Development and application of a new simple algorithm for stochastic modeling of daily ground snow loads: A case study in Northeastern China

The simulation or modeling of daily ground snow loads is a fundamental prerequisite for assessing the reliability of building structures subjected to the combined effects of snow and other loads. Taking northeastern China as an example, a novel and streamlined algorithm is introduced in this study to simulate daily ground snow loads, where the concept of daily snow declination rate was introduced, making the algorithm more logically explicit and more in line with reality when compared to existing models. The proposed algorithm models the arrival of snow events as a Poisson process, and five variables are involved in simulating the daily ground snow loads (represented by snow water equivalent, SWE), including the length of the snow period, T (days), the starting day, SD (dimensionless), the snow event arrival rate, v (dimensionless), the snow intensity, A (mm), and the daily snow declination rate, d (mm/day). Probabilistic models of the variables are determined from historical records of ground snow loads, and random samples of these variables are accordingly generated in the simulation. The implementation of this algorithm in the snowy region of northeastern China demonstrated its effectiveness. It's found that the simulated daily ground snow loads not only successfully replicate various patterns of snow accumulation but also align with the 50-year return period values of ground snow load. This validation underscores the algorithm's capability in simulating daily ground snow loads, thereby facilitating the investigation of the combined effects of snow load and other loading conditions.