No abstract

Improving lava flow hazard assessment is one of the most important and challenging fields of volcanology, and has an immediate and practical impact on society. Here, we present a methodology for the quantitative assessment of lava flow hazards based on a combination of field data, numerical simulations and probability analyses. With the extensive data available on historic eruptions of Mt. Etna, going back over 2000 years, it has been possible to construct two hazard maps, one for flank and the other for summit eruptions, allowing a quantitative analysis of the most likely future courses of lava flows. The effective use of hazard maps of Etna may help in minimizing the damage from volcanic eruptions through correct land use in densely urbanized area with a population of almost one million people. Although this study was conducted on Mt. Etna, the approach used is designed to be applicable to other volcanic areas.

No abstract

Since the mechanical properties of lava change over time, lava flows represent a challenge for physically based modeling. This change is ruled by a temperature field which needs to be modeled. MAGFLOW Cellular Automata (CA) model was developed for physically based simulations of lava flows in near real-time. We introduced an algorithm based on the Monte Carlo approach to solve the anisotropic problem. As transition rule of CA, a steadystate solution of Navier-Stokes equations was adopted in the case of isothermal laminar pressure-driven Bingham fluid. For the cooling mechanism, we consider only the radiative heat loss from the surface of the flow and the change of the temperature due to mixture of lavas between cells with different temperatures. The model was applied to reproduce a real lava flow that occurred during the 2004-2005 Etna eruption. The simulations were computed using three different empirical relationships between viscosity and temperature.

No abstract

Accurate mapping of recent lava flows can provide significant insight into the development of flow fields that may aid in predicting future flow behavior. The task is challenging, due to both intrinsic properties of the phenomenon (e.g., lava flow resurfacing processes) and technical issues (e.g., the difficulty to survey a spatially extended lava flow with either aerial or ground instruments while avoiding hazardous locations). The huge amount of moderate to high resolution multispectral satellite data currently provides new opportunities for monitoring of extreme thermal events, such as eruptive phenomena. While retrieving boundaries of an active lava flow is relatively straightforward, problems arise when discriminating a recently cooled lava flow from older lava flow fields. Here, we present a new supervised classifier based on machine learning techniques to discriminate recent lava imaged in the MultiSpectral Imager (MSI) onboard Sentinel-2 satellite. Automated classification evaluates each pixel in a scene and then groups the pixels with similar values (e.g., digital number, reflectance, radiance) into a specified number of classes. Bands at the spatial resolution of 10 m (bands 2, 3, 4, 8) are used as input to the classifier. The training phase is performed on a small number of pixels manually labeled as covered by fresh lava, while the testing characterizes the entire lava flow field. Compared with ground-based measurements and actual lava flows of Mount Etna emplaced in 2017 and 2018, our automatic procedure provides excellent results in terms of accuracy, precision, and sensitivity.

No abstract

Techniques capable of measuring lava discharge rates during an eruption are important for hazard prediction, warning, and mitigation. To this end, we developed an automated system that uses thermal infrared satellite MODIS data to estimate time-averaged discharge rate. MODIS-derived time-varying discharge rates were used to drive lava flow simulations calculated using the MAGFLOW cellular automata model, allowing us to simulate the discharge rate-dependent spread of lava as a function of time. During the July 2006 eruption of Mount Etna (Sicily, Italy), discharge rates were estimated at regular intervals (i.e., up to 2 times/day) using the MODIS data. The eruption lasted 10 days and produced a *3-km-long lava flow field. Time-averaged discharge rates extracted from 13 MODIS images were utilized to produce a detailed chronology of lava flow emplacement, demonstrating how infrared satellite data can be used to drive numerical simulations of lava flow paths during an ongoing eruptive event. The good agreement between simulated and mapped flow areas indicates that model-based inundation predictions, driven by timevarying discharge rate data, provide an excellent means for assessing the hazard posed by ongoing effusive eruptions.