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Landslides

pp 1–16 | Cite as

Spatial and temporal analysis of a fatal landslide inventory in China from 1950 to 2016

  • Qigen Lin
  • Ying Wang
Original Paper

Abstract

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.

Keywords

Fatal landslide Inventory Spatiotemporal patterns China Geographical detector method 

Notes

Acknowledgements

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 [41271544]. 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.

Supplementary material

10346_2018_1037_MOESM1_ESM.xlsx (34 kb)
ESM 1 (XLSX 34 kb)

References

  1. Cao F, Ge Y, Wang J-F (2013) Optimal discretization for geographical detectors-based risk assessment. GISci Remote Sens 50:78–92.  https://doi.org/10.1080/15481603.2013.778562 Google Scholar
  2. 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)
  3. Damm B, Klose M (2015) The landslide database for Germany: closing the gap at national level. Geomorphology 249:82–93.  https://doi.org/10.1016/j.geomorph.2015.03.021 CrossRefGoogle Scholar
  4. Donat MG, Alexander LV, Yang H, Durre I, Vose R, Caesar J (2013) Global land-based datasets for monitoring climatic extremes. Bull Am Meteorol Soc 94:997–1006.  https://doi.org/10.1175/BAMS-D-12-00109.1 CrossRefGoogle Scholar
  5. Fu G, Yu J, Yu X, Ouyang R, Zhang Y, Wang P, Liu W, Min L (2013) Temporal variation of extreme rainfall events in China, 1961–2009. J Hydrol 487:48–59.  https://doi.org/10.1016/j.jhydrol.2013.02.021 CrossRefGoogle Scholar
  6. Gariano SL, Guzzetti F (2016) Landslides in a changing climate. Earth Sci Rev 162:227–252.  https://doi.org/10.1016/j.earscirev.2016.08.011 CrossRefGoogle Scholar
  7. Guzzetti F (2000) Landslide fatalities and the evaluation of landslide risk in Italy. Eng Geol 58:89–107.  https://doi.org/10.1016/S0013-7952(00)00047-8 CrossRefGoogle Scholar
  8. Guzzetti F, Mondini AC, Cardinali M, Fiorucci F, Santangelo M, Chang KT (2012) Landslide inventory maps: new tools for an old problem. Earth Sci Rev 112:42–66.  https://doi.org/10.1016/j.earscirev.2012.02.001 CrossRefGoogle Scholar
  9. 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
  10. Hartmann J, Moosdorf N (2012) The new global lithological map database GLiM: a representation of rock properties at the earth surface. Geochem Geophys Geosyst 13:1–37.  https://doi.org/10.1029/2012GC004370 CrossRefGoogle Scholar
  11. Kendall MG (1948) Rank correlation methods. Griffin, OxfordGoogle Scholar
  12. Kirschbaum DB, Adler R, Hong Y, Hill S, Lerner-Lam A (2010) A global landslide catalog for hazard applications: method, results, and limitations. Nat Hazards 52:561–575.  https://doi.org/10.1007/s11069-009-9401-4 CrossRefGoogle Scholar
  13. Kirschbaum D, Stanley T, Zhou Y (2015) Spatial and temporal analysis of a global landslide catalog. Geomorphology 249:4–15.  https://doi.org/10.1016/j.geomorph.2015.03.016 CrossRefGoogle Scholar
  14. Klose M, Damm B, Highland LM (2015) Databases in geohazard science: an introduction. Geomorphology 249:1–3.  https://doi.org/10.1016/j.geomorph.2015.06.029 CrossRefGoogle Scholar
  15. Klose M, Maurischat P, Damm B (2016) Landslide impacts in Germany: a historical and socioeconomic perspective. Landslides 13:183–199.  https://doi.org/10.1007/s10346-015-0643-9 CrossRefGoogle Scholar
  16. 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
  17. Li G, Lei Y, Yao H, Wu S, Ge J (2017) The influence of land urbanization on landslides: an empirical estimation based on Chinese provincial panel data. Sci Total Environ 595:681–690.  https://doi.org/10.1016/j.scitotenv.2017.03.258 CrossRefGoogle Scholar
  18. 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
  19. Lin CW, Liu SH, Lee SY, Liu CC (2006) Impacts of the Chi-Chi earthquake on subsequent rainfall-induced landslides in central Taiwan. Eng Geol 86(2–3):87–101CrossRefGoogle Scholar
  20. Lin L, Lin Q, Wang Y (2017a) Landslide susceptibility mapping on a global scale using the method of logistic regression. Nat Hazards Earth Syst Sci 17:1411–1424.  https://doi.org/10.5194/nhess-17-1411-2017 CrossRefGoogle Scholar
  21. Lin Q, Wang Y, Liu T, Zhu Y, Sui Q (2017b) The vulnerability of people to landslides: a case study on the relationship between the casualties and volume of landslides in China. Int J Environ Res Public Health 14:212.  https://doi.org/10.3390/ijerph14020212 CrossRefGoogle Scholar
  22. 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
  23. 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
  24. Luo W, Liu CC (2017) Innovative landslide susceptibility mapping supported by geomorphon and geographical detector methods. Landslides 15:1–10.  https://doi.org/10.1007/s10346-017-0893-9 Google Scholar
  25. Luo W, Jasiewicz J, Stepinski T, Wang J, Xu C, Cang X (2016) Spatial association between dissection density and environmental factors over the entire conterminous United States. Geophys Res Lett 43:692–700.  https://doi.org/10.1002/2015GL066941 CrossRefGoogle Scholar
