Articles | Volume 16, issue 2
https://doi.org/10.5194/tc-16-505-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/tc-16-505-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
11 Feb 2022
Research article |
| 11 Feb 2022
| 11 Feb 2022
GNSS signal-based snow water equivalent determination for different snowpack conditions along a steep elevation gradient
WSL Institute for Snow and Avalanche Research SLF, Davos, Switzerland
Geophysical Institute, University of Alaska, Fairbanks, USA
Institute for Hydrology and Water Management, BOKU University of
Natural Resources and Life Sciences, Vienna, Austria
Patrick Henkel
ANavS GmbH, Munich, Germany
Markus Lamm
ANavS GmbH, Munich, Germany
Florian Appel
VISTA Remote Sensing in Geosciences GmbH, Munich, Germany
Christoph Marty
WSL Institute for Snow and Avalanche Research SLF, Davos, Switzerland
Jürg Schweizer
WSL Institute for Snow and Avalanche Research SLF, Davos, Switzerland
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Melin Walet, Jakob Schöttner, Valentin Adam, Florian Rheinschmidt, Philipp Rosendahl, Philipp Weißgraeber, Jürg Schweizer, and Alec van Herwijnen
EGUsphere, https://doi.org/10.5194/egusphere-2026-3554, https://doi.org/10.5194/egusphere-2026-3554, 2026
This preprint is open for discussion and under review for The Cryosphere (TC).
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Dry snow avalanches start when a weak snow layer fails and a crack spreads through the snowpack, but field data is limited. We tested a weak layer of coarse snow grains in field fracture experiments. Using videos of snow movement, we derived the stiffness of the weak layer and calculated the resistance to crack growth. Crack resistance increased during the season as the grains in the weak layer grew and bonded more strongly. This data can be used to improve avalanche release models.
Jürg Schweizer, Benjamin Reuter, Christoph Mitterer, Fabiano Monti, and Stephanie Mayer
EGUsphere, https://doi.org/10.5194/egusphere-2026-3913, https://doi.org/10.5194/egusphere-2026-3913, 2026
This preprint is open for discussion and under review for Natural Hazards and Earth System Sciences (NHESS).
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Avalanche forecasting increasingly relies on numerical predictions of snow stability. We present the first comparison of traditional as well as AI-based stability metrics and validate them against avalanche observations. The metrics show a similar level of performance. Combining both process-based and data-driven metrics provides the most reliable forecasts.
Harsh Beria, Sven Kotlarski, Adrien Michel, Jan Magnusson, and Christoph Marty
EGUsphere, https://doi.org/10.5194/egusphere-2026-3478, https://doi.org/10.5194/egusphere-2026-3478, 2026
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Snow is a vital water store for Switzerland, supporting hydropower, winter tourism, and aquatic ecosystems. Using the latest Swiss climate projections, we project future snow water across Switzerland at high spatial resolution. By the end of this century under high emission scenarios, low-lying regions are projected to become nearly snow-free, while even high mountains lose much of their snow. These results can support planning for future Alpine water resources under a warming climate.
Bettina Richter and Christoph Marty
Hydrol. Earth Syst. Sci., 30, 659–670, https://doi.org/10.5194/hess-30-659-2026, https://doi.org/10.5194/hess-30-659-2026, 2026
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We developed a literature-based approach for projecting future snow depths, which was applied to four measurement stations in Switzerland under a +2 °C temperature scenario, revealing significant declines in snow depths. Validation against published data shows that the approach captures key trends in snow loss. This resource-efficient method provides a practical tool for estimating climate change related snow depth declines, which are lacking highly resolved climate projections.
Philipp Maier, Caroline Ehrendorfer, Sophie Lücking, Thomas Pulka, Fabian Lehner, Mathew Herrnegger, Herbert Formayer, and Franziska Koch
EGUsphere, https://doi.org/10.5194/egusphere-2025-6382, https://doi.org/10.5194/egusphere-2025-6382, 2026
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Snow and rainfall measurements in mountain regions often underestimate the true amounts. We developed a method to correct Austrian precipitation data using weather stations and terrain features. The corrected data greatly improves runoff, snow and glacier modeling. This work shows that considering measurement errors is important for accurately assessing water resources, which is increasingly relevant as climate change affects alpine water systems supplying hydropower and downstream communities.
Christoph Marty, Adrien Michel, Tobias Jonas, Cynthia Steijn, Regula Muelchi, and Sven Kotlarski
The Cryosphere, 19, 4391–4407, https://doi.org/10.5194/tc-19-4391-2025, https://doi.org/10.5194/tc-19-4391-2025, 2025
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This work presents the first long-term (since 1962), daily, 1 km gridded dataset of snow depth and water storage for Switzerland. Its quality was assessed by comparing yearly, monthly, and weekly values to a higher-quality model and in situ measurements. Results show good overall performance, though some limitations exist at low elevations and short timescales. Despite this, the dataset effectively captures trends, offering valuable insights for climate monitoring and elevation-based changes.
