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Mapping debris flow susceptibility using analytical network process in Kodaikkanal Hills, Tamil Nadu (India)

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Abstract

Rapid debris flows, a mixture of unconsolidated sediments and water travelling at speeds > 10 m/s are the most destructive water related mass movements that affect hill and mountain regions. The predisposing factors setting the stage for the event are the availability of materials, type of materials, stream power, slope gradient, aspect and curvature, lithology, land use and land cover, lineament density, and drainage. Rainfall is the most common triggering factor that causes debris flow in the Palar subwatershed and seismicity is not considered as it is a stable continental region and moderate seismic zone. Also, there are no records of major seismic activities in the past. In this study, one of the less explored heuristic methods known as the analytical network process (ANP) is used to map the spatial propensity of debris flow. This method is based on top-down decision model and is a multi-criteria, decision-making tool that translates subjective assessment of relative importance to weights or scores and is implemented in the Palar subwatershed which is part of the Western Ghats in southern India. The results suggest that the factors influencing debris flow susceptibility in this region are the availability of material on the slope, peak flow, gradient of the slope, land use and land cover, and proximity to streams. Among all, peak discharge is identified as the chief factor causing debris flow. The use of micro-scale watersheds demonstrated in this study to develop the susceptibility map can be very effective for local level planning and land management.

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References

  • Aleotti P and Chowdhury R 1999 Landslide hazard assessment: Summary, review and new perspectives; Bull. Eng. Geol. Environ. 58(1) 21–44.

    Article  Google Scholar 

  • Althuwaynee O F, Pradhan B and Ahmad 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.

    Article  Google Scholar 

  • Arnold J G, Moriasi D N, Gassman P W, Abbaspour K C, White M J, Srinivasan R, Santhi C, Harmel R D, van Griensven A, Van Liew M W, Kannan N and Jha M K 2012 SWAT: Model use, calibration, and validation; Trans. ASABE 55(4) 1491–1508.

    Article  Google Scholar 

  • Baeza C and Corominas J 2001 Assessment of shallow landslide susceptibility by means of multivariate statistical techniques; Earth Surf. Process. Landf. 26 1251–1263.

  • Bhaskar Rao Y J, Chetty T R K, Janardhan A S and Gopalan K 1996 Sm–Nd and Rb–Sr ages and P–T history of the Archean–Sittampundi and Bhavani layered meta-anorthosite complexes in Cauvery shear zone, south India – evidence for Neoproterozoic reworking of Archean crust; Contrib. Mineral. Petrol. 125(2–3) 237–250.

    Google Scholar 

  • Bhaskar Rao Y J, Janardhan A S, Vijaya Kumar T, Narayana B L, Dayal A M, Taylor P N and Chetty T R K 2003 Sm–Nd model ages and Rb–Sr isotope systematics of charnockites and gneisses across the Cauvery shear zone, southern India: Implications for the Archaean–Neoproterozoic boundary in the southern granulite terrain; In: Tectonics of Southern Granulite Terrain (ed.) Ranmakrishnan M, Geol Soc. India 50 297–317.

  • Blahut J, van Westen J and Sterlacchini S 2010 Analysis of landslide inventories for accurate prediction of debris flow source areas; Geomorphology  119(1) 36–51.

    Article  Google Scholar 

  • Bottero M, Comino E and Riggio V 2011 Application of the analytic hierarchy process and the analytic network process for the assessment of different wastewater treatment systems; Environ. Model. Softw.  26 1211–1224.

    Article  Google Scholar 

  • Brakensiek D L and Rawls W J 1983 Green-Ampt infiltration model parameters for hydrologic classification of soils; In: Proceedings of ASCE, Specialty Conference (eds) Borelli J, Hasfurther V R and Burman R D, New York, pp. 226–233.

  • Carrara A, Corsta G and Frattini P 2008 Comparing models of debris flow susceptibility in the alpine environment; Geomorphology  94(3–4) 353–378.

    Article  Google Scholar 

  • Clark C, Collins A S, Kinny P D, Timms N E and Chetty T R K 2009 SHRIMP U–Pb age constraints on charnockite magmatism and high-grade metamorphism in the Salem Block, southern India; Gondwana Res.  16(1) 27–36.

    Article  Google Scholar 

  • Cruden D M and Varnes D J 1996 Landslide types and processes; In: Landslides: Investigations and Mitigation (eds) Turner A K and Schuster R L, Special Report 247, Transportation Research Board, US National Research Council, Washington DC, pp. 36–75.

  • D’Ambrosio D, Spataro W and Iovine G 2006 Parallel genetic algorithms for optimising cellular automata models of natural complex phenomena: An application to debris-flows; Comput. Geosci.  32 861–875.

    Article  Google Scholar 

  • Fell R, Coromina J, Bonnard C, Cascini L, Leroi E and Savage W 2008 Guidelines for landslide susceptibility, hazard and risk zoning for land use planning; Eng. Geol.  102 85–98.

