Risk of Compound Typhoon Disaster Chains: Insights from Southeastern China
Xiaoliu Yang , Youyi Yan , Xiang Zhou , Laiyin Zhu , Miaomiao Ma , Jiangbo Zhang , Ying Chen , Lu Gao
International Journal of Disaster Risk Science ›› : 1 -18.
Risk of Compound Typhoon Disaster Chains: Insights from Southeastern China
Typhoon disasters threaten sustainable development in southeastern China due to their complex spatiotemporal chaining and compounding effects. However, characteristics of risk and exposure of compound typhoon disaster chains remain unclear, particularly across geographic scales. This study proposed a multi-scale risk assessment framework to analyze compound typhoon disaster chains, using Fujian Province—a high-risk coastal region in southeastern China—as a case study. We quantified risks and exposures of four disaster chains: typhoon-rainstorm-urban waterlogging (TRU), typhoon-rainstorm-flood (TRF), typhoon-rainstorm-landslide (TRL), and typhoon-strong wind-storm surge (TWS), across nested scales (grid, county, city, and basin). Key findings include: (1) Pronounced spatial heterogeneity exists in Fujian Province, with compound risk hotspots dominated by TRU (216.71 km2), TRF (872.43 km2), and TWS (263.69 km2) high-risk areas in eastern coastal areas, while inland mountainous regions are primarily affected by the TRL single chain (24,993 km2, 20.4% of the total area of the province); (2) Approximately one-third of the provincial population (5.69 million) and GDP (RMB 552 billion yuan) are exposed to the high-risk zones of the TRF chain, whereas the TRU chain results in twice the exposure density of TRF, forming localized hotspots; (3) High-risk areas display dual patterns of single chain dominance and compound chain aggregation, with compound chain exposure densities 38–58 times higher (at population density 15,900 persons/km2) than single chain exposure density. Priority should be given to managing cascading risks of compound chains like TRU-TRF, alongside targeted interventions in multi-disaster hubs such as Fuzhou City and Jinjiang City. The findings advance our understanding of typhoon disaster risk compounding, informing targeted mitigation strategies and providing a framework for multi-hazard cascade analysis.
Compound pattern / Exposure / Risk / Southeastern China / Typhoon disaster chains
| [1] |
|
| [2] |
Ambily, P., N.R. Chithra, and F.C. Mohammed. 2024. A framework for urban pluvial flood resilient spatial planning through blue-green infrastructure. International Journal of Disaster Risk Reduction 103: Article 104342. |
| [3] |
Bevacqua, E., D. Maraun, I. Hobæk Haff, M. Widmann, and M. Vrac. 2017. Multivariate statistical modelling of compound events via pair-copula constructions: analysis of floods in Ravenna (Italy). International Journal of Hydrology and Earth System Sciences 21: 2701–2723. |
| [4] |
|
| [5] |
Cangialosi, J.P., A.S. Latto, and R. Berg. 2021. National hurricane center tropical cyclone report—Hurricane Irma. https://www.nhc.noaa.gov/data/tcr/AL112017. Accessed 24 Jun 2024. |
| [6] |
Chang, H., A. Pallathadka, J. Sauer, N.B. Grimm, R. Zimmerman, and C. Cheng. 2021. Assessment of urban flood vulnerability using the social-ecological-technological systems framework in six US cities. Sustainable Cities and Society 68: Article 102786. |
| [7] |
Chen, Y., and D. Alexamder. 2022. Integrated flood risk assessment of river basins: Application in the Dadu River basin, China. Journal of Hydrology 613: Article 128456. |
| [8] |
Czajkowski, J., G. Villarini, M. Montgomery, E. Michel-Kerjan, and R. Goska. 2017. Assessing current and future freshwater flood risk from North Atlantic tropical cyclones via insurance claims. Scientific Reports 7(1): Article 41609. |
| [9] |
Do, C., and Y. Kuleshov. 2023. Multi-hazard tropical cyclone risk assessment for Australia. Remote Sensing 15: Article 795. |
| [10] |
