Articles | Volume 15, issue 11
https://doi.org/10.5194/tc-15-5017-2021
© Author(s) 2021. 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-15-5017-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
01 Nov 2021
Research article |
| 01 Nov 2021
| 01 Nov 2021
Assimilating near-real-time mass balance stake readings into a model ensemble using a particle filter
Johannes Marian Landmann
CORRESPONDING AUTHOR
Laboratory of Hydraulics, Hydrology and Glaciology (VAW), ETH Zurich, Zurich, Switzerland
Swiss Federal Institute for Forest, Snow and Landscape Research (WSL), Birmensdorf, Switzerland
Hans Rudolf Künsch
Seminar for Statistics, ETH Zurich, Zurich, Switzerland
Matthias Huss
Laboratory of Hydraulics, Hydrology and Glaciology (VAW), ETH Zurich, Zurich, Switzerland
Swiss Federal Institute for Forest, Snow and Landscape Research (WSL), Birmensdorf, Switzerland
Department of Geosciences, University of Fribourg, Fribourg, Switzerland
Christophe Ogier
Laboratory of Hydraulics, Hydrology and Glaciology (VAW), ETH Zurich, Zurich, Switzerland
Swiss Federal Institute for Forest, Snow and Landscape Research (WSL), Birmensdorf, Switzerland
Markus Kalisch
Seminar for Statistics, ETH Zurich, Zurich, Switzerland
Daniel Farinotti
Laboratory of Hydraulics, Hydrology and Glaciology (VAW), ETH Zurich, Zurich, Switzerland
Swiss Federal Institute for Forest, Snow and Landscape Research (WSL), Birmensdorf, Switzerland
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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.
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Knowing the thickness of glacier ice is critical for predicting the rate of glacier loss and the myriad downstream impacts. To facilitate forecasts of future change, we have added 3 million measurements to our worldwide database of glacier thickness: 14 % of global glacier area is now within 1 km of a thickness measurement (up from 6 %). To make it easier to update and monitor the quality of our database, we have used automated tools to check and track changes to the data over time.
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Jérôme Lopez-Saez, Christophe Corona, Lenka Slamova, Matthias Huss, Valérie Daux, Kurt Nicolussi, and Markus Stoffel
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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.
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
Short summary
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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.
Matteo Guidicelli, Matthias Huss, Marco Gabella, and Nadine Salzmann
The Cryosphere, 17, 977–1002, https://doi.org/10.5194/tc-17-977-2023, https://doi.org/10.5194/tc-17-977-2023, 2023
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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.
Fabian Walter, Elias Hodel, Erik S. Mannerfelt, Kristen Cook, Michael Dietze, Livia Estermann, Michaela Wenner, Daniel Farinotti, Martin Fengler, Lukas Hammerschmidt, Flavia Hänsli, Jacob Hirschberg, Brian McArdell, and Peter Molnar
Nat. Hazards Earth Syst. Sci., 22, 4011–4018, https://doi.org/10.5194/nhess-22-4011-2022, https://doi.org/10.5194/nhess-22-4011-2022, 2022
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Debris flows are dangerous sediment–water mixtures in steep terrain. Their formation takes place in poorly accessible terrain where instrumentation cannot be installed. Here we propose to monitor such source terrain with an autonomous drone for mapping sediments which were left behind by debris flows or may contribute to future events. Short flight intervals elucidate changes of such sediments, providing important information for landscape evolution and the likelihood of future debris flows.
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.
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.
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.
Rebecca Gugerli, Matteo Guidicelli, Marco Gabella, Matthias Huss, and Nadine Salzmann
Adv. Sci. Res., 18, 7–20, https://doi.org/10.5194/asr-18-7-2021, https://doi.org/10.5194/asr-18-7-2021, 2021
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To obtain reliable snowfall estimates in high mountain remains a challenge. This study uses daily snow water equivalent (SWE) estimates by a cosmic ray sensor on two Swiss glaciers to assess three
readily-available high-quality precipitation products. We find a large bias between in situ SWE and snowfall, which differs among the precipitation products, the two sites, the winter seasons and in situ meteorological conditions. All products have great potential for various applications in the Alps.
