Articles | Volume 19, issue 10
https://doi.org/10.5194/tc-19-4437-2025
© Author(s) 2025. 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-19-4437-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
10 Oct 2025
Research article |
| 10 Oct 2025
| 10 Oct 2025
Quantification of capillary rise dynamics in snow using neutron radiography
Michael Lombardo
CORRESPONDING AUTHOR
WSL Institute for Snow and Avalanche Research SLF, Davos, Switzerland
Amelie Fees
WSL Institute for Snow and Avalanche Research SLF, Davos, Switzerland
Anders Kaestner
Laboratory for Neutron Scattering and Imaging, Paul Scherrer Institute, Villigen, Switzerland
Alec van Herwijnen
WSL Institute for Snow and Avalanche Research SLF, Davos, Switzerland
Jürg Schweizer
WSL Institute for Snow and Avalanche Research SLF, Davos, Switzerland
Peter Lehmann
Physics of Soils and Terrestrial Ecosystems, ETH Zurich, Zurich, Switzerland
Related authors
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
Short summary
Short summary
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.
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
Short summary
Short summary
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.
Nedal Aqel, Jannis Groh, Lutz Weihermüller, Ralf Gründling, Andrea Carminati, and Peter Lehmann
EGUsphere, https://doi.org/10.5194/egusphere-2025-5141, https://doi.org/10.5194/egusphere-2025-5141, 2025
This preprint is open for discussion and under review for Hydrology and Earth System Sciences (HESS).
Short summary
Short summary
This study investigates how soils respond to major climatic disturbances, such as the extreme drought in Germany in 2018. Using long-term lysimeter observations and an artificial intelligence model, we show that persistent shifts in soil water dynamics indicate changes in hydraulic properties that may affect soil health, emphasizing the need for continuous monitoring under a changing climate.
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
Short summary
Short summary
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.
Philipp L. Rosendahl, Johannes Schneider, Grégoire Bobillier, Florian Rheinschmidt, Bastian Bergfeld, Alec van Herwijnen, and Philipp Weißgraeber
Nat. Hazards Earth Syst. Sci., 25, 1975–1991, https://doi.org/10.5194/nhess-25-1975-2025, https://doi.org/10.5194/nhess-25-1975-2025, 2025
Short summary
Short summary
Avalanche formation depends on crack propagation in weak snow layers, but the conditions that stop a crack remain unclear. We show that slab touchdown reduces the energy driving crack growth, which can halt propagation even under static conditions. This suggests that crack arrest is influenced not only by snowpack variability or dynamics but also by mechanical interactions within the snowpack. Our findings refine avalanche prediction models and improve hazard assessment.
Cristina Pérez-Guillén, Frank Techel, Michele Volpi, and Alec van Herwijnen
Nat. Hazards Earth Syst. Sci., 25, 1331–1351, https://doi.org/10.5194/nhess-25-1331-2025, https://doi.org/10.5194/nhess-25-1331-2025, 2025
Short summary
Short summary
This study assesses the performance and explainability of a random-forest classifier for predicting dry-snow avalanche danger levels during initial live testing. The model achieved ∼ 70 % agreement with human forecasts, performing equally well in nowcast and forecast modes, while capturing the temporal dynamics of avalanche forecasting. The explainability approach enhances the transparency of the model's decision-making process, providing a valuable tool for operational avalanche forecasting.
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
Short summary
Short summary
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
Short summary
Short summary
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.
Bastian Bergfeld, Karl W. Birkeland, Valentin Adam, Philipp L. Rosendahl, and Alec van Herwijnen
Nat. Hazards Earth Syst. Sci., 25, 321–334, https://doi.org/10.5194/nhess-25-321-2025, https://doi.org/10.5194/nhess-25-321-2025, 2025
Short summary
Short summary
To release a slab avalanche, a crack in a weak snow layer beneath a cohesive slab has to propagate. Information on that is essential for assessing avalanche risk. In the field, information can be gathered with the propagation saw test (PST). However, there are different standards on how to cut the PST. In this study, we experimentally investigate the effect of these different column geometries and provide models to correct for imprecise field test geometry effects on the critical cut length.