  26. Mann HB (1945) Nonparametric tests against trend. Econometrica 13:245–259.  https://doi.org/10.2307/1907187 CrossRefGoogle Scholar
  27. 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)
  28. Nadim F, Kjekstad O, Peduzzi P, Herold C, Jaedicke C (2006) Global landslide and avalanche hotspots. Landslides 3:159–173.  https://doi.org/10.1007/s10346-006-0036-1 CrossRefGoogle Scholar
  29. Openshaw S (1984) Concepts and techniques in modern geography. Geobooks, NorwichGoogle Scholar
  30. Pennington C, Freeborough K, Dashwood C, Dijkstra T, Lawrie K (2015) The National Landslide Database of Great Britain: acquisition, communication and the role of social media. Geomorphology 249:44–51.  https://doi.org/10.1016/j.geomorph.2015.03.013 CrossRefGoogle Scholar
  31. Pereira S, Zêzere JL, Quaresma I, Santos PP, Santos M (2016) Mortality patterns of hydro-geomorphologic disasters. Risk Anal 36(6):1188–1210CrossRefGoogle Scholar
  32. Petley DN (2010) On the impact of climate change and population growth on the occurrence of fatal landslides in South, East and SE Asia. Q J Eng Geol Hydrogeol 43:487–496.  https://doi.org/10.1144/1470-9236/09-001 CrossRefGoogle Scholar
  33. Petley DN (2012) Global patterns of loss of life from landslides. Geology 40:927–930.  https://doi.org/10.1130/G33217.1 CrossRefGoogle Scholar
  34. 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
  35. Sen PK (1968) Estimates of the regression coefficient based on Kendall’s tau. J Am Stat Assoc 63:1379–1389.  https://doi.org/10.1080/01621459.1968.10480934 CrossRefGoogle Scholar
  36. Sepúlveda SA, Petley DN (2015) Regional trends and controlling factors of fatal landslides in Latin America and the Caribbean. Nat Hazards Earth Syst Sci 15:1821–1833.  https://doi.org/10.5194/nhess-15-1821-2015 CrossRefGoogle Scholar
  37. Shahid S (2011) Trends in extreme rainfall events of Bangladesh. Theor Appl Climatol 104(3–4):489–499.  https://doi.org/10.1007/s00704-010-0363-y CrossRefGoogle Scholar
  38. Sheng L, Wang W, Zhu W (2016) China statistical yearbook 2016. China Statistics Press, Beijing (in Chinese)Google Scholar
  39. Swift A, Liu L, Uber J (2008) Reducing MAUP bias of correlation statistics between water quality and GI illness. Comput Environ Urban Syst 32(2):134–148CrossRefGoogle Scholar
  40. Taylor FE, Malamud BD, Freeborough K, Demeritt D (2015) Enriching Great Britain’s national landslide database by searching newspaper archives. Geomorphology 249:52–68.  https://doi.org/10.1016/j.geomorph.2015.05.019 CrossRefGoogle Scholar
  41. Van Den Eeckhaut M, Hervás J (2012) State of the art of national landslide databases in Europe and their potential for assessing landslide susceptibility, hazard and risk. Geomorphology 139:545–558.  https://doi.org/10.1016/j.geomorph.2011.12.006 CrossRefGoogle Scholar
  42. Wang J, Xu C (2017) Geodetector: principle and prospective. Acta Geograph Sin 72:116–134 (Chinese with English abstract)Google Scholar
  43. Wang Y, Zhou L (2005) Observed trends in extreme precipitation events in China during 1961–2001 and the associated changes in large-scale circulation. Geophys Res Lett 32:L09707.  https://doi.org/10.1029/2005GL022574 CrossRefGoogle Scholar
  44. Wang JF, Li XH, Christakos G, Liao YL, Zhang T, X G, Zheng ZY (2010) Geographical detectors-based health risk assessment and its application in the neural tube defects study of the Heshun Region, China. Int J Geogr Inf Sci 24:107–127.  https://doi.org/10.1080/13658810802443457 CrossRefGoogle Scholar
  45. Wu Y, Wu SY, Wen, Xu M, Tan J (2016) Changing characteristics of precipitation in China during 1960–2012. Int J Climatol 36:1387–1402.  https://doi.org/10.1002/joc.4432 CrossRefGoogle Scholar
  46. Yin Y, Wang F, Sun P (2009) Landslide hazards triggered by the 2008 Wenchuan earthquake, Sichuan, China. Landslides 6:139–152.  https://doi.org/10.1007/s10346-009-0148-5 CrossRefGoogle Scholar
  47. Zhai P, Zhang X, Wan H, Pan X (2005) Trends in total precipitation and frequency of daily precipitation extremes over China. J Clim 18:1096–1108.  https://doi.org/10.1175/JCLI-3318.1 CrossRefGoogle Scholar
  48. Zhang S, Zhang LM, Glade T (2014) Characteristics of earthquake-and rain-induced landslides near the epicenter of Wenchuan earthquake. Eng Geol 175:58–73CrossRefGoogle Scholar
  49. Zhang M, Du S, Wu Y, Wen J, Wang C, Xu M, Wu SY (2017) Spatiotemporal changes in frequency and intensity of high-temperature events in China during 1961-2014. J Geogr Sci 27:1027–1043.  https://doi.org/10.1007/s11442-017-1419-z CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Key Laboratory of Environmental Change and Natural Disaster of Ministry of EducationBeijing Normal UniversityBeijingPeople’s Republic of China
  2. 2.State Key Laboratory of Earth Surface Processes and Resource EcologyBeijing Normal UniversityBeijingChina
  3. 3.Academy of Disaster Reduction and Emergency ManagementBeijing Normal UniversityBeijingChina

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