Predictions of river water quality models are subject to substantial uncertainties, which depend not only on parameterization and calibration strategies but also on the structure of the conceptual model itself. To evaluate the importance of this effect and associated implications for stochastic models, investigations were conducted based on a segment of the Potomac River in the eastern USA. Two commonly used conceptual representations of real-world processes were used, and their simulation of DO, BOD and NH(4) components scrutinized. A GLUE approach to the inverse problem was then used to examine how uncertainty changed along the river network for each conceptual model. Differences were observed not only between deterministic instances of each conceptual model, but also between their response surfaces as a whole. Uncertainties within the river network are substantially influenced by the selection of calibration data used, as well as the primary source of the constituent examined. The suggested methodology can be used to test conceptual model validity for specific applications. The results of this study will help users select and assess models for varied problems, and refine appropriate data collection and monitoring schemes.