Advertisement

Natural Hazards

, Volume 97, Issue 3, pp 1151–1173 | Cite as

Risk assessment of snowmelt-induced landslides based on GIS and an effective snowmelt model

  • Fasheng Miao
  • Yiping WuEmail author
  • Linwei Li
  • Kang Liao
  • Longfei Zhang
Original Paper
  • 22 Downloads

Abstract

In early 2008, southern China experienced a severe freezing snow event, causing many geological disasters (such as landslides). Based on combining the infinite slope model and the snowmelt effect, an effective snowmelt model (ESM) is proposed to calculate the stability of landslides. The geological mechanics model of snowmelt-induced landslides is established with the Enshi area as a case study. The landslide susceptibility in the Enshi area is evaluated based on the set pair analysis and analytical hierarchy process. Then, the hazard grade of snowmelt-induced landslides is predicted and classified by the calculation results of ESM. And the warning grade of Enshi is determined based on the landslide susceptibility and the hazard grade. The results indicate the following: (1) High-susceptibility areas in Enshi are mainly concentrated in the regions of Badong County and Lichuan County. (2) The snowmelt hazard in the rock group with a high susceptibility is considered a medium-level hazard and the other areas are low-level hazards. (3) A total of 94.73% of the study region is a no-warning area, and the levels 3 and 4 warning zones account for 1.04% and 4.23%, respectively. (4) The increases in the slope gradient α and the slip zone depth Z lead to a decreasing initial stability and decreased influence of snowmelt water infiltration. (5) The snowmelt threshold can be calculated by the ESM model, and different snowmelt risk levels can be classified according to the relationship between the snowmelt threshold and the slope gradient.

Keywords

Snowmelt-induced landslide Set pair analysis Analytical hierarchy process Effective snowmelt model Risk assessment 

Notes

Acknowledgements

This research is supported by the National Key R&D Program of China (2017YFC1501301), the National Natural Science Foundation of China (41572278), and the National Science-Technology Support Projects (2008BAC47B04). We would like to thank the colleagues in our laboratory for their constructive comments and assistance.

