Method selection in short-term eruption forecasting

For accurate and timely information on the evolving state of our volcanoes we need reliable short-term forecasts. These forecasts directly impact crisis management from evacuations, exclusion zones, and when it is safe to return. Eruption forecasting should not be viewed as an academic exercise or a theoretical discussion in a back room, nor is now the time for dramatic data interpretations or a set of 'we-told-you-so' hindcasting demonstrations. To produce a short-term eruption forecast, a systematic evaluation of options is required with a critical assessment of outstanding issues and assumption validity. We run this lens over a set of existing short-term eruption forecasting methods and provide a straightforward data-driven methodology for forecast selection. Six eruption forecasting methods are discussed here: (1) Expert interpretation, (2) Event trees, (3) Belief networks, (4) Failure forecasting, (5) Process / Source models, and (6) Machine-learning algorithms with a view to forecasting: (1) Eruption occurrence (onset time), (2) Eruptive vent location(s), (3) Eruption size, (4) Initial eruption style/phase, (5) Eruption phase duration, and (6) Eruption specific hazards. This work constitutes a decision tool that can be directly applied to a volcanic system of interest to determine which eruption forecasting methods are possible, plausible, and with what implementation steps. Accompanying this is an extensive evaluation of assumption validity (and assumption avoidance options) to ensure the accurate and transparent application of any eruption forecasting method. Significant potential is identified in methods that are generally data-hungry (e.g., belief networks and machine-learning algorithms), and/or by the coupling of probabilistic methods to process/source models. However, as most volcanic systems are data-poor, expert interpretation and event trees remain the only currently available forecasting methods that can be readily and widely applied during volcanic crises. (c) 2021 Elsevier B.V. All rights reserved.