The Joint Return Period Analysis of Natural Disasters Based on Monitoring and Statistical Modeling of Multidimensional Hazard Factors

Science of the Total Environment 538 (2015) 724–732 Contents lists available at ScienceDirect Science of the Total Environment journal homepage: www.elsevier.com/locate/scitotenv The joint return period analysis of natural disasters based on monitoring and statistical modeling of multidimensional hazard factors Xueqin Liu a,b,c,NingLia,ShuaiYuanb,⁎,NingXub,WenqinShib, Weibin Chen b a State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing 100875, China b National Marine Environmental Monitoring Center, State Oceanic Administration, Dalian 116023, China c School of Social Development and Public Policy, Beijing Normal University, Beijing 100875, China HIGHLIGHTS GRAPHICAL ABSTRACT • A method to estimate the multidimensional joint return periods is presented. • 2D function allows better fitting results at the lower tail of hazard factors. • Three-dimensional simulation has obvious advantages in extreme value fitting. • Joint return periods are closer to the reality than univariate return periods. • Copula method provides a new idea for multivariate analysis of natural disasters. article info abstract Article history: As a random event, a natural disaster has the complex occurrence mechanism. The comprehensive analysis of Received 5 April 2015 multiple hazard factors is important in disaster risk assessment. In order to improve the accuracy of risk analysis Received in revised form 2 August 2015 and forecasting, the formation mechanism of a disaster should be considered in the analysis and calculation of Accepted 16 August 2015 multi-factors. Based on the consideration of the importance and deficiencies of multivariate analysis of dust Available online xxxx storm disasters, 91 severe dust storm disasters in Inner Mongolia from 1990 to 2013 were selected as study Editor: Simon Pollard cases in the paper. Main hazard factors from 500-hPa atmospheric circulation system, near-surface meteorological system, and underlying surface conditions were selected to simulate and calculate the multidimensional joint Keywords: return periods. After comparing the simulation results with actual dust storm events in 54 years, we found Natural hazard that the two-dimensional Frank Copula function showed the better fitting results at the lower tail of hazard Hazard factor factors and that three-dimensional Frank Copula function displayed the better fitting results at the middle and Formation mechanism upper tails of hazard factors. However, for dust storm disasters with the short return period, three- Multidimensional return period dimensional joint return period simulation shows no obvious advantage. If the return period is longer than 10 Risk assessment years, it shows significant advantages in extreme value fitting. Therefore, we suggest the multivariate analysis method may be adopted in forecasting and risk analysis of serious disasters with the longer return period, such as earthquake and tsunami. Furthermore, the exploration of this method laid the foundation for the prediction and warning of other nature disasters. © 2015 Elsevier B.V. All rights reserved. ⁎ Corresponding author. E-mail address: [email protected] (S. Yuan). http://dx.doi.org/10.1016/j.scitotenv.2015.08.093 0048-9697/© 2015 Elsevier B.V. All rights reserved. X. Liu et al. / Science of the Total Environment 538 (2015) 724–732 725 1. Introduction weighted comprehensive analysis of multivariate variables. Dust storm occurrence mechanism or hazard factors are not considered Dust storm is one kind of weather-based disaster, in which wind from the perspective of disaster. raises a large amount of dust, makes air particularly turbid, and de- As a random event, a dust storm is the complex interaction process creases horizontal visibility below 1 km. Dust storm is one of the stron- among atmosphere, soil, and land surface from its occurrence to devel- gest wind and sand activities. It causes health problems, pollution and opment and ending. In the previous studies of dust storm disasters, haz- huge economic losses in downstream regions, thus leading to direct or ard factors considered relatively simple and mainly selected from the indirect climatic effects (Hamadneh et al., 2015; Tam et al., 2012). The underlying surface. It is because observation data near-surface can ac- arid area in China is one of the regions with the highest frequency and quire easier, and the multifactor study method of dust storm disasters the largest intensity of dust storms in Central Asia (Yang et al., 2009). was not mature. According to the findings of dust storm mechanism, a The majority of northwest China, the entire north China, and the west- dust storm has three basic formation conditions: high wind speed, ern region in northeast