Journal of Meteorological Research

, Volume 33, Issue 1, pp 138–148 | Cite as

Projection of Landslides in China during the 21st Century under the RCP8.5 Scenario

  • Shuangshuang He
  • Jun WangEmail author
  • Huijun Wang
Regular Articles


More and more rainstorms and other extreme weather events occur in the context of global warming, which may increase the risks of landslides. In this paper, changes of landslides in the 21st century of China under the high emission scenario RCP8.5 (Representative Concentration Pathway) are projected by using a statistical landslide forecasting model and the regional climate model RegCM4.0. The statistical landslide model is based on an improved landslide susceptibility map of China and a rainfall intensity–duration threshold. First, it is driven by observed rainfall and RegCM4.0 rainfall in 1980–99, and it can reproduce the spatial distribution of landslides in China pretty well. Then, it is used to forecast the landslide changes over China in the future under the RCP8.5 scenario. The results consistently reveal that landslides will increase significantly in most areas of China, especially in the southeastern, northeastern, and western parts of Northwest China. The change pattern at the end of the 21st century is generally consistent with that in the middle of the 21st century, but with larger increment and magnitude. In terms of the probability, the proportion of grid points that are very likely and extremely likely to experience landslides will also increase.

Key words

landslides projection statistical landslide forecasting model regional climate model 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.



