Agricultural and Biological Engineering Department, University of Florida, Gainesville, FL 32611-0570, USA.
J Environ Qual. 2010 Feb 19;39(2):630-41. doi: 10.2134/jeq2009.0300. Print 2010 Mar-Apr.
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 ( approximately 100-150 mm equivalent depths) and approximately 50% for the higher loading rate ( approximately 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.
植被过滤带(VFS)是一种用于减少地表径流中泥沙和农药运输的环境管理工具。VFS 的数值模型,如植被过滤带模型系统(VFSMOD-W),能够预测径流量、泥沙和农药的削减量,并且可以成为了解 VFS 有效性和其可能无效的环境条件的有用工具。然而,作为建模过程的一部分,确定输入因素的重要性并量化预测径流量、泥沙和农药削减量的不确定性至关重要。本研究使用了最先进的全局敏感性和不确定性分析工具,一种筛选方法(Morris)和一种基于方差的方法(扩展 Fourier 分析敏感性测试),在一系列野外场景下评估 VFSMOD-W。分析的三个 VFS 研究是在粉质粘壤土和粉壤土上进行的,采用均匀、片状流条件,并包括莠去津、毒死蜱、氰草津、甲草胺、二甲戊灵和特丁津的数据。饱和导水率是预测入渗和径流量的最重要输入因素,对水力负荷率较低(约 100-150mm 当量深度)的研究,解释了总输出方差的>75%,对水力负荷率较高(约 280mm 当量深度)的研究,解释了约 50%。预测泥沙沉降的重要输入因素包括导水率、平均粒径和过滤器的 Manning 糙率系数。农药截留的输入因子重要性受入渗控制,因此受导水率控制。全局不确定性分析表明,径流量(95%置信区间为 7-93%)、泥沙(84-100%)和农药(43-100%)的削减范围很广。农药截留概率分布介于径流量和泥沙削减分布之间,这是由于农药的吸附作用。看似等效的 VFS 表现出独特而复杂的截留响应,这取决于水力和泥沙负荷率,因此需要基于过程的 VFS 建模。