A Hierarchical Model for Serially-Dependent Extremes: A Study of Heat Waves in the Western US

Brian J. Reich, Benjamin Adam Shaby, Daniel Cooley

Research output: Contribution to journalArticlepeer-review

29 Scopus citations

Abstract

Heat waves take a major toll on human populations, with negative impacts on the economy, agriculture, and human health. As a result, there is great interest in studying the changes over time in the probability and magnitude of heat waves. In this paper we propose a hierarchical Bayesian model for serially-dependent extreme temperatures. We assume the marginal temperature distribution follows the generalized Pareto distribution (GPD) above a location-specific threshold, and capture dependence between subsequent days using a transformed max-stable process. Our model allows both the parameters in the marginal GPD and the temporal dependence function to change over time. This allows Bayesian inference on the change in likelihood of a heat wave. We apply this methodology to daily high temperatures in nine cities in the western US for 1979-2010. Our analysis reveals increases in the probability of a heat wave in several US cities. This article has supplementary material online.

Original languageEnglish (US)
Pages (from-to)119-135
Number of pages17
JournalJournal of Agricultural, Biological, and Environmental Statistics
Volume19
Issue number1
DOIs
StatePublished - Mar 1 2014

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Environmental Science(all)
  • Agricultural and Biological Sciences (miscellaneous)
  • Agricultural and Biological Sciences(all)
  • Statistics, Probability and Uncertainty
  • Applied Mathematics

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