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Improved rainfall threshold for landslides in data sparse and diverse geomorphic milieu: a cluster analysis based approach

Natural Hazards volume 103, pages 639–657 (2020)Cite this article

Abstract

Rainfall-triggered landslides are the most common type of mass movement seen along the tropical belt due to the prevalence of monsoons. These landslides can be forecasted by understanding the spatial and temporal rainfall distribution patterns, and subsequent generation of rainfall threshold (RT). However, deriving a regional RT in a geologically, geographically and physiographically diverse milieu is a formidable task. The data on spatial and intra-seasonal variability of monsoons can be widely dispersed in such diversified terrains. Clustering analysis provides a promising approach to handle such widely dispersed data. This study intends to develop a methodology using 2-stage clustering process to create RT in such terrains by using daily rainfall versus antecedent rainfall and rainfall versus antecedent rainfall versus soil depth. Sixteen rainfall-induced landslides, located in different terrains in the Western Ghats of India, were subjected to this analysis. Majority of the landslides were modeled, and different RTs were derived for different conditions. The landslides belong to four different classes, viz., landslides occurring at steep slopes; those occurring at knickpoints of highland and midland; in the plateau region and others characterized by a thin veneer of soil. Out of 16 landslides subjected to RT, this method was able to model 13 landslides with a success rate of 81.25%, which is a fair figure.

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(Source of image in b: Arc Earth; Landslide locations are mentioned in Table 1)

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(Profile source: Google Earth)

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Acknowledgements

The authors acknowledge the different agencies that provided rainfall data. Sajinkumar extend thanks to the officers of the Geological Survey of India for facilitating visits to the landslide sites. Sajinkumar thank the University Grants Commission (UGC), Government of India (Sanction No. F.No. 5-56/2016(IC) for granting Raman Postdoctoral Fellowship which enabled him to carry out the Postdoctoral Research work at Michigan Technological University, USA. Facility of LERIS, University of Kerala, India (Joint venture of University of Kerala and Indian Space Research Organization) has also been utilized for this work.

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Authors and Affiliations

  1. Department of Geology, University of Kerala, Thiruvananthapuram, Kerala, 695 581, India

    K. S. Sajinkumar, T. Oommen & C. L. Vishnu

  2. Department of Geological and Mining Engineering and Sciences, Michigan Technological University, 1400 Townsend Drive, Houghton, MI, 49931, USA

    K. S. Sajinkumar, T. Oommen & C. L. Vishnu

  3. Department of Civil Engineering and Environment, University of Texas at Arlington, 701 S Nedderman Drive, Arlington, TX, 76019, USA

    S. Rinu

  4. Geological Survey of India, Thiruvananthapuram, Kerala, 695 013, India

    K. R. Praveen & C. Muraleedharan

  5. Central Ground Water Board, Thiruvananthapuram, Kerala, 695 004, India

    V. R. Rani

Authors
  1. K. S. Sajinkumar
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  2. S. Rinu
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  3. T. Oommen
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  4. C. L. Vishnu
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  5. K. R. Praveen
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  6. V. R. Rani
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  7. C. Muraleedharan
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Correspondence to K. S. Sajinkumar.

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Sajinkumar, K.S., Rinu, S., Oommen, T. et al. Improved rainfall threshold for landslides in data sparse and diverse geomorphic milieu: a cluster analysis based approach. Nat Hazards 103, 639–657 (2020). https://doi.org/10.1007/s11069-020-04004-w

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  • DOI: https://doi.org/10.1007/s11069-020-04004-w

Keywords

  • Cluster analysis
  • Rainfall threshold analysis
  • Tropical landslides
  • Western Ghats
  • Landslide characterization

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