With the continuous advancement of urbanization, the risk of urban flooding is increasing, making establishment of emergency shelters crucial for mitigating flood disasters. This study uses the Jinshui River flood diversion pipeline project in Zhengzhou as a case to systematically investigate the effect of flood diversion measures on reducing urban flood risks and to optimize the site selection for emergency shelters based on flood risk assessments. First, the InfoWorks integrated catchment management model is used to simulate flooding under different rainfall scenarios. Second, integrating multi-source data, the technique for order preference by similarity to an ideal solution model with four weighting methods is applied to identify high-risk flood areas. Finally, the results of the flood risk assessment are used as weights in a multi-objective site selection model, which is solved by a multi-objective particle swarm optimization algorithm to determine the optimal shelter locations. The results show that: (1) In the 10, 50, and 200-years rainfall scenarios, flood diversion measures significantly reduce flood depth and inundated areas; however, the diversion effect is limited under the extreme “7·20” rainfall event. (2) High-risk flood areas are primarily concentrated in the highly urbanized northeast, and although flood diversion alleviates risk, the overall risk remains high under extreme rainfall events. (3) Under the “7·20” rainfall scenario after flood diversion, 13 optimal shelter locations are identified, with an average evacuation distance of 471.9 meters, covering 97.3% of the population in the study area. These findings provide scientific evidence for urban flood management.

The combined impacts of urban pluvial flooding and traffic congestion can severely delay emergency response. However, existing studies often focus on isolated scenarios, failing to systematically quantify the reduction in overall service capability and specific route disruptions to critical functional nodes under compound hazards. To address this problem, this study proposes a three-tier analytical framework to systematically evaluate the resilience of emergency services under compound hazards. The framework first utilizes spatial network analysis to simulate the overall spatial evolution of service capabilities for Emergency Medical Service (EMS) and Fire and Rescue Service (FRS) across various return periods and traffic conditions. It then delves into the emergency response coverage for vulnerable population places. Finally, the fastest-route analysis is employed to identify variations in rescue routing. The study reveals several critical insights. (1) As rainfall intensity and traffic congestion intensify, the coverage areas of EMS and FRS exhibit significant contraction and boundary erosion. Notably, the service areas of FRS show a distinct fragmentation pattern. (2) The protection levels for vulnerable population places (e.g., kindergartens, primary and secondary schools, and nursing homes) show a pronounced stepwise decline. Under extreme rainfall and the heaviest congestion, the 5 min coverage for these sites drops from 89.9% to 23.6% for EMS, and from 72.4% to only 15.1% for FRS, revealing a severe risk exposure for vulnerable groups. (3) Road inundation leads to a substantial extension of rescue routes and even results in the complete isolation of 141 primary and secondary schools. Overall, the framework provides actionable decision support to enhance urban emergency response under compound hazards.