Articles | Volume 20, issue 5
https://doi.org/10.5194/nhess-20-1441-2020
https://doi.org/10.5194/nhess-20-1441-2020
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

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Cited articles

AECOM Asia Company Limited: Detailed study of the 7 June 2008 landslides on the hillshade above Yu Tung Road, Tung Chung, Tech. rep., 2012. a, b, c, d
Aziz, S., Steward, B., Kaleita, A., and Karkee, M.: Assessing the effects of DEM uncertainty on erosion rate estimation in an agricultural field, T. ASABE, 55, 785–798, https://doi.org/10.13031/2013.41514, 2012. a, b, c
Bartelt, P., Salm, B., and Gruber, U.: Calculating dense-snow avalanche runout using a Voellmy-fluid model with active/passive longitudinal straining, J. Glaciol., 45, 242–254, https://doi.org/10.3189/002214399793377301, 1999. a
Berry, P., Garlick, J., and RG, S.: Near-global validation of the SRTM DEM using satellite radar altimetry, Remote Sens. Environ., 106, 17–27, https://doi.org/10.1016/j.rse.2006.07.011, 2007. a
Bolkas, D., Fotopoulos, G., Braun, A., and Tziavos, I.: Assessing digital elevation model uncertainty using GPS survey data, J. Surv. Eng., 142, 04016001, https://doi.org/10.1061/(ASCE)SU.1943-5428.0000169, 2016. a
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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.
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