Articles | Volume 8, issue 1
https://doi.org/10.5194/tc-8-73-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Special issue:
https://doi.org/10.5194/tc-8-73-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
10 Jan 2014
Research article |
| 10 Jan 2014
A satellite-based snow cover climatology (1985–2011) for the European Alps derived from AVHRR data
F. Hüsler
Institute of Geography and Oeschger Center for Climate Change Research, University of Bern, Bern, Switzerland
T. Jonas
WSL Institute for Snow and Avalanche Research SLF, Davos Dorf, Switzerland
M. Riffler
Institute of Geography and Oeschger Center for Climate Change Research, University of Bern, Bern, Switzerland
J. P. Musial
Institute of Geography and Oeschger Center for Climate Change Research, University of Bern, Bern, Switzerland
S. Wunderle
Institute of Geography and Oeschger Center for Climate Change Research, University of Bern, Bern, Switzerland
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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.
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Hydrol. Earth Syst. Sci., 28, 2745–2765, https://doi.org/10.5194/hess-28-2745-2024, https://doi.org/10.5194/hess-28-2745-2024, 2024
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Marius G. Floriancic, Wouter R. Berghuijs, Tobias Jonas, James W. Kirchner, and Peter Molnar
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Sebastian Würzer, Nander Wever, Roman Juras, Michael Lehning, and Tobias Jonas
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Nena Griessinger, Franziska Mohr, and Tobias Jonas
The Cryosphere Discuss., https://doi.org/10.5194/tc-2016-295, https://doi.org/10.5194/tc-2016-295, 2017
Revised manuscript not accepted
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Nena Griessinger, Jan Seibert, Jan Magnusson, and Tobias Jonas
Hydrol. Earth Syst. Sci., 20, 3895–3905, https://doi.org/10.5194/hess-20-3895-2016, https://doi.org/10.5194/hess-20-3895-2016, 2016
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Michal Jenicek, Jan Seibert, Massimiliano Zappa, Maria Staudinger, and Tobias Jonas
Hydrol. Earth Syst. Sci., 20, 859–874, https://doi.org/10.5194/hess-20-859-2016, https://doi.org/10.5194/hess-20-859-2016, 2016
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F. Kobierska, T. Jonas, J. W. Kirchner, and S. M. Bernasconi
Hydrol. Earth Syst. Sci., 19, 3681–3693, https://doi.org/10.5194/hess-19-3681-2015, https://doi.org/10.5194/hess-19-3681-2015, 2015
I. Gouttevin, M. Lehning, T. Jonas, D. Gustafsson, and M. Mölder
Geosci. Model Dev., 8, 2379–2398, https://doi.org/10.5194/gmd-8-2379-2015, https://doi.org/10.5194/gmd-8-2379-2015, 2015
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We improve the canopy module of a very detailed snow model, SNOWPACK, with a view of a more consistent representation of the sub-canopy energy balance with regard to the snowpack.
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N. Helbig, A. van Herwijnen, J. Magnusson, and T. Jonas
Hydrol. Earth Syst. Sci., 19, 1339–1351, https://doi.org/10.5194/hess-19-1339-2015, https://doi.org/10.5194/hess-19-1339-2015, 2015
Y. Bühler, M. Marty, L. Egli, J. Veitinger, T. Jonas, P. Thee, and C. Ginzler
The Cryosphere, 9, 229–243, https://doi.org/10.5194/tc-9-229-2015, https://doi.org/10.5194/tc-9-229-2015, 2015
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We are able to map snow depth over large areas ( > 100km2) using airborne digital photogrammetry. Digital photogrammetry is more economical than airborne Laser Scanning but slightly less accurate. Comparisons to independent snow depth measurements reveal an accuracy of about 30cm. Spatial continuous mapping of snow depth is a major step forward compared to point measurements usually applied today. Limitations are steep slopes (> 50°) and areas covered by trees and scrubs.
N. Wever, T. Jonas, C. Fierz, and M. Lehning
Hydrol. Earth Syst. Sci., 18, 4657–4669, https://doi.org/10.5194/hess-18-4657-2014, https://doi.org/10.5194/hess-18-4657-2014, 2014
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We simulated a severe rain-on-snow event in the Swiss Alps in October 2011 with a detailed multi-layer snow cover model. We found a strong modulating effect of the incoming rainfall signal by the snow cover. Initially, water from both rainfall and snow melt was absorbed by the snowpack. But once the snowpack released the stored water, simulated outflow rates exceeded rainfall and snow melt rates. The simulations suggest that structural snowpack changes enhanced the outflow during this event.
J. P. Musial, F. Hüsler, M. Sütterlin, C. Neuhaus, and S. Wunderle
Atmos. Meas. Tech., 7, 799–822, https://doi.org/10.5194/amt-7-799-2014, https://doi.org/10.5194/amt-7-799-2014, 2014
D. Finger, A. Hugentobler, M. Huss, A. Voinesco, H. Wernli, D. Fischer, E. Weber, P.-Y. Jeannin, M. Kauzlaric, A. Wirz, T. Vennemann, F. Hüsler, B. Schädler, and R. Weingartner
Hydrol. Earth Syst. Sci., 17, 3261–3277, https://doi.org/10.5194/hess-17-3261-2013, https://doi.org/10.5194/hess-17-3261-2013, 2013
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Here, we present ASIP: a new and comprehensive deep-learning-based methodology to retrieve high-resolution sea ice concentration with accompanying well-calibrated uncertainties from satellite-based active and passive microwave observations at a pan-Arctic scale for all seasons. In a comparative study against pan-Arctic ice charts and well-established passive-microwave-based sea ice products, we show that ASIP generalizes well to the pan-Arctic region.
Deniz Tobias Gök, Dirk Scherler, and Hendrik Wulf
The Cryosphere, 18, 5259–5276, https://doi.org/10.5194/tc-18-5259-2024, https://doi.org/10.5194/tc-18-5259-2024, 2024
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We derived Landsat Collection 2 land surface temperature (LST) trends in the Swiss Alps using a harmonic model with a linear trend. Validation with LST data from 119 high-altitude weather stations yielded robust results, but Landsat LST trends are biased due to unstable acquisition times. The bias varies with topographic slope and aspect. We discuss its origin and propose a simple correction method in relation to modeled changes in shortwave radiation.
