Extreme-rainfall flood risk to logistics facilities in the Pearl River Delta: A scenario-based modeling, flood risk dynamics, and spatial clustering

In the context of global climate change, extreme rainfall increasingly triggers urban flooding, yet facility-level flood risk for logistics facility—critical to urban operations and supply-chain—remains insufficiently assessed. This study applies a scenario-based SCS–CA modeling framework to simulate runoff generation and routing process and to produce high-resolution flood maps for logistics facilities across the Pearl River Delta (PRD), China. By running multi-return-period (10-, 20-, 50-, and 100-year) and multi-duration (0–180 min) rainfall scenarios, we quantify the dynamic process, magnitude, and spatial heterogeneity of facility-level flood risk across the region. Results reveal that: (1) Beyond the 50-year rainfall scenario, logistics facilities show sharply surging flood-risk dynamics, with flood risks escalating most rapidly during the 90–120-min duration.; (2) Under the 100-year scenario, prolonged rainfall further amplifies this dynamic escalation, with about 20% of facilities exceeding 300 mm flood depth after 180 min—indicating severe disruption potential under extreme events; (3) Spatial patterns exhibit pronounced heterogeneity, with high-risk hotspot clusters emerging in Guangzhou, along the Dongguan–Shenzhen corridor, and in Zhuhai; and (4) Local built-environment factors, exemplified by surrounding road density, show significant positive correlations with facility-level flood risk, though the strength of association varies across cities—most notably in Zhuhai, which demonstrates the strongest effect among Foshan, Dongguan, Zhuhai, Huizhou, and Zhaoqing. These findings may provide actionable evidence to support climate-adaptive site selection and supply-chain governance, offering both policymakers and private stakeholders a scientific basis for prioritizing operational strategies under increasingly intense extreme-rainfall events.