Articles | Volume 12, issue 11
https://doi.org/10.5194/tc-12-3617-2018
© Author(s) 2018. 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-12-3617-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
22 Nov 2018
Research article |
| 22 Nov 2018
| 22 Nov 2018
Interannual snow accumulation variability on glaciers derived from repeat, spatially extensive ground-penetrating radar surveys
Daniel McGrath
CORRESPONDING AUTHOR
Department of Geosciences, Colorado State University, Fort Collins, CO, USA
Louis Sass
U.S. Geological Survey Alaska Science Center, Anchorage, AK, USA
Shad O'Neel
U.S. Geological Survey Alaska Science Center, Anchorage, AK, USA
Chris McNeil
U.S. Geological Survey Alaska Science Center, Anchorage, AK, USA
Salvatore G. Candela
School of Earth Sciences and Byrd Polar Research Center, Ohio State University, Columbus, OH, USA
Emily H. Baker
U.S. Geological Survey Alaska Science Center, Anchorage, AK, USA
Hans-Peter Marshall
Department of Geosciences, Boise State University, Boise, ID, USA
Related authors
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
Short summary
Short summary
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.
Tate G. Meehan, Ahmad Hojatimalekshah, Hans-Peter Marshall, Elias J. Deeb, Shad O'Neel, Daniel McGrath, Ryan W. Webb, Randall Bonnell, Mark S. Raleigh, Christopher Hiemstra, and Kelly Elder
The Cryosphere, 18, 3253–3276, https://doi.org/10.5194/tc-18-3253-2024, https://doi.org/10.5194/tc-18-3253-2024, 2024
Short summary
Short summary
Snow water equivalent (SWE) is a critical parameter for yearly water supply forecasting and can be calculated by multiplying the snow depth by the snow density. We combined high-spatial-resolution snow depth information with ground-based radar measurements to solve for snow density. Extrapolated density estimates over our study area resolved detailed patterns that agree with the known interactions of snow with wind, terrain, and vegetation and were utilized in the calculation of SWE.
Alton C. Byers, Marcelo Somos-Valenzuela, Dan H. Shugar, Daniel McGrath, Mohan B. Chand, and Ram Avtar
The Cryosphere, 18, 711–717, https://doi.org/10.5194/tc-18-711-2024, https://doi.org/10.5194/tc-18-711-2024, 2024
Short summary
Short summary
In spite of enhanced technologies, many large cryospheric events remain unreported because of their remoteness, inaccessibility, or poor communications. In this Brief communication, we report on a large ice-debris avalanche that occurred sometime between 16 and 21 August 2022 in the Kanchenjunga Conservation Area (KCA), eastern Nepal.
Lucas Zeller, Daniel McGrath, Scott W. McCoy, and Jonathan Jacquet
The Cryosphere, 18, 525–541, https://doi.org/10.5194/tc-18-525-2024, https://doi.org/10.5194/tc-18-525-2024, 2024
Short summary
Short summary
In this study we developed methods for automatically identifying supraglacial lakes in multiple satellite imagery sources for eight glaciers in Nepal. We identified a substantial seasonal variability in lake area, which was as large as the variability seen across entire decades. These complex patterns are not captured in existing regional-scale datasets. Our findings show that this seasonal variability must be accounted for in order to interpret long-term changes in debris-covered glaciers.
Baptiste Vandecrux, Jason E. Box, Andreas P. Ahlstrøm, Signe B. Andersen, Nicolas Bayou, William T. Colgan, Nicolas J. Cullen, Robert S. Fausto, Dominik Haas-Artho, Achim Heilig, Derek A. Houtz, Penelope How, Ionut Iosifescu Enescu, Nanna B. Karlsson, Rebecca Kurup Buchholz, Kenneth D. Mankoff, Daniel McGrath, Noah P. Molotch, Bianca Perren, Maiken K. Revheim, Anja Rutishauser, Kevin Sampson, Martin Schneebeli, Sandy Starkweather, Simon Steffen, Jeff Weber, Patrick J. Wright, Henry Jay Zwally, and Konrad Steffen
Earth Syst. Sci. Data, 15, 5467–5489, https://doi.org/10.5194/essd-15-5467-2023, https://doi.org/10.5194/essd-15-5467-2023, 2023
Short summary
Short summary
The Greenland Climate Network (GC-Net) comprises stations that have been monitoring the weather on the Greenland Ice Sheet for over 30 years. These stations are being replaced by newer ones maintained by the Geological Survey of Denmark and Greenland (GEUS). The historical data were reprocessed to improve their quality, and key information about the weather stations has been compiled. This augmented dataset is available at https://doi.org/10.22008/FK2/VVXGUT (Steffen et al., 2022).
Brianna Rick, Daniel McGrath, William Armstrong, and Scott W. McCoy
The Cryosphere, 16, 297–314, https://doi.org/10.5194/tc-16-297-2022, https://doi.org/10.5194/tc-16-297-2022, 2022
Short summary
Short summary
Glacial lakes impact societies as both resources and hazards. Lakes form, grow, and drain as glaciers thin and retreat, and understanding lake evolution is a critical first step in assessing their hazard potential. We map glacial lakes in Alaska between 1984 and 2019. Overall, lakes grew in number and area, though lakes with different damming material (ice, moraine, bedrock) behaved differently. Namely, ice-dammed lakes decreased in number and area, a trend lost if dam type is not considered.
Suzanne L. Bevan, Adrian Luckman, Bryn Hubbard, Bernd Kulessa, David Ashmore, Peter Kuipers Munneke, Martin O'Leary, Adam Booth, Heidi Sevestre, and Daniel McGrath
The Cryosphere, 11, 2743–2753, https://doi.org/10.5194/tc-11-2743-2017, https://doi.org/10.5194/tc-11-2743-2017, 2017
Short summary
Short summary
Five 90 m boreholes drilled into an Antarctic Peninsula ice shelf show units of ice that are denser than expected and must have formed from refrozen surface melt which has been buried and transported downstream. We used surface flow speeds and snow accumulation rates to work out where and when these units formed. Results show that, as well as recent surface melt, a period of strong melt occurred during the 18th century. Surface melt is thought to be a factor in causing recent ice-shelf break-up.
Peter Kuipers Munneke, Daniel McGrath, Brooke Medley, Adrian Luckman, Suzanne Bevan, Bernd Kulessa, Daniela Jansen, Adam Booth, Paul Smeets, Bryn Hubbard, David Ashmore, Michiel Van den Broeke, Heidi Sevestre, Konrad Steffen, Andrew Shepherd, and Noel Gourmelen
The Cryosphere, 11, 2411–2426, https://doi.org/10.5194/tc-11-2411-2017, https://doi.org/10.5194/tc-11-2411-2017, 2017
Short summary
Short summary
How much snow falls on the Larsen C ice shelf? This is a relevant question, because this ice shelf might collapse sometime this century. To know if and when this could happen, we found out how much snow falls on its surface. This was difficult, because there are only very few measurements. Here, we used data from automatic weather stations, sled-pulled radars, and a climate model to find that melting the annual snowfall produces about 20 cm of water in the NE and over 70 cm in the SW.
