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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

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Abstract

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.

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Acknowledgments

The authors wish to thank USGS scientists make the TRIGRS materials available for research community. We also thank Rick Wooten and Anne Witt for providing critical data used in this study. We also thank Dave Jorgensen for letting the first author operating NOSS/NSSL mobile radars and learning the radar QPE algorithms. Financial support for the first author is from NASA Headquarter Applied Science Program and from Remote Sensing Hydrology group at University of Oklahoma (http://hydro.ou.edu).

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Authors and Affiliations

  1. School of Civil Engineering and Environmental Science, Center for Natural Hazard and Disaster Research, National Weather Center Suite 3630, The University of Oklahoma, Norman, OK, 73019, USA

    Zonghu Liao & Yang Hong

  2. NASA Goddard Space Flight Center, 8800 Greenbelt Road, Greenbelt, MD, 20771, USA

    Dalia Kirschbaum & Robert F. Adler

  3. NOAA National Severe Storm Laboratory, Norman, OK, 73072, USA

    Jonathan J. Gourley

  4. North Carolina Geological Survey, Swannanoa, NC, 28778, USA

    Rick Wooten

Authors
  1. Zonghu Liao
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  2. Yang Hong
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  3. Dalia Kirschbaum
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  4. Robert F. Adler
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  5. Jonathan J. Gourley
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  6. Rick Wooten
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Correspondence to Yang Hong.

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Liao, Z., Hong, Y., Kirschbaum, D. et al. 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. Nat Hazards 58, 325–339 (2011). https://doi.org/10.1007/s11069-010-9670-y

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  • DOI: https://doi.org/10.1007/s11069-010-9670-y

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