Bayesian hierarchical modeling of extreme hourly precipitation in Norway

By A. Dyrrdal, Alex Lenkoski, Thordis Linda Thorarinsdottir, F. Stordal. 2013

95 citations recorded by the source corpus.

Spatial maps of extreme precipitation are a critical component of flood estimation in hydrological modeling, as well as in the planning and design of important infrastructure. This is particularly relevant in countries, such as Norway, that have a high density of hydrological power generating facilities and are exposed to significant risk of infrastructure damage due to flooding. In this work, we estimate a spatially coherent map of the distribution of extreme hourly precipitation in Norway, in terms of return levels, by linking generalized extreme value (GEV) distributions with latent Gaussian fields in a Bayesian hierarchical model. Generalized linear models on the parameters of the GEV distribution are able to incorporate location‐specific geographic and meteorological information and thereby accommodate these effects on extreme precipitation. Our model incorporates a Bayesian model averaging component that directly assesses model uncertainty in the effect of the proposed covariates. Gaussian fields on the GEV parameters capture additional unexplained spatial heterogeneity and overcome the sparse grid on which observations are collected. Our framework is able to appropriately characterize both the spatial variability of the distribution of extreme hourly precipitation in Norway and the associated uncertainty in these estimates. Copyright © 2014 John Wiley & Sons, Ltd.

Subfields: Economics and Econometrics, Global and Planetary Change, Soil Science

View Paper: https://www.semanticscholar.org/paper/f3f8a1ae8abb13e096b4bb401302ab03d5efd3c8

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Citations in the corpus

A review of statistical methods to analyze extreme precipitation and temperature events in the Mediterranean region (2019, result): “Also in agreement with our results, Kyselý (2010), Anagnostopoulou and Tolika (2012), Dyrrdal et al. (2015), and Roth et al. (2014) proposed the GEVand GPD as the most appropriate distributions for extreme precipitation events.”
Spatial Bayesian hierarchical modeling of precipitation extremes over a large domain (2015, background): “, 2007] and since has been widely discussed in the literature [Cooley and Sain, 2010; Aryal et al., 2010; Atyeo and Walshaw, 2012; Davison et al., 2012; Ghosh and Mallick, 2011; Reich and Shaby, 2012; Sang and Gelfand, 2010, 2009; Apputhurai and Stephenson, 2013; Dyrrdal et al., 2014].”
Estimation of a non-stationary model for annual precipitation in southern Norway using replicates of the spatial field (2014, background): “Statistical modelling and spatial prediction of precipitation in Norway are challenging tasks (Orskaug et al., 2011; Ingebrigtsen et al., 2014; Dyrrdal et al., 2014).”