This paper addresses research into a new approach that couples precipitation predictions with GIS applications and hydrological modeling to predict flood potential and thus mitigate the impacts of these natural disasters in Texas. Flooding induced from storm events is a major concern in many regions of the world, including Texas, which receives extreme precipitation events numerous times annually. Even with recent advances in precipitation prediction from NWP models and advanced monitoring systems such as doppler radar, flooding continues to be a severe natural hazard throughout the United States. In a time period of six years (1989-1994), eighty percent of declared federal disasters were related to flooding; floods themselves average four billion dollars annually in property damage alone. In 2002, a major precipitation event caused extensive flooding in the San Antonio watershed, which is the case presented in this study. Urban areas such as San Antonio are especially prone to flooding due to the large proportion of impermeable surfaces such as concrete that do not allow rainfall to infiltrate into the soil. Rainfall that does not infiltrate or evaporate becomes runoff that moves through a drainage basin in the direction of the main river channel. Extreme runoff has the potential to exceed the carrying capacity of the stream, creating flood conditions. Thus, the goal of our research is to integrate precipitation forecasts issued by the National Weather Service (NWS) into a hydrological model, based upon high-resolution GIS data sets to predict flood potential should the forecast verify. Specifically, we seek to predict flood depth and areal coverage. In addition, the flood potential model has been designed to provide decision-makers with additional information that could result from inaccuracies in both magnitude and location of precipitation forecasts. The final product of this research will be a flood probablility forecast product that will enable decision makers to efficiently prepare for worst-case scenarios and mitigate flood damage, while continuing to model current conditions. While designed for use by Texas disaster managers, the approach used in this research can be extended to have applications in other areas of the country.