Zachary Hoppinen, Ross T. Palomaki, George Brencher, Devon Dunmire, Eric Gagliano, Adrian Marziliano, Jack Tarricone, and Hans-Peter Marshall
The Cryosphere, 18, 5407–5430, https://doi.org/10.5194/tc-18-5407-2024, https://doi.org/10.5194/tc-18-5407-2024, 2024
Short summary
Short summary
This study uses radar imagery from the Sentinel-1 satellite to derive snow depth from increases in the returning energy. These retrieved depths are then compared to nine lidar-derived snow depths across the western United State to assess the ability of this technique to be used to monitor global snow distributions. We also qualitatively compare the changes in underlying Sentinel-1 amplitudes against both the total lidar snow depths and nine automated snow monitoring stations.
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
Short summary
Short summary
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.
Tate G. Meehan, Ahmad Hojatimalekshah, Hans-Peter Marshall, Elias J. Deeb, Shad O'Neel, Daniel McGrath, Ryan W. Webb, Randall Bonnell, Mark S. Raleigh, Christopher Hiemstra, and Kelly Elder
The Cryosphere, 18, 3253–3276, https://doi.org/10.5194/tc-18-3253-2024, https://doi.org/10.5194/tc-18-3253-2024, 2024
Short summary
Short summary
Snow water equivalent (SWE) is a critical parameter for yearly water supply forecasting and can be calculated by multiplying the snow depth by the snow density. We combined high-spatial-resolution snow depth information with ground-based radar measurements to solve for snow density. Extrapolated density estimates over our study area resolved detailed patterns that agree with the known interactions of snow with wind, terrain, and vegetation and were utilized in the calculation of SWE.
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
Short summary
Short summary
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
Short summary
Short summary
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.
Ian E. McDowell, Kaitlin M. Keegan, S. McKenzie Skiles, Christopher P. Donahue, Erich C. Osterberg, Robert L. Hawley, and Hans-Peter Marshall
The Cryosphere, 18, 1925–1946, https://doi.org/10.5194/tc-18-1925-2024, https://doi.org/10.5194/tc-18-1925-2024, 2024
Short summary
Short summary
Accurate knowledge of firn grain size is crucial for many ice sheet research applications. Unfortunately, collecting detailed measurements of firn grain size is difficult. We demonstrate that scanning firn cores with a near-infrared imager can quickly produce high-resolution maps of both grain size and ice layer distributions. We map grain size and ice layer stratigraphy in 14 firn cores from Greenland and document changes to grain size and ice layer content from the extreme melt summer of 2012.
Rainey Aberle, Ellyn Enderlin, Shad O'Neel, Caitlyn Florentine, Louis Sass, Adam Dickson, Hans-Peter Marshall, and Alejandro Flores
EGUsphere, https://doi.org/10.5194/egusphere-2024-548, https://doi.org/10.5194/egusphere-2024-548, 2024
Short summary
Short summary
Tracking seasonal snow on glaciers is critical for understanding glacier health. However, current snow detection methods struggle to distinguish seasonal snow from glacier ice. To address this, we developed a new automated workflow for tracking seasonal snow on glaciers using satellite imagery and machine learning. Applying this method can help provide insights into glacier health, water resources, and the effects of climate change on snow cover over broad spatial scales.
Alton C. Byers, Marcelo Somos-Valenzuela, Dan H. Shugar, Daniel McGrath, Mohan B. Chand, and Ram Avtar
The Cryosphere, 18, 711–717, https://doi.org/10.5194/tc-18-711-2024, https://doi.org/10.5194/tc-18-711-2024, 2024
Short summary
Short summary
In spite of enhanced technologies, many large cryospheric events remain unreported because of their remoteness, inaccessibility, or poor communications. In this Brief communication, we report on a large ice-debris avalanche that occurred sometime between 16 and 21 August 2022 in the Kanchenjunga Conservation Area (KCA), eastern Nepal.
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
Short summary
Short summary
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.
Zachary Hoppinen, Shadi Oveisgharan, Hans-Peter Marshall, Ross Mower, Kelly Elder, and Carrie Vuyovich
The Cryosphere, 18, 575–592, https://doi.org/10.5194/tc-18-575-2024, https://doi.org/10.5194/tc-18-575-2024, 2024
Short summary
Short summary
We used changes in radar echo travel time from multiple airborne flights to estimate changes in snow depths across Idaho for two winters. We compared our radar-derived retrievals to snow pits, weather stations, and a 100 m resolution numerical snow model. We had a strong Pearson correlation and root mean squared error of 10 cm relative to in situ measurements. Our retrievals also correlated well with our model, especially in regions of dry snow and low tree coverage.
Lucas Zeller, Daniel McGrath, Scott W. McCoy, and Jonathan Jacquet
The Cryosphere, 18, 525–541, https://doi.org/10.5194/tc-18-525-2024, https://doi.org/10.5194/tc-18-525-2024, 2024
Short summary
Short summary
In this study we developed methods for automatically identifying supraglacial lakes in multiple satellite imagery sources for eight glaciers in Nepal. We identified a substantial seasonal variability in lake area, which was as large as the variability seen across entire decades. These complex patterns are not captured in existing regional-scale datasets. Our findings show that this seasonal variability must be accounted for in order to interpret long-term changes in debris-covered glaciers.
Baptiste Vandecrux, Jason E. Box, Andreas P. Ahlstrøm, Signe B. Andersen, Nicolas Bayou, William T. Colgan, Nicolas J. Cullen, Robert S. Fausto, Dominik Haas-Artho, Achim Heilig, Derek A. Houtz, Penelope How, Ionut Iosifescu Enescu, Nanna B. Karlsson, Rebecca Kurup Buchholz, Kenneth D. Mankoff, Daniel McGrath, Noah P. Molotch, Bianca Perren, Maiken K. Revheim, Anja Rutishauser, Kevin Sampson, Martin Schneebeli, Sandy Starkweather, Simon Steffen, Jeff Weber, Patrick J. Wright, Henry Jay Zwally, and Konrad Steffen
Earth Syst. Sci. Data, 15, 5467–5489, https://doi.org/10.5194/essd-15-5467-2023, https://doi.org/10.5194/essd-15-5467-2023, 2023
Short summary
Short summary
The Greenland Climate Network (GC-Net) comprises stations that have been monitoring the weather on the Greenland Ice Sheet for over 30 years. These stations are being replaced by newer ones maintained by the Geological Survey of Denmark and Greenland (GEUS). The historical data were reprocessed to improve their quality, and key information about the weather stations has been compiled. This augmented dataset is available at https://doi.org/10.22008/FK2/VVXGUT (Steffen et al., 2022).
