Citation

Aaron J, McDougall S, Kowalski J, Mitchell A, Nolde N. Landslides 2022; 19(12): 2853-2869.

Copyright

(Copyright © 2022, Holtzbrinck Springer Nature Publishing Group)

DOI

10.1007/s10346-022-01939-y

PMID

36338899

PMCID

PMC9630252

Abstract

Rock avalanches can be a significant hazard to communities located in mountainous areas. Probabilistic predictions of the 3D impact area of these events are crucial for assessing rock avalanche risk. Semi-empirical, calibration-based numerical runout models are one tool that can be used to make these predictions. When doing so, uncertainties resulting from both noisy calibration data and uncertain governing movement mechanism(s) must be accounted for. In this paper, a back-analysis of a database of 31 rock avalanche case histories is used to assess both of these sources of uncertainty. It is found that forecasting results are dominated by uncertainties associated with the bulk basal resistance of the path material. A method to account for both calibration and mechanistic uncertainty is provided, and this method is evaluated using pseudo-forecasts of two case histories. These pseudo-forecasts show that inclusion of expert judgement when assessing the bulk basal resistance along the path can reduce mechanistic uncertainty and result in more precise predictions of rock avalanche runout.

SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s10346-022-01939-y.

Language: en

Keywords

Probabilistic calibration; Probabilistic prediction; Rock avalanches; Runout modelling