Distributed hydrologic modeling holds significant promise for improved estimates of streamflow with high spatial resolution. However, uncertainty in model structure and parameters, which are distributed in space, and in operational weather radar rainfall estimates, which comprise the main input to the models, contributes to significant uncertainty in distributed model streamflow simulations over a wide range of space and time scales. Using the simulations produced for the Distributed Model Intercomparison Project (DMIP), this paper develops and applies sample-path methods to characterize streamflow simulation uncertainty by diverse distributed hydrologic models. The emphasis in this paper is on the model parameter and structure uncertainty given radar rainfall forcing. Multimodel ensembles are analyzed for six application catchments in the Central US to characterize model structure uncertainty within the sample of models (both calibrated and uncalibrated) participating in DMIP. Ensembles from single distributed and lumped models are also used for one of the catchments to provide a basis to characterize the impact of parametric uncertainty versus model structure uncertainty in flow simulation statistics. Two main science questions are addressed: (a) what is the value of multimodel streamflow ensembles in terms of the probabilistic characterization of simulation uncertainty? And (b) how do probabilistic skill measures of multimodel versus single-model ensembles compare? Discussed also are implications for the operational use of streamflow ensembles generated by distributed hydrologic models. The results support the serious consideration of ensemble simulations and predictions created by diverse models in real time flow prediction.