, Volume 16, Issue 4, pp 799–813 | Cite as

Early identification and dynamic processes of ridge-top rockslides: implications from the Su Village landslide in Suichang County, Zhejiang Province, China

  • Chaojun OuyangEmail author
  • Wei Zhao
  • Huicong An
  • Shu Zhou
  • Dongpo Wang
  • Qiang Xu
  • Weile Li
  • Dalei Peng
Technical Note


Ridge-top rockslides frequently cause huge property losses and casualties due to the difficulties involved in detecting their precursors by means of manual surveys. Their early identification and the surrounding area’s evaluation in terms of potential danger are essential for preventing disasters. The recent large rockslide which occurred at Su Village, which is in possession of data related to pre-failure images, real-time video, and post-failure boreholes, is helpful for providing new insights into the processes associated with these events. Due to a strong sustained rainfall, a large volume of granite blocks rapidly descended from the upper part of the hillside, causing 27 deaths and destroying more than 20 houses. It is found that the early identification of the hazard associated with such rockfalls can be made by the analysis of remote sensing images and Persistent Scatterer Interferometry (PSI) analysis of Synthetic Aperture Radar (SAR) data. The depth-integrated continuum method, including taking the entrainment effect into account, was adopted to analyze the dynamic processes and to identify the areas at risk. The computational results show that the evaluated runout distance and extent match well with the field investigation results. The parameter sensitivity surrounding cohesion, coefficients of lateral earth pressure, and volume amplification were analyzed. It is demonstrated that the cohesion plays a significant role in the dynamic processes and the deposited area. However, the effects from the earth pressure coefficient and volume bulking are comparatively weaker.


Rockslide Remote sensing image InSAR Dynamic process MacCormack-TVD scheme Numerical modeling 



The authors wish to thank the armed police traffic team who assisted us greatly during the field work. The Sentinel-1A SAR images were provided by the European Space Agency (EAS).

Funding information

Financial support was provided by the National Key Research and Development Program of China (Project No. 2017YFC1501000), the NSFC (Grant No. 41572303, 41520104002), and the CAS Youth Innovation Promotion Association and Joint Project of GAS and CAS (2017HZ-03).

Supplementary material

10346_2018_1128_MOESM1_ESM.kml (38.7 mb)
ESM 1 (KML 39677 kb)