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
Short summary
Short summary
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
Short summary
Short summary
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.
Nedal Aqel, Lea Reusser, Stephan Margreth, Andrea Carminati, and Peter Lehmann
Geosci. Model Dev., 17, 6949–6966, https://doi.org/10.5194/gmd-17-6949-2024, https://doi.org/10.5194/gmd-17-6949-2024, 2024
Short summary
Short summary
The soil water potential (SWP) determines various soil water processes. Since remote sensing techniques cannot measure it directly, it is often deduced from volumetric water content (VWC) information. However, under dynamic field conditions, the relationship between SWP and VWC is highly ambiguous due to different factors that cannot be modeled with the classical approach. Applying a deep neural network with an autoencoder enables the prediction of the dynamic SWP.
Tobias Karl David Weber, Lutz Weihermüller, Attila Nemes, Michel Bechtold, Aurore Degré, Efstathios Diamantopoulos, Simone Fatichi, Vilim Filipović, Surya Gupta, Tobias L. Hohenbrink, Daniel R. Hirmas, Conrad Jackisch, Quirijn de Jong van Lier, John Koestel, Peter Lehmann, Toby R. Marthews, Budiman Minasny, Holger Pagel, Martine van der Ploeg, Shahab Aldin Shojaeezadeh, Simon Fiil Svane, Brigitta Szabó, Harry Vereecken, Anne Verhoef, Michael Young, Yijian Zeng, Yonggen Zhang, and Sara Bonetti
Hydrol. Earth Syst. Sci., 28, 3391–3433, https://doi.org/10.5194/hess-28-3391-2024, https://doi.org/10.5194/hess-28-3391-2024, 2024
Short summary
Short summary
Pedotransfer functions (PTFs) are used to predict parameters of models describing the hydraulic properties of soils. The appropriateness of these predictions critically relies on the nature of the datasets for training the PTFs and the physical comprehensiveness of the models. This roadmap paper is addressed to PTF developers and users and critically reflects the utility and future of PTFs. To this end, we present a manifesto aiming at a paradigm shift in PTF research.
Gwendolyn Dasser, Valentin T. Bickel, Marius Rüetschi, Mylène Jacquemart, Mathias Bavay, Elisabeth D. Hafner, Alec van Herwijnen, and Andrea Manconi
EGUsphere, https://doi.org/10.5194/egusphere-2024-1510, https://doi.org/10.5194/egusphere-2024-1510, 2024
Short summary
Short summary
Understanding snowpack wetness is crucial for predicting wet snow avalanches, but detailed data is often limited to certain locations. Using satellite radar, we monitor snow wetness spatially continuously. By combining different radar tracks from Sentinel-1, we improved spatial resolution and tracked snow wetness over several seasons. Our results indicate higher snow wetness to correlate with increased wet snow avalanche activity, suggesting our method can help identify potential risk areas.
Andri Simeon, Cristina Pérez-Guillén, Michele Volpi, Christine Seupel, and Alec van Herwijnen
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-76, https://doi.org/10.5194/gmd-2024-76, 2024
Revised manuscript accepted for GMD
Short summary
Short summary
Avalanche seismic detection systems are key for forecasting, but distinguishing avalanches from other seismic sources remains challenging. We propose novel autoencoder models to automatically extract features and compare them with standard seismic attributes. These features are then used to classify avalanches and noise events. The autoencoder feature classifiers have the highest sensitivity to detect avalanches, while the standard seismic classifier performs better overall.
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
Short summary
Short summary
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.
Mathieu Le Breton, Éric Larose, Laurent Baillet, Yves Lejeune, and Alec van Herwijnen
The Cryosphere, 17, 3137–3156, https://doi.org/10.5194/tc-17-3137-2023, https://doi.org/10.5194/tc-17-3137-2023, 2023
Short summary
Short summary
We monitor the amount of snow on the ground using passive radiofrequency identification (RFID) tags. These small and inexpensive tags are wirelessly read by a stationary reader placed above the snowpack. Variations in the radiofrequency phase delay accurately reflect variations in snow amount, known as snow water equivalent. Additionally, each tag is equipped with a sensor that monitors the snow temperature.
