Preprints
https://doi.org/10.5194/hess-2022-273
https://doi.org/10.5194/hess-2022-273
 
31 Aug 2022
31 Aug 2022
Status: this preprint is currently under review for the journal HESS.

Canopy structure, topography and weather are equally important drivers of small-scale snow cover dynamics in sub-alpine forests

Giulia Mazzotti1,2, Clare Webster1,3, Louis Quéno1, Bertrand Cluzet1, and Tobias Jonas1 Giulia Mazzotti et al.
  • 1WSL Institute for Snow and Avalanche Research SLF, 7260 Davos Dorf, Switzerland
  • 2Univ. Grenoble Alpes, Université de Toulouse, Météo-France, CNRS, CNRM, Centre d’Études de la Neige, 38100 St. Martin d’Hères, France
  • 3Department of Geosciences, University of Oslo, 0316 Oslo, Norway

Abstract. In mountain regions, forests that overlap with seasonal snow mostly reside in complex terrain. Due to persisting major observational challenges in these environments, the combined impact of forest structure and topography on seasonal snow cover dynamics is still poorly understood. Recent advances in forest snow process representation and increasing availability of detailed canopy structure datasets, however, now allow for hyper-resolution (<5 m) snow model simulations capable of resolving tree-scale processes, which can shed light on the complex process interactions that govern forest snow dynamics. We present multi-year simulations at 2 m resolution obtained with FSM2, a mass- and energy-balance based forest snow model specifically developed and validated for meter-scale applications. We simulate a ~3 km2 model domain encompassing forested slopes of a sub-alpine valley in the Eastern Swiss Alps and six snow seasons. Simulations thus span a wide range of canopy structures, terrain characteristics, and meteorological conditions. We analyse spatial and temporal variations in forest snow energy balance partitioning, aiming to quantify and understand the contribution of individual energy exchange processes at different locations and times. Our results suggest that snow cover evolution is equally affected by canopy structure, terrain characteristics and meteorological conditions. We show that the interaction of these three factors can lead to snow distribution and melt patterns that vary between years. We further identify higher snow distribution variability and complexity in slopes that receive solar radiation early in winter. Our process-level insights corroborate and complement existing empirical findings that are largely based on snow distribution datasets only. Hyper-resolution simulations as presented here thus help to better understand how snowpacks and ecohydrological regimes in sub-alpine regions may evolve as a result of forest disturbances and a warming climate. They could further support the development of process-based sub-grid forest snow cover parametrizations or tiling approaches for coarse-resolution modelling applications.

Giulia Mazzotti et al.

Status: open (until 26 Oct 2022)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse

Giulia Mazzotti et al.

Giulia Mazzotti et al.

Viewed

Total article views: 265 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
208 54 3 265 18 0 1
  • HTML: 208
  • PDF: 54
  • XML: 3
  • Total: 265
  • Supplement: 18
  • BibTeX: 0
  • EndNote: 1
Views and downloads (calculated since 31 Aug 2022)
Cumulative views and downloads (calculated since 31 Aug 2022)

Viewed (geographical distribution)

Total article views: 211 (including HTML, PDF, and XML) Thereof 211 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 28 Sep 2022
Download
Short summary
This study analyzes snow cover evolution in mountainous forested terrain based on 2 m-resolution simulations from a process-based model. We show that snow accumulation patterns are controlled by canopy structure, but topographic shading modulates the timing of melt onset, and variability in weather can cause snow accumulation and melt patterns to vary between years. These findings advance our ability to predict how snow regimes will react to rising temperatures and forest disturbances.