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.
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
Related authors
No articles found.
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.
Kori L. Mooney and Ryan W. Webb
EGUsphere, https://doi.org/10.5194/egusphere-2024-2364, https://doi.org/10.5194/egusphere-2024-2364, 2024
Short summary
Short summary
This study observes the movement of snow water equivalence (SWE) during mid-winter surface melt and spring snowmelt periods. We observed the south facing slope that experienced mid-winter surface melt events, showed meltwater flowing downslope through the snow. The north facing slope saw similar redistribution of meltwater during the spring snowmelt period.
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.
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
Short summary
Short summary
Automated stations measure snow properties at a single point but are frequently used to validate data that represent much larger areas. We use lidar snow depth data to see how often the mean snow depth surrounding a snow station is within 10 cm of the snow station depth at different scales. We found snow stations overrepresent the area-mean snow depth in ~ 50 % of cases, but the direction of bias at a site is temporally consistent, suggesting a site could be calibrated to the surrounding area.
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.
Max Berkelhammer, Gerald F. Page, Frank Zurek, Christopher Still, Mariah S. Carbone, William Talavera, Laura Hildebrand, James Byron, Kyle Inthabandith, Angellica Kucinski, Melissa Carter, Kelsey Foss, Wendy Brown, Rosemary W. H. Carroll, Austin Simonpietri, Marshall Worsham, Ian Breckheimer, Anna Ryken, Reed Maxwell, David Gochis, Mark Raleigh, Eric Small, and Kenneth H. Williams
EGUsphere, https://doi.org/10.5194/egusphere-2023-3063, https://doi.org/10.5194/egusphere-2023-3063, 2024
Short summary
Short summary
Warming in montane systems is affecting the amount of snowmelt inputs. This will affect subalpine forests globally that rely on spring snowmelt to support their water demands. We use a network of sensors across in the Upper Colorado Basin to show that changing spring primarily impacts dense forest stands that have high peak water demands. On the other hand, open forest stands show a higher reliance on summer rain and were minimally sensitive to even historically low snow conditions like 2019.
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).
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.
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
Short summary
Short summary
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
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.
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
Short summary
Short summary
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
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.
Daniel McGrath, Louis Sass, Shad O'Neel, Chris McNeil, Salvatore G. Candela, Emily H. Baker, and Hans-Peter Marshall
The Cryosphere, 12, 3617–3633, https://doi.org/10.5194/tc-12-3617-2018, https://doi.org/10.5194/tc-12-3617-2018, 2018
Short summary
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.
Steven R. Fassnacht, Jared T. Heath, Niah B. H. Venable, and Kelly J. Elder
The Cryosphere, 12, 1121–1135, https://doi.org/10.5194/tc-12-1121-2018, https://doi.org/10.5194/tc-12-1121-2018, 2018
Short summary
Short summary
We conducted a series of experiments to determine how snowpack properties change with varying snowmobile traffic. Experiments were initiated at a shallow (30 cm) and deep (120 cm) snow depth at two locations. Except for initiation at 120 cm, snowmobiles significantly changed the density, hardness, ram resistance, and basal layer crystal size. Temperature was not changed. A density change model was developed and tested. The results inform management of lands with snowmobile traffic.
Ryan W. Webb, Steven R. Fassnacht, and Michael N. Gooseff
The Cryosphere, 12, 287–300, https://doi.org/10.5194/tc-12-287-2018, https://doi.org/10.5194/tc-12-287-2018, 2018
Short summary
Short summary
We observed how snowmelt is transported on a hillslope through multiple measurements of snow and soil moisture across a small headwater catchment. We found that snowmelt flows through the snow with less infiltration on north-facing slopes and infiltrates the ground on south-facing slopes. This causes an increase in snow water equivalent at the base of the north-facing slope by as much as 170 %. We present a conceptualization of flow path development to improve future investigations.
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.
