Critical Transitions at the Campi Flegrei Resurgent Caldera via Multiplatform and Multiparametric Data.
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| Title: | Critical Transitions at the Campi Flegrei Resurgent Caldera via Multiplatform and Multiparametric Data. |
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| Authors: | Vitale, Andrea1,2 (AUTHOR), Barone, Andrea2,3 (AUTHOR), Marotta, Enrica3,4 (AUTHOR), Vitale, Dino Franco4,5 (AUTHOR), Pepe, Susi2,3,5 (AUTHOR), Peluso, Rosario4,6 (AUTHOR), Castaldo, Raffaele2,3 (AUTHOR), Avino, Rosario4 (AUTHOR), Mercogliano, Francesco2,3,6 (AUTHOR), Pepe, Antonio3 (AUTHOR), Accomando, Filippo2,3 (AUTHOR), Avvisati, Gala4 (AUTHOR), Belviso, Pasquale4 (AUTHOR), Bellucci Sessa, Eliana4 (AUTHOR), Carandante, Antonio4 (AUTHOR), Perrini, Maddalena2,3 (AUTHOR), Sansivero, Fabio4 (AUTHOR), Tizzani, Pietro2,3 (AUTHOR) pietro.tizzani@cnr.it |
| Source: | Remote Sensing. Apr2026, Vol. 18 Issue 8, p1240. 33p. |
| Subjects: | Calderas, Multivariate analysis, Volcanic eruptions, Induced seismicity, Radar interferometry |
| Geographic Terms: | Phlegraean Plain (Italy), Italy |
| Abstract: | Highlights: What are the main findings? A time-lagged Multivariable Fractional Polynomial Analysis (MFPA) integrating InSAR deformation, seismicity, CO2 degassing and thermal/heat-flow signals at Solfatara–Pisciarelli was conducted, markedly improving model performance versus a no-lag approach. Global Critical Point Analysis (GCPA) on the normalized multiparametric series identified two system-wide transitions (30 November 2020 and 1 April 2023) consistent with regime shifts in the hydrothermal–magmatic system. What are the implications of the main findings? Explicitly accounting for delayed coupling among monitoring signals provides a more robust, interpretable way to characterize evolving unrest in complex calderas without relying on fixed thresholds. The integrated MFPA–GCPA workflow is transferable to other well-instrumented volcanoes and can support monitoring by objectively highlighting major reorganizations relevant to hazard contextualization. Understanding how volcanic systems evolve over time is a major challenge due to their complex behaviour and constantly changing conditions. This study explores a novel approach to detecting significant changes in multiparametric signals of volcanic unrest by analysing how different types of data, such as ground deformation, gas emissions, temperature, and earthquakes, interact with each other. Focusing on the Solfatara–Pisciarelli volcano system, which is a more active area in the Campi Flegrei Caldera (Southern Italy), we used two advanced methods to identify critical transitions in the system: one to model the nonlinear relationships between variables, and the other to detect key moments when the system's behaviour shifts. By including time delays between signals (LAG), we found that our model became much more accurate in identifying these changes. In contrast, models that ignored time lags showed higher uncertainty. The results highlight the importance and effectiveness of using integrated multivariate approaches such as Multivariable Fractional Polynomial Analysis (MFPA) and Global Critical Point Analysis (GCPA) to gain deeper insights into the systemic behaviour of the caldera and its temporal evolution within a complex area like the Campi Flegrei over the selected time period. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | Highlights: What are the main findings? A time-lagged Multivariable Fractional Polynomial Analysis (MFPA) integrating InSAR deformation, seismicity, CO2 degassing and thermal/heat-flow signals at Solfatara–Pisciarelli was conducted, markedly improving model performance versus a no-lag approach. Global Critical Point Analysis (GCPA) on the normalized multiparametric series identified two system-wide transitions (30 November 2020 and 1 April 2023) consistent with regime shifts in the hydrothermal–magmatic system. What are the implications of the main findings? Explicitly accounting for delayed coupling among monitoring signals provides a more robust, interpretable way to characterize evolving unrest in complex calderas without relying on fixed thresholds. The integrated MFPA–GCPA workflow is transferable to other well-instrumented volcanoes and can support monitoring by objectively highlighting major reorganizations relevant to hazard contextualization. Understanding how volcanic systems evolve over time is a major challenge due to their complex behaviour and constantly changing conditions. This study explores a novel approach to detecting significant changes in multiparametric signals of volcanic unrest by analysing how different types of data, such as ground deformation, gas emissions, temperature, and earthquakes, interact with each other. Focusing on the Solfatara–Pisciarelli volcano system, which is a more active area in the Campi Flegrei Caldera (Southern Italy), we used two advanced methods to identify critical transitions in the system: one to model the nonlinear relationships between variables, and the other to detect key moments when the system's behaviour shifts. By including time delays between signals (LAG), we found that our model became much more accurate in identifying these changes. In contrast, models that ignored time lags showed higher uncertainty. The results highlight the importance and effectiveness of using integrated multivariate approaches such as Multivariable Fractional Polynomial Analysis (MFPA) and Global Critical Point Analysis (GCPA) to gain deeper insights into the systemic behaviour of the caldera and its temporal evolution within a complex area like the Campi Flegrei over the selected time period. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 20724292 |
| DOI: | 10.3390/rs18081240 |