References

  1. Ayalew L, Yamagishi H (2005) The application of GIS-based logistic regression for landslide susceptibility mapping in the Kakuda-Yahiko Mountains, Central Japan. Geomorphology 65(1–2):15–31Google Scholar
  2. Bathrellos GD (2013) Assessment of rural community and agricultural development using geomorphological–geological factors and GIS in the Trikala prefecture (Central Greece). Stoch Env Res Risk Assess 27(2):573–588Google Scholar
  3. Bourenane H, Bouhadad Y, Guettouche MS et al (2015) GIS-based landslide susceptibility zonation using bivariate statistical and expert approaches in the city of Constantine (Northeast Algeria). Bull Eng Geol Env 74(2):337–355Google Scholar
  4. Brown G, Strickland-Munro J, Kobryn H et al (2017) Mixed methods participatory GIS: an evaluation of the validity of qualitative and quantitative mapping methods. Appl Geogr 79:153–166Google Scholar
  5. Chen W, Li W, Chai H et al (2016) GIS-based landslide susceptibility mapping using analytical hierarchy process (AHP) and certainty factor (CF) models for the Baozhong region of Baoji City, China. Environ Earth Sci 75(1):1–14Google Scholar
  6. Erener A, Mutlu A, Düzgün HS (2016) A comparative study for landslide susceptibility mapping using GIS-based multi-criteria decision analysis (MCDA), logistic regression (LR) and association rule mining (ARM). Eng Geol 203:45–55Google Scholar
  7. Gokceoglu C, Sonmez H, Nefeslioglu HA et al (2005) The 17 March 2005 Kuzulu landslide (Sivas, Turkey) and landslide-susceptibility map of its near vicinity. Eng Geol 81(1):65–83Google Scholar
  8. Hasekiogullar GD, Ercanoglu M (2012) A new approach to use AHP in landslide susceptibility mapping: a case study at Yenice (Karabuk, NW Turkey). Nat Hazards 63(2):1157–1179Google Scholar
  9. Hock R (1999) A distributed temperature-index ice- and snowmelt model including potential direct solar radiation. J Glaciol 45(149):101–111Google Scholar
  10. Hong H, Ilia I, Tsangaratos P et al (2017) A hybrid fuzzy weight of evidence method in landslide susceptibility analysis on the Wuyuan area, China. Geomorphology 290:1–16Google Scholar
  11. Iverson RM (2000) Landslide triggering by rain infiltration. Water Resour Res 36(7):1897–1910Google Scholar
  12. Karimi H, Zeinivand H, Tahmasebipour N et al (2016) Comparison of SRM and WetSpa models efficiency for snowmelt runoff simulation. Environ Earth Sci 75(8):664Google Scholar
  13. Kimura T, Katsura S, Maruyama K et al (2016) Topographic features of source and transfer-deposition areas of long-travelling landslides induced by snowmelt. J Jpn Landslide Soc 53(2):31–42Google Scholar
  14. Lee JH, Park HJ (2016) Assessment of shallow landslide susceptibility using the transient infiltration flow model and GIS-based probabilistic approach. Landslides 13(5):885–903Google Scholar
  15. Long NT, Smedt FD (2012) Application of an analytical hierarchical process approach for landslide susceptibility mapping in a Luoi district, Thua Thien Hue Province, Vietnam. Environ Earth Sci 66(7):1739–1752Google Scholar
  16. Meng Q, Miao F, Zhen J et al (2016) GIS-based landslide susceptibility mapping with logistic regression, analytical hierarchy process, and combined fuzzy and support vector machine methods: a case study from Wolong Giant Panda Natural Reserve, China. Bull Eng Geol Env 75(3):923–944Google Scholar
  17. Nakazato H, Shoda D, Inoue K et al (2013) A case study of behavior observation of landslide induced by snowmelt after an earthquake. Earthquake-induced landslides. Springer, Berlin, pp 341–345Google Scholar
  18. Pourghasemi HR, Pradhan B, Gokceoglu C (2012) Application of fuzzy logic and analytical hierarchy process (AHP) to landslide susceptibility mapping at Haraz watershed, Iran. Nat Hazards 63(2):1–32Google Scholar
  19. Riboust, P., Thirel, G., Moine, N. L., et al. (2016). Modelling the snowmelt and the snow water equivalent by creating a simplified energy balance conceptual snow model. In: EGU general assembly conference. vol 18. EGU general assembly conference abstractsGoogle Scholar
  20. Saaty TL (1980) The analytic hierarchy process. McGraw-Hill Book Co, New YorkGoogle Scholar
  21. Saaty TL (2000) Fundamentals of decision making and priority theory with the analytic hierarchy process, vol 6. RWS publicationsGoogle Scholar
  22. Shahabi H, Hashim M (2015) Landslide susceptibility mapping using GIS-based statistical models and remote sensing data in tropical environment. Sci Rep 5(3):9899Google Scholar
  23. Shamir E, Georgakakos KP (2006) Distributed snow accumulation and ablation modeling in the American river basin. Adv Water Resour 29(4):558–570Google Scholar
  24. Skempton AW, DeLory FA (1957) Stability of natural slopes in London clay. In: 4th International 551 Conference on Soil Mechanics and Foundation Engineering, pp 378–381.Google Scholar
  25. Torizin J (2016) Elimination of informational redundancy in the weight of evidence method: an application to landslide susceptibility assessment. Stoch Env Res Risk Assess 30(2):1–17Google Scholar
  26. Trandafir AC, Ertugrul OL, Giraud RE et al (2015) Geomechanics of a snowmelt-induced slope failure in glacial till. Environ Earth Sci 73(7):3709–3716Google Scholar
  27. Tsangaratos P, Ilia I, Hong H et al (2017) Applying information theory and GIS-based quantitative methods to produce landslide susceptibility maps in Nancheng County, China. Landslides 14:1091–1111Google Scholar
  28. Wang Y, Jing H, Yu L et al (2017) Set pair analysis for risk assessment of water inrush in karst tunnels. Bull Eng Geol Env 76:1199–1207Google Scholar
  29. Wei C, Dai X, Ye S et al (2016) Prediction analysis model of integrated carrying capacity using set pair analysis. Ocean Coast Manag 120:39–48Google Scholar
  30. Zhang F, Ahmad S, Zhang H et al (2016) Simulating low and high streamflow driven by snowmelt in an insufficiently gauged alpine basin. Stoch Env Res Risk Assess 30(1):59–75Google Scholar
  31. Zhao, K. (1989) Theory and analysis of set pair e a new concept and system analysis method. In: Conference thesis of system theory and regional planning, pp 87–91Google Scholar

Copyright information

© Springer Nature B.V. 2019

Authors and Affiliations

  1. 1.Faculty of EngineeringChina University of GeosciencesWuhanChina

Personalised recommendations