Feng, D., X. Shi, and F.G. Renaud. 2023. Risk assessment for hurricane-induced pluvial flooding in urban areas using a GIS-based multi-criteria approach: A case study of Hurricane Harvey in Houston, USA. Science of the Total Environment 904: Article 166891. |
| [11] |
Fox, S., F. Agyemang, L. Hawker, and J. Neal. 2024. Integrating social vulnerability into high-resolution global flood risk mapping. Nature Communications 15: Article 3155. |
| [12] |
Gao, J., and M. Bukovsky. 2023. Predicting future urban waterlogging-prone areas by coupling the maximum entropy and FLUS model. Nature Communications 14: Article 6536. |
| [13] |
Gao, J., and B.C. O’Neill. 2020. Mapping global urban land for the 21st century with data-driven simulations and Shared Socioeconomic Pathways. Nature Communications 11: Article 2302. |
| [14] |
|
| [15] |
Laura, D., N. Jeffrey, C. Gemma, S. James, and W. Thorsten. 2023. Flood hazard potential reveals global floodplain settlement patterns. Nature Communications 14: Article 2801. |
| [16] |
Liang, Y., C. Wang, G. Chen, and Z. Xie. 2024. Evaluation framework ACR-UFDR for urban form disaster resilience under rainstorm and flood scenarios: A case study in Nanjing, China. Sustainable Cities and Society 107: Article 05424. |
| [17] |
Lin, J., P. He, L. Yang, X. He, S. Lu, and D. Liu. 2022. Predicting future urban waterlogging-prone areas by coupling the maximum entropy and FLUS model. Sustainable Cities and Society 80: Article 103812. |
| [18] |
Liu, F., E. Xu, and H. Zhang. 2022. An improved typhoon risk model coupled with mitigation capacity and its relationship to disaster losses. Journal of Cleaner Production 357: Article 131913. |
| [19] |
Mabrouk, M., and H. Han. 2023. Urban resilience assessment: A multicriteria approach for identifying urban flood-exposed risky districts using multiple-criteria decision-making tools (MCDM). International Journal of Disaster Risk Reduction 91: Article 103684. |
| [20] |
Malakar, K., T. Mishra, V. Hari, and S. Karmakar. 2021. Risk mapping of Indian coastal districts using IPCC-AR5 framework and multi-attribute decision-making approach. Journal of Environmental Management 294: Article 112948. |
| [21] |
|
| [22] |
McMichael, C., S. Dasgupta, S. Ayeb-Karlsson, and I. Kelman. 2020. A review of estimating population exposure to sea-level rise and the relevance for migration. Environmental Research Letters 15(12): Article 123005. |
| [23] |
Messmer, M., and I. Simmonds. 2021. Global analysis of cyclone-induced compound precipitation and wind extreme events. Weather and Climate Extremes 32: Article 100324. |
| [24] |
Mind’je, R., L. Li, A.C. Amanambu, and L. Nahayo. 2019. Flood susceptibility modeling and hazard perception in Rwanda.International Journal of Disaster Risk Reduction 38: Article 101211. |
| [25] |
Mohammadifar, A., H. Gholami, and S. Golzari. 2023. Novel integrated modelling based on multiplicative long short-term memory (mLSTM) deep learning model and ensemble multi-criteria decision making (MCDM) models for mapping flood risk. Journal of Environmental Management 345: Article 118838. |
| [26] |
|
| [27] |
|
| [28] |
Qiao, R., S. Lin, L. Xiao, G. Zhang, X. Meng, Z. Liu, M. Wang, S. Zhou, and Z. Wu. 2024. Understanding the global subnational migration patterns driven by hydrological intrusion exposure. Nature Communications 15: Article 6285. |
| [29] |
Qin, L., L. Zhu, X. Liao, C. Meng, Q. Han, Z. Li, S. Shen, W. Xu, and J. Chen. 2024. Recent northward shift of tropical cyclone economic risk in China. Npj Natural Hazards 1: Article 8. |
| [30] |
Ravi, R., and K. Subhankar. 2024. Compound hazard mapping for tropical cyclone-induced concurrent wind and rainfall extremes over India. Npj Natural Hazards 1: Article 5. |
| [31] |
Rossi, M.W., R.S. Anderson, S.P. Anderson, and G.E. Tucker. 2020. Orographic controls on subdaily rainfall statistics and flood frequency in the Colorado front range, USA. Geophysical Research Letters 47: Article e2019GL085086. |
| [32] |
Seemuangngam, A., and H. Lin. 2024. The impact of urbanization on urban flood risk of Nakhon Ratchasima, Thailand. Applied Geography 162: Article 103152. |