Michael Lombardo, Amelie Fees, Anders Kaestner, Alec van Herwijnen, Jürg Schweizer, and Peter Lehmann
The Cryosphere, 19, 4437–4458, https://doi.org/10.5194/tc-19-4437-2025, https://doi.org/10.5194/tc-19-4437-2025, 2025
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Water flow in snow is important for many applications including snow hydrology and avalanche forecasting. This work investigated the role of capillary forces at the soil-snow interface during capillary rise experiments using neutron radiography. The results showed that the properties of both the snow and the transitional layer below the snow affected the water flow. This work will allow for better representations of water flow across the soil–snow interface in snowpack models.
Grégoire Bobillier, Bertil Trottet, Bastian Bergfeld, Ron Simenhois, Alec van Herwijnen, Jürg Schweizer, and Johan Gaume
Nat. Hazards Earth Syst. Sci., 25, 2215–2223, https://doi.org/10.5194/nhess-25-2215-2025, https://doi.org/10.5194/nhess-25-2215-2025, 2025
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Our study investigates the initiation of snow slab avalanches. Combining experimental data with numerical simulations, we show that on gentle slopes, cracks form and propagate due to compressive fractures within a weak layer. On steeper slopes, crack velocity can increase dramatically after approximately 5 m due to a fracture mode transition from compression to shear. Understanding these dynamics provides a crucial missing piece in the puzzle of dry-snow slab avalanche formation.
Amelie Fees, Michael Lombardo, Alec van Herwijnen, Peter Lehmann, and Jürg Schweizer
The Cryosphere, 19, 1453–1468, https://doi.org/10.5194/tc-19-1453-2025, https://doi.org/10.5194/tc-19-1453-2025, 2025
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Glide-snow avalanches release at the soil–snow interface due to a loss of friction, which is suspected to be linked to interfacial water. The importance of the interfacial water was investigated with a spatio-temporal monitoring setup for soil and local snow on an avalanche-prone slope. Seven glide-snow avalanches were released on the monitoring grid (winter seasons 2021/22 to 2023/24) and provided insights into the source, quantity, and spatial distribution of interfacial water before avalanche release.
Jan Svoboda, Marc Ruesch, David Liechti, Corinne Jones, Michele Volpi, Michael Zehnder, and Jürg Schweizer
Geosci. Model Dev., 18, 1829–1849, https://doi.org/10.5194/gmd-18-1829-2025, https://doi.org/10.5194/gmd-18-1829-2025, 2025
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Accurately measuring snow height is key for modeling approaches in climate science, snow hydrology, and avalanche forecasting. Erroneous snow height measurements often occur when snow height is low or changes, for instance during snowfall in summer. We prepare a new benchmark dataset with annotated snow height data and demonstrate how to improve the measurement quality using modern deep-learning approaches. Our approach can be easily implemented in a data pipeline for snow modeling.
Adrien Michel, Johannes Aschauer, Tobias Jonas, Stefanie Gubler, Sven Kotlarski, and Christoph Marty
Geosci. Model Dev., 17, 8969–8988, https://doi.org/10.5194/gmd-17-8969-2024, https://doi.org/10.5194/gmd-17-8969-2024, 2024
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We present a method to correct snow cover maps (represented in terms of snow water equivalent) to match better-quality maps. The correction can then be extended backwards and forwards in time for periods when better-quality maps are not available. The method is fast and gives good results. It is then applied to obtain a climatology of the snow cover in Switzerland over the past 60 years at a resolution of 1 d and 1 km. This is the first time that such a dataset has been produced.
Matthew Switanek, Gernot Resch, Andreas Gobiet, Daniel Günther, Christoph Marty, and Wolfgang Schöner
The Cryosphere, 18, 6005–6026, https://doi.org/10.5194/tc-18-6005-2024, https://doi.org/10.5194/tc-18-6005-2024, 2024
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Snow depth plays an important role in water resources, mountain tourism, and hazard management across the European Alps. Our study uses station-based historical observations to quantify how changes in temperature and precipitation affect average seasonal snow depth. We find that the relationship between these variables has been surprisingly robust over the last 120 years. This allows us to more accurately estimate how future climate will affect seasonal snow depth in different elevation zones.
Stephanie Mayer, Martin Hendrick, Adrien Michel, Bettina Richter, Jürg Schweizer, Heini Wernli, and Alec van Herwijnen
The Cryosphere, 18, 5495–5517, https://doi.org/10.5194/tc-18-5495-2024, https://doi.org/10.5194/tc-18-5495-2024, 2024
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Understanding the impact of climate change on snow avalanche activity is crucial for safeguarding lives and infrastructure. Here, we project changes in avalanche activity in the Swiss Alps throughout the 21st century. Our findings reveal elevation-dependent patterns of change, indicating a decrease in dry-snow avalanches alongside an increase in wet-snow avalanches at elevations above the current treeline. These results underscore the necessity to revisit measures for avalanche risk mitigation.