    Article  Google Scholar 

  • Fernandez Merodo J A, Pastor M, Mira P, Tonni L, Herreros M I, Gonzalez E and Tamagnini R 2004 Modeling of diffuse failure mechanisms of catastrophic landslides; Comput. Methods Appl. Mech. Eng.  193(27–29) 2911–2939.

    Article  Google Scholar 

  • Gabet E J and Mudd S M 2006 Mobilization of debris flow from shallow landslides; Geomorphology  74 204–218.

    Article  Google Scholar 

  • Ghosh J G, Dewit M J and Zartman R E 2004 Age and tectonic evolution of Neoproterozoic ductile shear zones in the southern granulite terrain of India, with implications for Gondwana studies; Tectonics  23(3) 1–38.

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Harris N B W, Bartlett J M and Santosh M 1996 Neodymium isotope constraints on the tectonic evolution of east Gondwana; \(J\). Southeast Asian Earth Sci.  14(3–4) 119–125.

    Article  Google Scholar 

  • Hungr O, McDougall S, Wise M and Cullen M 2008 Magnitude–frequency relationships of debris flows and debris avalanches in relation to slope relief; Geomorphology  96(3) 355–365.

    Article  Google Scholar 

  • Hürlimann M, Rickenmann D, Medina V and Bateman A 2008 Evaluation of approaches to calculate debris-flow parameters for hazard assessment; Eng. Geol.  102 152–163.

    Article  Google Scholar 

  • Irigaray C, Ferna’ndez T, El Hamdouni R and Chaco’n J 2007 Evaluation and validation of landslide-susceptibility maps obtained by a GIS matrix method: Examples from the Betic Cordillera (southern Spain); Nat. Hazards  41 61–79.

    Article  Google Scholar 

  • Iverson N R, Mann J E and Iverson R M 2010 Effects of soil aggregates on debris-flow mobilization: Results from ring-shear experiments; Eng. Geol.  114 84–92.

    Article  Google Scholar 

  • Keefer D K and Larsen M C 2007 Assessing landslide hazards; Science  316(5828) 1136–1137.

    Article  Google Scholar 

  • Liang W, Zhuang D, Jiang D, Pan J and Ren H 2012 Assessment of debris flow hazards using a Bayesian Network; Geomorphology  171–172 94–100.

    Article  Google Scholar 

  • Lorente A, Begueria S, Bathurst J C and Garcia-Ruiz J M 2003 Debris flow characteristics and relationships in the Central Spanish Pyrenees; Nat. Hazards Earth Syst. Sci.  3 683–692.

    Article  Google Scholar 

  • Lucà F, D’Ambrosio D, Robustelli G, Rongo R and Spataro W 2014 Integrating geomorphology, statistic and numerical simulations for landslide invasion hazard scenarios mapping: An example in the Sorrento peninsula (Italy); Comput. Geosci.  67 163–172.

    Article  Google Scholar 

  • Magliulo P, Di Lisio A, Russo F and Zelano A 2008 Geomorphology and landslide susceptibility assessment using GIS and bivariate statistics: A case study in southern Italy; Nat. Hazards  47 411–435.

    Article  Google Scholar 

  • McMillan P, Brown D J, Forster A and Winter M G 2005 Debris flow information sources; In: Scottish Road Network Landslides Study (eds) Winter M G, Macgregor F and Shackman L, The Scottish Executive, Edinburgh, pp. 25–44.

  • Meyer N K, Schwanghart W, Korup O, Romstad B and Etzelmüller B 2014 Estimating the topographic predictability of debris flows; Geomorphology  207 114–125.

    Article  Google Scholar 

  • Neaupane K M and Piantanakulchai M 2006 Analytical network model for landslide hazard zonation; Eng. Geol.  85 281–294.

    Article  Google Scholar 

  • Okano K, Suwa H and Kanno T 2012 Characterization of debris flows by rainstorm condition at a torrent on the Mount Yakedake volcano, Japan; Geomorphology  136(1) 88–94.

    Article  Google Scholar 

  • Plavsa D, Collins A S, Foden J F, Kropinski L, Santosh M, Chetty T R K and Clark C 2012 Delineating crustal domains in peninsular India: Age and chemistry of orthopyroxene-bearing felsic gneisses in the Madurai block; Precamb. Res.  198–199 77–93.

    Article  Google Scholar 

  • Quan Luna B, Blahut J, van Westen C J, Sterlacchini S, van Asch T W J and Akbas S O 2011 The application of numerical debris flow modelling for the generation of physical vulnerability curves; Nat. Hazards Earth Syst. Sci.  11 2047–2060.

    Article  Google Scholar 

  • Saaty R W and Saaty T L 2003 Decision making in complex environments: The Analytic Hierarchy Process (AHP) for decision making and the Analytic Network Process (ANP) for decision making with dependence and feedback; Pittsburgh, PA: SuperDecisions for Software for Decision Making with Dependence and Feedback, SuperDecisions Software, Creative Decisions Foundation, Ellsworth Avenue.

