Natural Hazards

, Volume 58, Issue 1, pp 325–339 | Cite as

Evaluation of TRIGRS (transient rainfall infiltration and grid-based regional slope-stability analysis)’s predictive skill for hurricane-triggered landslides: a case study in Macon County, North Carolina

  • Zonghu Liao
  • Yang Hong
  • Dalia Kirschbaum
  • Robert F. Adler
  • Jonathan J. Gourley
  • Rick Wooten
Original Paper


The key to advancing the predictability of rainfall-triggered landslides is to use physically based slope-stability models that simulate the transient dynamical response of the subsurface moisture to spatiotemporal variability of rainfall in complex terrains. TRIGRS (transient rainfall infiltration and grid-based regional slope-stability analysis) is a USGS landslide prediction model, coded in Fortran, that accounts for the influences of hydrology, topography, and soil physics on slope stability. In this study, we quantitatively evaluate the spatiotemporal predictability of a Matlab version of TRIGRS (MaTRIGRS) in the Blue Ridge Mountains of Macon County, North Carolina where Hurricanes Ivan triggered widespread landslides in the 2004 hurricane season. High resolution digital elevation model (DEM) data (6-m LiDAR), USGS STATSGO soil database, and NOAA/NWS combined radar and gauge precipitation are used as inputs to the model. A local landslide inventory database from North Carolina Geological Survey is used to evaluate the MaTRIGRS’ predictive skill for the landslide locations and timing, identifying predictions within a 120-m radius of observed landslides over the 30-h period of Hurricane Ivan’s passage in September 2004. Results show that within a radius of 24 m from the landslide location about 67% of the landslide, observations could be successfully predicted but with a high false alarm ratio (90%). If the radius of observation is extended to 120 m, 98% of the landslides are detected with an 18% false alarm ratio. This study shows that MaTRIGRS demonstrates acceptable spatiotemporal predictive skill for landslide occurrences within a 120-m radius in space and a hurricane-event-duration (h) in time, offering the potential to serve as a landslide warning system in areas where accurate rainfall forecasts and detailed field data are available. The validation can be further improved with additional landslide information including the exact time of failure for each landslide and the landslide’s extent and run out length.


Landslide Hurricane Hazard prediction LiDAR 


  1. Baum RL, Savage WZ, Godt JW (2002) TRIGR—a Fortran program for transient rainfall infiltration and grid-based regional slope-stability analysis. U.S. Geological Survey Open File ReportGoogle Scholar
  2. 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, 75 pGoogle Scholar
  3. Caine N (1980) The rainfall intensity duration control of shallow landslides and debris flows. Geogr Ann Ser A 62:23–27CrossRefGoogle Scholar
  4. Cannon S, Gartner J (2005) Wildfire related debris flow from a hazards perspective. In: Jacob M, Hungr O (eds) Debris-flow hazards and related phenomena: Springer-Praxis Books in Geophysical Sciences, pp 321–344Google Scholar
  5. Dietrich WE, Reiss R, Hsu ML, Montgomery DR (1995) A process-based model for colluvial soil depth and shallow landsliding using digital elevation data. Hydrol Process 9:383–400CrossRefGoogle Scholar
  6. Ebert EE, Janowiak JE, Kidd C (2007) Comparison of near-real-time precipitation estimates from satellite observations and numerical models. Bull Am Meteor Soc 88:47–64CrossRefGoogle Scholar
  7. Godt JW, Baum RL, Lu N (2009) Landsliding in partially saturated materials. Geophys Res Lett 36Google Scholar
  8. 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:L22402. doi:10.1029/2006GRL028010 CrossRefGoogle Scholar
  9. Hong Y, Adler RF, Huffman GJ (2007) Use of satellite remote sensing data in the mapping of global landslide susceptibility. Nat Hazards 43(2):245–256. doi:10.1007/s11069-006-9104-z CrossRefGoogle Scholar
  10. Iverson RM (2000) Landslide triggering by rain infiltration. Water Resour Res 36(7):1897–1910CrossRefGoogle Scholar
  11. Kirschbaum DB, Adler R, Hong Y, Hill S, Lerner-Lam AL (2009) A global landslide catalog for hazard applications—method, results and limitations. J Nat Hazards. doi:10.1007/s11069-009-9401-4
  12. Lu N, Godt JW (2008) Infinite-slope stability under steady unsaturated conditions. Water Resour Res 44:W11404. doi:10.1029/2008WR006976 CrossRefGoogle Scholar
  13. Meisina C, Scarabelli S (2007) A comparative analysis of terrain stability models for predicting 478 shallow landslides in colluvial soils. Geomorphology 87(3):207–223CrossRefGoogle Scholar
  14. Montrasio L, Valentino R (2008) A model for triggering mechanisms of shallow landslides. Nat Hazards Earth Syst Sci 8:1149–1159CrossRefGoogle Scholar
  15. Ren D, Leslie LM, Karoly D (2008) Mudslide risk analysis using a new constitutive relationship for granular flow. Earth Interact 12:1–16CrossRefGoogle Scholar
  16. Ren D, Wang J, Fu R, Karoly D, Yong Yang, Leslie LM, Fu C, Huang G (2009) Mudslide caused ecosystem degradation following Wenchuan earthquake 2008. GRL 36. doi:10.1029/2008GL036702
  17. Witt AC (2005) A brief history of debris flow occurrence in the French Broad River Watershed, western North Carolina. NC Geogr 13:58–82Google Scholar
  18. Wooten RM, Reid JC, Latham RS, Medina MA, Bechtel R, Clark TW (2005) An overview of the North Carolina Geological Survey’s Geologic hazards program-phase 1: In: Proceedings of the 56th highway geology symposium, Wilmington, NC, 4–6 May 2005, pp 291–307Google Scholar
  19. Wooten RM, Latham RS, Witt AC, Gillon KA, Douglas TD, Fuemmeler SJ, Bauer JB, Reid JC (2007) Landslide hazards and landslide hazard mapping in North Carolina: In: Schaefer VR, Schuster RL, Turner AK (eds) Conference presentations 1st North American landslide conference, Vail Colorado, AEG Special Publication 23, pp 458–471Google Scholar
  20. Wooten RM, Gillon KA, Witt AC, Latham RS, Douglas TJ, Bauer JB, Fuemmeler SJ, Lee LG (2008) Geologic, geomorphic, meteorological aspects of debris flows triggered by hurricanes Frances and Ivan during September 2004 in the southern Appalachian Mountains of Macon County, North Carolina (southeastern USA). Landslides 5(1):31–44CrossRefGoogle Scholar
  21. Wu W, Sidle RC (1995) A distributed slope stability model for steep forested basins. Water Resour Res 31:2097–2110CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2010

Authors and Affiliations

  • Zonghu Liao
    • 1
  • Yang Hong
    • 1
  • Dalia Kirschbaum
    • 2
  • Robert F. Adler
    • 2
  • Jonathan J. Gourley
    • 3
  • Rick Wooten
    • 4
  1. 1.School of Civil Engineering and Environmental Science, Center for Natural Hazard and Disaster Research, National Weather Center Suite 3630The University of OklahomaNormanUSA
  2. 2.NASA Goddard Space Flight CenterGreenbeltUSA
  3. 3.NOAA National Severe Storm LaboratoryNormanUSA
  4. 4.North Carolina Geological SurveySwannanoaUSA

Personalised recommendations