Adrian Wicki, Peter Lehmann, Christian Hauck, and Manfred Stähli
Nat. Hazards Earth Syst. Sci., 23, 1059–1077, https://doi.org/10.5194/nhess-23-1059-2023, https://doi.org/10.5194/nhess-23-1059-2023, 2023
Short summary
Short summary
Soil wetness measurements are used for shallow landslide prediction; however, existing sites are often located in flat terrain. Here, we assessed the ability of monitoring sites at flat locations to detect critically saturated conditions compared to if they were situated at a landslide-prone location. We found that differences exist but that both sites could equally well distinguish critical from non-critical conditions for shallow landslide triggering if relative changes are considered.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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.
Achille Capelli, Franziska Koch, Patrick Henkel, Markus Lamm, Florian Appel, Christoph Marty, and Jürg Schweizer
The Cryosphere, 16, 505–531, https://doi.org/10.5194/tc-16-505-2022, https://doi.org/10.5194/tc-16-505-2022, 2022
Short summary
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.
Antoine Guillemot, Alec van Herwijnen, Eric Larose, Stephanie Mayer, and Laurent Baillet
The Cryosphere, 15, 5805–5817, https://doi.org/10.5194/tc-15-5805-2021, https://doi.org/10.5194/tc-15-5805-2021, 2021
Short summary
Short summary
Ambient noise correlation is a broadly used method in seismology to monitor tiny changes in subsurface properties. Some environmental forcings may influence this method, including snow. During one winter season, we studied this snow effect on seismic velocity of the medium, recorded by a pair of seismic sensors. We detected and modeled a measurable effect during early snowfalls: the fresh new snow layer modifies rigidity and density of the medium, thus decreasing the recorded seismic velocity.
Nora Helbig, Michael Schirmer, Jan Magnusson, Flavia Mäder, Alec van Herwijnen, Louis Quéno, Yves Bühler, Jeff S. Deems, and Simon Gascoin
The Cryosphere, 15, 4607–4624, https://doi.org/10.5194/tc-15-4607-2021, https://doi.org/10.5194/tc-15-4607-2021, 2021
Short summary
Short summary
The snow cover spatial variability in mountains changes considerably over the course of a snow season. In applications such as weather, climate and hydrological predictions the fractional snow-covered area is therefore an essential parameter characterizing how much of the ground surface in a grid cell is currently covered by snow. We present a seasonal algorithm and a spatiotemporal evaluation suggesting that the algorithm can be applied in other geographic regions by any snow model application.
Adrian Wicki, Per-Erik Jansson, Peter Lehmann, Christian Hauck, and Manfred Stähli
Hydrol. Earth Syst. Sci., 25, 4585–4610, https://doi.org/10.5194/hess-25-4585-2021, https://doi.org/10.5194/hess-25-4585-2021, 2021
Short summary
Short summary
Soil moisture information was shown to be valuable for landslide prediction. Soil moisture was simulated at 133 sites in Switzerland, and the temporal variability was compared to the regional occurrence of landslides. We found that simulated soil moisture is a good predictor for landslides, and that the forecast goodness is similar to using in situ measurements. This encourages the use of models for complementing existing soil moisture monitoring networks for regional landslide early warning.
Bastian Bergfeld, Alec van Herwijnen, Benjamin Reuter, Grégoire Bobillier, Jürg Dual, and Jürg Schweizer
The Cryosphere, 15, 3539–3553, https://doi.org/10.5194/tc-15-3539-2021, https://doi.org/10.5194/tc-15-3539-2021, 2021
Short summary
Short summary
The modern picture of the snow slab avalanche release process involves a
dynamic crack propagation phasein which a whole slope becomes detached. The present work contains the first field methodology which provides the temporal and spatial resolution necessary to study this phase. We demonstrate the versatile capabilities and accuracy of our method by revealing intricate dynamics and present how to determine relevant characteristics of crack propagation such as crack speed.
