Partly Cloudy With a Chance of Lava Flows: Forecasting Volcanic Eruptions in the Twenty-First Century

A primary goal of volcanology is forecasting hazardous eruptive activity. Despite much progress over the last century, however, volcanoes still erupt with no detected precursors, lives and livelihoods are lost to eruptive activity, and forecasting the onsets of eruptions remains fraught with uncertainty. Long-term forecasts are generally derived from the geological and historical records, from which recurrence intervals and styles of activity can be inferred, while shorter-term forecasts are derived from patterns in monitoring data. Information from geology and monitoring data can be evaluated and combined using statistical analysis, expert elicitation, and conceptual and or mathematical models. Integrative frameworks, such as event trees, combine this diversity of information to produce probabilistic forecasts that can inform the style and scale of the societal response to a potential future eruption. Several developments show promise to revolutionize the utility and accuracy of these forecasts. These include growth in the quantity and quality of multidisciplinary monitoring data, coupled with increases in computing power; machine learning algorithms, which will allow far better utilization of this growing volume of data; and new physiochemical volcano models and data assimilation algorithms, which take advantage of a wide range of monitoring data and realistic physics to better predict the evolution of a given physical state. Although eruption forecasts may never be as generally reliable as weather forecasts, and great caution must be exercised when attempting to predict highly complex volcanic behavior, these and other innovations-particularly when combined in integrative, fully probabilistic forecasting frameworks-should help volcanologists to better issue warnings of volcanic activity on societally relevant time frames. Plain Language Summary Unlike many natural hazards, volcanoes often give warning signs from minutes to even years before they erupt. Detecting this activity, interpreting it, and using it to accurately forecast likely outcomes, however, remains a key challenge for volcanologists. A new generation of ground- and space-based sensors is recording volcanic unrest in unprecedented spatial and temporal detail, providing new views of eruption precursors that might not have been detected just a few years ago. Current forecasts are based largely on pattern recognition and analogy, but recent advances in volcano modeling offer hope that eruption forecasting may be augmented by knowledge of the fundamental physics governing eruptive processes. The growing availability of data will also be increasingly utilized by machine learning approaches that detect patterns and inform forecasts in ways not currently possible. Insights from these new techniques can be combined using probabilistic frameworks with the eruptive history of a volcano, changes in monitoring data, expert opinion, and other sources of information to yield forecasts of volcanic activity that might begin to resemble weather forecasts. Such an advance would be of obvious societal benefit in a world where millions of people live in the shadows of active volcanoes.