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A Bayesian spatio-temporal model for precipitation extremes—STOR team contribution to the EVA2017 challenge


This paper concerns our approach to the EVA2017 challenge, the aim of which was to predict extreme precipitation quantiles across several sites in the Netherlands. Our approach uses a Bayesian hierarchical structure, which combines Gamma and generalised Pareto distributions. We impose a spatio-temporal structure in the model parameters via an autoregressive prior. Estimates are obtained using Markov chain Monte Carlo techniques and spatial interpolation. This approach has been successful in the context of the challenge, providing reasonable improvements over the benchmark.

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The authors gratefully acknowledge the support of the Engineering and Physical Sciences Research Council (EPSRC) funded STOR-i Centre for Doctoral Training (Grant numbers EP/H023151/1 and EP/L015692/1). Rohrbeck also acknowledges funding by the Faculty of Science and Technology, Lancaster University. The authors also acknowledge sponsorship of EDF Energy (P. Sharkey), Shell Research (R. Shooter), JBA Risk Management (A. Barlow) and the Joint UK BEIS/Defra Met Office Hadley Centre Climate Programme (GA01101) (P. Sharkey). The authors also extend their thanks to Jonathan Tawn and the referees for helpful comments.

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Correspondence to Rob Shooter.

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Barlow, A.M., Rohrbeck, C., Sharkey, P. et al. A Bayesian spatio-temporal model for precipitation extremes—STOR team contribution to the EVA2017 challenge. Extremes 21, 431–439 (2018).

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