Related subject area
Discipline: Snow | Subject: Field Studies
Unlocking the potential of melting calorimetry: a field protocol for liquid water content measurement in snow
Elucidation of spatiotemporal structures from high-resolution blowing-snow observations
Assessing the key concerns in snow storage: a case study for China
Evaluating a prediction system for snow management
Implications of surface flooding on airborne estimates of snow depth on sea ice
A low-cost method for monitoring snow characteristics at remote field sites
The RHOSSA campaign: multi-resolution monitoring of the seasonal evolution of the structure and mechanical stability of an alpine snowpack
Measurement of specific surface area of fresh solid precipitation particles in heavy snowfall regions of Japan
The evolution of snow bedforms in the Colorado Front Range and the processes that shape them
Estimating the snow water equivalent on a glacierized high elevation site (Forni Glacier, Italy)
Snowmobile impacts on snowpack physical and mechanical properties
Riccardo Barella, Mathias Bavay, Francesca Carletti, Nicola Ciapponi, Valentina Premier, and Carlo Marin
The Cryosphere, 18, 5323–5345, https://doi.org/10.5194/tc-18-5323-2024, https://doi.org/10.5194/tc-18-5323-2024, 2024
Short summary
Short summary
This research revisits a classic scientific technique, melting calorimetry, to measure snow liquid water content. This study shows with a novel uncertainty propagation framework that melting calorimetry, traditionally less trusted than freezing calorimetry, can produce accurate results. The study defines optimal experiment parameters and a robust field protocol. Melting calorimetry has the potential to become a valuable tool for validating other liquid water content measuring techniques.
Kouichi Nishimura, Masaki Nemoto, Yoichi Ito, Satoru Omiya, Kou Shimoyama, and Hirofumi Niiya
The Cryosphere, 18, 4775–4786, https://doi.org/10.5194/tc-18-4775-2024, https://doi.org/10.5194/tc-18-4775-2024, 2024
Short summary
Short summary
It is crucial to consider organized structures such as turbulence sweeps and ejections when discussing the onset and development of snow transport. This study aims to systematically measure blowing and drifting snow to investigate their spatiotemporal structures. To achieve this goal, we have deployed 15 snow particle counters (SPCs) in designated test areas and are conducting measurements using an equal number of ultrasonic anemometers, providing high-temporal-resolution data.
Xing Wang, Feiteng Wang, Jiawen Ren, Dahe Qin, and Huilin Li
The Cryosphere, 18, 3017–3031, https://doi.org/10.5194/tc-18-3017-2024, https://doi.org/10.5194/tc-18-3017-2024, 2024
Short summary
Short summary
This work addresses snow storage at sports facilities in China. The snow pile at Big Air Shougang (BAS) lost 158.6 m3 snow (6.7 %) during pre-competition and Winter Olympic competition days in winter 2022. There were no significant variations in the snow quality of the snow piles at BAS and the National Biathlon Center except for in the upper part of the snow piles. The 0.7 and 0.4 m thick cover layers protected half the snow height over the summer at Beijing and Chongli, respectively.
Pirmin Philipp Ebner, Franziska Koch, Valentina Premier, Carlo Marin, Florian Hanzer, Carlo Maria Carmagnola, Hugues François, Daniel Günther, Fabiano Monti, Olivier Hargoaa, Ulrich Strasser, Samuel Morin, and Michael Lehning
The Cryosphere, 15, 3949–3973, https://doi.org/10.5194/tc-15-3949-2021, https://doi.org/10.5194/tc-15-3949-2021, 2021
Short summary
Short summary
A service to enable real-time optimization of grooming and snow-making at ski resorts was developed and evaluated using both GNSS-measured snow depth and spaceborne snow maps derived from Copernicus Sentinel-2. The correlation to the ground observation data was high. Potential sources for the overestimation of the snow depth by the simulations are mainly the impact of snow redistribution by skiers, compensation of uneven terrain, or spontaneous local adaptions of the snow management.
Anja Rösel, Sinead Louise Farrell, Vishnu Nandan, Jaqueline Richter-Menge, Gunnar Spreen, Dmitry V. Divine, Adam Steer, Jean-Charles Gallet, and Sebastian Gerland
The Cryosphere, 15, 2819–2833, https://doi.org/10.5194/tc-15-2819-2021, https://doi.org/10.5194/tc-15-2819-2021, 2021
Short summary
Short summary
Recent observations in the Arctic suggest a significant shift towards a snow–ice regime caused by deep snow on thin sea ice which may result in a flooding of the snowpack. These conditions cause the brine wicking and saturation of the basal snow layers which lead to a subsequent underestimation of snow depth from snow radar mesurements. As a consequence the calculated sea ice thickness will be biased towards higher values.
