Skip to main content
SpringerLink
Log in

A Review of Impacts of Climate Change on Slope Stability

  • Conference paper
  • First Online:
Climate Change and Water Security

Part of the book series: Lecture Notes in Civil Engineering ((LNCE,volume 178))

Abstract

Climate change has become an increasingly pressing issue that needs to be tackled by scientists and researchers around the globe in recent years. However, huge uncertainties are still associated with climate change and its impacts on our world as it is extremely difficult to quantify the effects of climate change. It is important to study the impact of climate change on slope stability in order to ensure that existing slopes and their design standards are able to accommodate the changes in environmental factors such as rainfall and temperature under climate change. The main objective of this paper is to review the relationship between rainfall variations under climate change and its impacts on slope stability. This review study revealed that there are still not many studies investigating the direct impacts of climate change on slope stability, particularly in the tropical regions, which receive intense monsoon rainfalls. Changes in extreme weather events under climate change have also resulted in the development of desiccated cracks in the soil. Studies have shown prolonged drying and intensified rainfall across various regions in which these phenomena would greatly affect the extent of cracks development in soil under repetitive wet-dry cycles. Consequently, this would result in excessive infiltrations into soil slopes during intense rainfall periods.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 189.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 249.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 249.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Abbot, J. and Marohasy, J. (2014) ‘Input selection and optimisation for monthly rainfall forecasting in queensland, australia, using artificial neural networks’, Atmospheric Research. Elsevier B.V., 138, pp. 166–178. https://doi.org/10.1016/j.atmosres.2013.11.002.

  2. Abdulla, F. (2020) ‘21st Century Climate Change Projections of Precipitation and Temperature in Jordan’, Procedia Manufacturing. Elsevier B.V., 44(2019), pp. 197–204. https://doi.org/10.1016/j.promfg.2020.02.222.

  3. Aftab S et al (2018) Rainfall prediction using data mining techniques: A systematic literature review. Int J Adv Comput Sci Appl 9(5):143–150. https://doi.org/10.14569/IJACSA.2018.090518

    Article  Google Scholar 

  4. Ahmadi A et al (2014) Assessment of climate change impacts on rainfall using large scale climate variables and downscaling models-a case study. J Earth Syst Sci 123(7):1603–1618. https://doi.org/10.1007/s12040-014-0497-x

    Article  Google Scholar 

  5. Alvioli M et al (2018) ‘Implications of climate change on landslide hazard in Central Italy’, Science of the Total Environment. The Authors 630:1528–1543. https://doi.org/10.1016/j.scitotenv.2018.02.315

    Article  CAS  Google Scholar 

  6. Antonio S.Cafino, Rafael Cano, Carmen Sordo, J. M. G. (2009) ‘Bayesian Networks for Probabilistic Weather Forecast’, 700, pp. 1–5.

    Google Scholar 

  7. Asare-Nuamah, P. and Botchway, E. (2019) Understanding climate variability and change: analysis of temperature and rainfall across agroecological zones in Ghana, Heliyon. Elsevier, 5(10), p. e02654. https://doi.org/10.1016/j.heliyon.2019.e02654

  8. Ashfaq M et al (2009) Suppression of south Asian summer monsoon precipitation in the 21st century. Geophys Res Lett 36(1):1–5. https://doi.org/10.1029/2008GL036500

    Article  Google Scholar 

  9. Bagirov, A. M., Mahmood, A. and Barton, A. (2017) ‘Prediction of monthly rainfall in Victoria, Australia: Clusterwise linear regression approach’, Atmospheric Research. Elsevier B.V., 188, pp. 20–29. doi: https://doi.org/10.1016/j.atmosres.2017.01.003.

  10. Bagirov, A. M., Ugon, J. and Mirzayeva, H. (2013) ‘Nonsmooth nonconvex optimization approach to clusterwise linear regression problems’, European Journal of Operational Research. Elsevier B.V., 229(1), pp. 132–142. doi: https://doi.org/10.1016/j.ejor.2013.02.059.

