Among other sources of uncertainties in hydrologic modeling, spatial rainfall variability, channel hydraulic variability, and model parameter uncertainty were evaluated. The Monte Carlo and Harr methods were used to assess 90% certainty bounds on simulated flows. The lumped watershed model, Hydrologic Simulation Program FORTRAN ? HSPF, was used to simulate streamflow at the outlet of the Luxapallila Creek watershed in Mississippi and Alabama. Analysis of parameter uncertainty propagation on streamflow simulations from 12 HSPF parameters was accomplished using 5,000 Monte Carlo random samples and 24 Harr selected points for each selected parameter. Spatial rainfall variability propagation on simulated flows was studied using six random grid point sets of Next Generation Weather Radar (NEXRAD) rainfall data (i.e., 109, 86, 58, 29, 6, and 2 grid points) from the baseline scenario (115 NEXRAD grid points). Uncertainty in channel hydraulic properties was assessed comparing the baseline scenario (USGS FTABLE) versus the EPA RF1 FTABLE scenario. The difference between the baseline scenario and the remaining scenarios in this study was evaluated using two criteria: the percentage of observed flows within the HSPF 90% certainty bounds (Reliability) and the width of the HSPF 90% certainty bounds (Sharpness). Daily observed streamflow data were clustered into three groups to assess the model performance by each class: below normal, normal, and above normal flows. The parameter uncertainty propagation results revealed that the higher the model Sharpness the lower the model Reliability. The model Sharpness and Reliability results using 2 NEXRAD grid points were markedly different from those results using the remaining NEXRAD data sets. The hydraulic property variability of the main channel affected storm event paths at the watershed outlet, especially the time to peak flow and recessing limbs of storm events. The comparison showed that Harr?s method could be an appropriate initial indicator of parameter uncertainty propagation on streamflow simulations, in particular for hydrology models with several parameters. Parameter uncertainty was still more important than those sources of uncertainty accomplished in this study because all of the median relative errors of model Reliability and Sharpness were lower than +/- 100%.