Comprehensive Hazard Assessment of Tropical Cyclone-Induced Wind, Rainfall, and Storm Surge: A Case Study of Zhejiang Province, China

Xinli Liao , Chenna Meng , Kai Tao , Peng Su , Qinmei Han , Lianjie Qin , Wei Xu

International Journal of Disaster Risk Science ›› : 1 -17.

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International Journal of Disaster Risk Science ›› :1 -17. DOI: 10.1007/s13753-026-00720-2
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Comprehensive Hazard Assessment of Tropical Cyclone-Induced Wind, Rainfall, and Storm Surge: A Case Study of Zhejiang Province, China
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Abstract

Tropical cyclones pose a significant threat to coastal regions through hazard-inducing factors such as wind, rainfall, and storm surge, whose interactions often lead to amplified impacts. Existing studies often fail to capture the complex dependence among these factors. This study focused on the coastal counties of Zhejiang Province, utilizing numerical simulation data of tropical cyclone-induced winds, rainfall, and storm surges from 1979 to 2022. A joint probability model based on the C-vine copula function was developed to characterize the synergistic mechanisms among these factors, and to analyze return periods and failure probabilities of engineering structures under different hazard scenarios. Furthermore, a comprehensive hazard index was introduced to assess the hazard of tropical cyclone events. The main findings are as follows: (1) The simulated data agreed well with observations, with root mean square errors below 4 m/s for wind and 0.2 m for storm surge, and correlation coefficients all above 0.75. (2) Neglecting multiple factors and their dependence introduced bias in the return period and failure probability estimates. For example, when the exceedance probability for each single factor was 0.05, the mean return period for the three factors under the independence assumption (1.760 years) was 35% shorter than that considering dependence (2.698 years). (3) The comprehensive tropical cyclone hazard in the coastal counties of Zhejiang exhibited a distinct spatial pattern, with higher values in the south and lower values in the north. This study provides a scientific basis for disaster risk management and the design of tropical cyclone protection infrastructure in coastal areas.

Keywords

C-vine copula / Hazard assessment / Tropical cyclone / Wind-rainfall-storm surge / Zhejiang Province

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Xinli Liao, Chenna Meng, Kai Tao, Peng Su, Qinmei Han, Lianjie Qin, Wei Xu. Comprehensive Hazard Assessment of Tropical Cyclone-Induced Wind, Rainfall, and Storm Surge: A Case Study of Zhejiang Province, China. International Journal of Disaster Risk Science 1-17 DOI:10.1007/s13753-026-00720-2

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References

[1]

Aas, K., C. Czado, A. Frigessi, and H. Bakken. 2009. Pair-copula constructions of multiple dependence. Insurance: Mathematics and Economics 44(2): 182–198.

[2]

Atkinson GD, Holliday CR. Tropical cyclone minimum sea level pressure/maximum sustained wind relationship for the western North Pacific. Monthly Weather Review, 1977, 105(4): 421-427

[3]

Bakkensen LA, Mendelsohn ROCollins J, Walsh K. Global tropical cyclone damages and fatalities under climate change: An updated assessment. Hurricane risk, 2019ChamSpringer179-197

[4]

Bedford T, Cooke RM. Probability density decomposition for conditionally dependent random variables modeled by vines. Annals of Mathematics and Artificial Intelligence, 2001, 32(1): 245-268

[5]

Bedford T, Cooke RM. Vines – A new graphical model for dependent random variables. The Annals of Statistics, 2002, 30(4): 1031-1068

[6]

Bloemendaal, N., H. de Moel, J.M. Mol, P.R. Bosma, A.N. Polen, and J.M. Collins. 2021. Adequately reflecting the severity of tropical cyclones using the new tropical cyclone severity scale. Environmental Research Letters 16(1): Article 014048.

[7]

Bushra N, Trepanier JC, Rohli RV. Joint probability risk modelling of storm surge and cyclone wind along the coast of Bay of Bengal using a statistical copula. International Journal of Climatology, 2019, 39(11): 4206-4217

[8]

Cho, E., E. Ahmadisharaf, J. Done, and C. Yoo. 2023. A multivariate frequency analysis framework to estimate the return period of hurricane events using event-based copula. Water Resources Research 59(12): Article e2023WR034786.

[9]

CMA (China Meteorological Administration). 2015. 2015 China climate impact assessment. Beijing: China Meteorological Press (in Chinese).

[10]

Feng, J., D. Li, Y. Li, and L. Zhao. 2023. Analysis of compound floods from storm surge and extreme precipitation in China. Journal of Hydrology 627: Article 130402.

[11]

Geiger T, Gütschow J, Bresch DN, Emanuel K, Frieler K. Double benefit of limiting global warming for tropical cyclone exposure. Nature Climate Change, 2021, 11(10): 861-866

[12]

Gori A, Lin N, Xi D, Emanuel K. Tropical cyclone climatology change greatly exacerbates US extreme rainfall-surge hazard. Nature Climate Change, 2022, 12(2): 171-178

[13]

Grimley, L.E., K.E. Hollinger Beatty, A. Sebastian, S. Bunya, and G.M. Lackmann. 2024. Climate change exacerbates compound flooding from recent tropical cyclones. npj Natural Hazards 1(1): Article 45.