Andrew G. Fountain, Bryce Glenn, and Christopher Mcneil
Earth Syst. Sci. Data, 15, 4077–4104, https://doi.org/10.5194/essd-15-4077-2023, https://doi.org/10.5194/essd-15-4077-2023, 2023
Short summary
Short summary
Glaciers are rapidly shrinking globally. To identify past change and provide a baseline for future change, we inventoried the extent of glaciers and perennial snowfields across the western USA excluding Alaska. Using mostly aerial imagery, we digitized the outlines of all glaciers and perennial snowfields equal to or larger than 0.01 km2 using a geographical information system. We identified 1331 (366.52 km2) glaciers and 1176 (31.00 km2) snowfields.
Jack Tarricone, Ryan W. Webb, Hans-Peter Marshall, Anne W. Nolin, and Franz J. Meyer
The Cryosphere, 17, 1997–2019, https://doi.org/10.5194/tc-17-1997-2023, https://doi.org/10.5194/tc-17-1997-2023, 2023
Short summary
Short summary
Mountain snowmelt provides water for billions of people across the globe. Despite its importance, we cannot currently measure the amount of water in mountain snowpacks from satellites. In this research, we test the ability of an experimental snow remote sensing technique from an airplane in preparation for the same sensor being launched on a future NASA satellite. We found that the method worked better than expected for estimating important snowpack properties.
Leung Tsang, Michael Durand, Chris Derksen, Ana P. Barros, Do-Hyuk Kang, Hans Lievens, Hans-Peter Marshall, Jiyue Zhu, Joel Johnson, Joshua King, Juha Lemmetyinen, Melody Sandells, Nick Rutter, Paul Siqueira, Anne Nolin, Batu Osmanoglu, Carrie Vuyovich, Edward Kim, Drew Taylor, Ioanna Merkouriadi, Ludovic Brucker, Mahdi Navari, Marie Dumont, Richard Kelly, Rhae Sung Kim, Tien-Hao Liao, Firoz Borah, and Xiaolan Xu
The Cryosphere, 16, 3531–3573, https://doi.org/10.5194/tc-16-3531-2022, https://doi.org/10.5194/tc-16-3531-2022, 2022
Short summary
Short summary
Snow water equivalent (SWE) is of fundamental importance to water, energy, and geochemical cycles but is poorly observed globally. Synthetic aperture radar (SAR) measurements at X- and Ku-band can address this gap. This review serves to inform the broad snow research, monitoring, and application communities about the progress made in recent decades to move towards a new satellite mission capable of addressing the needs of the geoscience researchers and users.
Juha Lemmetyinen, Juval Cohen, Anna Kontu, Juho Vehviläinen, Henna-Reetta Hannula, Ioanna Merkouriadi, Stefan Scheiblauer, Helmut Rott, Thomas Nagler, Elisabeth Ripper, Kelly Elder, Hans-Peter Marshall, Reinhard Fromm, Marc Adams, Chris Derksen, Joshua King, Adriano Meta, Alex Coccia, Nick Rutter, Melody Sandells, Giovanni Macelloni, Emanuele Santi, Marion Leduc-Leballeur, Richard Essery, Cecile Menard, and Michael Kern
Earth Syst. Sci. Data, 14, 3915–3945, https://doi.org/10.5194/essd-14-3915-2022, https://doi.org/10.5194/essd-14-3915-2022, 2022
Short summary
Short summary
The manuscript describes airborne, dual-polarised X and Ku band synthetic aperture radar (SAR) data collected over several campaigns over snow-covered terrain in Finland, Austria and Canada. Colocated snow and meteorological observations are also presented. The data are meant for science users interested in investigating X/Ku band radar signatures from natural environments in winter conditions.
Brianna Rick, Daniel McGrath, William Armstrong, and Scott W. McCoy
The Cryosphere, 16, 297–314, https://doi.org/10.5194/tc-16-297-2022, https://doi.org/10.5194/tc-16-297-2022, 2022
Short summary
Short summary
Glacial lakes impact societies as both resources and hazards. Lakes form, grow, and drain as glaciers thin and retreat, and understanding lake evolution is a critical first step in assessing their hazard potential. We map glacial lakes in Alaska between 1984 and 2019. Overall, lakes grew in number and area, though lakes with different damming material (ice, moraine, bedrock) behaved differently. Namely, ice-dammed lakes decreased in number and area, a trend lost if dam type is not considered.
Hans Lievens, Isis Brangers, Hans-Peter Marshall, Tobias Jonas, Marc Olefs, and Gabriëlle De Lannoy
The Cryosphere, 16, 159–177, https://doi.org/10.5194/tc-16-159-2022, https://doi.org/10.5194/tc-16-159-2022, 2022
Short summary
Short summary
Snow depth observations at high spatial resolution from the Sentinel-1 satellite mission are presented over the European Alps. The novel observations can improve our knowledge of seasonal snow mass in areas with complex topography, where satellite-based estimates are currently lacking, and benefit a number of applications including water resource management, flood forecasting, and numerical weather prediction.
Ahmad Hojatimalekshah, Zachary Uhlmann, Nancy F. Glenn, Christopher A. Hiemstra, Christopher J. Tennant, Jake D. Graham, Lucas Spaete, Arthur Gelvin, Hans-Peter Marshall, James P. McNamara, and Josh Enterkine
The Cryosphere, 15, 2187–2209, https://doi.org/10.5194/tc-15-2187-2021, https://doi.org/10.5194/tc-15-2187-2021, 2021
Short summary
Short summary
We describe the relationships between snow depth, vegetation canopy, and local-scale processes during the snow accumulation period using terrestrial laser scanning (TLS). In addition to topography and wind, our findings suggest the importance of fine-scale tree structure, species type, and distributions on snow depth. Snow depth increases from the canopy edge toward the open areas, but wind and topographic controls may affect this trend. TLS data are complementary to wide-area lidar surveys.
Rhae Sung Kim, Sujay Kumar, Carrie Vuyovich, Paul Houser, Jessica Lundquist, Lawrence Mudryk, Michael Durand, Ana Barros, Edward J. Kim, Barton A. Forman, Ethan D. Gutmann, Melissa L. Wrzesien, Camille Garnaud, Melody Sandells, Hans-Peter Marshall, Nicoleta Cristea, Justin M. Pflug, Jeremy Johnston, Yueqian Cao, David Mocko, and Shugong Wang
The Cryosphere, 15, 771–791, https://doi.org/10.5194/tc-15-771-2021, https://doi.org/10.5194/tc-15-771-2021, 2021
Short summary
Short summary
High SWE uncertainty is observed in mountainous and forested regions, highlighting the need for high-resolution snow observations in these regions. Substantial uncertainty in snow water storage in Tundra regions and the dominance of water storage in these regions points to the need for high-accuracy snow estimation. Finally, snow measurements during the melt season are most needed at high latitudes, whereas observations at near peak snow accumulations are most beneficial over the midlatitudes.
