
Snowpack
Main menu
Without snowpack there would be no avalanches. But snowpack also provides a surface for ski runs, protects flora and fauna and induces a cooling effect through snow-atmosphere coupling. These are just a few examples of its significance but they show how important snowpack is to both humans and the natural world.
Snowpack consists of layers, rather like a cream slice. Each snowfall adds another layer. Snowpack therefore tells the history of a winter, from the oldest layers on the ground to the most recent on the surface. Some layers are so distinctive that weeks later we can link them back to specific weather events, e.g. a thick melt-freeze crust to a warm snap. Weak layers, such as snow-covered surface hoar, can increase the risk of avalanches. SLF observers therefore monitor their development closely. To improve avalanche risk forecasting, we are working to better understand and reproduce snowpack structure and the processes that occur inside it.
Changeable snowpack
Snowpack is a master of transformation: a person might sink up to their waist or walk over the top, depending on its condition. This is the result of continuous changes in the snowpack itself, most of which cannot be seen from outside. The temperature has a particularly big influence on these changes. The warmer the snow and the greater the temperature differences between the snow surface and the ground, the faster the snow layers change.
The wind also shapes the snow surface (Fig. 1). It transports the snow grains and grinds them down. It forms hard wind slabs. It blows snow from summits and ridges and fills up hollows and channels, rendering traffic routes impassable.

We investigate the fundamentals of snow drift in detail at the SLF's wind tunnel facility and at test sites. The measurement results feed into computer models, which simulate snowpack properties.
Radiation
The specific physical properties of snow are also important in snowpack. The dazzling white of a fresh snow surface clearly shows that snow has a high diffuse reflectivity (albedo). In the visible range, 80‑90% of incident solar radiation is reflected back into the atmosphere, compared with 20-50% for a coniferous forest and just 5-25% for a calm water surface. This means that snow only absorbs a small fraction of solar energy compared to other surfaces. The little energy that does penetrate it affects the snow’s metamorphism.
FURTHER INFORMATION
Services and products
Alpine 3D
SnowMicroPen ® (SMP5 version)
SNOWPACK
Publications
Korzeniowska, K.; Bühler, Y.; Marty, M.; Korup, O., 2017: Regional snow-avalanche detection using object-based image analysis of near-infrared aerial imagery. Natural Hazards and Earth System Sciences, 17, 10: 1823-1836. doi: 10.5194/nhess-17-1823-2017
Wiese, M.; Schneebeli, M., 2017: Snowbreeder 5: a micro-CT device for measuring the snow-microstructure evolution under the simultaneous influence of a temperature gradient and compaction. Journal of Glaciology, 63, 238: 355-360. doi: 10.1017/jog.2016.143
Gaume, J.; Van Herwijnen, A.; Chambon, G.; Wever, N.; Schweizer, J., 2017: Snow fracture in relation to slab avalanche release: critical state for the onset of crack propagation. Cryosphere, 11, 1: 217-228. doi: 10.5194/tc-11-217-2017
Comola, F.; Kok, J.F.; Gaume, J.; Paterna, E.; Lehning, M., 2017: Fragmentation of wind-blown snow crystals. Geophysical Research Letters, 44, 9: 4195-4203. doi: 10.1002/2017GL073039
