Long-Term Trends in Annual Ground Snow Maxima for the Carpathian Region

Abstract

Abstract. The current structural design provisions are prevalently based on experience and on the assumption of stationary meteorological conditions. However, the observations of past decades and advanced climate models show that this assumption is debatable. Therefore, this paper examines the historical long-term trends in ground snow load maxima, and their effect on structural reliability. For this purpose, the Carpathian region is selected, and data from a joint research effort of nine countries of the region are used. Annual maxima snow water equivalents are taken, and univariate generalized extreme value distribution is adopted as a probabilistic model. Stationary and five nonstationary distributions are fitted to the observations utilizing the maximum likelihood method. Statistical and information theory based approaches are used to compare the models and to identify trends. Additionally, reliability analyses are performed on a simple structure to explore the practical significance of the trends. The calculations show decreasing trends in annual maxima for most of the region. Although statistically significant changes are detected at many locations, the practical significance -with respect to structural reliability -is considerable only for a few, and the effect is favourable. The results indicate that contrary to the widespread practice in extreme event modelling, the exclusive use of statistical techniques on the analysed extremes is insufficient to identify practically significant trends. This should be demonstrated using practically relevant examples, e.g. reliability of structures.

Investigating the Effects of Climate Change on Structural Actions

“…where snow accumulation is significant. A detailed analysis (Rózsás et al, 2016) focusing on the Carpathian region confirmed the Weibull distribution for mountains while arguing that a Fréchet (EVII) distribution provides the best fit for lowlands. The US experience advocates a lognormal distribution (ASCE 7-16, 2017) that has also been considered in some countries in Europe.…”

Non-stationary extreme value analysis of ground snow loads in the French Alps: a comparison with building standards

“…For details, we refer to Abidin et al (2012). We apply this test on the transformed data using Saeb (2018) and found that we cannot reject the null hypothesis (samples generated from the Gumbel model) at the 5 % significance level for almost all our selected models (98 %), justifying their good fit. As explained in Sect.…”

“…Moreover, according to the comparative study of Abidin et al (2012), the most powerful goodness-of-fit test for the Gumbel distribution is a combination of the Anderson-Darling test and the maximum likelihood estimator. We apply this test on the transformed data using Saeb (2018) and found that we cannot reject the null hypothesis (samples generated from the Gumbel model) at the 5 % significance level for almost all our selected models (98 %), justifying their good fit. We refer to Appendix B for more details.…”

Using statistical and interval-based approaches to propagate snow measurement uncertainty to structural reliability

“…The probabilistic model of ground snow load is inferred from the snow water equivalent data of CarpatClim database (Szalai et al, 2013). The observations are available for 49 full winter seasons; the time trend of annual maxima is statistically insignificant (Rózsás et al, 2016) and is neglected. The basic statistical parameters of the annual (winter season) maxima are given in Table 2.…”