Modeling the spatial variability of the snow cover in a high Arctic tundra with continuous permafrost using a physically based model

Abstract:

The high Arctic snow cover is extremely influenced by wind erosion, redistribution and deposition of snow during high wind events, especially over the winter season. Subsequently, the winter snow cover is determined by notable small-scale spatial variations in snow cover depth, density, and thus snow water equivalent (SWE), and runoff. The hydrology, ecology and climatology of the high Arctic will be significantly influenced by Future climate-related changes to snow cover depth and density. In this research, we examine the capability of the physically based model GEOtop to simulate snow dynamics at point and basin scales in the west part of Bylot island catchment (4.7 km²) in the Canadian High Arctic region. Geotop model reflects on the influence of wind compaction, vegetation, and water vapour flux. Measured snow depth data (the data period 2014 – 2019) from BYLOCAMP site were used to calibrate and validate simulated snow depths and SWE. To evaluate the performance of different model parameterizations within key parameters controlling the snowpack and the meteorological input data the statistical indices (R² and mean absolute error) were calculated. The model regenerates the physical features of snow cover fairly and demonstrates a significant consensus with the recorded data. Results showed that among all the parameters, SWE and snow depth were mostly charged by key parameters link with albedo, snow water saturation and by the accuracy of the input precipitation.