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Natural Hazards and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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Volume 9, issue 3
Nat. Hazards Earth Syst. Sci., 9, 673–686, 2009
https://doi.org/10.5194/nhess-9-673-2009
© Author(s) 2009. This work is distributed under
the Creative Commons Attribution 3.0 License.

Special issue: Methods and strategies to evaluate landslide hazard and risk

Nat. Hazards Earth Syst. Sci., 9, 673–686, 2009
https://doi.org/10.5194/nhess-9-673-2009
© Author(s) 2009. This work is distributed under
the Creative Commons Attribution 3.0 License.

  06 May 2009

06 May 2009

Evaluation of a preliminary satellite-based landslide hazard algorithm using global landslide inventories

D. B. Kirschbaum1,2, R. Adler2,3, Y. Hong4, and A. Lerner-Lam1 D. B. Kirschbaum et al.
  • 1Lamont-Doherty Earth Observatory and Department of Earth and Environmental Sciences, Columbia University, Palisades, New York, USA
  • 2Laboratory for Atmospheres, NASA Goddard Space Flight Center, Greenbelt, Maryland, USA
  • 3ESSIC, University of Maryland College Park, College Park, Maryland, USA
  • 4School of Civil Engineering and Environmental Sciences, University of Oklahoma, National Weather Center, Norman, Oklahoma, USA

Abstract. Most landslide hazard assessment algorithms in common use are applied to small regions, where high-resolution, in situ, observables are available. A preliminary global landslide hazard algorithm has been developed to estimate areas of potential landslide occurrence in near real-time by combining a calculation of landslide susceptibility with satellite derived rainfall estimates to forecast areas with increased potential for landslide conditions. This paper presents a stochastic methodology to compare this new, landslide hazard algorithm for rainfall-triggered landslides with a newly available inventory of global landslide events, in order to determine the predictive skill and limitations of such a global estimation technique. Additionally, we test the sensitivity of the global algorithm to its input observables, including precipitation, topography, land cover and soil variables. Our analysis indicates that the current algorithm is limited by issues related to both the surface-based susceptibility map and the temporal resolution of rainfall information, but shows skill in determining general geographic and seasonal distributions of landslides. We find that the global susceptibility model has inadequate performance in certain locations, due to improper weighting of surface observables in the susceptibility map. This suggests that the relative contributions of topographic slope and soil conditions to landslide susceptibility must be considered regionally. The current, initial forecast system, although showing some overall skill, must be improved considerably if it is to be used for hazard warning or detailed studies. Surface and remote sensing observations at higher spatial resolution, together with improved landslide event catalogues, are required if global landslide hazard forecasts are to become an operational reality.

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