Ethan Welty, Michael Zemp, Francisco Navarro, Matthias Huss, Johannes J. Fürst, Isabelle Gärtner-Roer, Johannes Landmann, Horst Machguth, Kathrin Naegeli, Liss M. Andreassen, Daniel Farinotti, Huilin Li, and GlaThiDa Contributors
Earth Syst. Sci. Data, 12, 3039–3055, https://doi.org/10.5194/essd-12-3039-2020, https://doi.org/10.5194/essd-12-3039-2020, 2020
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Knowing the thickness of glacier ice is critical for predicting the rate of glacier loss and the myriad downstream impacts. To facilitate forecasts of future change, we have added 3 million measurements to our worldwide database of glacier thickness: 14 % of global glacier area is now within 1 km of a thickness measurement (up from 6 %). To make it easier to update and monitor the quality of our database, we have used automated tools to check and track changes to the data over time.
Cited articles
Arulampalam, M. S., Maskell, S., Gordon, N., and Clapp, T.: A tutorial
on particle filters for online nonlinear/non-Gaussian Bayesian tracking, IEEE
T. Signal Proces., 50, 174–188, https://doi.org/10.1109/78.978374,
2002. a
Barry, R.: Mountain Weather and Climate, Physical Environment Series,
Routledge, 1992. a
Bauder, A., Funk, M., and Huss, M.: Ice-volume changes of selected glaciers in
the Swiss Alps since the end of the 19th century, Ann. Glaciol., 46,
145–149, https://doi.org/10.3189/172756407782871701, 2007. 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
Bernardo, J. M. and Smith, A. F.: Bayesian theory, vol. 405, John Wiley &
Sons, Hoboken, New Jersey, USA, 2009. a
Beven, K.: Environmental modelling: An uncertain future?, Routledge, New York,
2009. a
Biron, R. and Rabatel, A.: SmartStake: an autonomous measurement station
for high resolution glacier ablation monitoring, Data sheet, available at: https://bubbly-flow.com/wp-content/uploads/2021/02/SmartStake-v06-2020.pdf (last access: 28 October 2021), 2019. a
Bonan, B., Nodet, M., Ritz, C., and Peyaud, V.: An ETKF approach for initial state and parameter estimation in ice sheet modelling, Nonlin. Processes Geophys., 21, 569–582, https://doi.org/10.5194/npg-21-569-2014, 2014. a
Braithwaite, R. J.: Positive degree-day factors for ablation on the Greenland
ice sheet studied by energy-balance modelling, J. Glaciol., 41,
153–160, https://doi.org/10.3189/S0022143000017846, 1995. a
Braithwaite, R. J. and Olesen, O. B.: Calculation of glacier ablation from air
temperature, West Greenland, in: Glacier fluctuations and climatic change,
edited by: Oerlemans, J., Springer, Amsterdam, 219–233,
https://doi.org/10.1007/978-94-015-7823-3, 1989. a
Brehmer, J. R. and Gneiting, T.: Properization: constructing proper scoring
rules via Bayes acts, Annals of the Institute of Statistical Mathematics, pp.
1–15, 2019. a
Brock, B. W., Willis, I. C., and Sharp, M. J.: Measurement and parameterization
of albedo variations at Haut Glacier d'Arolla, Switzerland, J.
Glaciol., 46, 675–688, https://doi.org/10.3189/172756500781832675, 2000. a, b, c
Carturan, L., Cazorzi, F., dalla Fontana, G., and Zanoner, T.: Automatic
measurement of glacier ice ablation using thermistor strings, J.
