Effect of Climate Change on Snow Load on Ground: Bayesian Hierarchical Models for Snow Map Definition

Structural design verifications are currently based on extreme value analysis applied to observed data series, under the assumption of stationary climate conditions, but climatic change imposes to consider how they could change in the future, during the design life of structures and beyond. The aim of the present study is to set up a regional non-stationary model for the evaluation of the parameters of the Extreme Value distribution of ground snow load maxima, obtained from the analysis of climate projections. It is defined a Bayesian hierarchical model for extremes in which the standard extreme value representation at each site is combined with a latent process in order to take into account the parameter’s variation over the region. The model is implemented by means of Markov Chain Monte Carlo algorithm, within a Gibbs sampler; starting from the posterior pdfs of the parameters of the extreme value distribution, the characteristic values of ground snow load that serves as basis for structural design are estimated as 98% quantile, assessing its variation over time. The Bayesian framework will also enable a direct assessment of the uncertainties affecting the prediction using the posterior distribution of parameters. The main features and the potentialities of the proposed method are highlighted referring to a relevant case study, concerning the updating of snow load maps.