Rosamond J. Tutton and Robert G. Way
The Cryosphere, 15, 1–15, https://doi.org/10.5194/tc-15-1-2021, https://doi.org/10.5194/tc-15-1-2021, 2021
Short summary
Short summary
Snow cover is critical to everyday life for people around the globe. Regular measurements of snow cover usually occur only in larger communities because snow monitoring equipment is costly. In this study, we developed a new low-cost method for estimating snow depth and tested it continuously for 1 year at six remote field locations in coastal Labrador, Canada. Field testing suggests that this new method provides a promising option for researchers in need of a low-cost snow measurement system.
Neige Calonne, Bettina Richter, Henning Löwe, Cecilia Cetti, Judith ter Schure, Alec Van Herwijnen, Charles Fierz, Matthias Jaggi, and Martin Schneebeli
The Cryosphere, 14, 1829–1848, https://doi.org/10.5194/tc-14-1829-2020, https://doi.org/10.5194/tc-14-1829-2020, 2020
Short summary
Short summary
During winter 2015–2016, the standard program to monitor the structure and stability of the snowpack at Weissflujoch, Swiss Alps, was complemented by additional measurements to compare between various traditional and newly developed techniques. Snow micro-penetrometer measurements allowed monitoring of the evolution of the snowpack's internal structure at a daily resolution throughout the winter. We show the potential of such high-resolution data for detailed evaluations of snowpack models.
Satoru Yamaguchi, Masaaki Ishizaka, Hiroki Motoyoshi, Sent Nakai, Vincent Vionnet, Teruo Aoki, Katsuya Yamashita, Akihiro Hashimoto, and Akihiro Hachikubo
The Cryosphere, 13, 2713–2732, https://doi.org/10.5194/tc-13-2713-2019, https://doi.org/10.5194/tc-13-2713-2019, 2019
Short summary
Short summary
The specific surface area (SSA) of solid precipitation particles (PPs) includes detailed information of PP. This work is based on field measurement of SSA of PPs in Nagaoka, the city with the heaviest snowfall in Japan. The values of SSA strongly depend on wind speed (WS) and wet-bulb temperature (Tw) on the ground. An equation to empirically estimate the SSA of fresh PPs with WS and Tw was established and the equation successfully reproduced the fluctuation of SSA in Nagaoka.
Kelly Kochanski, Robert S. Anderson, and Gregory E. Tucker
The Cryosphere, 13, 1267–1281, https://doi.org/10.5194/tc-13-1267-2019, https://doi.org/10.5194/tc-13-1267-2019, 2019
Short summary
Short summary
Wind-blown snow does not lie flat. It forms dunes, ripples, and anvil-shaped sastrugi. These features ornament much of the snow on Earth and change the snow's effects on polar climates, but they have rarely been studied. We spent three winters watching snow move through the Colorado Front Range and present our findings here, including the first time-lapse videos of snow dune and sastrugi growth.
Antonella Senese, Maurizio Maugeri, Eraldo Meraldi, Gian Pietro Verza, Roberto Sergio Azzoni, Chiara Compostella, and Guglielmina Diolaiuti
The Cryosphere, 12, 1293–1306, https://doi.org/10.5194/tc-12-1293-2018, https://doi.org/10.5194/tc-12-1293-2018, 2018
Short summary
Short summary
We present and compare 11 years of snow data measured by an automatic weather station and corroborated by data from field campaigns on the Forni Glacier in Italy. The methodology we present is interesting for remote locations such as glaciers or high alpine regions, as it makes it possible to estimate the total snow water equivalent (SWE) using a relatively inexpensive, low-power, low-maintenance, and reliable instrument such as the sonic ranger.
Steven R. Fassnacht, Jared T. Heath, Niah B. H. Venable, and Kelly J. Elder
The Cryosphere, 12, 1121–1135, https://doi.org/10.5194/tc-12-1121-2018, https://doi.org/10.5194/tc-12-1121-2018, 2018
Short summary
Short summary
We conducted a series of experiments to determine how snowpack properties change with varying snowmobile traffic. Experiments were initiated at a shallow (30 cm) and deep (120 cm) snow depth at two locations. Except for initiation at 120 cm, snowmobiles significantly changed the density, hardness, ram resistance, and basal layer crystal size. Temperature was not changed. A density change model was developed and tested. The results inform management of lands with snowmobile traffic.