Jürg Schweizer, Christoph Mitterer, Benjamin Reuter, and Frank Techel
The Cryosphere, 15, 3293–3315, https://doi.org/10.5194/tc-15-3293-2021, https://doi.org/10.5194/tc-15-3293-2021, 2021
Short summary
Short summary
Snow avalanches threaten people and infrastructure in snow-covered mountain regions. To mitigate the effects of avalanches, warnings are issued by public forecasting services. Presently, the five danger levels are described in qualitative terms. We aim to characterize the avalanche danger levels based on expert field observations of snow instability. Our findings contribute to an evidence-based description of danger levels and to improve consistency and accuracy of avalanche forecasts.
Surya Gupta, Tomislav Hengl, Peter Lehmann, Sara Bonetti, and Dani Or
Earth Syst. Sci. Data, 13, 1593–1612, https://doi.org/10.5194/essd-13-1593-2021, https://doi.org/10.5194/essd-13-1593-2021, 2021
Michaela Wenner, Clément Hibert, Alec van Herwijnen, Lorenz Meier, and Fabian Walter
Nat. Hazards Earth Syst. Sci., 21, 339–361, https://doi.org/10.5194/nhess-21-339-2021, https://doi.org/10.5194/nhess-21-339-2021, 2021
Short summary
Short summary
Mass movements constitute a risk to property and human life. In this study we use machine learning to automatically detect and classify slope failure events using ground vibrations. We explore the influence of non-ideal though commonly encountered conditions: poor network coverage, small number of events, and low signal-to-noise ratios. Our approach enables us to detect the occurrence of rare events of high interest in a large data set of more than a million windowed seismic signals.
Bettina Richter, Alec van Herwijnen, Mathias W. Rotach, and Jürg Schweizer
Nat. Hazards Earth Syst. Sci., 20, 2873–2888, https://doi.org/10.5194/nhess-20-2873-2020, https://doi.org/10.5194/nhess-20-2873-2020, 2020
Short summary
Short summary
We investigated the sensitivity of modeled snow instability to uncertainties in meteorological input, typically found in complex terrain. The formation of the weak layer was very robust due to the long dry period, indicated by a widespread avalanche problem. Once a weak layer has formed, precipitation mostly determined slab and weak layer properties and hence snow instability. When spatially assessing snow instability for avalanche forecasting, accurate precipitation patterns have to be known.
Cited articles
Adachi, S., Yamaguchi, S., Ozeki, T., and Kose, K.: Application of a magnetic resonance imaging method for nondestructive, three-dimensional, high-resolution measurement of the water content of wet snow samples, Frontiers in Earth Science, 8, https://doi.org/10.3389/feart.2020.00179, 2020. a, b, c, d, e, f, g, h, i, j, k, l, m
Albert, M. R., Shultz, E. F., and Perron, F. E.: Snow and firn permeability at Siple Dome, Antarctica, Annals of Glaciology, 31, 353–356, https://doi.org/10.3189/172756400781820273, 2000. a
Barella, R., Bavay, M., Carletti, F., Ciapponi, N., Premier, V., and Marin, C.: Unlocking the potential of melting calorimetry: a field protocol for liquid water content measurement in snow, The Cryosphere, 18, 5323–5345, https://doi.org/10.5194/tc-18-5323-2024, 2024. a
Benson, C. H., Chiang, I., Chalermyanont, T., and Sawangsuriya, A.: Estimating van Genuchten Parameters α and n for Clean Sands from Particle Size Distribution Data, 410–427, https://doi.org/10.1061/9780784413265.033, 2014. a
Brun, E.: Investigation on Wet-Snow Metamorphism in Respect of Liquid-Water Content, Annals of Glaciology, 13, 22–26, https://doi.org/10.3189/S0260305500007576, 1989. a
