The identification and classification of flood-prone areas comprise a fundamental step in the Flood Risk Management approach, providing subsidies for land use planning, floodproofing policies, the design of mitigation measures and early warning systems. To address this issue, a frequently used preliminary tool is the flood susceptibility mapping of a region using a range of widely available data. Therefore, the present study introduces an index-based approach able to qualitatively assess flood-prone areas, named Physical Susceptibility to Floods Index (PhySFI), based on a multi-criteria decision-making method and developed in a GIS environment. The methodology presupposes a critical discussion of variables commonly used in other flood indexes, intending to simplify the proposed representation, and emphasizes the role of the user/modeler. PhySFI is composed of just four indicators, based on physical parameters of the assessed environment. This index was developed and first applied in the city of Rio de Janeiro, as part of the Rio de Janeiro Climate Change Adaptation Plan. The validation process was based on a comparative analysis with flood extent and height simulated by the hydrodynamic modeling of four watersheds within the study area, with different urbanization processes for each one. The results indicate that the index is a powerful preliminary tool to assess flood-prone areas in coastal cities.

Urbanization increases imperviousness and reduces infiltration, retention, and evapotranspiration, frequently aggravating urban flooding due to greater runoff and higher and faster discharge peaks. Effective strategies to mitigate flood risks require a better understanding of the watershed dynamics and space to reverse the negative impacts. However, often cities do not have proper data sets to feed mathematical models that would be helpful in mapping water dynamics. Attempts to reduce flood risks have been made for decades by means of structural interventions but were frequently designed within the logic of a local scale, using limited available spaces and often merely shifting flooding downstream. Therefore, assessing urban floods requires a modeling approach capable of reflecting the watershed scale, considering interactions between hydraulic structures and urban landscape, where best practices and non-structural measures aim to improve community flood resilience through the reduction of social and financial costs in the long run. This paper proposes an integrated approach to analyze low impact development (LID) practices complemented by non-structural measures in a case study in southern Italy, supported by mathematical modeling in a strategy to overcome a context of almost no available data and limited urban open spaces.