Long-term probabilistic volcanic hazard assessment using open and non-open data: observations and current issues

Tierz, Pablo. 2020 Long-term probabilistic volcanic hazard assessment using open and non-open data: observations and current issues. Frontiers in Earth Science, 8, 257. https://doi.org/10.3389/feart.2020.00257

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Official URL: http://dx.doi.org/10.3389/feart.2020.00257

Abstract/Summary

Probabilistic volcanic hazard assessment (PVHA) has become the paradigm to quantify volcanic hazard over the last decades. Substantial aleatory and epistemic uncertainties in PVHA arise from complexity of physico-chemical processes, impossibility of their direct observation and, importantly, a severe scarcity of observables from past eruptions. One factor responsible for data scarcity is the infrequency of moderate/large eruptions; other factors include lack of discoverability and accessibility to volcanological data. Open-access databases can help alleviate data scarcity and have significantly contributed to long-term PVHA of eruption onset and size, while are less common for data required in other PVHA components (e.g., vent opening). Making datasets open is complicated by economical, technological, ethical and/or policy-related challenges. International synergies (e.g., Global Volcanism Program, WOVOdat, Global Volcano Model, EPOS) will be key to facilitate the creation and maintenance of open-access databases that support Next-Generation PVHA. Additionally, clarification of some misconceptions about PVHA can also help progress. Firstly, PVHA should be understood as an expansion of deterministic, scenario-based hazard assessments. Secondly, a successful PVHA should sometimes be evaluated by its ability to deliver useful and usable hazard-related messages that help mitigate volcanic risk. Thirdly, PVHA is not simply an end product but a driver for research: identifying the most relevant sources of epistemic uncertainty can guide future efforts to reduce the overall uncertainty. Broadening of the volcanological community expertise to statistics or engineering has already brought major breakthroughs in long-term PVHA. A vital next step is developing and maintaining more open-access datasets that support PVHA worldwide.

Item Type:	Publication - Article
Digital Object Identifier (DOI):	https://doi.org/10.3389/feart.2020.00257
ISSN:	2296-6463
Date made live:	06 Oct 2020 08:54 +0 (UTC)
URI:	https://nora.nerc.ac.uk/id/eprint/528635

Item Type:

Publication - Article

Digital Object Identifier (DOI):

https://doi.org/10.3389/feart.2020.00257

ISSN:

2296-6463

Date made live:

06 Oct 2020 08:54 +0 (UTC)

URI:

https://nora.nerc.ac.uk/id/eprint/528635