Multi-Receptor Exposure to Compound Flooding in an Archipelagic City: Cross-Scale Hydrodynamic Modeling and Machine-Learning-Based Spatialization

Abstract

Against the backdrop of global climate change and rapid urbanization, island cities face increasing compound-flood risks due to their fragmented topography and limited terrestrial space, yet cross-scale modeling and high-resolution exposure assessment for archipelagic environments remain insufficient. Taking Zhoushan City, China, as a case study, this research develops an integrated compound-flood multi-receptor exposure assessment framework combining cross-scale hydrodynamic modeling with high-resolution spatialization. For hazard assessment, a one-way sequential coupling of a parametric typhoon model, FVCOM, and LISFLOOD-FP was employed to simulate storm-surge, pluvial, and compound flooding at 50-, 200-, and 1000-year design levels. Model performance was evaluated using tidal-gauge observations, historical storm-surge records, remotely sensed historical inundation, and documented urban waterlogging locations. For exposure assessment, Random Forest-based dasymetric mapping was used to generate 30 m population and secondary- and tertiary-sector GDP datasets; primary-sector GDP was spatialized using a land-cover-based approach, and 3314 critical-infrastructure facilities were compiled across eight subsystems. The results reveal the following: (1) compound-flood inundation areas were 289.95, 424.93, and 513.31 km2 under the 50-, 200-, and 1000-year design scenarios, respectively; (2) under the 1000-year compound scenario, 57.5% of the population, 51.3% of GDP, and 63.3% of critical infrastructure were exposed; (3) infrastructure exposure showed strong subsystem heterogeneity, with coastal facilities having the highest exposure ratio (76%) and emergency-rescue facilities reaching 65.3%; (4) sector-disaggregated GDP exposure revealed substantial economic-geographic differences among districts/counties, highlighting the need for differentiated adaptation strategies. The proposed framework provides spatially explicit information to support differentiated coastal adaptation planning and is transferable to other archipelagos and small island cities with similar data constraints.