Many hydrologic simulation studies, whether related to climate change scenarios, flood forecasting or water management, depend heavily on the availability of good-quality rainfall estimates. Difficulties in estimating rainfall arise in many remote parts of the world and particularly in developing countries where ground-based measurement networks (rain gauges or weather radar) are either sparse or non-existent. Recent improvements in the ability of satellite-based rainfall retrieval algorithms to produce estimates (with global coverage) at the high space and time resolutions makes them potentially attractive for hydrologic forecasting in poorly gauged and ungauged basins. This study explores the utility of satellite-remote-sensing-based rainfall estimates for watershed-scale hydrologic modeling at seven watersheds in the Southeastern U.S. where other sources of rainfall estimates (rain gauge, weather radar) exist for comparison. The results of this study indicate that satellite-based rainfall estimates may contain significant bias, which varies with watershed size and location. This bias, of course, then propagates into the hydrologic model simulations. However, the analysis showed that the model performance in large basins could be significantly improved if short-term streamflow observations are available for model calibration.