  Landslide run-out modeling involves various uncertainties originating from
model input data. It is therefore desirable to assess the model's sensitivity.
A global sensitivity analysis that is capable of exploring the entire input
space and accounts for all interactions, often remains limited due to
computational challenges resulting from a large number of necessary model runs.
We address this research gap by integrating Gaussian process emulation into
landslide run-out modeling and apply it to the open-source simulation tool
r.avaflow. The feasibility and efficiency of our approach is illustrated based
on the 2017 Bondo landslide event. The sensitivity of aggregated model outputs,
such as the apparent friction angle, impact area, as well as spatially resolved
maximum flow height and velocity, to the dry-Coulomb friction coefficient,
turbulent friction coefficient and the release volume are studied. The results
of first-order effects are consistent with previous results of common
one-at-a-time sensitivity analyses. In addition to that, our approach allows to
rigorously investigate interactions. Strong interactions are detected on the
margins of the flow path where the expectation and variation of maximum flow
height and velocity are small. The interactions generally become weak with
increasing variation of maximum flow height and velocity. Besides, there are
stronger interactions between the two friction coefficients than between the
release volume and each friction coefficient. In the future, it is promising to
extend the approach for other computationally expensive tasks like uncertainty
quantification, model calibration, and smart early warning.
