Framework Assesses the Economic Value of Volcanic Eruption Forecasts

Volcanic eruptions represent some of the most destructive natural hazards on Earth, capable of causing widespread disruption, loss of life, and long-term socioeconomic impacts. Globally, fifty to seventy eruptions occur each year, with a minority causing disproportionate losses. Recent events span a wide spectrum of local to global consequences, from the 2010 Merapi eruption in Indonesia, which killed more than 350 people and forced the evacuation of 400,000, to the 2022 Hunga Tonga–Hunga Ha’apai eruption, which primarily produced widespread infrastructure damage and aviation disruption. Smaller eruptions can also produce severe human impacts despite limited physical damage. The 2014 Ontake eruption in Japan resulted in 63 fatalities, while the 2019 Whakaari eruption in New Zealand caused 22 fatalities and dozens of severe injuries. These events exemplify how short-warning, exposure-driven events can be disproportionately deadly, raising critical questions about the true value of forecasting systems in preventing loss of life versus managing economic disruption.

Despite extended advances in monitoring over the last decades, anticipating eruptions remains challenging. Seismic, geodetic, and gas precursors often herald unrest, yet forecasts remain uncertain because genuine precursor signals vary across volcanoes and are difficult to identify robustly across eruptive and non-eruptive periods. Some eruptions occur with subtle or absent warnings, and most volcanoes lack sufficient recorded events to identify reliable patterns. Recent data-driven approaches have improved the detection of subtle patterns in monitoring data, producing likelihood of eruption forecasts that can be evaluated under retrospective conditions. However, most studies focus on pre-eruptive intervals without comparing against non-eruptive repose, and objective metrics that account for missed eruptions and false alarms over long periods are necessary to evaluate forecast reliability. This gap highlights the need for a framework that explicitly balances the short-term costs of precautionary action against the avoided losses from catastrophic events.

In a study published in Nature Communications, researchers Alberto Ardid, David Dempsey, Garry McDonald, and Shane Cronin introduce a Potential Economic Value (PEV) framework to volcanology. This framework evaluates forecast utility by balancing precautionary actions against avoidable catastrophic losses, such as mass casualties. Originally developed for meteorology and aviation, and now applied in flood, drought, and wildfire risk management, PEV balances the short-term costs of precautionary actions against the avoided losses when forecasts correctly anticipate eruptions. The study demonstrates the PEV framework on multi-year eruption forecasts derived from a machine-learning model at five volcanoes that collectively span a spectrum of eruption frequencies, monitoring histories, and socio-economic settings. Two cases, Whakaari and Ontake, provide documented human and economic losses, enabling retrospective evaluation of the framework, while three additional applications serve as stress tests for generalizability.

The retrospective analysis of Whakaari shows that the forecast model could have reduced cumulative costs by more than 30% relative to a no-forecasting baseline. This result was obtained using a decision threshold of 0.6, which represents a deliberately precautionary choice that favors reducing missed eruptions at the expense of more frequent false alarms. In a separate optimization analysis, the value-maximizing threshold near 0.85 to 0.90 was associated with up to approximately 220 million USD in modeled avoided losses. That estimate should not be attributed to the more precautionary 0.6 threshold. The PEV curve exhibits a characteristic hump shape, where low thresholds result in frequent false alarms dominating expected costs, while high thresholds lead to missed eruptions driving losses. Monte Carlo ensemble sampling suggests that while absolute PEV values shift under different cost assumptions, the location of the optimum remains relatively stable near a threshold of 0.85 to 0.90. This stability suggests that threshold selection can be robust to substantial uncertainty in economic valuation.

Ontake provides a complementary test of the PEV framework, representing a high-exposure, land-based volcanic setting with public access and minimal structural damage. A retrospective scenario analysis suggests that a short-term forecast could have substantially reduced exposure-related losses while requiring repeated precautionary alerts over the analysis period. If a forecasting method had been available and acted upon with evacuation planning, the analysis shows that a forecast at Ontake could have reduced human and exposure-related losses by approximately 90%, avoiding short-term impacts despite 40 false alarms. This finding is consistent with the Whakaari results, indicating that when catastrophic human losses dominate the cost structure, even imperfect forecasts with high false-alarm rates provide substantial value over the long term. The contrast between Whakaari and Ontake illustrates how PEV responds to differences in exposure structure and institutional control, with Ontake showing that precautionary actions are limited to temporary closure of public trails and summit access.

