176 TROPICAL CYCLONE RESEARCH AND REVIEW VOLUME 2, NO. 3 INTRODUCTION AND APPLICATION OF A NEW COMPREHENSIVE ASSESSMENT INDEX FOR DAMAGE CAUSED BY TROPICAL CYCLONES PEIYA N CHE N , XIAOTU LEI , A N D MI N G YI N G Shanghai Typhoon Institute, China Meteorological Administration, Shanghai, China ABSTRACT Using principal component analysis, a new comprehensive assessment index for damage caused by tropi- cal cyclones in mainland China is developed based on data from 1984 to 2008. It is a weighted average of four kinds of damage data: including the deaths and missing, affected crop area, destroyed houses, and rate of direct economic loss. The weighting coefficients are set by principal component analysis. Two indices are derived, which differ in the importance of the deaths and missing in severity assessment according to the sign of the second principal component of damage data. Trends in the damage caused by individual tropical cyclones and in the annual frequencies of the various levels of severity of damage caused by tropical cyclones are analyzed. No clear trend in damage from individual tropical cyclones is found. The annual frequency of tropical cyclones causing heavy and catastrophic damage shows a clear decrease from 1984 to 2008 with no trend in the total number of damaging tropical cyclones. Keywords: tropical cyclone, damage assessment, principal component analysis 1. Introduction and so on. It is useful to investigate objective and quantita- Tropical cyclones (TC) are one of the main causes of tive methods to describe the severity of damage caused by natural disasters in China, inflicting huge losses on coastal TCs. There is no recognized method for assessing the se- regions. Four of the worst 10 natural disasters in mainland verity of TC damage. The direct economic losses or casual- China in 2006 were caused by TCs; the worst two disasters ties are often analyzed as a single index of damage (Pielke were caused by the super typhoon “Saomai” and the severe and Landsea 1998; Watson and Johnson 2004; Zhang et tropical storm “Bilis” (The National Disaster Reduction al. 2009). The single index is simple and convenient, and Committee Office 2007). There were one or two TCs in the it is applied to most TCs with direct economic losses in list of the worst 10 natural disasters in China every year direct proportion to casualties. However, for many TCs from 2007 to 2012 (The National Disaster Reduction Com- there is a large gap between the ranking of damage based mittee Office 2007, 2008, 2009, 2010, 2011, 2012, 2013). on direct economic losses and that based on casualties. For Recently, researchers and forecasters have made encourag- example, the severe typhoon “Hagupit” in 2008 is ranked ing progress in the forecasting of TC track and intensity. more severely based on direct economic losses (0.3214% Tropical cyclone disaster research has also covered destruc- of the current year’s Gross Domestic Production (GDP) in tive power (Zhang et al. 2010), the distribution of risk and mainland China) than on casualties (47). Therefore some the nature of damage (Lei et al. 2009; Li and Duan 2010; researchers have tried to construct indices that provide a Yang et al. 2010), and damage assessment models or sys- comprehensive assessment of damage. These include indi- tems (Watson and Johnson 2004; Chen et al. 2009; Liu et ces using damage data normalized by a logarithmic func- al. 2010). The damage varies with the distribution of wind tion (Lu 1995; Qian et al. 2001; Lei et al. 2009), damage or rain, the building quality, rescue and relief procedures, indices of landfall TCs based on fuzzy mathematics (Liang et al. 1999; Ma et al. 2008), and the typhoon general disas- ter grade division model that uses the theory of grey asso- Corresponding author address: Ms. Peiyan Chen, Shanghai Typhoon ciation analysis (Wang et al. 2010). Although these indices Institute/CMA, No. 166 Puxi Road, Shanghai 200030, China. Tel: 86-21- are based on statistical theory, they still have shortcomings. 54896369. Fax: 86-21-64391966. E-mail: [email protected]. The main problem arises from the methods of determining DOI: 10.6057/2013TCRR03.05 SE P TEMBER 2013 CHEN ET AL. 177 weighting coefficients, which are not completely objective. in DEL data (Xu 1994; Pielke and Landsea 1998; Chen et Original damage data are nondimensionalized to values al. 2009; Zhang et al. 2009). The Rate of Direct Economic 10,000 or 1000 times smaller in the index developed by Losses is defined as follows: Lu (1995). Expert evaluation is used in processing damage indices with fuzzy mathematics. The method used to clas- (1) sify the grade of transfer function is subjective in the grey association analysis model. Thus, the indices are subjective to a greater or lesser extent. Another shortcoming is that Here, GDP is the current yearly gross domestic production some indices do not cope well with missing