  11. Baum SD, Haqq-Misra JD, Karmosky C (2012) Climate Change: Evidence of Human Causes and Arguments for Emissions Reduction. Sci Eng Ethics 18(2):393–410. https://doi.org/10.1007/s11948-011-9270-6

    Article  Google Scholar 

  12. Billa L, Mansor S, Mahmud AR (2004) Spatial information technology in flood early warning systems: An overview of theory, application and latest developments in Malaysia. Disaster Prevention and Management: An International Journal 13(5):356–363. https://doi.org/10.1108/09653560410568471

    Article  Google Scholar 

  13. Boucher O, Pham M (2002) History of sulfate aerosol radiative forcings. Geophys Res Lett 29(9):22–23. https://doi.org/10.1029/2001GL014048

    Article  Google Scholar 

  14. Brohan P et al (2006) Uncertainty estimates in regional and global observed temperature changes: A new data set from 1850. Journal of Geophysical Research Atmospheres 111(12):1–21. https://doi.org/10.1029/2005JD006548

    Article  Google Scholar 

  15. Buma J, Dehn M (1998) A method for predicting the impact of climate change on slope stability. Environ Geol 35(2–3):190–196. https://doi.org/10.1007/s002540050305

    Article  Google Scholar 

  16. Cascini L et al (2010) Modeling of rainfall-induced shallow landslides of the flow-type. Journal of Geotechnical and Geoenvironmental Engineering 136(1):85–98. https://doi.org/10.1061/(ASCE)GT.1943-5606.0000182

    Article  Google Scholar 

  17. Cascini L et al (2013) Modelling the post-failure stage of rainfall-induced landslides of the flow type. Can Geotech J 50(9):924–934. https://doi.org/10.1139/cgj-2012-0375

    Article  Google Scholar 

  18. Chapman S et al (2017) ‘The impact of urbanization and climate change on urban temperatures: a systematic review’, Landscape Ecology. Springer, Netherlands 32(10):1921–1935. https://doi.org/10.1007/s10980-017-0561-4

    Article  Google Scholar 

  19. Chaudhari, M. S. and Choudhari, N. K. (2017) ‘Study of Various Rainfall Estimation & Prediction Techniques Using Data Mining’, (7), pp. 137–139.

    Google Scholar 

  20. Chiu, Y. Y., Chen, H. E. and Yeh, K. C. (2019) ‘Investigation of the influence of rainfall runoff on shallow landslides in unsaturated soil using a mathematical model’, Water (Switzerland), 11(6). doi: https://doi.org/10.3390/w11061178.

  21. Cifrodelli M et al (2015) The Influence of Climate Change on Heavy Rainfalls in Central Italy. Procedia Earth and Planetary Science 15:694–701. https://doi.org/10.1016/j.proeps.2015.08.097

    Article  Google Scholar 

  22. Coe JA, Godt JW (2012) Review of approaches for assessing the impact of climate change on landslide hazards’, Landslides and Engineered Slopes: Protecting Society through Improved Understanding - Proceedings of the 11th International and 2nd North American Symposium on Landslides and Engineered Slopes, 2012, (December 2017), pp. 371–377

    Google Scholar 

  23. Collobert R, Bengio S (2001) SVMTorch: Support Vector Machines for large-scale regression problems. J Mach Learn Res 1(2):143–160. https://doi.org/10.1162/15324430152733142

    Article  Google Scholar 

  24. Cook, J. et al. (2016) ‘Consensus on consensus: A synthesis of consensus estimates on human-caused global warming’, Environmental Research Letters, 11(4). doi: https://doi.org/10.1088/1748-9326/11/4/048002.

  25. Cramer S et al (2017) ‘An extensive evaluation of seven machine learning methods for rainfall prediction in weather derivatives’, Expert Systems with Applications. Elsevier Ltd 85:169–181. https://doi.org/10.1016/j.eswa.2017.05.029

    Article  Google Scholar 

  26. Dash Y, Mishra SK, Panigrahi BK (2018) Rainfall prediction for the Kerala state of India using artificial intelligence approaches. Computers and Electrical Engineering. Elsevier 70(May):66–73. https://doi.org/10.1016/j.compeleceng.2018.06.004

    Article  Google Scholar 

  27. DeSarbo WS, Cron WL (1988) A maximum likelihood methodology for clusterwise linear regression. J Classif 5(2):249–282. https://doi.org/10.1007/BF01897167

    Article  Google Scholar 

  28. Dehn M et al (2000) Impact of climate change on slope stability using expanded downscaling. Eng Geol 55(3):193–204. https://doi.org/10.1016/S0013-7952(99)00123-4

    Article  Google Scholar 

  29. Diel J, Vogel HJ, Schlüter S (2018) (2019) ‘Impact of wetting and drying cycles on soil structure dynamics.’ Geoderma 345:63–71. https://doi.org/10.1016/j.geoderma.2019.03.018