Cited articles
Andrews, D. F.: A robust method for multiple linear regression, Technometrics, 16, 523–531, https://doi.org/10.1080/00401706.1974.10489233, 1974.
Bentley, J. L.: Multidimensional Binary Search Trees Used for Associative Searching, Commun. ACM, 18, 509–517, https://doi.org/10.1145/361002.361007, 1975.
Besso, H., Shean, D., and Lundquist, J. D.: Mountain snow depth retrievals from customized processing of ICESat-2 satellite laser altimetry, Remote Sens. Environ., 300, 113 843, https://doi.org/10.1016/j.rse.2023.113843, 2024.
Bonnell, R., McGrath, D., Hedrick, A. R., Trujillo, E., Meehan, T. G., Williams, K., Marshall, H. P., Sexstone, G., Fulton, J., Ronayne, M. J., Fassnacht, S. R., Webb, R. W., and Hale, K. E.: Snowpack relative permittivity and density derived from near-coincident lidar and ground-penetrating radar, Hydrol. Process., 37, e14996, https://doi.org/10.1002/hyp.14996, 2023.
Bonner, H. M., Raleigh, M. S., and Small, E. E.: Isolating forest process effects on modelled snowpack density and snow water equivalent, Hydrol. Process., 36, e14475, https://doi.org/10.1002/hyp.14475, 2022.
Booth, A. D., Clark, R., and Murray, T.: Semblance response to a ground-penetrating radar wavelet and resulting errors in velocity analysis, Near Surf. Geophys., 8, 235–246. https://doi.org/10.3997/1873-0604.2010008, 2010.
Boyd, D. R., Alam, A. M., Kurum, M., Gurbuz, A. C., and Osmanoglu, B.: Preliminary Snow Water Equivalent Retrieval of SnowEX20 Swesarr Data, in: Proceedings of the 42nd IEEE International Symposium on Geoscience and Remote Sensing IGARSS, 17–22 July 2022, Kuala Lumpur, Malaysia, vol. 2022-July, ISBN 9781665427920, https://doi.org/10.1109/IGARSS46834.2022.9883412, pp. 3927–3930, 2022.
Breiman, L.: Random Forests, Mach. Learn., 45, 5–32, https://doi.org/10.1023/A:1010933404324, 2001.
Broxton, P. D., Leeuwen, W. J. D., and Biederman, J. A.: Improving Snow Water Equivalent Maps With Machine Learning of Snow Survey and Lidar Measurements, Water Resour. Res., 55, 3739–3757, https://doi.org/10.1029/2018WR024146, 2019.
Cressie, N.: Fitting variogram models by weighted least squares, J. Int. Ass. Math. Geol., 17, 563–586, https://doi.org/10.1007/BF01032109, 1985.
Deems, J. S., Fassnacht, S. R., and Elder, K. J.: Fractal Distribution of Snow Depth from Lidar Data, J. Hydrometeorol., 7, 285–297, https://doi.org/10.1175/JHM487.1, 2006.
Deems, J. S., Painter, T. H., and Finnegan, D. C.: Lidar measurement of snow depth: A review, J. Glaciol., 59, 467–479, https://doi.org/10.3189/2013JoG12J154, 2013.
Deschamps-Berger, C., Gascoin, S., Shean, D., Besso, H., Guiot, A., and López-Moreno, J. I.: Evaluation of snow depth retrievals from ICESat-2 using airborne laser-scanning data, The Cryosphere, 17, 2779–2792, https://doi.org/10.5194/tc-17-2779-2023, 2023.
Dewitz, J.: National Land Cover Database (NLCD) 2016 Products (ver. 3.0, November 2023), U.S. Geological Survey [data set], https://doi.org/10.5066/P96HHBIE, 2019.
Efron, B. and Tibshirani, R.: Bootstrap Methods for Standard Errors, Confidence Intervals, and Other Measures of Statistical Accuracy, Stat. Sci., 1, 77–77, https://doi.org/10.1214/ss/1177013817, 1986.
Elder, K., Dozier, J., and Michaelsen, J.: Snow accumulation and distribution in an Alpine Watershed, Water Resour. Res., 27, 1541–1552, https://doi.org/10.1029/91WR00506, 1991.
