Recent trends in predicting N dynamics of agricultural catchments have led to the development of increasingly complex simulation models. However, the parameter calibration of these models is often limited by the availability and temporal resolution of appropriate measurement data. This study addressed the problem of evaluating the predictive uncertainty of a simple N budget simulation model when applied across a winter period by using the Bayesian Generalized Likelihood Uncertainty Estimation (GLUE) methodology. Within a Monte Carlo simulation analysis, it was shown that parameter equifinality was obtained across wide areas of the model parameter space. Equifinality is used here in the sense that many different parameter combinations will produce similar good simulation results with respect to available calibration data. The GLUE methodology assigned a likelihood weight to each acceptable simulation from where uncertainty bounds for the predicted amounts of mineral N in a soil profile and for N drainage fluxes were calculated. It was demonstrated that the equifinality of different parameter sets results in large uncertainty in the predictions. This study also suggested that introducing more complexity into the process description is very unlikely to allow this uncertainty to be constrained.