Full TGIF Record # 161315
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DOI:10.2134/jeq2009.0300
Web URL(s):https://dl.sciencesocieties.org/publications/jeq/articles/39/2/630
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https://www.dl.sciencesocieties.org/publications/jeq/pdfs/39/2/630
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Publication Type:
i
Refereed
Author(s):Muñoz-Carpena, Rafael; Fox, Garey A.; Sabbagh, George J.
Author Affiliation:Muñoz-Carpena: Agricultural and Biological Engineering Dep., Univ of Florida, Gainesville, FL; Fox: Dep. of Biosystems and Agricultural Engineering, Oklahoma State Univ., Stillwater, OK; Sabbagh: Bayer Crop Science, Oklahoma State University, Stilwell, KS
Title:Parameter importance and uncertainty in predicting runoff pesticide reduction with filter strips
Section:Technical reports: Surface water quality
Other records with the "Technical reports: Surface water quality" Section
Source:Journal of Environmental Quality. Vol. 39, No. 2, March/April 2010, p. 630-641.
Publishing Information:Madison, Wisconsin: American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America
# of Pages:12
Keywords:TIC Keywords: Cynodon dactylon; Cynodon transvaalensis; Filter strips; Leaching; Models; Pesticides; Runoff control
Abstract/Contents:"Vegetative filter strips (VFS) are an environmental management tool used to reduce sediment and pesticide transport from surface runoff. Numerical models of VFS such as the Vegetative Filter Strip Modeling System (VFSMOD-W) are capable of predicting runoff, sediment, and pesticide reduction and can be useful tools to understand the effectiveness of VFS and environmental conditions under which they may be ineffective. However, as part of the modeling process, it is critical to identify input factor importance and quantify uncertainty in predicted runoff, sediment, and pesticide reductions. This research used state-of-the-art global sensitivity and uncertainty analysis tools, a screening method (Morris) and a variance-based method (extended Fourier Analysis Sensitivity Test), to evaluate VFSMOD-W under a range of field scenarios. The three VFS studies analyzed were conducted on silty clay loam and silt loam soils under uniform, sheet flow conditions and included atrazine, chlorpyrifos, cyanazine, metolachlor, pendimethalin, and terbuthylazine data. Saturated hydraulic conductivity was the most important input factor for predicting infiltration and runoff, explaining >75% of the total output variance for studies with smaller hydraulic loading rates (~100150 mm equivalent depths) and ~50% for the higher loading rate (~280-mm equivalent depth). Important input factors for predicting sedimentation included hydraulic conductivity, average particle size, and the filter's Manning's roughness coefficient. Input factor importance for pesticide trapping was controlled by infiltration and, therefore, hydraulic conductivity. Global uncertainty analyses suggested a wide range of reductions for runoff (95% confidence intervals of 7-93%), sediment (84-100%), and pesticide (43-100%) . Pesticide trapping probability distributions fell between runoff and sediment reduction distributions as a function of the pesticides' sorption. Seemingly equivalent VFS exhibited unique and complex trapping responses dependent on the hydraulic and sediment loading rates, and therefore, process-based modeling of VFS is required."
Language:English
References:47
Note:Tables
Graphs
ASA/CSSA/SSSA Citation (Crop Science-Like - may be incomplete):
Muñoz-Carpena, R., G. A. Fox, and G. J. Sabbagh. 2010. Parameter importance and uncertainty in predicting runoff pesticide reduction with filter strips. J. Environ. Qual. 39(2):p. 630-641.
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DOI: 10.2134/jeq2009.0300
Web URL(s):
https://dl.sciencesocieties.org/publications/jeq/articles/39/2/630
    Last checked: 11/02/2016
    Access conditions: Item is within a limited-access website
https://www.dl.sciencesocieties.org/publications/jeq/pdfs/39/2/630
    Last checked: 11/02/2016
    Requires: PDF Reader
    Access conditions: Item is within a limited-access website
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