    Article  CAS  Google Scholar 

  30. Ding Y et al (2007) Detection, causes and projection of climate change over China: An overview of recent progress. Adv Atmos Sci 24(6):954–971. https://doi.org/10.1007/s00376-007-0954-4

    Article  Google Scholar 

  31. Diodato N, Bellocchi G (2018) Using historical precipitation patterns to forecast daily extremes of rainfall for the coming decades in naples (Italy). Geosciences (Switzerland) 8(8):1–11. https://doi.org/10.3390/geosciences8080293

    Article  Google Scholar 

  32. Dixon, N. (no date) ‘( https://dspace.lboro.ac.uk/) by the author and is made available under the following Creative Commons Licence conditions . For the full text of this licence , please go to : Climate change and slope stability in the UK : Challenges and approaches’.

  33. Dutta, P. S. and Tahbilder, H. (2014) ‘Prediction of Rainfall Using Datamining Technique Over Assam’, 5(2), pp. 85–90.

    Google Scholar 

  34. El-Shafie AH et al (2011) Artificial neural network technique for rainfall forecasting applied to Alexandria, Egypt. International Journal of Physical Sciences 6(6):1306–1316. https://doi.org/10.5897/IJPS11.143

    Article  Google Scholar 

  35. Fustos I et al (2020) ‘Rainfall-Induced Landslides forecast using local precipitation and global climate indexes’, Natural Hazards. Springer, Netherlands 102(1):115–131. https://doi.org/10.1007/s11069-020-03913-0

    Article  Google Scholar 

  36. Gaffney, S. and Smyth, P. (1999) ‘[doi 10.1145%2F312129.312198] Gaffney, Scott; Smyth, Padhraic -- [ACM Press the fifth ACM SIGKDD international conference - San Diego, California, United States (1999.08.15–1999.08.18)] Procee.pdf’, pp. 63–72.

    Google Scholar 

  37. García-Escudero, L. A. et al. (2010) ‘Robust clusterwise linear regression through trimming’, Computational Statistics and Data Analysis. Elsevier B.V., 54(12), pp. 3057–3069. doi: https://doi.org/10.1016/j.csda.2009.07.002.

  38. Gariano SL, Guzzetti F (2016) ‘Landslides in a changing climate’, Earth-Science Reviews. The Authors 162:227–252. https://doi.org/10.1016/j.earscirev.2016.08.011

    Article  Google Scholar 

  39. Grenci, L. M. and Nese, J. M. (2006) A World of Weather: Fundamentals of Meteorology. Available at https://he.kendallhunt.com/product/world-weather-fundamentals-meteorology

  40. Gunawardhana LN, Al-Rawas GA, Al-Hadhrami G (2018) ‘Quantification of the changes in intensity and frequency of hourly extreme rainfall attributed climate change in Oman’, Natural Hazards. Springer, Netherlands 92(3):1649–1664. https://doi.org/10.1007/s11069-018-3271-6

    Article  Google Scholar 

  41. Huang JB et al (2012) ‘Debates on the causes of global warming’, Advances in Climate Change Research. Elsevier Masson SAS 3(1):38–44. https://doi.org/10.3724/sp.j.1248.2012.00038

    Article  CAS  Google Scholar 

  42. Huang Z et al (2019) ‘Surface Crack Development Rules and Shear Strength of Compacted Expansive Soil Due to Dry-Wet Cycles’, Geotechnical and Geological Engineering. Springer International Publishing 37(4):2647–2657. https://doi.org/10.1007/s10706-018-00784-y

    Article  Google Scholar 

  43. Huang C-C, Tsai C, Chen Y-H (2002) (2002) ‘Base Stability of Circular Excavations in Soft Clay.’ Journal of Geotechnical and Geoenvironmental Engineering 0241:836–848. https://doi.org/10.1061/(ASCE)1090-0241(2002)128

    Article  Google Scholar 

  44. Huggel, C. et al. (2013) ‘Physical impacts of climate change on landslide occurrence and related adaptation’, Landslides, pp. 121–133. doi: https://doi.org/10.1017/cbo9780511740367.012.