Philipp Sebastian Arndt and Helen Amanda Fricker
The Cryosphere, 18, 5173–5206, https://doi.org/10.5194/tc-18-5173-2024, https://doi.org/10.5194/tc-18-5173-2024, 2024
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We develop a method for ice-sheet-scale retrieval of supraglacial meltwater depths using ICESat-2 photon data. We report results for two drainage basins in Greenland and Antarctica during two contrasting melt seasons, where our method reveals a total of 1249 lake segments up to 25 m deep. The large volume and wide variety of accurate depth data that our method provides enable the development of data-driven models of meltwater volumes in satellite imagery.
Brenton A. Wilder, Joachim Meyer, Josh Enterkine, and Nancy F. Glenn
The Cryosphere, 18, 5015–5029, https://doi.org/10.5194/tc-18-5015-2024, https://doi.org/10.5194/tc-18-5015-2024, 2024
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Remotely sensed properties of snow are dependent on accurate terrain information, which for a lot of the cryosphere and seasonal snow zones is often insufficient in accuracy. However, as we show in this paper, we can bypass this issue by optimally solving for the terrain by utilizing the raw radiance data returned to the sensor. This method performed well when compared to validation datasets and has the potential to be used across a variety of different snow climates.
Benjamin J. Wallis, Anna E. Hogg, Yikai Zhu, and Andrew Hooper
The Cryosphere, 18, 4723–4742, https://doi.org/10.5194/tc-18-4723-2024, https://doi.org/10.5194/tc-18-4723-2024, 2024
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The grounding line, where ice begins to float, is an essential variable to understand ice dynamics, but in some locations it can be challenging to measure with established techniques. Using satellite data and a new method, Wallis et al. measure the grounding line position of glaciers and ice shelves in the Antarctic Peninsula and find retreats of up to 16.3 km have occurred since the last time measurements were made in the 1990s.
Clemens von Baeckmann, Annett Bartsch, Helena Bergstedt, Aleksandra Efimova, Barbara Widhalm, Dorothee Ehrich, Timo Kumpula, Alexander Sokolov, and Svetlana Abdulmanova
The Cryosphere, 18, 4703–4722, https://doi.org/10.5194/tc-18-4703-2024, https://doi.org/10.5194/tc-18-4703-2024, 2024
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Lakes are common features in Arctic permafrost areas. Land cover change following their drainage needs to be monitored since it has implications for ecology and the carbon cycle. Satellite data are key in this context. We compared a common vegetation index approach with a novel land-cover-monitoring scheme. Land cover information provides specific information on wetland features. We also showed that the bioclimatic gradients play a significant role after drainage within the first 10 years.
Nils Risse, Mario Mech, Catherine Prigent, Gunnar Spreen, and Susanne Crewell
The Cryosphere, 18, 4137–4163, https://doi.org/10.5194/tc-18-4137-2024, https://doi.org/10.5194/tc-18-4137-2024, 2024
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Passive microwave observations from satellites are crucial for monitoring Arctic sea ice and atmosphere. To do this effectively, it is important to understand how sea ice emits microwaves. Through unique Arctic sea ice observations, we improved our understanding, identified four distinct emission types, and expanded current knowledge to include higher frequencies. These findings will enhance our ability to monitor the Arctic climate and provide valuable information for new satellite missions.
Melody Sandells, Nick Rutter, Kirsty Wivell, Richard Essery, Stuart Fox, Chawn Harlow, Ghislain Picard, Alexandre Roy, Alain Royer, and Peter Toose
The Cryosphere, 18, 3971–3990, https://doi.org/10.5194/tc-18-3971-2024, https://doi.org/10.5194/tc-18-3971-2024, 2024
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Satellite microwave observations are used for weather forecasting. In Arctic regions this is complicated by natural emission from snow. By simulating airborne observations from in situ measurements of snow, this study shows how snow properties affect the signal within the atmosphere. Fresh snowfall between flights changed airborne measurements. Good knowledge of snow layering and structure can be used to account for the effects of snow and could unlock these data to improve forecasts.
Veit Helm, Alireza Dehghanpour, Ronny Hänsch, Erik Loebel, Martin Horwath, and Angelika Humbert
The Cryosphere, 18, 3933–3970, https://doi.org/10.5194/tc-18-3933-2024, https://doi.org/10.5194/tc-18-3933-2024, 2024
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We present a new approach (AWI-ICENet1), based on a deep convolutional neural network, for analysing satellite radar altimeter measurements to accurately determine the surface height of ice sheets. Surface height estimates obtained with AWI-ICENet1 (along with related products, such as ice sheet height change and volume change) show improved and unbiased results compared to other products. This is important for the long-term monitoring of ice sheet mass loss and its impact on sea level rise.
Fabrizio Troilo, Niccolò Dematteis, Francesco Zucca, Martin Funk, and Daniele Giordan
The Cryosphere, 18, 3891–3909, https://doi.org/10.5194/tc-18-3891-2024, https://doi.org/10.5194/tc-18-3891-2024, 2024
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The study of glacier sliding along slopes is relevant in many aspects of glaciology. We processed Sentinel-2 satellite optical images of Mont Blanc, obtaining surface velocities of 30 glaciers between 2016 and 2024. The study revealed different behaviours and velocity variations that have relationships with glacier morphology. A velocity anomaly was observed in some glaciers of the southern side in 2020–2022, but its origin needs to be investigated further.
Benoit Montpetit, Joshua King, Julien Meloche, Chris Derksen, Paul Siqueira, J. Max Adam, Peter Toose, Mike Brady, Anna Wendleder, Vincent Vionnet, and Nicolas R. Leroux
The Cryosphere, 18, 3857–3874, https://doi.org/10.5194/tc-18-3857-2024, https://doi.org/10.5194/tc-18-3857-2024, 2024
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This paper validates the use of free open-source models to link distributed snow measurements to radar measurements in the Canadian Arctic. Using multiple radar sensors, we can decouple the soil from the snow contribution. We then retrieve the "microwave snow grain size" to characterize the interaction between the snow mass and the radar signal. This work supports future satellite mission development to retrieve snow mass information such as the future Canadian Terrestrial Snow Mass Mission.
Randall Bonnell, Daniel McGrath, Jack Tarricone, Hans-Peter Marshall, Ella Bump, Caroline Duncan, Stephanie Kampf, Yunling Lou, Alex Olsen-Mikitowicz, Megan Sears, Keith Williams, Lucas Zeller, and Yang Zheng
The Cryosphere, 18, 3765–3785, https://doi.org/10.5194/tc-18-3765-2024, https://doi.org/10.5194/tc-18-3765-2024, 2024
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Snow provides water for billions of people, but the amount of snow is difficult to detect remotely. During the 2020 and 2021 winters, a radar was flown over mountains in Colorado, USA, to measure the amount of snow on the ground, while our team collected ground observations to test the radar technique’s capabilities. The technique yielded accurate measurements of the snowpack that had good correlation with ground measurements, making it a promising application for the upcoming NISAR satellite.
