Spatial and temporal analysis of a fatal landslide inventory in China from 1950 to 2016
Landslides result in severe casualties every year in China. However, there are few historical fatal landslide catalogs available to quantitatively assess the impact as well as the temporal and spatial patterns of landslides. The Fatal Landslide Event Inventory of China (FLEIC), which spans from 1950 to 2016, was compiled based on multiple data sources. The inventory contains 1911 non-seismically triggered landslides, which resulted in a total of 28,139 deaths in China during 1950–2016. The occurrence frequency of fatal landslides presented significantly different trends for different grades of events. Very large fatal landslide events (fatalities > = 30) were on the rise during 1950–1999 and declined from 2000 to 2016. The decreasing trend after 2000 can be attributed to the increase in landslide mitigation investments. The small and medium-sized fatal landslide events (fatalities < 10) showed a significant increasing trend between 1950 and 2016, especially during the period of 2000–2016. This significant increasing trend is partly due to the improvement of the availability of landslide data online and may also be related to other factors including an increase in extreme precipitation events, the effects of land urbanization, and so on. This suggested that the inherent incompleteness of the landslide time series should be considered when analyzing. The fatal landslides mainly occurred between April and September (82.15%), which is consistent with the monthly precipitation variation in China. Spatially, most of the fatal landslides occurred in 14 provinces: five southwestern provinces (Yunnan, Sichuan, Guangxi, Guizhou, and Chongqing), five southeastern provinces (Hunan, Guangdong, Fujian, Jiangxi, and Zhejiang), Shaanxi and Shanxi, Hubei and Gansu. These 14 provinces account for 86% of the total fatal landslides and their associated fatalities. The spatial association between the fatal landslide density and possible influencing factors was assessed based on a geographical detector method. The results showed that the interacting factors between the precipitation and topography, soil, lithology, vegetation and population density are more closely related to the spatial distribution of fatal landslides than each individual factor.
KeywordsFatal landslide Inventory Spatiotemporal patterns China Geographical detector method
This work was supported primarily by the National Key Research and Development Program of China [No. 2016YFA0602403, No. 2017YFC1502505] and the National Natural Science Funds . Acknowledgement for the data support from “National Earth System Science Data Sharing Infrastructure, National Science & Technology Infrastructure of China. (http://www.geodata.cn)” and Data Center for Resources and Environmental Sciences, Chinese Academy of Sciences (RESDC). Finally, we would like to thank the three anonymous reviewers and the handing editor for their valuable comments and suggestions, which helped to improved the manuscript.
- China Institute for Geo-Environment Monitoring (CIGEM) (2016) China geological hazard bulletin 2016. China Geological Environmental Monitoring Institute Web. http://www.cigem.gov.cn/. Accessed 10 Aug 2017. (in Chinese)
- Haque U, Blum P, da Silva PF, Andersen P, Pilz J, Chalov SR, Malet JP, Auflič MJ, Andres N, Poyiadji E, Lamas PC, Zhang W, Peshevski I, Pétursson HG, Kurt T, Dobrev N, García-Davalillo JC, Halkia M, Ferri S, Gaprindashvili G, Engström J, Keellings D (2016) Fatal landslides in Europe. Landslides 13:1545–1554. https://doi.org/10.1007/s10346-016-0689-3 CrossRefGoogle Scholar
- Kendall MG (1948) Rank correlation methods. Griffin, OxfordGoogle Scholar
- Li WY, Liu C, Hong Y, Zhang XH, Wan ZM, Saharia M, Sun WW, Yao DJ, Chen W, Chen S, Yang XQ, Yue Y (2016) A public cloud-based China’s landslide inventory database (CsLID): development, zone, and spatiotemporal analysis for significant historical events, 1949-2011. J Mt Sci 13:1275–1285. https://doi.org/10.1007/s11629-015-3659-7 CrossRefGoogle Scholar
- Liang Y, Liu J, Li L et al (2015) Study of estimating critical rainfall of landslide based on soil erosion model. Resour Environ Yangtze Basin 24(03):464–468 (Chinese with English abstract)Google Scholar
- Liu Y, Yang R (2012) Spatial characteristics and mechanisms of county level urbanization in China. Acta Geograph Sin 67:1011–1020 (Chinese with English abstract)Google Scholar
- Lu J, Fan W, Lu Y (2017) Research on early warning of shallow landslide based on soil erosion model. Bull Soil Water Conserv 37(3):227–230 (Chinese with English abstract)Google Scholar
- Ministry of Land and Resources of China (MLRC) (2017) Report on geological disaster situation. Ministry of Land and Resources of China Web. http://www.mlr.gov.cn/dzhj/dzzh/zqxqbg/201706/t20170626_1512282.htm. Accessed 10 Aug 2017. (in Chinese)
- Openshaw S (1984) Concepts and techniques in modern geography. Geobooks, NorwichGoogle Scholar
- Pradhan B, Chaudhari A, Adinarayana J, Buchroithner MF (2012) Soil erosion assessment and its correlation with landslide events using remote sensing data and GIS: a case study at Penang Island, Malaysia. Environ Monit Assess 184(2):715–727. https://doi.org/10.1007/s10661-011-1996-8 CrossRefGoogle Scholar
- Sheng L, Wang W, Zhu W (2016) China statistical yearbook 2016. China Statistics Press, Beijing (in Chinese)Google Scholar
- Wang J, Xu C (2017) Geodetector: principle and prospective. Acta Geograph Sin 72:116–134 (Chinese with English abstract)Google Scholar