The effects of disproportional load contributions on quantifying vegetated filter strip sediment trapping efficiencies

Heather E. Gall, Daniel Schultz, Tamie L. Veith, Sarah C. Goslee, Alfonso Mejia, Ciaran J. Harman, Cibin Raj, Paul H. Patterson

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Abstract

Vegetated filter strips (VFSs) are a best management practice (BMP) commonly implemented adjacent to row-cropped fields to trap overland transport of sediment and other constituents present in agricultural runoff. Although they have been widely adopted, insufficient data exist to understand their short and long-term effectiveness. High inter-event variability in performance has been observed, yet the majority of studies report average removal efficiencies over observed or simulated events, ignoring the disproportional effects of loads into and out of VFSs over longer periods of time. We argue that due to positively correlated sediment concentration-discharge relationships, disproportional contribution of runoff events transporting sediment over the course of a year (i.e., temporal inequality), decreased performance with increasing flow rates, and effects of antecedent moisture condition, VFS removal efficiencies over annual time scales may be significantly lower than reported per-event averages. By applying a stochastic approach, we investigated the extent of disparity between reporting average efficiencies from each runoff event over the course of 1 year versus the total annual load reduction. Additionally, we examined the effects of soil texture, concentration-discharge relationship, and VFS slope in contributing to this disparity, with the goal of revealing potential errors that may be incurred by ignoring the effects of temporal inequality in quantifying VFS performance. Simulation results suggest that ignoring temporal inequality can lead to overestimation of annual performance by as little as < 2% and to as much as > 20%, with the greatest disparities observed for soils with high clay content.

Original languageEnglish (US)
Pages (from-to)2369-2380
Number of pages12
JournalStochastic Environmental Research and Risk Assessment
Volume32
Issue number8
DOIs
StatePublished - Aug 1 2018

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All Science Journal Classification (ASJC) codes

  • Environmental Engineering
  • Environmental Chemistry
  • Safety, Risk, Reliability and Quality
  • Water Science and Technology
  • Environmental Science(all)

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