Miguel A. Aguayo, Alejandro N. Flores, James P. McNamara, Hans-Peter Marshall, and Jodi Mead
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2020-451, https://doi.org/10.5194/hess-2020-451, 2020
Manuscript not accepted for further review
Gabriel Lewis, Erich Osterberg, Robert Hawley, Hans Peter Marshall, Tate Meehan, Karina Graeter, Forrest McCarthy, Thomas Overly, Zayta Thundercloud, and David Ferris
The Cryosphere, 13, 2797–2815, https://doi.org/10.5194/tc-13-2797-2019, https://doi.org/10.5194/tc-13-2797-2019, 2019
Short summary
Short summary
We present accumulation records from sixteen 22–32 m long firn cores and 4436 km of ground-penetrating radar, covering the past 20–60 years of accumulation, collected across the western Greenland Ice Sheet percolation zone. Trends from both radar and firn cores, as well as commonly used regional climate models, show decreasing accumulation over the 1996–2016 period.
Suzanne L. Bevan, Adrian Luckman, Bryn Hubbard, Bernd Kulessa, David Ashmore, Peter Kuipers Munneke, Martin O'Leary, Adam Booth, Heidi Sevestre, and Daniel McGrath
The Cryosphere, 11, 2743–2753, https://doi.org/10.5194/tc-11-2743-2017, https://doi.org/10.5194/tc-11-2743-2017, 2017
Short summary
Short summary
Five 90 m boreholes drilled into an Antarctic Peninsula ice shelf show units of ice that are denser than expected and must have formed from refrozen surface melt which has been buried and transported downstream. We used surface flow speeds and snow accumulation rates to work out where and when these units formed. Results show that, as well as recent surface melt, a period of strong melt occurred during the 18th century. Surface melt is thought to be a factor in causing recent ice-shelf break-up.
Peter Kuipers Munneke, Daniel McGrath, Brooke Medley, Adrian Luckman, Suzanne Bevan, Bernd Kulessa, Daniela Jansen, Adam Booth, Paul Smeets, Bryn Hubbard, David Ashmore, Michiel Van den Broeke, Heidi Sevestre, Konrad Steffen, Andrew Shepherd, and Noel Gourmelen
The Cryosphere, 11, 2411–2426, https://doi.org/10.5194/tc-11-2411-2017, https://doi.org/10.5194/tc-11-2411-2017, 2017
Short summary
Short summary
How much snow falls on the Larsen C ice shelf? This is a relevant question, because this ice shelf might collapse sometime this century. To know if and when this could happen, we found out how much snow falls on its surface. This was difficult, because there are only very few measurements. Here, we used data from automatic weather stations, sled-pulled radars, and a climate model to find that melting the annual snowfall produces about 20 cm of water in the NE and over 70 cm in the SW.
Gabriel Lewis, Erich Osterberg, Robert Hawley, Brian Whitmore, Hans Peter Marshall, and Jason Box
The Cryosphere, 11, 773–788, https://doi.org/10.5194/tc-11-773-2017, https://doi.org/10.5194/tc-11-773-2017, 2017
Short summary
Short summary
We analyze 25 flight lines from NASA's Operation IceBridge Accumulation Radar totaling to determine snow accumulation throughout the dry snow and percolation zone of the Greenland Ice Sheet. Our results indicate that regional differences between IceBridge and model accumulation are large enough to significantly alter the Greenland Ice Sheet surface mass balance, with implications for future global sea-level rise.
M. Pelto, J. Kavanaugh, and C. McNeil
Earth Syst. Sci. Data, 5, 319–330, https://doi.org/10.5194/essd-5-319-2013, https://doi.org/10.5194/essd-5-319-2013, 2013
Related subject area
Discipline: Glaciers | Subject: Mass Balance Obs
Reanalysis of the longest mass balance series in Himalaya using nonlinear model: Chhota Shigri Glacier (India)
Accumulation by avalanches as significant contributor to the mass balance of a High Arctic mountain glacier
Brief communication: The Glacier Loss Day as an indicator of a record-breaking negative glacier mass balance in 2022
European heat waves 2022: contribution to extreme glacier melt in Switzerland inferred from automated ablation readings
Central Asia's spatiotemporal glacier response ambiguity due to data inconsistencies and regional simplifications
Recent contrasting behaviour of mountain glaciers across the European High Arctic revealed by ArcticDEM data
Characteristics of mountain glaciers in the northern Japanese Alps
Assimilating near-real-time mass balance stake readings into a model ensemble using a particle filter
Geodetic point surface mass balances: a new approach to determine point surface mass balances on glaciers from remote sensing measurements
Applying artificial snowfall to reduce the melting of the Muz Taw Glacier, Sawir Mountains
Satellite-observed monthly glacier and snow mass changes in southeast Tibet: implication for substantial meltwater contribution to the Brahmaputra
Brief communication: Ad hoc estimation of glacier contributions to sea-level rise from the latest glaciological observations
Heterogeneous spatial and temporal pattern of surface elevation change and mass balance of the Patagonian ice fields between 2000 and 2016
Long-range terrestrial laser scanning measurements of annual and intra-annual mass balances for Urumqi Glacier No. 1, eastern Tien Shan, China
Multi-year evaluation of airborne geodetic surveys to estimate seasonal mass balance, Columbia and Rocky Mountains, Canada
Local topography increasingly influences the mass balance of a retreating cirque glacier
Multi-decadal mass balance series of three Kyrgyz glaciers inferred from modelling constrained with repeated snow line observations
Changing pattern of ice flow and mass balance for glaciers discharging into the Larsen A and B embayments, Antarctic Peninsula, 2011 to 2016
Mohd Farooq Azam, Christian Vincent, Smriti Srivastava, Etienne Berthier, Patrick Wagnon, Himanshu Kaushik, Arif Hussain, Manoj Kumar Munda, Arindan Mandal, and Alagappan Ramanathan
EGUsphere, https://doi.org/10.5194/egusphere-2024-644, https://doi.org/10.5194/egusphere-2024-644, 2024
Short summary
Short summary
Mass balance series on Chhota Shigri Glacier has been reanalysed by combining the traditional mass balance reanalysis framework and a nonlinear model. The nonlinear model is preferred over traditional glaciological method to compute the mass balances as the former can capture the spatiotemporal variability of point mass balances from a heterogeneous in-situ point mass balance network. The nonlinear model outperforms the traditional method and agrees better with the geodetic estimates.
Bernhard Hynek, Daniel Binder, Michele Citterio, Signe Hillerup Larsen, Jakob Abermann, Geert Verhoeven, Elke Ludewig, and Wolfgang Schöner
The Cryosphere Discuss., https://doi.org/10.5194/tc-2023-157, https://doi.org/10.5194/tc-2023-157, 2023
Revised manuscript accepted for TC
Short summary
Short summary
A strong avalanche event in winter 2018 caused thick snow deposits on Freya Glacier, a mountain glacier in Northeast Greenland. The avalanche deposits led to positive elevation changes during the study period 2013–2021 and altered the mass balance of the glacier significantly. The eight year mass balance was positive, it would have been negative without avalanches. The contribution from snow avalanches might become more important with rising temperatures in the Arctic.
