VUELCO deliverable 7.5: Guidelines for the best practice of scientific management of volcanic unrest

By dmitri rouwet

INGV-Sezione di Bologna, Italy

You must login to access the full resource.

Published on


Volcanic eruption forecasting and hazard assessment are multi-disciplinary processes with scientific and social implications. Our limited knowledge and the randomness of the processes behind a volcanic eruption yields the need of quantifying uncertainties on volcano dynamics. With deterministic and probabilistic methods for volcanic hazard assessment being often in disagreement, we propose a combined approach that bridges the two schools of thoughts in order to improve future volcano monitoring. Expert elicitation has proven to be an effective way to bind deterministic research within a probabilistic framework aiming to reduce the uncertainties related to any hazard forecast; yet, numerous exercises based on expert elicitation have revealed that the attempt to reduce uncertainties led to the creation of new ones, often unquantifiable, created by human nature and reasoning during stressful situations. Such reasoning interferes with the complexity of volcanic processes and the fact that every scenario has a probability to occur. The recent probabilistic methods and tools syntonise probabilistic and deterministic researchers and lead to unprecedented models. Nevertheless, the novelty of probabilistic hazard assessment is often misunderstood as not all of the researchers involved have backgrounds in such matters. A probabilistic method cannot stand-alone as it depends on data input obtained by deterministic approaches. We propose that, given the symbiotic relationship between the two methods, a probabilistic framework can play a role of moderator between various deterministic disciplines, thus creating a coherent environment for discussion and debate among seismologists, geodesists, geochemists. This can be achieved by training all scientists involved in hazard assessment, probability theory and data interpretation, whereas another group member objectively uses the information provided to produce the probabilities. Hence, numerical outcomes can be interpreted transparently as they represent the quantification of experts’ knowledge and related uncertainties. A probabilistic method that incorporates the joint-opinions of a group of multi-disciplinary researchers facilitates a more straightforward way of communicating scientific information to decision-makers.