Exploring the role of model classification, complexity, and selection in volcanic hazard forecasting

Abstract

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.