Taha Sadeghi Chorsi, Franz J. Meyer, and Timothy H. Dixon
The Cryosphere, 18, 3723–3740, https://doi.org/10.5194/tc-18-3723-2024, https://doi.org/10.5194/tc-18-3723-2024, 2024
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The active layer thaws and freezes seasonally. The annual freeze–thaw cycle of the active layer causes significant surface height changes due to the volume difference between ice and liquid water. We estimate the subsidence rate and active-layer thickness (ALT) for part of northern Alaska for summer 2017 to 2022 using interferometric synthetic aperture radar and lidar. ALT estimates range from ~20 cm to larger than 150 cm in area. Subsidence rate varies between close points (2–18 mm per month).
Jukes Liu, Madeline Gendreau, Ellyn Mary Enderlin, and Rainey Aberle
The Cryosphere, 18, 3571–3590, https://doi.org/10.5194/tc-18-3571-2024, https://doi.org/10.5194/tc-18-3571-2024, 2024
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There are sometimes gaps in global glacier velocity records produced using satellite image feature-tracking algorithms during times of rapid glacier acceleration, which hinders the study of glacier flow processes. We present an open-source pipeline for customizing the feature-tracking parameters and for including images from an additional source. We applied it to five glaciers and found that it produced accurate velocity data that supplemented their velocity records during rapid acceleration.
Annett Bartsch, Xaver Muri, Markus Hetzenecker, Kimmo Rautiainen, Helena Bergstedt, Jan Wuite, Thomas Nagler, and Dmitry Nicolsky
EGUsphere, https://doi.org/10.5194/egusphere-2024-2518, https://doi.org/10.5194/egusphere-2024-2518, 2024
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We developed a robust freeze/thaw detection approach, applying a constant threshold on Copernicus Sentinel-1 data, that is suitable for tundra regions. All global, coarser resolution products, tested with the resulting benchmarking dataset, are of value for freeze/thaw retrieval, although differences were found depending on seasons, in particular during spring and autumn transition.
Jordan N. Herbert, Mark S. Raleigh, and Eric E. Small
The Cryosphere, 18, 3495–3512, https://doi.org/10.5194/tc-18-3495-2024, https://doi.org/10.5194/tc-18-3495-2024, 2024
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Automated stations measure snow properties at a single point but are frequently used to validate data that represent much larger areas. We use lidar snow depth data to see how often the mean snow depth surrounding a snow station is within 10 cm of the snow station depth at different scales. We found snow stations overrepresent the area-mean snow depth in ~ 50 % of cases, but the direction of bias at a site is temporally consistent, suggesting a site could be calibrated to the surrounding area.
Andreas Stokholm, Jørgen Buus-Hinkler, Tore Wulf, Anton Korosov, Roberto Saldo, Leif Toudal Pedersen, David Arthurs, Ionut Dragan, Iacopo Modica, Juan Pedro, Annekatrien Debien, Xinwei Chen, Muhammed Patel, Fernando Jose Pena Cantu, Javier Noa Turnes, Jinman Park, Linlin Xu, Katharine Andrea Scott, David Anthony Clausi, Yuan Fang, Mingzhe Jiang, Saeid Taleghanidoozdoozan, Neil Curtis Brubacher, Armina Soleymani, Zacharie Gousseau, Michał Smaczny, Patryk Kowalski, Jacek Komorowski, David Rijlaarsdam, Jan Nicolaas van Rijn, Jens Jakobsen, Martin Samuel James Rogers, Nick Hughes, Tom Zagon, Rune Solberg, Nicolas Longépé, and Matilde Brandt Kreiner
The Cryosphere, 18, 3471–3494, https://doi.org/10.5194/tc-18-3471-2024, https://doi.org/10.5194/tc-18-3471-2024, 2024
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The AutoICE challenge encouraged the development of deep learning models to map multiple aspects of sea ice – the amount of sea ice in an area and the age and ice floe size – using multiple sources of satellite and weather data across the Canadian and Greenlandic Arctic. Professionally drawn operational sea ice charts were used as a reference. A total of 179 students and sea ice and AI specialists participated and produced maps in broad agreement with the sea ice charts.
Enrico Mattea, Etienne Berthier, Amaury Dehecq, Tobias Bolch, Atanu Bhattacharya, Sajid Ghuffar, Martina Barandun, and Martin Hoelzle
EGUsphere, https://doi.org/10.5194/egusphere-2024-2169, https://doi.org/10.5194/egusphere-2024-2169, 2024
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We reconstruct the evolution of terminus position, ice thickness and surface flow velocity of the reference Abramov glacier (Kyrgyzstan) from 1968 to present. We describe a front pulsation in the early 2000s and the multi-annual present-day buildup of a new pulsation. Such dynamic instabilities can challenge the representativity of Abramov as reference glacier. For our work we used satellite‑based optical remote sensing from multiple platforms, including recently declassified archives.
Livia Piermattei, Michael Zemp, Christian Sommer, Fanny Brun, Matthias H. Braun, Liss M. Andreassen, Joaquín M. C. Belart, Etienne Berthier, Atanu Bhattacharya, Laura Boehm Vock, Tobias Bolch, Amaury Dehecq, Inés Dussaillant, Daniel Falaschi, Caitlyn Florentine, Dana Floricioiu, Christian Ginzler, Gregoire Guillet, Romain Hugonnet, Matthias Huss, Andreas Kääb, Owen King, Christoph Klug, Friedrich Knuth, Lukas Krieger, Jeff La Frenierre, Robert McNabb, Christopher McNeil, Rainer Prinz, Louis Sass, Thorsten Seehaus, David Shean, Désirée Treichler, Anja Wendt, and Ruitang Yang
The Cryosphere, 18, 3195–3230, https://doi.org/10.5194/tc-18-3195-2024, https://doi.org/10.5194/tc-18-3195-2024, 2024
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Satellites have made it possible to observe glacier elevation changes from all around the world. In the present study, we compared the results produced from two different types of satellite data between different research groups and against validation measurements from aeroplanes. We found a large spread between individual results but showed that the group ensemble can be used to reliably estimate glacier elevation changes and related errors from satellite data.
