Advertisement

Journal of Mountain Science

, Volume 16, Issue 4, pp 870–883 | Cite as

Probabilistic rainfall thresholds in Chibo, India: estimation and validation using monitoring system

  • Abhirup DikshitEmail author
  • Neelima Satyam
Article
  • 22 Downloads

Abstract

The Himalayan region has been severely affected by landslides especially during the monsoons. In particular, Kalimpong region in Darjeeling Himalayas has recorded several landslides and has caused significant loss of life, property and agricultural land. The study region, Chibo has experienced several landslides in the past which were mainly debris and earth slide. Globally, several types of rainfall thresholds have been used to determine rainfall-induced landslide incidents. In this paper, probabilistic thresholds have been defined as it would provide a better understanding compared to deterministic thresholds which provide binary results, i.e., either landslide or no landslide for a particular rainfall event. Not much research has been carried out towards validation of rainfall thresholds using an effective and robust monitoring system. The thresholds are then validated using a reliable system utilizing Microelectromechanical Systems (MEMS) tilt sensor and volumetric water content sensor installed in the region. The system measures the tilt of the instrument which is installed at shallow depths and is ideal for an early warning system for shallow landslides. The change in observed tilt angles due to rainfall would give an understanding of the applicability of the probabilistic model. The probabilities determined using Bayes’ theorem have been calculated using the rainfall parameters and landslide data in 2010–2016. The rainfall values were collected from an automatic rain gauge setup near the Chibo region. The probabilities were validated using the MEMS based monitoring system setup in Chibo for the monsoon season of 2017. This is the first attempt to determine probabilities and validate it with a robust and effective monitoring system in Darjeeling Himalayas. This study would help in developing an early warning system for regions where the installation of monitoring systems may not be feasible.

Keywords

Early warning Probabilistic thresholds Kalimpong Monitoring 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Notes

Acknowledgement

The authors are extremely grateful to the Department of Science & Technology (DST), New Delhi for funding the research project Landslide hazard assessment and monitoring at Chibo Pashyar, Kalimpong (Grant No. NRDMS/02/31/015(G)). We thank Praful Rao, President, Save The Hills for great support in logistics. We are also thankful to Prof. Ikuo Towhata, Tokyo University, Japan, Rajat Singh and Yeshu Sharma, International Institute of Information Technology, Hyderabad for technical and GIS expertise. The authors acknowledge the two anonymous reviewers for their useful comments and suggestions.

