Impact of a new sediment parameterization method in VFSMOD on PECsw/sed in FOCUS step4

The most widely implemented mitigation measure to reduce transfer of pesticides and other pollutants to surface water bodies via surface runoff are vegetative filter strips (VFS). The most commonly used model for assessing the reduction of surface runoff, eroded sediment and pesticide inputs into surface water by VFS is VFSMOD, which simulates reduction of total inflow (∆Q) and reduction of incoming eroded sediment load (∆E) mechanistically. These variables are subsequently used to calculate the reduction of pesticide load by the VFS (∆P). Since errors in ∆Q and ∆E will propagate to ∆P, for strongly sorbing compounds, an accurate prediction of ∆E is crucial for a reliable prediction of ∆P. The most important parameter characterizing the incoming sediment in VFSMOD is the median particle diameter d50, which is currently fixed to 20 µm in the regulatory tool SWAN 5.01. The objective of this study was to derive an improved, generic d50 parameterization methodology that can be readily used for regulatory VFS scenarios.

A test dataset of d50 values and explanatory variables has been compiled from heterogeneous data sources. The established test dataset (n = 93) was analysed using Machine Learning techniques (Random Forest, Gradient Boosting) and multiple regression analysis (MLR). With the help of the knowledge gained with Machine Learning, a MLR equation with six predictor variables was established and thoroughly tested. Since three of the predictors are event-specific (eroded sediment yield, rainfall intensity and peak runoff rate), the predicted d50 values vary between runoff events according to their magnitude and intensity.

A modified version of SWAN-VFSMOD containing the improved d50 parameterization method was run for a number of contrasting compounds and application scenarios. The obtained ∆E and ∆P values as well as the resulting pesticide concentrations in surface water and sediment (PECsw/sed) were compared with the current FOCUS step4 approach.