Isis Brangers, Hans-Peter Marshall, Gabrielle De Lannoy, Devon Dunmire, Christian Mätzler, and Hans Lievens
The Cryosphere, 18, 3177–3193, https://doi.org/10.5194/tc-18-3177-2024, https://doi.org/10.5194/tc-18-3177-2024, 2024
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To better understand the interactions between C-band radar waves and snow, a tower-based experiment was set up in the Idaho Rocky Mountains. The reflections were collected in the time domain to measure the backscatter profile from the various snowpack and ground surface layers. The results demonstrate that C-band radar is sensitive to seasonal patterns in snow accumulation but that changes in microstructure, stratigraphy and snow wetness may complicate satellite-based snow depth retrievals.
Lanqing Huang and Irena Hajnsek
The Cryosphere, 18, 3117–3140, https://doi.org/10.5194/tc-18-3117-2024, https://doi.org/10.5194/tc-18-3117-2024, 2024
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Interferometric synthetic aperture radar can measure the total freeboard of sea ice but can be biased when radar signals penetrate snow and ice. We develop a new method to retrieve the total freeboard and analyze the regional variation of total freeboard and roughness in the Weddell and Ross seas. We also investigate the statistical behavior of the total freeboard for diverse ice types. The findings enhance the understanding of Antarctic sea ice topography and its dynamics in a changing climate.
Michael Studinger, Benjamin E. Smith, Nathan Kurtz, Alek Petty, Tyler Sutterley, and Rachel Tilling
The Cryosphere, 18, 2625–2652, https://doi.org/10.5194/tc-18-2625-2024, https://doi.org/10.5194/tc-18-2625-2024, 2024
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We use green lidar data and natural-color imagery over sea ice to quantify elevation biases potentially impacting estimates of change in ice thickness of the polar regions. We complement our analysis using a model of scattering of light in snow and ice that predicts the shape of lidar waveforms reflecting from snow and ice surfaces based on the shape of the transmitted pulse. We find that biased elevations exist in airborne and spaceborne data products from green lidars.
James Dillon, Christopher Donahue, Evan Schehrer, Karl Birkeland, and Kevin Hammonds
The Cryosphere, 18, 2557–2582, https://doi.org/10.5194/tc-18-2557-2024, https://doi.org/10.5194/tc-18-2557-2024, 2024
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Surface hoar crystals are snow grains that form when vapor deposits on a snow surface. They create a weak layer in the snowpack that can cause large avalanches to occur. Thus, determining when and where surface hoar forms is a lifesaving matter. Here, we developed a means of mapping surface hoar using remote-sensing technologies. We found that surface hoar displayed heightened texture, hence the variability of brightness. Using this, we created surface hoar maps with an accuracy upwards of 95 %.
Riley Culberg, Roger J. Michaelides, and Julie Z. Miller
The Cryosphere, 18, 2531–2555, https://doi.org/10.5194/tc-18-2531-2024, https://doi.org/10.5194/tc-18-2531-2024, 2024
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Ice slabs enhance meltwater runoff from the Greenland Ice Sheet. Therefore, it is important to understand their extent and change in extent over time. We present a new method for detecting ice slabs in satellite radar data, which we use to map ice slabs at 500 m resolution across the entire ice sheet in winter 2016–2017. Our results provide better spatial coverage and resolution than previous maps from airborne radar and lay the groundwork for long-term monitoring of ice slabs from space.
Andreas Wernecke, Dirk Notz, Stefan Kern, and Thomas Lavergne
The Cryosphere, 18, 2473–2486, https://doi.org/10.5194/tc-18-2473-2024, https://doi.org/10.5194/tc-18-2473-2024, 2024
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The total Arctic sea-ice area (SIA), which is an important climate indicator, is routinely monitored with the help of satellite measurements. Uncertainties in observations of sea-ice concentration (SIC) partly cancel out when summed up to the total SIA, but the degree to which this is happening has been unclear. Here we find that the uncertainty daily SIA estimates, based on uncertainties in SIC, are about 300 000 km2. The 2002 to 2017 September decline in SIA is approx. 105 000 ± 9000 km2 a−1.
Steven J. Pestana, C. Chris Chickadel, and Jessica D. Lundquist
The Cryosphere, 18, 2257–2276, https://doi.org/10.5194/tc-18-2257-2024, https://doi.org/10.5194/tc-18-2257-2024, 2024
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We compared infrared images taken by GOES-R satellites of an area with snow and forests against surface temperature measurements taken on the ground, from an aircraft, and by another satellite. We found that GOES-R measured warmer temperatures than the other measurements, especially in areas with more forest and when the Sun was behind the satellite. From this work, we learned that the position of the Sun and surface features such as trees that can cast shadows impact GOES-R infrared images.
Stephen E. L. Howell, David G. Babb, Jack C. Landy, Isolde A. Glissenaar, Kaitlin McNeil, Benoit Montpetit, and Mike Brady
The Cryosphere, 18, 2321–2333, https://doi.org/10.5194/tc-18-2321-2024, https://doi.org/10.5194/tc-18-2321-2024, 2024
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The CAA serves as both a source and a sink for sea ice from the Arctic Ocean, while also exporting sea ice into Baffin Bay. It is also an important region with respect to navigating the Northwest Passage. Here, we quantify sea ice transport and replenishment across and within the CAA from 2016 to 2022. We also provide the first estimates of the ice area and volume flux within the CAA from the Queen Elizabeth Islands to Parry Channel, which spans the central region of the Northwest Passage.
Karl Kortum, Suman Singha, Gunnar Spreen, Nils Hutter, Arttu Jutila, and Christian Haas
The Cryosphere, 18, 2207–2222, https://doi.org/10.5194/tc-18-2207-2024, https://doi.org/10.5194/tc-18-2207-2024, 2024
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A dataset of 20 radar satellite acquisitions and near-simultaneous helicopter-based surveys of the ice topography during the MOSAiC expedition is constructed and used to train a variety of deep learning algorithms. The results give realistic insights into the accuracy of retrieval of measured ice classes using modern deep learning models. The models able to learn from the spatial distribution of the measured sea ice classes are shown to have a clear advantage over those that cannot.
Christoph Posch, Jakob Abermann, and Tiago Silva
The Cryosphere, 18, 2035–2059, https://doi.org/10.5194/tc-18-2035-2024, https://doi.org/10.5194/tc-18-2035-2024, 2024
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Radar beams from satellites exhibit reflection differences between water and ice. This condition, as well as the comprehensive coverage and high temporal resolution of the Sentinel-1 satellites, allows automatically detecting the timing of when ice cover of lakes in Greenland disappear. We found that lake ice breaks up 3 d later per 100 m elevation gain and that the average break-up timing varies by ±8 d in 2017–2021, which has major implications for the energy budget of the lakes.
