On the problem of estimating the snowiness of winters by different methods by the example of the Yuzhno-Sakhalinsk city

Abstract

The problem of estimating a snowiness (a snow coverage) of a territory is considered by many authors, but still nowadays no common approach to its solution is reached. Different authors use different characteristics in their works for estimating the winter snowiness. In this work the snowiness of winters was estimated using different parameters to determine the most representative one. The area ща study is the Yuzhno-Sakhalinsk urban district. The data for the past 36 years (1986–2022) were used to determine the types of the winter snowiness in this district. The following nine methods have been applied: the maximum winter snow reserve; by the amount of solid precipitation; by the average winter thickness of snow cover from the weather station and by snow survey; by the average greatest ten-day thickness of snow cover; by the amount of precipitation in the form of snow; the method of N. N. Galakhov; the Schultz coefficient; and the maximum winter snow cover thickness. The results obtained are very contradictory. Thus, the types of snowiness completely coincided in only 17 % of winters; while in 58 % of winters the types of snowiness coincided by 2/3 of the above indicators. Estimation of snowiness using various parameters gives closer results when using data on snow reserve at the beginning of snowmelt and the average winter thickness of snow cover (coincidence in 78 % of cases). The reason is that a major part of the above methods uses the values of only one parameter. But given that snowiness is a complex indicator, it would be reasonable to consider all possible parameters at the same time. It is impossible to develop a unified approach to estimating winter snowiness, since the parameters chosen for this problem depend on the goal of the determination. It would also be worthwhile to select a methodology for estimating winter snowiness depending on the tasks set, and on the availability and reliability of the initial meteorological data for the analysis. Such work is also complicated by the insufficient volume of meteorological data, as well as due to gaps in them.