Predictability of a Physically Based Model for Rainfall-induced Shallow Landslides: Model Development and Case Studies

  • Yang Hong
  • Xiaogang He
  • Amy Cerato
  • Ke Zhang
  • Zhen Hong
  • Zonghu Liao
Chapter

Abstract

A cost-effective physical model (SLope-Infiltration-distributed Equilibrium—SLIDE) has been developed to identify the spatial and temporal occurrences of rainfall-induced landslides, employing a range of remotely sensed and in situ data. The main feature of SLIDE is that it takes into account of some simplified hypotheses on water infiltration and defines a direct relationship between the factor of safety and the rainfall depth on an infinite slope model. This prototype has been applied to two case studies in Indonesia and Honduras during heavy rainfall events brought by typhoon and hurricane, respectively. Simulation results from SLIDE demonstrated good skills in predicting rainfall-induced shallow landslides by assimilating the most important dynamic triggering factor (i.e., rainfall) quantitatively. The model’s prediction performance also suggested that SLIDE could serve as a potential tool for the future landslide early-warning system. Despite positive model performance, the SLIDE model is limited by several assumptions including using general parameter calibration rather than in situ tests and neglecting geotechnical information and some of the hydrological processes in deep soil layers. Advantages and limitations of this physically based model are also discussed with respect to future applications of landslide assessment and prediction over large scales.

Keywords

Physically based models Prediction Rainfall-induced Shallow landslides SLIDE TRMM 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • Yang Hong
    • 1
    • 2
  • Xiaogang He
    • 1
  • Amy Cerato
    • 1
  • Ke Zhang
    • 3
  • Zhen Hong
    • 1
  • Zonghu Liao
    • 1
  1. 1.Department of Civil Engineering and Environmental Science, and Advanced Radar Research CenterThe University of OklahomaNormanUSA
  2. 2.National Weather Center ARRC Suite 4610NormanUSA
  3. 3.Cooperative Institute for Mesoscale Meteorological StudiesThe University of OklahomaNormanUSA

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