Amelie Fees, Alec van Herwijnen, Michael Lombardo, Jürg Schweizer, and Peter Lehmann
Nat. Hazards Earth Syst. Sci., 24, 3387–3400, https://doi.org/10.5194/nhess-24-3387-2024, https://doi.org/10.5194/nhess-24-3387-2024, 2024
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Glide-snow avalanches release at the ground–snow interface, and their release process is poorly understood. To investigate the influence of spatial variability (snowpack and basal friction) on avalanche release, we developed a 3D, mechanical, threshold-based model that reproduces an observed release area distribution. A sensitivity analysis showed that the distribution was mostly influenced by the basal friction uniformity, while the variations in snowpack properties had little influence.
Stephanie Mayer, Frank Techel, Jürg Schweizer, and Alec van Herwijnen
Nat. Hazards Earth Syst. Sci., 23, 3445–3465, https://doi.org/10.5194/nhess-23-3445-2023, https://doi.org/10.5194/nhess-23-3445-2023, 2023
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We present statistical models to estimate the probability for natural dry-snow avalanche release and avalanche size based on the simulated layering of the snowpack. The benefit of these models is demonstrated in comparison with benchmark models based on the amount of new snow. From the validation with data sets of quality-controlled avalanche observations and danger levels, we conclude that these models may be valuable tools to support forecasting natural dry-snow avalanche activity.
Johannes Aschauer, Adrien Michel, Tobias Jonas, and Christoph Marty
Geosci. Model Dev., 16, 4063–4081, https://doi.org/10.5194/gmd-16-4063-2023, https://doi.org/10.5194/gmd-16-4063-2023, 2023
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Snow water equivalent is the mass of water stored in a snowpack. Based on exponential settling functions, the empirical snow density model SWE2HS is presented to convert time series of daily snow water equivalent into snow depth. The model has been calibrated with data from Switzerland and validated with independent data from the European Alps. A reference implementation of SWE2HS is available as a Python package.
Moritz Buchmann, Gernot Resch, Michael Begert, Stefan Brönnimann, Barbara Chimani, Wolfgang Schöner, and Christoph Marty
The Cryosphere, 17, 653–671, https://doi.org/10.5194/tc-17-653-2023, https://doi.org/10.5194/tc-17-653-2023, 2023
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Our current knowledge of spatial and temporal snow depth trends is based almost exclusively on time series of non-homogenised observational data. However, like other long-term series from observations, they are susceptible to inhomogeneities that can affect the trends and even change the sign. To assess the relevance of homogenisation for daily snow depths, we investigated its impact on trends and changes in extreme values of snow indices between 1961 and 2021 in the Swiss observation network.
Bastian Bergfeld, Alec van Herwijnen, Grégoire Bobillier, Philipp L. Rosendahl, Philipp Weißgraeber, Valentin Adam, Jürg Dual, and Jürg Schweizer
Nat. Hazards Earth Syst. Sci., 23, 293–315, https://doi.org/10.5194/nhess-23-293-2023, https://doi.org/10.5194/nhess-23-293-2023, 2023
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For a slab avalanche to release, the snowpack must facilitate crack propagation over large distances. Field measurements on crack propagation at this scale are very scarce. We performed a series of experiments, up to 10 m long, over a period of 10 weeks. Beside the temporal evolution of the mechanical properties of the snowpack, we found that crack speeds were highest for tests resulting in full propagation. Based on these findings, an index for self-sustained crack propagation is proposed.
Stephanie Mayer, Alec van Herwijnen, Frank Techel, and Jürg Schweizer
The Cryosphere, 16, 4593–4615, https://doi.org/10.5194/tc-16-4593-2022, https://doi.org/10.5194/tc-16-4593-2022, 2022
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Information on snow instability is crucial for avalanche forecasting. We introduce a novel machine-learning-based method to assess snow instability from snow stratigraphy simulated with the snow cover model SNOWPACK. To develop the model, we compared observed and simulated snow profiles. Our model provides a probability of instability for every layer of a simulated snow profile, which allows detection of the weakest layer and assessment of its degree of instability with one single index.
Cristina Pérez-Guillén, Frank Techel, Martin Hendrick, Michele Volpi, Alec van Herwijnen, Tasko Olevski, Guillaume Obozinski, Fernando Pérez-Cruz, and Jürg Schweizer
Nat. Hazards Earth Syst. Sci., 22, 2031–2056, https://doi.org/10.5194/nhess-22-2031-2022, https://doi.org/10.5194/nhess-22-2031-2022, 2022
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A fully data-driven approach to predicting the danger level for dry-snow avalanche conditions in Switzerland was developed. Two classifiers were trained using a large database of meteorological data, snow cover simulations, and danger levels. The models performed well throughout the Swiss Alps, reaching a performance similar to the current experience-based avalanche forecasts. This approach shows the potential to be a valuable supplementary decision support tool for assessing avalanche hazard.
