We examine the manner with which rainfall input and parametric uncertainty influence the character of the flow simulation uncertainty in a validated distributed hydrologic model. An extensive Monte Carlo numerical experiment was undertaken for two study watersheds in the state of Oklahoma of the United States. The study examines the sensitivity of ensemble flow simulations produced by the distributed model HRCDHM to uncertainty in parametric and radar rainfall input. The watersheds were included in the Distributed Model Intercomparison Project (DMIP) organized by the US National Weather Service Office of Hydrologic Development, and HRCDHM validated well in DMIP for both watersheds. The uncertainty scenarios included parametric uncertainty involving multiple soil model parameters simultaneously, and radar-rainfall uncertainty based on radar-pixel scale uncertainty scale to the subcatchment scale. Flow sensitivities are summarized in terms of a relative measure of the dispersion in the flow ensembles computed for selected events between May 1993 and July 1999. The results consistently show that the flow simulation uncertainty is strongly dependent on catchment scale. Simulation uncertainty is significantly reduced for larger scales of distributed model resolution. The consistency of this result for the selected watershed locations allows for the development of scaling relationships between catchment size and the flow uncertainty measure. The derived scaling relationship may be used to infer pronounced small-scale simulation uncertainties in distributed hydrologic model applications.