  45. Hung NQ et al (2009) An artificial neural network model for rainfall forecasting in Bangkok, Thailand. Hydrol Earth Syst Sci 13(8):1413–1425. https://doi.org/10.5194/hess-13-1413-2009

    Article  Google Scholar 

  46. IPCC (2001) Climate Change 2001: The Scientific Basis (Online). http://www.ipcc.ch/ (Accessed: 20 June 2020)

  47. Ibrahim B et al (2014) Changes in rainfall regime over Burkina Faso under the climate change conditions simulated by 5 regional climate models. Clim Dyn 42(5–6):1363–1381. https://doi.org/10.1007/s00382-013-1837-2

    Article  Google Scholar 

  48. Inversion, O. (1998) ‘Algorithmica On a Kernel-Based Method for Pattern Recognition ’, New York, pp. 211–231.

    Google Scholar 

  49. Iverson RM (2000) Landslide triggering by rain infiltration. Water Resour Res 36(7):1897–1910. https://doi.org/10.1029/2000WR900090

    Article  Google Scholar 

  50. Jakob, M. and Lambert, S. (2009) ‘Climate change effects on landslides along the southwest coast of British Columbia’, Geomorphology. Elsevier B.V., 107(3–4), pp. 275–284. doi: https://doi.org/10.1016/j.geomorph.2008.12.009.

  51. Jiang R et al (2017) Historical and potential changes of precipitation and temperature of Alberta subjected to climate change impact: 1900–2100. Theoret Appl Climatol 127(3–4):725–739. https://doi.org/10.1007/s00704-015-1664-y

    Article  Google Scholar 

  52. Joseph J, T K, R. (2013) Rainfall Prediction using Data Mining Techniques. International Journal of Computer Applications 83(8):11–15. https://doi.org/10.5120/14467-2750

    Article  Google Scholar 

  53. K. Poorani, K. Brindha (2013) ‘Data Mining Based on Principal Component Analysis for Rainfall Forecasting in India’, International Journal of Advanced Research in Computer Science and Software Engineering, 3(9).

    Google Scholar 

  54. KIN C. LUK, J. E. B. A. A. S. (2001) ‘1-s2.0-S0895717700002727-main.pdf’, 33.

    Google Scholar 

  55. Kashiwao T et al (2017) ‘A neural network-based local rainfall prediction system using meteorological data on the Internet: A case study using data from the Japan Meteorological Agency’, Applied Soft Computing Journal. Elsevier B.V., 56, pp. 317–330. https://doi.org/10.1016/j.asoc.2017.03.015

  56. Khalili, N. et al. (2011) ‘“Daily Rainfall Forecasting for Mashhad Synoptic Station using Artificial Neural Networks”’, 2011 International Conference on Environmental and Computer Science, 19(May 2014), pp. 118–123.

    Google Scholar 

  57. Khandelwal N, Davey R (2012) Climatic Assessment Of Rajasthan ’ s Region For Drought With Concern Of Data Mining Techniques. International Journal of Engineering Research and Application 2(5):1695–1697

    Google Scholar 

  58. Lal M, Meehl GA, Arblaster JM (2000) Simulation of Indian summer monsoon rainfall and its intraseasonal variability in the NCAR climate system model. Reg Environ Change 1(3–4):163–179. https://doi.org/10.1007/s101130000017

    Article  Google Scholar 

  59. Lee, J. et al. (2018) ‘Application of artificial neural networks to rainfall forecasting in the Geum River Basin, Korea’, Water (Switzerland), 10(10). doi: https://doi.org/10.3390/w10101448.

  60. Lee, L. M., Gofar, N. and Rahardjo, H. (2009) ‘A simple model for preliminary evaluation of rainfall-induced slope instability’, Engineering Geology. Elsevier B.V., 108(3–4), pp. 272–285. doi: https://doi.org/10.1016/j.enggeo.2009.06.011.

  61. Li JH, Zhang LM (2011) Study of desiccation crack initiation and development at ground surface. Eng Geol 123(4):347–358. https://doi.org/10.1016/j.enggeo.2011.09.015

    Article  Google Scholar 

  62. Lima CHR, Kwon HH, Kim YT (2018) A local-regional scaling-invariant Bayesian GEV model for estimating rainfall IDF curves in a future climate. Journal of Hydrology. Elsevier 566(September):73–88. https://doi.org/10.1016/j.jhydrol.2018.08.075

    Article  Google Scholar 

  63. Lima, C. H. R., Kwon, H. H. and Kim, J. Y. (2016) ‘A Bayesian beta distribution model for estimating rainfall IDF curves in a changing climate’, Journal of Hydrology. Elsevier B.V., 540, pp. 744–756. doi: https://doi.org/10.1016/j.jhydrol.2016.06.062.