Elder, K., Rosenthal, W., and Davis, R. E.: Estimating the spatial distribution of snow water equivalence in a montane watershed, Hydrol. Process., 12, 1793–1808, https://doi.org/10.1002/(SICI)1099-1085(199808/09)12:10/11<1793::AID-HYP695>3.0.CO;2-K, 1998.
Essery, R., Morin, S., Lejeune, Y., and Ménard, C. B.: A comparison of 1701 snow models using observations from an alpine site, Adv. Water Resour., 55, 131–148, https://doi.org/10.1016/j.advwatres.2012.07.013, 2013.
Fassnacht, S. R., Heun, C. M., López-Moreno, J., and Latron, J.: Snow Density Variability in the Rio Esera Valley, Pyrenees Mountains, 2. Study Site, Cuadernos de Ivestigación Geográfica, 36, 59–72, 2010.
Goh, A.: Back-propagation neural networks for modeling complex systems, Artif. Intell. Eng., 9, 143–151, https://doi.org/10.1016/0954-1810(94)00011-S, 1995.
Griessinger, N., Mohr, F., and Jonas, T.: Measuring snow ablation rates in alpine terrain with a mobile multioffset ground-penetrating radar system, Hydrol. Process., 32, 3272–3282, https://doi.org/10.1002/hyp.13259, 2018.
Hapfelmeier, A., Hothorn, T., Ulm, K., and Strobl, C.: A new variable importance measure for random forests with missing data, Stat. Comput., 24, 21–34, https://doi.org/10.1007/s11222-012-9349-1, 2014.
Hedrick, A. R., Marks, D., Havens, S., Robertson, M., Johnson, M., Sandusky, M., Marshall, H., Kormos, P. R., Bormann, K. J., and Painter, T. H.: Direct Insertion of NASA Airborne Snow Observatory-Derived Snow Depth Time Series Into the iSnobal Energy Balance Snow Model, Water Resour. Res., 54, 8045–8063, https://doi.org/10.1029/2018WR023190, 2018.
Hiemstra, C., Marshall, H., Vuyovich, C., Elder, K., Mason, M., and Durand, M.: SnowEx20 Community Snow Depth Probe Measurements, Version 1, NASA National Snow and Ice Data Center Distributed Active Archive Center [data set], https://doi.org/10.5067/9IA978JIACAR, 2020.
Hiemstra, C. A., Vuyovich, C. M., and Marshall, H.-P.: SnowEx20 Grand Mesa Reference GIS Data Sets, Version 1, NASA National Snow and Ice Data Center Distributed Active Archive Center [data set], https://doi.org/10.5067/YDZXY4Q79VIJ, 2021.
Hill, D. F., Burakowski, E. A., Crumley, R. L., Keon, J., Hu, J. M., Arendt, A. A., Wikstrom Jones, K., and Wolken, G. J.: Converting snow depth to snow water equivalent using climatological variables, The Cryosphere, 13, 1767–1784, https://doi.org/10.5194/tc-13-1767-2019, 2019.
Hojatimalekshah, A., Uhlmann, Z., Glenn, N. F., Hiemstra, C. A., Tennant, C. J., Graham, J. D., Spaete, L., Gelvin, A., Marshall, H.-P., McNamara, J. P., and Enterkine, J.: Tree canopy and snow depth relationships at fine scales with terrestrial laser scanning, The Cryosphere, 15, 2187–2209, https://doi.org/10.5194/tc-15-2187-2021, 2021.
Houser, P., Rudisill, W., Johnston, J., Elder, K., Marshall, H., Vuyovich, C. M., Kim, E. J., and Mason, M.: SnowEx Meteorological Station Measurements from Grand Mesa, CO, Version 1, Boulder, Colorado USA, NASA National Snow and Ice Data Center Distributed Active Archive Center [data set], https://doi.org/10.5067/497NQVJ0CBEX, 2022.
Hu, X., Hao, X., Wang, J., Huang, G., Li, H., and Yang, Q.: Can the Depth of Seasonal Snow be Estimated From ICESat-2 Products: A Case Investigation in Altay, Northwest China, IEEE Geosci Remote S., 19, 1–5, https://doi.org/10.1109/LGRS.2021.3078805, 2021.
Isaaks, E. H. and Srivastava, R. M.: Applied Geostatistics, Oxford University Press, New York, NY, ISBN 9780195050134, 1989.