The study also tests the generalizability of PEV to volcanoes lacking recent major loss events but facing credible high-impact scenarios. In high-exposure alpine settings like Mount Ruapehu, where lahars threaten people working in and visiting ski fields, short-term forecasts could reduce expected losses by enabling temporary access restrictions. Although only two minor eruptions occurred during the 14-year observation period at Ruapehu, ensemble modeling indicates that precautionary access restrictions could reduce expected losses by more than 40% under plausible assumptions about the probability that a missed eruption coincides with high exposure. Sensitivity analysis reveals that balanced thresholds maintain positive PEV across wide cost ranges, whereas risk-averse thresholds eliminate value in the absence of recent catastrophic losses. This illustrates a practical challenge: in the absence of recent damaging eruptions to calibrate perceived risk, high alert thresholds may seem cautious, yet they increase expected losses by allowing rare but catastrophic events to be missed.

Applications to Copahue and Bezymianny further demonstrate framework generalizability across volcanic systems with contrasting dominant avoidable loss pathways. At Bezymianny, short-term forecast value is driven primarily by aviation risk mitigation, as explosive eruptions frequently generate ash clouds that intersect major trans-Pacific air routes. In this context, precautionary actions consist mainly of airspace management and flight rerouting rather than evacuation of exposed populations. In contrast, the Copahue application models hypothetical evacuation and access-control costs involving nearby communities, people, livestock, and infrastructure in the Chile–Argentina border region. The analysis shows that positive forecast value is not confined to a single optimal threshold, but arises across a range of intermediate thresholds where reducing the likelihood of rare, high-impact misses outweighs the cumulative cost of precautionary actions.

To assess how PEV depends on forecast model, the researchers evaluated the metric on three different models: a machine-learning model tailored to a specific volcano, a model that uses transfer-learning to pool precursor information from multiple volcanoes, and a benchmark forecast based on unmodified seismic data. For Whakaari, the generalized model yielded the highest PEV and greatest cost savings, with the tailored model performing slightly worse. The same comparison for Copahue and Bezymianny indicates that generalized models achieve the highest positive economic value, despite training on data from other volcanic systems. This suggests that models incorporating generalized multi-volcano precursory signals offer operational advantages for data-limited settings where local eruption histories are insufficient. These comparisons suggest potential benefits from transferring precursor information between volcanoes, but the small set of case studies does not establish universal performance across volcanic systems.

Operational challenges remain in applying the PEV framework, as the decision threshold that maximizes expected value can only be identified retrospectively. In practice, agencies must choose alert thresholds under uncertainty, informed by imperfect knowledge of future eruption behavior, exposure, and societal tolerance for false alarms versus missed events. The researchers recommend that agencies adopt a robust initial threshold that maintains positive PEV across a broad range of plausible cost assumptions, rather than attempting to identify a value-maximizing threshold a priori. Balanced thresholds sustain positive PEV across wide cost-ratio ranges, making them robust initial choices. This approach parallels earthquake early warning practice, where alert thresholds are set below the target damage threshold to bound miss probability at a policy-specified ceiling, accepting a measurable increase in false alerts. As forecast-outcome data accumulate, thresholds can be refined through Bayesian updating of the posterior expected loss, following established methods in flood and weather forecasting.

Successful implementation of PEV-informed forecasting requires addressing both technical and behavioral dimensions. Research on warning compliance shows that repeated false alarms can erode public response by reducing perceived credibility and willingness to act, a phenomenon commonly referred to as the cry-wolf effect. However, evidence from risk-communication research suggests that transparent communication of uncertainty can sustain credibility even when forecasts are imperfect. A central behavioral challenge is managing societal response to frequent precautionary warnings, which can be addressed by reframing alerts as indicators of elevated risk rather than definitive eruption statements. Operationally, this behavioral framing must be supported by alert designs that translate high-frequency forecast outputs into warnings that are actionable but not overwhelming. The study concludes that the PEV framework provides a structured means of comparing alternative warning strategies under uncertainty, supporting the use of more precautionary warning thresholds provided that the resulting increase in alert frequency is managed in ways that maintain public compliance and trust.

Subject of Research: Socio-economic evaluation of data-driven volcanic eruption forecasting systems

Article Title: Socio-economic value of data-driven eruption forecasts to balance false alarms against catastrophic loss

Article References: Ardid, A., Dempsey, D., McDonald, G., & Cronin, S. (2026). Socio-economic value of data-driven eruption forecasts to balance false alarms against catastrophic loss. Nature Communications, 17(1), Article 9356. https://doi.org/10.1038/s41467-026-77242-4

Image Credits: AI Generated

DOI: 10.1038/s41467-026-77242-4

Keywords: volcanology, machine learning, risk management, seismic monitoring, economic impact, disaster preparedness, forecasting, public safety

The cited source study is licensed under CC BY 4.0. This article is an edited news summary; the original authors and DOI are credited above.