Floods are the most common natural disaster in the U.S. as reported by the Federal Emergency Management Administration (FEMA), and there is a need to provide advance warning to vulnerable communities on the potential risks of flooding after intense storms. The key drivers of urban hydrological research include climate change impacts and adaption, city resilience to hydrological extremes, and integration with emergency management and city planning disciplines. Significant advances in modeling techniques and computational resources have made real-time flood forecasting tools in urban and rural areas an achievable goal, but there is no universal method for flood modeling. Urban landscapes pose a challenge because of fine-scale features and heterogeneities in the landscape including streets, buildings, pipes, and impervious land cover. A nested regional-local modeling approach was used to evaluate its capabilities to provide useful and accurate flood related information to a small community in Iowa. The advantage of a nested approach is the ability to harness the computational efficiency of the regional model while providing reasonably accurate streamflow boundary conditions to the local model. The nested model incorporates the tools and products maintained at the Iowa Flood Center (IFC) including the streamflow bridge sensors, rain gauges, radar rainfall product, and statewide model. A one-way connection was made between the regional model of the upper Maquoketa Watershed (275 mi2) and the local model of the City of Manchester (5 mi2). The uncalibrated, nested model was validated using photos and streamflow records for flood events that occurred in July 2010 and September 2016. Multiple radar rainfall estimates were used as input to the model to better understand the impacts of the spatial and temporal resolution and variations of rainfall on streamflow predictions. A local storm event analysis was completed to determine the vulnerable areas of the stormwater network in eastern Manchester. The two main sources of flooding in Manchester are from the river and from local runoff. During extreme flood events caused by the river, the hydrologic impacts of the urban catchment are masked and the stormwater network system is overwhelmed. The coarse, regional model is limited in producing streamflow results for the small tributaries draining the eastern areas of Manchester. In the case of localized rainfall, a fine resolution model that takes into account the stormwater network and rainfall-runoff dynamics are crucial to capturing the hydrologic response of the urban area. Overall, the nested model showed skill in reproducing the hydrographs and the flood extents. Using an ensemble of rainfall input, the multiple model realizations envelope the observed streamflow indicating that the uncertainty of the rainfall is implicitly captured in the model results. The simulated streamflow at the outlet varies significantly depending on the spatial resolution of the rainfall but shows small sensitivity to the temporal resolution of the rainfall input. However, the local rainfall-runoff volumes vary significantly depending on the spatial and temporal resolution of the rainfall input. Recommendations are given to Manchester to highlight areas at risk to flooding. Recommendations are given to the IFC on the capabilities of the nested regional-local modeling approach along with suggestions for future work to incorporate urban areas into the statewide flood forecasting system.