Calonne, N., Geindreau, C., Flin, F., Morin, S., Lesaffre, B., Rolland du Roscoat, S., and Charrier, P.: 3-D image-based numerical computations of snow permeability: links to specific surface area, density, and microstructural anisotropy, The Cryosphere, 6, 939–951, https://doi.org/10.5194/tc-6-939-2012, 2012. a, b, c, d, e, f, g, h
Carminati, C., Boillat, P., Schmid, F., Vontobel, P., Hovind, J., Morgano, M., Raventos, M., Siegwart, M., Mannes, D., Gruenzweig, C., Trtik, P., Lehmann, E., Strobl, M., and Kaestner, A.: Implementation and assessment of the black body bias correction in quantitative neutron imaging, PLoS ONE, 14, 1–24, https://doi.org/10.1371/journal.pone.0210300, 2019. a, b, c, d
Clerx, N., Machguth, H., Tedstone, A., Jullien, N., Wever, N., Weingartner, R., and Roessler, O.: In situ measurements of meltwater flow through snow and firn in the accumulation zone of the SW Greenland Ice Sheet, The Cryosphere, 16, 4379–4401, https://doi.org/10.5194/tc-16-4379-2022, 2022. a
Colbeck, S.: Grain and bond growth in wet snow, IAHS Publication, 114, 51–61, 1975. a
Colbeck, S.: Statistics of coarsening in water-saturated snow, Acta Metallurgica, 34, 347–352, https://doi.org/10.1016/0001-6160(86)90070-2, 1986. a, b
Colbeck, S. C.: The capillary effects on water percolation in homogeneous snow, Journal of Glaciology, 13, 85–97, https://doi.org/10.3189/s002214300002339x, 1974. a
Coléou, C. and Lesaffre, B.: Irreducible water saturation in snow: experimental results in a cold laboratory, Annals of Glaciology, 26, 64–68, 1998. a
Coléou, C., Xu, K., Lesaffre, B., and Brzoska, J.-B.: Capillary rise in snow, Hydrological Processes, 13, 1721–1732, https://doi.org/10.1002/(SICI)1099-1085(199909)13:12/13<1721::AID-HYP852>3.0.CO;2-D, 1999. a, b, c
Courville, Z., Hörhold, M., Hopkins, M., and Albert, M.: Lattice-Boltzmann modeling of the air permeability of polar firn, Journal of Geophysical Research: Earth Surface, 115, https://doi.org/10.1029/2009JF001549, 2010. a
D'Amboise, C. J. L., Müller, K., Oxarango, L., Morin, S., and Schuler, T. V.: Implementation of a physically based water percolation routine in the Crocus/SURFEX (V7.3) snowpack model, Geosci. Model Dev., 10, 3547–3566, https://doi.org/10.5194/gmd-10-3547-2017, 2017. a, b
Faybishenko, B. A.: Hydraulic Behavior of Quasi-Saturated Soils in the Presence of Entrapped Air: Laboratory Experiments, Water Resources Research, 31, 2421–2435, https://doi.org/10.1029/95WR01654, 1995. a
Fees, A., van Herwijnen, A., Altenbach, M., Lombardo, M., and Schweizer, J.: Glide-snow avalanche characteristics at different timescales extracted from time-lapse photography, Annals of Glaciology, 65, https://doi.org/10.1017/aog.2023.37, 2025. a
Feistl, T., Bebi, P., Dreier, L., Hanewinkel, M., and Bartelt, P.: Quantification of basal friction for technical and silvicultural glide-snow avalanche mitigation measures, Nat. Hazards Earth Syst. Sci., 14, 2921–2931, https://doi.org/10.5194/nhess-14-2921-2014, 2014. a, b
Fierz, C., Armstrong, R., Durand, Y., Etchevers, P., Greene, E., McClung, D., Nishimura, K., Satyawali, P. K., and Sokratov, S.: The International Classification for Seasonal Snow on the Ground, vol. 83, HP-VII Technical Documents in Hydrology, UNESCO-IHP, Paris, France, https://unesdoc.unesco.org/ark:/48223/pf0000186462 (last access: 12 November 2020), 2009. a, b
