Extreme Ground Snow Loads in Europe from 1951 to 2100

Pietro Croce,Filippo Landi,Paolo Formichi

doi:10.3390/cli9090133

Abstract

Lightweight roofs are extremely sensitive to extreme snow loads, as confirmed by recently occurring failures all over Europe. Obviously, the problem is further emphasized in warmer climatic areas, where low design values are generally foreseen for snow loads. Like other climatic actions, representative values of snow loads provided in structural codes are usually derived by means of suitable elaborations of extreme statistics, assuming climate stationarity over time. As climate change impacts are becoming more and more evident over time, that hypothesis is becoming controversial, so that suitable adaptation strategies aiming to define climate resilient design loads need to be implemented. In the paper, past and future trends of ground snow load in Europe are assessed for the period 1950–2100, starting from high-resolution climate simulations, recently issued by the CORDEX program. Maps of representative values of snow loads adopted for structural design, associated with an annual probability of exceedance p = 2%, are elaborated for Europe. Referring to the historical period, the obtained maps are critically compared with the current European maps based on observations. Factors of change maps, referred to subsequent time windows are presented considering RCP4.5 and RCP8.5 emission trajectories, corresponding to medium and maximum greenhouse gas concentration scenarios. Factors of change are thus evaluated considering suitably selected weather stations in Switzerland and Germany, for which high quality point measurements, sufficiently extended over time are available. Focusing on the investigated weather stations, the study demonstrates that climate models can appropriately reproduce historical trends and that a decrease of characteristic values of the snow loads is expected over time. However, it must be remarked that, if on one hand the mean value of the annual maxima tends to reduce, on the other hand, its standard deviation tends to increase, locally leading to an increase of the extreme values, which should be duly considered in the evaluation of structural reliability over time.

Highlights

Impacts of climate change in several relevant societal and political sectors will become more severe in coming decades [1]
Climate 2021, 9, 133 where Ce, the exposure coefficient, depending on the orographic conditions of the neighborhood of the construction, on the shelter effect of trees and other surrounding construction works, and on wind velocity, accounts for the removal or the accumulation of snow on roof exerted by wind; μi, the shape coefficient, which is a function of the shape and extension of the roof, and on the presence of taller adjacent constructions, is used to convert the ground into the roof snow load as well as to model possible non-uniform snow distributions on roof; Ct, the thermal coefficient, accounts for changes in the snow cover caused by the heat flux through the roof
The snow water equivalent (SWE) was measured directly only in some weather stations of European countries such as Germany, Finland, Switzerland and partially UK, in the other cases, the height of snow cover was converted into snow load by means of analytical snow density laws depending on the climatic conditions and the snow lasting period [33]

Summary

Introduction

Impacts of climate change in several relevant societal and political sectors will become more severe in coming decades [1]. Looking at the available climate observations, a clear consequence of global warming is that the probability of occurrence of extreme weather and climate events has generally increased [2,3,4,5]. Focusing on civil engineering structures and infrastructures, the challenge is the assessment of projected changes in extremes of climatic actions over time [6,7,8,9]. The representative values of such actions are associated with specified probabilities of being exceeded during a given reference period: e.g., the characteristic value in EN1990 corresponds to p = 2% in one year [10]. The representative values are usually determined by elaborating extreme measurements covering a period of 40–50 years, assuming the climate as stationary over time. To consider the influence of changing climate, suitably extended data series should be handled, including available past observations as well as future projections provided by climate models [12]

Methods

Discussion

Conclusion