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Spatiotemporal quantile regression for detecting distributional changes in environmental processes.

Brian J Reich1

  • 1Department of Statistics, North Carolina State University.

Journal of the Royal Statistical Society. Series C, Applied Statistics
|July 18, 2013
PubMed
Summary
This summary is machine-generated.

Spatiotemporal quantile regression models climate variable distribution changes. This method, applied to US temperature data, revealed significant spatial trends in temperature, outperforming traditional methods.

Keywords:
Bayesian hierarchical modelUS temperature dataclimate changenon-Gaussian datawarming hole

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Area of Science:

  • Environmental Science
  • Statistical Modeling
  • Climate Science

Background:

  • Climate change impacts climate variable distribution, including mean, variability, and extreme events.
  • Detecting these changes requires advanced statistical methods capable of analyzing complex spatiotemporal patterns.

Purpose of the Study:

  • To introduce spatiotemporal quantile regression (SQT-Reg) as a method for detecting changes in climate variable distributions.
  • To jointly model all quantiles of the response distribution, providing a comprehensive view of climate trends.

Main Methods:

  • Developed a spatiotemporal quantile regression model allowing quantile functions to evolve over time at each spatial location.
  • Employed Gaussian process priors for spatial smoothing of quantile functions and a basis expansion for closed-form likelihood.
  • Incorporated residual correlation modeling using a Gaussian spatial copula.

Main Results:

  • SQT-Reg identified more significant time trends in temperature data (1931-2009) for the southeastern US compared to non-spatial methods.
  • A decreasing time trend was observed for monthly mean and maximum temperatures across much of the study area.
  • Lower quantiles of monthly minimum temperatures showed regional variations, decreasing in Georgia and Florida, and increasing in Virginia and the Carolinas.

Conclusions:

  • Spatiotemporal quantile regression offers a flexible and interpretable approach to analyzing climate data.
  • The model effectively captures complex spatiotemporal variations in climate variables, enhancing trend detection.
  • Findings highlight regional climate change impacts, underscoring the need for localized adaptation strategies.