With the increasing frequency of extreme rainfall events, pluvial flooding has become a critical challenge to the safety and sustainable development of megacities worldwide. This study proposes a multi-dimensional framework for assessing urban pluvial flood resilience (UPFR) by integrating a coupled hydrological-hydrodynamic model with system performance curves. The framework characterizes the dynamic evolution of resilience across three dimensions: rainfall characteristics, risk thresholds, and spatial scales. Results show that short-duration intense rainfall triggers instantaneous pipe overloading, whereas long-duration storms impose cumulative stress that leads to sustained systemic weakening, with the lowest resilience observed under extreme prolonged rainfall conditions. The specification of risk thresholds strongly influences resilience ranking, with the vehicle stalling risk (VSR) consistently showing the lowest resilience, followed by building inundation risk (BIR) and human instability risk (HIR). Spatially, pipes represent the weakest components, nodes maintain resilience under moderate stress, and the regional system exhibits a pattern of local weakness but overall stability, accompanied by delayed recovery. These findings highlight the importance of incorporating multi-threshold and multi-scale perspectives in flood resilience assessment and management. The proposed framework provides a scientific basis to support staged prevention measures and adaptive emergency response strategies, thereby enhancing urban flood resilience in megacities.

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.

Climate change and rapid urbanization are intensifying urban pluvial flooding and threatening sustainable urban development. This study proposes a three-stage, four-dimensional framework (TSFD-UPFR) to assess urban pluvial flood resilience across resistance, response, and recovery phases that integrate natural, infrastructural, social, and economic dimensions. Using a representative urban catchment affected by a typical extreme rainfall event, we couple hydrological–hydrodynamic simulations with multi-source remote sensing and socio-economic indicators at a 100 m grid resolution to enable spatially explicit assessment. The results indicate moderate overall resilience with pronounced spatial heterogeneity. Resistance is primarily constrained by drainage capacity and impervious surfaces, response is shaped by road connectivity and public service accessibility, and recovery is determined by essential facility restoration and economic support. Low-resilience clusters are concentrated in dense built-up areas and transport hubs, revealing structural weaknesses in adaptive capacity. By linking flood processes with socio-economic recovery dynamics, the framework captures cross-stage interactions within urban systems. The findings support climate-adaptive planning, targeted infrastructure investment, and resilience-oriented governance, contributing to sustainable and equitable urban transformation in megacities facing intensifying extreme rainfall.