Cities exhibit unique spatial patterns, and thus a distinctive heterogeneity. At different scales of influence, they introduce changes in the physical properties of the natural environment, as the diffusion of impervious surfaces. While climate change is expected to increase the frequency of hazards, patterns of urbanization might be critical in balancing the exposure of cities to extreme weather events, such as heavy rainfalls. Therefore, the adaptability of urban structures to stormwater management measures is vital to safeguard cities against increasing flooding. Yet, planning and design practices are challenged to address the resiliency of future urban landscapes. There is necessity for a new set of planning tools able to analyse the performance of specific urban patterns to extreme stormwater events. This study describes a new hydrologic model designed to operate on the meso-scale level, which uses a limited dataset but yet is able to identify flood prone areas in dense urban environments. Initial experiments on case-study areas has been develop to determine the behavior and robustness of the model. Although further research needs to be performed, the use of urban form metrics, in assessing future developments, has a strategic role in bringing together urban planning and flood impact reduction.