Integrating rapid assessment of flood proneness into urban planning under data constraints: a fuzzy logic and bricolage approach

To increase city climate resilience, a rapid data-constrained flood-proneness assessment (FPA) technique for the Kolkata Metropolitan Region (KMR) in India is proposed, and a conceptual urban-climate plan (UCP) framework capable of incorporating the FPA outputs into the mainstream planning process is developed. To overcome data and computational demands of FPA mapping, this integrated framework is based on the concept of data bricolage and integrates four flood-conditioning factors – surface elevation, normalized difference water index (NDWI), flow accumulation and surface run-off – into a single output using fuzzy inference. The KMR results show that elevation had the most significant impact on flood proneness, and that NDWI and surface run-off played a vital role in more urbanized areas.