Glaciol., 65, 188–194, https://doi.org/10.1017/jog.2018.103, 2019. a
Cogley, J., Hock, R., Rasmussen, L., Arendt, A., Bauder, A., Braithwaite, R.,
Jansson, P., Kaser, G., Möller, M., Nicholson, L., and Zemp, M.: Glossary
of glacier mass balance and related terms, IHP-VII technical documents in
hydrology No. 86, IACS Contribution No. 2, Tech. rep., International
Association of Cryospheric Sciences (IACS), Paris, 2011. a, b
Corripio, J. G.: Vectorial algebra algorithms for calculating terrain
parameters from DEMs and solar radiation modelling in mountainous terrain,
Int. J. Geogr. Inf. Sci., 17, 1–23,
https://doi.org/10.1080/713811744, 2003. a
Dumont, M., Durand, Y., Arnaud, Y., and Six, D.: Variational assimilation of
albedo in a snowpack model and reconstruction of the spatial mass-balance
distribution of an alpine glacier, J. Glaciol., 58, 151–164,
https://doi.org/10.3189/2012JoG11J163, 2012. a
Eis, J., Maussion, F., and Marzeion, B.: Initialization of a global glacier model based on present-day glacier geometry and past climate information: an ensemble approach, The Cryosphere, 13, 3317–3335, https://doi.org/10.5194/tc-13-3317-2019, 2019. a
Elsberg, D. H., Harrison, W. D., Echelmeyer, K. A., and Krimmel, R. M.:
Quantifying the effects of climate and surface change on glacier mass
balance, J. Glaciol., 47, 649–658,
https://doi.org/10.3189/172756501781831783, 2001. a
Euronews: From Siberia to Switzerland, scorching August leads to more fires,
less ice, available at: https://bit.ly/2BLLrfU (last access: 28 October 2021), 2019. a
Farinotti, D., Magnusson, J., Huss, M., and Bauder, A.: Snow accumulation
distribution inferred from time-lapse photography and simple modelling,
Hydrol. Process., 24, 2087–2097,
https://doi.org/10.1002/hyp.7629, 2010. a
Farinotti, D., Usselmann, S., Huss, M., Bauder, A., and Funk, M.: Runoff
evolution in the Swiss Alps: projections for selected high-alpine catchments
based on ENSEMBLES scenarios, Hydrol. Process., 26, 1909–1924,
https://doi.org/10.1002/hyp.8276, 2012. a, b
Fausto, R. S., Van As, D., Ahlstrøm, A. P., and Citterio, M.: Assessing the
accuracy of Greenland ice sheet ice ablation measurements by pressure
transducer, J. Glaciol., 58, 1144–1150, 2012. a
Fettweis, X., Franco, B., Tedesco, M., van Angelen, J. H., Lenaerts, J. T. M., van den Broeke, M. R., and Gallée, H.: Estimating the Greenland ice sheet surface mass balance contribution to future sea level rise using the regional atmospheric climate model MAR, The Cryosphere, 7, 469–489, https://doi.org/10.5194/tc-7-469-2013, 2013. a
Fischer, M., Huss, M., and Hoelzle, M.: Surface elevation and mass changes of all Swiss glaciers 1980–2010, The Cryosphere, 9, 525–540, https://doi.org/10.5194/tc-9-525-2015, 2015. a
Frei, C.: Interpolation of temperature in a mountainous region using nonlinear
profiles and non-Euclidean distances, Int. J. Climatol.,
34, 1585–1605, https://doi.org/10.1002/joc.3786, 2014. a
Gabbi, J., Carenzo, M., Pellicciotti, F., Bauder, A., and Funk, M.: A
comparison of empirical and physically based glacier surface melt models for
long-term simulations of glacier response, J. Glaciol., 60,
1140–1154, https://doi.org/10.3189/2014JoG14J011, 2014. a, b
German Meteorological Service: Frontal Analysis Europe 2019-09-01,
available at: http://www1.wetter3.de/archiv_dwd_dt.html (last access: 28 October 2021), 2019. a
Glacier Monitoring Switzerland: Swiss Glacier Mass Balance, release 2018, GLAMOS Data [data set],
https://doi.org/10.18750/massbalance.2018.r2018, 2018. a
GLAMOS: GLAMOS web page, Web site, available at: https://www.glamos.ch/en/,
last access: 6 August 2020. a
Golledge, N. R.: Long-term projections of sea-level rise from ice sheets, WIREs
Clim. Change, 11, e634, https://doi.org/10.1002/wcc.634, 2020. a
Gugerli, R., Salzmann, N., Huss, M., and Desilets, D.: Continuous and autonomous snow water equivalent measurements by a cosmic ray sensor on an alpine glacier, The Cryosphere, 13, 3413–3434, https://doi.org/10.5194/tc-13-3413-2019, 2019. a
Hersbach, H.: Decomposition of the Continuous Ranked Probability Score for
Ensemble Prediction Systems, Weather Forecast., 15, 559–570,
https://doi.org/10.1175/1520-0434(2000)015<0559:DOTCRP>2.0.CO;2, 2000. a
Hock, R., Jansson, P., and Braun, L. N.: Modelling the Response of Mountain
Glacier Discharge to Climate Warming, 243–252, Springer Netherlands,
Dordrecht, 2005. a
Hock, R., Bliss, A., marzeion, b., Giesen, R. H., Hirabayashi, Y., Huss, M.,
Radic, V., and Slangen, A. B. A.: GlacierMIP – A model intercomparison of
global-scale glacier mass-balance models and projections, J. Glaciol., 65, 453–467, https://doi.org/10.1017/jog.2019.22, 2019. a
Hulth, J.: Using a draw-wire sensor to continuously monitor glacier melt,
J. Glaciol., 56, 922–924, https://doi.org/10.3189/002214310794457290, 2010. a
Huss, M., Farinotti, D., Bauder, A., and Funk, M.: Modelling runoff from highly
glacierized alpine drainage basins in a changing climate, Hydrol.
