Haagmans et al. (2026) How montane forests shape snow cover dynamics across the central European Alps

Identification

• Journal: Hydrology and earth system sciences
• Year: 2026
• Date: 2026-03-30
• Authors: Vincent Haagmans, Giulia Mazzotti, Clare Webster, Tobias Jonas
• DOI: 10.5194/hess-30-1691-2026

Research Groups

• WSL Institute for Snow and Avalanche Research SLF, Davos, Switzerland
• Department of Civil, Environmental and Geomatic Engineering, ETH Zürich, Zürich, Switzerland
• Université Grenoble Alpes, INRAE, CNRS, IRD, Grenoble INP, IGE, Grenoble, France
• Université Grenoble Alpes, Université de Toulouse, Météo-France, CNRS, CNRM, Centre d'Études de la Neige, St. Martin d'Hères, France
• Department of Geosciences, University of Oslo, Oslo, Norway
• Department of Geography, University of Zurich, Zürich, Switzerland

Short Summary

This study comprehensively analyzed how montane forests influence snow cover dynamics across the central European Alps using a process-based snow model over eight hydrological years. It found that forests store 20–30% of midwinter snow, generally reducing peak snow water equivalent (SWE) but often delaying snow disappearance, with these effects varying significantly by elevation, aspect, region, and interannual weather variability.

Objective

• To determine the overall impact of forests on snow storage in the central European Alps.
• To investigate how topography and climate across the study region affect forest impacts on seasonal snow dynamics.
• To analyze how forest impacts on seasonal snow dynamics vary between years.

Study Configuration

• Spatial Scale: The study covered nearly 58 000 km² across the central European Alps, with elevations ranging from 184 to 4806 meters above sea level. Simulations were performed at a 250 meter spatial resolution.
• Temporal Scale: The analysis spanned eight consecutive hydrological years, from 2017 to 2024, with model simulations conducted at an hourly temporal resolution.

Methodology and Data

• Models used:
  • FSM2oshd: A process-based, mass- and energy-balance-based forest snow model, specifically developed for large spatial scales with sub-grid parameterization for snow-canopy-atmosphere interactions.
  • COSMO: A 1 kilometer numerical weather prediction system (MeteoSwiss) used for meteorological forcing, downscaled to 250 meters.
  • CanRad: Used for detailed representation of radiation transmission through the three-dimensional canopy structure.
• Data sources:
  • Meteorological forcing: Output from the COSMO numerical weather prediction system.
  • Data assimilation: Observations from 444 snow monitoring stations (snow height and snow water equivalent).
  • Canopy structure: Derived from high-resolution lidar acquisitions.
  • Land cover: Land cover datasets used for model partitioning into open, forested, and glacierized areas.
  • Model validation: High-resolution (approximately 3 meters) optical satellite imagery from the PlanetScope constellation.

Main Results

• Forests accounted for 20–30% of total snow storage in midwinter across the central European Alps.
• The presence of forests generally led to a decrease in peak snow water equivalent (SWE), with an average reduction of 5 millimeters (3.7% of mean total peak SWE) if forests were absent.
• Forests typically decelerated snowmelt, often resulting in a later snow disappearance date (SDD), particularly on south-facing slopes where snow persisted longer in forests for a few days to two weeks.
• On north-facing slopes, snow generally persisted longer in open areas for a few days to two weeks, and forests advanced the center time of snowmelt runoff (CT) by up to two weeks.
• Interannual variability was significant, with snow-scarce years accentuating relative differences in forest effects on snow cover, sometimes reversing typical trends in peak SWE, SDD, and CT.
• The impact of forests on snow cover dynamics was strongly dependent on elevation, aspect, and regional climatic conditions, with distinct patterns observed across the six focus regions.
• Differences in peak SWE were correlated with Leaf Area Index (LAI), highlighting the role of canopy interception in reducing snow accumulation on the ground.

Contributions

• This study provides the first comprehensive, in-depth analysis of forest snow water resources across the central European Alps at large spatiotemporal scales.
• It demonstrates that complex forest-snow process interactions, previously studied at meter-scale resolutions, are relevant and observable at coarser, regional scales (250 meters).
• The research highlights the intricate interplay of forest structure, topography, and interannual weather variability in shaping snow cover dynamics, showing how these factors can shift or reverse typical forest impacts.
• The work underscores the importance of extensive, high-resolution datasets (model-based or observational) for understanding forest snow processes in a changing environment, with implications for water resource and forest management.

Funding

• Swiss National Science Foundation (grant-nos.: 500PN202741 and 5R5PN225378)

Citation

@article{Haagmans2026How,
  author = {Haagmans, Vincent and Mazzotti, Giulia and Webster, Clare and Jonas, Tobias},
  title = {How montane forests shape snow cover dynamics across the central European Alps},
  journal = {Hydrology and earth system sciences},
  year = {2026},
  doi = {10.5194/hess-30-1691-2026},
  url = {https://doi.org/10.5194/hess-30-1691-2026}
}