  64. Lin, G. F. and Jhong, B. C. (2015) ‘A real-time forecasting model for the spatial distribution of typhoon rainfall’, Journal of Hydrology. Elsevier B.V., 521, pp. 302–313. doi: https://doi.org/10.1016/j.jhydrol.2014.12.009.

  65. Lin, G. F., Jhong, B. C. and Chang, C. C. (2013) ‘Development of an effective data-driven model for hourly typhoon rainfall forecasting’, Journal of Hydrology. Elsevier B.V., 495, pp. 52–63. doi: https://doi.org/10.1016/j.jhydrol.2013.04.050.

  66. Loginov VF (2014) Global and regional changes of climate: Causes, consequences and adaptation of the economic activities. Geogr Nat Resour 35(1):7–17. https://doi.org/10.1134/S1875372814010028

    Article  Google Scholar 

  67. Loo YY, Billa L, Singh A (2015) Effect of climate change on seasonal monsoon in Asia and its impact on the variability of monsoon rainfall in Southeast Asia. Geoscience Frontiers. Elsevier Ltd 6(6):817–823. https://doi.org/10.1016/j.gsf.2014.02.009

    Article  Google Scholar 

  68. Lu N et al (2012) Analysis of rainfall-induced slope instability using a field of local factor of safety. Water Resour Res 48(9):1–14. https://doi.org/10.1029/2012WR011830

    Article  Google Scholar 

  69. Luo, Y., Y. Ding, Z. Zhao, X. Gao, Y. Xu, and Z. Xie. (2005) ‘Projection of the future anthropogenic climate change in China’. Assessment of Climate and Environment Changes in China (1): Climate and Environment Changes in China and their Projection, Qin et al. Eds., China Science Press, p507–555. (in Chinese)

    Google Scholar 

  70. Maracchi G, Baldi M (2006) Climate change: Causes and medium range perspectives. Vet Res Commun 30(SUPPL. 1):69–74. https://doi.org/10.1007/s11259-006-0015-y

    Article  Google Scholar 

  71. Matteis A (2019) ‘Decomposing the anthropogenic causes of climate change’, Environment, Development and Sustainability. Springer, Netherlands 21(1):165–179. https://doi.org/10.1007/s10668-017-0028-4

    Article  Google Scholar 

  72. May W (2004) Simulation of the variability and extremes of daily rainfall during the Indian summer monsoon for present and future times in a global time-slice experiment. Clim Dyn 22(2–3):183–204. https://doi.org/10.1007/s00382-003-0373-x

    Article  Google Scholar 

  73. May W (2011) The sensitivity of the Indian summer monsoon to a global warming of 2°C with respect to pre-industrial times. Clim Dyn 37(9–10):1843–1868. https://doi.org/10.1007/s00382-010-0942-8

    Article  Google Scholar 

  74. May, W. (2002) ‘Simulated changes of the Indian summer monsoon under enhanced greenhouse gas conditions in a global time-slice experiment’, Geophysical Research Letters, 29(7), pp. 22–1–22–4. doi: https://doi.org/10.1029/2001GL013808.

  75. Meehl GA, Arblaster JM (2003) Mechanisms for projected future changes in south Asian monsoon precipitation. Clim Dyn 21(7–8):659–675. https://doi.org/10.1007/s00382-003-0343-3

    Article  Google Scholar 

  76. Melchiorre C, Frattini P (2012) Modelling probability of rainfall-induced shallow landslides in a changing climate, Otta, Central Norway. Clim Change 113(2):413–436. https://doi.org/10.1007/s10584-011-0325-0

    Article  Google Scholar 

  77. Mirhosseini, G., Srivastava, P. and Stefanova, L. (2013) ‘The impact of climate change on rainfall Intensity-Duration-Frequency (IDF) curves in Alabama’, Regional Environmental Change, 13(SUPPL.1), pp. 25–33. doi: https://doi.org/10.1007/s10113-012-0375-5.

  78. Mislan et al. (2015) ‘Rainfall Monthly Prediction Based on Artificial Neural Network: A Case Study in Tenggarong Station, East Kalimantan - Indonesia’, Procedia Computer Science. Elsevier Masson SAS, 59(Iccsci), pp. 142–151. doi: https://doi.org/10.1016/j.procs.2015.07.528.

