The Effects of Vent Location, Event Scale, and Time Forecasts on Pyroclastic Density Current Hazard Maps at Campi Flegrei Caldera (Italy)

Abstract

This study presents a new method for producing long-term hazard maps for pyroclastic density currents (PDC) originating at Campi Flegrei caldera. Such method is based on a doubly stochastic approach and is able to combine the uncertainty assessments on the spatial location of the volcanic vent, the size of the flow and the expected time of such an event. The results are obtained by using a Monte Carlo approach and adopting a simplified invasion model based on the box model integral approximation. Temporal assessments are modeled through a Cox-type process including self-excitement effects, based on the eruptive record of the last 15 kyr. Mean and percentile maps of PDC invasion probability are produced, exploring their sensitivity to some sources of uncertainty and to the effects of the dependence between PDC scales and the caldera sector where they originated. Conditional maps representative of PDC originating inside limited zones of the caldera, or of PDC with a limited range of scales are also produced. Finally, the effect of assuming different time windows for the hazard estimates is explored, also including the potential occurrence of a sequence of multiple events. Assuming that the last eruption of Monte Nuovo (A.D. 1538) marked the beginning of a new epoch of activity similar to the previous ones, results of the statistical analysis indicate a mean probability of PDC invasion above 5% in the next 50 years on almost the entire caldera (with a probability peak of ∼25% in the central part of the caldera). In contrast, probability values reduce by a factor of about 3 if the entire eruptive record is considered over the last 15 kyr, i.e., including both eruptive epochs and quiescent periods.

Dynamic Probabilistic Hazard Mapping in the Long Valley Volcanic Region CA: Integrating Vent Opening Maps and Statistical Surrogates of Physical Models of Pyroclastic Density Currents

“…Nevertheless, the careful accounting of uncertainty in the dynamic probabilistic hazard map construction outlined here is insightful and implicitly overcomes some of the inadequacy of the model while providing support for decision making by experts. Potential further investigations include the following: ‐As part of a holistic probabilistic hazard study at LVVR, calculating frequency and volume models of flow hazards and incorporate those using the presented methodology to make probabilistic hazard maps and study the impacts of various uncertainties (see Appendix for a preliminary analysis of past volumes. ) ‐Making it possible for civil authorities to communicate probabilistic hazard forecasts and uncertainties as part of a hazard analysis or risk assessment. ‐Taking temporal eruption frequency into consideration, providing time‐space assessments (Bebbington & Cronin, ; Bebbington, ; Bevilacqua et al, ; Connor & Hill, ; Jaquet et al, ) and even volume‐space assessments (Bebbington, ; Bevilacqua, Neri, et al, ). …”

“…In contrast, our goal is to produce dynamic probabilistic hazard maps—maps of probabilities indicating the likelihood of a hazard affecting the mapped locations—that are consistent with past events but can reflect both aleatory uncertainty inherent in the system and epistemic uncertainty due to imperfect models and limited data (Marzocchi & Bebbington, ; Sparks, ). Further, a single probabilistic forecast map is not the goal—instead, we wish to see how dynamic probabilistic hazard maps change as we explore these uncertainties (Bevilacqua, Neri, et al, ; Neri et al, ). Ultimately, we envision these dynamic probabilistic hazard maps as a tool to explore the impacts of uncertainties on probabilistic hazard forecasts for those charged with making a hazard map.…”

Towards Quantitative Volcanic Risk of Pyroclastic Density Currents: Probabilistic Hazard Curves and Maps Around Somma‐Vesuvius (Italy)

“…This allows computation of statistical moments (e.g., the mean) or full distributions for these model outputs. Monte Carlo methods are still a popular choice to achieve this goal but they suffer from slow convergence rates, so precise uncertainty assessment requires many simulations of the PDC model, something that is only achievable if very simple models are used (e.g., Bevilacqua et al, ; Neri et al, ; Sandri et al, ; Tierz, Sandri, Costa, Sulpizio, et al, ). An alternative approach to Monte Carlo, when the PDC model is more computationally expensive, is PCQ (e.g., Dalbey et al, ), a nonintrusive polynomial chaos expansion method.…”

“…However, they are becoming more customary over recent years. The majority of these hazard assessments have quantified the probability of PDC invasion around the target volcano without considering other hazard intensity measures such as the flow depth or speed (e.g., Bevilacqua et al, ; Neri et al, ; Sandri et al, , , ; Tierz, Sandri, Costa, Sulpizio, et al, ; Tierz, Sandri, Costa, Zaccarelli, et al, ). Some studies that have quantified uncertainty in relation with intensity measures have mostly focused on dividing the PDC model parameter space into regions that do or do not lead to a catastrophe (defined as a given flow depth being overcome) occurring at selected locations (e.g., Bayarri et al, ; Spiller et al, ).…”

Late Quaternary Eruption Record and Probability of Future Volcanic Eruptions in the Long Valley Volcanic Region (CA, USA)

“…It is worth remarking that we are always considering long‐term forecasting models, that is, probability estimates for the time of the future events based on past record analysis. All the models described here are doubly stochastic, and the probability estimates are affected by uncertainty and represented by mean and percentile bounds (Bevilacqua, ; Bevilacqua, Bursik, et al, ; Bevilacqua, Neri, et al, ; Tadini, Bevilacqua, et al, ). Doubly stochastic methods account for two different probability frameworks: one describes the physical and intrinsic variability of the system (sometimes also called aleatoric uncertainty), while the other describes the epistemic uncertainty due to the imperfect knowledge of the system under study.…”