray drift deposition and resulting aquatic estimated environmental concentrations (EECs) challenging at the watershed scale. The variability in wind conditions further complicates the simulation of the environmental processes leading to pesticide spray drift contributions to surface water. This study investigates the use of the Soil Water Assessment Tool (SWAT) for predicting concentrations of malathion (O,O-deimethyl thiophosphate of diethyl mercaptosuccinate) in a flowing water body when exposure is a result of off-target spray drift, and assesses the model’s performance using a parameterization typical of a screening-level regulatory assessment. Six SWAT parameterizations, each including incrementally more sitespecific data, are then evaluated to quantify changes in model performance. Results indicate that the SWAT model is an appropriate tool for simulating watershed scale concentrations of pesticides resulting from off-target spray drift deposition. The model predictions are significantly more accurate when the inputs and assumptions accurately reflect application practices and environmental conditions. Inclusion of detailed wind data had the most significant impact on improving model-predicted EECs in comparison to observed concentrations.