Moritz Buchmann, John Coll, Johannes Aschauer, Michael Begert, Stefan Brönnimann, Barbara Chimani, Gernot Resch, Wolfgang Schöner, and Christoph Marty
The Cryosphere, 16, 2147–2161, https://doi.org/10.5194/tc-16-2147-2022, https://doi.org/10.5194/tc-16-2147-2022, 2022
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Knowledge about inhomogeneities in a data set is important for any subsequent climatological analysis. We ran three well-established homogenization methods and compared the identified break points. By only treating breaks as valid when detected by at least two out of three methods, we enhanced the robustness of our results. We found 45 breaks within 42 of 184 investigated series; of these 70 % could be explained by events recorded in the station history.
Yves Bühler, Peter Bebi, Marc Christen, Stefan Margreth, Lukas Stoffel, Andreas Stoffel, Christoph Marty, Gregor Schmucki, Andrin Caviezel, Roderick Kühne, Stephan Wohlwend, and Perry Bartelt
Nat. Hazards Earth Syst. Sci., 22, 1825–1843, https://doi.org/10.5194/nhess-22-1825-2022, https://doi.org/10.5194/nhess-22-1825-2022, 2022
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To calculate and visualize the potential avalanche hazard, we develop a method that automatically and efficiently pinpoints avalanche starting zones and simulate their runout for the entire canton of Grisons. The maps produced in this way highlight areas that could be endangered by avalanches and are extremely useful in multiple applications for the cantonal authorities, including the planning of new infrastructure, making alpine regions more safe.
Johannes Aschauer and Christoph Marty
Geosci. Instrum. Method. Data Syst., 10, 297–312, https://doi.org/10.5194/gi-10-297-2021, https://doi.org/10.5194/gi-10-297-2021, 2021
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Methods for reconstruction of winter long data gaps in snow depth time series are compared. The methods use snow depth data from neighboring stations or calculate snow depth from temperature and precipitation data. All methods except one are able to reproduce the average snow depth and maximum snow depth in a winter reasonably well. For reconstructing the number of snow days with snow depth ≥ 1 cm, results suggest using a snow model instead of relying on data from neighboring stations.
Moritz Buchmann, Michael Begert, Stefan Brönnimann, and Christoph Marty
The Cryosphere, 15, 4625–4636, https://doi.org/10.5194/tc-15-4625-2021, https://doi.org/10.5194/tc-15-4625-2021, 2021
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We investigated the impacts of local-scale variations by analysing snow climate indicators derived from parallel snow measurements. We found the largest relative inter-pair differences for all indicators in spring and the smallest in winter. The findings serve as an important basis for our understanding of uncertainties of commonly used snow indicators and provide, in combination with break-detection methods, the groundwork in view of any homogenization efforts regarding snow time series.
Christian Voigt, Karsten Schulz, Franziska Koch, Karl-Friedrich Wetzel, Ludger Timmen, Till Rehm, Hartmut Pflug, Nico Stolarczuk, Christoph Förste, and Frank Flechtner
Hydrol. Earth Syst. Sci., 25, 5047–5064, https://doi.org/10.5194/hess-25-5047-2021, https://doi.org/10.5194/hess-25-5047-2021, 2021
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A continuously operating superconducting gravimeter at the Zugspitze summit is introduced to support hydrological studies of the Partnach spring catchment known as the Zugspitze research catchment. The observed gravity residuals reflect total water storage variations at the observation site. Hydro-gravimetric analysis show a high correlation between gravity and the snow water equivalent, with a gravimetric footprint of up to 4 km radius enabling integral insights into this high alpine catchment.
Cited articles
Appel, F., Koch, F., Rösel, A., Klug, P., Henkel, P., Lamm, M., Mauser,
W., and Bach, H.: Advances in snow hydrology using a combined approach of
GNSS in situ stations, hydrological modelling and Earth observation: a case
study in Canada, Geosciences, 9, 44,
https://doi.org/10.3390/geosciences9010044, 2019.
Avanzi, F., Bianchi, A., Cina, A., De Michele, C., Maschio, P., Pagliari,
D., Passoni, D., Pinto, L., Piras, M., and Rossi, L.: Centimetric accuracy
in snow depth using unmanned aerial system photogrammetry and a
MultiStation, Remote Sensing, 10, 765, https://doi.org/10.3390/rs10050765, 2018.
Bojinski, S., Verstraete, M., Peterson, T. C., Richter, C., Simmons, A., and
Zemp, M.: The concept of essential climate variables in support of climate
research, applications, and policy, B. Am. Meteorol.
Soc., 95, 1431–1443, https://doi.org/10.1175/bams-d-13-00047.1, 2014.
Boniface, K., Braun, J. J., McCreight, J. L., and Nievinski, F. G.:
Comparison of snow data assimilation system with GPS reflectometry snow
depth in the Western United States, Hydrol. Process., 29, 2425–2437,
https://doi.org/10.1002/hyp.10346, 2015.
