Historical Earthquakes and Their Socioeconomic Consequences in China: 1950–2017

Abstract

Understanding the spatiotemporal pattern of historical earthquake disasters and resultant socioeconomic consequences is essential for designing effective disaster risk reduction measures. Based on historical earthquake disaster records, this study compiles a Chinese earthquake disaster catalog (CH-CAT) that includes records of 722 earthquake disasters that occurred during 1950–2017 in the mainland of China. This catalog includes more complete data records than other existing global earthquake databases for China as a whole. Statistical results demonstrate that the number of earthquake disasters and the resultant direct economic losses (DELs) exhibit significant increasing trends (p < 0.01) over the studied 68-year period. Earthquake-induced deaths vary greatly between individual years and exhibit no significant trend. The Qinghai-Tibet seismic zone is the area with the highest frequency of earthquake disasters and the largest accumulated DELs, whereas the North China seismic zone is associated with the highest number of deaths. Among the 722 earthquake disasters, nearly 99.0% of deaths and 95.0% of DELs are attributable to 1.8% and 3.9% of the earthquake disasters, respectively. Approximately 54.2% of recorded earthquake disasters have earthquake magnitude (Ms) values between 5.0 and 5.9, while earthquake disasters with Ms greater than or equal to 7.0 account for 88.5% of DELs and 98.8% of deaths. On average, earthquake-induced DELs and deaths increase nonlinearly with increasing Ms per earthquake. DELs have a positive correlation with deaths and casualties on a logarithmic scale. This study further discusses that during different stages of socioeconomic development, changes in both exposure and vulnerability may be the major factors leading to change differences in earthquake-induced socioeconomic consequences. This study is a beneficial supplement to the global earthquake database and is useful for calibrating global or regional empirical loss models.