We thank Xuejie Gao for assistance with the data


  1. Bălteanu, D., V. Chendeş, M. Sima, et al., 2010: A country-wide spatial assessment of landslide susceptibility in Romania. Geomorphology, 124: 102–112, doi: 10.1016/j.geomorph.20 10.03.005.CrossRefGoogle Scholar
  2. Caine, N., 1980: The rainfall intensity–duration control of shallow landslides and debris flows. Geogr. Ann., 62: 23–27, doi: 10.1080/04353676.1980.11879996.Google Scholar
  3. Carrara, A., M. Cardinali, R. Detti, et al., 1991: GIS techniques and statistical models in evaluating landslide hazard. Earth Surf. Proc. Land., 16: 427–445, doi: 10.1002/esp.3290160505.CrossRefGoogle Scholar
  4. Chen, H. P., J. Q. Sun, and X. L. Chen, 2012: The projection and uncertainty analysis of summer precipitation in China and the variations of associated atmospheric circulation field. Climatic Environ. Res., 17: 171–183, doi: 10.3878/j.issn.1006-9585.2011.10137. (in Chinese)Google Scholar
  5. Coe, J. A., J. W. Godt, R. L. Baum, et al., 2004: Landslide susceptibility from topography in Guatemala. Landslides: Evaluation and Stabilization, W. A. Lacerda, M. Ehrlich, S. A. B. Fontoura, et al., Eds., Taylor & Francis, London, 69–78.Google Scholar
  6. Dai, F. C., C. F. Lee, and Y. Y. Nagi, 2002: Landslide risk assessment and management: An overview. Eng. Geol., 64: 65–87, doi: 10.1016/S0013-7952(01)00093-X.CrossRefGoogle Scholar
  7. Ding, Y. H., G. Y. Ren, G. Y. Shi, et al., 2006: National assessment report of climate change (I): Climate change in China and its future trend. Progressus Inquisitiones de Mutatione Climatis, 2: 3–8. (in Chinese)Google Scholar
  8. Fabbri, A. G., C. J. F. Chung, A. Cendrero, et al., 2003: Is prediction of future landslides possible with a GIS? Nat. Hazards, 30: 487–503, doi: 10.1023/B:NHAZ.0000007282.62071.75.CrossRefGoogle Scholar
  9. Fredlund, D. G., A. Q. Xing, M. D. Fredlund, et al., 1996: The relationship of the unsaturated soil shear to the soil-water characteristic curve. Can. Geotech. J., 33: 440–448, doi: 10.1139/t96-065.CrossRefGoogle Scholar
  10. Gao, X. J., Y. Shi, D. F. Zhang, et al., 2012: Climate change in China in the 21st century as simulated by a high resolution regional climate model. Chinese Sci. Bull., 57: 1188–1195, doi: 10.1007/s11434-011-4935-8.CrossRefGoogle Scholar
  11. Gao, X. J., M. L. Wang, and F. Giorgi, 2013: Climate change over China in the 21st century as simulated by BCC_CSM1.1-RegCM4.0. Atmos. Ocean. Sci. Lett., 6: 381–386, doi: 10.38 78/j.issn.1674-2834.13.0029.CrossRefGoogle Scholar
  12. Guzzetti, F., S. Peruccacci, M. Rossi, et al., 2007: Rainfall thresholds for the initiation of landslides in central and southern Europe. Meteor. Atmos. Phys., 98: 239–267, doi: 10.100 7/s00703-007-0262-7.CrossRefGoogle Scholar
  13. Guzzetti, F., S. Peruccacci, M. Rossi, et al., 2008: The rainfall intensity–duration control of shallow landslides and debris flows: An update. Landslides, 5: 3–17, doi: 10.1007/s10346-007-0112-1.CrossRefGoogle Scholar
  14. Hong, Y., and R. F. Adler, 2008: Predicting global landslide spatiotemporal distribution: Integrating landslide susceptibility zoning techniques and real-time satellite rainfall estimates. Int. J. Sediment Res., 23: 249–257, doi: 10.1016/S1001-6279(08)60022-0.CrossRefGoogle Scholar
  15. Hong, Y., H. Hiura, K. Shino, et al., 2005: The influence of intense rainfall on the activity of large-scale crystalline schist landslides in Shikoku Island, Japan. Landslides, 2: 97–105, doi: 10.1007/s10346-004-0043-z.CrossRefGoogle Scholar
  16. Hong, Y., R. Adler, and G. Huffman, 2006: Evaluation of the potential of NASA multi-satellite precipitation analysis in global landslide hazard assessment. Geophys. Res. Lett., 33, L22402, doi: 10.1029/2006GL028010.CrossRefGoogle Scholar
  17. Hong, Y., R. Adler, and G. Huffman, 2007: Use of satellite remote sensing data in the mapping of global landslide susceptibility. Nat. Hazards, 43: 245–256, doi: 10.1007/s11069-006-9104-z. IPCC, 2014: Climate Change 2014: Synthesis Report. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, R. K. Pachauri, et al., Eds., IPCC, Geneva, Switzerland, 151 pp.CrossRefGoogle Scholar
  18. Iverson, R. M., 2000: Landslide triggering by rain infiltration. Water Resour. Res., 36: 1897–1910, doi: 10.1029/2000WR 900090.CrossRefGoogle Scholar
  19. Jibson, R. W., 1989: Debris flows in southern Puerto Rico. Spec. Pap.-Geol. Soc. Am., 236: 29–56, doi: 10.1130/SPE236-p29.Google Scholar
  20. Kirschbaum, D. B., R. Adler, Y. Hong, et al., 2012: Advances in landslide nowcasting: Evaluation of a global and regional modeling approach. Environ. Earth Sci., 66: 1683–1696, doi: 10.1007/s12665-011-0990-3.CrossRefGoogle Scholar
  21. Lee, S., and K. Min, 2001: Statistical analysis of landslide susceptibility at Yongin, Korea. Environ. Geol., 40: 1095–1113, doi: 10.1007/s002540100310.CrossRefGoogle Scholar
  22. Li, W. Y., C. Liu, M. Scaioni, et al., 2017: Spatiotemporal analysis and simulation on shallow rainfall-induced landslides in China using landslide susceptibility dynamics and rainfall I-D thresholds. Sci. China Earth Sci., 60: 720–732, doi: 10.1007/s11430-016-9008-4.CrossRefGoogle Scholar
  23. Liao, Z. H., Y. Hong, J. Wang, et al., 2010: Prototyping an experimental early warning system for rainfall-induced landslides in Indonesia using satellite remote sensing and geospatial datasets. Landslides, 7: 317–324, doi: 10.1007/s10346-010-0219-7.CrossRefGoogle Scholar
  24. Liu, C., W. Y. Li, H. B. Wu, et al., 2013: Susceptibility evaluation and mapping of China’s landslides based on multisource data. Nat. Hazards, 69: 1477–1495, doi: 10.1007/s11069-013-0759-y.CrossRefGoogle Scholar
  25. Ma, L., P. Cui, G. B. Zhou, et al., 2009: Geological Meteorological Hazard. China Meteorological Press, Beijing, 156 pp. (in Chinese)Google Scholar
  26. Montrasio, L., and R. Valentino, 2008: A model for triggering mechanisms of shallow landslides. Nat. Hazards Earth Syst. Sci., 8: 1149–1159, doi: 10.5194/nhess-8-1149-2008.CrossRefGoogle Scholar
  27. Niu, X. R., S. Y. Wang, J. P. Tang, et al., 2015: Multimodel ensemble projection of precipitation in eastern China under A1B emission scenario. J. Geophys. Res. Atmos., 120: 9965–9980, doi: 10.1002/2015JD023853.CrossRefGoogle Scholar
  28. Salciarini, D., E. Volpe, S. A. Kelley, et al., 2016: Modeling the effects induced by the expected climatic trends on landslide activity at large scale. Procedia Eng., 158: 541–545, doi: 10.1016/j.proeng.2016.08.486.CrossRefGoogle Scholar
  29. Sarkar, S., and D. P. Kanungo, 2004: An integrated approach for landslide susceptibility mapping using remote sensing and GIS. Photogramm. Eng. Rem. S., 70: 617–625, doi: 10.14358/PERS.70.5.617.CrossRefGoogle Scholar
  30. Segoni, S., D. Lagomarsino, R. Fanti, et al., 2015: Integration of rainfall thresholds and susceptibility maps in the Emilia Romagna (Italy) regional-scale landslide warning system. Landslides, 12: 773–785, doi: 10.1007/s10346-014-0502-0.CrossRefGoogle Scholar
  31. Segoni, S., L. Piciullo, and S. L. Gariano, 2018: A review of the recent literature on rainfall thresholds for landslide occurrence. Landslides, 15: 1483–1501, doi: 10.1007/s10346-018-0966-4.CrossRefGoogle Scholar
  32. Wang, J., H. J. Wang, and Y. Hong, 2016: A Realtime Monitoring and Dynamical Forecasting System for Floods fand Landslides in China. China Meteorological Press, Beijing, 164 pp. (in Chinese)Google Scholar
  33. Wu, J., and X. J. Gao, 2013: A gridded daily observation dataset over China region and comparison with the other datasets. Chinese J. Geophys., 56: 1102–1111, doi: 10.6038/cjg20130 406. (in Chinese)Google Scholar

Copyright information

© The Chinese Meteorological Society and Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Nansen–Zhu International Research Centre, Institute of Atmospheric PhysicsChinese Academy of SciencesBeijingChina
  2. 2.Collaborative Innovation Center on Forecast and Evaluation of Meteorological DisastersNanjing University of Information Science & TechnologyNanjingChina
  3. 3.Key Laboratory of Meteorological DisasterNanjing University of Information Science & TechnologyNanjingChina
  4. 4.University of Chinese Academy of SciencesBeijingChina

Personalised recommendations