Articles | Volume 17, issue 2
https://doi.org/10.5194/tc-17-977-2023
© Author(s) 2023. 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-17-977-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
01 Mar 2023
Research article |
| 01 Mar 2023
| 01 Mar 2023
Spatio-temporal reconstruction of winter glacier mass balance in the Alps, Scandinavia, Central Asia and western Canada (1981–2019) using climate reanalyses and machine learning
Matteo Guidicelli
CORRESPONDING AUTHOR
Department of Geosciences, University of Fribourg, Fribourg, Switzerland
Matthias Huss
Department of Geosciences, University of Fribourg, Fribourg, Switzerland
Laboratory of Hydraulics, Hydrology and Glaciology (VAW), ETH Zurich, Zurich, Switzerland
Swiss Federal Institute for Forest, Snow and Landscape Research (WSL), Birmensdorf, Switzerland
Marco Gabella
Federal Office of Meteorology and Climatology MeteoSwiss, Locarno-Monti, Switzerland
Nadine Salzmann
WSL Institute for Snow and Avalanche Research SLF, Davos, Switzerland
Climate Change, Extremes and Natural Hazards in Alpine Regions Research Center CERC, Davos, Switzerland
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Aaron Cremona, Matthias Huss, Johannes Marian Landmann, Mauro Marty, Marijn van der Meer, Christian Ginzler, and Daniel Farinotti
The Cryosphere, 20, 3111–3130, https://doi.org/10.5194/tc-20-3111-2026, https://doi.org/10.5194/tc-20-3111-2026, 2026
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Our study provides daily mass balance estimates for every Swiss glacier from 2010–2024 using modelling, remote sensing observations, and machine learning. Over the period, Swiss glaciers lost nearly a quarter of their ice volume. The approach enables investigating the spatio-temporal variability of glacier mass balance in relation to the driving climatic factors.
Andrea Francesco Battaglia and Marco Gabella
EGUsphere, https://doi.org/10.5194/egusphere-2026-2097, https://doi.org/10.5194/egusphere-2026-2097, 2026
This preprint is open for discussion and under review for Atmospheric Measurement Techniques (AMT).
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Weather radars must be checked regularly to ensure reliable measurements of atmospheric conditions. We used the Sun as a natural radio source to compare observations from European radars and radio astrophysical telescopes with models. These models are needed as the reference and radar frequencies differ. Using observations at both frequencies simultaneously, we developed a new model that better captures the varying flux of the Sun. This helps reduce calibration bias and its uncertainty.
Talia Rosin, Francesco Marra, Marco Gabella, Urs Germann, Daniel Wolfensberger, and Efrat Morin
EGUsphere, https://doi.org/10.5194/egusphere-2026-1800, https://doi.org/10.5194/egusphere-2026-1800, 2026
This preprint is open for discussion and under review for Hydrology and Earth System Sciences (HESS).
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Extreme rainfall in mountainous regions is difficult to assess as it varies strongly across space and time, and rain gauges are often too sparse to capture it. Using 9 years of radar data, we analyse summer rainfall extremes across Switzerland for different durations and areas. We show that rainfall intensity depends strongly on scale and topography, and that radar better captures local extremes, improving flood hazard assessment, as demonstrated for three recent major flood-producing storms.
Marit van Tiel, Matthias Huss, Massimiliano Zappa, Tobias Jonas, and Daniel Farinotti
Hydrol. Earth Syst. Sci., 30, 23–43, https://doi.org/10.5194/hess-30-23-2026, https://doi.org/10.5194/hess-30-23-2026, 2026
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The summer of 2022 was extremely warm and dry in Europe, severely impacting water availability. We calculated water balance anomalies for 88 glacierized catchments in Switzerland, showing that glaciers played a crucial role in alleviating the drought situation by melting at record rates, partially compensating for the lack of rain and snowmelt. By comparing 2022 with past extreme years, we show that while glacier meltwater remains essential during droughts, its contribution is declining.
Alexandra von der Esch, Matthias Huss, Marit van Tiel, Justine Berg, and Daniel Farinotti
Hydrol. Earth Syst. Sci., 29, 6761–6780, https://doi.org/10.5194/hess-29-6761-2025, https://doi.org/10.5194/hess-29-6761-2025, 2025
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Glaciers are vital water sources, especially in alpine regions. Using the Glacier Evolution Runoff Model (GERM), we examined how forcing data and model resolution impact glacio-hydrological model results. We find that precipitation biases greatly affect results, and coarse resolutions miss critical small-scale details. This highlights the trade-offs between computational efficiency and model accuracy, emphasizing the need for high-resolution data and precise calibration for reliable predictions.
Kamilla Hauknes Sjursen, Jordi Bolibar, Marijn van der Meer, Liss Marie Andreassen, Julian Peter Biesheuvel, Thorben Dunse, Matthias Huss, Fabien Maussion, David R. Rounce, and Brandon Tober
The Cryosphere, 19, 5801–5826, https://doi.org/10.5194/tc-19-5801-2025, https://doi.org/10.5194/tc-19-5801-2025, 2025
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Understanding glacier mass changes is crucial for assessing freshwater availability in many regions of the world. We present the Mass Balance Machine, a machine learning model that learns from sparse measurements of glacier mass change to make predictions on unmonitored glaciers. Using data from Norway, we show that the model provides accurate estimates of mass changes at different spatiotemporal scales. Our findings show that machine learning can be a valuable tool to improve such predictions.