  79. Nayak DR, Mahapatra, Amitav\Mishra, P. (2013) A Survey on Rainfall Prediction using Artificial Neural Network. International Journal of Computer Applications 72(16):32–40. https://doi.org/10.5120/12580-9217

    Article  Google Scholar 

  80. Poorani K, Brindha K (2013) Data Mining Based on Principal Component Analysis for Rainfall Forecasting in India. International Journal of Advanced Research in Computer Sciences and Software Engineering 3(9):1254–1256

    Google Scholar 

  81. Rajeevan M et al (2007) New statistical models for long-range forecasting of southwest monsoon rainfall over India. Clim Dyn 28(7–8):813–828. https://doi.org/10.1007/s00382-006-0197-6

    Article  Google Scholar 

  82. Ran Q et al (2018) A modelling study of rainfall-induced shallow landslide mechanisms under different rainfall characteristics. Journal of Hydrology. Elsevier 563(May):790–801. https://doi.org/10.1016/j.jhydrol.2018.06.040

    Article  Google Scholar 

  83. Ran, Q. et al. (2018b) ‘A modelling study of rainfall-induced shallow landslide mechanisms under different rainfall characteristics’, Journal of Hydrology. Elsevier B.V., 563, pp. 790–801. doi: https://doi.org/10.1016/j.jhydrol.2018.06.040.

  84. Rana, A. et al. (2014a) ‘Impact of climate change on rainfall over Mumbai using Distribution-based Scaling of Global Climate Model projections’, Journal of Hydrology: Regional Studies. Elsevier B.V., 1, pp. 107–128. doi: https://doi.org/10.1016/j.ejrh.2014.06.005.

  85. Rana, A. et al. (2014b) ‘Impact of climate change on rainfall over Mumbai using Distribution-based Scaling of Global Climate Model projections’, Journal of Hydrology: Regional Studies. Elsevier B.V., 1(August), pp. 107–128. doi: https://doi.org/10.1016/j.ejrh.2014.06.005.

  86. Robinson JD, Vahedifard F, Aghakouchak A (2017) Rainfall-triggered slope instabilities under a changing climate: Comparative study using historical and projected precipitation extremes. Can Geotech J 54(1):117–127. https://doi.org/10.1139/cgj-2015-0602

    Article  Google Scholar 

  87. Rumelhart, D. E., Hinton, G. E. and Williams, R. J. (1986) ‘Learning internal representations by error propagation. In: Rumelhart D E, McClelland J L et al. (eds.) Parallel Distributed Processing: Explorations in the Microstructure of Cognition.’, MIT Press, Cambridge, MA, 1(V), pp. 318–362. Available at: https://apps.dtic.mil/docs/citations/ADA164453.

  88. Rupa Kumar, K. et al. (2006) ‘High-resolution climate change scenarios for India for the 21st century’, Current Science, 90(3), pp. 334–345

    Google Scholar 

  89. Salimi, M. and Al-Ghamdi, S. G. (2020) ‘Climate change impacts on critical urban infrastructure and urban resiliency strategies for the Middle East’, Sustainable Cities and Society. Elsevier, 54(November 2019), p. 101948. doi: https://doi.org/10.1016/j.scs.2019.101948

  90. Sangelantoni, L., Gioia, E. and Marincioni, F. (2018) Impact of climate change on landslides frequency: the Esino river basin case study (Central Italy), Natural Hazards. Springer Netherlands. https://doi.org/10.1007/s11069-018-3328-6.

  91. Schewe, J. and Levermann, A. (2012) ‘A statistically predictive model for future monsoon failure in India’, Environmental Research Letters, 7(4). https://doi.org/10.1088/1748-9326/7/4/044023.

  92. Shan W et al (2015) The impact of climate change on landslides in southeastern of high-latitude permafrost regions of China. Front Earth Sci 3(February):1–11. https://doi.org/10.3389/feart.2015.00007

    Article  Google Scholar 

  93. Sharma, A. and Goyal, M. K. (2020) ‘Assessment of the changes in precipitation and temperature in Teesta River basin in Indian Himalayan Region under climate change’, Atmospheric Research. Elsevier, 231(August 2019), p. 104670. doi: https://doi.org/10.1016/j.atmosres.2019.104670

  94. Shrivastava G et al (2012) Application of Artificial Neural Networks in Weather Forecasting: A Comprehensive Literature Review. International Journal of Computer Applications 51(18):17–29. https://doi.org/10.5120/8142-1867

    Article  Google Scholar 

  95. Sidle, R.C. (2007) ‘Using weather and climate information for landslide prevention and mitigation’, Climate and land degradation, p285–307.

