Earth Systems and Environment

, Volume 3, Issue 3, pp 575–584 | Cite as

Estimation of Rainfall-Induced Landslides Using the TRIGRS Model

  • Abhirup Dikshit
  • Neelima Satyam
  • Biswajeet PradhanEmail author
Original Article


Rainfall-induced landslides have become the biggest threat in the Indian Himalayas and their increasing frequency has led to serious calamities. Several models have been built using various rainfall characteristics to determine the minimum rainfall amount for landslide occurrences. The utilisation of such models depends on the quality of available landslide and rainfall data. However, these models do not consider the effect of local soil, geology, hydrology and topography, which varies spatially. This study is to analyse the triggering process for shallow landslides using physical-based models for the Indian Himalayan region. This research focuses on the utilisation and dependability of physical models in the Kalimpong area of Darjeeling Himalayas, India. The approach utilised the transient rainfall infiltration and grid-based regional slope-stability (TRIGRS) model, which is a widely used model in assessing the variations in pore water pressure and determining the change in the factor of safety. TRIGRS uses an infinite slope model to calculate the change in the factor of safety for every pixel. Moreover, TRIGRS is used to compare historical rainfall scenarios with available landslide database. This study selected the rainfall event from 30th June to 1st July 2015 as input for calibration because the amount of rainfall in this period was higher than the monthly average and caused 18 landslides. TRIGRS depicted variations in the factor of safety with duration before, during and after the heavy rainfall event in 2015. This study further analysed the landslide event and evaluated the predictive capability using receiver operating characteristics. The model was able to successfully predict 71.65% of stable pixels after the landslide event, however, the availability of more datasets such as hourly rainfall, accurate time of landslide event would further improve the results. The results from this study could be replicated and used in other unstable Indian Himalayan regions to establish an operational landslide early warning system.


Shallow landslides Physical models GIS Rainfall threshold Kalimpong 



This research was supported by the Department of Science & Technology (DST), New Delhi, with Grant no. NRDMS/02/31/015(G). We are also thankful to Mr. Praful Rao, President, Save The Hills, Kalimpong for logistical support during field visit. The authors acknowledge the two anonymous reviewers for their useful comments and suggestions.