Botteron, C., Dawes, N., Leclere, J., Skaloud, J., Weijs, S. V., and Farine,
P. A.: Soil moisture & snow properties determination with GNSS in Alpine
environments: Challenges, status, and perspectives, Remote Sensing, 5,
3516–3543, https://doi.org/10.3390/rs5073516, 2013.
Bühler, Y., Adams, M. S., Stoffel, A., and Boesch, R.: Photogrammetric
reconstruction of homogenous snow surfaces in alpine terrain applying
near-infrared UAS imagery, Int. J. Remote Sens., 38,
3135–3158, https://doi.org/10.1080/01431161.2016.1275060, 2017.
Capelli, A., Koch, F., Marty, C., Henkel, P., and Schweizer, J.: Snow water
equivalent measurements with low-cost GNSS receivers along a steep elevation
gradient in the eastern Swiss Alps, EnviDat [data set],
https://doi.org/10.16904/envidat.186, 2020.
Deems, J. S., Painter, T. H., and Finnegan, D. C.: Lidar measurements of
snow depth: a review, J. Glaciol., 59, 467–479,
https://doi.org/10.3189/2013JoG12J154, 2013.
Denoth, A.: An electronic device for long-term snow wetness recording,
Ann. Glaciol., 19, 104–106,
https://doi.org/10.3189/S0260305500011058, 1994.
Deschamps-Berger, C., Gascoin, S., Berthier, E., Deems, J., Gutmann, E., Dehecq, A., Shean, D., and Dumont, M.: Snow depth mapping from stereo satellite imagery in mountainous terrain: evaluation using airborne laser-scanning data, The Cryosphere, 14, 2925–2940, https://doi.org/10.5194/tc-14-2925-2020, 2020.
Dozier, J., Bair, E. H., and Davis, R. E.: Estimating the spatial
distribution of snow water equivalent in the world's mountains, Wiley
Interdisciplinary Reviews: Water, 3, 461–474,
https://doi.org/10.1002/wat2.1140, 2016.
Fierz, C. and Föhn, P. M. B.: Long-term observation of the water
content of an alpine snowpack, Proceedings ISSW 1994, International Snow
Science Workshop, Snowbird, Utah, USA, 30 October–3 November 1994,
117–131, available at: http://arc.lib.montana.edu/snow-science/item/1332 (last access: 7 February 2022), 1995.
Griessinger, N., Schirmer, M., Helbig, N., Winstral, A., Michel, A., and
Jonas, T.: Implications of observation-enhanced energy-balance snowmelt
simulations for runoff modeling of Alpine catchments, Adv. Water
Resour., 133, 103410, https://doi.org/10.1016/j.advwatres.2019.103410, 2019.
Grossi, G., Lendvai, A., Peretti, G., and Ranzi, R.: Snow precipitation
measured by gauges: systematic error estimation and data series correction
in the Central Italian Alps, Water, 9, 461,
https://doi.org/10.3390/w9070461, 2017.
Grünewald, T., Schirmer, M., Mott, R., and Lehning, M.: Spatial and temporal variability of snow depth and ablation rates in a small mountain catchment, The Cryosphere, 4, 215–225, https://doi.org/10.5194/tc-4-215-2010, 2010.
Gugerli, R., Salzmann, N., Huss, M., and Desilets, D.: Continuous and autonomous snow water equivalent measurements by a cosmic ray sensor on an alpine glacier, The Cryosphere, 13, 3413–3434, https://doi.org/10.5194/tc-13-3413-2019, 2019.
Haberkorn, A. (Ed.): European Snow Booklet – an Inventory of Snow
Measurements in Europe, edited by: Haberkorn, A.,
363 pp., https://doi.org/10.16904/envidat.59, 2019.
Helfricht, K., Kuhn, M., Keuschnig, M., and Heilig, A.: Lidar snow cover studies on glaciers in the Ötztal Alps (Austria): comparison with snow depths calculated from GPR measurements, The Cryosphere, 8, 41–57, https://doi.org/10.5194/tc-8-41-2014, 2014.
Henkel, P., Koch, F., Appel, F., Bach, H., Prasch, M., Schmid, L.,
Schweizer, J., and Mauser, W.: Snow water equivalent of dry snow derived
from GNSS carrier phases, IEEE T. Geosci. Remote, 56, 3561–3572,
https://doi.org/10.1109/TGRS.2018.2802494, 2018.
Jin, S. and Najibi, N.: Sensing snow height and surface temperature
variations in Greenland from GPS reflected signals, Adv. Space
Res., 53, 1623–1633,
https://doi.org/10.1016/j.asr.2014.03.005, 2014.
Johnson, J. B. and Schaefer, G. L.: The influence of thermal, hydrologic,
and snow deformation mechanisms on snow water equivalent pressure sensor
accuracy, Hydrol. Process., 16, 3529–3542,
https://doi.org/10.1002/hyp.1236, 2002.
