| Popis: |
The eruption of Cumbre Vieja (also known as Tajogaite volcano, 19 September–13 December 2021, Spain) is an example of successful emergency management. The lessons learnt are yet to be fully disclosed as is whether the response can be further improved. The latter may include tools to predict lava flow inundation rheological characteristics, amongst other issues related to volcanic eruptions (i.e., ash fall and gas emission). The aim of this study was to explore if a scientific open-source, readily available, lava-flow-modelling code (VolcFlow) would suffice for lava emplacement forecasting, focusing on the first seven days of the eruption. We only the open data that were released during the crisis and previously available data sets. The rheology of the lava, as well as the emission rate, are of utmost relevance when modelling lava flow, and these data were not readily available. Satellite lava extent analysis allowed us to preliminarily estimate its velocity, the average flow emitted, and flow viscosity. These estimates were numerically adjusted by maximising the Jaccard morphometric index and comparing the area flooded by the lava for a simulated seven-day advance with the real advance of the lava in the same timescale. The manual search for the solution to this optimization problem achieved morphometric matches of 85% and 60%. We obtained an estimated discharge rate of about 140 m3/s of lava flow during the first 24 h of the eruption. We found the emission rate then asymptotically decreased to 60 m3/s. Viscosity varied from 8 × 106 Pa s, or a yield strength of 42 × 103 Pa, in the first hours, to 4 × 107 Pa s and 35 × 103 Pa, respectively, during the remainder of the seven days. The simulations of the lava emplacement up to 27 September showed an acceptable distribution of lava thickness compared with the observations and an excellent geometrical fit. The calculations of the calibrated model required less time than the simulated time span; hence, flow modelling can be used for emergency management. However, both speed and accuracy can be improved with some extra developments and guidance on the data to be collected. Moreover, the available time for management, once the model is ready, quasi-linearly increases as the forecasting time is extended. This suggests that a predictive response during an emergency with similar characteristics is achievable, provided that an adequate rheological description of the lava is available. |
