An assessment framework for evaluating urban flood vulnerabilities using Night-Time Light data

Urban floods pose significant socio-economic and environmental challenges, particularly in rapidly urbanizing regions. We utilized Night-Time Light (NTL) data as a dynamic proxy for human activity and urban density to enhance the assessment of urban flood vulnerability. Unlike traditional methods that rely on static datasets or post-disaster surveys, this approach incorporates real-time NTL data to better capture the evolving patterns of urban exposure. Focusing on the Kelani River watershed, the most flood-prone and densely populated region in Sri Lanka, we integrated nine conditioning factors, including slope, precipitation, and soil type, with NTL intensity to generate comprehensive vulnerability maps. To ensure the accessibility and practical application of the results, a web application was developed using Google Earth Engine (GEE), offering an interactive platform for real-time visualization of urban flood risks. The findings underlined the transformative potential of NTL data in flood vulnerability mapping and demonstrated its applicability as a cost-effective, scalable, and open-access decision-support tool adaptable to various urban contexts.