A new method is proposed for modelling the yearly maxima of sub-daily precipitation, with the aim of producing spatial maps of return level estimates. Yearly precipitation maxima are modelled using a Bayesian hierarchical model with a latent Gaussian field, with the blended generalised extreme value (bGEV) distribution used as a substitute for the more standard generalised extreme value (GEV) distribution. Inference is made less wasteful with a novel two-step procedure that performs separate modelling of the scale parameter of the bGEV distribution using peaks over threshold data. Fast inference is performed using integrated nested Laplace approximations (INLA) together with the stochastic partial differential equation approach, both implemented in R-INLA. Heuristics for improving the numerical stability of R-INLA with the GEV and bGEV distributions are also presented. The model is fitted to yearly maxima of sub-daily precipitation from the south of Norway and is able to quickly produce high-resolution return level maps with uncertainty. The proposed two-step procedure provides an improved model fit over standard inference techniques when modelling the yearly maxima of sub-daily precipitation with the bGEV distribution. Supplementary materials accompanying this paper appear on-line.
|Original language||English (US)|
|Journal||JOURNAL OF AGRICULTURAL BIOLOGICAL AND ENVIRONMENTAL STATISTICS|
|State||Published - Jun 4 2022|
Bibliographical noteKAUST Repository Item: Exported on 2022-06-17
Acknowledgements: Open access funding provided by orgName. This publication is part of the World of Wild Waters (WoWW) project, which falls under the umbrella of Norwegian University of Science and Technology (NTNU)’s Digital Transformation initiative. We thank Thordis L. Thorarinsdottir and Geir-Arne Fuglstad for helpful discussions.
ASJC Scopus subject areas
- Agricultural and Biological Sciences(all)
- Environmental Science(all)
- Applied Mathematics
- Statistics and Probability
- Statistics, Probability and Uncertainty
- Agricultural and Biological Sciences (miscellaneous)