Jain, A., Mao, J., and Mohiuddin, K.: Artificial neural networks: a tutorial, Computer, 29, 31–44, https://doi.org/10.1109/2.485891, 1996.
Jonas, T., Marty, C., and Magnusson, J.: Estimating the snow water equivalent from snow depth measurements in the Swiss Alps, J. Hydrol., 378, 161–167, https://doi.org/10.1016/j.jhydrol.2009.09.021, 2009.
Kahaner, D., Moler, C., and Nash, S.: Numerical methods and software, Prentice Hall, Englewood Cliffs, ISBN 0-13-627258-4, 1989.
Kim, J. H., Cho, S. J., and Yi, M. J.: Removal of ringing noise in GPR data by signal processing, Geosci. J., 11, 75–81, https://doi.org/10.1007/BF02910382, 2007.
Kuwahara, M., Hachimura, K., Eiho, S., and Kinoshita, M.: Processing of RI-Angiocardiographic Images, Springer US, Boston, MA, https://doi.org/10.1007/978-1-4684-0769-3_13, 187–202, 1976.
Lague, D., Brodu, N., and Leroux, J.: Accurate 3D comparison of complex topography with terrestrial laser scanner: Application to the Rangitikei canyon (NZ), ISPRS J. Photogramm., 82, 10–26, https://doi.org/10.1016/j.isprsjprs.2013.04.009, 2013.
Lettenmaier, D. P., Alsdorf, D., Dozier, J., Huffman, G. J., Pan, M., and Wood, E. F.: Inroads of remote sensing into hydrologic science during the WRR era, Water Resour. Res., 51, 7309–7342, https://doi.org/10.1002/2015WR017616, 2015.
Li, L. and Pomeroy, J. W.: Estimates of Threshold Wind Speeds for Snow Transport Using Meteorological Data, J. Appl. Meteorol., 36, 205–213, https://doi.org/10.1175/1520-0450(1997)036<0205:EOTWSF>2.0.CO;2, 1997.
Lievens, H., Demuzere, M., Marshall, H.-P., Reichle, R. H., Brucker, L., Brangers, I., de Rosnay, P., Dumont, M., Girotto, M., Immerzeel, W. W., Jonas, T., Kim, E. J., Koch, I., Marty, C., Saloranta, T., Schöber, J., and Lannoy, G. J. M. D.: Snow depth variability in the Northern Hemisphere mountains observed from space, Nat. Commun., 10, 4629, https://doi.org/10.1038/s41467-019-12566-y, 2019.
Lievens, H., Brangers, I., Marshall, H.-P., Jonas, T., Olefs, M., and De Lannoy, G.: Sentinel-1 snow depth retrieval at sub-kilometer resolution over the European Alps, The Cryosphere, 16, 159–177, https://doi.org/10.5194/tc-16-159-2022, 2022.
López-Moreno, J. I., Fassnacht, S. R., Heath, J. T., Musselman, K. N., Revuelto, J., Latron, J., Morán-Tejeda, E., and Jonas, T.: Small scale spatial variability of snow density and depth over complex alpine terrain: Implications for estimating snow water equivalent, Adv. Water Resour., 55, 40–52, https://doi.org/10.1016/j.advwatres.2012.08.010, 2013.
Lukas, V. and Baez, V.: 3D Elevation Program—Federal best practices, U. S. Geological Survey Fact Sheet 2020–3062, U.S. Geological Survey, https://doi.org/10.3133/fs20203062, 2021.
Lv, Z. and Pomeroy, J. W.: Assimilating snow observations to snow interception process simulations, Hydrol. Process., 34, 2229–2246, https://doi.org/10.1002/hyp.13720, 2020.
Marks, D., Dozier, J., and Davis, R. E.: Climate and energy exchange at the snow surface in the Alpine Region of the Sierra Nevada: 1. Meteorological measurements and monitoring, Water Resour. Res., 28, 3029–3042, https://doi.org/10.1029/92WR01482, 1992.
Marshall, H., Vuyovich, C., Hiemstra, C., Brucker, L., Elder, K., Deems, J., Newlin, J., Bales, R., Nolin, A., and Trujillo, E.: NASA SnowEx 2020 Experiment Plan, NASA, https://snow.nasa.gov/campaigns/snowex/experimental-plan-2020 (last access: 7 June 2024), pp. 1–100, 2019.