Hagenmuller, P., Chambon, G., Lesaffre, B., Flin, F., and Naaim, M.: Energy-based binary segmentation of snow microtomographic images, Journal of Glaciology, 59, 859–873, https://doi.org/10.3189/2013JoG13J035, 2013. a
Hammond, J. C. and Kampf, S. K.: Subannual Streamflow Responses to Rainfall and Snowmelt Inputs in Snow-Dominated Watersheds of the Western United States, Water Resources Research, 56, e2019WR026132, https://doi.org/10.1029/2019WR026132, 2020. a
Hendrick, M., Techel, F., Volpi, M., Olevski, T., Pérez-Guillén, C., van Herwijnen, A., and Schweizer, J.: Automated prediction of wet-snow avalanche activity in the Swiss Alps, Journal of Glaciology, 69, 1365–1378, https://doi.org/10.1017/jog.2023.24, 2023. a
Hirashima, H., Yamaguchi, S., Sato, A., and Lehning, M.: Numerical modeling of liquid water movement through layered snow based on new measurements of the water retention curve, Cold Regions Science and Technology, 64, 94–103, https://doi.org/10.1016/j.coldregions.2010.09.003, 2010. a
Hirashima, H., Avanzi, F., and Wever, N.: Wet-Snow Metamorphism Drives the Transition From Preferential to Matrix Flow in Snow, Geophysical Research Letters, 46, 14548–14557, https://doi.org/10.1029/2019GL084152, 2019. a
Jordan, P.: Meltwater movement in a deep snowpack 1. Field observations, Water Resources Research, 19, 971–978, https://doi.org/10.1029/WR019i004p00971, 1983. a
Jordan, R.: Effects of capillary discontinuities on water flow and water retention in layered snowcovers, Defence Science Journal, 45, 79–91, https://doi.org/10.14429/dsj.45.4107, 1995. a
Jordan, R. E., Hardy, J. P., Perron, F. E., and Fisk, D. J.: Air permeability and capillary rise as measures of the pore structure of snow: An experimental and theoretical study, Hydrological Processes, 13, 1733–1753, https://doi.org/10.1002/(SICI)1099-1085(199909)13:12/13<1733::AID-HYP863>3.0.CO;2-2, 1999. a
Katsushima, T., Yamaguchi, S., Kumakura, T., and Sato, A.: Experimental analysis of preferential flow in dry snowpack, Cold Regions Science and Technology, 85, 206–216, https://doi.org/10.1016/j.coldregions.2012.09.012, 2013. a, b, c, d
Kool, J. B. and Parker, J. C.: Development and evaluation of closed-form expressions for hysteretic soil hydraulic properties, Water Resources Research, 23, 105–114, https://doi.org/10.1029/WR023i001p00105, 1987. a
Lehmann, E. H., Vontobel, P., and Wiezel, L.: Properties of the radiography facility NEUTRA at SINQ and its potential for use as European reference facility, Nondestructive Testing and Evaluation, 16, 191–202, https://doi.org/10.1080/10589750108953075, 2001. a, b
Lehmann, P., Assouline, S., and Or, D.: Characteristic lengths affecting evaporative drying of porous media, Phys. Rev. E, 77, 056309, https://doi.org/10.1103/PhysRevE.77.056309, 2008. a, b
Leroux, N. R., Marsh, C. B., and Pomeroy, J. W.: Simulation of preferential flow in snow with a 2-D non-equilibrium Richards model and evaluation against laboratory data, Water Resources Research, 56, e2020WR027466, https://doi.org/10.1029/2020WR027466, 2020. a, b, c, d
Lombardo, M., Fees, A., Kaestner, A., van Herwijnen, A., Schweizer, J., and Lehmann, P.: Capillary rise experiments in snow using neutron radiography, enviDat [data set], https://doi.org/10.16904/envidat.572, 2025a. a, b, c, d
Lombardo, M., Fees, A., Udke, A., Meusburger, K., van Herwijnen, A., Schweizer, J., and Lehmann, P.: Capillary suction across the soil-snow interface as a mechanism for the formation of wet basal layers under gliding snowpacks, Journal of Glaciology, 1–46, https://doi.org/10.1017/jog.2025.2, 2025b. a, b, c, d
