Rapid Damage Prediction and Risk Assessment for Tropical Cyclones at a Fine Grid in Guangdong Province, South China

Abstract 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... Mehr ...

Verfasser: Yazhou Ning
Xianwei Wang
Qi Yu
Du Liang
Jianqing Zhai
Dokumenttyp: Artikel
Erscheinungsdatum: 2023
Reihe/Periodikum: International Journal of Disaster Risk Science, Vol 14, Iss 2, Pp 237-252 (2023)
Verlag/Hrsg.: SpringerOpen
Schlagwörter: Damage prediction / Holland model / Risk assessment / South China / Tropical cyclones / Wind disasters / Disasters and engineering / TA495
Sprache: Englisch
Permalink: https://search.fid-benelux.de/Record/base-27100260
Datenquelle: BASE; Originalkatalog
Powered By: BASE
Link(s) : https://doi.org/10.1007/s13753-023-00485-y

Abstract 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.