References

  1. Agterberg FP, Bonham-Carter GF, Wright DF (1990) Statistical pattern integration for mineral exploration, Computer Applications in Resource Estimation, Prediction and Assessment of Metals and Petroleum (G. Gaal and D.F. Merriam, editors), Pergamon Press, New York. pp 1–21.Google Scholar
  2. Bean, MA (2009) Probability: The Science of Uncertainty with Applications to Investments, Insurance, and Engineering, 448 pp., American Mathematical Society, Providence, R. I.Google Scholar
  3. Bureau of Indian Standards, 2002a. Seismic Zonation Map of India: IS: 1893 (Part — I); Revised.Google Scholar
  4. Berti M, Martina MLV, Franceschini S, et al. (2012) Probabilistic rainfall thresholds for landslide occurrence using a Bayesian approach. Journal of Geophysical Research: Earth Surface 117: F04006. https://doi.org/10.1029/2012JF002367 CrossRefGoogle Scholar
  5. Chang KT, Chiang SH, Lei F (2008) Analysing the relationship between Typhoon-triggered landslides and critical rainfall conditions. Earth Surface Processes and Landforms 33: 1261–1271. https://doi.org/10.1002/esp.1611 CrossRefGoogle Scholar
  6. Chung CF, Fabbri AG (1999) Probabilistic prediction models for landslide hazard mapping. Photogrammetric Engineering and Remote Sensing 65–12: 1389–1399.Google Scholar
  7. Costanzo S, Di Massa G, Costanzo A, et al. (2016) Software-defined radar system for landslides monitoring. New Advances in Information Systems and Technologies 445: 325–331. https://doi.org/10.1007/978-3-319-31307-8_34 CrossRefGoogle Scholar
  8. Cruden DM, Varnes DJ (1996) Landslide types and processes. In: Turner AK, Schuster RL (eds.), Landslides: investigation and mitigation. Transportation Research Board special report 247. National Academy Press, Washington DC. pp 36–75.Google Scholar
  9. Dikshit A, Satyam DN (2018) Estimation of rainfall thresholds for landslide occurrences in Kalimpong, India. Innovative Infrastructure Solutions 3: 24. https://doi.org/10.1007/s41062-018-0132-9 CrossRefGoogle Scholar
  10. Dikshit A, Satyam N (2017) Rainfall Thresholds for the Prediction of Landslides using Empirical Methods in Kalimpong, Darjeeling, India. In: Workshop on Advances in Landslide Understanding, JTC1, Barcelona. pp 255–259.Google Scholar
  11. Dikshit A, Satyam N, Towhata I (2018) Early warning system using tilt sensors in Chibo Kalimpong, Darjeeling Himalayas, India. Natural Hazards 94: 727. https://doi.org/10.1007/s11069-018-3417-6 CrossRefGoogle Scholar
  12. Do H, Yin K (2018) Rainfall Threshold Analysis and Bayesian Probability Method for Landslide Initiation Based on Landslides and Rainfall Events in the Past. Open Journal of Geology 8: 674–696. https://doi.org/10.4236/ojg.2018.87040 CrossRefGoogle Scholar
  13. Dowling CA, Santi PM (2014) Debris flows and their toll on human life: a global analysis of debis-flow fatalities from 1950 to 2011. Natural Hazards 71: 203. https://doi.org/10.1007/s11069-013-0907-4 CrossRefGoogle Scholar
  14. Frodella W, Salvatici T, Pazzi V, et al. (2017) GB-InSAR monitoring of slope deformations in a mountainous area affected by debris flow events. Natural Hazards and Earth System Sciences 17: 1779–1793. https://doi.org/10.5194/nhess-17-1779-2017 CrossRefGoogle Scholar
  15. Gariano SL, Sarkar R, Dikshit A, et al. (2018) Automatic calculation of rainfall thresholds for landslide occurrence in Chukha Dzongkhag, Bhutan. Bulletin of Engineering Geology and the Environment. https://doi.org/10.1007/s10064-018-1415-2
  16. Ghosh S, Carranza EJM, van Westen CJ, et al. (2011) Selecting and weighting spatial predictors for empirical modeling of landslide susceptibility in the Darjeeling Himalayas (India). Geomorphology 131: 35–56. https://doi.org/10.1016/j.geomorph.2011.04.019 CrossRefGoogle Scholar
  17. Glade T, Crozier M, Smith P (2000) Applying probability determination to refine landslide-triggering rainfall thresholds using an empirical Antecedent Daily Rainfall Model. Pure and Applied Geophysics 157: 1059–1079. https://doi.org/10.1007/s000240050017 CrossRefGoogle Scholar
  18. González A, Caetano E (2017) Probabilistic rainfall thresholds for landslide episodes in the Sierra Norte De Puebla, Mexico. Natural Resources 8: 254–267. https://doi.org/10.4236/nr.2017.83014 CrossRefGoogle Scholar
  19. Guzzetti F, Peruccacci S, Rossi M, et al. (2007) Rainfall thresholds for the initiation of landslides in central and southern Europe Meteorology and Atmospheric Physics 98: 239–267. https://doi.org/10.1007/s00703-007-0262-7 CrossRefGoogle Scholar