    Google Scholar 

  96. Singhrattna N et al (2005) Seasonal forecasting of Thailand summer monsoon rainfall. Int J Climatol 25(5):649–664. https://doi.org/10.1002/joc.1144

    Article  Google Scholar 

  97. Snapshot, C. S. (2016) ‘TEACR Engineering Assessment’, (September), pp. 1–77.

    Google Scholar 

  98. Sohn KT et al (2005) Statistical prediction of heavy rain in South Korea. Adv Atmos Sci 22(5):703–710. https://doi.org/10.1007/BF02918713

    Article  Google Scholar 

  99. Solomon, M. (2013) ‘The structure of scientific revolutions (Thomas S. Kuhn, 1970, 2nd ed. Chicago, London: University of Chicago Press Ltd. 210 pages)’, Philosophical Papers and Review, 4(4), pp. 41–48. doi: https://doi.org/10.5897/ppr2013.0102.

  100. Späth H (1979) Algorithm 39 Clusterwise linear regression. Computing 22(4):367–373. https://doi.org/10.1007/BF02265317

    Article  Google Scholar 

  101. Stern DI, Kaufmann RK (2014) Anthropogenic and natural causes of climate change. Clim Change 122(1–2):257–269. https://doi.org/10.1007/s10584-013-1007-x

    Article  Google Scholar 

  102. Stirling RA, Glendinning S, Davie CT (2017) Modelling the deterioration of the near surface caused by drying induced cracking. Applied Clay Science. Elsevier 146(June):176–185. https://doi.org/10.1016/j.clay.2017.06.003

    Article  CAS  Google Scholar 

  103. Stocker, T., Qin, D., Plattner, G. K., Tignor, M. Allen, S. K., Boschung, J., Nauels, A., Xia, Y., Bex, V. and Midgley, P. M. (2014) Climate Change 2013: The physical science basis (Online). Available at: http://www.ipcc.ch/

  104. Tang, C. S. et al. (2011) ‘Desiccation and cracking behaviour of clay layer from slurry state under wetting-drying cycles’, Geoderma. Elsevier B.V., 166(1), pp. 111–118. doi: https://doi.org/10.1016/j.geoderma.2011.07.018.

  105. Trafalis TB et al (2002) Data mining techniques for improved WSR-88D rainfall estimation. Comput Ind Eng 43(4):775–786. https://doi.org/10.1016/S0360-8352(02)00139-0

    Article  Google Scholar 

  106. Turkington T et al (2016) Assessing debris flow activity in a changing climate. Climatic Change. Climatic Change 137(1–2):293–305. https://doi.org/10.1007/s10584-016-1657-6

    Article  Google Scholar 

  107. Vamsidhar E et al (2010) Prediction of rainfall using backpropagation neural network model. International Journal on Computer Science and Engineering 2(4):1119–1121

    Google Scholar 

  108. Vapnik, V. (1998) Statistical Learning Theory. Available at: https://www.wiley.com/en-my/Statistical+Learning+Theory-p-9780471030034 (Accessed: 22 June 2020)

  109. Vathsala H, Koolagudi SG (2015) ‘Closed Item-Set Mining for Prediction of Indian Summer Monsoon Rainfall A Data Mining Model with Land and Ocean Variables as Predictors’, Procedia Computer Science. Elsevier Masson SAS 54:271–280. https://doi.org/10.1016/j.procs.2015.06.032

    Article  Google Scholar 

  110. Vathsala H, Koolagudi SG (2016) (2017) ‘Prediction model for peninsular Indian summer monsoon rainfall using data mining and statistical approaches.’ Computers and Geosciences. Elsevier 98:55–63. https://doi.org/10.1016/j.cageo.2016.10.003

    Article  Google Scholar 

  111. Vaughan, P. R., Kovacevic, N. and Ridley, A. M. (2002) ‘The influence of climate and climate change on the stability of embankment dam slopes.’, Reservoirs in a Changing World, pp. 353–366. doi: https://doi.org/10.1680/riacw.31395.0028.

  112. Vijayavenkataraman S, Iniyan S, Goic R (2012) ‘A review of climate change, mitigation and adaptation’, Renewable and Sustainable Energy Reviews. Elsevier Ltd 16(1):878–897. https://doi.org/10.1016/j.rser.2011.09.009

    Article  Google Scholar 

  113. Vishal, S. and Sudesh, C. (2016) ‘Monsoon Rain Fall Prediction of Haryana 2016 on the Basis of Historical Data’, pp. 608–612.

