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A Hierarchical Model for Serially-Dependent Extremes: A Study of Heat Waves in the Western US
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.
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“[33] demonstrated the efficacy of GPD within a hierarchical framework to account for serial dependencies in heatwave occurrences, particularly in the western United States, where temporal clustering of extreme temperatures is prominent.”
From Advancing Heatwave Forecasting via Distribution Informed-Graph Neural Networks (DI-GNNs): Integrating Extreme Value Theory with GNNs (2024) · cites this paper as [33]
In the citing paper
“The Generalized Pareto Distribution (GPD) is a cornerstone of extreme value theory, particularly well-suited for modeling the tails of distributions to predict rare and extreme events such as heatwaves.”
From Advancing Heatwave Forecasting via Distribution Informed-Graph Neural Networks (DI-GNNs): Integrating Extreme Value Theory with GNNs (2024) · cites this paper as [33]
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Research works printed but not linked: 29
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Cited by
Sorted by citations
Chishtie, F. A., Brunet, D., White, R. H., Michelson, D., Jiang, J., Lucas, V., Ruboonga, E., Imaash, S., Westland, M., Chui, T., et al. (2024) · arXiv · doi:10.48550/arxiv.2411.13496
“[33] demonstrated the efficacy of GPD within a hierarchical framework to account for serial dependencies in heatwave occurrences, particularly in the western United States, where temporal clustering of extreme temperatures is prominent.”
Metadata
- Authors
- Reich, B. J., Shaby, B. A., & Cooley, D.
- Affiliations
- North Carolina State University (US)
- Pennsylvania State University (US)
- Colorado State University (US)
- Corresponding author
- Reich, Brian J.
- Venue
- Journal of Agricultural, Biological, and Environmental Statistics
- Published
- 2013-09-26
- Volume
- 19
- Issue
- 1
- Pages
- 119-135
- ISSN
- 1085-7117 (ISSN-L)
- 1537-2693
- OpenAlex
- W2032045639
- Cited by
- 45
- References
- 0
- Citations by year
- 2026: 2 · 2024: 2 · 2023: 2 · 2022: 2 · 2021: 6 · 2020: 6 · 2019: 4 · 2018: 5 · 2017: 7 · 2016: 6 · 2015: 1 · 2014: 1 · 2013: 1
- FWCI
- 2.28
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Journal of Agricultural Biological and Environmental Statistics (publishedVersion)
- Topics
- Physical Sciences › Environmental Science › Global and Planetary Change › Climate variability and models
- Social Sciences › Decision Sciences › Management Science and Operations Research › Probability and Risk Models
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Distribution Estimation and Applications
- Keywords
- heat waves, temperature extremes, serial dependence, Bayesian hierarchical model, generalized Pareto distribution, max-stable processes, time-varying dependence, temperature thresholds, American West, climate change detection
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