Process., 22, 3888–3902, https://doi.org/10.1002/hyp.7055, 2008. 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, b, c, d
Huss, M., Hock, R., Bauder, A., and Funk, M.: Conventional versus
reference-surface mass balance, J. Glaciol., 58, 278–286,
https://doi.org/10.3189/2012JoG11J216, 2012. a
Huss, M., Dhulst, L., and Bauder, A.: New long-term mass-balance series for the
Swiss Alps, J. Glaciol., 61, 551–562,
https://doi.org/10.3189/2015JoG15J015, 2015. a, b
Hydrique: Example hydrological nowcast, available at: https://fribourg.swissrivers.ch/appSite/index/site/fribourg,
last access: 6 October 2020. a
Iqbal, M.: An introduction to solar radiation, Academic Press,
https://doi.org/10.1016/B978-0-12-373750-2.X5001-0, 1983. a
Isotta, F. A., Frei, C., Weilguni, V., Perčec Tadić, M., Lassègues, P.,
Rudolf, B., Pavan, V., Cacciamani, C., Antolini, G., Ratto, S. M., Munari,
M., Micheletti, S., Bonati, V., Lussana, C., Ronchi, C., Panettieri, E.,
Marigo, G., and Vertačnik, G.: The climate of daily precipitation in the
Alps: development and analysis of a high-resolution grid dataset from
pan-Alpine rain-gauge data, Int. J. Climatol., 34,
1657–1675, https://doi.org/10.1002/joc.3794, 2014. a
Isotta, F. A., Begert, M., and Frei, C.: Long-term consistent monthly
temperature and precipitation grid datasets for Switzerland over the past 150
years, J. Geophys. Res.-Atmos., 124, 3783–3799, 2019. a
Jouvet, G., Huss, M., Funk, M., and Blatter, H.: Modelling the retreat of
Grosser Aletschgletscher, Switzerland, in a changing climate, J. Glaciol., 57, 1033–1045, https://doi.org/10.3189/002214311798843359, 2011. a
Keeler, M. L. and Brugger, K. A.: A method for recording ice ablation using a
low-cost ultrasonic rangefinder, J. Glaciol., 58, 565–568, 2012. a
Kreucher, C., Hero, A., and Kastella, K.: Multiple model particle filtering for
multitarget tracking, in: Proceedings of the Twelfth Annual Workshop on
Adaptive Sensor Array Processing, 16–18 March 2004, Massachusetts, USA, 2004. a
Landmann, J. M.: Time lapse video melt at Holfuy station PLM-1, TIB AV-Portal [video supplement], https://doi.org/10.5446/48826, 2020a. a
Landmann, J. M.: Time lapse video melt at Holfuy station FIN-1, TIB AV-Portal [video supplement], https://doi.org/10.5446/48824, 2020b. a
Landmann, J. M.: Time lapse video melt at Holfuy station FIN-2, TIB AV-Portal [video supplement], https://doi.org/10.5446/48825, 2020c. a
Landmann, J. M.:
Time lapse video melt at Holfuy station RHO-1, TIB AV-Portal [video supplement], https://doi.org/10.5446/48820, 2020d. a
Landmann, J. M.: Time lapse video melt at Holfuy station RHO-2, TIB AV-Portal [video supplement], https://doi.org/10.5446/48821, 2020e. a
