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Landslides

, Volume 14, Issue 3, pp 961–980 | Cite as

Performance evaluation of a physically based model for shallow landslide prediction

  • Jui-Yi Ho
  • Kwan Tun LeeEmail author
Original Paper

Abstract

Evaluating the performance of a physically based model for landslide prediction was conducted in this study. The model was developed based on the basis of the infinite slope instability analysis and TOPMODEL for saturated water level estimation, which enabled to predict the location and time of occurrence of shallow landslides. Field data from 2008 to 2013 in two areas vulnerable to landslide in Taiwan were collected to test the applicability of the model for landslide prediction. Three indexes including the probability of detection (POD), false alarm ratio (FAR), and threat score (TS) were adopted to assess the advantages and disadvantages of the model. The results indicated that the POD for the landslide prediction by using the proposed model was 1.00, the FAR was lower than 0.25, and the overall TS value was higher than 0.75. It is promising to apply the proposed model for landslide early warnings to reduce the loss of life and property.

Keywords

Shallow landslide Performance evaluation Infinite slope instability analysis Factor of safety Rainfall threshold TOPMODEL 

References

  1. Baum RL, Savage WZ, Godt JW (2008) TRIGRS—a fortran program for transient rainfall infiltration and grid-based regional slope-stability analysis, VersionGoogle Scholar
  2. Beven KJ (1997) TOPMODEL: A critique. Hydrol Process 11:1069–1085CrossRefGoogle Scholar
  3. Beven KJ, Kirkby MJ (1979) A physically based variable contributing area model of basin hydrology. Hydrol Sci Bull 24(1):43–69CrossRefGoogle Scholar
  4. Borga M, Fontana GD, Cazorzi F (2002) Analysis of topographic and climatic control on rainfall-triggeredshallow landsliding using a quasi-dynamic wetness index. J Hydrol 268:56–71CrossRefGoogle Scholar
  5. Caine N (1980) The rainfall intensity-duration control of shallow landslides and debris flows. Geogr Ann 62:23–27CrossRefGoogle Scholar
  6. Cannon SH, Ellen S (1985) Rainfall conditions for abundant debris avalanches San Francisco Bay region. Calif Geol 38:267–272Google Scholar
  7. Capparelli G, Versace P (2011) FLaIR and SUSHI: two mathematical models for early warning of landslides induced by rainfall. Landslides 8:67–79Google Scholar
  8. Casadei M, Dietrich WE, Miller NL (2003) Testing a model for predicting the time and location of shallow landslide initiation in soil-mantled landscapes. Earth Surf Process landf 28:925–950CrossRefGoogle Scholar
  9. Catani F, Segoni S, Falorni G (2010) An empirical geomorphology-based approach to the spatial prediction of soil thickness at catchment scale. Water Resour Res 46:W05508CrossRefGoogle Scholar
  10. Delmonaco G, Leoni G, Margottini C, Puglisi C, Spizzichino D (2003) Large scale debris-flow hazard assessment: a geotechnical approach and GIS modelling. Nat Hazards Earth Syst Sci 3:443–455CrossRefGoogle Scholar
  11. Dietrich WE, Reiss R, Hus M, Montgomery DR (1995) A process-based model for colluvial soil depth and shallow landslideing using digital elevation data. Hydrol Process 9:383–400CrossRefGoogle Scholar
  12. Dunne T, Black RD (1970) Partial area contributions to storm runoff in a small New England watershed. Water Resour Res 6:1296–1311CrossRefGoogle Scholar
  13. Heimsath A, Dietrich W, Nishizumi K, Finkel R (2001) Stochastic processes of soil production and transport: erosion rates, topographic variation and cosmogenic nuclides in the Oregon coast range. Earth Surf Process Landf 26:531–552CrossRefGoogle Scholar
  14. Ho J-Y, Lee KT, Chang T-C, Wang Z-Y, Liao Y-H (2012) Influences of spatial distribution of soil thickness on shallow landslide prediction. Eng Geol 124:38–46CrossRefGoogle Scholar
