Technical note: Literature based approach to estimate future snow

Abstract. The seasonal snow cover in the European Alps is increasingly threatened by rising temperatures due to climate change. Still, downscaled climate projections are lacking for many regions. To address this gap, we developed a literature-based approach for projecting future snow depths, that is applicable to all locations where historical snow depth data is available. We harmonized heterogeneous literature on future snow depth and snow water equivalent by translating emission scenarios to corresponding temperature scenarios and standardizing seasonal periods. Then, we parameterized localized reduction curves based on elevation, temperature scenarios and local climatologies, such as mean snow cover length and mean maximum snow depth. This method was applied to four measurement stations in Switzerland under a +2 °C temperature scenario, revealing significant declines in snow depth and season length, especially at lower elevations. Validation against published data shows that the approach captures key trends in snow loss, despite the simplification of climate dynamics. This resource-efficient method provides a practical tool for estimating climate change related snow depth declines in snow dominated regions, which are lacking highly resolved climate projections, and can support decision-makers in developing adaptation strategies for climate-related challenges.

Authors

Publication Details

Journal
Hydrology and earth system sciences
Published
2026-02-05
DOI
https://doi.org/10.5194/hess-30-659-2026
Primary Topic
Cryospheric studies and observations
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Technical note: Literature based approach to estimate future snow

Bettina Richter, Christoph Marty
Hydrology and earth system sciences
Cryospheric studies and observations
article

Technical note: Literature based approach to estimate future snow

Bettina Richter, Christoph Marty
article en

Abstract

Abstract. The seasonal snow cover in the European Alps is increasingly threatened by rising temperatures due to climate change. Still, downscaled climate projections are lacking for many regions. To address this gap, we developed a literature-based approach for projecting future snow depths, that is applicable to all locations where historical snow depth data is available. We harmonized heterogeneous literature on future snow depth and snow water equivalent by translating emission scenarios to corresponding temperature scenarios and standardizing seasonal periods. Then, we parameterized localized reduction curves based on elevation, temperature scenarios and local climatologies, such as mean snow cover length and mean maximum snow depth. This method was applied to four measurement stations in Switzerland under a +2 °C temperature scenario, revealing significant declines in snow depth and season length, especially at lower elevations. Validation against published data shows that the approach captures key trends in snow loss, despite the simplification of climate dynamics. This resource-efficient method provides a practical tool for estimating climate change related snow depth declines in snow dominated regions, which are lacking highly resolved climate projections, and can support decision-makers in developing adaptation strategies for climate-related challenges.

Hydrology and earth system sciencesVol. 30(3)
Board of the Swiss Federal Institutes of Technology
Climate action
Openalex Percentile: Top 74%
Cryospheric studies and observations
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.