  1. Anderson BL, Winawer J (2005) Image segmentation and lightness perception. Nature 434(7029):79–83. CrossRefGoogle Scholar
  2. Banton J, Villard P, Jongmans D, Scavia C (2009) Two-dimensional discrete element models of debris avalanches: parameterization and the reproducibility of experimental results. J Geophys Res Earth Surf 114:F04013. CrossRefGoogle Scholar
  3. Chen XW, Lin X (2014) Big data deep learning: challenges and perspectives. IEEE Access 2:514–525CrossRefGoogle Scholar
  4. Chen GP, Zhao QH, Huang HQ (2011) Sliding characteristics of high-speed and long run-out giant rockslide landslide at Wenjiagou Stream. J Eng Geol 19(3):404–408Google Scholar
  5. Chen HX, Zhang S, Peng M, Zhang LM (2016) A physically-based multi-hazard risk assessment platform for regional rainfall-induced slope failures and debris flows. Eng Geol 203:15–29. CrossRefGoogle Scholar
  6. Cui YF, Nouri A, Chan D, Rahmati E (2016) A new approach to DEM simulation of sand production. J Pet Sci Eng 147:56–67. CrossRefGoogle Scholar
  7. Cui YF, Chan D, Nouri A (2017) Coupling of solid deformation and pore pressure for undrained deformation—a discrete element method approach. Int J Numer Anal Methods Geomech 41(18):1943–1961. CrossRefGoogle Scholar
  8. Delaney KB, Evans SG (2015) The 2000 Yigong landslide (Tibetan Plateau), rockslide-dammed lake and outburst flood: review, remote sensing analysis, and process modelling. Geomorphology 246:377–393. CrossRefGoogle Scholar
  9. Feng Z, Yin YP, Li B, Zhang M (2012) Mechanism analysis of apparent dip landslide of Jiweishan in Wulong, Chongqing. Rock Soil Mech 33(9):2704–2712Google Scholar
  10. Hu XW, Huang RQ, Shi YB, Lu XP, Zhu HY, Wang XR (2009) Analysis of blocking river mechanism of Tangjiashan landslide and dam-breaking mode of its barrier dam. Chin J Rock Mech Eng 28(1):181–189Google Scholar
  11. Huang RQ, Pei XJ, Cui SH (2016) Cataclastic characteristics and formation mechanism of rock mass in sliding zone of Daguangbao landslide. Chin J Rock Mech Eng 35(1):1–15Google Scholar
  12. Hungr O, Evans SG (2004) Entrainment of debris in rock avalanches: an analysis of a long run-out mechanism. Geol Soc Am Bull 116(9–10):1240–1252. CrossRefGoogle Scholar
  13. Intrieri E, Raspini F, Fumagalli A, Lu P, Del Conte S, Farina P, Allievi J, Ferretti A, Casagli N (2018) The Maoxian landslide as seen from space: detecting precursors of failure with Sentinel-1 data. Landslides 15(1):123–133. CrossRefGoogle Scholar
  14. Iverson RM (2015) Scaling and design of landslide and debris-flow experiments. Geomorphology 244:9–20. CrossRefGoogle Scholar
  15. Iverson RM, George DL (2015) Modelling landslide liquefaction, mobility bifurcation and the dynamics of the 2014 Oso disaster. Geotechnique 66(3):175–187. CrossRefGoogle Scholar
  16. Iverson RM, Ouyang CJ (2015) Entrainment of bed material by earth-surface mass flows: review and reformulation of depth-integrated theory. Rev Geophys 53(1):27–58. CrossRefGoogle Scholar
  17. Jones N (2014) Computer science: the learning machines. Nature 505(7482):146–148. CrossRefGoogle Scholar
  18. Lecun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436–444. CrossRefGoogle Scholar
  19. Liang QH (2010) Flood simulation using a well-balanced shallow flow model. J Hydraul Eng 136(9):669–675. CrossRefGoogle Scholar
  20. Lin CH, Lin ML (2015) Evolution of the large landslide induced by Typhoon Morakot: a case study in the Butangbunasi River, southern Taiwan using the discrete element method. Eng Geol 197:172–187. CrossRefGoogle Scholar
  21. Lo CM, Lee CF, Chou HT, Lin ML (2014) Landslide at Su-Hua Highway 115.9k triggered by Typhoon Megi in Taiwan. Landslides 11(2):293–304. CrossRefGoogle Scholar
  22. McDougall S (2016) Landslide runout analysis—current practice and challenges. Can Geotech J 54(5):605–620. CrossRefGoogle Scholar
  23. McDougall S, Hungr O (2005) Dynamic modelling of entrainment in rapid landslides. Can Geotech J 42(5):1437–1448. CrossRefGoogle Scholar
  24. Ouyang CJ, He SM, Xu Q, Luo Y, Zhang WC (2013) A MacCormack-TVD finite difference method to simulate the mass flow in mountainous terrain with variable computational domain. Comput Geosci 52:1–10. CrossRefGoogle Scholar
  25. Ouyang CJ, He SM, Tang CA (2015a) Numerical analysis of dynamics of debris flow over erodible beds in Wenchuan earthquake-induced area. Eng Geol 194:62–72. CrossRefGoogle Scholar
  26. Ouyang CJ, He SM, Xu Q (2015b) MacCormack-TVD finite difference solution for dam break hydraulics over erodible sediment beds. J Hydraul Eng 141(5):06014026. CrossRefGoogle Scholar
  27. Ouyang CJ, Zhao W, He SM, Wang DP, Zhou S, An HC, Wang ZW, Cheng DX (2017a) Numerical modeling and dynamic analysis of the 2017 Xinmo landslide in Maoxian County, China. J Mt Sci 14(9):1701–1711. CrossRefGoogle Scholar
  28. Ouyang CJ, Zhou KQ, Xu Q, Yin JH, Peng DL, Wang DP, Li WL (2017b) Dynamic analysis and numerical modeling of the 2015 catastrophic landslide of the construction waste landfill at Guangming, Shenzhen, China. Landslides 14(2):705–718. CrossRefGoogle Scholar
  29. Ouyang CJ, Zhao W, Xu Q, Peng DL, Li WL, Wang DP, Zhou S, Hou SW (2018) Failure mechanisms and characteristics of the 2016 catastrophic rockslide at Su village, Lishui, China. Landslides 15(7):1391–1400. CrossRefGoogle Scholar
  30. Papandreou G, Kokkinos I, Savalle PA (2015) Modeling local and global deformations in deep learning: epitomic convolution, multiple instance learning, and sliding window detection. 2015 IEEE Conference on Computer Vision and Pattern 390–399Google Scholar
  31. Sarmap (2005)
  32. Savage SB, Hutter K (1989) The motion of a finite mass of granular material down a rough incline. J Fluid Mech 199:177–215. CrossRefGoogle Scholar
  33. Sosio R, Crosta GB, Chen JH, Hungr O (2012) Modelling rock avalanche propagation onto glaciers. Quat Sci Rev 47:23–40. CrossRefGoogle Scholar
  34. Tantianuparp P, Shi XG, Zhang L, Balz T, Liao MS (2013) Characterization of landslide deformations in Three Gorges area using multiple InSAR data stacks. Remote Sens 5(6):2704–2719. CrossRefGoogle Scholar
  35. Tommasi P, Campedel P, Consorti C, Ribacchi R (2008) A discontinuous approach to the numerical modelling of rock avalanches. Rock Mech Rock Eng 41(1):37–58. CrossRefGoogle Scholar
  36. Xing AG, Wang GH, Li B, Jiang Y, Feng Z, Toshitaka K (2014) Long-runout mechanism and landsliding behavior of large catastrophic landslide triggered by heavy rainfall in Guanling, Guizhou, China. Can Geotech J 52(7):971–981. CrossRefGoogle Scholar
  37. Xu Q, Fan XM, Huang RQ, Yin YP, Hou SS, Dong XJ, Tang MG (2010) A catastrophic rockslide-debris flow in Wulong, Chongqing, China in 2009: background, characterization, and causes. Landslides 7(1):75–87. CrossRefGoogle Scholar
  38. Zhang H, Liu SG, Wang W, Zheng L, Zhang YB, Wu YQ, Han Z, Li YG, Chen GQ (2016) A new DDA model for kinematic analyses of rockslides on complex 3-D terrain. Bull Eng Geol Environ 77(2):555–571. CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Key Laboratory of Mountain Hazards and Surface Process & Institute of Mountain Hazards and Environment (IMHE)Chinese Academy of SciencesChengduChina
  2. 2.University of Chinese Academy of SciencesBeijingChina
  3. 3.State Key Laboratory of Geohazard Prevention and Geoenvironment ProtectionChengdu University of TechnologyChengduChina

Personalised recommendations