Landmann, J. M.: Time lapse video melt at Holfuy station RHO-3, TIB AV-Portal [video supplement], https://doi.org/10.5446/48822, 2020f. a
Landmann, J. M.: Time lapse video melt at Holfuy station RHO-4, TIB AV-Portal [video supplement], https://doi.org/10.5446/48823, 2020g. a
Landmann, J. M.: Glacier mass balance stake readings and videos from automated real-time cameras in summer 2019, ETH Zürich [data set], https://doi.org/10.3929/ethz-b-000508515, 2021. a
Lang, H. and Braun, L.: On the information content of air temperature in the
context of snow melt estimation, IAHS Publ., 190, 347–354, 1990. a
Leclercq, P., Aalstad, K., Elvehøy, H., and Altena, B.: Modelling of
glacier surface mass balance with assimilation of glacier mass balance and
snow cover observations from remote sensing, in: EGU General Assembly
Conference Abstracts, vol. 19 of EGU General Assembly Conference
Abstracts, p. 17591, 2017. a
Magnusson, J., Winstral, A., Stordal, A. S., Essery, R., and Jonas, T.:
Improving physically based snow simulations by assimilating snow depths using
the particle filter, Water Resour. Res., 53, 1125–1143,
https://doi.org/10.1002/2016WR019092, 2017. a
Marzeion, B., Hock, R., Anderson, B., Bliss, A., Champollion, N., Fujita, K.,
Huss, M., Immerzeel, W., Kraaijenbrink, P., Malles, J.-H., Maussion, F.,
Radić, V., Rounce, D. R., Sakai, A., Shannon, S., van de Wal, R., and
Zekollari, H.: Partitioning the Uncertainty of Ensemble Projections of Global
Glacier Mass Change, Earth's Future, https://doi.org/10.1029/2019EF001470, 2020. a
Maussion, F., Butenko, A., Champollion, N., Dusch, M., Eis, J., Fourteau, K., Gregor, P., Jarosch, A. H., Landmann, J., Oesterle, F., Recinos, B., Rothenpieler, T., Vlug, A., Wild, C. T., and Marzeion, B.: The Open Global Glacier Model (OGGM) v1.1, Geosci. Model Dev., 12, 909–931, https://doi.org/10.5194/gmd-12-909-2019, 2019. a, b
MeteoSwiss: Daily, monthly and yearly satellite-based global radiation, Tech.
rep., MeteoSwiss,
available at: https://www.meteoswiss.admin.ch/content/dam/meteoswiss/en/climate/swiss-climate-in-detail/doc/ProdDoc_SIS.pdf (last access: 28 October 2021),
2018. a
MeteoSwiss: Documentation of MeteoSwiss Grid-Data Products: Daily Mean, Minimum
and Maximum Temperature: TabsD, TminD, TmaxD,
available at: https://www.meteoswiss.admin.ch/content/dam/meteoswiss/de/service-und-publikationen/produkt/raeumliche-daten-temperatur/doc/ProdDoc_TabsD.pdf last access: September 2021a. a
Müller, H. and Kappenberger, G.: Claridenfirn-Messungen 1914-1984: Daten
und Ergebnisse eines gemeinschaftlichen Forschungsprojektes, Verlag d.
