Spatial modelling framework for the characterisation of rainfall extremes at different durations and under climate change

Abstract

This paper describes a statistical modelling framework for the characterisation of rainfall extremes over a region of interest. Using a Bayesian hierarchical approach, the data are assumed to follow the generalised extreme value distribution, whose parameters are modelled as spatial Gaussian processes in the latent process layer. We also integrate a parametric relationship between precipitation maxima accumulated over increasing durations. The inference of the model parameters is thus improved by pooling information across both space and accumulation duration. In addition, we propose and investigate two different approaches for the integration of daily and sub-daily rainfall data within the framework. We also demonstrate how information from a regional climate model can be integrated to enable the investigation of future projections of extreme rainfall characteristics. We apply the proposed methodology to precipitation datasets from two large-scale study regions located on the east coast of Australia. The models are fitted using Markov chain Monte Carlo simulations, and we present estimated model parameters and posterior inferences of return levels at various durations and sites of interest. We demonstrate the effectiveness of the framework in spatially extrapolating the inference to locations other than those at which direct rainfall measurements are available. We also provide comparisons between rainfall extremes at various durations obtained for the current climate and those based on future projections from a regional climate model. Both methods proposed for the integration of daily and sub-daily records were found to yield similar results in terms of model performance and computational requirements.