Physics-based distributed (PBD) hydrologic models predict flooding throughout a basin using the laws of conservation of mass and momentum, which requires accurate and representative quantitative precipitation (QPE) input. Uncertainties in operational QPE data streams are evaluated with the distributed hydrologic model Vflo. Vflo is a gridded distributed hydrologic model that predicts runoff and continuously updates soil moisture for operational prediction. As a participating model in the Distributed Model Intercomparison Project (DMIP2), Vflo is applied to the Blue River basin in South Central Oklahoma and the Illinois River in Eastern Oklahoma and Western Arkansas. Setup of the physics-based distributed hydrologic model, Vflo, is accomplished using geospatial data to derive physical parameters. Model parameter calibration seeks to reduce model uncertainty between simulated and observed streamflow for a ten year period. Hydrologic prediction accuracy is known to be affected by both the forcing inputs radar/gauge observations and from model uncertainty. Using a calibrated hydrologic model, the propagation of uncertainties in precipitation estimates is examined. The achievable accuracy for various radar input datasets is investigated in this presentation to gain an understanding of uncertainties associated with data quality, sampling errors, and bias correction procedures applied to the radar used in DMIP2.