[14]

Grinsted A, Moore JC, Jevrejeva S. Homogeneous record of Atlantic hurricane surge threat since 1923. Proceedings of the National Academy of Sciences, 2012, 109(48): 19601-19605

[15]

Guzman, O., and H. Jiang. 2021. Global increase in tropical cyclone rain rate. Nature Communications 12(1): Article 5344.

[16]

Harr PA, Jordi A, Madaus LCollins JM, Done JM. Analysis of the future change in frequency of tropical cyclone-related impacts due to compound extreme events. Hurricane risk in a changing climate, 2022ChamSpringer87-120

[17]

He F, Liang Z, Dong G. Analysis on the compound probability and future change of flood and waterlogging disaster factors in Shanghai. Journal of Catastrophology, 2021, 36(2): 9-13(in Chinese)

[18]

Howard RA, Matheson JE, North DW. The decision to seed hurricanes: On the basis of present information, the probability of severe damage is less if a hurricane is seeded. Science, 1972, 176(4040): 1191-1202

[19]

Huang S, Li Y, Zhao X, Xie Y. Numerical analysis of storm surge due to a super typhoon in coastal region of Zhejiang Province. Ocean Engineering, 2008, 26(3): 58-64(in Chinese)

[20]

Jalili Pirani, F., and M.R. Najafi. 2022. Multivariate analysis of compound flood hazard across Canada’s Atlantic, Pacific and Great Lakes coastal areas. Earth’s Future 10(8): Article e2022EF002655.

[21]

Jelesnianski CP. A numerical calculation of storm tides induced by a tropical storm impinging on a continental shelf. Monthly Weather Review, 1965, 93(6): 343-358

[22]

Jiang H, Zipser EJ. Contribution of tropical cyclones to the global precipitation from eight seasons of TRMM data: Regional, seasonal, and interannual variations. Journal of Climate, 2010, 23(6): 1526-1543

[23]

Jing R, Heft-Neal S, Chavas DR, Griswold M, Wang Z, Clark-Ginsberg A, Guha-Sapir D, Bendavid E, Wagner Z. Global population profile of tropical cyclone exposure from 2002 to 2019. Nature, 2024, 626(7999): 549-554

[24]

Knutson T, Camargo SJ, Chan JCL, Emanuel K, Ho CH, Kossin J, Mohapotra M, Satoh M, et al.. Tropical cyclones and climate change assessment: Part I: Detection and attribution. Bulletin of the American Meteorological Society, 2019, 100(10): 1987-2007

[25]

Kossin JP, Knapp KR, Olander TL, Velden CS. Global increase in major tropical cyclone exceedance probability over the past four decades. Proceedings of the National Academy of Sciences, 2020, 117(22): 11975-11980

[26]

Lai Y, Li J, Gu X, Liu C, Chen YD. Global compound floods from precipitation and storm surge: Hazards and the roles of cyclones. Journal of Climate, 2021, 34(20): 8319-8339

[27]

Liu, M., G.A. Vecchi, J.A. Smith, and T.R. Knutson. 2019. Causes of large projected increases in hurricane precipitation rates with global warming. npj Climate and Atmospheric Science 2(1): Article 38.

[28]

Liu, Z., L. Xu, and Q. Lu. 2024. Comprehensive typhoon hazard zoning in China based on historical records. Geomatics, Natural Hazards and Risk 15(1): Article 2300813.

[29]

Massey FJJr. The Kolmogorov-Smirnov test for goodness of fit. Journal of the American Statistical Association, 1951, 46(253): 68-78

[30]

Maxwell, J.T., J.C. Bregy, S.M. Robeson, P.A. Knapp, P.T. Soulé, and V. Trouet. 2021. Recent increases in tropical cyclone precipitation extremes over the US east coast. Proceedings of the National Academy of Sciences 118(41): Article e2105636118.

[31]

Meng, C., W. Xu, P. Su, L. Qin, X. Liao, and J. Zhang. 2024. Quantitative assessment of population risk to tropical cyclones using hybrid modeling combining GAM and XGBoost: A case study of Hainan Province. International Journal of Disaster Risk Reduction 110: Article 104650.

[32]

Moftakhari HR, Salvadori G, AghaKouchak A, Sanders BF, Matthew RA. Compounding effects of sea level rise and fluvial flooding. Proceedings of the National Academy of Sciences, 2017, 114(37): 9785-9790

[33]

Murnane, R.J., and J.B. Elsner. 2012. Maximum wind speeds and US hurricane losses. Geophysical Research Letters 39(16): Article L16707.

[34]

NHC (National Hurricane Center). 2025. National hurricane center tropical cyclone report: Hurricane Helene. https://www.nhc.noaa.gov/data/tcr/AL092024_Helene.pdf. Accessed 31 Mar 2026.