Inés Dussaillant, Romain Hugonnet, Matthias Huss, Etienne Berthier, Jacqueline Bannwart, Frank Paul, and Michael Zemp
Earth Syst. Sci. Data, 17, 1977–2006, https://doi.org/10.5194/essd-17-1977-2025, https://doi.org/10.5194/essd-17-1977-2025, 2025
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Our research observes glacier mass changes worldwide from 1976 to 2024, revealing an alarming increase in melt, especially in the last decade and the record year of 2023. By combining field and satellite observations, we provide annual mass changes for all glaciers in the world, showing significant contributions to global sea level rise. This work underscores the need for ongoing local monitoring and global climate action to mitigate the effects of glacier loss and its broader environmental impacts.
Finn Wimberly, Lizz Ultee, Lilian Schuster, Matthias Huss, David R. Rounce, Fabien Maussion, Sloan Coats, Jonathan Mackay, and Erik Holmgren
The Cryosphere, 19, 1491–1511, https://doi.org/10.5194/tc-19-1491-2025, https://doi.org/10.5194/tc-19-1491-2025, 2025
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Glacier models have historically been used to understand glacier melt’s contribution to sea level rise. The capacity to project seasonal glacier runoff is a relatively recent development for these models. In this study we provide the first model intercomparison of runoff projections for the glacier evolution models capable of simulating future runoff globally. We compare model projections from 2000 to 2100 for all major river basins larger than 3000 km2 with over 30 km2 of initial glacier cover.
Janneke van Ginkel, Fabian Walter, Fabian Lindner, Miroslav Hallo, Matthias Huss, and Donat Fäh
The Cryosphere, 19, 1469–1490, https://doi.org/10.5194/tc-19-1469-2025, https://doi.org/10.5194/tc-19-1469-2025, 2025
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This study on Glacier de la Plaine Morte in Switzerland employs various passive seismic analysis methods to identify complex hydraulic behaviours at the ice–bedrock interface. In 4 months of seismic records, we detect spatio-temporal variations in the glacier's basal interface, following the drainage of an ice-marginal lake. We identify a low-velocity layer, whose properties are determined using modelling techniques. This low-velocity layer results from temporary water storage subglacially.
Frédéric G. Jordan, Clément Cosson, Marco Gabella, Ioannis V. Sideris, Adrien Liernur, Alexis Berne, and Urs Germann
Abstr. Int. Cartogr. Assoc., 9, 19, https://doi.org/10.5194/ica-abs-9-19-2025, https://doi.org/10.5194/ica-abs-9-19-2025, 2025
Marijn van der Meer, Harry Zekollari, Matthias Huss, Jordi Bolibar, Kamilla Hauknes Sjursen, and Daniel Farinotti
The Cryosphere, 19, 805–826, https://doi.org/10.5194/tc-19-805-2025, https://doi.org/10.5194/tc-19-805-2025, 2025
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Glacier retreat poses big challenges, making understanding how climate affects glaciers vital. But glacier measurements worldwide are limited. We created a simple machine-learning model called miniML-MB, which estimates annual changes in glacier mass in the Swiss Alps. As input, miniML-MB uses two climate variables: average temperature (May–Aug) and total precipitation (Oct–Feb). Our model can accurately predict glacier mass from 1961 to 2021 but struggles for extreme years (2022 and 2023).
Mette K. Gillespie, Liss M. Andreassen, Matthias Huss, Simon de Villiers, Kamilla H. Sjursen, Jostein Aasen, Jostein Bakke, Jan M. Cederstrøm, Hallgeir Elvehøy, Bjarne Kjøllmoen, Even Loe, Marte Meland, Kjetil Melvold, Sigurd D. Nerhus, Torgeir O. Røthe, Eivind W. N. Støren, Kåre Øst, and Jacob C. Yde
Earth Syst. Sci. Data, 16, 5799–5825, https://doi.org/10.5194/essd-16-5799-2024, https://doi.org/10.5194/essd-16-5799-2024, 2024
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We present an extensive ice thickness dataset from Jostedalsbreen ice cap that will serve as a baseline for future studies of regional climate-induced change. Results show that Jostedalsbreen currently (~2020) has a maximum ice thickness of ~630 m, a mean ice thickness of 154 ± 22 m and an ice volume of 70.6 ±10.2 km3. Ice of less than 50 m thickness covers two narrow regions of Jostedalsbreen, and the ice cap is likely to separate into three parts in a warming climate.
Alfonso Ferrone, Jérôme Kopp, Martin Lainer, Marco Gabella, Urs Germann, and Alexis Berne
Atmos. Meas. Tech., 17, 7143–7168, https://doi.org/10.5194/amt-17-7143-2024, https://doi.org/10.5194/amt-17-7143-2024, 2024
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Estimates of hail size have been collected by a network of hail sensors, installed in three regions of Switzerland, since September 2018. In this study, we use a technique called “double-moment normalization” to model the distribution of diameter sizes. The parameters of the method have been defined over 70 % of the dataset and tested over the remaining 30 %. An independent distribution of hail sizes, collected by a drone, has also been used to evaluate the method.
Harry Zekollari, Matthias Huss, Lilian Schuster, Fabien Maussion, David R. Rounce, Rodrigo Aguayo, Nicolas Champollion, Loris Compagno, Romain Hugonnet, Ben Marzeion, Seyedhamidreza Mojtabavi, and Daniel Farinotti
The Cryosphere, 18, 5045–5066, https://doi.org/10.5194/tc-18-5045-2024, https://doi.org/10.5194/tc-18-5045-2024, 2024
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Glaciers are major contributors to sea-level rise and act as key water resources. Here, we model the global evolution of glaciers under the latest generation of climate scenarios. We show that the type of observations used for model calibration can strongly affect the projections at the local scale. Our newly projected 21st century global mass loss is higher than the current community estimate as reported in the latest Intergovernmental Panel on Climate Change (IPCC) report.
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.
