Abstract Understanding how volcanic systems evolve over time is a major challenge due to their complex behavior and constantly changing conditions. This study explores a new way to detect early warning signals of volcanic unrest by analyzing 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, that is a more active area in 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 behavior shifts. By including time delays between signals (LAG) we found that our model becomes much more accurate in identifying these changes. In contrast, models that ignore time lags show higher uncertainty. These results highlight the importance of considering how different warning signs may not appear all at once, and they offer a promising tool for improving the monitoring of active volcanoes and supporting early risk management.
Vitale et al. (Tue,) studied this question.