Jiahui Xu, Yao Tang, Linxin Dong, Shujie Wang, Bailang Yu, Jianping Wu, Zhaojun Zheng, and Yan Huang
The Cryosphere, 18, 1817–1834, https://doi.org/10.5194/tc-18-1817-2024, https://doi.org/10.5194/tc-18-1817-2024, 2024
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Understanding snow phenology (SP) and its possible feedback are important. We reveal spatiotemporal heterogeneous SP on the Tibetan Plateau (TP) and the mediating effects from meteorological, topographic, and environmental factors on it. The direct effects of meteorology on SP are much greater than the indirect effects. Topography indirectly effects SP, while vegetation directly effects SP. This study contributes to understanding past global warming and predicting future trends on the TP.
Niklas Bohn, Edward H. Bair, Philip G. Brodrick, Nimrod Carmon, Robert O. Green, Thomas H. Painter, and David R. Thompson
EGUsphere, https://doi.org/10.2139/ssrn.4671920, https://doi.org/10.2139/ssrn.4671920, 2024
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A new type of Earth-observing satellite is measuring reflected sunlight in all its colors. These measurements can be used to characterize snow properties, which give us important information about climate change. In our work, we emphasize the difficulties of obtaining these properties from rough mountainous regions and present a solution to the problem. Our research was inspired by the growing number of new satellite technologies and the increasing challenges associated with climate change.
Xinwei Chen, Muhammed Patel, Fernando J. Pena Cantu, Jinman Park, Javier Noa Turnes, Linlin Xu, K. Andrea Scott, and David A. Clausi
The Cryosphere, 18, 1621–1632, https://doi.org/10.5194/tc-18-1621-2024, https://doi.org/10.5194/tc-18-1621-2024, 2024
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This paper introduces an automated sea ice mapping pipeline utilizing a multi-task U-Net architecture. It attained the top score of 86.3 % in the AutoICE challenge. Ablation studies revealed that incorporating brightness temperature data and spatial–temporal information significantly enhanced model accuracy. Accurate sea ice mapping is vital for comprehending the Arctic environment and its global climate effects, underscoring the potential of deep learning.
Jinmei Pan, Michael Durand, Juha Lemmetyinen, Desheng Liu, and Jiancheng Shi
The Cryosphere, 18, 1561–1578, https://doi.org/10.5194/tc-18-1561-2024, https://doi.org/10.5194/tc-18-1561-2024, 2024
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We developed an algorithm to estimate snow mass using X- and dual Ku-band radar, and tested it in a ground-based experiment. The algorithm, the Bayesian-based Algorithm for SWE Estimation (BASE) using active microwaves, achieved an RMSE of 30 mm for snow water equivalent. These results demonstrate the potential of radar, a highly promising sensor, to map snow mass at high spatial resolution.
Luisa von Albedyll, Stefan Hendricks, Nils Hutter, Dmitrii Murashkin, Lars Kaleschke, Sascha Willmes, Linda Thielke, Xiangshan Tian-Kunze, Gunnar Spreen, and Christian Haas
The Cryosphere, 18, 1259–1285, https://doi.org/10.5194/tc-18-1259-2024, https://doi.org/10.5194/tc-18-1259-2024, 2024
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Leads (openings in sea ice cover) are created by sea ice dynamics. Because they are important for many processes in the Arctic winter climate, we aim to detect them with satellites. We present two new techniques to detect lead widths of a few hundred meters at high spatial resolution (700 m) and independent of clouds or sun illumination. We use the MOSAiC drift 2019–2020 in the Arctic for our case study and compare our new products to other existing lead products.
Siddharth Singh, Michael Durand, Edward Kim, and Ana P. Barros
The Cryosphere, 18, 747–773, https://doi.org/10.5194/tc-18-747-2024, https://doi.org/10.5194/tc-18-747-2024, 2024
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Seasonal snowfall accumulation plays a critical role in climate. The water stored in it is measured by the snow water equivalent (SWE), the amount of water released after completely melting. We demonstrate a Bayesian physical–statistical framework to estimate SWE from airborne X- and Ku-band synthetic aperture radar backscatter measurements constrained by physical snow hydrology and radar models. We explored spatial resolutions and vertical structures that agree well with ground observations.
Jérôme Messmer and Alexander Raphael Groos
The Cryosphere, 18, 719–746, https://doi.org/10.5194/tc-18-719-2024, https://doi.org/10.5194/tc-18-719-2024, 2024
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The lower part of mountain glaciers is often covered with debris. Knowing the thickness of the debris is important as it influences the melting and future evolution of the affected glaciers. We have developed an open-source approach to map variations in debris thickness on glaciers using a low-cost drone equipped with a thermal infrared camera. The resulting high-resolution maps of debris surface temperature and thickness enable more accurate monitoring and modelling of debris-covered glaciers.
Shadi Oveisgharan, Robert Zinke, Zachary Hoppinen, and Hans Peter Marshall
The Cryosphere, 18, 559–574, https://doi.org/10.5194/tc-18-559-2024, https://doi.org/10.5194/tc-18-559-2024, 2024
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The seasonal snowpack provides water resources to billions of people worldwide. Large-scale mapping of snow water equivalent (SWE) with high resolution is critical for many scientific and economics fields. In this work we used the radar remote sensing interferometric synthetic aperture radar (InSAR) to estimate the SWE change between 2 d. The error in the estimated SWE change is less than 2 cm for in situ stations. Additionally, the retrieved SWE using InSAR is correlated with lidar snow depth.
Dhiraj Kumar Singh, Srinivasarao Tanniru, Kamal Kant Singh, Harendra Singh Negi, and RAAJ Ramsankaran
The Cryosphere, 18, 451–474, https://doi.org/10.5194/tc-18-451-2024, https://doi.org/10.5194/tc-18-451-2024, 2024
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In situ techniques for snow depth (SD) measurement are not adequate to represent the spatiotemporal variability in SD in the Western Himalayan region. Therefore, this study focuses on the high-resolution mapping of daily snow depth in the Indian Western Himalayan region using passive microwave remote-sensing-based algorithms. Overall, the proposed multifactor SD models demonstrated substantial improvement compared to the operational products. However, there is a scope for further improvement.
