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Chinese Science Bulletin

, Volume 48, Issue 7, pp 703–708 | Cite as

Spatial hazard analysis and prediction on rainfall-induced landslide using GIS

  • Hengxing Lan
  • Faquan Wu
  • Chenghu Zhou
  • Lingjuan Wang
Reports

Abstract

The application of landslide hazard model coupled with GIS provides an effective means to spatial hazard analysis and prediction on rainfall-induced landslides. A modified SINMAP model is established based upon the systematic investigation on previous GIS-based landslide analysis models. By integrating the landslide deterministic model with the hydrological distribution model based on DEM, this model deeply studied the effect of underground water distribution due to rainfall on the slope stability and landslide occurrence, including the effect of dynamic water pressure resulting from the down slope seepage process as well as that of static water pressure. Its applicability has been testified on the Xiaojiang watershed, the rainfall-induced landslides widespread area in Southeast China. Detailed discussion was carried out on the spatial distribution characteristics of landslide hazard and its extending trend, as well as the quantitative relationship between landslide hazard with precipitation, slope angle and specific catchment area in the Xiaojiang watershed. And the precipitation threshold for landslide occurrence was estimated. These analytical results are proved useful for geohazard control and engineering decision-making in the Xiaojiang watershed.

Keywords

rainfall-induced landslide spatial analysis and prediction Geographical Information System 

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References

  1. 1.
    Wen, B. P., The state of the art and trend of the landslide prediction, Earth Science Frontiers (in Chinese), 1996, 3(1–2): 86–92.Google Scholar
  2. 2.
    Yin, K. L., Yan, T. Z., Landslide prediction and related models, Chinese Journal of Rock Mechanics and Engieering (in Chinese), 1996, 15(1): 1–8.Google Scholar
  3. 3.
    Sun, G. Z., Yao, B. K., Landslide hazard in China and its research, (ed. Chinese Society of Rock Mechanics and Engineering) Typical landslides in China (in Chinese), Beijing: Science Press, 1988, 1–11.Google Scholar
  4. 4.
    Carrara, A., Guzzetti, F., Use of GIS technology in the prediction and monitoring of landslide hazard, Natural Hazards, 1999, 20(2): 117–135.CrossRefGoogle Scholar
  5. 5.
    Guzzetti, F., Carrara, A., Cardinali, M. et al., Landslide evaluation: a review of current techniques and their application in a multi-scale study, Central Italy, Geomorphology, 1999, 31: 181–216.CrossRefGoogle Scholar
  6. 6.
    Pike, R. J., Quantifying landslide-terrain types from digital elevation models, Mathematical Geology, 1988, 20(5): 491–511.CrossRefGoogle Scholar
  7. 7.
    Carrara, A., Cardinali, M., Guzzetti, F. et al., GIS technology in mapping landslide hazard (eds. Carrara, A., Guzzetti, F.), Geographical Information Systems in Assessing Natural Hazards, Dordrecht: Kluwer Academic Publishers, 1995, 135–175.CrossRefGoogle Scholar
  8. 8.
    Carrara, A., Multivariate methods for landslide hazard evaluation, Mathematical Geology, 1983, 15: 403–426.CrossRefGoogle Scholar
  9. 9.
    Chung, C. F., Fabbri, A. G., Van Westen, C. J., Multivariate regression analysis for landslide hazard zonation (eds. Carrara, A., Guzzetti, F.), Geographical Information Systems in Assessing Natural Hazards, Dordrecht: Kluwer Academic Publishers, 1995, 107–133.CrossRefGoogle Scholar
  10. 10.
    Heckerman, Probabilistic interpretation of MYCIN’s certainty factors (eds. Kanal, L. N., Lemmer, J. E.), Uncertainty in Artificial Intelligence, New York: Elsevier, 1986, 298–311.Google Scholar
  11. 11.
    Chung, C. F., Fabbri, A. G., Probabilistic prediction models for landslide hazard mapping, Photogrammetric Engineering & Remote Sensing (PE&RS), 1999, 65(12): 1388–1399.Google Scholar
  12. 12.
    Montgomery, D. R., Dietrich, W. E., A physically based model for the topographic control on shallow landsliding, Water Resources Research, 1994, 30(4): 1153–1171.CrossRefGoogle Scholar
  13. 13.
    Dietrich, E. W., Reisss, R., Hsu, M. L. et al., A process-based model for colluvial soil depth and shallow landsliding using digital elevation data, Hydrological Processes, 1995, 9: 383–400.CrossRefGoogle Scholar
  14. 14.
    Wu, W., Sidle, R. C., A distributed slope stability model for steep forested watersheds, Water Resources Research, 1995, 31(8): 2097–2110.CrossRefGoogle Scholar
  15. 15.
    Chung, C. F., Fabbri, A. G., Prediction models for landslide hazard using fuzzy set approach (eds. Marchetti, M., Rivas, V.), Geomorphology and Environmental Impact Assessment, Rotterdam: A. A. Balkema Publisher, 2001, 31–47.Google Scholar
  16. 16.
    Binaghi, E., Luzi, L., Madella, P., Slope instability zonation: a comparison between certainty factor and fuzzy Dempster-Shafer approaches, Natural Hazards, 1998, 17: 77–97.CrossRefGoogle Scholar
  17. 17.
    Zhou, C. H., Lee, C. F., Li, J., On the spatial relationship between landslides and causative factors on Lantau Island, Hong Kong. Geomorphology, 2002, 43: 197–207.CrossRefGoogle Scholar
  18. 18.
    Pack, R. T., Tarboton, D. G., Goodwin, C. N. The SINMAP approach to terrain stability mapping, Proceedings of 8th Congress of the International Assocation of Engineering Geology (eds. Moore, D., Hungr, O.), Rotterdam, A. A. Balema Publisher, 1998, 1157–1165.Google Scholar
  19. 19.
    Du, R. H., Kang, Z. C., Chen, X. Q. et al., Synthetic Investigation and control on the debris flow in the Xiaojiang watershed, Yunnan (in Chinese), Chongqing: Science Technology Press, 1987, 57–71.Google Scholar
  20. 20.
    Wu, J. S., Kang, Z. C., Tian, L. Q. et al., Observation on the Debris Flow in Jiangjiagou, Yunnan (in Chinese), Beijing: Science Press, 1990, 26–47.Google Scholar

Copyright information

© Science in China Press 2003

Authors and Affiliations

  • Hengxing Lan
    • 1
  • Faquan Wu
    • 2
  • Chenghu Zhou
    • 1
  • Lingjuan Wang
    • 3
  1. 1.State Key Laboratory of Resources and Environmental System, Institute of Geographic Science and Natural Resources ResearchChinese Academy of SciencesBeijingChina
  2. 2.Institute of Geology and GeophysicsChinese Academy of SciencesBeijingChina
  3. 3.Institute of Remote Sensing ApplicationChinese Academy of SciencesBeijingChina

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