Marshall, H. P., Koh, G., Sturm, M., Johnson, J. B., Demuth, M., Landry, C., Deems, J. S., and Gleason, J. A.: Spatial variability of the snowpack: Experiences with measurements at a wide range of length scales with several different high precision instruments, in: Proceedings ISSW 2006, International Snow Science Workshop, Telluride CO, USA, 1–6 October 2006, http://arc.lib.montana.edu/snow-science/item/947 (last access: 7 June 2024), pp. 359–364, 2006.
Marti, R., Gascoin, S., Berthier, E., de Pinel, M., Houet, T., and Laffly, D.: Mapping snow depth in open alpine terrain from stereo satellite imagery, The Cryosphere, 10, 1361–1380, https://doi.org/10.5194/tc-10-1361-2016, 2016.
McCreight, J. L. and Small, E. E.: Modeling bulk density and snow water equivalent using daily snow depth observations, The Cryosphere, 8, 521–536, https://doi.org/10.5194/tc-8-521-2014, 2014.
McGrath, D., Webb, R., Shean, D., Bonnell, R., Marshall, H. P., Painter, T. H., Molotch, N. P., Elder, K., Hiemstra, C., and Brucker, L.: Spatially Extensive Ground-Penetrating Radar Snow Depth Observations During NASA's 2017 SnowEx Campaign: Comparison With In Situ, Airborne, and Satellite Observations, Water Resour. Res., 55, 10026–10036, https://doi.org/10.1029/2019WR024907, 2019.
McGrath, D., Bonnell, R., Zeller, L., Olsen-Mikitowicz, A., Bump, E., Webb, R., and Marshall, H.-P.: A Time Series of Snow Density and Snow Water Equivalent Observations Derived From the Integration of GPR and UAV SfM Observations, Frontiers in Remote Sensing, 3, 1–15, https://doi.org/10.3389/frsen.2022.886747, 2022.
Meehan, T. G.: SnowEx20 Grand Mesa IOP BSU 1 GHz Multi-polarization GPR, Version 1, NASA National Snow and Ice Data Center Distributed Active Archive Center [data set], https://doi.org/10.5067/Q2LFK0QSVGS2, 2021.
Meehan, T.: tatemeehan/SnowEx2020_BSU_pE_GPR: Multipolarization Radargram Processing SnowEx 2020 Grand Mesa IOP (v1.0.0), Zenodo [code], https://doi.org/10.5281/zenodo.11521496, 2024.
Meehan, T. G. and Hojatimalekshah, A.: SnowEx20 Grand Mesa IOP Lidar and GPR-Derived Snow Water Equivalent and Snow Density, Version 1, NASA National Snow and Ice Data Center Distributed Active Archive Center [data set], https://doi.org/10.5067/LANQ53RTJ2DR, 2024a.
Meehan, T. G. and Hojatimalekshah, A.: SnowEx20 Grand Mesa IOP QSI Lidar Snow Depth Data, Version 1, NASA National Snow and Ice Data Center Distributed Active Archive Center [data set], https://doi.org/10.5067/M9TPF6NWL53K, 2024b.
Meehan, T. G., Marshall, H. P., Bradford, J. H., Hawley, R. L., Overly, T. B., Lewis, G., Graeter, K., Osterberg, E., and McCarthy, F.: Reconstruction of historical surface mass balance, 1984–2017 from GreenTrACS multi-offset ground-penetrating radar, J. Glaciol., 67, 219–228, https://doi.org/10.1017/jog.2020.91, 2021.
Meløysund, V., Leira, B., Høiseth, K. V., and Lisø, K. R.: Predicting snow density using meteorological data, Meteorol. Appl., 14, 413–423, https://doi.org/10.1002/met.40, 2007.
Mote, P. W., Li, S., Lettenmaier, D. P., Xiao, M., and Engel, R.: Dramatic declines in snowpack in the western US, NPJ Clim. Atmos. Sci., 1, 2, https://doi.org/10.1038/s41612-018-0012-1, 2018.
National Academies of Sciences Engineering and Medicine: Thriving on Our Changing Planet: A Decadal Strategy for Earth Observation from Space, National Academies Press, Washington, D.C., ISBN 978-0-309-46757-5, https://doi.org/10.17226/24938, 2018.
