Lawrence Berkeley National Laboratory

Abstract Snow plays a critical role in carbon cycling, vegetation dynamics, and permafrost hydrology at high latitudes by influencing surface energy exchange. Predicting snow distribution patterns is essential for understanding the evolution of Arctic ecosystems, yet scaling process‐level knowledge to landscape predictions remains challenging. Here, we analyze snow depth (2019 and 2022), terrain elevation, and vegetation height from a watershed on the Seward Peninsula, Alaska, to examine how topography and shrubs shape snow redistribution across spatial scales. We find that snow depth is strongly coupled to terrain at scales below ∼60 m but becomes increasingly decoupled at larger scales. The topographic model of snow depth variation, which transforms terrain data to align with these scale‐dependent snow patterns, is well correlated with local snow depth variations (linear fit R 2  > 0.5 for 85% of 100‐m patches). A machine learning reconstruction of shrub canopy snow trapping reveals a simple exponential relationship between canopy structure and snow accumulation ( R 2  = 0.59), highlighting the combined influence of topography and vegetation on snow distribution. Together, these empirical relationships capture much of the observed snow variability in the watershed ( R 2  = 0.49, root mean square error (RMSE) = 30 cm), though systematic limitations persist in areas of strong scour and at coarser scales where wind‐terrain interactions are more complex. These findings provide a framework for more efficient snow depth prediction and offer insights to improve snow‐vegetation feedback representation in Earth System Models. Plain Language Summary In Arctic regions, snow acts as a winter blanket that influences everything from soil temperature to plant growth. While we know that wind moves snow around the landscape, creating deeper drifts in some areas and scouring others bare, predicting these patterns has proven challenging. We studied a watershed in Alaska where different‐sized landscape features interact with wind to create complex snow patterns. By analyzing high‐resolution maps of the ground surface and snow depth, we found that local terrain features strongly control where snow accumulates at scales up to about 60 m. We developed a simple model that uses this insight to predict snow patterns and showed how shrubs create additional snow drifts in tandem with the terrain effects. Our approach helps explain why snow ends up where it does and provides new ways to predict Arctic snow patterns that could improve climate models. This matters because as the Arctic continues to warm and shrubs expand across the landscape, changes in snow distribution will affect permafrost thaw, plant growth, and ecosystem carbon cycling. Key Points Snow depth variations are tightly coupled to terrain features below 60‐m scales but follow more complex patterns at larger scales Shrub effects on snow accumulation depend on canopy height above the snow surface rather than total shrub height Scale‐dependent empirical relationships enable efficient prediction of snow depth patterns in complex Arctic terrain