    Google Scholar 

  114. Wang X, Shi G (2001) Numerical investigation on the climate effects of anthropogenic sulfate aerosols. Plateau Meteorology 20(3):258–263 (in Chinese)

    Google Scholar 

  115. Wayllace, A. et al. (2019) ‘Hydrological behavior of an infiltration-induced landslide in Colorado, USA’, Geofluids, 2019. doi: https://doi.org/10.1155/2019/1659303

  116. Wei SC et al (2018) Potential impact of climate change and extreme events on slope land hazard - A case study of Xindian watershed in Taiwan. Nat Hazard 18(12):3283–3296. https://doi.org/10.5194/nhess-18-3283-2018

    Article  Google Scholar 

  117. Xiang, Y. et al. (2018) ‘A SVR–ANN combined model based on ensemble EMD for rainfall prediction’, Applied Soft Computing Journal. Elsevier B.V., 73, pp. 874–883. doi: https://doi.org/10.1016/j.asoc.2018.09.018.

  118. Yang Y et al (2007) A data mining approach for heavy rainfall forecasting based on satellite image sequence analysis. Comput Geosci 33(1):20–30. https://doi.org/10.1016/j.cageo.2006.05.010

    Article  Google Scholar 

  119. Zaw, W. and Naing, T. (2008) ‘Empirical Statistical Modeling of Rainfall Prediction over Myanmar’, World Acad Sci Eng Technol, 2(10), pp. 565–568. Available at: http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.193.4595&rep=rep1&type=pdf.

  120. Zhang, J., Zhu, D. and Zhang, S. (2020) ‘Shallow slope stability evolution during rainwater infiltration considering soil cracking state’, Computers and Geotechnics. Elsevier, 117(July 2018), p. 103285. doi: https://doi.org/10.1016/j.compgeo.2019.103285

  121. Zhao Z, Ding Y, Luo Y, Wang S (2005) Recent studies on attributions of climate change in China. Acta Meteor Sin 19:389–400

    Google Scholar 

  122. Zhao Z, S, Wang, Y. Xu, G. Ren, Y. Luo, and X. Gao. (2005) Attribution of the 20th century climate warming in China. Climatic and Environmental Research 10(4):808–817 (in Chinese)

    Google Scholar 

  123. Zhou T, Yu R (2006) 20th century surface air temperature over China and the globe simulated by coupled climate models. J Climate 19(22):5843–5858

    Article  Google Scholar 

  124. Zhou T, Zhao Z (2006) Attribution of the climate warming in China for the 20th century. Adv Clim Chang Res 2(1):28–31 (in Chinese)

    Google Scholar 

Download references

Acknowledgements

This study is funded by the Fundamental Research Grant Scheme (FRGS), Malaysia, Ref no.: FRGS/1/2019/TK01/UNIM/02/2. The first author would also like to thank the University of Nottingham Malaysia for providing a full scholarship for his Ph.D. study.

Author information

Authors and Affiliations

  1. Faculty of Science and Engineering, University of Nottingham Malaysia, 43500, Selangor, Malaysia

    Jun Lim Wong, Min Lee Lee, Fang Yenn Teo & Kian Wah Liew

Authors
  1. Jun Lim Wong
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Min Lee Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Fang Yenn Teo
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Kian Wah Liew
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Min Lee Lee .

Editor information

Editors and Affiliations

  1. Department of Civil Engineering, National Institute of Technology Karnataka, Surathkal, India

    Sreevalsa Kolathayar

  2. Department of Civil Engineering, Indian Institute of Technology Bombay, Mumbai, Maharashtra, India

    Arpita Mondal

  3. Department of Civil and Environmental Engineering, National University of Singapore, Singapore, Singapore

    Siau Chen Chian

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Wong, J.L., Lee, M.L., Teo, F.Y., Liew, K.W. (2022). A Review of Impacts of Climate Change on Slope Stability. In: Kolathayar, S., Mondal, A., Chian, S.C. (eds) Climate Change and Water Security. Lecture Notes in Civil Engineering, vol 178. Springer, Singapore. https://doi.org/10.1007/978-981-16-5501-2_13

Download citation

  • DOI: https://doi.org/10.1007/978-981-16-5501-2_13

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-16-5500-5

  • Online ISBN: 978-981-16-5501-2

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation