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Risk Zoning of Typhoon Disasters in Zhejiang Province, China

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Risk Zoning of Typhoon Disasters in Zhejiang Province, China Nat. Hazards Earth Syst. Sci., 18, 2921–2932, 2018 https://doi.org/10.5194/nhess-18-2921-2018 © Author(s) 2018. This work is distributed under the Creative Commons Attribution 4.0 License. Risk zoning of typhoon disasters in Zhejiang Province, China Yi Lu1, Fumin Ren2, and Weijun Zhu3 1Shanghai Typhoon Institute of China Meteorological Administration, Shanghai 200030, China 2State Key Laboratory of severe weather; Chinese Academy of Meteorological Sciences, Beijing 100081, China 3Key Laboratory of Meteorological Disaster of Ministry of Education, Nanjing University of Information, Science & Technology, Nanjing 210044, China Correspondence: Fumin Ren ([email protected]) Received: 18 January 2018 – Discussion started: 23 February 2018 Revised: 22 October 2018 – Accepted: 25 October 2018 – Published: 8 November 2018 Abstract. In this paper, typhoon simply means tropical cy- especially in the boundary region between Taizhou and Wen- clone. As risk is future probability of hazard events, when es- zhou cities. Although the inland mountainous areas are not timated future probability is the same as historical probability directly affected by typhoons, they are in the medium-risk for a specific period, we can understand risk by learning from category for vulnerability. past events. Based on precipitation and wind data over the mainland of China during 1980–2014 and disaster and social data at the county level in Zhejiang Province from 2004 to 2012, a study on risk zoning of typhoon disasters (typhoon 1 Introduction disasters in this paper refer to affected population or direct economic losses caused by typhoons in Zhejiang Province) Typhoon, which means tropical cyclone in this paper, often is carried out. Firstly, characteristics of typhoon disasters and causes some of the most serious natural disasters in China, factors causing typhoon disasters are analyzed. Secondly, an with an average annual direct economic loss of about USD 9 intensity index of factors causing typhoon disasters and a billion. The arrival of a typhoon is often accompanied by population vulnerability index are developed. Thirdly, com- heavy rain, high winds and storm surges, with the main im- bining the two indexes, a comprehensive risk index for ty- pacts in southern coastal areas of China (Zhang et al., 2009). phoon disasters is obtained and used to zone areas of risk. Zhejiang Province is seriously affected by typhoons – for ex- The above analyses show that southeastern Zhejiang is the ample, in 2006, super-typhoon Sang Mei caused 153 deaths area most affected by typhoon disasters. The annual proba- in Cangnan County in Wenzhou City, with CNY 11.25 billion bility of the occurrence of typhoon rainstorms >50 mm de- of direct economic losses. Therefore it would be of practical creases from the southeast coast to inland areas, with a max- significance to develop a system for the risk assessment of imum in the boundary region between Fujian and Zhejiang, typhoon disasters in Zhejiang Province. which has the highest risk of rainstorms. Southeastern Zhe- Major risk assessment models include the disaster risk jiang and the boundary region between Zhejiang and Fujian index system of the United Nations Development Program provinces and the Hangzhou Bay area are most frequently (global scale, focusing on human vulnerability), the Euro- affected by extreme typhoon winds and have the highest risk pean multiple risk assessment (with emphasis on factors of wind damage. The population of southwestern Zhejiang is causing disasters and vulnerability) and the American Hazus- the most vulnerable to typhoons as a result of the relatively MH hurricane module and disaster risk management sys- undeveloped economy, mountainous terrain and the high risk tem. Vickery et al. (2009), Fang and Shi (2012), and Fang of geological disasters in this region. Vulnerability is lower and Lin (2013) reviewed the factors causing typhoon dis- in the cities due to better disaster prevention and reduction asters. Rain and wind are direct causes of typhoon disas- strategies and a more highly educated population. The south- ters (Emanuel, 1988, 1992, 1995; Holland, 1997; Kunreuther east coastal areas face the highest risk of typhoon disasters, and Roth, 1998); stronger typhoons produce heavier rain and stronger winds, resulting in a greater number of casualties Published by Copernicus Publications on behalf of the European Geosciences Union. 