Fast and accurate simulation method improves hazard assessment for geophysical flows
A Highly Efficient Depth-Averaged Material Point Method for Geophysical Mass Flows
Computational Engineering, Finance, and Science
Summary
Simulating natural events like landslides or mudflows is slow, making it hard to predict risks quickly. The authors developed a computer method that tracks these flows using particles, running much faster than previous tools while still being accurate. Their model can simulate real disasters with high detail on a regular computer, helping to forecast hazards more efficiently. Different friction models in their simulation produce similar results, making the method flexible for various situations.
What this means in practice
- •For natural hazard assessment teams: Run large, detailed landslide and debris flow simulations rapidly for probabilistic hazard forecasting on standard desktop computers.
- •For civil engineering firms: Perform fast and reliable simulations of geophysical mass flows to inform infrastructure design and risk management decisions.
Authors
Johann Kammholz, Yidong Zhao, Lars Blatny, Thomas Pauze, Johan Gaume
Abstract
Probabilistic hazard assessment of geophysical mass flows requires large simulation ensembles and thus fast and robust numerical models. We present an open-source depth-averaged material point method formulated in global Cartesian coordinates, using Lagrangian particles to track the flow margin. A separate basal friction step based on the Coulomb, Voellmy, or $μ(I)$-rheology brings the flow to rest without artificial stopping criteria. Our model reproduces analytical solutions for a sliding block and dam-break problems and matches the deposit of a laboratory granular column collapse. A back-calculation of the 2023 Brienz/Brinzauls rockslide (Switzerland) at 1m resolution (190'000 particles) runs faster than real time on a single CPU core. Calibrated to the observed runout, the three friction laws yield very similar results, but Coulomb friction shows a slightly wider affected area than Voellmy and $μ(I)$. A probabilistic 1632-simulation ensemble at 5m resolution was computed in 76s. The model is one to two orders of magnitude faster than established depth-averaged models used in engineering practice, making catchment-style ensemble simulations feasible on a desktop computer.