Loris Foresti, Bernat Puigdomènech Treserras, Daniele Nerini, Aitor Atencia, Marco Gabella, Ioannis V. Sideris, Urs Germann, and Isztar Zawadzki
Nonlin. Processes Geophys., 31, 259–286, https://doi.org/10.5194/npg-31-259-2024, https://doi.org/10.5194/npg-31-259-2024, 2024
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We compared two ways of defining the phase space of low-dimensional attractors describing the evolution of radar precipitation fields. The first defines the phase space by the domain-scale statistics of precipitation fields, such as their mean, spatial and temporal correlations. The second uses principal component analysis to account for the spatial distribution of precipitation. To represent different climates, radar archives over the United States and the Swiss Alpine region were used.
Jérôme Lopez-Saez, Christophe Corona, Lenka Slamova, Matthias Huss, Valérie Daux, Kurt Nicolussi, and Markus Stoffel
Clim. Past, 20, 1251–1267, https://doi.org/10.5194/cp-20-1251-2024, https://doi.org/10.5194/cp-20-1251-2024, 2024
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Glaciers in the European Alps have been retreating since the 1850s. Monitoring glacier mass balance is vital for understanding global changes, but only a few glaciers have long-term data. This study aims to reconstruct the mass balance of the Silvretta Glacier in the Swiss Alps using stable isotopes and tree ring proxies. Results indicate increased glacier mass until the 19th century, followed by a sharp decline after the Little Ice Age with accelerated losses due to anthropogenic warming.
Marco Gabella, Martin Lainer, Daniel Wolfensberger, and Jacopo Grazioli
Atmos. Meas. Tech., 16, 4409–4422, https://doi.org/10.5194/amt-16-4409-2023, https://doi.org/10.5194/amt-16-4409-2023, 2023
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A still wind turbine observed with a fixed-pointing radar antenna has shown distinctive polarimetric signatures: the correlation coefficient between the two orthogonal polarization states was persistently equal to 1. The differential reflectivity and the radar reflectivity factors were also stable in time. Over 2 min (2000 Hz, 128 pulses were used; consequently, the sampling time was 64 ms), the standard deviation of the differential backscattering phase shift was only a few degrees.
Lander Van Tricht, Harry Zekollari, Matthias Huss, Daniel Farinotti, and Philippe Huybrechts
The Cryosphere Discuss., https://doi.org/10.5194/tc-2023-87, https://doi.org/10.5194/tc-2023-87, 2023
Manuscript not accepted for further review
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Detailed 3D models can be applied for well-studied glaciers, whereas simplified approaches are used for regional/global assessments. We conducted a comparison of six Tien Shan glaciers employing different models and investigated the impact of in-situ measurements. Our results reveal that the choice of mass balance and ice flow model as well as calibration have minimal impact on the projected volume. The initial ice thickness exerts the greatest influence on the future remaining ice volume.
Christian Sommer, Johannes J. Fürst, Matthias Huss, and Matthias H. Braun
The Cryosphere, 17, 2285–2303, https://doi.org/10.5194/tc-17-2285-2023, https://doi.org/10.5194/tc-17-2285-2023, 2023
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Knowledge on the volume of glaciers is important to project future runoff. Here, we present a novel approach to reconstruct the regional ice thickness distribution from easily available remote-sensing data. We show that past ice thickness, derived from spaceborne glacier area and elevation datasets, can constrain the estimated ice thickness. Based on the unique glaciological database of the European Alps, the approach will be most beneficial in regions without direct thickness measurements.
Aaron Cremona, Matthias Huss, Johannes Marian Landmann, Joël Borner, and Daniel Farinotti
The Cryosphere, 17, 1895–1912, https://doi.org/10.5194/tc-17-1895-2023, https://doi.org/10.5194/tc-17-1895-2023, 2023
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Summer heat waves have a substantial impact on glacier melt as emphasized by the extreme summer of 2022. This study presents a novel approach for detecting extreme glacier melt events at the regional scale based on the combination of automatically retrieved point mass balance observations and modelling approaches. The in-depth analysis of summer 2022 evidences the strong correspondence between heat waves and extreme melt events and demonstrates their significance for seasonal melt.
Pau Wiersma, Jerom Aerts, Harry Zekollari, Markus Hrachowitz, Niels Drost, Matthias Huss, Edwin H. Sutanudjaja, and Rolf Hut
Hydrol. Earth Syst. Sci., 26, 5971–5986, https://doi.org/10.5194/hess-26-5971-2022, https://doi.org/10.5194/hess-26-5971-2022, 2022
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We test whether coupling a global glacier model (GloGEM) with a global hydrological model (PCR-GLOBWB 2) leads to a more realistic glacier representation and to improved basin runoff simulations across 25 large-scale basins. The coupling does lead to improved glacier representation, mainly by accounting for glacier flow and net glacier mass loss, and to improved basin runoff simulations, mostly in strongly glacier-influenced basins, which is where the coupling has the most impact.
Erik Schytt Mannerfelt, Amaury Dehecq, Romain Hugonnet, Elias Hodel, Matthias Huss, Andreas Bauder, and Daniel Farinotti
The Cryosphere, 16, 3249–3268, https://doi.org/10.5194/tc-16-3249-2022, https://doi.org/10.5194/tc-16-3249-2022, 2022
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How glaciers have responded to climate change over the last 20 years is well-known, but earlier data are much more scarce. We change this in Switzerland by using 22 000 photographs taken from mountain tops between the world wars and find a halving of Swiss glacier volume since 1931. This was done through new automated processing techniques that we created. The data are interesting for more than just glaciers, such as mapping forest changes, landslides, and human impacts on the terrain.