Annelies Voordendag, Rainer Prinz, Lilian Schuster, and Georg Kaser
The Cryosphere, 17, 3661–3665, https://doi.org/10.5194/tc-17-3661-2023, https://doi.org/10.5194/tc-17-3661-2023, 2023
Short summary
Short summary
The Glacier Loss Day (GLD) is the day on which all mass gained from the accumulation period is lost, and the glacier loses mass irrecoverably for the rest of the mass balance year. In 2022, the GLD was already reached on 23 June at Hintereisferner (Austria), and this led to a record-breaking mass loss. We introduce the GLD as a gross yet expressive indicator of the glacier’s imbalance with a persistently warming climate.
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
Short summary
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.
Martina Barandun and Eric Pohl
The Cryosphere, 17, 1343–1371, https://doi.org/10.5194/tc-17-1343-2023, https://doi.org/10.5194/tc-17-1343-2023, 2023
Short summary
Short summary
Meteorological and glacier mass balance data scarcity introduces large uncertainties about drivers of heterogeneous glacier mass balance response in Central Asia. We investigate the consistency of interpretations derived from various datasets through a systematic correlation analysis between climatic and static drivers with mass balance estimates. Our results show in particular that even supposedly similar datasets lead to different and partly contradicting assumptions on dominant drivers.
Jakub Małecki
The Cryosphere, 16, 2067–2082, https://doi.org/10.5194/tc-16-2067-2022, https://doi.org/10.5194/tc-16-2067-2022, 2022
Short summary
Short summary
This study presents a snapshot of the recent state of small mountain glaciers across the European High Arctic, where severe climate warming has been occurring over the past years. The analysis revealed that this class of ice mass might melt away from many study sites within the coming two to five decades even without further warming. Glacier changes were, however, very variable in space, and some glaciers have been gaining mass, but the exact drivers behind this phenomenon are unclear.
Kenshiro Arie, Chiyuki Narama, Ryohei Yamamoto, Kotaro Fukui, and Hajime Iida
The Cryosphere, 16, 1091–1106, https://doi.org/10.5194/tc-16-1091-2022, https://doi.org/10.5194/tc-16-1091-2022, 2022
Short summary
Short summary
In recent years, seven glaciers are confirmed in the northern Japanese Alps. However, their mass balance has not been clarified. In this study, we calculated the seasonal and continuous annual mass balance of these glaciers during 2015–2019 by the geodetic method using aerial images and SfM–MVS technology. Our results showed that the mass balance of these glaciers was different from other glaciers in the world. The characteristics of Japanese glaciers provide new insights for earth science.
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
Short summary
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.
Christian Vincent, Diego Cusicanqui, Bruno Jourdain, Olivier Laarman, Delphine Six, Adrien Gilbert, Andrea Walpersdorf, Antoine Rabatel, Luc Piard, Florent Gimbert, Olivier Gagliardini, Vincent Peyaud, Laurent Arnaud, Emmanuel Thibert, Fanny Brun, and Ugo Nanni
The Cryosphere, 15, 1259–1276, https://doi.org/10.5194/tc-15-1259-2021, https://doi.org/10.5194/tc-15-1259-2021, 2021
Short summary
Short summary
In situ glacier point mass balance data are crucial to assess climate change in different regions of the world. Unfortunately, these data are rare because huge efforts are required to conduct in situ measurements on glaciers. Here, we propose a new approach from remote sensing observations. The method has been tested on the Argentière and Mer de Glace glaciers (France). It should be possible to apply this method to high-spatial-resolution satellite images and on numerous glaciers in the world.
Feiteng Wang, Xiaoying Yue, Lin Wang, Huilin Li, Zhencai Du, Jing Ming, and Zhongqin Li
The Cryosphere, 14, 2597–2606, https://doi.org/10.5194/tc-14-2597-2020, https://doi.org/10.5194/tc-14-2597-2020, 2020
Short summary
Short summary
How to mitigate the melting of most mountainous glaciers is a disturbing issue for scientists and the public. We chose the Muz Taw Glacier of the Sawir Mountains as our study object. We carried out two artificial precipitation experiments on the glacier to study the role of precipitation in mitigating its melting. The average mass loss from the glacier decreased by over 14 %. We also propose a possible mechanism describing the role of precipitation in mitigating the melting of the glaciers.
Shuang Yi, Chunqiao Song, Kosuke Heki, Shichang Kang, Qiuyu Wang, and Le Chang
The Cryosphere, 14, 2267–2281, https://doi.org/10.5194/tc-14-2267-2020, https://doi.org/10.5194/tc-14-2267-2020, 2020
Short summary
Short summary
High-Asia glaciers have been observed to be retreating the fastest in the southeastern Tibeten Plateau, where vast amounts of glacier and snow feed the streamflow of the Brahmaputra. Here, we provide the first monthly glacier and snow mass balance during 2002–2017 based on satellite gravimetry. The results confirm previous long-term decreases but reveal strong seasonal variations. This work helps resolve previous divergent model estimates and underlines the importance of meltwater.
Michael Zemp, Matthias Huss, Nicolas Eckert, Emmanuel Thibert, Frank Paul, Samuel U. Nussbaumer, and Isabelle Gärtner-Roer
The Cryosphere, 14, 1043–1050, https://doi.org/10.5194/tc-14-1043-2020, https://doi.org/10.5194/tc-14-1043-2020, 2020
Short summary
Short summary
Comprehensive assessments of global glacier mass changes have been published at multi-annual intervals, typically in IPCC reports. For the years in between, we present an approach to infer timely but preliminary estimates of global-scale glacier mass changes from glaciological observations. These ad hoc estimates for 2017/18 indicate that annual glacier contributions to sea-level rise exceeded 1 mm sea-level equivalent, which corresponds to more than a quarter of the currently observed rise.
Wael Abdel Jaber, Helmut Rott, Dana Floricioiu, Jan Wuite, and Nuno Miranda
The Cryosphere, 13, 2511–2535, https://doi.org/10.5194/tc-13-2511-2019, https://doi.org/10.5194/tc-13-2511-2019, 2019
Short summary
Short summary
We use topographic maps from two radar remote-sensing missions to map surface elevation changes of the northern and southern Patagonian ice fields (NPI and SPI) for two epochs (2000–2012 and 2012–2016). We find a heterogeneous pattern of thinning within the ice fields and a varying temporal trend, which may be explained by complex interdependence between surface mass balance and effects of flow dynamics. The contribution to sea level rise amounts to 0.05 mm a−1 for both ice fields for 2000–2016.