  • Saaty T L 1980 The Analytic Hierarchy Process; McGraw-Hill, New York, NY.

    Google Scholar 

  • Saaty T L 1999 Fundamentals of the analytic network process; In: Proceedings of International Symposium on Analytical Hierarchy Process, Kobe, Japan, pp. 48–63.

  • Saaty T L 2001 Decision Making with Dependence and Feedback: The Analytic Network Process; 2nd edn, RWS Publications, Pittsburgh, PA, 370p.

    Google Scholar 

  • Saaty T L 2005 Theory and Applications of the Analytic Network Process; RWS Publications, Pittsburgh, PA, 352p.

    Google Scholar 

  • Saaty T L and Vargas L G 2006 Decision Making with the Analytic Network Process; Springer Science, New York, NY, 333p.

    Google Scholar 

  • Shieh C L, Chen Y S, Tsai Y J and Wu J H 2009 Variability in rainfall threshold for debris flow after the Chi–Chi earthquake in central Taiwan, China; Int. J. Sedim. Res.  24 177–188.

    Article  Google Scholar 

  • Sujatha E R, Kumaravel P and Rajamanickam G V 2012 Landslide susceptibility mapping using remotely sensed data through conditional probability analysis using seed cell and point sampling techniques; J. Indian Soc. Remote Sens.  40(4) 669–678.

    Article  Google Scholar 

  • Sujatha E R and Rajamanickam G V 2011 Landslide susceptibility mapping of Tevankarai Ar subwatershed, Kodaikkanal Taluk, India, using weighted similar choice fuzzy model; Nat. Hazards  59 401–425.

    Article  Google Scholar 

  • Sujatha E R and Rajamanickam G V 2015 Landslide hazard and risk mapping using weighted linear combination applied to Tevankarai Stream Watershed, Kodaikkanal, India; Hum. Ecol. Risk Assess.  21(6) 1445–1461.

    Article  Google Scholar 

  • Tang C, Zhu J, Li W L and Liang J T 2009 Rainfall triggered debris flow following the Wenchuan earthquake; Bull. Eng. Geol. Environ.  68(2) 187–194.

    Article  Google Scholar 

  • Tomson J K, Bhaskar Rao Y J, Vijaya Kumar T and Mallikharjuna Rao J 2006 Charnockite genesis across the Archaean–Proterozoic terrane boundary in the South Indian Granulite Terrain: Constraints from major-trace element geochemistry and Sr–Nd isotopic systematics; Gondwana Res.  10(1–2) 115–127.

    Article  Google Scholar 

  • van Westen C J, van Asch T W J and Soeters R 2006 Landslide hazard and risk zonation – why is it still so difficult? Bull. Eng. Geol. Environ.  65 167–184.

    Article  Google Scholar 

  • Winter M G 2013 DS 4.4 Lead discussion: Landslides; In: Geotechnics of Hard Soils–Weak Rocks: Proc. 15th European Conference on Soil Mech. and Geotech. Eng., Athens, Greece (eds) Anagnostopoulos A, Pachakis M and Tsatsanifos C, IOS Press, Amsterdam, The Netherlands, vol. 4.

  • Yilmaz I 2009 A case study from Koyulhisar (Sivas–Turkey) for landslide susceptibility mapping by artificial neural networks; Bull. Eng. Geol. Environ.  68 297–306.

    Article  Google Scholar 

  • Zhen C, Heng L and Wong C T C 2005 Environal planning: Analytic Network Process Model for environmentally conscious construction planning; J. Constr. Eng. Manag.  131 92–101.

    Article  Google Scholar 

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Acknowledgements

We would like to thank the Vice Chancellor, SASTRA University and Virginia Tech for facilitating the collaboration. We acknowledge Kumar Mallikarjunan for providing the opportunity and Mirz Billah for GIS modeling at Virginia Tech.

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Authors and Affiliations

  1. Centre for Advanced Research on Environment, School of Civil Engineering, SASTRA University, Thanjavur, 613 401, India

    Evangelin Ramani Sujatha

  2. Biological Systems Engineering Department, Virginia Tech, Blacksburg, VA, 24061, USA

    Venkataramana Sridhar

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  1. Evangelin Ramani Sujatha
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  2. Venkataramana Sridhar
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Correspondence to Evangelin Ramani Sujatha.

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Corresponding editor: Rajib Maity

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Sujatha, E.R., Sridhar, V. Mapping debris flow susceptibility using analytical network process in Kodaikkanal Hills, Tamil Nadu (India). J Earth Syst Sci 126, 116 (2017). https://doi.org/10.1007/s12040-017-0899-7

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  • DOI: https://doi.org/10.1007/s12040-017-0899-7

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