Abstract GPUSPH is a fully three-dimensional model for the simulation of the thermal and rheological evolution of lava flows that relies on the Smoothed Particle Hydrodynamics (SPH) numerical method. Thanks to the Lagrangian, meshless nature of SPH, the model incorporates a more complete physical description of the emplacement process and rheology of lava that considers the free surface, the irregular boundaries represented by the topography, the solidification fronts and the non-Newtonian rheology with temperature-dependent parameters. GPUSPH follows the very general Herschel–Bulkley rheological model, which encompasses Newtonian, power-law and Bingham flow behaviours, with both constant and temperature-dependent parameters, and can thus be used to explore in detail the impact of rheology on the behaviour of lava flows and on their emplacement. To illustrate this possibility, we present some preliminary applications of the model for studying the rheology of lava flows with different constitutive relationships and thermal regimes using the real topography of the Mt Etna volcano.

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.

Abstract RED SEED stands for Risk Evaluation, Detection and Simulation during Effusive Eruption Disasters, and combines stakeholders from the remote sensing, modelling and response communities with experience in tracking volcanic effusive events. The group first met during a three day-long workshop held in Clermont Ferrand (France) between 28 and 30 May 2013. During each day, presentations were given reviewing the state of the art in terms of (a) volcano hot spot detection and parameterization, (b) operational satellite-based hot spot detection systems, (c) lava flow modelling and (d) response protocols during effusive crises. At the end of each presentation set, the four groups retreated to discuss and report on requirements for a truly integrated and operational response that satisfactorily combines remote sensors, modellers and responders during an effusive crisis. The results of collating the final reports, and follow-up discussions that have been on-going since the workshop, are given here. We can reduce our discussions to four main findings. (1) Hot spot detection tools are operational and capable of providing effusive eruption onset notice within 15 min. (2) Spectral radiance metrics can also be provided with high degrees of confidence. However, if we are to achieve a truly global system, more local receiving stations need to be installed with hot spot detection and data processing modules running on-site and in real time. (3) Models are operational, but need real-time input of reliable time-averaged discharge rate data and regular updates of digital elevation models if they are to be effective; the latter can be provided by the radar/photogrammetry community. (4) Information needs to be provided in an agreed and standard format following an ensemble approach and using models that have been validated and recognized as trustworthy by the responding authorities. All of this requires a sophisticated and centralized data collection, distribution and reporting hub that is based on a philosophy of joint ownership and mutual trust. While the next chapter carries out an exercise to explore the viability of the last point, the detailed recommendations behind these findings are detailed here.