Fachvereine, Zürich, 1991. a
Netto, G. T. and Arigony-Neto, J.: Open-source Automatic Weather Station and
Electronic Ablation Station for measuring the impacts of climate change on
glaciers, HardwareX, 5, e00053, https://doi.org/10.1016/j.ohx.2019.e00053, 2019. a
NSIDC: Greenland Ice Sheet Today, available at: https://nsidc.org/greenland-today/, last access: 4 September
2020a. a
NSIDC: Snow Today, available at: https://nsidc.org/snow-today,
last access: 22 May 2020b. a
Ohmura, A.: Physical Basis for the Temperature-Based Melt-Index Method, J. Appl. Meteorol., 40, 753–761,
https://doi.org/10.1175/1520-0450(2001)040<0753:PBFTTB>2.0.CO;2, 2001. a
Pappenberger, F., Pagano, T. C., Brown, J. D., Alfieri, L., Lavers, D. A.,
Berthet, L., Bressand, F., Cloke, H. L., Cranston, M., Danhelka, J.,
Demargne, J., Demuth, N., de Saint-Aubin, C., Feikema, P. M., Fresch, M. A.,
Garçon, R., Gelfan, A., He, Y., Hu, Y. Z., Janet, B., Jurdy, N.,
Javelle, P., Kuchment, L., Laborda, Y., Langsholt, E., Le Lay, M., Li, Z. J.,
Mannessiez, F., Marchandise, A., Marty, R., Meißner, D., Manful, D.,
Organde, D., Pourret, V., Rademacher, S., Ramos, M. H., Reinbold, D.,
Tibaldi, S., Silvano, P., Salamon, P., Shin, D., Sorbet, C., Sprokkereef, E.,
Thiemig, V., Tuteja, N. K., van Andel, S. J., Verkade, J. S.,
Vehviläinen, B., Vogelbacher, A., Wetterhall, F., Zappa, M., Van der
Zwan, R. E., and Thielen-del Pozo, J.: Hydrological Ensemble Prediction
Systems Around the Globe, Springer Berlin Heidelberg, Berlin,
Heidelberg, 1–35, https://doi.org/10.1007/978-3-642-40457-3_47-1, 2016. a
Pellicciotti, F., Brock, B., Strasser, U., Burlando, P., Funk, M., and
Corripio, J.: An enhanced temperature-index glacier melt model including the
shortwave radiation balance: development and testing for Haut Glacier
d'Arolla, Switzerland, J. Glaciol., 51, 573–587,
https://doi.org/10.3189/172756505781829124, 2005. a, b, c, d
Ristic, B., Arulampalam, S., and Gordon, N.: Beyond the Kalman filter: Particle
filters for tracking applications, vol. 685, Artech house, Boston, 2004. a
Ritz, C., Edwards, T. L., Durand, G., Payne, A. J., Peyaud, V., and Hindmarsh,
R. C.: Potential sea-level rise from Antarctic ice-sheet instability
constrained by observations, Nature, 528, 115–118, 2015. a
Rounce, D. R., Khurana, T., Short, M. B., Hock, R., Shean, D. E., and
Brinkerhoff, D. J.: Quantifying parameter uncertainty in a large-scale
glacier evolution model using Bayesian inference: application to High
Mountain Asia, J. Glaciol., 66, 1–13, https://doi.org/10.1017/jog.2019.91,
2020. a
Ruiz, J. J., Pulido, M., and Miyoshi, T.: Estimating Model Parameters with
Ensemble-Based Data Assimilation: A Review, Journal of the Meteorological
Society of Japan. Ser. II, 91, 79–99, https://doi.org/10.2151/jmsj.2013-201, 2013. a
Salzmann, N., Machguth, H., and Linsbauer, A.: The Swiss Alpine glaciers'
response to the global “2 ∘C air temperature
target”, Environ. Res. Lett., 7, 044001,
https://doi.org/10.1088/1748-9326/7/4/044001, 2012. a
Saucan, A.-A., Chonavel, T., Sintes, C., and Le Caillec, J.-M.: Interacting
multiple model particle filters for side scan bathymetry, in: 2013 MTS/IEEE
OCEANS – Bergen, 1–5, https://doi.org/10.1109/OCEANS-Bergen.2013.6608125, 2013. a
Science Magazine: Europe's record heat melted Swiss glaciers,
available at: https://bit.ly/2VpvAL3 (last access: 28 October 2021), 2019. a
Seroussi, H., Nowicki, S., Payne, A. J., Goelzer, H., Lipscomb, W. H., Abe-Ouchi, A., Agosta, C., Albrecht, T., Asay-Davis, X., Barthel, A., Calov, R., Cullather, R., Dumas, C., Galton-Fenzi, B. K., Gladstone, R., Golledge, N. R., Gregory, J. M., Greve, R., Hattermann, T., Hoffman, M. J., Humbert, A., Huybrechts, P., Jourdain, N. C., Kleiner, T., Larour, E., Leguy, G. R., Lowry, D. P., Little, C. M., Morlighem, M., Pattyn, F., Pelle, T., Price, S. F., Quiquet, A., Reese, R., Schlegel, N.-J., Shepherd, A., Simon, E., Smith, R. S., Straneo, F., Sun, S., Trusel, L. D., Van Breedam, J., van de Wal, R. S. W., Winkelmann, R., Zhao, C., Zhang, T., and Zwinger, T.: ISMIP6 Antarctica: a multi-model ensemble of the Antarctic ice sheet evolution over the 21st century, The Cryosphere, 14, 3033–3070, https://doi.org/10.5194/tc-14-3033-2020, 2020. a
Sevruk, B.: Systematischer Niederschlagsmessfehler in der Schweiz, Der
Niederschlag in der Schweiz, Kommissionsverlag Geographischer Verlag Kümmerly + Frey, Bern, 1985. a
Shannon, S., Smith, R., Wiltshire, A., Payne, T., Huss, M., Betts, R., Caesar, J., Koutroulis, A., Jones, D., and Harrison, S.: Global glacier volume projections under high-end climate change scenarios, The Cryosphere, 13, 325–350, https://doi.org/10.5194/tc-13-325-2019, 2019.
