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Regional-scale landslide susceptibility assessment for the hilly state of Uttarakhand, NW Himalaya, India

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Abstract

Landslide and related mass movement activities are common and one of the most destructive natural hazards in the mountainous terrain including the Himalayas. Of the 11 administrative states in the Indian Himalayan region, the state of Uttarakhand has witnessed enhanced activities of these phenomena. It is therefore essential to understand the regional scale landslide susceptibility assessment of the state and in the present study, landslide susceptibility mapping for the entire state has been carried out using bivariate weight of evidence and information value methods which depict that around 51% of the area is located in the high and very high landslide susceptible zones, 22–23% in the moderate and ~26–27% in the low and very low landslide susceptible zones, and slopes ranging between 40° and 60°, located at an elevation of 2000–4000 m, facing towards southern sides and covered with limestone, gneiss, quartzite and phyllite, have higher propensity towards development of landslides in the region.

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References

  • Abella E A C and Van Weste C J 2008 Qualitative landslide susceptibility assessment by multicriteria analysis: A case study from San Antonio del Sur, Guantánamo, Cuba; Geomorphology 94(3–4) 453–466.

    Google Scholar 

  • Agterberg Bonham-Carter G F, Cheng Q M and Wright D F 1993 Weights of evidence modeling and weighted logistic regression for mineral potential mapping; Comput. Geol. 25 13–32.

    Google Scholar 

  • Bonham-Carter G F 1994 Geographic information systems for geoscientists – modeling with GIS; Pergamon Press, 416p.

  • Brabb E E 1984 Innovative approaches to landslide hazard mapping; Proc. 4th International Symposium on Landslides, Toronto 1 307–324.

  • Cao Y, Wei X, Fan W, Nan Y, Xiong W and Zhang S 2021 Landslide susceptibility assessment using the weight of evidence method: A case study in Xunyang area, China; PLoS ONE 16(1) e0245668.

    Google Scholar 

  • Carrara A, Cardinali M, Detti R, Guzzetti F, Pasqui V and Reichenbach P 1991 GIS techniques and statistical models in evaluating landslide hazard; Earth Surf. Process. Landf. 16(5) 427–445.

    Google Scholar 

  • Carrara A, Cardinali M, Guzzetti F and Reichenbach P 1995 GIS technology in mapping landslide hazard; In: Geographical Information Systems in assessing natural hazards, pp. 135–175.

  • Champatiray P K 1996 Landslide hazard zonation using fuzzy logic and probability analysis in western Himalayas; Project report under IIRS-ITC Programme, Internal Publication, ITC, Netherlands.

  • Chaudhary S, Gupta V and Sundriyal Y P 2010 Surface and sub-surface characterization of Byung landslide in Mandakini valley, Garhwal Himalaya; Him. Geol. 31(2) 125–132.

    Google Scholar 

  • Chauhan S, Sharma M and Arora M K 2010 Landslide susceptibility zonation of the Chamoli region, Garhwal Himalayas, using logistic regression model; Landslides 7(4) 411–423.

    Google Scholar 

  • Chen T, Niu R and Jia X 2016 A comparison of information value and logistic regression models in landslide susceptibility mapping by using GIS; Environ. Earth Sci. 75(10) 867.

    Google Scholar 

  • Chung C J F and Fabbri A G 1999 Probabilistic prediction models for landslide hazard mapping; Photogram. Eng. Remote Sens. 65(12) 1389–1399.

    Google Scholar 

  • Corsini A, Cervi F and Ronchetti F 2009 Weight of evidence and artificial neural networks for potential groundwater spring mapping: An application to the Mt. Modino area (Northern Apennines, Italy); Geomorphology 111(1–2) 79–87.

  • DMMC https://dmmc.uk.gov.in/pages/display/96-landslide-zone.

  • DST 2011 Landslide Hazard Zonation Atlas; Department of Science and Technology, Govt. of India.

  • Fleiss J L 1991 Statistical methods for rates and proportions; Wiley.

    Google Scholar 

  • Gupta R P and Joshi B C 1990 Landslide hazard zoning using the GIS approach – a case study from the Ramganga catchment, Himalayas; Eng. Geol. 28(1–2) 119–131.

    Google Scholar 

  • Gupta V and Bist K S 2004 The 23 September 2003 Varunavat Parvat landslide in Uttarkashi township, Uttaranchal; Curr. Sci. 87(11) 1600–1605.

    Google Scholar 

  • Gupta V, Bhasin R K, Kaynia A M, Kumar V, Saini A S, Tandon R S and Pabst T 2016 Finite element analysis of failed slope by shear strength reduction technique: A case study for Surabhi Resort Landslide, Mussoorie township, Garhwal Himalaya; Geomat. Nat. Hazards Risk 7(5) 1677–1690.

