RECONSTRUCTION OF SPATIAL DISTRIBUTION FIELDS OF SNOW COVER CHARACTERISTICS IN PERM KRAI USING INTERPOLATION TOOLS IN GIS APPLICATIONS

Abstract

The article presents the results of a study on the reconstruction of the spatial distribution of snow cover characteristics (snow depth and snow water equivalent) in Perm Krai conducted with the use of interpolation methods in GIS applications. Basing on hydrometeorological observation data for the 2020–2024 seasons, we performed an assessment of the accuracy of fields reconstruction for the northern, central, and southern parts of the region. Three interpolation methods were compared: spline, kriging, and inverse distance weighting (IDW). The study has found that the kriging method demonstrates the best accuracy (errors of less than 15 %) for the northern and central parts of the region, while the spline method shows significant errors, especially in the southern part and during periods of unstable snow cover. The IDW method produces stable but slightly smoothed results. All the methods tend to underestimate values compared to actual data. The highest reconstruction errors (up to 45 %) are observed during periods of active snowmelt (March–April). In spatial terms, due to the lack of sufficient source information for the southern part of Perm Krai, the quality of interpolation suffers when using all the methods under consideration. All the tools, on average, understate moisture reserves. For Chernushka, the relative error of the spline approach reaches 33 %, of the kriging approach and IDW – about 20 %. During the season, the errors increase until February, then decrease, and increase again with snowmelt. The conclusion is made about the advisability of using kriging as the most balanced method for reconstructing spatial fields of snow cover in the conditions of Perm Krai.