Benchmarking lava-flow models

Abstract

Prediction of the emplacement of volcanic mass flows (lava flows, pyroclastic density currents, debris avalanches and debris flows) is required for hazard and risk assessment, and for the planning of risk-mitigation measures. Numerical computer-based models now exist that are capable of approximating the motion of a given volume of volcanic material from its source to the deposition area. With these advances in technology, it is useful to compare the various codes in order to evaluate their respective suitability for real-time forecasting, risk preparedness and post-eruptive response. A 'benchmark' compares codes or methods, all aimed at simulating the same physical process using common initial and boundary conditions and outputs, but using different physical formulations, mathematical approaches and numerical techniques. We set up the basis for a future general benchmarking exercise on volcanic mass-flow models and, more specifically, establish a benchmark series for computational lava-flow modelling. We describe a set of benchmarks in this paper, and present a few sample results to demonstrate output analysis and code evaluation methodologies. The associated web-based communal facility for sharing test scenarios and results is also described.

Benchmarking computational fluid dynamics models of lava flow simulation for hazard assessment, forecasting, and risk management

“…We use the benchmarks defined by Cordonnier et al (2015) as a guide to define our set of benchmarks. These cover a range of physics and complexity, from simple scenarios with analytical solutions, to the complexity of cooling molten basalt.…”

“…This is based on a benchmark previously defined for lava flow models by Cordonnier et al (2015;BM2). It is a scenario that was explored analytically and experimentally by Lister (1992), and is well understood, capturing the dominant behavior of a lava flow as a viscous gravity current.…”

“…This framework has been applied to many geologic cases, including mantle convection and climate models (e.g., Blankenbach et al, 1989;Schmeling, et al, 2008;Charbonnier and Gertisser, 2009;Harrison et al 2014;Costa et al, 2016), but only recently introduced for lava flow models (Cordonnier et al, 2015). Cordonnier et al (2015) define a set of benchmarks based on analytical theory, experiments, and wellobserved natural lava flows that we use for our study. We also extend their study by (1) including additional experimental data for benchmarking, (2) testing multiple codes for multiple benchmarks, (3) evaluating both model accuracy and CPU efficiency, and (4) interpreting experimentally based benchmarking results in the context of natural lava flows.…”

Evaluating the impacts of volcanic eruptions using RiskScape

“…Inundation extent is crude, but is an output of the majority of lava flow models (Cordonnier et al, 2015). It is also the diagnostic HEMU in the vast majority of cases -was the area impacted by any amount of lava.…”

“…It is also the diagnostic HEMU in the vast majority of cases -was the area impacted by any amount of lava. Thickness is a closely related HEMU which provides slightly more information, and is an output of a number of lava flow models (Cordonnier et al, 2015). Lava flow thickness may become important during recovery, particularly for buried infrastructure -e.g., there may be a difference when determining whether a network is abandoned or maintained if it is covered in 1 or 10 m of lava.…”