Chunhai Xu, Zhongqin Li, Huilin Li, Feiteng Wang, and Ping Zhou
The Cryosphere, 13, 2361–2383, https://doi.org/10.5194/tc-13-2361-2019, https://doi.org/10.5194/tc-13-2361-2019, 2019
Short summary
Short summary
We take Urumqi Glacier No. 1 as an example and validate a long-range terrestrial laser scanner (TLS) as an efficient tool for monitoring annual and intra-annual mass balances, especially for inaccessible glacier areas where no glaciological measurements are available. The TLS has application potential for glacier mass-balance monitoring in China. For wide applications of the TLS, we can select some benchmark glaciers and use stable scan positions and in-situ-measured densities of snow–firn.
Ben M. Pelto, Brian Menounos, and Shawn J. Marshall
The Cryosphere, 13, 1709–1727, https://doi.org/10.5194/tc-13-1709-2019, https://doi.org/10.5194/tc-13-1709-2019, 2019
Short summary
Short summary
Changes in glacier mass are the direct response to meteorological conditions during the accumulation and melt seasons. We derived multi-year, seasonal mass balance from airborne laser scanning surveys and compared them to field measurements for six glaciers in the Columbia and Rocky Mountains, Canada. Our method can accurately measure seasonal changes in glacier mass and can be easily adapted to derive seasonal mass change for entire mountain ranges.
Caitlyn Florentine, Joel Harper, Daniel Fagre, Johnnie Moore, and Erich Peitzsch
The Cryosphere, 12, 2109–2122, https://doi.org/10.5194/tc-12-2109-2018, https://doi.org/10.5194/tc-12-2109-2018, 2018
Martina Barandun, Matthias Huss, Ryskul Usubaliev, Erlan Azisov, Etienne Berthier, Andreas Kääb, Tobias Bolch, and Martin Hoelzle
The Cryosphere, 12, 1899–1919, https://doi.org/10.5194/tc-12-1899-2018, https://doi.org/10.5194/tc-12-1899-2018, 2018
Short summary
Short summary
In this study, we used three independent methods (in situ measurements, comparison of digital elevation models and modelling) to reconstruct the mass change from 2000 to 2016 for three glaciers in the Tien Shan and Pamir. Snow lines observed on remote sensing images were used to improve conventional modelling by constraining a mass balance model. As a result, glacier mass changes for unmeasured years and glaciers can be better assessed. Substantial mass loss was confirmed for the three glaciers.
Helmut Rott, Wael Abdel Jaber, Jan Wuite, Stefan Scheiblauer, Dana Floricioiu, Jan Melchior van Wessem, Thomas Nagler, Nuno Miranda, and Michiel R. van den Broeke
The Cryosphere, 12, 1273–1291, https://doi.org/10.5194/tc-12-1273-2018, https://doi.org/10.5194/tc-12-1273-2018, 2018
Short summary
Short summary
We analysed volume change, mass balance and ice flow of glaciers draining into the Larsen A and Larsen B embayments on the Antarctic Peninsula for 2011 to 2013 and 2013 to 2016. The mass balance is based on elevation change measured by the radar satellite mission TanDEM-X and on the mass budget method. The glaciers show continuing losses in ice mass, which is a response to ice shelf break-up. After 2013 the downwasting of glaciers slowed down, coinciding with years of persistent sea ice cover.
Cited articles
Akaike, H.: A new look at the statistical model identification, IEEE Trans. Automat. Contr.,
AC-19(6), 716–723, 1974.
Anderson, B. T., McNamara, J. P., Marshal, H. P., and Flores, A. N.:
Insights into the physical processes controlling correlations between snow
distribution and terrain properties, Water Res. Res., 50, 4545–4563,
https://doi.org/10.1002/2013WR013714, 2014.
Bauder, A. (Ed.): The Swis Glaciers, 2013/14 and 2014/15, Glaciological
Report (Glacier) No. 135/136, https://doi.org/10.18752/glrep_135-136,
2017.
Bair, E. H., Abreu Calfa, A., Rittger, K., and Dozier, J.: Using machine learning for real-time estimates of
snow water equivalent in the watersheds of Afghanistan, The Cryosphere, 12, 1579–1594, https://doi.org/10.5194/tc-12-1579-2018, 2018.
Balk, B. and Elder, K.: Combining binary regression tree and geostatistical
methods to estimate snow distribution in a mountain watershed, Water Res.
Res., 36, 13–26, 2000.
Beamer, J. P., Hill, D. F., McGrath, D., Arendet, A., and Kienholz, C.:
Hydrologic impacts of changes in climate and glacier extent in the Gulf of
Alaska watershed, Water. Res. Res., 53, 7502–7520, https://doi.org/10.1002/2016WR020033, 2017.
Burrows, R.: Annual report on vital signs monitoring of glaciers in the
Central Alaska Network 2011–2013, Natural Resource Technical Report
NPS/CAKN/NRTR–2014/905, National Park Service, Fort Collins, Colorado,
2014.
Breiman, L.: Bagging predictors, Mach. Learn., 24, 123–140,
https://doi.org/10.1023/A:1018054314350, 1996.
Breiman, L.: Random forests, Mach. Learn., 45, 5–32,
https://doi.org/10.1023/A:1010933404324, 2001.
Breiman, L., Friedman, J. H., Olshen, R. A., and Stone, C. J.:
Classification and Regression Trees, Chapman and Hall, New York, 368 pp.,
1984.
Cathles, L. C., Abbot, S. D., Bassis, J. N., and MacAyeal, D.R.: Modeling
surface-roughness/solar-ablation feedback: application to small-scale
surface channels and crevasses of the Greenland ice sheet, Ann. Glaciol.,
52, 99–108, 2011.
Cogley, J. G., Hock, R., Rasmussen, L. A., Arendt, A. A., Bauder, A.,
Braithwaite, R. J., 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, UNESCO-IHP, Paris, 2011.
Colgan, W., Rajaram, H., Abdalati, W., McCutchan, C., Mottram, R., Moussavi,
M. S., and Grigsby, S.: Glacier crevasses: Observations, models, and mass
balance implications, Rev. Geophys., 54, 119–161, https://doi.org/10.1002/2015RG000504, 2016.
Dadic, R., Mott, R., Lehning, M., and Burlando, P.: Wind influence on snow
depth distribution and accumulation over glaciers, J. Geophys. Res., 115,
F01012, https://doi.org/10.1029/2009JF001261, 2010.
Deems, J. S., Fassnacht, S. R., and Elder, K. J.: Interannual consistency in
fractal snow depth patterns at two Colorado mountain sites, J. Hydrometeorol., 9,
977–988, https://doi.org/10.1175/2008JHM901.1, 2008.
Elder, K., Michaelsen, J., and Dozier, J.: Small basin modeling of snow
water equivalence using binary regression tree methods, IAHS Publ., 228,
129–139, 1995.