  1. Althuwaynee OF, Pradhan B, Omar N (2015) Estimation of rainfall threshold and its use in landslide hazard mapping of Kuala Lumpur metropolitan and surrounding areas. Landslides 12(5):861–875. CrossRefGoogle Scholar
  2. Baum RL, Savage WZ, Godt JW (2002) TRIGRS—a Fortran program for transient rainfall infiltration and grid-based regional slope-stability analysis, US Geological Survey, Open-File Report 02-424Google Scholar
  3. Baum RL, Savage WZ, Godt JW (2008) TRIGRS—a Fortran program for transient rainfall infiltration and grid-based regional slope-stability analysis, version 2.0, US Geological Survey, Open-File Report 2008-1159Google Scholar
  4. Chatterjee R (2010) Landslide hazard zonation mapping of Kalimpong. VDM Verlag Dr. Muller GmbH & Co, CologneGoogle Scholar
  5. 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 36–75Google Scholar
  6. Dikshit A, Satyam DN (2018) Estimation of rainfall thresholds for landslide occurrences in Kalimpong, India. Innov Infrastruct Solut 3:24CrossRefGoogle Scholar
  7. Dikshit A, Satyam N (2019) Probabilistic rainfall thresholds in Chibo, India: estimation and validation using monitoring system. J Mt Sci 16(4):870–883CrossRefGoogle Scholar
  8. Dikshit A, Satyam DN, Towhata I (2018a) Early warning system using tilt sensors in Chibo, Kalimpong, Darjeeling Himalayas, India. Nat Hazards 94(2):727–741CrossRefGoogle Scholar
  9. Dikshit A, Sarkar R, Satyam N (2018b) Probabilistic approach toward Darjeeling Himalayas landslides—a case study. Cogent Eng 5:1–11CrossRefGoogle Scholar
  10. Dikshit A, Sarkar R, Pradhan B, Acharya S, Dorji K (2019) Estimating rainfall thresholds for landslide occurrence in the Bhutan Himalayas. Water 11:1616CrossRefGoogle Scholar
  11. Fawcett T (2006) An introduction to ROC analysis. Pattern Recogn Lett 27(8):861–874CrossRefGoogle Scholar
  12. Gardner WR (1958) Some steady-state solutions of the unsaturated moisture flow equation with application to evaporation from a water table. Soil Sci 85(4):228–231CrossRefGoogle Scholar
  13. Ghosh S, Ghoshal TB, Mukherjee S, Bhowmik S (2016) Landslide compendium on Darjeeling-Sikkim Himalayas, India. In: Geological Survey of India. GSI, India (ISSN0 254-0436)Google Scholar
  14. Ghosh S, van Westen CJ, Carranza EJM, Jetten VG (2012) Integrating spatial, temporal, and magnitude probabilities for medium-scale landslide risk analysis in Darjeeling Himalayas, India. Landslides 9(3):371–384CrossRefGoogle Scholar
  15. Ghoshal TB, Sarkar NK, Ghosh S, Surendranath M (2008) GIS based landslide susceptibility mapping-a study from Darjeeling-Kalimpong area, Eastern Himalaya, India. J Geol Soc India 72:763–773Google Scholar
  16. Guzzetti F, Peruccacci S, Rossi M, Stark CP (2007) Rainfall thresholds for the initiation of landslides in Central and southern Europe. Meteorol Atmos Phys 98(3–4):239–267CrossRefGoogle Scholar
  17. Guzzetti F, Peruccacci S, Rossi M, Stark CP (2008) The rainfall intensity–duration control of shallow landslides and debris flows: an update. Landslides 5(1):3–17CrossRefGoogle Scholar
  18. Iverson RM (2000) Landslide triggering by rain infiltration. Water Resour Res 36:1897–1910CrossRefGoogle Scholar
  19. Jensen DT, Hargreaves GH, Temesgen B, Allen RG (1997) Computation of ETo under nonideal conditions. J Irrig Drain Eng 123(5):394–400CrossRefGoogle Scholar
  20. Kanungo DP, Sharma S (2014) Rainfall thresholds for prediction of shallow landslides around Chamoli-Joshimath region, Garhwal Himalayas, India. Landslides 11(4):629–638CrossRefGoogle Scholar
  21. Kim D, Im S, Lee SH et al (2010) Predicting the rainfall-triggered landslides in a forested mountain region using TRIGRS model. J Mt Sci 7(1):83–91CrossRefGoogle Scholar
  22. Kuriakose SL, van Beek LPH, van Westen CJ (2009) Parameterizing a physically based shallow landslide model in a data poor region. Earth Surf Process Landf 34(6):867–881CrossRefGoogle Scholar
  23. Montrasio L, Valentino R, Losi GL (2011) Towards a real-time susceptibility assessment of rainfall-induced shallow landslides on a regional scale. Nat Hazards Earth Syst Sci 11:1927–1947CrossRefGoogle Scholar
  24. Mukherjee A, Mitra AK (2001) Geotechnical study of mass movements along the Kalimpong approach road in the Eastern Himalayas. Indian J Geol 73(4):271–279Google Scholar
  25. Park DW, Nikhil NV, Lee SR (2013) Landslide and debris flow susceptibility zonation using TRIGRS for the 2011 Seoul landslide event. Nat Hazards Earth Syst Sci 13:2833–2849CrossRefGoogle Scholar
  26. Rao P (2009) Landslide hazard case study: the dire need for a comprehensive, long term solution to the landslide problem at Chibo—Pashyor villages, Kalimpong, District Darjeeling, W Bengal. Proceedings of Second India Disaster Management Congress, DelhiGoogle Scholar
  27. Schaap MG, Leij FJ, van Genuchten MT (2001) ROSETTA: a computer program for estimating soil hydraulic parameters with hierarchical pedotransfer functions. J Hydrol 251(3–4):163–176CrossRefGoogle Scholar
  28. Schilirò L, Esposito C, Scarascia Mugnozza G (2015) Evaluation of shallow landslide-triggering scenarios through a physically based approach: an example of application in the southern Messina area (northeastern Sicily, Italy). Nat Hazards Earth Syst Sci 15:2091–2109CrossRefGoogle Scholar
  29. Segoni S, Piciullo L, Gariano SL (2018) A review of the recent literature on rainfall thresholds for landslide occurrence. Landslides 15(8):1483–1501CrossRefGoogle Scholar
  30. Sengupta A, Gupta S, Anbarasu K (2010) Rainfall thresholds for the initiation of landslide at Lanta Khola in north Sikkim, India. Nat Hazards 52(1):31–42CrossRefGoogle Scholar
  31. Šimůnek J, Huang M, Šejna M, van Genuchten M (1998) The HYDRUS-1D software package for simulating the one dimensional movement of water, heat, and multiple solutes in variably-saturated media. Version 1.0, International Ground Water Modeling Center, Colorado School of Mines, Golden, ColoradoGoogle Scholar
  32. Surendranath M, Ghosh S, Ghoshal TB, Rajendran N (2008) Landslide hazard zonation in darjeeling Himalayas: a case study on integration of IRS and SRTM data. In: Nayak S, Zlatanova S (eds) Remote sensing and GIS technologies for monitoring and prediction of disasters. Environmental science and engineering (environmental science). Springer, BerlinGoogle Scholar
  33. Teja TS, Dikshit A, Satyam N (2019) Determination of rainfall thresholds for landslide prediction using an algorithm-based approach: case study in Darjeeling Himalayas, India. Geosciences 9:302CrossRefGoogle Scholar
  34. Weidner L, Oommen T, Escobar-Wolf R et al (2018) Regional-scale back-analysis using TRIGRS: an approach to advance landslide hazard modeling and prediction in sparse data regions. Landslides 15(12):2343–2356CrossRefGoogle Scholar
  35. Zizioli D, Meisina C, Valentino R, Montrasio L (2013) Comparison between different approaches to modeling shallow landslide susceptibility: a case history in Oltrepo Pavese, Northern Italy. Nat Hazards Earth Syst Sci 13:559–573CrossRefGoogle Scholar

Copyright information

© King Abdulaziz University and Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Discipline of Civil EngineeringIndian Institute of Technology IndoreIndoreIndia
  2. 2.Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS), Faculty of Engineering and Information TechnologyUniversity of Technology SydneySydneyAustralia

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