Natural Hazards and Earth System Sciences
Natural Hazards and Earth System Sciences
NHESS
Articles | Volume 20, issue 5
Articles | Volume 20, issue 5
https://doi.org/10.5194/nhess-20-1441-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-20-1441-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 20, issue 5
Research article
 | 
26 May 2020
Research article |  | 26 May 2020

Topographic uncertainty quantification for flow-like landslide models via stochastic simulations

Hu Zhao and Julia Kowalski

Viewed

Total article views: 3,507 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
2,490 929 88 3,507 117 127
  • HTML: 2,490
  • PDF: 929
  • XML: 88
  • Total: 3,507
  • BibTeX: 117
  • EndNote: 127
Views and downloads (calculated since 02 Jan 2020)
Cumulative views and downloads (calculated since 02 Jan 2020)

Viewed (geographical distribution)

Total article views: 3,507 (including HTML, PDF, and XML) Thereof 3,132 with geography defined and 375 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 14 Mar 2026
Download
Short summary
We study the impact of topographic uncertainty on landslide run-out modeling using conditional and unconditional stochastic simulation. First, we propose a generic workflow and then apply it to a historic flow-like landslide. We find that topographic uncertainty can greatly affect landslide run-out modeling, depending on how well the underlying flow path is captured by topographic data. The difference between unconditional and conditional stochastic simulation is discussed in detail.
Read more
Share
Altmetrics
Final-revised paper
Preprint