Flood Insurance Rate Maps (FIRMs) managed by the Federal Emergency Management Agency (FEMA) have been providing ongoing flood information to most of the communities in the United States over the past half century. However, the uncertainty associated with the modeling of the FIRMs, some of which are created by using a single HEC-RAS one-dimensional (1D) steady flow model, may have adverse effects on the reliability of flood stage and inundation extent. Therefore, a systematic understating of the uncertainty in the modeling process of FIRMs is important and necessary. The Bayesian model averaging (BMA), which is a statistical approach that can combine estimations from multiple models and produce reliable probabilistic predictions, is applied to evaluating the uncertainty associated with the FIRMs. In this study, both the BMA and HBMA approaches are used to quantify the uncertainty within the detailed FEMA models of the Deep River and the Saint Marys River in the state of Indiana based on water stage predictions from 150 HEC-RAS 1D unsteady flow model configurations that incorporate four uncertainty sources including the bridges, channel roughness, floodplain roughness, and upstream flow input. The BMA weight and the variance for each model member are obtained given the ensemble predictions and the observed water stage data in the training period, and then the BMA prediction ability is validated for the observed data from the later period. The results indicate that BMA prediction is more robust than the original FEMA model as well as the ensemble mean. Different types of uncertainty coefficients based on the BMA prediction distribution are also proposed to evaluate the FEMA models. Furthermore, the HBMA framework shows that both the channel roughness and the upstream flow input have a larger impact on prediction variance than bridges, and hence provides some insights for modelers into the relative impact of individual uncertainty sources in the flood modeling process.