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One of the major flaws of modern flood maps is the lack of consideration towards precipitation flooding, which can turn into a type flash flood known as pluvial flooding. This type of urban flooding has not been explored in depth because of variations in urban infrastructure and a heavy focus on coastal flooding. However, urban flash flooding does happen, and residents are often unprepared and uninformed. The prominence of impervious surfaces – artificial structures that do not absorb water like asphalt and concrete – makes urban areas particularly vulnerable to pluvial flash flooding. Therefore, our project addresses the question: where will water accumulate in the event of a pluvial flash flood in Houston, Texas? This research uses previous data related to elevation, precipitation, and flash flooding in Houston, Texas. The data, collected from several sources including the GLOBE Observer land covers, OpenTopography elevation data, and QGIS analysis features, was developed into a Python program that predicts areas at high risk for severe flash flooding. The model analyzes digital elevation model rasters using a D8 flow algorithm and determines specific elevation points that collect large amounts of water from other points, indicating areas particularly vulnerable to water accumulation in a flash flood. After analyzing 6 areas of interest in the Houston area, we found that water is most likely to pool on impervious surfaces such as streets and roads. This knowledge can help urban planners and citizens to prepare for flash floods through informed drain placement and avoiding building houses on areas at high risk of flooding. Furthermore, our model can be used by other scientists studying flooding for similar urban environments, providing a valuable tool for predicting and mitigating the impacts of urban pluvial flash flooding. Key words: Pluvial flooding, digital elevation model, flow analysis