Advancing Urban Flood Resilience: A Systematic Review of Urban Flood Risk Mitigation Model, Research Trends, and Future Directions - Anahita Azadgar; Lucyna Nyka; Stefano Salata (Land)
Climate change has increased the frequency of extreme weather events, challenging traditional flood risk assessments. New methods, like InVEST’s urban flood risk mitigation model (UFRM), are crucial for assessing flood-prone areas, especially those vulnerable to pluvial flooding. This systematic review examines the utilization and limitations of the UFRM model for identifying flood-prone areas and designing adaptation plans in response to climate change, based on research articles published between 2019 and 2024. The articles were identified through Scinapse and Google Scholar using predefined criteria, including relevance to UFRM applications and publication within the specified timeframe. An analysis of the 21 selected papers using bibliometric techniques reveals geographic concentrations of the case studies that utilized the UFRM model primarily in Italy, Turkey, China, and the United States, with notable research gaps in Central Europe. The journal publication trends highlight the prominence of certain journals, such as the Journal of Land. A network analysis using VOSviewer identifies thematic clusters aligned with the UFRM research areas, including Urban Planning and Nature-Based Solutions (NBSs), Urbanization and Sustainable Development, the Economic and Social Effects of Flood Mitigation, Adaptation and Risk Management, and Resilience and Vulnerability. This review contributes to gaining a deeper understanding of the research landscape regarding the utilization of the UFRM model and provides recommendations for advancing sustainable and resilient urban development practices. The review was not preregistered in a formal registry. The finding reveals that integrating automation and optimization tools to suggest different solutions for problematic nodes in urban areas is crucial for addressing multiple issues simultaneously and optimizing adaptation plans effectively.