2922 Y. Lu et al.: Risk zoning of typhoon disasters in Zhejiang Province, China and higher economic losses. Many of the studies on the fac- 2 Data and methods tors causing typhoon disasters used a grade index and the probability of occurrence (H. Y. Chen et al., 2011; Su et al., This study is carried out in Zhejiang Province (Fig. 1) in- 2008; Ding and Shi, 2002; Chen, 2007). Recently, some stud- cluding 11 cities along the Yangtze River Delta. Zhejiang ies built quantitative assessments in some provinces and car- Province borders the eastern part of the East China Sea, is ried out preliminary studies on pre-evaluating typhoon disas- adjacent to Fujian Province, and is one of the most economi- ters (Huang and Wang, 2015; Yin and Li, 2017). cally powerful provinces in China. In terms of vulnerability, Pielke and Landsea (1998) and Pielke et al. (2008) combined the characteristics of typhoons 2.1 Data and socioeconomic factors, suggesting that both the vulnera- 2.1.1 Typhoon, precipitation and wind data bility of the population and economic factors were important in estimating disaster losses. The vulnerability of a popula- The typhoon data used in this study are the best-track tropi- tion is a preexisting condition that influences its ability to cal cyclone datasets from the Shanghai Typhoon Institute for face typhoon disasters. Among the most widely used indexes the time period 1960–2014 (Eunjeong and Ying, 2009; Li is the Social Vulnerability Index (SoVI) (Cutter et al., 2003; and Hong, 2015). Daily precipitation data for 2479 stations W. F. Chen et al., 2011). Other researches have focused on and daily wind data for 2419 stations during the time period the vulnerability of buildings, obtaining a fragility curve by 1960–2014 over the mainland of China are obtained from the combining historical loss with the characteristics of build- National Meteorological Information Center. The maximum ings and typhoons (Hendrick and Friedman, 1966; Howard wind speed is given as the maximum 10 min mean. In this et al., 1972; Friedman, 1984; Kafali and Jain, 2009; Pita et paper, two time periods of precipitation and wind data are al., 2014). Studies in China have assessed vulnerabilities to used. typhoon disasters (Yin et al., 2010; Niu et al., 2011). Evalu- Because of limited access to county-level typhoon disaster ation indexes for the assessment of disaster losses were es- data, we have only obtained data from 2004 to 2012. So when tablished based on the number of deaths, direct economic calculating intensity index of factors causing typhoon disas- losses, the area of crops affected and the number of col- ters, the time period of typhoon precipitation and typhoon lapsed houses. These indexes were used to construct differ- wind is the same as for the typhoon disasters and is 2004– ent disaster assessment models (Liang and Fan, 1999; Lei et 2012. al., 2009; Wang et al., 2010). Xu et al. (2015) comprehen- For risk analyses of typhoon precipitation and typhoon sively assessed the impact of typhoons across China using wind (please see details in Sect. 3.1 and 3.2), estimated fu- the geographical information system. The future direction of ture probability is the same as historical probability, and we tropical cyclone risk management is quantitative risk models then select the period of 1980–2014. As Lu et al. (2016) men- (Chen et al., 2017). tioned, considering the homogeneity of wind data, we use the Previous studies have concentrated on semiquantitative period of 1980–2014 for wind analysis. To ensure the consis- large-scale research, with less emphasis on quantitative re- tency between wind and precipitation data, 1980–2014 is se- search at the county level based on large numbers of accu- lected as the period. In addition, the objective synoptic analy- rate data. In addition, the studies have paid more attention to sis technique (OSAT) is needed to identify typhoon wind and disaster losses. Few studies have focused on a comprehen- precipitation from a wider range than just Zhejiang Province sive risk assessment of typhoon disasters coupled with fac- (please see details in Sect. 2.2.1); thus 2419 stations of pre- tors causing typhoon disasters and population vulnerability. cipitation data and 2479 stations of wind data over mainland In this study, Zhejiang Province, which is frequently affected China are used, 71 stations of which correspond to counties by the strongest landfall typhoons (Ren et al., 2008) and ex- in Zhejiang Province. periences most serious typhoon disasters (Liu and Gu, 2002) on the mainland of China, is selected as the study area. This 2.1.2 Disaster and social data paper does not consider the impact of storm surges. The fac- tors causing typhoon disasters are represented by typhoon Disaster data for each typhoon that affected Zhejiang rain and typhoon wind. Section 2 introduces the data and Province from 2004 to 2012 are obtained from the National methods used in this study. Section 3 provides analyses on ty- Climate Center and the number of records for each county is phoon disaster losses and factors causing typhoons. Section 4 shown in Fig. 2. Of the 11 cities in Zhejiang Province, Wen- presents risk assessment and regionalization of typhoon dis- zhou and Taizhou record the most typhoon disasters, with asters. Summary and discussions are given in the final sec- the maximum being 17 at Wenzhou. Fewer typhoon disasters tion. are recorded in the central and western regions of Zhejiang Province, particularly in Changshan and Quzhou, which may be because the strength of typhoons weakened after land- fall.
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