Effect of climate change on snow load on ground: Bayesian approach for snow map refinement

Alteration of ground snow loads due to the climate change may significantly impact the reliability of existing structures, as well as design Codes for new ones. In the paper a novel technique for snow load map refinement is proposed where ground snow loads derived starting from gridded climate data provided by climate models are combined with observed point measurements of snow loads and then suitably updated. First, an a priori random field of characteristic ground snow loads at the sea level is deduced from the analysis of gridded climate data. This prior random field is discretized by the truncated Karhunen-Loeve expansion, to separate the spatial and the stochastic domain and to reduce the dimension of the problem. The distribution of the resulting standard normal random variables are then updated incorporating point measurements of ground snow loads collected in the past and using the Markov Chain Monte Carlo method to sample the posterior. The Bayesian approach results in a more trustable, refined snow load map, and furthermore prospects a dynamic, sequential model updating procedure as new observed data becomes available.