Abstract
Compound flooding from tropical cyclones (TCs), driven by the concurrent or sequential occurrence of storm surge, heavy rainfall, and river discharge, poses escalating risks under climate change. This study develops a Bayesian network (BN) model to probabilistically assess compound flood risk in coastal regions, integrating multiple hazard drivers and their dependencies. We apply the model to the Bay of Bengal and South China Sea regions, using historical TC data (1980–2020) and future projections under RCP4.5 and RCP8.5 scenarios (2070–2100). The BN structure captures causal links among sea surface temperature, TC intensity, precipitation, storm surge, and flood depth. Results indicate that under RCP8.5, the probability of extreme compound flood events (exceeding 2-m inundation) increases by 40–60% relative to historical baselines, with the largest increases in the Ganges-Brahmaputra delta and the Pearl River Delta. Sensitivity analysis reveals that TC intensity and antecedent soil moisture are the most influential drivers. The BN approach effectively quantifies uncertainties and supports adaptive planning. Our findings underscore the need for integrated risk management strategies that account for compound interactions under warming climates.