How well can rock avalanche runout be forecasted? GPU-Accelerated validation of a method for forecasting rock avalanche motion using a numerical model

Abstract Rock avalanches are extremely rapid, flowlike landslides that can threaten people and infrastructure, making accurate forecasts of their motion using numerical models critical for risk assessment. These forecasts are a primary input into land use and evacuation decisions; however, a comprehensive analysis of the effectiveness of various forecasting approaches applied to a wide variety of rock avalanche types has never been done. This is primarily due to a lack of field data, as well as the prohibitive computational cost. In this work, we address both limitations by applying a Bayesian posterior analysis and leave-one-out cross validation strategy to a dataset of 22 case histories. This enables us to calibrate the GPU (Graphics Processing Unit)-accelerated runout model ORIN-3D (parallel simulatOR of landslIde motioN over 3D terrain) for three rheological parameterizations: (1) Frictional, (2) Voellmy, and (3) a combined Frictional-Voellmy rheology. The analysis involves around 400,000 simulations, which is a computational effort that would be impractical with CPU (Central Processing Unit)-based models. Probabilistic forecasts for each case history are conducted using calibration results across eight forecast sets and evaluated with the Tversky loss function (TLF). The results show that the best performing model parameterization uses a volume-dependent friction angle in the source zone (Frictional rheology) and Voellmy rheology in the path zone. This work thus provides systematically validated, probabilistic forecasts of a database of 22 rock avalanche case histories, enabling improved hazard and risk assessments of this damaging phenomenon.