Mallett, R., Nandan, V., Stroeve, J., Willatt, R., Saha, M., Yackel, J., Veysière, G., and Wilkinson, J.: Dye tracing of upward brine migration in snow, Annals of Glaciology, 1–14, https://doi.org/10.1017/aog.2024.27, 2024. a
Mannes, D., Schmid, F., Wehmann, T., and Lehmann, E.: Design and Applications of a Climatic Chamber for in-situ Neutron Imaging Experiments, Physics Procedia, 88, 200–207, https://doi.org/10.1016/j.phpro.2017.06.028, 2017. a
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. a
Marshall, H., Conway, H., and Rasmussen, L.: Snow densification during rain, Cold Regions Science and Technology, 30, 35–41, https://doi.org/10.1016/S0165-232X(99)00011-7, 1999. a
Mayer, S., Hendrick, M., Michel, A., Richter, B., Schweizer, J., Wernli, H., and van Herwijnen, A.: Impact of climate change on snow avalanche activity in the Swiss Alps, The Cryosphere, 18, 5495–5517, https://doi.org/10.5194/tc-18-5495-2024, 2024. a
Mitterer, C. and Schweizer, J.: Towards a Better Understanding of Glide-Snow Avalanche Formation, in: Proceedings ISSW 2012. International Snow Science Workshop, Anchorage AK, USA, 16-21 September 2012, 610–616, https://arc.lib.montana.edu/snow-science/item.php?id=1612 (last access: 4 November 2020), 2012. a, b, c
Mualem, Y.: A new model for predicting the hydraulic conductivity of unsaturated porous media, Water Resources Research, 12, 513–522, https://doi.org/10.1029/WR012i003p00513, 1976. a, b
Quéno, L., Fierz, C., van Herwijnen, A., Longridge, D., and Wever, N.: Deep ice layer formation in an alpine snowpack: monitoring and modeling, The Cryosphere, 14, 3449–3464, https://doi.org/10.5194/tc-14-3449-2020, 2020. a, b, c
Richards, L. A.: Capillary conduction of liquids through porous mediums, Journal of Applied Physics, 1, 318–333, https://doi.org/10.1063/1.1745010, 1931. a
Richards, L. A. and Gardner, W.: Tensiometers for Measuring the Capillary Tension of Soil Water, Journal of the American Society of Agronomy, 28, 352–358, https://doi.org/10.2134/agronj1936.00021962002800050002x, 1936. a
Rösel, A., Farrell, S. L., Nandan, V., Richter-Menge, J., Spreen, G., Divine, D. V., Steer, A., Gallet, J.-C., and Gerland, S.: Implications of surface flooding on airborne estimates of snow depth on sea ice, The Cryosphere, 15, 2819–2833, https://doi.org/10.5194/tc-15-2819-2021, 2021. a
Savitzky, A. and Golay, M. J. E.: Smoothing and Differentiation of Data by Simplified Least Squares Procedures, Analytical Chemistry, 36, 1627–1639, https://doi.org/10.1021/ac60214a047, 1964. a
Schleef, S., Jaggi, M., Löwe, H., and Schneebeli, M.: An improved machine to produce nature-identical snow in the laboratory, Journal of Glaciology, 60, 94–102, https://doi.org/10.3189/2014JoG13J118, 2014. a
Shimizu, H.: Air permeability of deposited snow, Contributions from the Institute of Low Temperature Science, A22, 1–32, 1970. a
Siegwart, M., Woracek, R., Damián, J. I. M., Tremsin, A. S., Manzi-Orezzoli, V., Strobl, M., Schmidt, T. J., and Boillat, P.: Distinction between super-cooled water and ice with high duty cycle time-of-flight neutron imaging, Review of Scientific Instruments, 90, 1–15, https://doi.org/10.1063/1.5110288, 2019. a
Šimůnek, J., van Genuchten, M. T., and Šejna, M.: Development and Applications of the HYDRUS and STANMOD Software Packages and Related Codes, Vadose Zone Journal, 7, 587–600, https://doi.org/10.2136/vzj2007.0077, 2008. a
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. a