  20. Kanungo DP, Sharma S (2014) Rainfall thresholds for prediction of shallow landslides around Chamoli-Joshimath region, Garhwal Himalayas, India. Landslides 11(4): 629–638. https://doi.org/10.1007/s10346-013-0438-9 CrossRefGoogle Scholar
  21. Lagomarsino D, Segoni S, Rosi A, et al. (2015) Quantitative comparison between two different methodologies to define rainfall thresholds for landslide forecasting. Natural Hazards and Earth System Sciences 15: 2413–2423. https://doi.org/10.5194/nhess-15-2413-2015 CrossRefGoogle Scholar
  22. Marques R, Zêzere J, Trigo R, et al. (2008) Rainfall patterns and critical values associated with landslides in Povoşcão County (São Miguel Island, Azores): relationships with the North Atlantic Oscillation. Hydrological Processes 22: 478–494. https://doi.org/10.1002/hyp.6879 CrossRefGoogle Scholar
  23. Melillo M, Brunetti MT, Peruccacci S, et al. (2018) A tool for the automatic calculation of rainfall thresholds for landslide occurrence. Environmental Modelling and Software 105: 230–243. https://doi.org/10.1016/j.envsoft.2018.03.024 CrossRefGoogle Scholar
  24. Refice, A, Capolongo D (2004) Probabilistic modeling of uncertainties in earthquake-induced landslide hazard assessment. Computers and Geosciences 28: 735–749. https://doi.org/10.1016/S0098-3004(01)00104-2 CrossRefGoogle Scholar
  25. Rosi A, Lagomarsino D, Rossi G, et al. (2015) Updating EWS rainfall thresholds for the triggering of landslides. Natural Hazards 78: 297–308. https://doi.org/10.1007/s11069-015-1717-7 CrossRefGoogle Scholar
  26. Spiegelhalter DJ (1986) A statistical view of uncertainty in expert systems, Artificial Intelligence and Statistics, (W.A. Gale, editor), Addison-Wesley Publ. Co., Reading, Massachusetts. pp 17–55.Google Scholar
  27. Staley DM, Kean JW, Cannon SH, et al. (2013) Objective definition of rainfall intensity-duration thresholds for the initiation of post-fire debris flows in southern California. Landslides 10: 547–562. https://doi.org/10.1007/s10346-012-0341-9 CrossRefGoogle Scholar
  28. Sumantra SB, Raghunath P (2016) Causes of Landslides in Darjeeling Himalayas during June-July, 2015. Journal of Geography and Natural Disasters 6: 173. https://doi.org/10.4172/2167-0587.1000173 Google Scholar
  29. Tessari G, Floris M, Pasquali P (2017) Phase and amplitude analyses of SAR data for landslide detection and monitoring in non-urban areas located in the North-Eastern Italian pre-Alps. Environmental Earth Sciences 76: 85. https://doi.org/10.1007/s12665-017-6403-5 CrossRefGoogle Scholar
  30. Uchimura T, Towhata I, Wang L, et al. (2009) Development of low-cost early warning system of slope instability for civilian use. In: Proceedings of the 17th ISSMGE, Alexandria. vol. 3, pp 1897–1900.Google Scholar
  31. Uchimura T, Towhata I, Trinh TLA, et al. (2010) Simple monitoring method for precaution of landslides watching tilting and water contents on slopes surface. Landslides 7(3): 351–358. https://doi.org/10.1007/s10346-009-0178-z CrossRefGoogle Scholar
  32. Uchimura T, Towhata I, Wang L, et al. (2015) Precaution and early warning of surface failure of slopes using tilt sensors. Soils and Foundations 55(5): 1086–1099. https://doi.org/10.1016/j.sandf.2015.09.010 CrossRefGoogle Scholar
  33. Vessia G, Pisano L, Vennari C, et al. (2016) Mimic expert judgement through automated procedure for selecting rainfall events responsible for shallow landslide: a statistical approach to validation. Computers and Geosciences 86: 146–153. https://doi.org/10.1016/j.cageo.2015.10.015 CrossRefGoogle Scholar
  34. Wilson RC, Wieczorek GF (1995), Rainfall thresholds for the initiation of debris flows at La Honda, California. Environmental and Engineering Geoscience 1: 11–2. https://doi.org/10.2113/gseegeosci.I.1.11 CrossRefGoogle Scholar
  35. Yang Z, Cai H, Shao W, et al. (2018) Clarifying the hydrological mechanisms and thresholds for rainfall-induced landslide: in situ monitoring of big data to unsaturated slope stability analysis. Bulletin of Engineering Geology and the Environment. https://doi.org/10.1007/s10064-018-1295-5
  36. Yin Y, Zheng W, Liu Y, et al. (2010) Integration of GPS with InSAR to monitoring of the Jiaju landslide in Sichuan, China. Landslides 7(3): 359–365. https://doi.org/10.1007/s10346-010-0225-9 CrossRefGoogle Scholar

Copyright information

© Science Press, Institute of Mountain Hazards and Environment, CAS and Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Discipline of Civil EngineeringIndian Institute of Technology Indore SimrolIndoreIndia

Personalised recommendations