a
SLF: WSL Institute for Snow and Avalanche Research (SLF) Operational
Snow-Hydrological Service, available at: https://www.slf.ch/en/snow/snow-as-a-water-resource/snow-hydrological-forecasting.html,
last access: 10 June 2020. a
Sommer, C., Malz, P., Seehaus, T. C., Lippl, S., Zemp, M., and Braun, M. H.:
Rapid glacier retreat and downwasting throughout the European Alps in the
early 21 st century, Nat. Commun., 11, 1–10, 2020. a
Stöckli, R.: The HelioMont Surface Solar Radiation Processing, Tech.
Rep. 93, MeteoSwiss, Zürich, 2013. a
Swiss Academy of Sciences: Press Release on Glacier Melt 2019,
available at: https://bit.ly/2UK6YfD (last access: 18 October 2021), 2019. a
van Leeuwen, P. J., Künsch, H. R., Nerger, L., Potthast, R., and Reich, S.:
Particle filters for high-dimensional geoscience applications: A review,
Q. J. Roy. Meteor. Soc., 145, 2335–2365,
https://doi.org/10.1002/qj.3551, 2019. a, b, c
Wang, R., Work, D. B., and Sowers, R.: Multiple model particle filter for
traffic estimation and incident detection, IEEE T. Intell.
Transp., 17, 3461–3470, 2016. a
Werder, M. A., Huss, M., Paul, F., Dehecq, A., and Farinotti, D.: A Bayesian
ice thickness estimation model for large-scale applications, J. Glaciol., 66, 137–152, https://doi.org/10.1017/jog.2019.93, 2020. a
WSL: Swiss Federal Institute for Forest, Snow and Landscape Research (WSL)
platform for drought monitoring drought.ch, available at: http://www.drought.ch/Messungen/index_DE#, last access: 8 June 2020. a
Wu, W., Emerton, R., Duan, Q., Wood, A. W., Wetterhall, F., and Robertson,
D. E.: Ensemble flood forecasting: Current status and future opportunities,
WIREs Water, 7, e1432, https://doi.org/10.1002/wat2.1432, 2020. a
Zappa, M., Rotach, M. W., Arpagaus, M., Dorninger, M., Hegg, C., Montani, A.,
Ranzi, R., Ament, F., Germann, U., Grossi, G., Jaun, S., Rossa, A., Vogt, S.,
Walser, A., Wehrhan, J., and Wunram, C.: MAP D-PHASE: real-time demonstration
of hydrological ensemble prediction systems, Atmos. Sci. Lett., 9,
80–87, https://doi.org/10.1002/asl.183, 2008. a
Zappa, M., van Andel, S. J., and Cloke, H. L.: Introduction to Ensemble
Forecast Applications and Showcases, pp. 1–5, Springer Berlin Heidelberg,
Berlin, Heidelberg, https://doi.org/10.1007/978-3-642-40457-3_45-1, 2018. a
Zekollari, H., Huss, M., and Farinotti, D.: Modelling the future evolution of glaciers in the European Alps under the EURO-CORDEX RCM ensemble, The Cryosphere, 13, 1125–1146, https://doi.org/10.5194/tc-13-1125-2019, 2019. a
Zellner, A.: On assessing prior distributions and Bayesian regression analysis
with g-prior distributions, Bayesian inference and decision techniques, in: Bayesian Inference and Decision Techniques: Essays in Honor of Bruno de Finetti, edited by: Goel, P. and Zellner, A., Elsevier Science Publishers, Inc., New York, 233–243, 1986. a
Short summary
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.
In this study, we (1) acquire real-time information on point glacier mass balance with...
