Assessment of Disaster Loss Index and Characteristics of Gale Disaster in Typical Arid and Semiarid Lands

Table 1

Overview of the most related to gale/wind loss analysis.

Authors	Time period	Method	Study region	Purpose/aim	Major findings
Baredo [26]	1970–2008	Normalization is used to account for changes in the socioeconomic factors	Across 29 European countries	To put windstorm kyrill into a historical context by examining large historical windstorm event losses	No trend in the normalized windstorm losses
					Increasing disaster losses are driven by societal factors and increasing exposure
Xiao et al. [27]	1949–2009	Developed a Tropical Cyclone Potential Impact Index (TCPI)	China	To assess the regional impact of TCs, analyzed the spatial pattern, trends, and interannual variation of the TCPI	A weak decreasing TCPI trend over the period; quoted the air mass trajectories, disaster information, intensity, duration, and frequency of tropical cyclones and constituted the TCPI
Pinto et al. [28]	1960–2000	Both rank statistics and return periods (RP) are estimated by fitting an extreme value distribution using the peak over threshold method to potential storm losses	“Core Europe,” which comprises countries of Western Europe	Quantify possible changes of the associated event-based storm losses	An increased risk of occurrence of windstorm-associated losses, which can be attributed mainly to changes in the meteorological severity of the events
Lou et al. [29]	1970–2008	The principal component as the input of a BP neural network model	Zhejiang Province, China	To establish an assessment model and process disaster-inducing assessment factors, disaster-formative environments and disaster-affected bodies	Loss assessment values of tropical cyclones were higher than the actual losses, but the gap was smaller in severe storms
Li and Fang [30]	1990–2009	Correlation analysis; develop a loss index for rapidly assessing tropical cyclone (TC) disaster loss	China	Effective for rapid damage assessment	Developed a loss index to assess TC disasters rapidly
Cusack [31]	Flushing from 1910 to 1914 and 1995 to 2010 for de Bilt	Storm damage using a model measuring loss impacts upon society	Netherlands	To have a wind speed time series solely reflecting changes in storm strength	A 101-year time-series of storm losses is developed from the near-surface wind speed records at five Dutch stations
Kruger et al. [32]	2004–2014	Application of extreme value distribution, estimation of four factors using the peak-over-threshold method, relative categorization of overall wind hazard	South Africa	To develop strong wind statistics, disaster models for the built environment and estimations of tornado risk, and a general analysis of the strong wind hazard	Identified high hazard areas with strong winds
Chen et al. [33]	2006–2015	Gamma hurdle model (GHM)	Taiwan, China	To assess typhoon damages： return period analysis and loss prediction	Accounted for the combined effect of rainfall and wind by a loss prediction model
Chen et al. [33]	1983–2015	A hazard footprint-based normalization method	China	To improve the spatial resolution of affected areas and the associated exposures to influential tropical cyclones	Contributed to a more realistic estimation of the population and wealth affected by the influential tropical cyclones for the original year and the present scenario
Chen et al. [34]	1993–2009	Comprehensive evaluation by model combination method	Guangdong, China	To predict tropical cyclone (TC) disaster loss	Constructed a more accurate and stable individual model to predict TC disaster loss
Guo and Li [35]	1985–2014	Confirmatory factor analysis	Guangdong, China	Accurately estimate the economic losses inflicted by typhoon storm surge	Impact indicators from various risk factors at different time periods have not changed significantly, while their degree of relevance has varied with each risk factor
Chen et al. [36]	1949–2018	Kernel Density Estimation (KDE) index as the hazard index	Six typical provinces of China	To describe the occurrence probability of hazards; evaluation mapping and result analysis	Master the characteristics and pattern of typhoon activity for typhoon warning and disaster prevention and mitigation
Wang et al. [37]	2009–2020	Inverse distance weighted interpolation technique	Guangdong–Hong Kong–Macau greater bay area (GBA)	To provide scientific support for typhoon disaster prevention and mitigation in the GBA	Constructed the hazard index, vulnerability index, and comprehensive risk index better reflecting the actual losses, verified the spatial correlation between typhoon disaster risk indexes and actual losses