Intra-landscape distribution of snow storage in the Mayma river basin (low-mountain area of the Russian Altai) - L. F. Lubenets; D. V. Chernykh (Лëд и снег, 2019)
The paper presents the results of observations and cartographic modeling of distribution of maximum snow storages (snowpack water equivalent) in the low-mountain forest landscapes of the Maima river basin in different by snow amount winters. The weather conditions during cold periods of 2014/15-2017/18 were analyzed. The data on precipitation and mean monthly temperatures for the above mentioned seasons were compared with averaged values for 1998-2018 as the whole. It was found that the values of snow storages as well as losses for evaporation here are mainly conditioned by the meteorological parameters of the cold period. The algorithm of mapping with the use of the landscape cartographic basis and corrections for features of the relief and the character of the underlying surface is proposed. This algorithm may be used in the studies of snow accumulation in low-mountain forest landscapes. It had been found that the cold periods of 2014/15 and 2016/17 were characterized by the highest excess of winter precipitation over the averaged long-term values, whereas the lowest values were typical for 2017/18 and 2015/16. According to mean monthly air temperatures, the investigated winters were warm. The data on the in situ measurements and calculations confirm the dependence of snow accumulation in the basin on an altitude gradient, vegetation features, expositions and surface steepness. Consistently high values of snow storages are characteristic for the black-taiga landscapes on the shadow slopes (North-Western, Northern and North-Eastern expositions) with an angle of up to 20° with small-leaved forests. Minimum values of snow storages are observed in the sub-taiga part of the basin on the slopes of light (South-Western, Southern and South-Eastern) and transitional (Western and Eastern) expositions with an angle of more than 20° with the domination of original coniferous forests.