Development of Percolation Features After a Rain-on-Snow Event in the Southern Taiga

In this study, we investigate the development of percolation columns in fine-grained snow triggered by the accumulation of liquid precipitation on a cold, dry snowpack during a rain-on-snow (ROS) event in the Southern Taiga. We analyze snow physical properties, stratigraphy, and meteorological conditions before and after the percolation event, documenting changes in snow layering and the formation of percolation columns. Furthermore, we examine how local-scale factors, such as ground surface microtopography and vegetation cover, influence the spatial distribution of these features by comparing snow properties at three adjacent sites with distinctly different surface and vegetation characteristics.Our results demonstrate that, under certain conditions, percolation columns can form even within fine-grained, low-density snow. Their spatial distribution appears strongly influenced by ground microtopography, with preferential formation between tussocks, while the presence of deciduous vegetation may inhibit their development. Additionally, we discuss the development of preferential flow paths on the adjacent slope that formed simultaneously to the development of percolation columns on flat surfaces and describe the major morphological features we observed. These findings contribute to a deeper understanding of preferential flow in snow and highlight the need to consider localized environmental conditions and evolving climate patterns in future snow hydrology research and hazard forecasting models.Our observations also provide valuable information for improving the representation of preferential flow processes, which remain a major source of uncertainty in snow models. The distinct vertical icy features associated with percolation columns are also likely to affect radar signal penetration and backscatter, with potential implications for the interpretation of remote sensing observations. Moreover, the fact that such features can be identified from above, for example using drone imagery, offers opportunities for model evaluation and spatial validation under natural conditions.