Lea Geibel, Matthias Huss, Claudia Kurzböck, Elias Hodel, Andreas Bauder, and Daniel Farinotti
Earth Syst. Sci. Data, 14, 3293–3312, https://doi.org/10.5194/essd-14-3293-2022, https://doi.org/10.5194/essd-14-3293-2022, 2022
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Glacier monitoring in Switzerland started in the 19th century, providing exceptional data series documenting snow accumulation and ice melt. Raw point observations of surface mass balance have, however, never been systematically compiled so far, including complete metadata. Here, we present an extensive dataset with more than 60 000 point observations of surface mass balance covering 60 Swiss glaciers and almost 140 years, promoting a better understanding of the drivers of recent glacier change.
Tim Steffen, Matthias Huss, Rebekka Estermann, Elias Hodel, and Daniel Farinotti
Earth Surf. Dynam., 10, 723–741, https://doi.org/10.5194/esurf-10-723-2022, https://doi.org/10.5194/esurf-10-723-2022, 2022
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Climate change is rapidly altering high-alpine landscapes. The formation of new lakes in areas becoming ice free due to glacier retreat is one of the many consequences of this process. Here, we provide an estimate for the number, size, time of emergence, and sediment infill of future glacier lakes that will emerge in the Swiss Alps. We estimate that up to ~ 680 potential lakes could form over the course of the 21st century, with the potential to hold a total water volume of up to ~ 1.16 km3.
Loris Compagno, Matthias Huss, Evan Stewart Miles, Michael James McCarthy, Harry Zekollari, Amaury Dehecq, Francesca Pellicciotti, and Daniel Farinotti
The Cryosphere, 16, 1697–1718, https://doi.org/10.5194/tc-16-1697-2022, https://doi.org/10.5194/tc-16-1697-2022, 2022
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We present a new approach for modelling debris area and thickness evolution. We implement the module into a combined mass-balance ice-flow model, and we apply it using different climate scenarios to project the future evolution of all glaciers in High Mountain Asia. We show that glacier geometry, volume, and flow velocity evolve differently when modelling explicitly debris cover compared to glacier evolution without the debris-cover module, demonstrating the importance of accounting for debris.
Rebecca Gugerli, Darin Desilets, and Nadine Salzmann
The Cryosphere, 16, 799–806, https://doi.org/10.5194/tc-16-799-2022, https://doi.org/10.5194/tc-16-799-2022, 2022
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Monitoring the snow water equivalent (SWE) in high mountain regions is highly important and a challenge. We explore the use of muon counts to infer SWE temporally continuously. We deployed muonic cosmic ray snow gauges (µ-CRSG) on a Swiss glacier over the winter 2020/21. Evaluated with manual SWE measurements and SWE estimates inferred from neutron counts, we conclude that the µ-CRSG is a highly promising method for remote high mountain regions with several advantages over other current methods.
Monika Feldmann, Urs Germann, Marco Gabella, and Alexis Berne
Weather Clim. Dynam., 2, 1225–1244, https://doi.org/10.5194/wcd-2-1225-2021, https://doi.org/10.5194/wcd-2-1225-2021, 2021
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Mesocyclones are the rotating updraught of supercell thunderstorms that present a particularly hazardous subset of thunderstorms. A first-time characterisation of the spatiotemporal occurrence of mesocyclones in the Alpine region is presented, using 5 years of Swiss operational radar data. We investigate parallels to hailstorms, particularly the influence of large-scale flow, daily cycles and terrain. Improving understanding of mesocyclones is valuable for risk assessment and warning purposes.
Christophe Ogier, Mauro A. Werder, Matthias Huss, Isabelle Kull, David Hodel, and Daniel Farinotti
The Cryosphere, 15, 5133–5150, https://doi.org/10.5194/tc-15-5133-2021, https://doi.org/10.5194/tc-15-5133-2021, 2021
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Glacier-dammed lakes are prone to draining rapidly when the ice dam breaks and constitute a serious threat to populations downstream. Such a lake drainage can proceed through an open-air channel at the glacier surface. In this study, we present what we believe to be the most complete dataset to date of an ice-dammed lake drainage through such an open-air channel. We provide new insights for future glacier-dammed lake drainage modelling studies and hazard assessments.
Johannes Marian Landmann, Hans Rudolf Künsch, Matthias Huss, Christophe Ogier, Markus Kalisch, and Daniel Farinotti
The Cryosphere, 15, 5017–5040, https://doi.org/10.5194/tc-15-5017-2021, https://doi.org/10.5194/tc-15-5017-2021, 2021
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In this study, we (1) acquire real-time information on point glacier mass balance with autonomous real-time cameras and (2) assimilate these observations into a mass balance model ensemble driven by meteorological input. For doing so, we use a customized particle filter that we designed for the specific purposes of our study. We find melt rates of up to 0.12 m water equivalent per day and show that our assimilation method has a higher performance than reference mass balance models.
Hannah R. Field, William H. Armstrong, and Matthias Huss
The Cryosphere, 15, 3255–3278, https://doi.org/10.5194/tc-15-3255-2021, https://doi.org/10.5194/tc-15-3255-2021, 2021
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The growth of a glacier lake alters the hydrology, ecology, and glaciology of its surrounding region. We investigate modern glacier lake area change across northwestern North America using repeat satellite imagery. Broadly, we find that lakes downstream from glaciers grew, while lakes dammed by glaciers shrunk. Our results suggest that the shape of the landscape surrounding a glacier lake plays a larger role in determining how quickly a lake changes than climatic or glaciologic factors.
Loris Compagno, Sarah Eggs, Matthias Huss, Harry Zekollari, and Daniel Farinotti
The Cryosphere, 15, 2593–2599, https://doi.org/10.5194/tc-15-2593-2021, https://doi.org/10.5194/tc-15-2593-2021, 2021
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Recently, discussions have focused on the difference in limiting the increase in global average temperatures to below 1.0, 1.5, or 2.0 °C compared to preindustrial levels. Here, we assess the impacts that such different scenarios would have on both the future evolution of glaciers in the European Alps and the water resources they provide. Our results show that the different temperature targets have important implications for the changes predicted until 2100.
