Spatiotemporal sensitivity analysis of vertical transport of pesticides in soil. (July 2018)

• Record Type: Journal Article
• Authors: Hong, Tao; Purucker, S. Thomas
• Is Part Of: Environmental modelling & software. Volume 105(2018)
• Page Start: 24
• Page End: 38
• Publication Date: 2018-07
• Subjects: Sobol' sensitivity analysis -- Global sensitivity analysis -- Vertical transport -- Fate and transport -- Pesticides; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2018.03.018
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: Environmental fate and transport processes are influenced by many factors. Simulation models that mimic these processes often have complex implementations, which can lead to over-parameterization. Sensitivity analyses are subsequently used to identify critical parameters whose uncertainties can be further reduced or better described and prediction variability minimized. In this study, a variance decomposition based global sensitivity analysis technique (Sobol' method) is conducted based on estimated concentrations in vertical soil compartments using the Pesticide Root Zone Model (PRZM). Daily simulations are performed that explore the input parameter space. Estimated concentrations are compared to data collected over the course of a growing season from an experimental site in Georgia. Our results suggest that model sensitivity is conditional and should be examined at appropriate spatial and temporal resolution to avoid omitting important parameters. This approach can yield a better understanding about the interplay between sensitivity/uncertainty and model dynamics in non-monotonic, non-linear systems. Highlights: We conditionally identify the sensitive inputs of the Pesticide Root Zone Model (PRZM) using Sobol' global sensitivity technique. Model parameter sensitivities change when evaluated over time and with depth due to changes in forcing variables and dynamic process components. Sensitivity analysis conducted by applying local sensitivity methods can omit important parameters.