Advertisement

Journal of Mountain Science

, Volume 15, Issue 6, pp 1342–1353 | Cite as

Early warning model for slope debris flow initiation

  • Ming-li Li
  • Yuan-jun Jiang
  • Tao Yang
  • Qiang-bing Huang
  • Jian-ping Qiao
  • Zong-ji Yang
Article

Abstract

Early warning model of debris flow is important for providing local residents with reliable and accurate warning information to escape from debris flow hazards. This research studied the debris flow initiation in the Yindongzi gully in Dujiangyan City, Sichuan province, China with scaled-down model experiments. We set rainfall intensity and slope angle as dominating parameters and carried out 20 scaled-down model tests under artificial rainfall conditions. The experiments set four slope angles (32°, 34°, 37°, 42°) and five rainfall intensities (60 mm/h, 90 mm/h, 120 mm/h, 150 mm/h, and 180 mm/h) treatments. The characteristic variables in the experiments, such as, rainfall duration, pore water pressure, moisture content, surface inclination, and volume were monitored. The experimental results revealed the failure mode of loose slope material and the process of slope debris flow initiation, as well as the relationship between the surface deformation and the physical parameters of experimental model. A traditional rainfall intensity-duration early warning model (I-D model) was firstly established by using a mathematical regression analysis, and it was then improved into ISD model and ISM model (Here, I is rainfall Intensity, S is Slope angle, D is rainfall Duration, and M is Moisture content). The warning model can provide reliable early warning of slope debris flow initiation.

Keywords

Slope debris flow Artificial rainfall model Early warning model Model experiment 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Notes

Acknowledgements

This study was financially supported by the CAS Pioneer Hundred Talents Program and the Institute of Mountain Hazards and Environment (Grant No. SDS-135-1705). The authors also gratefully acknowledge support from the National Natural Science Foundation of China (Grant No. 41771021, 41471429, and 41790443) and the National Key Research and Development Program of China (Grant No. 2017YFD0800501). We also acknowledge the help and advice of several experts and editors.

