Risk assessment for the typhoon-storm surge-flood-dike burst disaster chain based on risk-transmission network

Abstract

[Objective] In recent years, frequent typhoon strikes on coastal areas have caused severe casualties, economic losses and environmental degradation. Therefore, it is important to explore the formation mechanism and risk assessment of the easily occurring typhoon disaster chain to provide scientific support for flood control and tide prevention in coastal cities. [Methods] Focus is paid on a typical “typhoon-storm surge-flood-dike burst”(TSFD) disaster chain event, conducting an evolution analysis of the disaster chain mechanism based on the risk transmission and response among various disasters. A risk transmission network structure is established for the TSFD disaster chain, and a risk assessment model for the TSFD disaster chain is constructed using Bayesian theory. This model is applied in Zhuhai, Zhongshan, Jiangmen, and Foshan in Guangdong Province to simulate and analyze the distribution of TSFD disaster chain risk under the three typical typhoon scenarios. [Results] The result show that coastal areas are at higher risk of TSFD disaster chain than inland regions. When a landing-type typhoon with a wind force above Category 16 occurs, the total area of “high” and “medium” risk zones in the study area accounts for 29.9% of the total, with “high” risk zones mainly concentrating in the coastal areas of Zhuhai and Zhongshan, and “medium” risk zones mainly distributing in the east-central part of Foshan and the southern part of Jiangmen. [Conclusion] The result indicate that: under different typhoon scenarios, typhoon intensity change has a greater impact on storm surge and dike burst than flood, while the impact of typhoon path change on flood and dike burst is greater than that of storm surge. Research result can provide valuable references for the disaster prevention and mitigation capacity building in the Guangdong-Hong Kong-Macao Greater Bay Area.

Keywords

typhoon storm surge / disaster chain / risk assessment / Bayesian network / Guangdong Province / climate change / urban flood control / rainfall