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.

Abstract This paper presents numerical models of the Vulcanian explosions that occurred in 1997 at Soufrière Hills Volcano. Plume evolution and velocities were calculated for the well-documented and typical explosions of 6 and 7 August 1997, and these data and other observations were compared to transient, axisymmetric, multiphase flow simulations of coupled conduit evacuation and pyroclastic dispersal. Pre-explosion conduit conditions were estimated from Montserrat data, using a simple gas solubility law and assuming that conduit magma flow had stagnated with a constant overpressure prior to the explosions. Reference simulation input parameters include conduit diameter of 30 m, crater diameter of 300 m, meltwater content of 4.3±0.5%, grain sizes of 30, 2000 and 5000 µm, and conduit overpressure of l0MPa. The numerical simulations of the explosions resolved highly unsteady vent exit conditions such as velocity, pressure and mass flux, and the spatial and temporal dispersal of pyroclasts during the initial few minutes was investigated using one gas phase and two or three solid phases representing pyroclasts of different size. Our simulations produced transitional eruptive regime behaviour, dividing the erupted mass into a portion that generated a radial pyroclastic current fed by a collapsing column, and a convective portion that generated a buoyant plume. This behaviour generally mimicked the observed explosions. The movement of different particle sizes was tracked, with fine particles dominantly influencing the convective behaviour of the central plume and ash plume thermals generated above the pyroclastic currents. Simulated initial vent velocities ranged from 85 to 120ms −1 , collapse heights ranged from 450 to 1370 m above the vent, initial pyroclastic current velocities ranged from 40 to 60 ms −1 with surge runouts to 1.8 km, drawdown depths in the conduit were a few hundred metres, and simulated pyroclastic current deposit temperatures ranged between 135 and 430°C. Subsets of these results are in reasonable agreement with observed and measured parameters of the 1997 explosions. The best match was intermediate between our reference simulation, which assumed no loss of volatiles from the conduit during rise from the magma reservoir and which appeared too energetic, and another simulation in which much volatile leakage was assumed. The results suggest that volatile depletion in the conduit was an important factor in influencing the dynamic behaviour of the Vulcanian explosions on Montserrat.

Abstract Hazardous effects of tephra fallout on Montserrat include roof collapse, aviation threats, health hazards from respirable crystalline silica, crop pollution, road safety and lahar generation. An advection-diffusion model was developed to investigate tephra dispersal from dome collapses and Vulcanian explosions, which generated most of the fallout tephra during the 1995–1999 eruptive period of Soufrière Hills Volcano. Wind field, atmospheric diffusion, gravity settling, aggregation and elutriation processes are considered. Computed isomass maps compare well with field observations and require aggregation of fine ash for good agreement. Probability maps were also compiled. Individual probability maps (for individual dome collapses and Vulcanian explosions) are based on the statistics of wind profiles and show that fallout tephra generated by individual eruptive events on a Montserrat scale do not cause serious damage in any area on Montserrat. Cumulative probability maps (for a given scenario of activity) are generated by sampling statistical distributions of wind profiles and eruptive events over an extended period of time. They show that persistent tephra fallout can accumulate enough material to cause roof collapses and serious damage to vegetation in the SW part of the island, and minor damage to vegetation in the north, as also confirmed by field data.