An Enhanced Framework for Assessing Pluvial Flooding Risk with Integrated Dynamic Population Vulnerability at Urban Scale

Under the combined influence of climate change, accelerated urbanization, and inadequate urban flood defense standards, urban pluvial flooding has become an increasingly severe issue. This not only poses significant challenges to social stability and economic development but also makes accurate flood risk assessment crucial for improving urban flood control and drainage capabilities. This study uses Jinan, a typical foothill plain city in Shandong Province, as a case study to compare the performance of differential evolution (DE), genetic algorithm (GA), and particle swarm optimization (PSO) in calibrating the SWMM. By constructing a hydrological-hydrodynamic coupled model using the SWMM and LISFLOOD-FP, this study evaluates the drainage capacity of the pipe network and surface inundation characteristics under both historical and design rainfall scenarios. An agent-based model (ABM) is developed to analyze the dynamic risks and vulnerabilities of population and building agents under different rainfall scenarios, capturing macroscopic emergent patterns from individual behavior rules and analyzing them in both time and space dimensions. Additionally, using multi-source remote sensing data, dynamic population vulnerability, and flood hazard processes, a quantitative dynamic flood risk analysis is conducted based on cloud models. The results demonstrated the following: (1) PSO performed best in calibrating the SWMM in the study area, with Nash-Sutcliffe efficiency (NSE) values ranging from 0.93 to 0.69. (2) Drainage system capacity was low, with over 90% of the network exceeding capacity in scenarios with return periods of 1 to 100 years. (3) The vulnerability of people and buildings increased with higher flood intensity and duration. Most affected individuals were located on roads. In Event 6, 11.41% of buildings were at risk after 1440 min; in the 20-year flood event, 26.69% of buildings were at risk after 180 min. (4) Key features influencing vulnerability included the DEM, PND, NDVI, and slope. High-risk areas in the study area expanded from 36.54% at 30 min to 38.05% at 180 min.