damage data. in mainland China in units of billion RMB. As there is a In summary, it is necessary to develop a representative, considerable difference in magnitude between DEL and objective, practically applicable damage index to assess the GDP, the unit of RDEL is 0.01%. There was no clear trend severity of TC disasters. in RDEL from 1983 to 2006, although DEL had a clear up- The remainder of the manuscript is organized as fol- ward trend (Zhang et al. 2009). lows. The data are introduced in Section 2. Sections 3 and Tropical cyclones that cause damage measured by at 4 describe the development of a comprehensive assessment least one of the four damage data types (DM, ACA, NDH, index of TC damage based on principal component analysis and DEL) are referred to as TCs causing damage (DTCs). (PCA), and the severity grade classification standards. The There were 187 DTCs in 24 provinces of mainland China annual trends of damage from single TCs and of frequency during 1984–2008. Guangdong province suffered the most of TCs with all severity grades of disaster are introduced (98 DTCs), followed by Fujian and Zhejiang provinces. in Sections 4 and 5. Finally, Section 6 contains the conclu- Guangxi and Hainan suffered more than 50 DTCs. Three sions and discussion. provinces (Jiangsu, Shanghai, and Jiangxi) suffered more than 20 DTCs. 2. Data Damage data, such as number of casualties, affected 3. Comprehensive assessment index for damage crop area, number of destroyed and damaged houses, and caused by tropical cyclones direct economic losses, have often been used in damage Principal component analysis (PCA) is a mathematical assessment models (Lu 1995; Qian et al. 2001). However, procedure that uses an orthogonal transformation to con- in recent years, difficulties in collecting data on casualties vert a set of observations of possibly correlated variables and damaged houses meant that these data were often miss- into a set of values of linearly uncorrelated variables called ing. To ensure comparability of data and interoperability of principal components. A comprehensive assessment index models, these items should be adjusted. In recent decades, based on a weighted linear function of multiple indices the main types of data that have been always available are (factors) can be deduced from the principal components. the deaths and missing, affected crop area, number of hous- The weighting coefficients are determined from the vari- es destroyed, and direct economic losses. The first three ance contribution of the principal components. This re- items describe the extent of damage to life, agriculture, and moves the impact of subjective factors in deriving weight- housing, respectively. Direct economic losses also describe ing coefficients and ensures objective ordering (Yu 1993; the extent of economic damage to other sectors, such as the Ye 2004; Hou 2006). direct losses in power systems, transport systems, aqua- To eliminate the effect of data dimensionality, the data culture, and so on (Lu et al. 2002). Therefore, in this paper need to be nondimensionalized before applying PCA. A the Deaths and Missing (DM; unit: person), Affected Crop number of methods can be used, including min-max nor- Area(ACA; unit: thousand hectare), Number of Destroyed malization, range analysis, the proportion method, Z-score, Houses (NDH; unit: room), and Direct Economic Losses and mean value analysis (Ding et al. 2001; Han 2008). The (DEL; unit: billion RMB)(RMB is Renminbi) are selected most popular method is standard deviation normalization. as measures of the severity of TC damage. Damage data However, this assumes that the data have a Gaussian dis- for all TC events during 1984–2008 were collected by the tribution, which is not the case for TC damage data. Mean Shanghai Typhoon Institute of the China Meteorological value analysis is often used in comprehensive evaluations Administration (CMA) and the National Climate Center, by PCA (Fu et al. 2001; Ye 2001; Ye 2004), as it preserves supported by a national ‘85’ project and a CMA project to the correlativity and deformation in the original data, but it disseminate new technology. can be easily affected by a few extreme values (Gao et al. To produce a robust dependence on DM, ACA, NDH and 2011). Median normalization has the two main advantages DEL, these data must be validated and calibrated to ensure of mean value analysis, but is not so strongly affected by model objectivity and stability. The extent of damage is extremes. A comparison of mean value analysis and median determined not only by the severity of the disaster but also normalization based on the criteria for nondimensionaliza- by the level of social development. This explains the higher tion methods introduced in Bai et al. (1995), shows that value of damage data in recent years than earlier, especially median normalization is more appropriate for this topic.