Yungang Cao, Rumeng Pan, Meng Pan, Ruodan Lei, Puying Du, and Xueqin Bai
The Cryosphere, 18, 153–168, https://doi.org/10.5194/tc-18-153-2024, https://doi.org/10.5194/tc-18-153-2024, 2024
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This study built a glacial lake dataset with 15376 samples in seven types and proposed an automatic method by two-stage (the semantic segmentation network and post-processing) optimizations to detect glacial lakes. The proposed method for glacial lake extraction has achieved the best results so far, in which the F1 score and IoU reached 0.945 and 0.907, respectively. The area of the minimum glacial lake that can be entirely and correctly extracted has been raised to the 100 m2 level.
Michael Durand, Joel T. Johnson, Jack Dechow, Leung Tsang, Firoz Borah, and Edward J. Kim
The Cryosphere, 18, 139–152, https://doi.org/10.5194/tc-18-139-2024, https://doi.org/10.5194/tc-18-139-2024, 2024
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Seasonal snow accumulates each winter, storing water to release later in the year and modulating both water and energy cycles, but the amount of seasonal snow is one of the most poorly measured components of the global water cycle. Satellite concepts to monitor snow accumulation have been proposed but not selected. This paper shows that snow accumulation can be measured using radar, and that (contrary to previous studies) does not require highly accurate information about snow microstructure.
Andrew O. Hoffman, Knut Christianson, Ching-Yao Lai, Ian Joughin, Nicholas Holschuh, Elizabeth Case, Jonathan Kingslake, and the GHOST science team
EGUsphere, https://doi.org/10.5194/egusphere-2023-2956, https://doi.org/10.5194/egusphere-2023-2956, 2024
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We use satellite and ice-penetrating radar technology to segment crevasses in the Amundsen Sea Embayment. Inspection of satellite time series reveals inland expansion of crevasses where surface stresses have increased. We develop a simple model for the strength of densifying snow and show that these crevasses are likely restricted to the near surface. This result bridges discrepancies between satellite and lab experiments and reveals the importance of porosity on surface crevasse formation.
Qin Zhang and Nick Hughes
The Cryosphere, 17, 5519–5537, https://doi.org/10.5194/tc-17-5519-2023, https://doi.org/10.5194/tc-17-5519-2023, 2023
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To alleviate tedious manual image annotations for training deep learning (DL) models in floe instance segmentation, we employ a classical image processing technique to automatically label floes in images. We then apply a DL semantic method for fast and adaptive floe instance segmentation from high-resolution airborne and satellite images. A post-processing algorithm is also proposed to refine the segmentation and further to derive acceptable floe size distributions at local and global scales.
Daniel Falaschi, Atanu Bhattacharya, Gregoire Guillet, Lei Huang, Owen King, Kriti Mukherjee, Philipp Rastner, Tandong Yao, and Tobias Bolch
The Cryosphere, 17, 5435–5458, https://doi.org/10.5194/tc-17-5435-2023, https://doi.org/10.5194/tc-17-5435-2023, 2023
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Because glaciers are crucial freshwater sources in the lowlands surrounding High Mountain Asia, constraining short-term glacier mass changes is essential. We investigate the potential of state-of-the-art satellite elevation data to measure glacier mass changes in two selected regions. The results demonstrate the ability of our dataset to characterize glacier changes of different magnitudes, allowing for an increase in the number of inaccessible glaciers that can be readily monitored.
Cited articles
Agrawala, S.: Climate change in the European Alps: adapting winter tourism and natural hazards management, Organisation for Economic Co-operation and Development, 2007.
Armstrong, R. L. and Brodzik, M. J.: Recent Northern Hemisphere snow extent: A comparison of data derived from visible and microwave satellite sensors, Geophys. Res. Lett., 28, 3673–3676, 2001.
Auer, I., Böhm, R., Jurkovic, A., Lipa, W., Orlik, A., Potzmann, R., Schöner, W., Ungersböck, M., Matulla, C., Briffa, K., Jones, P., Efthymiadis, D., Brunetti, M., Nanni, T., Maugeri, M., Mercalli, L., Mestre, O., Moisselin, J.-M., Begert, M., Müller-Westermeier, G., Kveton, V., Bochnicek, O., Stastny, P., Lapin, M., Szalai, S., Szentimrey, T., Cegnar, T., Dolinar, M., Gajic-Capka, M., Zaninovic, K., Majstorovic, Z., and Nieplova, E.: HISTALP-historical instrumental climatological surface time series of the Greater Alpine Region, Int. J. Climatol., 27, 17–46, https://doi.org/10.1002/joc.1377, 2007.
Auer, M., Meister, R., Stoffel, A., and Weingartner, R.: Analyse und Darstellung der mittleren monatlichen Schneehöhen in der Schweiz, Wasser Energ. Luft, 96 (7/8), 173–178, 2004.
Bartolini, E., Claps, P., and D'Odorico, P.: Connecting European snow cover variability with large scale atmospheric patterns, Adv. Geosci., 26, 93–97, https://doi.org/10.5194/adgeo-26-93-2010, 2010.
Beniston, M.: Variations of snow depth and duration in the Swiss Alps over the last 50 years: links to changes in large-scale climatic forcings, Clim. Change, 36, 281–300, 1997.
Cihlar, J., Latifovic, R., Chena, J., Trishchenko, A., Duc, Y., Fedosejevsa, G., and Guindona, B.: S}ystematic corrections of AVHRR image composites for temporal studies, {Remote Sens. Environ., 89, 217–233, https://doi.org/10.1016/j.rse.2002.06.007, 2004.
Dietz, A. J., Kuenzer, C., Gessner, U., and Dech, S.: Remote sensing of snow – a review of available methods, Int. J. Remote Sens., 33, 4094–4134, https://doi.org/10.1080/01431161.2011.640964, 2012.
Durand, Y., Giraud, G., Laternser, M., Etchevers, P., Mérindol, L., and Lesaffre, B.: Reanalysis of 47 Years of Climate in the French Alps (1958–2005): Climatology and Trends for Snow Cover, J. Appl. Meteorol. Clim., 48, 2487–2512, https://doi.org/10.1175/2009JAMC1810.1, 2009.
EEA: Regional climate change and adaptation, Tech. rep., European Environmental Agency, https://doi.org/10.2800/12552, Copenhagen, 2009.
Elsasser, H. and Bürki, R.: Climate change as a threat to tourism in the Alps, Clim. Res., 20, 253–257, https://doi.org/10.3354/cr020253, 2002.
Fontana, F. M. A., Trishchenko, A. P., Luo, Y., Khlopenkov, K. V., Nussbaumer, S. U., and Wunderle, S.: Perennial snow and ice variations (2000–2008) in the Arctic circumpolar land area from satellite observations, J. Geophys. Res., 115, F04020, https://doi.org/10.1029/2010JF001664, 2010.
