  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.
