Vegetation effects on snow duration and soil microclimate in a marginal snowpack environment

Marginal snowpacks in shrub‑dominated mountain ecosystems are key drivers of ecological and hydrological processes in the central Pyrenees, yet remain poorly understood. These shallow, patchy snowpacks are highly dynamic, exhibiting repeated accumulation–ablation cycles within a single season, making their distribution highly sensitive to vegetation structure and local topography. To quantify these controls, we established an intensive monitoring network in a site-specific study area (8 ha) mainly dominated by Buxus sempervirens, Echinospartum horridum and Juniperus communis.

Since 2021, we have collected distributed soil temperature and moisture data from sensors placed beneath shrubs and in adjacent open areas in a subalpine site at 1700 m a.s.l. Additionally, data from 24 UAV flights were used to derive high‑resolution (0.20 m) spatial products of snow depth, snow presence, vegetation structure, and local topographic metrics.

Results demonstrate that ground temperatures were buffered during snow‑covered periods, ranging from 1 to 2°C (±0.5°C) with low daily oscillation (1ºC), as evidenced by temperatures that remained constant once the ground was insulated from air temperatures, even by a thick snowpack (<1 m). In general, ground sensors at 8 cm depth presented higher temperatures than the sensors at ground surface. Shrubs act as mechanical snow traps that enhance leeward accumulation and as thermal insulators that elevate near‑surface soil temperatures by 1.5 to >3°C compared to open sites. Buxus and Echinospartum sites exhibited higher average ground temperatures than Pinus or open sites. Thawing events were rare, but they occurred more frequently in vegetated areas. Soil moisture peaked following snowmelt events and then decreased slowly until the next snowfall, thus soil humidity variability is clearly driven by melt out date. UAV‑based snow maps and machine learning models (gradient boosted models) reveal that shrubs presence, local topographic and wind‑exposure variables consistently explain >60% of snow distribution variance where interannual variability in snow persistence was pronounced, with no with similar interannual patters.

This integrated approach which combines distributed soil temperature and humidity monitoring and UAV‑based snow mapping, improves the understanding of marginal snowpack dynamics. Our findings underscore the importance of explicitly incorporating fine‑scale vegetation and wind‑topographic interactions into snow models to improve predictions in complex alpine mountain terrain under changing climate and land cover conditions that can affect plant communities and water availability.