Cited articles
Adger, W. N., Huq, S., Brown, K., Conway, D., and M., H.: Adaptation to climate
change in the developing world, Prog. Dev. Stud., 3,
179–195, https://doi.org/10.1191/1464993403ps060oa, 2003. a
Barandun, M., Fiddes, J., Scherler, M., Mathys, T., Saks, T., Petrakov, D., and
Hoelzle, M.: The state and future of the cryosphere in Central Asia, Water
Security, 11, 100072, https://doi.org/10.1016/j.wasec.2020.100072, 2020. a
Beniston, M.: Is snow in the Alps receding or disappearing?, Wiley
Interdisciplin. Rev. Clim. Change, 3, 349–358, https://doi.org/10.1002/wcc.179, 2012. a
Beniston, M., Stoffel, M., Harding, R., Kernan, M., Ludwing, R., Moors, E.,
Samuels, P., and Tockner, K.: Obstacles to data access for research related
to climate and water: Implications for science and EU policy-making,
Environ. Sci. Policy, 17, 41–48,
https://doi.org/10.1016/j.envsci.2011.12.002, 2012. a
Beniston, M., Farinotti, D., Stoffel, M., Andreassen, L. M., Coppola, E., Eckert, N., Fantini, A., Giacona, F., Hauck, C., Huss, M., Huwald, H., Lehning, M., López-Moreno, J.-I., Magnusson, J., Marty, C., Morán-Tejéda, E., Morin, S., Naaim, M., Provenzale, A., Rabatel, A., Six, D., Stötter, J., Strasser, U., Terzago, S., and Vincent, C.: The European mountain cryosphere: a review of its current state, trends, and future challenges, The Cryosphere, 12, 759–794, https://doi.org/10.5194/tc-12-759-2018, 2018. a
Bormann, K., Brown, R., Derksen, C., and Painter, T.: Estimating snow-cover
trends from space, Nat. Clim. Change, 8, 924–928,
https://doi.org/10.1038/s41558-018-0318-3, 2018. a
Breiman, L.: Random Forests, Mach. Learn., 45, 5–32,
https://doi.org/10.1023/A:1010933404324, 2001. a
Chen, Y., Sharma, S. andX Zhou, X., Yang, K., Li, X., Niu, X., Hu, X., and
Khadka, N.: Spatial performance of multiple reanalysis precipitation datasets
on the southern slope of central Himalaya, Atmos. Res., 250,
105365, https://doi.org/10.1016/j.atmosres.2020.105365, 2021. a
Dadic, R., Mott, R., Lehning, M., and Burlando, P.: Wind influence on snow
depth distribution and accumulation over glaciers, J. Geophys.
Res.-Earth Surf., 115, F01012, https://doi.org/10.1029/2009JF001261,
2010. a, b
Fiddes, J. and Gruber, S.: TopoSCALE v.1.0: downscaling gridded climate data in complex terrain, Geosci. Model Dev., 7, 387–405, https://doi.org/10.5194/gmd-7-387-2014, 2014. a
Freitas, A.: Comprehensible classification models: a position paper, ACM SIGKDD
explorations newsletter, 15, 1–10, 2014. a
Friedman, J. H.: Greedy function approximation: a gradient boosting machine,
Ann. Statist., 29, 1189–1232, https://doi.org/10.1214/aos/1013203451, 2001. a
Gascoin, S., Lhermitte, S., Kinnard, C., Bortels, K., and Liston, G. E.: Wind
effects on snow cover in Pascua-Lama, Dry Andes of Chile, Adv. Water
Resour., 55, 25–39, https://doi.org/10.1016/j.advwatres.2012.11.013, 2013. a
Gelaro, R., McCarty, W., Suarez, M., Todling, R., Molod, A., Takacs, L.,
Randles, C. A., Darmenov, A., Bosilovich, M. G., Reichle, R., Wargan, K.,
Coy, L., Cullather, R., Draper, C., Akella, S., Buchard, V., Conaty, A.,
Da Silva, A., Gu, W., Kim, G.-K., Koster, R., Lucchesi, R., Merkova, D.,
Nielsen, J. E., Partyka, G., Pawson, S., Putman, W., Rienecker, M., Schubert,
S. D., Sienkiewicz, M., and Zhao, B.: The Modern-Era Retrospective Analysis
for Research and Applications, Version 2 (MERRA-2), J. Climate, 30,
5419–5454, https://doi.org/10.1175/JCLI-D-16-0758.1, 2017. a, b
Gerber, F., Mott, R., and Lehning, M.: The importance of near-surface winter
precipitation processes in complex alpine terrain, J.
