Evaluating and educating communities on their flood risks is an integral part of adapting to climate change and more extreme precipitation patterns. Low-income communities are often the most affected by in-land floods. They are more likely to live in floodplains and have less socioeconomic mobility. This thesis takes a two-pronged approach to evaluating flood risk. First, a flood risk model that identifies areas of high runoff in Giles County, Virginia was developed. The model accounts for land cover, soil type and elevation. The soil retention layer and Digital Elevation Model (DEM) produced a cell by cell layer of runoff. Storm data was collected from the NEXRAD program and integrated into the runoff layer. Countywide maps of low, moderate and high risk were produced. Semi structured interviews were then conducted to gauge the usability of the maps within high risk areas. Interview feedback was transcribed and coded for analysis. Themes identified throughout the interviews, were used to make adjustments to the model and maps. This experience suggests that combining technical analysis and community engagement can create a more accurate depiction of flood risk that is meaningful to those most affected. This paper also suggests that evaluating riverine flooding based on the most severe, recent storm yields a more accurate and impactful tool than previous flood modeling methods. This work takes flood modeling a step farther by receiving community input and shows that models are only as useful as they are meaningful to the most impacted communities. / Master of Urban and Regional Planning / Climate change models suggest that many communities will experience fewer precipitation events, but when precipitation events do occur they will be more intense and thus destructive. Communities can expect a higher risk of flooding, which is why it is important to plan proactively and understand where the most at-risk areas are. To help Giles County, VA understand its flood risk we created a tool that displays storm runoff. Elevation, soil type and vegetation were incorporated into the model. Storm data was integrated into the model and compared to annual precipitation levels. Countywide maps of low, moderate and high risk were produced. In order for the maps to have the maximum impact they need to be accessible to at risk populations. To gauge the accessibility of the maps to the community semi structured interviews were conducted within the high risk areas. Issues and opportunities identified through the interviews were used to make adjustments to the model and maps. The findings suggest that combining technical analysis and community engagement can create more accurate depictions of flood risks that are meaningful to those most affected. Future research efforts should include incorporating anticipated development schemes into the model and hosting more in depth community engagement activities. The importance of community engagement is highlighted in this research, as the interviews has a major impact on the outcome of the model.