Calonne, N.; Montagnat, M.; Matzl, M.; Schneebeli, M., 2017: The layered evolution of fabric and microstructure of snow at Point Barnola, Central East Antarctica. Earth and Planetary Sciences Letters, 460: 293-301. doi: 10.1016/j.epsl.2016.11.041
Würzer, S.; Wever, N.; Juras, R.; Lehning, M.; Jonas, T., 2017: Modelling liquid water transport in snow under rain-on-snow conditions – considering preferential flow. Hydrology and Earth System Sciences, 21, 3: 1741-1756. doi: 10.5194/hess-21-1741-2017
Haberkorn, A.; Wever, N.; Hoelzle, M.; Phillips, M.; Kenner, R.; Bavay, M.; Lehning, M., 2017: Distributed snow and rock temperature modelling in steep rock walls using Alpine3D. Cryosphere, 11, 1: 585-607. doi: 10.5194/tc-11-585-2017
Steger, C.R.; Reijmer, C.H.; Van den Broeke, M.R.; Wever, N.; Forster, R.R.; Koenig, L.S.; Kuipers Munneke, P.; Lehning, M.; Lhermitte, S.; Ligtenberg, S.R.M.; Miège, C.; Noël, B.P.Y., 2017: Firn meltwater retention on the Greenland Ice Sheet: a model comparison. Frontiers in Earth Science, 5: 3 (16 pp.). doi: 10.3389/feart.2017.00003
Avanzi, F.; Petrucci, G.; Matzl, M.; Schneebeli, M.; De Michele, C., 2017: Early formation of preferential flow in a homogeneous snowpack observed by micro-CT. Water Resources Research, 53, 5: 3713-3729. doi: 10.1002/2016WR019502
Magnusson, J.; Winstral, A.; Stordal, A.S.; Essery, R.; Jonas, T., 2017: Improving physically based snow simulations by assimilating snow depths using the particle filter. Water Resources Research, 53, 2: 1125-1143. doi: 10.1002/2016WR019092
Comola, F.; Lehning, M., 2017: Energy- and momentum-conserving model of splash entrainment in sand and snow saltation. Geophysical Research Letters, 44, 3: 1601-1609. doi: 10.1002/2016GL071822
Helbig, N.; Mott, R.; Van Herwijnen, A.; Winstral, A.; Jonas, T., 2017: Parameterizing surface wind speed over complex topography. Journal of Geophysical Research D: Atmospheres, 122, 2: 651-667. doi: 10.1002/2016JD025593
Sammonds, P.; Montagnat, M.; Bons, P.; Schneebeli, M., 2017: Ice microstructures and microdynamics. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 375, 2086: 20160438 (5 pp.). doi: 10.1098/rsta.2016.0438
Sammonds, P.; Montagnat, M.; Bons, P.; Schneebeli, M., 2017: Microdynamics of ice. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 375, 2086: 20160437 (3 pp.). doi: 10.1098/rsta.2016.0437
Winstral, A.; Jonas, T.; Helbig, N., 2017: Statistical downscaling of gridded wind speed data using local topography. Journal of Hydrometeorology, 18: 335-348. doi: 10.1175/JHM-D-16-0054.1
Capelli, A.; Kapil, J.C.; Reiweger, I.; Or, D.; Schweizer, J., 2016: Speed and attenuation of acoustic waves in snow: laboratory experiments and modeling with Biot's theory. Cold Regions Science and Technology, 125: 1-11. doi: 10.1016/j.coldregions.2016.01.004
Paterna, E.; Crivelli, P.; Lehning, M., 2016: Decoupling of mass flux and turbulent wind fluctuations in drifting snow. Geophysical Research Letters, 43, 9: 4441-4447. doi: 10.1002/2016GL068171
Monti, F.; Gaume, J.; Van Herwijnen, A.; Schweizer, J., 2016: Snow instability evaluation: calculating the skier-induced stress in a multi-layered snowpack. Natural Hazards and Earth System Sciences, 16, 3: 775-788. doi: 10.5194/nhess-16-775-2016
Maslanka, W.; Leppänen, L.; Kontu, A.; Sandells, M.; Lemmetyinen, J.; Schneebeli, M.; Proksch, M.; Matzl, M.; Hannula, H.; Gurney, R., 2016: Arctic snow microstructure experiment for the development of snow emission modelling. Geoscientific Instrumentation, Methods and Data Systems, 5, 1: 85-94. doi: 10.5194/gi-5-85-2016