Hydrometeorol., 20, 77–96, https://doi.org/10.1175/JHM-D-18-0055.1, 2019. a
GLAMOS: Swiss Glacier Point Mass Balance Observations, release 2021, Glacier
Monitoring Switzerland [data set], https://doi.org/10.18750/massbalance.point.2021.r2021, 2021. a, b
Global Modeling and Assimilation Office (GMAO): MERRA-2
inst6_3d_ana_Np: 3d,6-Hourly,
Instantaneous, Pressure-Level, Analysis, Analyzed Meteorological Fields
V5.12.4, Greenbelt, MD, USA, Goddard Earth Sciences Data and Information
Services Center (GES DISC) [data set], https://doi.org/10.5067/A7S6XP56VZWS, last access: 13 June 2021,
2015a. a, b
Global Modeling and Assimilation Office (GMAO): MERRA-2
tavg1_2d_ lnd_ Nx: 2d,1-Hourly,
Time-Averaged, Single-Level, Assimilation, Land Surface Diagnostics V5.12.4,
Greenbelt, MD, USA, Goddard Earth Sciences Data and Information Services
Center (GES DISC) [data set], https://doi.org/10.5067/RKPHT8KC1Y1T, last access: 13 June 2021,
2015b. a, b
Global Modeling and Assimilation Office (GMAO): MERRA-2
tavg1_2d_slv_Nx: 2d,1-Hourly,
Time-Averaged, Single-Level, Assimilation, Single-Level Diagnostics V5.12.4,
Greenbelt, MD, USA, Goddard Earth Sciences Data and Information Services
Center (GES DISC) [data set], https://doi.org/10.5067/VJAFPLI1CSIV, last access: 13 June 2021,
2015c. a, b
Gobiet, A., Kotlarski, S., Beniston, M., Heinrich, G., Rajczak, J., and
Stoffel, M.: 21st century climate change in the European Alps – A review,
Sci. Total Environ., 493, 1138–1151,
https://doi.org/10.1016/j.scitotenv.2013.07.050, 2014. a
Grünewald, T., Stötter, J., Pomeroy, J. W., Dadic, R., Moreno Baños, I., Marturià, J., Spross, M., Hopkinson, C., Burlando, P., and Lehning, M.: Statistical modelling of the snow depth distribution in open alpine terrain, Hydrol. Earth Syst. Sci., 17, 3005–3021, https://doi.org/10.5194/hess-17-3005-2013, 2013. a
Gugerli, R., Gabella, M., Huss, M., and Salzmann, N.: Can weather radars be
used to estimate snow accumulation on alpine glaciers? – an evaluation based
on glaciological surveys, J. Hydrometeorol., 21, 2943–2962,
https://doi.org/10.1175/JHM-D-20-0112.1, 2020. a
Guidicelli, M., Gugerli, R., Gabella, M., Marty, C., and Salzmann, N.:
Continuous spatio-temporal high-resolution estimates of SWE across the Swiss
Alps – a statistical two-step approach for high-mountain topography,
Front. Earth Sci., 9, 399, https://doi.org/10.3389/feart.2021.664648, 2021. a
Gupta, A. S. and Tarboton, D. G.: A tool for downscaling weather data from
large-grid reanalysis products to finer spatial scales for distributed
hydrological applications, Environ. Model. Softw., 84, 50–69,
https://doi.org/10.1016/j.envsoft.2016.06.014, 2016. a, b, c
Helfricht, K., Kuhn, M., Keuschnig, M., and Heilig, A.: Lidar snow cover studies on glaciers in the Ötztal Alps (Austria): comparison with snow depths calculated from GPR measurements, The Cryosphere, 8, 41–57, https://doi.org/10.5194/tc-8-41-2014, 2014. a
Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A.,
Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers,
D., Simmons, A., Soci, C., Dee, D., and Thépaut, J.-N.: ERA5 hourly data on
pressure levels from 1979 to present, Copernicus Climate Change Service (C3S)
Climate Data Store (CDS) [data set], https://doi.org/10.24381/cds.bd0915c6, last access: 1 June 2021, 2018a. a, b
Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A.,
Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers,
D., Simmons, A., Soci, C., Dee, D., and Thépaut, J.-N.: ERA5 hourly data on
single levels from 1979 to present, Copernicus Climate Change Service (C3S)
Climate Data Store (CDS) [data set], https://doi.org/10.24381/cds.adbb2d47, last access:
1 June 2021, 2018b. a, b
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A.,
Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons,
A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati,
G., Bidlot, J., Bonavita, M., Chiara, G., Dahlgren, P., Dee, D., Diamantakis,
M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer, A., Haimberger,
L., Healy, S., Hogan, R. J., Hólm, E., Janisková, M., Keeley, S., Laloyaux,
P., Lopez, P., Lupu, C., Radnoti, G., Rosnay, P., Rozum, I., Vamborg, F.,
Villaume, S., , and Thépaut, J.-N.: The ERA5 global reanalysis, Q.
J. Roy. Meteor. Soc., 146, 1999–2049,
https://doi.org/10.1002/qj.3803, 2020. a, b
Hock, R., Rasul, G., Adler, C., Cáceres, B., Gruber, S., Hirabayashi, Y.,
Jackson, M., Kääb, A., Kang, S., Kutuzov, S., Milner, A., Molau, U., Morin,
S., Orlove, B., and Steltzer, H.: High Mountain Areas, in:
IPCC Special Report on the Ocean and Cryosphere in a Changing
Climate, edited by: Pörtner, H.-O., Roberts, D. C., Masson-Delmotte, V., Zhai, P.,
Tignor, M., Poloczanska, E., Mintenbeck, K., Alegría, A., Nicolai, M., Okem, A.,
Petzold, J., Rama, B., and Weyer, N. M., Cambridge University Press, Cambridge,
UK and New York, NY, USA, 755 pp., https://doi.org/10.1017/9781009157964.004, 2019. a
Huss, M., Bauder, A., and Funk, M.: Homogenization of long-term mass-balance
time series, Ann. Glaciol., 50, 198–206,
https://doi.org/10.3189/172756409787769627, 2009. a
Huss, M., Bookhagen, B., Huggel, C., Jacobsen, D., Bradley, R. S., Clague,
J. J., Vuille, M., Buytaert, W., Cayan, D. R., Greenwood, G., Mark, B. G.,
Milner, A., Weingartner, R., and Winder, M.: The state and future of the
cryosphere in Central Asia, Earth's Future, 5, 418–435,
https://doi.org/10.1002/2016EF000514, 2017. a
Huss, M., Bauder, A., Linsbauer, A., Gabbi, J., Kappenberger, G., Steinegger,
U., and Farinotti, D.: More than a century of direct glacier mass-balance
observations on Claridenfirn, Switzerland, J. Glaciol., 67,
697–713, https://doi.org/10.1017/jog.2021.22, 2021. a, b, c, d
Huysmans, J., Dejaeger, K., Mues, C., Vanthienen, J., and Baesens, B.: An
empirical evaluation of the comprehensibility of decision table, tree and
rule based predictive models, Decision Support Systems, 51, 141–154,
https://doi.org/10.1016/j.dss.2010.12.003, 2011. a
Immerzeel, W. W., Lutz, A. F., Andrade, M., Bahl, A., Biemans, H., Bolch, T.,
Hyde, S., Brumby, S., Davies, B. J., Elmore, A. C., Emmer, A., Feng, M.,
Fernández, A., Haritashya, U., Kargel, J. S., Koppes, M., Kraaijenbrink, P.
