Preprints
https://doi.org/10.5194/nhess-2021-213
https://doi.org/10.5194/nhess-2021-213

  23 Jul 2021

23 Jul 2021

Review status: a revised version of this preprint is currently under review for the journal NHESS.

A Unified Probabilistic Framework for Volcanic Hazard and Eruption Forecasting

Warner Marzocchi1, Jacopo Selva2, and Thomas H. Jordan3 Warner Marzocchi et al.
  • 1Department of Earth, Environmental, and Resources Sciences, University of Naples, Federico II, Complesso di Monte Sant'Angelo, Via Cupa Nuova Cintia, 21 - 80126 Napoli, Italy
  • 2Istituto Nazionale di Geofisica e Vulcanologia, Via Donato Creti 12, 40128 Bologna, Italy
  • 3Department of Earth Sciences Southern California Earthquake Center, University of Southern California, Los Angeles, California 90089, USA

Abstract. 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 seeks 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 (aleatory variability); (2) the uncertainty in our knowledge of how volcanic systems operate and evolve, often represented as subjective probabilities based on expert opinion (epistemic uncertainty); 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 (ontological error). Here we put forward a probabilistic framework for hazard analysis recently proposed by Marzocchi & 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 experimental concept 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.

Warner Marzocchi et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on nhess-2021-213', Anonymous Referee #1, 16 Aug 2021
    • AC1: 'Reply on RC1', Warner Marzocchi, 08 Sep 2021
  • RC2: 'Comment on nhess-2021-213', Anonymous Referee #2, 16 Aug 2021
    • AC2: 'Reply on RC2', Warner Marzocchi, 08 Sep 2021

Warner Marzocchi et al.

Warner Marzocchi et al.

Viewed

Total article views: 396 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
334 51 11 396 3 3
  • HTML: 334
  • PDF: 51
  • XML: 11
  • Total: 396
  • BibTeX: 3
  • EndNote: 3
Views and downloads (calculated since 23 Jul 2021)
Cumulative views and downloads (calculated since 23 Jul 2021)

Viewed (geographical distribution)

Total article views: 381 (including HTML, PDF, and XML) Thereof 381 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 16 Sep 2021
Download
Short summary
Eruption forecasting and volcanic hazard analysis are pervaded by uncertainty of different kinds, such as the natural randomness, our lack of knowledge, and the so-called unknown unknowns. After discussing the limits of how classical probabilistic frameworks handle these uncertainties, we put forward a unified probabilistic framework which define unambiguously uncertainty of different kinds and it allows scientific validation of the hazard model against independent observations.
Altmetrics