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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. (C) 2018 Published by Elsevier Ltd.