    Google Scholar 

  • Gupta V, Tandon R S, Venkateshwarlu B, Bhasin R K and Kaynia A M 2017 Accelerated mass movement activities due to increased rainfall in the Nainital township, Kumaun Lesser Himalaya, India; Zeitschrift Fur Geomorph. 61(1) 29–42.

    Google Scholar 

  • Guri P K and Patel R C 2015 Spatial prediction of landslide susceptibility in parts of Garhwal Himalaya, India, using the weight of evidence modelling; Environ. Monit. Assess. 187(6) 1–25.

    Google Scholar 

  • Guzzetti F, Salvati P and Stark C P 2005 Evaluation of risk to the population posed by natural hazards in Italy; In: Landslide risk management Hungr O, Fell R, Couture R and Eberhardt E (eds) Taylor & Francis Group, London, pp. 381–389.

    Google Scholar 

  • Guzzetti F, Carrara A, Cardinali M and Reichenbach P 1999 Landslide hazard evaluation: A review of current techniques and their application in a multi-scale study, Central Italy; Geomorphology 31(1–4) 181–216.

    Google Scholar 

  • Guzzetti F, Reichenbach P, Ardizzone F, Cardinali M and Galli M 2006 Estimating the quality of landslide susceptibility models; Geomorphology 81(1–2) 166–184.

    Google Scholar 

  • Haque U, Da Silva P F, Devoli G, Pilz J, Zhao B, Khaloua A, Wilopo W, Andersen P, Lu P, Lee J and Yamamoto T 2019 The human cost of global warming: Deadly landslides and their triggers (1995–2014); Sci Total Environ. 682 673–684.

    Google Scholar 

  • Heim A and Gansser A 1939 Central Himalayan geological observations of the Swiss expeditions 1936; Helv. Sci. Nat. 73(1) 1–245.

    Google Scholar 

  • Jamir I, Gupta V, Kumar V and Thong G T 2018 Evaluation of Potential Surface Instability in Kharsali Village, Yamuna Valley, NW Himalaya; J. Mount. Sci. 14(8) 1666–1676.

    Google Scholar 

  • Jamir I, Gupta V, Thong G T and Kumar V 2020 Litho-tectonic and precipitation implications on landslides, Yamuna valley, NW Himalaya; Phys. Geogr. 41(4) 365–388.

    Google Scholar 

  • Kumar R and Anbalagan R 2015 Landslide susceptibility zonation in part of Tehri reservoir region using frequency ratio, fuzzy logic and GIS; J. Earth Syst. Sci. 124(2) 431–448.

    Google Scholar 

  • Kumar S and Gupta V 2021 Evaluation of spatial probability of landslides using bivariate and multivariate approaches in the Goriganga valley, Kumaun Himalaya, India; Nat. Hazards, doi: https://doi.org/10.1007/s11069-021-04928-x.

  • Kumar V, Jamir I, Gupta V and Bhasin R K 2021 Inferring potential landslide damming using slope stability, geomorphic constraints and run-out analysis: Case study from the NW Himalaya; Earth Surf. Dyn. 9 351–377.

    Google Scholar 

  • Kumari S, Haustein K, Javid H, Burton C, Allen M R, Paltan H, Dadson S and Otto F E 2019 Return period of extreme rainfall substantially decreases under 1.5°C and 2.0°C warming: A case study for Uttarakhand, India; Environ. Res. Lett. 14(4) 044033.

    Google Scholar 

  • Kundu V and Patel R C 2019 Susceptibility status of landslides in Yamuna valley, Uttarakhand, NW-Himalaya, India; Him. Geol. 40(1) 30–49.

    Google Scholar 

  • Lee S 2005 Application of logistic regression model and its validation for landslide susceptibility mapping using GIS and remote sensing data; Int. J. Remote Sens. 26(7) 1477–1491.

    Google Scholar 

  • Lee S 2019 Current and future status of GIS-based landslide susceptibility mapping: A literature review; Korean J. Remote Sens. 35(1) 179–193.

    Google Scholar 

  • Martha T R, Roy P, Govindharaj K B, Kumar K V, Diwakar P G and Dadhwal V K 2015 Landslides triggered by the June 2013 extreme rainfall event in parts of Uttarakhand state, India; Landslides 12(1) 135–146.

    Google Scholar 

  • Mathew J, Jha V K and Rawat G S 2009 Landslide susceptibility zonation mapping and its validation in part of Garhwal Lesser Himalaya, India, using binary logistic regression analysis and receiver operating characteristic curve method; Landslides 6(1) 17–26.

    Google Scholar 

  • NRSA 2001 Atlas - Landslide Hazard Zonation Mapping in the Himalayas of Uttarakhand and Himachal Pradesh states using remote sensing and GIS techniques. National Remote Sensing Agency, Department of Space, Hyderabad.

