ABSTRACT. We present an analysis of temporal trends inThe snow-depth and temperature control on these patterns seems significant (R = 0.4-0.6), but is stronger at high frequencies for occurrences, and at lower frequencies for runout altitudes. Occurrences between the northern and southern French Alps are partially coupled (R $ $ 0.4, higher at low frequencies). In the north, the main change-point was an earlier shift in $ $1977, and winter snow depth seems to be the main control parameter. In the south, the main change-point occurred later, $ $1979-84, was more gradual, and trends are more strongly correlated with winter temperature.

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Although modeling complex environmental spatio‐temporal processes, separability between time and space effects is often assumed as a first approach, even though it may be unrealistic. Hence, the development of adapted non‐separable spatio‐temporal models is a challenging issue. In this work, we are interested in modeling avalanche counts over the whole French Alps, a large area that exhibits zones characterized by distinct climatic behaviors. We have at our disposal a data set of 405, 63‐year series of avalanche counts, one series for one township. We assume the series can be divided into several spatial clusters presenting specific temporal trends. On the latent layer of a hierarchical negative binomial‐lognormal structure, the clusters are characterized by independent temporal evolutions, modeled using smoothing splines. The clusters arise as the result of a multinomial probit regression, with spatial coordinates used as covariates. Inference is performed within the Bayesian hierarchical framework. The number of clusters is assessed using cross validation. The best score is obtained for a three‐cluster model, but other configurations also share some evidence. The first cluster is located in the east of the French Alps, whereas the others are located together in the west part of the French Alps. The three contrasted temporal trends corresponding to the three clusters result from regional climate change effects, interacting with a strong altitudinal control and data limitation. The proposed model could be used for analyzing various other climatic and environmental data sets. Copyright © 2012 John Wiley & Sons, Ltd.

We address the question of introducing expert knowledge in a hierarchical Bayesian spatiotemporal model. Specifically, we combine avalanche count data and an expert prior judgement about avalanche climatology to cluster French Alpine townships into two coherent groups. A spatial regression defines the a priori probability of belonging to each group and an innovative method is proposed to elicit its parameters by working with an expert on replicates of simulated maps. As a benefit of the extra data knowledge, we show the existence of robust north-south clusters of decreasing-increasing avalanche activity resulting from the interaction of local climate change patterns with altitude distribution. Spatial dependence of avalanche activity has also been inferred and is characterized by an anisotropy, with longer dependence along the north-west-south-east axis