D. A., Kulkarni, A. V., Mayewski, P. A., Nepal, S., Pacheco, P., Painter,
T. H., Pellicciotti, F., Rajaram, H., Rupper, S., Sinisalo, A., Shrestha,
A. B., Viviroli, D., Wada, Y., Xiao, C., Yao, T., and M., B. J. E.:
Importance and vulnerability of the world’s water towers, Nature, 577,
364–369, https://doi.org/10.1038/s41586-019-1822-y, 2020. a
Liu, Y. and Margulis, S. A.: Deriving Bias and Uncertainty in MERRA-2 Snowfall
Precipitation Over High Mountain Asia, Front. Earth Sci., 7, 280,
https://doi.org/10.3389/feart.2019.00280, 2019. a
Maraun, D., Wetterhall, F., Ireson, A. M., Chandler, R. E., Kendon, E. J.,
Widmann, M., Brienen, S., Rust, H. W., Sauter, T., Themeßl, M., Venema, V.
K. C., Chun, K. P., Goodess, C. M., Jones, R. G., Onof, C., Vrac, M., and
Thiele-Eich, I.: Precipitation downscaling under climate change: Recent
developments to bridge the gap between dynamical models and the end user,
Rev. Geophys., 48, RG3003, https://doi.org/10.1029/2009RG000314, 2010. a
Marty, C.: Regime shift of snow days in Switzerland, Geophys. Res.
Lett., 35, L12501, https://doi.org/10.1029/2008GL033998, 2008. a
Mayo, L., Meier, M., and Tangborn, W.: A system to combine stratigraphic and
annual mass-balance systems: a contribution to the International Hydrological
Decade, J. Glaciol, 11, 3–14, https://doi.org/10.3189/S0022143000022449, 1972. a
Mölg, T. and Kaser, G.: A new approach to resolving climate-cryosphere
relations: Downscaling climate dynamics to glacier-scale mass and energy
balance without statistical scale linking, J. Geophys. Res.-Atmos., 116,
D16101, https://doi.org/10.1029/2011JD015669, 2011. a
Najafi, M. R., Moradkhani, H., and Wherry, S. A.: Statistical Downscaling of
Precipitation Using Machine Learning with Optimal Predictor Selection,
J. Hydrol. Eng., 16, 650–664,
https://doi.org/10.1061/(ASCE)HE.1943-5584.0000355, 2011. a
O'Neel, S., McNeil, C., Sass, L., Florentine, C., Baker, E., Peitzsch, E.,
McGrath, D., Fountain, A., and Fagre, D.: Reanalysis of the US Geological
Survey Benchmark Glaciers: Long-term insight into climate forcing of glacier
mass balance, J. Glaciol., 65, 850–866,
https://doi.org/10.1017/jog.2019.66, 2019. a
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel,
O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J.,
Passos, A., Cournapeau, D., Brucher, M., Perrot, M., and Duchesnay, E.:
Scikit-learn: Machine Learning in Python, J. Mach. Learn.
Res., 12, 2825–2830, 2011. a
Pepin, N. C., Arnone, E., Gobiet, A., Haslinger, K., Kotlarski, S.,
Notarnicola, C., Palazzi, E., Seibert, P., Serafin, S., Schöner, W.,
Terzago, ., Thornton, J., Vuille, M., and Adler, C.: Climate Changes and
Their Elevational Patterns in the Mountains of the World, Rev.
Geophys., 60, 601–606, https://doi.org/10.1029/2020RG000730, 2022. a
Rasmussen, L. and Andreassen, L.: Seasonal mass-balance gradients in Norway,
J. Glaciol., 51, 601–606, https://doi.org/10.3189/172756505781828990,
2005. a
Rasul, G. and Molden, D.: The Global Social and Economic Consequences of
Mountain Cryospheric Change, Front. Environ. Sci., 7, 91,
https://doi.org/10.3389/fenvs.2019.00091, 2019. a
RGI Consortium: Randolph Glacier Inventory – A Dataset of Global Glacier
Outlines: Version 6.0, Technical Report, Global
Land Ice Measurements from Space, Colorado, USA, Digital Media, RGI Consortium [data set], https://doi.org/10.7265/N5-RGI-60, 2017. a, b, c
Rienecker, M. M., Suarez, M. J., Gelaro, R., Todling, R., Bacmeister, J., Liu,
E., Bosilovich, M. G., Schubert, S. D., Takacs, L., Kim, G.-K., Bloom, S.,
Chen, J., Collins, D., Conaty, A., da Silva, A., Gu, W., Joiner, J., Koster,
R. D., Lucchesi, R., Molod, A., Owens, T., Pawson, S., Pegion, P., Redder,
C. R., Reichle, R., Robertson, F. R., Ruddick, A. G., Sienkiewicz, M., and
Woollen, J.: MERRA: NASA's Modern-Era Retrospective Analysis for Research
and Applications, J. Climate, 24, 3624–3648,
https://doi.org/10.1175/JCLI-D-11-00015.1, 2011. a
Sachindra, D. A., Ahmed, K., Mamunur Rashid, M., Shahid, S., and Perera, B. J. C.:
Statistical downscaling of precipitation using machine learning techniques,
Atmos. Res., 212, 240–258, https://doi.org/10.1016/j.atmosres.2018.05.022,
2018. a
Salzmann, N. and Mearns, L. O.: Assessing the Performance of Multiple Regional
Climate Model Simulations for Seasonal Mountain Snow in the Upper Colorado
River Basin, J. Hydrometeorol., 13, 539–556,
https://doi.org/10.1175/2011JHM1371.1, 2012. a
Seiz, G. and Foppa, N.: National Climate Observing System of Switzerland (GCOS Switzerland), Adv. Sci. Res., 6, 95–102, https://doi.org/10.5194/asr-6-95-2011, 2011. a, b
Serifi, A., Günther, T., and Ban, N.: Spatio-Temporal Downscaling of Climate
Data Using Convolutional and Error-Predicting Neural Networks, Front.
