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Journal of Environmental Quality Abstract - Surface Water Quality

Parameter Importance and Uncertainty in Predicting Runoff Pesticide Reduction with Filter Strips


This article in JEQ

  1. Vol. 39 No. 2, p. 630-641
    Received: Aug 3, 2009

    * Corresponding author(s): carpena@ufl.edu
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  1. Rafael Muñoz-Carpena *a,
  2. Garey A. Foxb and
  3. George J. Sabbaghc
  1. a Agricultural and Biological Engineering Dep., Univ. of Florida, 287 Frazier Rogers Hall, Gainesville, FL 32611-0570;
    b Dep. of Biosystems and Agricultural Engineering, Oklahoma State Univ., 120 Agricultural Hall, Stillwater, OK 74078;
    c Bayer Crop Science, Oklahoma State Univ., 17745 South Metcalf, Stilwell, KS 66085. Assigned to Associate Editor Pamela Rice


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 (∼100–150 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.

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