  15. Hong Y, Adler R, Huffman G (2006) Evaluation of the potential of NASA multi-satellite precipitation analysis in global landslide hazard assessment. Geophys Res Lett 33:L22402CrossRefGoogle Scholar
  16. Iverson RM (2000) Landslide triggering by rain infiltration. Water Resour Res 36(7):1897–1910CrossRefGoogle Scholar
  17. Jakob M, Owen T, Simpson T (2012) A regional real-time debris-flow warning system for the District of North Vancouver, Canada. Landslides 9:165–178CrossRefGoogle Scholar
  18. Keefer DK, Wilson RC, Mark RK, Brabb EE, Brown W, Ellen SD, Harp EL, Wieczorek GF, Alger CS, Zatkin RS (1987) Real time landslide warning during heavy rainfall. Science 238:921–925CrossRefGoogle Scholar
  19. Kirkby MJ (1975) Hydrograph modelling strategies. In: Peel R, Chisholm M, Haggett P (eds) Process in physical and human geography, pp 69–90Google Scholar
  20. Lee KT (1998) Generating design hydrographs by DEM assisted geomorphic runoff simulation: a case study. J Am Water Resour Assoc 34(2):375–384Google Scholar
  21. Lee KT, Ho J-Y (2009) Prediction of landslide occurrence based on slope instability analysis and hydrological model simulation. J Hydrol 375:489–497CrossRefGoogle Scholar
  22. Lo C-M, Lee C-F, Chou H-T (2013) Landslide at Su-Hua highway 115.9 k triggered by Typhoon Megi in Taiwan. Landslides 11(2):293–304CrossRefGoogle Scholar
  23. Lu N, Godt J (2008) Infinite slope stability under steady unsaturated seepage conditions. Water Resour Res 44:W11404Google Scholar
  24. Montgomery DR, Dietrich WE (1994) A physically based model for the topographic control on shallow landsliding. Water Resour Res 30(4):1153–1171CrossRefGoogle Scholar
  25. Montgomery DR, Sullivan K, Greenberg HM (1998) Regional test of a model for shallow landsliding. Hydrol Process 12:943–955CrossRefGoogle Scholar
  26. Park HJ, Lee JH, Woo I (2013) Assessment of rainfall-induced shallow landslide susceptibility using a GIS-based probabilistic approach. Eng Geol 161:1–15CrossRefGoogle Scholar
  27. Salciarini D, Godt JW, Savage WZ, Conversini P, Baum RL, Michael JA (2006) Modeling regional initiation of rainfall-induced shallow landslides in the eastern Umbria region of central Italy. Landslides 3:181–194CrossRefGoogle Scholar
  28. Saulnier GM, Beven KJ, Obled C (1997) Including spatially variable effective soil depths in TOPMODEL. J Hydrol 202:158–172CrossRefGoogle Scholar
  29. Schaefer JT (1990) The critical success index as an indicator of warning skill. Weather Forecast 5:570–575CrossRefGoogle Scholar
  30. Skempton AW, Delory FA (1957) Stability of natural slopes in London clay. ASCE J 2:378–381Google Scholar
  31. Takara K, Yamashiki Y, Sassa K, Ibrahim HF (2010) A distributed hydrological-geotechnical model using satellite-derived rainfall estimates for shallow landslide prediction system at a catchment scale. Landslides 7:237–258CrossRefGoogle Scholar
  32. Van Westen CJ, Terlien MTJ (1996) An approach towards deterministic landslide hazard analysis in GIS – a case study from Manizales (Colombia). Earth Surf Process Landf 21:853–868CrossRefGoogle Scholar
  33. Wieczorek GF (1987) Effect of rainfall intensity and duration on the debris flows in central Santa Cruz Mountains California. Geol Soc Am Rev Eng Geol 7:93–104CrossRefGoogle Scholar
  34. Wilks DS (2005) Statistical methods in the atmospheric sciences, 2nd edn. ElsevierGoogle Scholar
  35. World Bank (2005) Natural disaster hotspots: a global risk analysis. World Bank Group, Washington, DCGoogle Scholar
  36. Wu W, Sidle R (1995) A distributed slope stability model for steep forested basins. Water Resour Res 31(8):2097–2110CrossRefGoogle Scholar
  37. 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

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  1. 1.National Applied Research LaboratoriesTaiwan Typhoon and Flood Research InstituteTaipeiTaiwan
  2. 2.Department of River & Harbor EngineeringNational Taiwan Ocean UniversityKeelungTaiwan

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