On the driving forces of historical changes in the fatalities of tropical cyclone disasters in China from 1951 to 2014

  • Yi Li
  • Weihua FangEmail author
  • Xiaogang Duan
Original Paper


Tropical cyclone (TC) disasters have frequently caused casualties in the coastal areas of China. According to the statistics of dead and missing people due to TCs from 1951 to 2014, the number of fatalities has been significantly decreasing over time. However, deadly TC events have still caused great losses of life in recent years, which are characterized as significant abrupt fluctuations superimposed along the downward trend of the long-term fatality time series. The numbers of fatalities caused by TC disasters are influenced by variables such as the intensity of TC hazards, the population exposed to TCs and the vulnerability of people to TC hazards. It is thus of great significance to analyze their temporal characteristics and understand the forces driving these changes. First, the time series of the TC wind, precipitation, spatial distribution of population, fatality and disaster risk reduction (DRR) measures of China from 1951 to 2014 are reconstructed. Second, the improved power dissipation index, total precipitation, integrated intensity and index of exposed population are calculated, and the population vulnerability indices, including mean and relative fatality rates, are derived. Third, the change trend of each index is detected using the Mann–Kendall test. Finally, the main driving factors of the long-term change trend and fluctuations of the TC fatalities are analyzed by a negative binomial regression model and standard deviation statistics. It is found that the decrease in vulnerability based on the improvement in structural and non-structural measures is the main driving force of the decreases in fatalities over the past six decades. Although the total population and exposure have increased dramatically in the coastal areas of China, their contributions to the increase in the fatality risk were counteracted by the decrease in vulnerability. Abrupt and catastrophic disasters were mostly caused by TCs with hazards of high intensity that surpassed the capacity of structural measures; the lack of forecasting or early warning, as well as improper emergency response actions, may also have triggered the great loss of lives. To reduce the fatalities of future TCs, especially those that may exceed the capacity of structural measures, the enhancement of non-structural measures and the adaptation of resilience strategies should be priorities for future people-centered disaster management.


Tropical cyclone Fatality change Driving force Disaster China 

List of symbols

\({\text{AE}}^{\left( t \right)}\)

Annual population exposure in the tth year

\({\text{AF}}^{\left( t \right)}\)

Annual number of fatalities in the tth year

\({\text{AI}}^{\left( t \right)}\)

Annual intensity of the tth year

\({\text{APDI}}^{\left( t \right)}\)

Annual power dissipation index in the tth year

\({\text{APV}}^{\left( t \right)}\)

Annual precipitation volume in the tth year

\({\text{ARFR}}^{\left( t \right)}\)

Annual relative fatality rate in the tth year

\({\text{CI}}^{\left( t \right)}\)

DRR capacity index of the tth year


Population exposure in the nth TC


Number of event fatalities of the nth TC


Event intensity of the nth TC


Event power dissipation index for the nth TC


Event precipitation volume in the nth TC

\({\text{len}}^{\left( t \right)}\)

Normalized embankment length in the tth year


Mean fatality rate of historic TCs


Mean event intensity of historic TCs

\(\bar{p}_{{\left( {i,j} \right)}}\)

Expected precipitation with a 10-year return period

\(p_{{n\left( {i,j} \right)}}\)

Precipitation height in grid \((i,j)\) in the nth TC

\({\text{pop}}_{{k\left( {i,j} \right)}}^{\left( t \right)}\)

Population in grid \((i,j)\) of the kth county in the tth year

\(r_{k}^{\left( t \right)}\)

Proportion of the provincial population of the kth county in the tth year

\(r_{\text{mb}}^{\left( t \right)}\)

Penetration rate of mobile phones in the tth year


Standard deviation of each indicator in the negative binomial regression model

\({\text{SI}}_{{\left( {i,j} \right)}}\)

Scaling index of grid \((i,j)\) reflecting the effects of wind and precipitation

\(S_{{{\text{PDI}}\left( {i,j} \right)}}\)

Scaling index of grid \((i,j)\) for wind

\(S_{{{\text{PV}}\left( {i,j} \right)}}\)

Scaling index of grid \((i,j)\) for precipitation

\(\bar{v}_{{\left( {i,j} \right)}}\)

Expected wind speed of 10-year return period

\(v_{{n\left( {i,j} \right)}}\)

Wind speed in grid \((i,j)\) of the wind footprint of the nth TC


Mann–Kendall trend test statistic


Coefficients in the negative binomial regression model


Relative contribution rates of driving force factors

\(\xi^{\left( t \right)}\)

Normalized CMA 24-h forecasting error in the tth year



This work is mainly supported by the National Key Research and Development Program of China (No. 2018YFC1508803), and jointly supported by the National Key Research and Development Program of China (No. 2017YFA0604903).


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Copyright information

© Springer Nature B.V. 2019

Authors and Affiliations

  1. 1.Academy of Disaster Reduction and Emergency Management, Ministry of Emergency Management and Ministry of EducationBeijing Normal UniversityBeijingPeople’s Republic of China
  2. 2.National Disaster Reduction Center of ChinaMinistry of Emergency ManagementBeijingPeople’s Republic of China
  3. 3.Key Laboratory of Environmental Change and Natural Disaster of Ministry of Education, Faculty of Geographical ScienceBeijing Normal UniversityBeijingPeople’s Republic of China
  4. 4.School of StatisticsBeijing Normal UniversityBeijingPeople’s Republic of China

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