| [33] |
Seneviratne, S.I., X. Zhang, and M. Adnan. 2021. Weather and climate extreme events in a changing climate. In Climate change 2021: The physical science basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, ed. V. MassonDelmotte, P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. et al., 1513–1766. Cambridge, UK: Cambridge University Press. |
| [34] |
|
| [35] |
|
| [36] |
Wang, Z., N. Xia, X. Zhao, X. Ji, and J. Wang. 2024. Comprehensive risk assessment of typhoon disasters in China’s coastal areas based on multi-source geographic big data. Science of the Total Environment 926: Article 171815. |
| [37] |
Wang, Y., C. Zhang, A.S. Chen, and G. Fu. 2023. Exploring the relationship between urban flood risk and resilience at a high-resolution grid cell scale. Science of the Total Environment 893: Article 164852. |
| [38] |
|
| [39] |
|
| [40] |
Xiao, S., L. Zou, J. Xia, Y. Dong, Z. Yang, and T. Yao. 2023. Assessment of the urban waterlogging resilience and identification of its driving factors: A case study of Wuhan City, China. Science of the Total Environment 866: Article 161321. |
| [41] |
Xu, P., D. Wang, Y. Wang, V. Singh, J. Qiu, J. Wu, A. Zhang, and X. Ju. 2023. Dynamic identification and risk analysis of compound dry-hot events considering nonstationarity. Journal of Hydrology 616: Article 128852. |
| [42] |
Yan, Y., G. Wang, H. Wu, G. Gu, and N. Nanding. 2022. Characteristics of precipitation and floods during typhoons in Guangdong Province. Remote Sensing 14: Article 1945. |
| [43] |
Yang, X., X. Qin, X. Zhou, Y. Chen, and L. Gao. 2024. Assessment of disaster mitigation capability oriented to typhoon disaster chains: A case study of Fujian Province, China. Ecological Indicators 167: Article 112621. |
| [44] |
Yang, M., W.F.M. Yusoff, M.F. Mohamed, S. Jiao, and Y. Dai. 2024. Flood economic vulnerability and risk assessment at the urban mesoscale based on land use: A case study in Changsha, China. Journal of Environmental Management 351: Article 119798. |
| [45] |
Ye, M., J. Wu, W. Liu, X. He, and C. Wang. 2020. Dependence of tropical cyclone damage on maximum wind speed and socioeconomic factors. Environmental Research Letters 15: Article 094061. |
| [46] |
Yu, S., X. Kong, Q. Wang, Z. Yang, and J. Peng. 2023. A new approach of Robustness-Resistance-Recovery (3Rs) to assessing flood resilience: A case study in Dongting Lake Basin. Landscape and Urban Planning 230: Article 104605. |
| [47] |
Zhang, W., G. Liu, J.C. Chiaka, and Z. Yang. 2023. Flood risk cascade analysis and vulnerability assessment of watershed based on Bayesian network. Journal of Hydrology 626: Article 130144. |
| [48] |
Zhang, C., H. Rong, W. Yang, J. Lin, and C. Zhang. 2024. A novel integrated urban flood risk assessment approach coupling Geodetector-dematel and clustering method. Journal of Environmental Management 354: Article 120308. |
| [49] |
Zhang, Y., K. Wei, Z. Shen, X. Bai, X. Lu, and C.G. Soares. 2020. Economic impact of typhoon-induced wind disasters on port operations: A case study of ports in China. International Journal of Disaster Risk Reduction 50: Article 101719. |
| [50] |
|
| [51] |
Zhang, Q., G. Zhang, X. Xiao, Y. Zhang, N. You, Y. Di, T. Yang, Y. He, et al. 2024. Unraveling the spatial-temporal patterns of typhoon impacts on maize during the milk stage in Northeast China in 2020. European Journal of Agronomy 156: Article 127169. |
| [52] |
Zhang, X., C. Zhou, H. Lou, X. Zeng, Z. Shu, L. Jiang, Z. Wang, and Z. Fei. 2024. Study on the risk of rainstorm waterlogging disaster in hilly cities based on sponge city construction-liking Suining. Urban Climate 53: Article 101829. |
| [53] |
Zhao, B., T. Wang, D. Yang, S. Yang, W. Lu, and J. Santisirisomboon. 2023. The impacts of climatic and land surface characteristics on the storm-flood relationship in a tropical monsoon basin of Thailand. Journal of Hydrology 616: Article 128809. |
The Author(s)
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