Climatic Change

, Volume 127, Issue 2, pp 353–369 | Cite as

Non-stationary extreme value analysis in a changing climate

  • Linyin Cheng
  • Amir AghaKouchakEmail author
  • Eric Gilleland
  • Richard W Katz


This paper introduces a framework for estimating stationary and non-stationary return levels, return periods, and risks of climatic extremes using Bayesian inference. This framework is implemented in the Non-stationary Extreme Value Analysis (NEVA) software package, explicitly designed to facilitate analysis of extremes in the geosciences. In a Bayesian approach, NEVA estimates the extreme value parameters with a Differential Evolution Markov Chain (DE-MC) approach for global optimization over the parameter space. NEVA includes posterior probability intervals (uncertainty bounds) of estimated return levels through Bayesian inference, with its inherent advantages in uncertainty quantification. The software presents the results of non-stationary extreme value analysis using various exceedance probability methods. We evaluate both stationary and non-stationary components of the package for a case study consisting of annual temperature maxima for a gridded global temperature dataset. The results show that NEVA can reliably describe extremes and their return levels.


Markov Chain Monte Carlo Return Period Generalize Extreme Value Return Level Exceedance Probability 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



The authors would like to thank Professor Balaji Rajagopalan for his thoughtful comments on an earlier draft of this paper. We also acknowledge the comments of Dr. Francesco Serinaldi and two other anonymous reviewers which led to substantial improvements in the current version. This study is supported by the National Science Foundation (NSF) Award No. EAR-1316536, and the United States Bureau of Reclamation (USBR) Award No. R11AP81451. The first author acknowledges partial financial support from the National Center for Atmospheric Research (NCAR) Graduate Student Visitor Program. NCAR is sponsored by the National Science Foundation.

Supplementary material

10584_2014_1254_MOESM1_ESM.docx (510 kb)
ESM 1 (DOCX 509 kb)


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Copyright information

© Springer Science+Business Media Dordrecht 2014

Authors and Affiliations

  • Linyin Cheng
    • 1
  • Amir AghaKouchak
    • 1
    Email author
  • Eric Gilleland
    • 2
  • Richard W Katz
    • 2
  1. 1.University of California IrvineIrvineUSA
  2. 2.National Center for Atmospheric ResearchBoulderUSA

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