Subaerial landslides and volcano flank collapses can generate tsunamis with devastating consequences. The lack of comprehensive models incorporating both the landslide and the wave mechanics represents a gap in providing consistent predictions of real events. Here, we present a novel three-dimensional granular landslide and tsunami model and apply it to the 2014 Lake Askja landslide tsunami. For the first time, we consistently simulate small-scale laboratory experiments as well as full scale catastrophic events with the same model. The model captures the complete event chain from the landslide dynamics to the wave generation and inundation. Unique and complete field data, along with the limited geographic extent of Lake Askja enabled a rigorous validation. The model gives deep insights into the physical landslide processes and improves our understanding and prediction capabilities of frequent and catastrophic landslide tsunamis.

Snow avalanche simulation software is a commonly used tool for hazard estimation and mitigation planning. In this study a depth-averaged flow model, combining a simple entrainment and friction relation, is implemented in the software SamosAT. Computational results strongly depend on the simulation input, in particular on the employed model parameters. A long-standing problem is to quantify the influence of these parameters on the simulation results. We present a new multivariate optimization approach for avalanche simulation in three-dimensional terrain. The method takes into account the entire physically relevant range of the two friction parameters (Coulomb friction, turbulent drag) and one entrainment parameter. These three flow model parameters are scrutinized with respect to six optimization variables (runout, matched and exceeded affected area, maximum velocity, average deposition depth and mass growth). The approach is applied to a documented extreme avalanche event, recorded in St Anton, Austria. The final results provide adjusted parameter distributions optimizing the simulation–observation correspondence. At the same time, the degree of parameter–variable correspondence is determined. We show that the specification of optimal values for certain model parameters is near-impossible, if corresponding optimization variables are neglected or unavailable.

Numerical models for dense snow avalanches have become central to hazard zone mapping and mitigation. Several commercial and free applications, which are used on a regular basis, implement such models. In this study we present a tool based on the open-source toolkit OpenFOAM ^® as an alternative to the established solutions. The proposed tool implements a depth-integrated shallow flow model in accordance with current practice. The solver combines advantages of the extensive OpenFOAM infrastructure with popular models from the avalanche community. OpenFOAM allows assembling custom physical models with built-in primitives and implements the numerical solution at a high level. OpenFOAM supports an extendable solver structure, making the tool well-suited for future developments and rapid prototyping. We introduce the basic solver, implementing an incompressible, single-phase model for natural terrain, including entrainment. The respective workflow, consisting of meshing, pre-processing, numerical solution and post-processing, is presented. We demonstrate data transfer from and to a geographic information system (GIS) to allow a simple application in practice. The tool chain is based entirely on open-source applications and libraries and can be easily customised and extended. Simulation results for a well-documented avalanche event are presented and compared to previous numerical studies and historical data.