Spatial modeling of extreme snow depth

TL;DR

This paper applies max-stable processes to model the spatial dependence of extreme snow depth in Switzerland, accounting for climate regions and weather patterns, leading to improved risk assessment.

Contribution

It introduces a novel application of max-stable processes with climate transformations for spatial extreme snow modeling, enhancing fit over traditional models.

Findings

Max-stable models outperform independence and full dependence models.

Climate transformation improves model accuracy.

Pairwise likelihood inference effectively estimates model parameters.

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.