Ground-based remote sensing and uncertainty analysis of the mass eruption rate associated with the 3-5 December 2015 paroxysm of Mt.Etna

Mt. Etna, in Italy, is one of the most active volcanoes in the world, whose explosive eruptions represent a serious threat to the nearby populations and producing various dangerous effects mainly on properties, crops and transports. During explosive eruptions, the real-time estimation of the mass eruption rate (MER) is challenging although crucial to mitigate the impact due to the erupted tephra. Microwave radar techniques at L- and/or X-bands, as well as thermal infrared imagery, can provide a reliable MER estimation in real-time. Using the Etna lava fountains of 3–5 December 2015 as test cases, we investigate the differences among different approaches to estimate the MER: i) the mass continuity approach (MCA); ii) the top plume approach (TPA); and iii) the surface flux approach (SFA). We also introduce a new approach, called the near source approach (NSA) that is based on the X-band radar data alone. Finally, we extend the volcanic advanced radar retrieval methodology to estimate for the first time the gas-tephra mixture density near the volcanic crater. The analysis allows us to identify the optimal real-time MER retrieval strategy, showing the potential and limitations of each method. We show that the MCA method, entirely based on the X-band radar data processing, is the best strategy with a percentage uncertainty in the MER estimation of 22.3%, whereas other approaches exhibit a higher uncertainty (26.4% for NSA, 30% for TPA, and 31.6% for SFA). We investigate and optimize the different strategies for the volume eruption rate (VER), total erupted mass and volume estimations (TEM and TEV, respectively) including their uncertainties. The MER retrieval methods, described and applied in this work, showed promising results that can be exploited to improve the tephra dispersal and fallout forecasts at Etna in near real-time. Further work might be devoted to explore new techniques, using low-cost sensors for the MER estimation and employing microwave radars as validation tools.