Abstract Etna volcano is characterized by frequent effusive eruptions from the summit craters or from flank fissures, and these have often threatened villages, infrastructures and tourist facilities. Considerable experience of lava-flow mitigation has been gained by scientists working on this volcano, and in this paper we principally discuss the problems arising from lava flows emplaced during the 2002–03 flank eruption, when eruptive fissures opened both on the northern and southern flanks of the volcano, feeding lava flows towards several villages, tourist facilities and forests. We highlight the importance of the monitoring system to follow the spreading of eruptive fissures and predict when they stopped propagating. We illustrate the value of thermal mapping in identifying active lava flows, in measuring effusion rates to estimate the maximum distance that flows can travel, and in obtaining reliable lava-flow simulations in real time in order to predict possible paths of the lava flow and to adopt the most appropriate solutions to limit its damage. Collaborations between scientists from different institutions and fields once again proved essential to understand and model the eruptive processes, to mitigate hazards and to obtain the best results.

Abstract The use of thematic volcanic hazard maps is essential for policy managers and administrators in land use planning and to determine the best form of action during emergencies. In particular, hazard maps are a key tool in emergency management and are used to describe the threat expected at a certain location in the event of future eruptions. We applied the latest version of the SCIARA lava flow cellular automata model using parallel computing through general purpose graphics processing units technology to derive lava flow hazard maps for Mt Etna, Sicily. The methodology relies on an accurate analysis of the past behaviour of the volcano and is appropriate for land use planning and civil defence applications.

Abstract The MAGFLOW model for lava-flow simulations is based on the cellular automaton (CA) approach, and uses a physical model for the thermal and rheological evolution of the flowing lava. We discuss the potential of MAGFLOW to improve our understanding of the dynamics of lava-flow emplacement and our ability to assess lava-flow hazards. Sensitivity analysis of the input parameters controlling the evolution function of the automaton demonstrates that water content and solidus temperatures are the parameters to which MAGFLOW is most sensitive. Additional tests also indicate that temporal changes in effusion rate strongly influence the accuracy of the predictive modelling of lava-flow paths. The parallel implementation of MAGFLOW on graphic processing units (GPUs) can achieve speed-ups of two orders of magnitude relative to the corresponding serial implementation, providing a lava-flow simulation spanning several days of eruption in just a few minutes. We describe and demonstrate the operation of MAGFLOW using two case studies from Mt Etna: one is a reconstruction of the detailed chronology of the lava-flow emplacement during the 2006 flank eruption; and the other is the production of the lava-flow hazard map of the persistent eruptive activity at the summit craters.

Abstract One of the most common and straightforward ways to explicitly represent spatial heterogeneity in simulations is with the use of some form of a lattice. Lattices are two- or three-dimensional grids in which entities are connected using various forms of local rules. They are thus ideal for representing systems with different levels of local interactions and, thus, for exploring the processes and impacts of self-organization. Models based on lattices have found wide usage in ecology and geology and often use the same basic formalism, despite the differences in the entities being studied. Groups of models, such as cellular automata, self-organized criticality, and diffusion limited aggregation, show how complex spatial structures and temporal behaviors can arise from local interactions only in the absence of external forcing. Other models that incorporate external processes, such as percolation-based models of fire and diseases, demonstrate that self-organization can strongly affect the signal produced by exogenous disturbances. Most lattice models are best used as tools for improving understanding of the dynamics of systems under various sets of assumptions of internal dynamics and external forcing, rather than as a means for accurate predictions of actual system behaviors. Lattice models that integrate ecology and sedimentology could be used to introduce an explicit spatial component into studies of Earth system history.

A major objective of science is to provide a fundamental understanding of natural phenomena. In “the old kind of science,” this was done primarily by using partial differential equations. Boundary and initial value conditions were specified and solutions were obtained either analytically or numerically. However, many phenomena in geology are complex and statistical in nature and thus require alternative approaches. But the observed statistical distributions often are not Gaussian (normal) or lognormal, instead they are power laws. A power-law (fractal) distribution is a direct consequence of scale invariance, but it is now recognized to also be associated with self-organized complexity. Relatively simple cellular automata (CA) models provide explanations for a range of complex geological observations. The “sand-pile” model of Bak—the context for “self-organized criticality”—has been applied to landslides and turbidite deposits. The “forest-fire” model provides an explanation for the frequency-magnitude statistics of actual forest and wild fires. The slider-block model reproduces the Guttenberg-Richter frequency-magnitude scaling for earthquakes. Many of the patterns generated by the CA approach can be recognized in geological contexts. The use of CA models to provide an understanding of a wide range of natural phenomena has been popularized in Stephen Wolfram's bestselling book A New Kind of Science (2002) . Since CA models are basically computer games, they are accepted enthusiastically by many students who find other approaches to the quantification of geological problems both difficult and boring.