Massey Documents by Type

Now showing 1 - 3 of 3

Exploring the role of model classification, complexity, and selection in volcanic hazard forecasting
(Elsevier Ltd, 2026-02-01) Scott E; Whitehead M; Procter J
This review examines the current landscape of computational volcanic hazard models, focusing on their creation and application, for a diverse set of end-users’ short-term and long-term forecasting requirements. We provide a comprehensive classification of volcanic hazard models, categorising them according to their theoretical foundations. This is central to understanding the diversity of hazard characterisation and simulation approaches, from empirical models to computationally demanding physics-based numerical models. The classification framework helps contextualise the strengths and limitations of different models and their suitability for specific forecasting demands. We discuss the fundamental principles behind model construction, considering factors such as input parameters, conceptual frameworks, and the incorporation of uncertainties. We also synthesise existing literature on model testing, covering aspects such as model verification, validation, calibration, and benchmarking, and provide a systematic and transparent framework for model selection, considering data availability, computational constraints, and specific forecasting needs. We explore the balance between model complexity, computational efficiency, and accuracy, addressing the uncertainties inherent in both input parameters and model processes. A key focus is the role of input parameters in forecasting and the need to select models that are detailed enough to capture essential hazard dynamics, yet simple enough to minimise error and computational costs.
Global sensitivity analysis of models for volcanic ash forecasting
(Elsevier B V, 2025-10-01) Scott E; Whitehead M; Mead S; Bebbington M; Procter J
Volcanic ash is a widespread and destructive volcanic hazard. Timely and accurate forecasts for ash deposition and dispersal help mitigate the risks of volcanic hazards to society. Producing these forecasts requires numerous simulations with varying input parameters to encapsulate uncertainty and accurately capture the actual event to deliver a reliable forecast. However, exploring all possible combinations of input parameters is computationally infeasible in the lead up to an eruption. This research explores the input space of two volcanic ash transport and dispersion models, Tephra2, which is based on a simplified analytical solution, and Fall3D, which is a computational model based on more general assumptions, in the context of forecasting an unknown future eruption. We use the exemplar of Taranaki Mounga (Mount Taranaki), Aotearoa New Zealand, which has an estimated 30% to 50% chance of an explosive eruption in the next 50 years. We statistically determine how much each input parameter contributes to model output variance through a global sensitivity analysis via Sobol’ indices and the extended Fourier Amplitude Sensitivity Test (eFAST). Our findings show that grain size distribution, diffusion, plume shape, and plume duration (Fall3D only) have a substantial first-order impact on model output variance. In contrast, mass, particle density, and plume height have minimal impact in the first-order but become influential when considering parameter-parameter inter-relationships (total-order). The results not only enhance our understanding of model sensitivities but also point to improved efficiency in forecasting efforts.
Development of a Bayesian event tree for short-term eruption onset forecasting at Taupō volcano
(Elsevier BV, 2022-12) Scott E; Bebbington M; Wilson T; Kennedy B; Leonard G
Taupō volcano, located within the Taupō Volcanic Zone (TVZ) in the central North Island of Aotearoa-New Zealand, is one of the world's most active silicic caldera systems. Silicic calderas such as Taupō are capable of a broad and complex range of volcanological activity, ranging from minor unrest episodes to large destructive supereruptions. A critical tool for volcanic risk management is eruption forecasting. The Bayesian Event Tree for Eruption Forecasting (BET_EF) is one probabilistic eruption forecasting tool that can be used to produce short-term eruption forecasts for any volcano worldwide. A BET_EF model is developed for Taupō volcano, informed by geologic and historic data. Monitoring parameters for the model were obtained through a structured expert elicitation workshop with 30 of Aotearoa-New Zealand's volcanologists and volcano monitoring scientists. The eruption probabilities output by the BET_EF model for Taupō volcano's 17 recorded unrest episodes (between 1877 and 2019) were examined. We found time-inhomogeneity in the probabilities stemming from both the changes over time in the monitoring network around Taupō volcano and increasing level of past data (number of non-eruptive unrest episodes). We examine the former issue through the lens of the latest episodes, and the latter by re-running the episodes assuming knowledge of all 16 other episodes (calibration to 2021 data). The time variable monitoring network around Taupō volcano and parameter weights had a substantial impact on the estimated probabilities of magmatic unrest and eruption. We also note the need for improved monitoring and data processing at Taupō volcano, the existence of which would prompt updates and therefore refinements in the BET_EF model.

Massey Documents by Type

Browse

Filters

Settings

Sort By

Results per page

Search Results