Johnson, J. B., Gelvin, A. B., Duvoy, P., Schaefer, G. L., Poole, G., and
Horton, G. D.: Performance characteristics of a new electronic snow water
equivalent sensor in different climates, Hydrol. Process., 29,
1418–1433, https://doi.org/10.1002/hyp.10211, 2015.
Jonas, T., Marty, C., and Magnusson, J.: Estimating the snow water
equivalent from snow depth measurements in the Swiss Alps, J.
Hydrol., 378, 161–167, https://doi.org/10.1016/j.jhydrol.2009.09.021, 2009.
Koch, F., Prasch, M., Schmid, L., Schweizer, J., and Mauser, W.: Measuring
snow liquid water content with low-cost GPS receivers, Sensors, 14,
20975–20999, https://doi.org/10.3390/s141120975, 2014.
Koch, F., Henkel, P., Appel, F., Schmid, L., Bach, H., Lamm, M., Prasch, M.,
Schweizer, J., and Mauser, W.: Retrieval of snow water equivalent, liquid
water content, and snow height of dry and wet snow by combining GPS signal
attenuation and time delay, Water Resour. Res., 55, 4465–4487,
https://doi.org/10.1029/2018WR024431, 2019.
Lamm, M., Koch, F., Appel, F., and Henkel, P.: Estimation of snow parameters
with GPS and Galileo, 2018 International Symposium ELMAR, 16–19 September 2018, 4903, 109–112, https://doi.org/10.23919/ELMAR.2018.8534690, 2018.
Largeron, C., Dumont, M., Morin, S., Boone, A., Lafaysse, M., Metref, S.,
Cosme, E., Jonas, T., Winstral, A., and Margulis, S. A.: Toward snow cover
estimation in mountainous areas using modern data assimilation methods: a
review, Front. Earth Sci., 8, 325,
https://doi.org/10.3389/feart.2020.00325, 2020.
Larson, K. M., Gutmann, E. D., Zavorotny, V. U., Braun, J. J., Williams, M.
W., and Nievinski, F. G.: Can we measure snow depth with GPS receivers?,
Geophys. Res. Lett., 36, L17502,
https://doi.org/10.1029/2009gl039430, 2009.
Larue, F., Royer, A., De Sève, D., Langlois, A., Roy, A., and Brucker,
L.: Validation of GlobSnow-2 snow water equivalent over Eastern Canada,
Remote Sens. Environ., 194, 264–277,
https://doi.org/10.1016/j.rse.2017.03.027, 2017.
Le Roux, E., Evin, G., Eckert, N., Blanchet, J., and Morin, S.: Non-stationary extreme value analysis of ground snow loads in the French Alps: a comparison with building standards, Nat. Hazards Earth Syst. Sci., 20, 2961–2977, https://doi.org/10.5194/nhess-20-2961-2020, 2020.
Lehning, M., Völksch, I., Gustafsson, D., Nguyen, T. A., Stähli, M.,
and Zappa, M.: ALPINE3D: a detailed model of mountain surface processes and
its application to snow hydrology, Hydrol. Process., 20, 2111–2128,
https://doi.org/10.1002/hyp.6204, 2006.
Lievens, H., Demuzere, M., Marshall, H.-P., Reichle, R. H., Brucker, L.,
Brangers, I., de Rosnay, P., Dumont, M., Girotto, M., Immerzeel, W. W.,
Jonas, T., Kim, E. J., Koch, I., Marty, C., Saloranta, T., Schöber, J.,
and De Lannoy, G. J. M.: Snow depth variability in the Northern Hemisphere
mountains observed from space, Nat. Commun., 10, 4629,
https://doi.org/10.1038/s41467-019-12566-y, 2019.
Lievens, H., Brangers, I., Marshall, H.-P., Jonas, T., Olefs, M., and De Lannoy, G.: Sentinel-1 snow depth retrieval at sub-kilometer resolution over the European Alps, The Cryosphere Discuss. [preprint], https://doi.org/10.5194/tc-2021-74, in review, 2021.
López-Moreno, J. I., Leppänen, L., Luks, B., Holko, L., Picard, G.,
Sanmiguel-Vallelado, A., Alonso-González, E., Finger, D. C., Arslan, A.
N., Gillemot, K., Sensoy, A., Sorman, A., Ertaş, M. C., Fassnacht, S.
R., Fierz, C., and Marty, C.: Intercomparison of measurements of bulk snow
density and water equivalent of snow cover with snow core samplers:
Instrumental bias and variability induced by observers, Hydrol.
Process., 34, 3120–3133, https://doi.org/10.1002/hyp.13785, 2020.
Magnusson, J., Winstral, A., Stordal, A. S., Essery, R., and Jonas, T.:
Improving physically based snow simulations by assimilating snow depths
using the particle filter, Water Resour. Res., 53, 1125–1143,
https://doi.org/10.1002/2016WR019092, 2017.
