This study investigates the effects of uncertainties associated with pollutant wash-off dynamics in the context of stormwater management practices modeling. A formal Bayesian approach is adopted for the calibration and the uncertainty analysis of a commonly used wash-off model under (1) the unverified assumption of homoscedastic, independent, and normally distributed residuals; and (2) using a more correct heteroscedastic and autoregressive error model. The results obtained for each of these approaches are compared, and the uncertainty associated with water quality modeling is later propagated through a conceptual best management practices (BMP) model for various stormwater management scenarios so as to assess the effect of this uncertainty for BMP modeling and clarify the benefits of a robust description of error structure. This study indicates that the violation of the statistical assumptions about the residuals may result in unreliable estimation of model parameters and total predictive uncertainty. However, the effect of the uncertainty in the intraevent variability of concentrations in runoff is found to have only a limited effect on the outputs of the BMP model, regardless of the error model adopted for calibration.