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Natural Hazards and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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Discussion papers
https://doi.org/10.5194/nhess-2019-358
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-2019-358
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: research article 02 Jan 2020

Submitted as: research article | 02 Jan 2020

Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Natural Hazards and Earth System Sciences (NHESS).

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

Hu Zhao and Julia Kowalski Hu Zhao and Julia Kowalski
  • RWTH Aachen University, Templergraben 55, 52062 Aachen, Germany

Abstract. Topography representing digital elevation models (DEMs) are essential inputs for computational models capable of simulating the run-out of flow-like landslides. Yet, DEMs are often subject to error, a fact that is mostly overlooked in landslide modeling. We address this research gap and investigate the impact of topographic uncertainty on landslide-run-out models. In particular, we will describe two different approaches to account for DEM uncertainty, namely unconditional and conditional stochastic simulation methods. We investigate and discuss their feasibility, as well as whether DEM uncertainty represented by stochastic simulations critically affects landslide run-out simulations. Based upon a historic flow-like landslide event in Hong Kong, we present a series of computational scenarios to compare both methods using our modular Python-based workflow. Our results show that DEM uncertainty can significantly affect simulation-based landslide run-out analyses, depending on how well the underlying flow path is captured by the DEM, as well as further topographic characteristics and the DEM error's variability. We further find that in the absence of systematic bias in the DEM, a performant root mean square error based unconditional stochastic simulation yields similar results than a computationally intensive conditional stochastic simulation that takes actual DEM error values at reference locations into account. In all other cases the unconditional stochastic simulation overestimates the variability of the DEM error, which leads to an increase of the potential hazard area as well as extreme values of dynamic flow properties.

Hu Zhao and Julia Kowalski
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Status: open (until 27 Feb 2020)
Status: open (until 27 Feb 2020)
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Hu Zhao and Julia Kowalski
Hu Zhao and Julia Kowalski
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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.
We study the impact of topographic uncertainty on landslide run-out modeling using conditional...
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