A bibliometric analysis of trends in rainfall-runoff modeling techniques for urban flood mitigation (2005–2024)

• Bibliometric analysis of 618 documents (2005–2024) reveals trends in rainfall-runoff modeling for urban flood mitigation. • Integration of Artificial Intelligence (AI), Geographic Information Systems (GIS), and Low-Impact Development (LID) enhance hydrological model accuracy and sustainability. • China, the United States (US), and the United Kingdom (UK) lead research, with rising contributions from Southeast Asia. • The Storm Water Management Model (SWMM) and the Hydrologic Engineering Center's Hydrologic Modeling System (HEC HMS) remain dominant, with growing adoption of nature-based solutions. • Future research must assess long-term Low-Impact Development (LID) effectiveness and climate-adaptive modeling. Urban flooding poses significant challenges globally, driven by climate change and rapid urbanization. This bibliometric study reviewed 618 documents published between 2005 and 2024, focusing on rainfall-runoff modelling for urban flood mitigation. Key findings reveal that China (100 publications), the United States (81), and the United Kingdom (55) dominate research output, with emerging contributions from Southeast Asia and the Middle East. Traditional models such as the Storm Water Management Model (SWMM) and the Hydrologic Modelling System (HEC HMS) remain widely used, while machine learning (ML), Geographic Information Systems (GIS), and Low-Impact Development (LID) approaches drive innovation in model precision and adaptability. However, gaps persist in evaluating long-term LID effectiveness and incorporating real-time data to address extreme climate variability. By offering quantitative insights into current research efforts, this analysis highlights the critical need for integrating advanced technologies and sustainable strategies to further enhance resilience in urban flood management frameworks.