Erickson, T. A., Williams, M. W., and Winstral, A.: Persistence of
topographic controls on the spatial distribution of snow in rugged mountain
terrain, Colorado, United States, Water Res. Res., 41, W04014,
https://doi.org/10.1029/2003WR002973, 2005.
Escher-Vetter, H., Kuhn, M., and Weber, M.: Four decades of winter mass
balance of Vernagtferner and Hintereisferner, Austria: methodology and
results, Ann. Glaciol., 50, 87–95, 2009.
Fausto, R. S., Box, J. E., Vandecrux, B., van
As, D., Steffen, K., MacFerrin, M. J.,
Machguth, H., Colgan, W., Koenig, L. S.,
McGrath, D., Charalampidis, C., and
Braithwaite, R. J.: A Snow Density Dataset for Improving Surface
Boundary Conditions in Greenland Ice Sheet Firn Modeling, Front. Earth Sci.,
6, https://doi.org/10.3389/feart.2018.00051, 2018.
Fitzpatrick, N., Radić, V., and Menounos, B.: Surface energy balance
closure and turbulent flux parameterization on a mid-latitude mountain
glacier, Purcell Mountains, Canada, Front. Earth Sci., 5,
51, https://doi.org/10.3389/feart.2017.00067, 2017.
Grünewald, T. and Lehning, M.: Altitudinal dependency of
snow amounts in two alpine catchments: Can catchment-wide snow amounts be
estimated via single snow or precipitation stations?, Ann. Glaciol., 52,
153–158, 2011.
Helfricht, K., Schöber, J., Schneider, K., Sailer, R., and Kuhn, M.:
Interannual persistence of the seasonal snow cover in a glacierized
catchment, J. Glaciol., 60, 889–904, https://doi.org/10.3189/2014JoG13J197, 2014.
Hock, R.: Glacier melt: a review of processes and their modeling, Prog.
Phys. Geog., 29, 362–391, https://doi.org/10.1191/0309133305pp453ra, 2005.
Hock, R., Hutchings, J. K., and Lehning, M.: Grand challenges in cryospheric
sciences: toward better predictability of glaciers, snow and sea ice, Front.
Earth Sci., 5, 64, https://doi.org/10.3389/feart.2017.00064, 2017.
Hodgkins, R., Cooper, R., Wadham, J., and Tranter, M.: Interannual
variability in the spatial distribution of winter accumulation at a
high-Arctic glacier (Finsterwalderbreen, Svalbard), and its relationship
with topography, Ann. Glaciol., 42, 243–248, 2005.
Huss, M. and Hock, R.: A new model for global glacier change and sea-level
rise, Front. Earth Sci., 3, 54, https://doi.org/10.3389/feart.2015.00054, 2015.
Kaser, G., Großhauser, M., and Marzeion, B.: Contribution potential of
glaciers to water availability in different climate regimes, P. Natl.
Acad. Sci. USA, 107, 20223–20227, https://doi.org/10.1073/pnas.1008162107, 2010.
Kjøllmoen, B. (Ed.), Andreassen L. M., Elvehøy, H., Jackson, M., and
Melvold, J.: Glaciological investigations in Norway 2016, NVE Rapport 76
2017, 108 pp, 2017.
Klos, P. Z., Link, T. E., and Abatzoglou, J. T.: Extent of the rain-snow
transition zone in the western U.S. under historic and projected climate,
Geophys. Res. Lett., 41, 4560–4568, https://doi.org/10.1002/2014GL060500, 2014.
Kovacs, A., Gow, A. J., and Morey, R. M.: The in-situ dielectric constant of
polar firn revisited, Cold Reg. Sci. Tech., 23, 245–256, 1995.
Kuhn, M.: The mass balance of very small glaciers, Z. Gletscherkd. Glazialgeol., 31, 171–179, 1995.
Lehning, M., Völksch, I., Gustafsson, D., Nguyen, T. A.,
Stähli1, 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.
Lehning, M., Grünewald, T., and Schirmer, M.: Mountain snow distribution
governed by altitudinal gradient and terrain roughness, Geophys. Res. Lett.,
38, L19504, https://doi.org/10.1029/2011GL048927, 2011.
Li, L. and Pomeroy, J. W.: Estimates of threshold wind speeds for snow
transport using meteorological data, J. Appl. Meteorol., 36, 205–213, 1997.
Liston, G. E.: Representing subgride snow cover heterogeneities in regional and global models, J. Climate, 17, 1381–1397, 2004.
Liston, G. E. and Elder, K.: A distributed snow-evolution modeling system
(SnowModel), J. Hydrometeorol., 7, 1259–1276, 2006.
Littell, J. S., McAfee, S. A., and Hayward, G. D.: Alaska snowpack response
to climate change: statewide snowfall equivalent and snowpack water
scenarios, Water, 10, 668, https://doi.org/10.3390/w10050668, 2018.
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, 1999.
Machguth, H., Eisen, O., Paul, F., and Hoelzle, M.: Strong spatial
variability of snow accumulation observed with helicopter-borne GPR on two
adjacent Alpine glaciers, Geophys. Res. Lett., 33, L13503,
https://doi.org/10.1029/2006GL026576, 2006.
March, R. S. and Trabant, D. C.: Mass balance, meteorological, ice motion,
surface altitude, and runoff data at Gulkana Glacier, Alaska, 1992 balance
year, Water-Resources Investigations Report, 95–4277, 1996.
McAfee, S., Walsh, J., and Rupp, T. S.: Statistically downscaled projections
of snow/rain partitioning for Alaska, Hydrol. Process., 28, 3930–3946,
https://doi.org/10.1002/hyp.9934, 2013.
McGrath, D., Sass, L., O'Neel, S., Arendt, A., Wolken, G., Gusmeroli,
A., Kienholz, C., and McNeil, C.: End-of-winter snow depth variability on
glaciers in Alaska, J. Geophys. Res.-Earth, 120, 1530–1550,
https://doi.org/10.1002/2015JF003539, 2015.
McGrath, D., Sass, L., O'Neel, S., Arendt, A., and Kienholz, C.: Hypsometric
control on glacier mass balance sensitivity in Alaska and northwest Canada,
Earth's Future, 5, 324–336, https://doi.org/10.1002/2016EF000479, 2017.
Meromy, L., Molotch, N. P., Link, T. E., Fassnacht, S. R., and Rice, R.:
Subgrid variability of snow water equivalent at operational snow stations in
the western USA, Hydrol. Process., 27, 2383–2400, https://doi.org/10.1002/hyp.9355, 2013.
Molotch, N. P., Colee, M. T., Bales, R. C., and Dozier, J.: Estimating the
spatial distribution of snow water equivalent in an alpine basin using
binary regression tree models: the impact of digital elevation data and
independent variable selection, Hydrol. Process., 19, 1459–1479,
https://doi.org/10.1002/hyp.5586, 2005.
