Typhoon Disaster Chain Evolution Modelling in the Guangdong–Hong Kong–Macao Greater Bay Area Based on GERT Stochastic Networks: A Case Study of Typhoon Mangkhut

Typhoon disaster chains—cascading sequences of hazards triggered by tropical cyclones—pose significant risks to highly urbanized coastal regions. However, quantitative modelling of their stochastic evolution, including both propagation probabilities and temporal dynamics, remains limited. This study develops a typhoon disaster chain evolution model by integrating the FBIREC (Factor-Bearing body- Incident-Response-Environment-Consequence) scenario representation framework with a Graphical Evaluation and Review Technique (GERT) stochastic network. The model is applied to the Guangdong–Hong Kong–Macao Greater Bay Area using Typhoon Mangkhut (2018) as a case study. Ten cascading event sequences are identified, and their transmission probabilities and expected durations are calculated. The model reproduces the observed event probabilities for Typhoon Mangkhut with high fidelity, with a mean absolute error of 4.31% and a coefficient of determination R 2 = 0.816. Power facility damage was identified as one of the most influential infrastructure nodes, exhibiting high propagation potential to multiple downstream service systems. The disaster chain unfolds across two temporal scales: short-term risks (0–48 h) and long-term risks (>48 h), providing quantifiable targets for phased emergency response planning. The proposed framework provides a methodological reference that can be adapted to other typhoon-prone regions following case-specific re-parameterization.