References

  1. Baum RL, Godt JW (2010) Early warning of rainfall-induced shallow landslides and debris flows in the USA. Landslides 7: 259–272. https://doi.org/10.1007/s10346-009-0177-0 CrossRefGoogle Scholar
  2. Berti M, Martian MLV, Franceshini S, et al. (2012) Probabilistic rainfall threshold for landslide occurrence using a Bayesian approach. Journal of Geophysical Research 117: F04006. https://doi.org/10.1029/2012JF002367 Google Scholar
  3. Caine N (1980) The rainfall intensity: Duration control of shallow landslide and debris flows. Geografiska Annaler. Series A, Physical Geography 62(1/2): 23–27. https://doi.org/10.2307/520449 Google Scholar
  4. Chen XQ, Cui P, Feng ZL, et al. (2006) Artificial rainfall experimental study on landslide translation to debris flow. Chinese Journal of Rock Mechanics and Engineering 25(1): 106–116. (In Chinese)Google Scholar
  5. Chen G, Meng X, Qiao L, et al. (2017) Response of a loess landslide to rainfall: observations from a field artificial rainfall experiment in Bailong river basin, China. Landslides (1-2): 1–17. https://doi.org/10.1007/s10346-017-0924-6 Google Scholar
  6. Cui P, Lin Y, Chen C (2012) Destruction of vegetation due to geo-hazards and its environmental impacts in the Wenchuan earthquake areas. Ecological Engineering 44: 61–69. https://doi.org/10.1016/j.ecoleng.2012.03.012 CrossRefGoogle Scholar
  7. Deangeli C (2009) Pore water pressure contribution to debris flow mobility. American Journal of Environmental Sciences 5(4): 487–493. https://doi.org/10.3844/ajessp.2009.486.492 CrossRefGoogle Scholar
  8. Huang J, Ju NP, Liao YJ, et al. (2015) Determination of rainfall thresholds for shallow landslides by a probabilistic and empirical method. Natural Hazards and Earth System Sciences 15: 2715–2723. https://doi.org/10.5194/nhess-15-2715-2015 CrossRefGoogle Scholar
  9. Gao B, Zhou J, Zhang J (2011) Macro-meso analysis of watersoil interaction mechanism of debris flow starting process. Chinese Journal of Rock Mechanics and Engineering 30(12): 2567–2573. (In Chinese)Google Scholar
  10. Hu MJ, Wang N (2003) Testing study in the correlation among landslide. debris flow and rainfall in Jiangjia valley. Chinese Journal of Rock Mechanics and Engineering 22(5): 824–828. (In Chinese)Google Scholar
  11. Hungr O, Leroueil S, Picarelli L (2014) The Varnes classification of landslide types, an update. Landslides 11: 167–194. https://doi.org/10.1007/s10346-013-0436-y CrossRefGoogle Scholar
  12. Jakob M, Owen T, Simpson T (2012) A regional real-time debris-flow warning system for the District of North Vancouver, Canada. Landslides 9: 165–178. https://doi.org/10.1007/s10346-011-0282-8 CrossRefGoogle Scholar
  13. Khan YA, Lateh H, Baten MA, et al. (2012) Critical antecedent rainfall conditions for shallow landslides in Chittagong City of Bangladesh. Environmental Earth Sciences 67(1): 97–106. https://doi.org/10.1007/s12665-011-1483-0 CrossRefGoogle Scholar
  14. Lv L, Chen N, Lu Y, et al. (2013) Mechanical model of slope debris flow initiation based on artificial rainfall experiment. Journal of Natural Disasters 6(1): 19–34. (In Chinese) https://doi.org/10.13577/j.jnd.2013.0108 Google Scholar
  15. Ni HY, Tang C (2014) Advances in the physical simulation experiment on debris flow initiation in China. Advances in Water Science 25(4): 606–613. (In Chinese) https://doi.org/10.14042/j.cnki.32.1309.2014.04.005 Google Scholar
  16. Raia S, Alvioli M, Rossi M, et al. (2014) Improving predictive power of physical based rainfall induced shallow landslide models: a probabilistic approach. Geoscientific Model Development 7: 495–514. https://doi.org/10.5194/gmdd-6-1367-2013 CrossRefGoogle Scholar
  17. Staleya DM, Negria JA, Keana JW, et al. (2017) Prediction of spatially explicit rainfall intensity–duration thresholds for post-fire debris-flow generation in the western United States. Geomorphology 278: 149–162. https://doi.org/10.1016/j.geomorph.2016.10.019 CrossRefGoogle Scholar
  18. Tsaparas I, Rahardjo H, Toll DG, Leong EC (2002) Controlling parameters for rainfall-induced landslides. Computers and Geotechnics 29: 1–27. https://doi.org/10.1016/S0266-352X(01)00019-2 CrossRefGoogle Scholar
  19. Vennari C, Gariano SL, Antronico L, et al. (2014) Rainfall thresholds for shallow landslide occurrence in Calabria, southern Italy. Natural Hazards and Earth System Sciences 14: 317–330. https://doi.org/10.5194/nhessd-1-5141-2013 CrossRefGoogle Scholar
  20. Wang YY, Zhou RY, Li CZ (1999) Study on relationship between erosion of debris flows and critical rain quantity. Journal of Soil Erosion and Soil and Water Conservation 5(6): 34–38. (In Chinese) https://doi.org/10.13870/j.cnki.stbcxb.1999.s1.008 Google Scholar
  21. Yang ZJ, Qiao JP, Taro Uchimura, et al. (2017) Unsaturated hydro-mechanical behaviour of rainfall-induced mass remobilization in post-earthquake landslides. Engineering Geology 222: 102–110. https://doi.org/10.1016/j.enggeo.2017.04.001 CrossRefGoogle Scholar
  22. Zhang S, Zhang LM (2017) Impact of the 2008 Wenchuan earthquake in China on subsequent long-term debris flow activities in the epicentral area. Geomorphology 276: 86–103. https://doi.org/10.1016/j.geomorph.2016.10.009 CrossRefGoogle Scholar

Copyright information

© Science Press, Institute of Mountain Hazards and Environment, CAS and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Key Laboratory of Mountain Hazards and Earth Surface Process of Chinese Academy of Sciences, Institute of Mountain Hazards and EnvironmentChinese Academy of SciencesChengduChina
  2. 2.University of Chinese Academy of SciencesBeijingChina
  3. 3.Chengdu Center of Hydrogeology and Engineering Geology of Sichuan Bureau of Geology & Mineral ResourcesChengduChina
  4. 4.Department of Geological EngineeringChang’an UniversityXi’anChina

Personalised recommendations