Nolan, M., Larsen, C., and Sturm, M.: Mapping snow depth from manned aircraft on landscape scales at centimeter
resolution using structure-from-motion photogrammetry, The Cryosphere, 9, 1445–1463, https://doi.org/10.5194/tc-9-1445-2015, 2015.
Noh, M. J. and Howat, I. M.: Automated stereo-photogrammetric DEM generation
at high latitudes: Surface Extraction with TIN-based Search-space
Minimization (SETSM) validation and demonstration over glaciated
regions, GIScience & Remote Sensing, 52, 198–217,
https://doi.org/10.1080/15481603.2015.1008621, 2015.
O'Neel, S., Hood, E., Arendt, A., and Sass, L.: Assessing streamflow
sensitivity to variations in glacier mass balance, Climatic Change, 123,
329–341, https://doi.org/10.1007/s10584-013-1042-7, 2014.
O'Neel, S., Fagre, D. B., Baker, E. H., Sass, L. C., McNeil, C. J.,
Peitzsch, E. H., McGrath, D., and Florentine, C. E.: Glacier-Wide Mass
Balance and Input Data: USGS Benchmark Glaciers, 1966–2016 (ver. 2.1, May
2018), U.S. Geological Survey data release, https://doi.org/10.5066/F7HD7SRF, 2018.
Painter, T., Berisford, D., Boardman, J., Bormann, K., Deems, J., Gehrke,
F., Hedrick, A., Joyce, M., Laidlaw, R., Marks, D., Mattmann, C., Mcgurk,
B., Ramirez, P., Richardson, M., Skiles, S .M., Seidel, F., and Winstral, A.:
The Airborne Snow Observatory: fusion of scanning lidar, imaging
spectrometer, and physically-based modeling for mapping snow water
equivalent and snow albedo, Remote Sens. Environ., 184, 139–152,
https://doi.org/10.1016/j.rse.2016.06.018, 2016.
Pelto, M.: Utility of late summer transient snowline migration rate on Taku Glacier, Alaska,
The Cryosphere, 5, 1127–1133, https://doi.org/10.5194/tc-5-1127-2011, 2011.
Pelto, M., Kavanaugh, J., and McNeil, C.: Juneau Icefield Mass Balance Program 1946–2011,
Earth Syst. Sci. Data, 5, 319–330, https://doi.org/10.5194/essd-5-319-2013, 2013.
Pérez-Ruiz, M., Carballido, J., and Agüera, J.: Assessing GNSS
correction signals for assisted guidance systems in agricultural vehicles,
Precis. Agric., 12, 639–652, https://doi.org/10.1007/s11119-010-9211-4, 2011.
Pfeffer, W. T. and Bretherton, C.: The effect of crevasses on the solar
heating of a glacier surface, IAHS Publ., 170, 191–205, 1987.
Pfeffer, W. T., Arendt, A. A., Bliss, A., Bolch, T., Cogley, J. G., Gardner, A.
S.,
Hagen, J.-O., Hock, R., Kaser, G., Kienholz, C., Miles, E. S., Moholdt, G., Mölg, N., Paul, F., Radić,
V.,
Rastner, P., Rau, B. H., Rich, J., Sharp, M. J., and The Randolph Consortium: The Randolph Glacier Inventory: A globally complete
inventory of glaciers, J. Glaciol., 60, 537–552,
https://doi.org/10.3189/2014JoG13J176, 2014.
Porter, C., Morin, P., Howat, I., Noh, M.-J., Bates, B., Peterman, K., Keesey, S., Schlenk, M., Gardiner, J., Tomko, K., Willis, M., Kelleher, C.,
Cloutier, M., Husby, E., Foga, S., Nakamura, H., Platson, M., Wethington Jr., M., Williamson, C., Bauer, G., Enos, J., Arnold, G., Kramer, W.,
Becker, P., Doshi, A., D'Souza, C., Cummens, P., Laurier, F., and
Bojesen, M.: “ArcticDEM”, Harvard Dataverse, V1, [5/2018], https://doi.org/10.7910/DVN/OHHUKH, 2018.
Schirmer, M., Wirz, V., Clifton, A., and Lehning, M.: Persistence in
intra-annual snow depth distribution: 1. Measurements and topographic
control, Water Resour. Res., 47, W09516, https://doi.org/10.1029/2010WR009426, 2011.
Sold, L., Huss, M., Hoelzle, M., Andereggen, H., Joerg, P., and Zemp, M.:
Methodological approaches to infer end-of-winter snow distribution on alpine
glaciers, J. Glaciol., 59, 1047–1059, https://doi.org/10.3189/2013JoG13J015, 2013.
Sold, L., Huss, M., Machguth, H., Joerg, P. C., Vieli, 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.
Sturm, M. and Wagner, A. M.: Using repeated patterns in snow distribution
modeling: An Arctic example, Water Res. Res., 46, W12549,
https://doi.org/10.1029/2010WR009434, 2010.
Van Beusekom, A. E., O'Neel, S., March, R. S., Sass, L., and Cox, L. H.:
Re-analysis of Alaskan Benchmark Glacier mass balance data using the index
method, U.S. Geological Survey Scientific Investigations Report 2010–5247,
p. 16, 2010.
Vincent, C., Fischer, A., Mayer, C., Bauder, A., Galos, S.P., Funk, M.,
Thibert, E., Six, D., Braun, L., and Huss, M.: Common climatic signal from
glaciers in the European Alps over the last 50 years, Geophys. Res. Lett.,
44, 1376–1383, https://doi.org/10.1002/2016GL072094, 2017.
Winstral, A. and Marks, D.: Long-term snow distribution observations in a
mountain catchment: Assessing variability, time stability, and the
representativeness of an index site, Water Res. Res., 50, 293–305,
https://doi.org/10.1002/2012WR013038, 2014.
Winstral, A., Elder, K., and Davis, R. E.: Spatial snow modeling of
wind-redistributed snow using terrain-based parameters, J. Hydrometeorol., 3,
524–538, 2002.
Winstral, A., Marks, D., and Gurney, R.: Simulating wind-affected snow
accumulations at catchment to basin scales, Adv. Water Res., 55, 64–79,
https://doi.org/10.1016/j.advwatres.2012.08.011, 2013.
Woo, M.-K. and Marsh, P.: Analysis of error in the determination of snow
storage for small high Arctic basins, J. Appl. Meteorol., 17, 1537–1541,
1978.
Yelf, R. and Yelf, D.: Where is true time zero?, Electro. Phenom., 7,
158–163, 2006.
Short summary
Measuring the amount and spatial pattern of snow on glaciers is essential for monitoring glacier mass balance and quantifying the water budget of glacierized basins. Using repeat radar surveys for 5 consecutive years, we found that the spatial pattern in snow distribution is stable over the majority of the glacier and scales with the glacier-wide average. Our findings support the use of sparse stake networks for effectively measuring interannual variability in winter balance on glaciers.
Measuring the amount and spatial pattern of snow on glaciers is essential for monitoring glacier...
