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Explosive volcanic eruptions inject gases and ash into the atmosphere, posing major hazards for human health, infrastructure, and aviation. A new article in Reviews of Geophysics examines recent advances in estimating Eruption Source Parameters (ESPs), the key conditions at the volcanic vent that are a necessity for modeling the behavior of volcanic plumes. Here, we asked the authors to explain what ESPs are, what technologies are used to observe eruptions, and which scientific challenges and future research directions remain for improving volcanic plume monitoring and modeling.
In simple terms, what are Eruption Source Parameters?
Eruption Source Parameters (ESPs) describe the key conditions at the volcanic vent during an eruption.
Eruption Source Parameters (ESPs) describe the key conditions at the volcanic vent during an eruption, such as the mass eruption rate, exit velocity, temperature, and particle size distribution. These parameters define how material is injected into the atmosphere and are essential inputs for models that simulate plume rise and subsequent dispersal of volcanic gases and ash in the atmosphere. In simple terms, ESPs represent the boundary conditions that control the behavior of volcanic plumes. Because they cannot usually be measured during an eruption, they must be estimated from indirect observations and models, which introduces significant uncertainty.
Why is it important to understand how volcanic ash and gases disperse after an eruption?
Volcanic ash and gases can travel long distances and affect aviation safety, human health, infrastructure, and even climate. Fine ash particles are particularly hazardous for aircrafts, while ash fallout can disrupt communities and critical services on the ground. Gas emissions may also impact air quality and alter the atmospheric radiative budget. Understanding volcanic dispersion is therefore essential for forecasting the movement of volcanic clouds and issuing timely warnings. Reliable forecasts support risk mitigation strategies and enable more effective responses by civil protection agencies and aviation authorities.
What technologies are used to observe volcanic plumes?
Volcanic plumes are observed using a combination of satellite, ground-based, and, more rarely, airborne measurements. Satellite observations are crucial for tracking ash and gas clouds over large spatial scales and in near real time. Ground-based instruments, such as radar, cameras, and infrasound sensors, provide detailed information on plume dynamics close to the source. Increasingly, these observations are integrated with numerical models to infer eruption conditions. The combination of multiple data streams is essential for constraining ESPs and improving the reliability of plume simulations.
What are some of the recent advances in estimating Eruption Source Parameters?
Recent advances have focused on combining observations with numerical models to better constrain ESPs. Multi-sensor approaches, data inversion techniques, and improved plume models have significantly enhanced our ability to estimate eruption rates and plume dynamics. At the same time, high-resolution computational fluid dynamics (CFD) simulations provide deeper insights into the complex fluid dynamic processes governing plume behavior. However, these models are computationally expensive and unsuitable for real-time applications, highlighting the need for approaches that bridge the gap between physical realism and operational efficiency.
What strategies do you propose in your review to improve Eruption Source Parameters estimation?
A central contribution of this review is the proposal of a new class of operational models for volcanic plumes.
A central contribution of this review is the proposal of a new class of operational models for volcanic plumes. These models integrate the physical realism of high-fidelity CFD simulations with the efficiency of simplified models used in forecasting. In particular, the review highlights the potential of artificial intelligence and machine learning techniques to “learn” from CFD results and optimally calibrate the key variables controlling plume dynamics. This hybrid approach allows complex physical processes to be represented in a computationally efficient framework, making it suitable for real-time applications while retaining improved accuracy.
How does improved volcanic plume monitoring lead to more effective volcanic hazard assessment?
Improved monitoring leads to more accurate estimates of ESPs, which directly translate into better forecasts of plume rise and ash dispersion. This reduces uncertainty in hazard assessments and supports more reliable decision-making. For example, more accurate forecasts can help aviation authorities minimize disruptions while maintaining safety and enable civil protection agencies to issue targeted warnings. Ultimately, better integration of observations and models enhances the capacity to respond effectively during eruptions and to mitigate their societal and economic impacts.
What are the remaining questions or knowledge gaps where additional research is needed?
Further research is needed to improve the coupling between observations, physics-based models, and data-driven approaches.
Despite progress, significant challenges remain. ESPs are still difficult to constrain in real time, and uncertainties in both observations and models propagate into forecasts. The integration of diverse data sources is not yet fully optimized, and different estimation methods can yield inconsistent results. Further research is needed to improve the coupling between observations, physics-based models, and data-driven approaches. In particular, developing robust hybrid frameworks that combine CFD, simplified models, and machine learning represents a key direction for advancing both scientific understanding and operational forecasting.
—Antonio Costa (antonio.costa@ingv.it, 
0000-0002-4987-6471), Istituto Nazionale di Geofisica e Vulcanologia, Italy
Citation: Costa, A. (2026), From volcanic vents to safer skies, Eos, 107, https://doi.org/10.1029/2026EO265022. Published on 27 May 2026.
This article does not represent the opinion of AGU, Eos, or any of its affiliates. It is solely the opinion of the author(s).
Text © 2026. The authors. CC BY-NC-ND 3.0
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