Crivelli, P.; Paterna, E.; Horender, S.; Lehning, M., 2016: Quantifying particle numbers and mass Flux in drifting snow. Boundary-Layer Meteorology, 161, 3: 519-542. doi: 10.1007/s10546-016-0170-9
Schweizer, J.; Reuter, B.; Van Herwijnen, A.; Richter, B.; Gaume, J., 2016: Temporal evolution of crack propagation propensity in snow in relation to slab and weak layer properties. Cryosphere, 10, 6: 2637-2653. doi: 10.5194/tc-10-2637-2016
Moeser, D.; Mazzotti, G.; Helbig, N.; Jonas, T., 2016: Representing spatial variability of forest snow: implementation of a new interception model. Water Resources Research, 52, 2: 1208-1226. doi: 10.1002/2015WR017961
Ebner, P.P.; Schneebeli, M.; Steinfeld, A., 2016: Metamorphism during temperature gradient with undersaturated advective airflow in a snow sample. Cryosphere, 10, 2: 791-797. doi: 10.5194/tc-10-791-2016
Revuelto, J.; Jonas, T.; López-Moreno, J., 2016: Backward snow depth reconstruction at high spatial resolution based on time-lapse photography. Hydrological Processes, 30, 17: 2976-2990. doi: 10.1002/hyp.10823
Wever, N.; Vera Valero, C.; Fierz, C., 2016: Assessing wet snow avalanche activity using detailed physics based snowpack simulations. Geophysical Research Letters, 43, 11: 5732-5740. doi: 10.1002/2016GL068428
Lemmetyinen, J.; Schwank, M.; Rautiainen, K.; Kontu, A.; Parkkinen, T.; Mätzler, C.; Wiesmann, A.; Wegmüller, U.; Derksen, C.; Toose, P.; Roy, A.; Pulliainen, J., 2016: Snow density and ground permittivity retrieved from L-band radiometry: application to experimental data. Remote Sensing of Environment, 180: 377-391. doi: 10.1016/j.rse.2016.02.002
Würzer, S.; Jonas, T.; Wever, N.; Lehning, M., 2016: Influence of initial snowpack properties on runoff formation during rain-on-snow events. Journal of Hydrometeorology, 17, 6: 1801-1815. doi: 10.1175/JHM-D-15-0181.1
Bokhorst, S.; Højlund Pedersen, S.; Brucker, L.; Anisimov, O.; Bjerke, J.W.; Brown, R.D.; Ehrich, D.; Essery, R.L.H.; Heilig, A.; Ingvander, S.; Johansson, C.; Johansson, M.; Jónsdóttir, I.S.; Inga, N.; Luojus, K.; Macelloni, G.; Mariash, H.; McLennan, D.; Rosqvist, G.N.; ... Callaghan, T.V., 2016: Changing arctic snow cover: a review of recent developments and assessment of future needs for observations, modelling, and impacts. Ambio, 45, 5: 516-537. doi: 10.1007/s13280-016-0770-0
Webster, C.; Rutter, N.; Zahner, F.; Jonas, T., 2016: Modeling subcanopy incoming longwave radiation to seasonal snow using air and tree trunk temperatures. Journal of Geophysical Research D: Atmospheres, 121, 3: 1220-1235. doi: 10.1002/2015JD024099
Webster, C.; Rutter, N.; Zahner, F.; Jonas, T., 2016: Measurement of incoming radiation below forest canopies: a comparison of different radiometer configurations. Journal of Hydrometeorology, 17, 3: 853-864. doi: 10.1175/JHM-D-15-0125.1
Pellarin, T.; Mialon, A.; Biron, R.; Coulaud, C.; Gibon, F.; Kerr, Y.; Lafaysse, M.; Mercier, B.; Morin, S.; Redor, I.; Schwank, M.; Völksch, I., 2016: Three years of L-band brightness temperature measurements in a mountainous area: Topography, vegetation and snowmelt issues. Remote Sensing of Environment, 180: 85-98. doi: 10.1016/j.rse.2016.02.047
Wever, N.; Würzer, S.; Fierz, C.; Lehning, M., 2016: Simulating ice layer formation under the presence of preferential flow in layered snowpacks. Cryosphere, 10, 6: 2731-2744. doi: 10.5194/tc-10-2731-2016
Griessinger, N.; Seibert, J.; Magnusson, J.; Jonas, T., 2016: Assessing the benefit of snow data assimilation for runoff modeling in Alpine catchments. Hydrology and Earth System Sciences, 20, 9: 3895-3905. doi: 10.5194/hess-20-3895-2016