[35]

Noy I. The socio-economics of cyclones. Nature Climate Change, 2016, 6(4): 343-345

[36]

Pan Y, Chen YP, Li JX, Ding XL. Improvement of wind field hindcasts for tropical cyclones. Water Science and Engineering, 2016, 9(1): 58-66

[37]

Phillips, R.C., S. Samadi, D.B. Hitchcock, M.E. Meadows, and C.A.M.E. Wilson. 2022. The devil is in the tail dependence: An assessment of multivariate copula‐based frameworks and dependence concepts for coastal compound flood dynamics. Earth’s Future 10(9): Article e2022EF002705.

[38]

Prat OP, Nelson BR. Mapping the world’s tropical cyclone rainfall contribution over land using the TRMM multi-satellite precipitation analysis. Water Resources Research, 2013, 49(11): 7236-7254

[39]

Qin, L., L. Zhu, X. Liao, C. Meng, Q. Han, Z. Li, S. Shen, W. Xu, and J. Chen. 2024a. Recent northward shift of tropical cyclone economic risk in China. npj Natural Hazards 1(1): Article 8.

[40]

Qin, L., L. Zhu, B. Liu, Z. Li, Y. Tian, G. Mitchell, S. Shen, W. Xu, and J. Chen. 2024b. Global expansion of tropical cyclone precipitation footprint. Nature Communications 15(1): Article 4824.

[41]

Ranjan, R., and S. Karmakar. 2024. Compound hazard mapping for tropical cyclone-induced concurrent wind and rainfall extremes over India. npj Natural Hazards 1(1): Article 15.

[42]

Reimann, L., A.T. Vafeidis, and L.E. Honsel. 2023. Population development as a driver of coastal risk: Current trends and future pathways. Cambridge Prisms: Coastal Futures 1: Article e14.

[43]

Salvadori G, Durante F, De Michele C, Bernardi M, Petrella L. A multivariate copula-based framework for dealing with hazard scenarios and failure probabilities. Water Resources Research, 2016, 52(5): 3701-3721

[44]

Sarker S, Adnan MSG. Evaluating multi-hazard risk associated with tropical cyclones using the fuzzy analytic hierarchy process model. Natural Hazards Research, 2024, 4(1): 97-109

[45]

Trepanier JC, Yuan J, Jagger TH. The combined risk of extreme tropical cyclone winds and storm surges along the US Gulf of Mexico Coast. Journal of Geophysical Research: Atmospheres, 2017, 122(6): 3299-3316

[46]

Tripathy, S.S., K. Jafarzadegan, H. Moftakhari, and H. Moradkhani. 2024. Dynamic bivariate hazard forecasting of hurricanes for improved disaster preparedness. Communications Earth & Environment 5(1): Article 12.

[47]

UNISDR (United Nations International Strategy for Disaster Reduction)Terminology on disaster risk reduction, 2009GenevaUNISDR

[48]

Vecchi GA, Delworth TL, Murakami H, Underwood SD, Wittenberg AT, Zeng F, Zhang W, Baldwin JW, et al.. Tropical cyclone sensitivities to CO2 doubling: Roles of atmospheric resolution, synoptic variability and background climate changes. Climate Dynamics, 2019, 53: 5999-6033

[49]

Warren IR, Bach H. MIKE 21: A modelling system for estuaries, coastal waters and seas. Environmental Software, 1992, 7(4): 229-240

[50]

Xu H, Lian J, Bin L, Xu K. Joint distribution of multiple typhoon hazard factors. Scientia Geographica Sinica, 2018, 38(12): 2118-2124(in Chinese)

[51]

Xu H, Tian Z, Sun L, Ye Q, Ragno E, Bricker J, Mao G, Tan J, et al.. Compound flood impact of water level and rainfall during tropical cyclone periods in a coastal city: The case of Shanghai. Natural Hazards and Earth System Sciences, 2022, 22(7): 2347-2358

[52]

Yang X, Qian J. Joint occurrence probability analysis of typhoon-induced storm surges and rainstorms using trivariate Archimedean copulas. Ocean Engineering, 2019, 171: 533-539

[53]

Ying M, Zhang W, Yu H, Lu X, Feng J, Fan Y, Zhu Y, Chen D. An overview of the China Meteorological Administration tropical cyclone database. Journal of Atmospheric and Oceanic Technology, 2014, 31(2): 287-301

[54]

Zhou, Z., S. Yang, S. Wang, X. Liu, F. Hu, Y. Wu, and Y. Chen. 2025. ComHazAsTC-RRE: Compound hazard assessment of tropical cyclones within repeatable, reproducible, and expandable framework. International Journal of Applied Earth Observation and Geoinformation 136: Article 104314.

[55]

Zhu, L., and S.M. Quiring. 2022. Exposure to precipitation from tropical cyclones has increased over the continental United States from 1948 to 2019. Communications Earth & Environment 3(1): Article 312.

[56]

Zhuang Y, Bao R, Zhang R, Gao S, Pan H, Chen S, Lin X. Refined risk assessment of tropical cyclone disasters in Fujian. Journal of Applied Meteorological Science, 2022, 33(3): 319-328(in Chinese)

[57]

Zhuang, Y., X. Tang, and Y. Wang. 2020. Impact of track forecast error on tropical cyclone quantitative precipitation forecasts over the coastal region of China. Journal of Hydrology 589: Article 125347.

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