Foppa, N. and Seiz, G.: Inter-annual variations of snow days over Switzerland from 2000–2010 derived from MODIS satellite data, The Cryosphere, 6, 331–342, https://doi.org/10.5194/tc-6-331-2012, 2012.
Fuchs, S. and Brandl, M.: Damage Potential and Losses Resulting from Snow Avalanches in Settlements of the Canton of Grisons, Switzerland, Nat. Hazards, 34, 53–69, https://doi.org/10.1007/s11069-004-0784-y, 2005.
Gafurov, A. and Bárdossy, A.: Cloud removal methodology from MODIS snow cover product, Hydrol. Earth Syst. Sci., 13, 1361–1373, https://doi.org/10.5194/hess-13-1361-2009, 2009.
Gao, J., Williams, M. W., Fu, X., and Gong, T.: Spatiotemporal distribution of snow in eastern Tibet and the response to climate change, Remote Sens. Environ., 121, 1–9, https://doi.org/10.1016/j.rse.2012.01.006, 2012.
Gutman, G. and Masek, J. G.: Long-term time series of the Earth's land-surface observations from space, Int. J. Remote Sens., 33, 4700–4719, https://doi.org/10.1080/01431161.2011.638341, 2012.
Hall, D. K., Riggs, G. A., Foster, J. L., and Kumar, S. V.: Development and evaluation of a cloud-gap-filled MODIS daily snow-cover product, Remote Sens. Environ., 114, 496–503, https://doi.org/10.1016/j.rse.2009.10.007, 2010.
Hänggi, P. and Weingartner, R.: Variations in Discharge Volumes for Hydropower Generation in Switzerland, Water Resour. Manag., 26, 1231–1252, https://doi.org/10.1007/s11269-011-9956-1, 2012.
Hantel, M. and Hirtl-Wielke, L. M.: Sensitivity of Alpine snow cover to European temperature, Int. J. Climatol., 27, 1265–1275, https://doi.org/10.1002/joc.1472, 2007.
Haylock, M. R., Hofstra, N., Klein Tank, A. M. G., Klok, E. J., Jones, P. D., and New, M.: A European daily high-resolution gridded data set of surface temperature and precipitation for 1950-2006, J. Geophys. Res., 113, D20119, https://doi.org/10.1029/2008JD010201, 2008.
Heidinger, A. K., Straka, W. C., Molling, C. C., Sullivan, J. T., and Wu, X. Q.: Deriving an inter-sensor consistent calibration for the AVHRR solar reflectance data record, Int. J. Remote Sens., 31, 6493–6517, https://doi.org/10.1080/01431161.2010.496472, 2010.
Hosmer, D. W. and Lemeshow, S.: Applied logistic regression, Wiley series in probability and statistics: Texts and references section, Wiley, 2000.
Hüsler, F., Fontana, F., Riffler, M., Neuhaus, C., Musial, J., and Wunderle, S.: AVHRR Archive and Processing Facility at the University of Bern: A Comprehensive 1-km Satellite Data Set for Climate Change Studies, EARSeL eProc, 10, 83–101, 2011.
Hüsler, F., Jonas, T., Wunderle, S., and Albrecht, S.: Validation of a modified snow cover retrieval algorithm from historical 1-km AVHRR data over the European Alps, Remote Sens. Environ., 121, 497–515, https://doi.org/10.1016/j.rse.2012.02.018, 2012.
IPCC: Climate Change 2007: The Physical Science Basis, Tech. rep., Intergovernmental Panel on Climate Change (IPCC), Geneva, Switzerland, 2007.
James, M. E. and Kalluri, S. N.: The Pathfinder AVHRR land data set: An improved coarse resolution data set for terrestrial monitoring, I}nt. J. {Remote Sens., 15, 3347–3363, 1994.
Jasper, K., Gurtz, J., and Lang, H.: Advanced flood forecasting in Alpine watersheds by coupling meteorological observations and forecasts with a distributed hydrological model, J. Hydrol., 267, 40–52, https://doi.org/10.1016/S0022-1694(02)00138-5, 2002.
Jonas, T., Rixen, C., Sturm, M., and Stöckli, V.: How alpine plant growth is linked to snow cover and climate variability, J. Geophys. Res.-Biogeo., 113, G03013, https://doi.org/10.1029/2007JG000680, 2008.
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.
Jones, J.: Habitat selection studies in avian ecology: a critical review, The Auk, 118, 557–562, 2001.
Khlopenkov, K. V. and Trishchenko, A. P.: SPARC: New cloud, snow, and cloud shadow detection scheme for historical 1-km AVHRR data over Canada, J. Atmos. Ocean. Tech., 24, 322–343, 2007.
Kim, Y., Kim, K.-Y., and Kim, B.-M.: Physical mechanisms of European winter snow cover variability and its relationship to the NAO, Clim. Dynam., 40, 1657–1669, https://doi.org/10.1007/s00382-012-1365-5, 2012.
König, M., Winther, J.-G., and Isaksson, E.: Measuring snow and glacier ice properties from satellite, Rev. Geophys., 39, 1–27, https://doi.org/10.1029/1999RG000076, 2001.
Laternser, M. and Schneebeli, M.: Long-term snow climate trends of the Swiss Alps (1931-99), Int. J. Climatol., 23, 733–750, 2003.
Lehning, M., Bartelt, P., Brown, B., Russi, T., Stockli, U., and Zimmerli, M.: SNOWPACK model calculations for avalanche warning based upon a new network of weather and snow stations, Cold Reg. Sci. Technol., 30, 145–157, 1999.
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.
Magnusson, J., Farinotti, D., Jonas, T., and Bavay, M.: Quantitative evaluation of different hydrological modelling approaches in a partly glacierized Swiss watershed, Hydrol. Process., 25, 2071–2084, https://doi.org/10.1002/hyp.7958, 2011.
Marks, D., Domingo, J., Susong, D., Link, T., and Garen, D.: A spatially distributed energy balance snowmelt model for application in mountain basins, Hydrol. Process., 13, 1935–1959, https://doi.org/10.1002/(SICI)1099-1085(199909)13:12/13<1935::AID-HYP868>3.0.CO;2-C, 1999.
Marty, C.: Regime shift of snow days in Switzerland, Geophys. Res. Lett., 35, L12501, https://doi.org/10.1029/2008GL033998, 2008.
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.
Metsämäki, S., Mattila, O.-P., Pulliainen, J., Niemi, K., Luojus, K., and Böttcher, K.: An optical reflectance model-based method for fractional snow cover mapping applicable to continental scale, Remote Sens. Environ., 123, 508–521, https://doi.org/10.1016/j.rse.2012.04.010, 2012.