China belong to the area with frequent dust abundant dust sources, and unstable atmospheric stratification. The pe- storms. However, the area affected by dust storms is much larger than riodic change, frequency, and intensity of dust storms are closely related the above regions. Especially, severe dust storms characterized by to the large-scale circulation background, local weather systems, and strong wind speed, low visibility, and high dust content can cause seri- underlying surface conditions. The sand and dust exposed on the sur- ous damages to the environment and human beings within a very short face is the dust source. A dust storm requires a strong wind. Meanwhile, time (Hsieh and Liao, 2013). Under the background of the overexploita- the unstable stratification state of atmosphere is an important local tion of land resources, global warming, and shortage of water resources, thermodynamic condition. The three predisposing factors are comple- dust storm has seriously affected local socio-economic development mentary and indispensable (Wang et al., 2005). Therefore, dust storms and ecological environment construction in northwest China. It is an involve several hazard factors. In the study of dust storms, in order to ecological environment problem that cannot be ignored, and also a objectively, accurately, and quickly calculate the return period, enhance hot topic in current research fields of atmospheric science, disaster sci- its extension prediction capability, and improve the accuracy and depth ence, resources, and environmental science. (Natsagdorj et al., 2003; of risk assessment of severe dust storms, it is necessary to trace the dust Wang et al., 2005; Zhou et al., 2013). At present, the risk assessment source and establish the joint distribution model of multi-hazards study and return period calculation of severe dust storms are still diffi- through the long-term observation and the exploration of the relation- cult because severe dust storm disasters are characterized by the com- ship and interaction among various hazard factors. Currently, more dust plex formation mechanism, large influencing area, and huge loss. In storm researchers recognized the importance of multivariate analysis order to reduce economic loss caused by severe dust storm disasters and achieved some results. Based on the comprehensive consideration and improve corresponding risk management level, it is necessary to of multi-hazards, Xu and Chen (2003) calculated the risk index of dust evaluate the return periods timely, accurately, quickly, and objectively, storms in the Tarim Region and applied the index in the study of dust especially for extremely severe dust storms, and establish an effective storm disasters. With the data of dust storms recorded from 1954 to early warning system and dust storm control measures based on dust 2001, Wang et al. (2008) calculated the annual risk of severe dust storm hazard mechanism and monitoring data (Frans et al., 2006). storms in eastern northwest China. J.L. Liu et al. (2012) analyzed the Disaster return period refers to the average recurrence interval of risk of spring sand-dust storm disasters in northwestern China based certain event repeated for many times in several tests. The occurrences on information diffusion method. X.Q. Liu et al. (2012) performed mul- of an independent event Q for different magnitudes q are illustrated in tivariate analysis with the underlying surface, meteorology, and other Fig. 1,LetL denote the time period between any two successive events factors and supplemented the mechanism and risk assessment of dust without consideration of the magnitude, called the interarrival time. storms in northern China. Because hazard factors of dust storms belong The events with a magnitude equal to or greater than any value q, to different distribution types, these hazard factors show the non-linear Q ≥ q, are denoted by ● in Fig. 1, while the events with a magnitude correlation relationship. The non-linear correlation was not fully less than any value q, Q b q, are denoted by ○ in Fig. 1.Hence,the considered in previous studies. time period between two ● is the recurrence interval, denoted by TQ, Using copula theory is a good way to solve these problems. The pre- which is equal to the summation of the interarrival time for all events vious application studies of storms, floods, and droughts with the copula between them. TQ is called the return period for Q ≥ q (Shiau, 2003; theory were focused on the disaster characteristic variables during or Loaiciga and Leipnik, 1996; Shiau et al., 2007). The return period of an after natural disasters, such as storm peak, storm amount and discharge, event is calculated based on the records of the event or an analytical volume and duration of flood, duration

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