Bayesian spatial clustering of extremal behaviour for hydrological variables

Christian Rohrbeck, Jonathan Tawn

Research output: Contribution to journalArticle

1 Citation (Scopus)


To address the need for efficient inference for a range of hydrological extreme value
problems, spatial pooling of information is the standard approach for marginal tail
estimation. We propose the first extreme value spatial clustering methods which
account for both the similarity of the marginal tails and the spatial dependence
structure of the data to determine the appropriate level of pooling. Spatial depen-
dence is incorporated in two ways: to determine the cluster selection and to account
for dependence of the data over sites within a cluster when making the marginal
inference. We introduce a statistical model for the pairwise extremal dependence
which incorporates distance between sites, and accommodates our belief that sites
within the same cluster tend to exhibit a higher degree of dependence than sites
in different clusters. We use a Bayesian framework which learns about both the
number of clusters and their spatial structure, and that enables the inference of
site-specific marginal distributions of extremes to incorporate uncertainty in the
clustering allocation. The approach is illustrated using simulations, the analysis of
daily precipitation levels in Norway and daily river flow levels in the UK.
Original languageEnglish
JournalJournal of Computational and Graphical Statistics
Early online date9 Jul 2020
Publication statusE-pub ahead of print - 9 Jul 2020

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