Nolin, A. W.: Recent advances in remote sensing of seasonal snow, J. Glaciol., 56, 1141-1150, https://doi.org/10.3189/002214311796406077, 2011.
Parajka, J. and Blöschl, G.: Validation of MODIS snow cover images over Austria, Hydrol. Earth Syst. Sci., 10, 679–689, https://doi.org/10.5194/hess-10-679-2006, 2006.
Parajka, J. and Blöschl, G.: Spatio-temporal combination of MODIS images-potential for snow cover mapping, Water Resour. Res., 44, W03406, https://doi.org/10.1029/2007WR006204, 2008.
Parajka, J., Pepe, M., Rampini, A., Rossi, S., and Blöschl, G.: A regional snow-line method for estimating snow cover from MODIS during cloud cover, J. Hydrol., 381, 203–212, https://doi.org/10.1016/j.jhydrol.2009.11.042, 2010.
Rammig, A., Jonas, T., Zimmermann, N. E., and Rixen, C.: Changes in alpine plant growth under future climate conditions, Biogeosciences, 7, 2013–2024, https://doi.org/10.5194/bg-7-2013-2010, 2010.
Rango, A. and Martinec, J.: Revisiting the degree-day method for snowmelt computations, Water Resour. Bull., 31, 657–669, 1995.
Rees, W. G.: Remote Sensing of Snow and Ice, CRC Press, Boca Raton (FL), 2005.
Rhyner, J., Bründl, M., Etter, H. J., Steiniger, M., Stöckli, U., Stucki, T., Zimmerli, M., and Amman, W.: Avalanche warning Switzerland – consequences of the avalanche winter 1999, in: Proceedings of the 13th Int. Snow Science Workshop, 30 September–3 October, Penticton, B.C., Canada, 2002.
Robinson, D. A. and Frei, A.: Seasonal Variability of Northern Hemisphere Snow Extent Using Visible Satellite Data, The Professional Geographer, 582, 307–314, 2000.
Romanov, P., Gutman, G., and Csiszar, I.: Satellite-derived snow cover maps for north America: Accuracy Assesement, Adv. Space Res., 30, 2455–2460, 2002.
Scherrer, S. C. and Appenzeller, C.: Swiss Alpine snow pack variability: major patterns and links to local climate and large-scale flow, Clim. Res., 32, 187–199, https://doi.org/10.3354/cr032187, 2006.
Scherrer, S. C., Appenzeller, C., and Laternser, M.: Trends in Swiss Alpine snow days: The role of local- and large-scale climate variability, Geophys. Res. Lett., 31, L13215, https://doi.org/10.1029/2004GL020255, 2004.
Scherrer, S. C., Wüthrich, C., Croci-Maspoli, M., Weingartner, R., and Appenzeller, C.: Analyses of newly digitized and reconstructed snow series over the last 100+ years in Switzerland, EMS Annual Meeting Abstracts Vol. 7, EMS2010-514, 13–17 September , Zürich, 2010.
Scherrer, S. C., Wüthrich, C., Croci-Maspoli, M., Weingartner, R., and Appenzeller, C.: Snow variability in the Swiss Alps 1864-2009, Int. J. Climatol., 33, 3162–3173, https://doi.org/10.1002/joc.3653, 2013.
Stucki, T., Duerr, L., and Etter, H.: Schnee und Lawinen in den Schweizer Alpen. Winter 2005/06. Wetter, Schneedecke und Lawinengefahr. Winterbericht SLF, Tech. rep., Davos, WSL-Institut für Schnee- und Lawinenforschung SLF, 2011.
Tiao, G. C., Reinsel, G. C., Xu, D., Pedrick, J. H., Zhu, X., Miller, A. J., DeLuisi, J. J., Mateer, C. L., and Wuebbles, D. J.: Effects of autocorrelation and temporal sampling schemes on estimates of trend and spatial correlation, J. Geophys. Res., 95, 20507–20517, 1990.
Valt, M. and Cianfarra, P.: Recent snow cover variability in the Italian Alps, Cold Reg. Sci. Technol., 64, 146–157, https://doi.org/10.1016/j.coldregions.2010.08.008, 2010.
Voigt, S.: Advanced methods for operational mapping of Alpine snow cover using medium resolution optical satellite data, Inauguraldissertation, University of Bern, Switzerland, 2000.
Wang, X. and Xie, H.: New methods for studying the spatiotemporal variation of snow cover based on combination products of MODIS Terra and Aqua, J. Hydrol., 371, 192–200, https://doi.org/10.1016/j.jhydrol.2009.03.028, 2009.
Wang, X., Xie, H., and Liang, T.: Evaluation of MODIS snow cover and cloud mask and its application in Northern Xinjiang, China, Remote Sens. Environ., 112, 1497–1513, 2008.
Wang, X., Xie, H., Liang, T., and Huang, X.: Comparison and validation of MODIS standard and new combination of Terra and Aqua snow cover products in northern Xinjiang , China, Hydrol. Process., 429, 419–429, https://doi.org/10.1002/hyp.7151, 2009.
Weatherhead, E. C., Reinsel, G. C., Tiao, G. C., Meng, X.-L., Choi, D., Cheang, W.-K., Keller, T., DeLuisi, J., Wuebbles, D. J., Kerr, J. B., Miller, A. J., Oltmans, S. J., and Frederick, J. E.: Factors affecting the detection of trends: Statistical considerations and applications to environmental data, J. Geophys. Res., 103, 17149–17161, https://doi.org/10.1029/98JD00995, 1998.
Wiscombe, W. J. and Warren, S. G.: Model for the Spectral Albedo of Snow. I : Pure Snow, Engineering, 37, 2712–2733, 1980.
WMO: Implementation Plan for the Global Observing System for Climate in Support of the UNFCCC (2010 Update), Tech. Rep. 1523, Global Climate Observing System, http://eprints.soton.ac.uk/162953/ (last access: 18 June 2012), 2010.
WMO: Systematic Observation Requirement for Satellite-based Data Products for Climate (Update 2011), GCOS-154, Tech. rep., World Meteorological Organization, Geneva, 2011.
Zhao, H. and Fernandes, R.: Daily snow cover estimation from Advanced Very High Resolution Radiometer Polar Pathfinder data over Northern Hemisphere land surfaces during 1982–2004, J. Geophys. Res., 114, D05113, https://doi.org/10.1029/2008JD011272, 2009.
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