Climate, 3, 656479, https://doi.org/10.3389/fclim.2021.656479, 2021. a
Sold, L., Huss, M., Machguth, H., Joerg, P. C., Veli, G. L., Linsbauer, A., Salzmann, N., Zemp, M., and Hoelzle, M.: Mass Balance Re-analysis of Findelengletscher,
Switzerland; Benefits of Extensive Snow Accumulation Measurements, Front. Earth Sci., 4, 18, https://doi.org/10.3389/feart.2016.00018, 2016. a, b, c, d
Stone, D., Auffhammer, M., Carey, M., Hansen, G., Huggel, C., Cramer, W.,
Lobell, D., Molau, U., Solow, A., Tibig, L., and Yohe, G.: The challenge to
detect and attribute effects of climate change on human and natural systems,
Clim. Change, 121, 381–395, https://doi.org/10.1007/s10584-013-0873-6, 2013. a
Sun, A. Y. and Tang, G.: Downscaling Satellite and Reanalysis Precipitation
Products Using Attention-Based Deep Convolutional Neural Nets, Front.
Water, 2, 536743, https://doi.org/10.3389/frwa.2020.536743, 2020. a
Sun, Q., Miao, C., Duan, Q., Ashouri, H., Sorooshian, S., , and Hsu, K.-L.: A
review of global precipitation data sets: Data sources, estimation, and
inter-comparison, Geophys. Res. Lett., 56, 79–107,
https://doi.org/10.1002/2017RG000574, 2018. a, b
Tapiador, F. J., Turk, F. J., Petersen, W., Hou, A. Y., García-Ortega, E.,
Machado, L. A. T., Angelis, C. F., Salio, P., Kidd, C., Huffman, G. J., and
de Castro, M.: Global precipitation measurement: Methods, datasets and
applications, Atmos. Res., 104–105, 70–97,
https://doi.org/10.1016/j.atmosres.2011.10.021, 2012. a
Viviroli, D., Dürr, H. H., Messerli, B., Meybeck, M., and Weingartner, R.:
Mountains of the world, water towers for humanity: typology, mapping, and
global significance, Water Resour. Res., 43, W07447,
https://doi.org/10.1029/2006WR005653, 2007. a
Vorkauf, M., Marty, C., Kahmen, A., and Hiltbrunner, E.: Past and future
snowmelt trends in the Swiss Alps: the role of temperature and snowpack,
Clim. Change, 165, 44, https://doi.org/10.1007/s10584-021-03027-x, 2021. a
Wang, F., Tian, D., Lowe, L., Kalin, L., and Lehrter, J.: Deep learning for
daily precipitation and temperature downscaling, Water Resour. Res.,
57, e2020WR029308, https://doi.org/10.1029/2020WR029308, 2021.
a
Zandler, H., Haag, I., and Samimi, C.: Evaluation needs and temporal performance differences of gridded precipitation products in peripheral mountain regions, Sci. Rep., 9, 15118, https://doi.org/10.1038/s41598-019-51666-z, 2019. a, b
Zemp, M., Thibert, E., Huss, M., Stumm, D., Rolstad Denby, C., Nuth, C., Nussbaumer, S. U., Moholdt, G., Mercer, A., Mayer, C., Joerg, P. C., Jansson, P., Hynek, B., Fischer, A., Escher-Vetter, H., Elvehøy, H., and Andreassen, L. M.: Reanalysing glacier mass balance measurement series, The Cryosphere, 7, 1227–1245, https://doi.org/10.5194/tc-7-1227-2013, 2013. a, b
Zemp, M., Nussbaumer, S., Gärtner-Roer, I., Bannwart, J., Paul, F., and
Hoelzle, M. E.: WGMS (2021): Global Glacier Change Bulletin No. 4
(2018–2019), iSC(WDS)/IUGG(IACS)/UNEP/UNESCO/WMO, World Glacier Monitoring
Service, Zurich, Switzerland, 278 pp.,
https://doi.org/10.5904/wgms-fog-2021-05, 2021. a
Short summary
Spatio-temporal reconstruction of winter glacier mass balance is important for assessing long-term impacts of climate change. However, high-altitude regions significantly lack reliable observations, which is limiting the calibration of glaciological and hydrological models. We aim at improving knowledge on the spatio-temporal variations in winter glacier mass balance by exploring the combination of data from reanalyses and direct snow accumulation observations on glaciers with machine learning.
Spatio-temporal reconstruction of winter glacier mass balance is important for assessing...
