Simulation and statistical analysis of ground snow loads based on a multi-layer snow accumulation and melt model

Abstract

Ground snow loads, the study of which relies on the snowpack data from historical records of meteorological stations, are the basis of calculating the roof snow loads. In the absence of observation data, the snow accumulation and melt model, also called the snowmelt model, can be used to simulate the annual extreme samples of ground snow loads in each locality, and then the ground snow loads under a given recurrence interval can be obtained through probability analysis. In this paper, a multi-layer snowmelt model is used to input meteorological data such as precipitation, air temperature, wind speed, and relative humidity to obtain detailed information on the long-term snowpack, including snow density, snow depth, and snow water equivalent (SWE), which overcomes the problem of insufficient sample data for observed snow loads. Firstly, the precision of multi-layer snowmelt is checked through data of snow depth and SWE. Then, based on the model, meteorological stations in thirty-seven localities of six Chinese regions are selected to simulate the ground snow loads for nearly 60 years. Subsequently, the maximum snow depth and its occurrence time are compared, and the relationship between snow depth, snow density, and SWE in each region is analyzed. Finally, a variety of probabilistic models are used to calculate the ground snow loads at 50-year recurrence intervals. The goodness of fit of each probabilistic model for the snow loads sample is examined, and the calculated values of ground snow loads are compared with recommended values by the Chinese code. The results indicate that the snowmelt model can accurately reproduce the detailed process of ground snow events, which can be used to obtain the samples of ground snow load extreme. Due to gravity compaction, ground snow loads corresponding to the maximum depth in a certain year differ from the maximum ground snow loads in that year, which requires more attention. The goodness of fit is higher and the results are larger than the values of the code when generalized extreme distribution and logarithmic normal distribution are adopted to estimate extreme ground snow loads.