Rapid Damage Prediction and Risk Assessment for Tropical Cyclones at a Fine Grid in Guangdong Province, South China
Yazhou Ning , Xianwei Wang , Qi Yu , Du Liang , Jianqing Zhai
International Journal of Disaster Risk Science ›› 2023, Vol. 14 ›› Issue (2) : 237 -252.
Rapid Damage Prediction and Risk Assessment for Tropical Cyclones at a Fine Grid in Guangdong Province, South China
Rapid damage prediction for wind disasters is significant in emergency response and disaster mitigation, although it faces many challenges. In this study, a 1-km grid of wind speeds was simulated by the Holland model using the 6-h interval records of maximum wind speed (MWS) for tropical cyclones (TC) from 1949 to 2020 in South China. The MWS during a TC transit was used to build damage rate curves for affected population and direct economic losses. The results show that the Holland model can efficiently simulate the grid-level MWS, which is comparable to the ground observations with R 2 of 0.71 to 0.93 and mean absolute errors (MAEs) of 3.3 to 7.5 m/s. The estimated damage rates were in good agreement with the reported values with R 2 = 0.69–0.87 for affected population and R 2 = 0.65–0.84 for GDP loss. The coastal areas and the Guangdong-Hong Kong-Macao Greater Bay Area have the greatest risk of wind disasters, mainly due to the region’s high density of population and developed economy. Our proposed method is suitable for rapid damage prediction and supporting emergency response and risk assessment at the community level for TCs in the coastal areas of China.
Damage prediction / Holland model / Risk assessment / South China / Tropical cyclones / Wind disasters
| [1] |
|
| [2] |
Bloemendaal, N., H. de Moel, J.M. Mol, P.R.M. 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. |
| [3] |
|
| [4] |
|
| [5] |
CMA (China Meteorological Administration) |
| [6] |
CMDN (China Meteorological Data Network). 2021. Basic ground-based meteorological observation data in China. http://data.cma.cn/data/cdcdetail/dataCode/A.0012.0001.html. Accessed 11 Nov 2021 (in Chinese). |
| [7] |
COIN (China Ocean Information Network). 2020. China marine disaster bulletin. http://www.nmdis.org.cn/hygb/zghyzhgb/. Accessed 18 Jan 2022 (in Chinese). |
| [8] |
|
| [9] |
|
| [10] |
Ding, Y., H. Duan, M. Xie, R. Mao, J. Wang, and W. Zhang. 2022. Carbon emissions and mitigation potentials of 5G base station in China. Resources, Conservation and Recycling 182: Article 106339. |
| [11] |
|
| [12] |
GPBS (Guangdong Provincial Bureau of Statistics). 2021. Guangdong statistical yearbook. http://tjnj.gdstats.gov.cn:8080/tjnj/2021/. Accessed 18 Jan 2022 (in Chinese). |
| [13] |
GPDNR (Guangdong Provincial Department of Natural Resources). 2020. Guangdong marine disaster bulletin. http://nr.gd.gov.cn/zwgknew/sjfb/tjsj/content/post_3316131.html. Accessed 18 Jan 2022 (in Chinese). |
| [14] |
He, Y., B. Wu, P. He, W. Gu, and B. Liu. 2021. Wind disasters adaptation in cities in a changing climate: A systematic review. PLOS ONE 16(3): Article e248503. |
| [15] |
|
| [16] |
|
| [17] |
|
| [18] |
|
| [19] |
IPCC (Intergovernmental Panel on Climate Change). 2014. Climate change 2014 – Impacts, adaptation, and vulnerability: Part A: Global and sectoral aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, ed. C.B. Field, V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea,T.E. Bilir, et al. Cambridge: Cambridge University Press. |
| [20] |
|
| [21] |
|
| [22] |
Liu, G., B. Chen, Z. Gao, H. Fu, S. Jiang, L. Wang, and K. Yi. 2019. Calculation of joint return period for connected edge data. Water 11(2): Article 300. |
| [23] |
Lloyd, C.T., A. Sorichetta, and A.J. Tatem. 2017. High resolution global gridded data for use in population studies. Scientific Data 4(1): Article 170001. |
| [24] |
|
| [25] |
Meng, C., W. Xu, Y. Qiao, X. Liao, and L. Qin. 2021. Quantitative risk assessment of population affected by tropical cyclones through joint consideration of extreme precipitation and strong wind—A case study of Hainan province. Earth's Future 9(12): Article e2021EF002365. |
| [26] |
|
| [27] |
|
| [28] |
Nellipudi, N.R., Y. Viswanadhapalli, V.S. Challa, N.K. Vissa, and S. Langodan. 2021. Impact of surface roughness parameterizations on tropical cyclone simulations over the Bay of Bengal using WRF-OML model. Atmospheric Research 262: Article 105779. |
| [29] |
|
| [30] |
|
| [31] |
RESD (Resource and Environment Science and Data Center). 2017. Spatial distribution of GDP in China with kilometer grid dataset. http://www.resdc.cn/DOI. Accessed 15 Mar 2022 (in Chinese). |
| [32] |
Ruiz-Salcines, P., P. Salles, L. Robles-Díaz, G. Díaz-Hernández, A. Torres-Freyermuth, and C.M. Appendini. 2019. On the use of parametric wind models for wind wave modeling under tropical cyclones. Water 11(10): Article 2044. |
| [33] |
|
| [34] |
|
| [35] |
|
| [36] |
Song, J.Y., A. Alipour, H.R. Moftakhari, and H. Moradkhani. 2020. Toward a more effective hurricane hazard communication. Environmental Research Letters 15(6): Article 64012. |
| [37] |
|
| [38] |
|
| [39] |
|
| [40] |
|
| [41] |
WorldPop. 2018. The spatial distribution of population in 2020 China. https://hub.worldpop.org/geodata/summary?id=29818. Accessed 18 Mar 2022. |
| [42] |
|
| [43] |
Yang, J., Y. Chen, Y. Tang, G. Yan, and Z. Duan. 2021. A high-fidelity parametric model for tropical cyclone boundary layer wind field by considering effects of land cover and terrain. Atmospheric Research 260: Article 105701. |
| [44] |
Zhang, C., K. Yin, X. Shi, and X. Yan. 2021. Risk assessment for typhoon storm surges using geospatial techniques for the coastal areas of Guangdong, China. Ocean & Coastal Management 213: Article 105880. |
| [45] |
|
/
| 〈 |
|
〉 |