The Annals of Applied Statistics

Spatial modeling of extreme snow depth

Juliette Blanchet and Anthony C. Davison

Full-text: Open access

Abstract

The spatial modeling of extreme snow is important for adequate risk management in Alpine and high altitude countries. A natural approach to such modeling is through the theory of max-stable processes, an infinite-dimensional extension of multivariate extreme value theory. In this paper we describe the application of such processes in modeling the spatial dependence of extreme snow depth in Switzerland, based on data for the winters 1966–2008 at 101 stations. The models we propose rely on a climate transformation that allows us to account for the presence of climate regions and for directional effects, resulting from synoptic weather patterns. Estimation is performed through pairwise likelihood inference and the models are compared using penalized likelihood criteria. The max-stable models provide a much better fit to the joint behavior of the extremes than do independence or full dependence models.

Article information

Source
Ann. Appl. Stat., Volume 5, Number 3 (2011), 1699-1725.

Dates
First available in Project Euclid: 13 October 2011

Permanent link to this document
https://projecteuclid.org/euclid.aoas/1318514282

Digital Object Identifier
doi:10.1214/11-AOAS464

Mathematical Reviews number (MathSciNet)
MR2884920

Zentralblatt MATH identifier
1228.62154

Keywords
Climate space extremal coefficient extreme value theory Max-stable process pairwise likelihood snow depth data

Citation

Blanchet, Juliette; Davison, Anthony C. Spatial modeling of extreme snow depth. Ann. Appl. Stat. 5 (2011), no. 3, 1699--1725. doi:10.1214/11-AOAS464. https://projecteuclid.org/euclid.aoas/1318514282


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Supplemental materials

  • Supplementary material: Supplementary Material for “Spatial modeling of extreme snow depth”. This contains example time series of data, and further discussion of the estimation algorithm and of the fitted models.