A unified probabilistic framework for volcanic hazard and eruption forecasting
<p>The main purpose of this article is to emphasize the importance of clarifying the probabilistic framework adopted for volcanic hazard and eruption forecasting. Eruption forecasting and volcanic hazard analysis seek to quantify the deep uncertainties that pervade the modeling of pre-, sin-,...
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| Autores principales: | , , |
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| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
Copernicus Publications
2021
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/1934ced471d04f4eb149b36ba32e6695 |
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| Sumario: | <p>The main purpose of this article is to emphasize the
importance of clarifying the probabilistic framework adopted for volcanic
hazard and eruption forecasting. Eruption forecasting and volcanic hazard
analysis seek to quantify the deep uncertainties that pervade the modeling
of pre-, sin-, and post-eruptive processes. These uncertainties can be
differentiated into three fundamental types: (1) the natural variability of
volcanic systems, usually represented as stochastic processes with
parameterized distributions (<i>aleatory variability</i>); (2) the uncertainty in our knowledge of how
volcanic systems operate and evolve, often represented as subjective
probabilities based on expert opinion (<i>epistemic uncertainty</i>); and (3) the possibility that our
forecasts are wrong owing to behaviors of volcanic processes about which we
are completely ignorant and, hence, cannot quantify in terms of
probabilities (<i>ontological error</i>). Here we put forward a probabilistic framework for hazard
analysis recently proposed by Marzocchi and Jordan (2014), which unifies
the treatment of all three types of uncertainty. Within this framework, an
eruption forecasting or a volcanic hazard model is said to be complete only
if it (a) fully characterizes the epistemic uncertainties in the model's
representation of aleatory variability and (b) can be unconditionally tested
(in principle) against observations to identify ontological errors.
Unconditional testability, which is the key to model validation, hinges on
an <i>experimental concept</i> that characterizes hazard events in terms of exchangeable data sequences
with well-defined frequencies. We illustrate the application of this unified
probabilistic framework by describing experimental concepts for the
forecasting of tephra fall from Campi Flegrei. Eventually, this example may
serve as a guide for the application of the same probabilistic framework to
other natural hazards.</p> |
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