Hagenmuller, P.; Matzl, M.; Chambon, G.; Schneebeli, M., 2016: Sensitivity of snow density and specific surface area measured by microtomography to different image processing algorithms. Cryosphere, 10, 3: 1039-1054. doi: 10.5194/tc-10-1039-2016
Lemmetyinen, J.; Kontu, A.; Pulliainen, J.; Vehviläinen, J.; Rautiainen, K.; Wiesmann, A.; Mätzler, C.; Werner, C.; Rott, H.; Nagler, T.; Schneebeli, M.; Proksch, M.; Schüttemeyer, D.; Kern, M.; Davidson, M.W.J., 2016: Nordic Snow Radar Experiment. Geoscientific Instrumentation, Methods and Data Systems, 5, 2: 403-415. doi: 10.5194/gi-5-403-2016
Proksch, M.; Rutter, N.; Fierz, C.; Schneebeli, M., 2016: Intercomparison of snow density measurements: bias, precision, and vertical resolution. Cryosphere, 10, 1: 371-384. doi: 10.5194/tc-10-371-2016
Jenicek, M.; Seibert, J.; Zappa, M.; Staudinger, M.; Jonas, T., 2016: Importance of maximum snow accumulation for summer low flows in humid catchments. Hydrology and Earth System Sciences, 20, 2: 859-874. doi: 10.5194/hess-20-859-2016
Boesch, R.; Bühler, Y.; Marty, M.; ... Ginzler, C., 2016: Comparison of digital surface models for snow depth mapping with UAV and aerial cameras. In: Halounova, L.; Šafář, V.; Raju, P.L.N.; Plánka, L.; Ždímal, V.; Srinivasa Kumar, T.; Faruque, F.S.; Kerr, Y.; Ramasamy, S.M.; Comiso, J.; (Yousif) Hussin, Y.A.; ... Thenkabail, P.S.; Lavender, S.; Skidmore, A.; Yue, P.; Weng, Q. (eds), 2016: XXIII ISPRS congress, commission VIII. XXIII ISPRS congress, Prague, Czech Republic, July 12-19, 2016. 453-458. doi: 10.5194/isprs-archives-XLI-B8-453-2016
Gallice, A.; Bavay, M.; Brauchli, T.; Comola, F.; Lehning, M.; Huwald, H., 2016: StreamFlow 1.0: an extension to the spatially distributed snow model Alpine3D for hydrological modelling and deterministic stream temperature prediction. Geoscientific Model Development, 9, 12: 4491-4519. doi: 10.5194/gmd-9-4491-2016
Steinkogler, W.; Gaume, J.; Löwe, H.; Sovilla, B.; Lehning, M., 2015: Granulation of snow: from tumbler experiments to discrete element simulations. Journal of Geophysical Research F: Earth Surface, 120, 6: 1107-1126. doi: 10.1002/2014JF003294
Bühler, Y.; Marty, M.; Egli, L.; Veitinger, J.; Jonas, T.; Thee, P.; Ginzler, C., 2015: Snow depth mapping in high-alpine catchments using digital photogrammetry. Cryosphere, 9, 1: 229-243. doi: 10.5194/tc-9-229-2015
Meijer zu Schlochtern, M.P.; Rixen, C.; Wipf, S.; Cornelissen, J.H.C., 2014: Management, winter climate and plant-soil feedbacks on ski slopes: a synthesis. Ecological Research, 29, 4: 583-592. doi: 10.1007/s11284-014-1141-6
Maysenhölder, W.; Heggli, M.; Zhou, X.; Zhang, T.; Frei, E.; Schneebeli, M., 2012: Microstructure and sound absorption of snow. Cold Regions Science and Technology, 83-84: 3-12. doi: 10.1016/j.coldregions.2012.05.001
Heggli, M.; Köchle, B.; Matzl, M.; Pinzer, B.R.; Riche, F.; Steiner, S.; Steinfeld, D.; Schneebeli, M., 2011: Measuring snow in 3-D using X-ray tomography: assessment of visualization techniques. Annals of Glaciology, 52, 58: 231-236. doi: 10.3189/172756411797252202
Heggli, M.; Frei, E.; Schneebeli, M., 2009: Snow replica method for three-dimensional X-ray microtomographic imaging. . Journal of Glaciology, 55, 192: 631-639. doi: 10.3189/002214309789470932
Sovilla, B.; Bartelt, P., 2002: Observations and modelling of snow avalanche entrainment. Natural Hazards and Earth System Sciences, 2, 3-4: 169-179. doi: 10.5194/nhess-2-169-2002