Mapping snow gliding distances: Bridging modelled and field observations

Snow gliding affects soil erosion patterns and depends on various factors such as slope angle, precipitation amount, and vegetation roughness. Snow gliding distance can be assessed through measurement or modeling. However, the comparison of measured with modelled data remains limited due to the scarcity of measured data. We present a long-term dataset (2010−2021) of measured snow gliding distances for two Swiss alpine Valleys (Urseren and Val Piora) using glide shoes to address this gap. We also predicted snow gliding between 2010 and 2021 using the spatial snow gliding distance model developed by Leitinger et al. (2018). Our analysis of the measured data indicates that sites with a north aspect generally exhibit shorter snow gliding distances than those facing south. Moreover, we observed an increase in snow gliding distance with steeper slope angles and a decrease with a higher roughness coefficient. Comparing measured and modelled values, the R 2 and Concordance Correlation Coefficient (CCC) values are 0.23 and 0.12 for the Urseren Valley and 0.24 and 0.35 for the Val Piora. Generally, the model tended to predict higher values than the measured data for both Valleys, potentially due to the large small-scale variability observed in the replicates of the measured data that cannot be caught with large-scale models. This variability highlights the dynamic nature of snow gliding distance, making it challenging to model or measure accurately. Furthermore, a covariate importance analysis revealed precipitation and slope angle as the dominant drivers of modelled snow gliding distances versus vegetation roughness (a rather local feature) for measured values. • Measured and modelled snow gliding distances of two Swiss Alpine Valleys. • North aspect generally exhibits shorter snow gliding distances than South aspect. • The snow gliding model predicted consistently higher values as measured data. • Precipitation and slope angle drive modelled while surface roughness measured data.

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Publication Details

Journal
Cold Regions Science and Technology
Published
2024-12-16
DOI
https://doi.org/10.1016/j.coldregions.2024.104402
Primary Topic
Cryospheric studies and observations
Type
article
Field-Weighted Citation Impact
0.00

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article

Mapping snow gliding distances: Bridging modelled and field observations

Katrin Di Bella Meusburger, Christine Alewell, Georg Leitinger, Lauren Zweifel et al.
Cold Regions Science and Technology
Cryospheric studies and observations
article

Mapping snow gliding distances: Bridging modelled and field observations

Katrin Di Bella Meusburger, Christine Alewell, Georg Leitinger, Lauren Zweifel, Surya Gupta, Axel Birkholz
article en

Abstract

Snow gliding affects soil erosion patterns and depends on various factors such as slope angle, precipitation amount, and vegetation roughness. Snow gliding distance can be assessed through measurement or modeling. However, the comparison of measured with modelled data remains limited due to the scarcity of measured data. We present a long-term dataset (2010−2021) of measured snow gliding distances for two Swiss alpine Valleys (Urseren and Val Piora) using glide shoes to address this gap. We also predicted snow gliding between 2010 and 2021 using the spatial snow gliding distance model developed by Leitinger et al. (2018). Our analysis of the measured data indicates that sites with a north aspect generally exhibit shorter snow gliding distances than those facing south. Moreover, we observed an increase in snow gliding distance with steeper slope angles and a decrease with a higher roughness coefficient. Comparing measured and modelled values, the R 2 and Concordance Correlation Coefficient (CCC) values are 0.23 and 0.12 for the Urseren Valley and 0.24 and 0.35 for the Val Piora. Generally, the model tended to predict higher values than the measured data for both Valleys, potentially due to the large small-scale variability observed in the replicates of the measured data that cannot be caught with large-scale models. This variability highlights the dynamic nature of snow gliding distance, making it challenging to model or measure accurately. Furthermore, a covariate importance analysis revealed precipitation and slope angle as the dominant drivers of modelled snow gliding distances versus vegetation roughness (a rather local feature) for measured values. • Measured and modelled snow gliding distances of two Swiss Alpine Valleys. • North aspect generally exhibits shorter snow gliding distances than South aspect. • The snow gliding model predicted consistently higher values as measured data. • Precipitation and slope angle drive modelled while surface roughness measured data.

Cold Regions Science and TechnologyVol. 231
University of Basel (CH), Universität Innsbruck (AT), Swiss Federal Institute for Forest, Snow and Landscape Research (CH)
Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung
Climate action
Openalex Percentile: Top 74%
Cryospheric studies and observations
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