Marin, C., Bertoldi, G., Premier, V., Callegari, M., Brida, C., Hürkamp, K., Tschiersch, J., Zebisch, M., and Notarnicola, C.: Use of Sentinel-1 radar observations to evaluate snowmelt dynamics in alpine regions, The Cryosphere, 14, 935–956, https://doi.org/10.5194/tc-14-935-2020, 2020.
Marty, C. and Meister, R.: Long-term snow and weather observations at
Weissfluhjoch and its relation to other high-altitude observatories in the
Alps, Theor. Appl. Climatol., 110, 573–583,
https://doi.org/10.1007/s00704-012-0584-3, 2012.
Marty, C., Tilg, A.-M., and Jonas, T.: Recent evidence of large-scale
receding snow water equivalents in the European Alps, J.
Hydrometeorol., 18, 1021–1031, https://doi.org/10.1175/jhm-d-16-0188.1, 2017.
Mavrovic, A., Madore, J.-B., Langlois, A., Royer, A., and Roy, A.: Snow
liquid water content measurement using an open-ended coaxial probe (OECP),
Cold Reg. Sci. Technol., 171, 102958,
https://doi.org/10.1016/j.coldregions.2019.102958, 2020.
Mote, P. W., Li, S., Lettenmaier, D. P., Xiao, M., and Engel, R.: Dramatic
declines in snowpack in the western US, NPJ Climate and Atmospheric Science,
1, 2397–3722, https://doi.org/10.1038/s41612-018-0012-1, 2018.
Pirazzini, R., Leppänen, L., Picard, G., Lopez-Moreno, J., Marty, C.,
Macelloni, G., Kontu, A., von Lerber, A., Tanis, C., Schneebeli, M., de
Rosnay, P., and Arslan, A.: European in-situ snow measurements: practices
and purposes, Sensors, 18, 2016,
https://doi.org/10.3390/s18072016, 2018.
Prokop, A., Schirmer, M., Rub, M., Lehning, M., and Stocker, M.: A
comparison of measurement methods: terrestrial laser scanning, tachymetry
and snow probing, for the determination of the spatial snow depth
distribution on slopes, Ann. Glaciol., 49, 210–216,
https://doi.org/10.3189/172756408787814726, 2008.
Pulliainen, J. and Hallikainen, M.: Retrieval of regional Snow Water
Equivalent from space-borne passive microwave observations, Remote Sens.
Environ., 75, 76–85, https://doi.org/10.1016/s0034-4257(00)00157-7, 2001.
Roth, K., Schulin, R., Flühler, H., and Attinger, W.: Calibration of
time domain reflectometry for water-content measurement using a composite
dielectric approach, Water Resour. Res., 26, 2267–2273,
https://doi.org/10.1029/WR026i010p02267, 1990.
Rover, S. and Vitti, A.: GNSS-R with low-cost receivers for retrieval of
antenna height from snow surfaces using single-frequency observations,
Sensors, 19, 5536, https://doi.org/10.3390/s19245536, 2019.
Royer, A., Roy, A., Jutras, S., and Langlois, A.: Review article: Performance assessment of radiation-based field sensors for monitoring the water equivalent of snow cover (SWE), The Cryosphere, 15, 5079–5098, https://doi.org/10.5194/tc-15-5079-2021, 2021.
Schattan, P., Baroni, G., Oswald, S. E., Schöber, J., Fey, C., Kormann,
C., Huttenlau, M., and Achleitner, S.: Continuous monitoring of snowpack
dynamics in alpine terrain by aboveground neutron sensing, Water Resour.
Res., 53, 3615–3634, https://doi.org/10.1002/2016wr020234,
2017.
Schattan, P., Köhli, M., Schrön, M., Baroni, G., and Oswald, S. E.:
Sensing area-average snow water equivalent with cosmic-ray neutrons: the
influence of fractional snow cover, Water Resour. Res., 55,
10796–10812, https://doi.org/10.1029/2019WR025647, 2019.
Schmid, L.: Deriving snow properties with upward-looking radar systems,
PhD, ETH Zurich, Zurich, Switzerland, 156 pp., https://doi.org/10.3929/ethz-a-010544465, 2015.
Schmid, L., Heilig, A., Mitterer, C., Schweizer, J., Maurer, H., Okorn, R.,
and Eisen, O.: Continuous snowpack monitoring using upward-looking
ground-penetrating radar technology, J. Glaciol., 60, 509–525,
https://doi.org/10.3189/2014JoG13J084, 2014.
Schmid, L., Koch, F., Heilig, A., Prasch, M., Eisen, O., Mauser, W., and
Schweizer, J.: A novel sensor combination (upGPR – GPS) to continuously and
non-destructively derive snow cover properties, Geophys. Res. Lett., 42, 3397–3405, https://doi.org/10.1002/2015GL063732,
2015.
Serreze, M. C., Clark, M. P., Armstrong, R. L., McGinnis, D. A., and
Pulwarty, R. S.: Characteristics of the western United States snowpack from
snowpack telemetry (SNOTEL) data, Water Resour. Res., 35, 2145–2160,
https://doi.org/10.1029/1999WR900090, 1999.