  • Onagh M, Kumra V K and Rai P K 2012 Landslide susceptibility mapping in a part of Uttarkashi district (India) by multiple linear regression method; Int. J. Geol., Earth and Environ. Sci. 2(2) 102–120.

  • Ozdemir A and Altural T 2013 A comparative study of frequency ratio, weights of evidence and logistic regression methods for landslide susceptibility mapping: Sultan Mountains, SW Turkey; J. Asian Earth Sci. 64 180–197.

    Google Scholar 

  • Petley D 2012 Global patterns of loss of life from landslides; Geology 40(10) 927–930.

    Google Scholar 

  • Pradhan S P and Siddique T 2020 Stability assessment of landslide-prone road cut rock slopes in Himalayan terrain: A finite element method based approach; J. Rock Mech. Geotech. Eng. 12(1) 59–73.

    Google Scholar 

  • Ram P, Gupta V, Devi M and Vishwakarma N 2020 Landslide susceptibility mapping using bivariate statistical method for the hilly township of Mussoorie and its surrounding areas, Uttarakhand Himalaya; J. Earth Syst. Sci. 129(1) 1–18.

    Google Scholar 

  • Ram P and Gupta V 2021 Landslide hazard, vulnerability and risk assessment (HVRA), Mussoorie township, Lesser Himalaya, India; Environ. Develop. Sustain., https://doi.org/10.1007/s10668-021-01449-2.

  • Sarkar S, Roy A K and Martha T R 2013 Landslide susceptibility assessment using information value method in parts of the Darjeeling Himalayas; J. Geol. Soc. India 82(4) 351–362.

    Google Scholar 

  • Sati S P, Naithani A and Rawat G S 1998 Landslides in the Garhwal Lesser Himalaya, UP, India; Environmentalist 18(3) 149–155.

    Google Scholar 

  • Sevgen E, Kocaman S, Nefeslioglu H A and Gokceoglu C 2019 A novel performance assessment approach using photogrammetric techniques for landslide susceptibility mapping with logistic regression, ANN and random forest; Sensors 19(18) 3940.

    Google Scholar 

  • Shano L, Raghuvanshi T K and Meten M 2020 Landslide susceptibility evaluation and hazard zonation techniques – a review; Geoenviron. Disasters 7 1–19.

    Google Scholar 

  • Sharma S and Mahajan A K 2019 A comparative assessment of information value, frequency ratio and analytical hierarchy process models for landslide susceptibility mapping of a Himalayan watershed, India; Bull. Eng. Geol. Environ. 78(4) 2431–2448.

    Google Scholar 

  • Solanki A, Gupta V, Bhakuni S S, Ram P and Joshi M 2019 Geological and geotechnical characterisation of the Khotila landslide in the Dharchula region, NE Kumaun Himalaya; J. Earth Syst. Sci. 128(4) 1–14.

    Google Scholar 

  • Tandon R S, Gupta V and Venkateshwarlu B 2021 Geological, geotechnical, and GPR investigations along the Mansa Devi hill-bypass (MDHB) Road, Uttarakhand, India; Landslides 18 849–863, https://doi.org/10.1007/s10346-020-01546-9.

    Article  Google Scholar 

  • Thakur V C 1992 Geology of western Himalaya; Phys. Chem. Earth 19 1–355.

    Google Scholar 

  • van Westen C J, Castellanos E and Kuriakose S L 2008 Spatial data for landslide susceptibility, hazard, and vulnerability assessment: An overview; Eng. Geol. 102(3–4) 112–131.

    Google Scholar 

  • Yin K L and Yan T Z 1988 Statistical prediction models for instability of metamorphosed rocks; In: Int. Symp. on Landslides 5 1269–1272.

  • Yu C and Chen J 2020 Landslide susceptibility mapping using the slope unit for southeastern Helong City, Jilin Province, China: A comparison of ANN and SVM; Symmetry 12(6) 1047.

    Google Scholar 

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Acknowledgements

The authors thank the Director, Wadia Institute of Himalayan Geology, Dehradun for his constant encouragement to carry out the present study and publish the paper. Funding was provided by Department of Science and Technology, Govt of India (Grant Number DST/SPLICE/CCP/NMSHE/TF-3/WIHG/2015(G)).

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V Gupta conceived the idea. V Gupta, S Kumar, R Kaur and R S Tandon helped in data collection. S Kumar, R Kaur analyzed the data. All the authors contributed towards writing the manuscript.

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Correspondence to Vikram Gupta.

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Communicated by Navin Juyal

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Gupta, V., Kumar, S., Kaur, R. et al. Regional-scale landslide susceptibility assessment for the hilly state of Uttarakhand, NW Himalaya, India. J Earth Syst Sci 131, 2 (2022). https://doi.org/10.1007/s12040-021-01746-4

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