Short-Term Eruption Forecasting for Crisis Decision-Support in the Auckland Volcanic Field, New Zealand - Wild, Alec J.; Bebbington, Mark S.; Lindsay, Jan M. (Frontiers in Earth Science, 2022)
Auckland, a city of 1.6 million people, is situated atop the active monogenetic Auckland Volcanic Field (AVF). Thus, short-term eruption forecasting is critical to support crisis management in a future event, especially to inform decisions such as calling evacuations. Here we present an updated BET_EF for the AVF incorporating new data and the results of an expert-opinion workshop, and test the performance of the resulting BETEF_AVF on eight hypothetical eruption scenarios with pre-eruptive sequences. We carry out a sensitivity analysis into the selection of prior distributions for key model parameters to explore the utility of using BET_EF outputs as a potential input for evacuation decision making in areas of distributed volcanism such as the AVF. BETEF_AVF performed well based on the synthetic unrest dataset for assessing the probability of eruption, with the vent outbreaks eventuating within the zone of high spatial likelihood. Our analysis found that the selection of different spatial prior model inputs affects the estimated vent location due to the weighting between prior models and monitoring inputs within the BET_EF, which as unrest escalates may not be appropriate for distributed volcanic fields. This issue is compounded when the outputs are combined with cost-benefit analysis to inform evacuation decisions, leading to areas well beyond those with observed precursory activity being included in evacuation zones. We find that several default settings used in past work for the application of BET_EF and CBA to inform evacuation decision-support are not suitable for distributed volcanism; in particular, the default 50-50 weighting between priors and monitoring inputs for assessing spatial vent location does not produce useful results. We conclude by suggesting future cost-benefit analysis applications in volcanic fields appropriately consider the spatial and temporal variability and uncertainty characteristic of such systems.