Keywords
sensitivity analysis, pesticides, vernal pools, agricultural watersheds
Start Date
26-6-2018 3:40 PM
End Date
26-6-2018 5:00 PM
Abstract
Environmental simulations of the fate of pesticide can be complex and highly parameterized with uncertain values. A Monte Carlo (MC) shell wrapper was developed to execute the Pesticide Water Calculator (PWC) model and to characterize parameter sensitivity. The goal of this analysis was to identify the effects of input variability on site-specific model predictions, particularly surface water and sediment outputs, and compare the results to field observations. An executable graphical user interface of PWC (version 1.59) was parameterized for three agricultural vernal pool watersheds, located in the San Joaquin River basin in the Central Valley of California, and used to estimate exposure concentrations for chlorpyrifos, diazinon and malathion. R scripts were structured in a sequential way to create and load multiple PWC input files based on prior probability distributions, execute the PWC submodels, write input/output files for each Monte Carlo simulation, load the resulting output files and post-process for sensitivity analysis purposes. Partial correlation coefficients were used as a primary sensitivity metric for analyzing model outputs. The simulations indicate that soil properties (the universal soil loss equation [USLE] soil erodibility factor [USLE-K] and bulk density), degradation half-life, suspended solid and dissolved organic carbon concentrations were critical inputs in simulating pesticide concentrations in the surface water of vernal pools. Depth of benthic region, curve number, soil factors (e.g., bulk density, USLE-K, USLE topographic factor) and suspended solid concentrations in water column were identified as critical parameters to predict pesticide concentrations in the sediment.
Sensitivity analysis for pesticide transport in a vernal pool watershed using the Pesticide Water Calculator
Environmental simulations of the fate of pesticide can be complex and highly parameterized with uncertain values. A Monte Carlo (MC) shell wrapper was developed to execute the Pesticide Water Calculator (PWC) model and to characterize parameter sensitivity. The goal of this analysis was to identify the effects of input variability on site-specific model predictions, particularly surface water and sediment outputs, and compare the results to field observations. An executable graphical user interface of PWC (version 1.59) was parameterized for three agricultural vernal pool watersheds, located in the San Joaquin River basin in the Central Valley of California, and used to estimate exposure concentrations for chlorpyrifos, diazinon and malathion. R scripts were structured in a sequential way to create and load multiple PWC input files based on prior probability distributions, execute the PWC submodels, write input/output files for each Monte Carlo simulation, load the resulting output files and post-process for sensitivity analysis purposes. Partial correlation coefficients were used as a primary sensitivity metric for analyzing model outputs. The simulations indicate that soil properties (the universal soil loss equation [USLE] soil erodibility factor [USLE-K] and bulk density), degradation half-life, suspended solid and dissolved organic carbon concentrations were critical inputs in simulating pesticide concentrations in the surface water of vernal pools. Depth of benthic region, curve number, soil factors (e.g., bulk density, USLE-K, USLE topographic factor) and suspended solid concentrations in water column were identified as critical parameters to predict pesticide concentrations in the sediment.
Stream and Session
F3: Modelling and Decision Making Under Uncertainty