van Genuchten, M.: A Closed-form Equation for Predicting the Hydraulic Conductivity of Unsaturated Soils, Soil Science Society of America Journal, 44, 892–898, https://doi.org/10.2136/sssaj1980.03615995004400050002x, 1980. a, b, c, d
van Lier, Q. d. J. and Pinheiro, E. A. R.: An Alert Regarding a Common Misinterpretation of the Van Genuchten α Parameter, Revista Brasileira de Ciência do Solo, 42, e0170343, https://doi.org/10.1590/18069657rbcs20170343, 2018. a, b
Walter, B., Horender, S., Gromke, C., and Lehning, M.: Measurements of the pore-scale water flow through snow using Fluorescent Particle Tracking Velocimetry, Water Resources Research, 49, 7448–7456, https://doi.org/10.1002/2013WR013960, 2013. a
Wankiewicz, A.: Water pressure in ripe snowpacks, Water Resources Research, 14, 593–600, https://doi.org/10.1029/WR014i004p00593, 1978. a
Wankiewicz, A. C.: Water percolation within a deep snowpack field investigations at a site on Mt. Seymour, British Columbia, PhD thesis, University of British Columbia, https://doi.org/10.14288/1.0053186, 1976. a
Webb, R. W., Fassnacht, S. R., Gooseff, M. N., and Webb, S. W.: The Presence of Hydraulic Barriers in Layered Snowpacks: TOUGH2 Simulations and Estimated Diversion Lengths, Transport in Porous Media, 123, 457–476, https://doi.org/10.1007/s11242-018-1079-1, 2018. a, b
Webb, R. W., Wigmore, O., Jennings, K., Fend, M., and Molotch, N. P.: Hydrologic connectivity at the hillslope scale through intra-snowpack flow paths during snowmelt, Hydrological Processes, 34, 1616–1629, https://doi.org/10.1002/hyp.13686, 2020. a
Wever, N., Fierz, C., Mitterer, C., Hirashima, H., and Lehning, M.: Solving Richards Equation for snow improves snowpack meltwater runoff estimations in detailed multi-layer snowpack model, The Cryosphere, 8, 257–274, https://doi.org/10.5194/tc-8-257-2014, 2014. a, b, c, d
Wever, N., Comola, F., Bavay, M., and Lehning, M.: Simulating the influence of snow surface processes on soil moisture dynamics and streamflow generation in an alpine catchment, Hydrol. Earth Syst. Sci., 21, 4053–4071, https://doi.org/10.5194/hess-21-4053-2017, 2017. a
Wever, N., Vera Valero, C., and Techel, F.: Coupled Snow Cover and Avalanche Dynamics Simulations to Evaluate Wet Snow Avalanche Activity, Journal of Geophysical Research: Earth Surface, 123, 1772–1796, https://doi.org/10.1029/2017JF004515, 2018. a
Wever, N., Rossmann, L., Maaß, N., Leonard, K. C., Kaleschke, L., Nicolaus, M., and Lehning, M.: Version 1 of a sea ice module for the physics-based, detailed, multi-layer SNOWPACK model, Geosci. Model Dev., 13, 99–119, https://doi.org/10.5194/gmd-13-99-2020, 2020. a
Würzer, S., Wever, N., Juras, R., Lehning, M., and Jonas, T.: Modelling liquid water transport in snow under rain-on-snow conditions – considering preferential flow, Hydrol. Earth Syst. Sci., 21, 1741–1756, https://doi.org/10.5194/hess-21-1741-2017, 2017. a
Yamaguchi, S., Katsushima, T., Sato, A., and Kumakura, T.: Water retention curve of snow with different grain sizes, Cold Regions Science and Technology, 64, 87–93, https://doi.org/10.1016/j.coldregions.2010.05.008, 2010. a, b, c, d
Yamaguchi, S., Adachi, S., and Sunako, S.: A novel method to visualize liquid distribution in snow: superimposition of MRI and X-ray CT images, Annals of Glaciology, 65, https://doi.org/10.1017/aog.2023.77, 2025. a, b
Short summary
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.
Water flow in snow is important for many applications including snow hydrology and avalanche...
