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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 urban developments. There is necessity for a new set of planning tools able to assess the performance of specific spatial patterns on the meso-scale level to extreme stormwater events. This study describes a new hydrologic model designed to analyse the spatial configuration of the urban landscape and local distribution of natural drainage sites, identifying flood prone areas in densely urbanized districts. Initial experiments on case-study cities has been develop to determine the behavior, outcomes and robustness of the model. Although further research needs to be performed, the use of spatial metrics, in assessing future developments, has a strategic role in bringing together urban planning and flood impact reduction.

Urban pluvial flood management requires detailed spatial and temporal information on flood characteristics and damaging consequences. There is lack of quantitative field data on pluvial flooding resulting in large uncertainties in urban flood model calculations and ensuing decisions for investments in flood protection. In this paper four different data sources are discussed, based on literature and expert consultation, that are believed to be of value for the acquisition of quantitative data on pluvial flooding. Data assembled by insurance agencies on flood damage, call databases held by water authorities and emergency services and remote sensing images cover years of observational data that can be mined to obtain data on flood characteristics and occurrence. Flood monitoring using sensor technology can be effective to collect additional pluvial flood data, that is not captured by existing data sources.