Articles | Volume 18, issue 7
https://doi.org/10.5194/tc-18-3253-2024
© Author(s) 2024. 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-18-3253-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
22 Jul 2024
Research article |
| 22 Jul 2024
| 22 Jul 2024
Spatially distributed snow depth, bulk density, and snow water equivalent from ground-based and airborne sensor integration at Grand Mesa, Colorado, USA
Tate G. Meehan
CORRESPONDING AUTHOR
Cold Regions Research and Engineering Laboratory, US Army Corps of Engineers, Hanover, NH, USA
Department of Geosciences, Boise State University, Boise, ID, USA
Ahmad Hojatimalekshah
Department of Geosciences, Boise State University, Boise, ID, USA
Hans-Peter Marshall
Department of Geosciences, Boise State University, Boise, ID, USA
Elias J. Deeb
Cold Regions Research and Engineering Laboratory, US Army Corps of Engineers, Hanover, NH, USA
Shad O'Neel
Cold Regions Research and Engineering Laboratory, US Army Corps of Engineers, Hanover, NH, USA
Department of Geosciences, Boise State University, Boise, ID, USA
Daniel McGrath
Department of Geosciences, Colorado State University, Fort Collins, CO, USA
Ryan W. Webb
Department of Civil and Architectural Engineering & Construction Management, University of Wyoming, Laramie, WY, USA
Randall Bonnell
Department of Geosciences, Colorado State University, Fort Collins, CO, USA
Mark S. Raleigh
College of Earth, Ocean, and Atmospheric Sciences, Oregon State University, Corvallis, OR, USA
Christopher Hiemstra
Geospatial Management Office, USDA Forest Service, Salt Lake City, UT, USA
Kelly Elder
Rocky Mountain Research Station, USDA Forest Service, Fort Collins, CO, USA
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Bareera N. Mirza, Eric E. Small, and Mark S. Raleigh
EGUsphere, https://doi.org/10.5194/egusphere-2025-978, https://doi.org/10.5194/egusphere-2025-978, 2025
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Max Berkelhammer, Gerald F. M. Page, Frank Zurek, Christopher Still, Mariah S. Carbone, William Talavera, Laura Hildebrand, James Byron, Kyle Inthabandith, Angellica Kucinski, Melissa Carlson, Kelsey Foss, Wendy Brown, Rosemary W. H. Carroll, Austin Simonpietri, Marshall Worsham, Ian Breckheimer, Anna Ryken, Reed Maxwell, David Gochis, Mark S. Raleigh, Eric Small, and Kenneth H. Williams
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Jordan N. Herbert, Mark S. Raleigh, and Eric E. Small
The Cryosphere, 18, 3495–3512, https://doi.org/10.5194/tc-18-3495-2024, https://doi.org/10.5194/tc-18-3495-2024, 2024
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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
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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
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Shadi Oveisgharan, Robert Zinke, Zachary Hoppinen, and Hans Peter Marshall
The Cryosphere, 18, 559–574, https://doi.org/10.5194/tc-18-559-2024, https://doi.org/10.5194/tc-18-559-2024, 2024
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The seasonal snowpack provides water resources to billions of people worldwide. Large-scale mapping of snow water equivalent (SWE) with high resolution is critical for many scientific and economics fields. In this work we used the radar remote sensing interferometric synthetic aperture radar (InSAR) to estimate the SWE change between 2 d. The error in the estimated SWE change is less than 2 cm for in situ stations. Additionally, the retrieved SWE using InSAR is correlated with lidar snow depth.
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
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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
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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
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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).
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
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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.
Timbo Stillinger, Karl Rittger, Mark S. Raleigh, Alex Michell, Robert E. Davis, and Edward H. Bair
The Cryosphere, 17, 567–590, https://doi.org/10.5194/tc-17-567-2023, https://doi.org/10.5194/tc-17-567-2023, 2023
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Understanding global snow cover is critical for comprehending climate change and its impacts on the lives of billions of people. Satellites are the best way to monitor global snow cover, yet snow varies at a finer spatial resolution than most satellite images. We assessed subpixel snow mapping methods across a spectrum of conditions using airborne lidar. Spectral-unmixing methods outperformed older operational methods and are ready to to advance snow cover mapping at the global scale.
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
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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
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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
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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
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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
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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.
Ryan W. Webb, Keith Jennings, Stefan Finsterle, and Steven R. Fassnacht
The Cryosphere, 15, 1423–1434, https://doi.org/10.5194/tc-15-1423-2021, https://doi.org/10.5194/tc-15-1423-2021, 2021
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We simulate the flow of liquid water through snow and compare results to field experiments. This process is important because it controls how much and how quickly water will reach our streams and rivers in snowy regions. We found that water can flow large distances downslope through the snow even after the snow has stopped melting. Improved modeling of snowmelt processes will allow us to more accurately estimate available water resources, especially under changing climate conditions.
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
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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
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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.
Snow water equivalent (SWE) is a critical parameter for yearly water supply forecasting and can...