Shi, J. and Dozier, J.: Estimation of snow water equivalence using
SIR-C/X-SAR. I. Inferring snow density and subsurface properties, IEEE
T. Geosci. Remote, 38, 2465–2474, https://doi.org/10.1109/36.885195, 2000.
Steiner, L., Meindl, M., Fierz, C., and Geiger, A.: An assessment of sub-snow GPS for quantification of snow water equivalent, The Cryosphere, 12, 3161–3175, https://doi.org/10.5194/tc-12-3161-2018, 2018.
Steiner, L., Meindl, M., Fierz, C., Marty, C., and Geiger, A.: Monitoring
snow water equivalent using low-cost GPS antennas buried underneath a
snowpack, 2019 13th European Conference on Antennas and Propagation (EuCAP), 2019 13th European Conference on Antennas and Propagation (EuCAP), 31 March-5 April 2019,
1–5, ISBN 978-88-907018-8-7, 2019a.
Steiner, L., Meindl, M., Marty, C., and Geiger, A.: Impact of GPS processing
on the estimation of snow water equivalent using refracted GPS signals, IEEE
T. Geosci. Remote, 58, 1–13, https://doi.org/10.1109/TGRS.2019.2934016, 2019b.
Techel, F. and Pielmeier, C.: Point observations of liquid water content in wet snow – investigating methodical, spatial and temporal aspects, The Cryosphere, 5, 405–418, https://doi.org/10.5194/tc-5-405-2011, 2011.
Tiuri, M. E., Sihvola, A. H., Nyfors, E. G., and Hallikainen, M. T.: The
complex dielectric constant of snow at microwave frequencies, IEEE J. Ocean.
Eng., 9, 377–382, https://doi.org/10.1109/joe.1984.1145645,
1984.
Tsang, L., Durand, M., Derksen, C., Barros, A. P., Kang, D.-H., Lievens, H., Marshall, H.-P., Zhu, J., Johnson, J., King, J., Lemmetyinen, J., Sandells, M., Rutter, N., Siqueira, P., Nolin, A., Osmanoglu, B., Vuyovich, C., Kim, E. J., Taylor, D., Merkouriadi, I., Brucker, L., Navari, M., Dumont, M., Kelly, R., Kim, R. S., Liao, T.-H., and Xu, X.: Review Article: Global Monitoring of Snow Water Equivalent using High Frequency Radar Remote Sensing, The Cryosphere Discuss. [preprint], https://doi.org/10.5194/tc-2021-295, in review, 2021.
Wiese, M. and Schneebeli, M.: Early-stage interaction between settlement
and temperature-gradient metamorphism, J. Glaciol., 63, 652–662,
https://doi.org/10.1017/jog.2017.31, 2017.
Wiesmann, A., Caduff, R., Werner, C., Frey, O., Schneebeli, M., Löwe,
H., Jaggi, M., Schwank, M., Naderpour, R., and Fehr, T.: ESA SnowLab
Project: 4 years of wide band scatterometer measurements of seasonal snow,
IGARSS 2019 – 2019 IEEE Int. Geosci. Remote Se., 5745–5748, https://doi.org/10.1109/IGARSS.2019.8898961, 2019.
Winkler, M., Schellander, H., and Gruber, S.: Snow water equivalents exclusively from snow depths and their temporal changes: the Δsnow model, Hydrol. Earth Syst. Sci., 25, 1165–1187, https://doi.org/10.5194/hess-25-1165-2021, 2021.
Winstral, A., Magnusson, J., Schirmer, M., and Jonas, T.: The bias-detecting
ensemble: a new and efficient technique for dynamically incorporating
observations into physics-based, multilayer snow models, Water Resour.
Res., 55, 613–631, https://doi.org/10.1029/2018wr024521,
2019.
WMO: Technical Regulations, Volume I – General Meteorological Standards and
Recommended Practices, WMO-No. 49, edited by: WMO, World Meteorological
Organization, Geneva, Switzerland, Geneva, Switzerland, WMO-No. 49, 48 pp., ISBN 978-92-63-10049-8 2019.
Zhang, Z., Guo, F., and Zhang, X.: Triple-frequency multi-GNSS reflectometry
snow depth retrieval by using clustering and normalization algorithm to
compensate terrain variation, GPS Solut., 24, 52,
https://doi.org/10.1007/s10291-020-0966-4, 2020.
Short summary
Snow occurrence, snow amount, snow density and liquid water content (LWC) can vary considerably with climatic conditions and elevation. We show that low-cost Global Navigation Satellite System (GNSS) sensors as GPS can be used for reliably measuring the amount of water stored in the snowpack or snow water equivalent (SWE), snow depth and the LWC under a broad range of climatic conditions met at different elevations in the Swiss Alps.
Snow occurrence, snow amount, snow density and liquid water content (LWC) can vary considerably...