Neidell, N. S. and Taner, M. T.: Semblance and Other Coherency Measrues for Multichannel Data, Geophysics, 36, 482–497, https://doi.org/10.1190/1.1440186, 1971.
NOAA: VDatum 4.3 Vertical Datum Transformation, National Ocean Service NOAA Department of Commerce [software], https://vdatum.noaa.gov/ (last access: 7 June 2024), 2021.
Painter, T.: ASO L4 Lidar Snow Depth 3m UTM Grid, Version 1, NASA National Snow and Ice Data Center Distributed Active Archive Center [data set], https://doi.org/10.5067/KIE9QNVG7HP0, 2018a.
Painter, T.: ASO L4 Lidar Snow Water Equivalent 50m UTM Grid, Version 1, NASA National Snow and Ice Data Center Distributed Active Archive Center [data set], https://doi.org/10.5067/M4TUH28NHL4Z, 2018b.
Painter, T. H. and Bormann, K. J.: ASO L4 Lidar Point Cloud Digital Terrain Model 3 m UTM Grid, Version 1, NASA National Snow and Ice Data Center Distributed Active Archive Center [data set], https://doi.org/10.5067/2EHMWG4IT76O, 2020.
Painter, T. H., Berisford, D. F., Boardman, J. W., Bormann, K. J., Deems, J. S., Gehrke, F., Hedrick, A., Joyce, M., Laidlaw, R., Marks,D., Mattmann, C., McGurk, B., Ramirez, P., Richardson, M., Skiles, S. M. K., Seidel, F. C., 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.
Pierce, D. W., Barnett, T. P., Hidalgo, H. G., Das, T., Bonfils, C., Santer, B. D., Bala, G., Dettinger, M. D., Cayan, D. R., Mirin, A., Wood, A. W., and Nozawa, T.: Attribution of declining Western U. S. Snowpack to human effects, J. Climate, 21, 6425–6444, https://doi.org/10.1175/2008JCLI2405.1, 2008.
Raleigh, M. S. and Small, E. E.: Snowpack density modeling is the primary source of uncertainty when mapping basin-wide SWE with lidar, Geophys. Res. Lett., 44, 3700–3709, https://doi.org/10.1002/2016GL071999, 2017.
Rovansek, R. J., Kane, D. L., and Hinzman, L. D.: Improving estimates of snowpack water equivalent using double sampling, in: Proceedings 61st Western Snow Conference, 9–11 June 1993, Quebec City, Quebec, pp. 157–163, 1993.
Siirila-Woodburn, E. R., Rhoades, A. M., Hatchett, B. J., Huning, L. S., Szinai, J., Tague, C., Nico, P. S., Feldman, D. R., Jones, A. D., Collins, W. D., and Kaatz, L.: A low-to-no snow future and its impacts on water resources in the western United States, Nat. Rev. Earth Environ., 2, 800–819, https://doi.org/10.1038/s43017-021-00219-y, 2021.
Singh, S., Durand, M., Kim, E., Pan, J., Kang, D. H., and Barros, A. P.: A Physical-Statistical Retrieval Framework to Estimate SWE from X and Ku-Band SAR Observations, vol. 2023-July, 16–21 July 2023, Pasadena, CA, USA, IEEE, ISBN 9798350320107, https://doi.org/10.1109/IGARSS52108.2023.10281838, pp. 17–20, 2023.
Sturm, M. and Holmgren, J.: Differences in compaction behavior of three climate classes of snow, Ann. Glaciol., 26, 125 130, https://doi.org/10.3189/1998AoG26-1-125-130, 1998.
Sturm, M., Taras, B., Liston, G. E., Derksen, C., Jonas, T., and Lea, J.: Estimating snow water equivalent using snow depth data and climate classes, J. Hydrometeorol., 11, 1380–1394, https://doi.org/10.1175/2010JHM1202.1, 2010.
Štroner, M., Urban, R., Lidmila, M., Kolář, V., and Křemen, T.: Vegetation Filtering of a Steep Rugged Terrain: The Performance of Standard Algorithms and a Newly Proposed Workflow on an Example of a Railway Ledge, Remote Sens.-Basel, 13, 3050, https://doi.org/10.3390/rs13153050, 2021.
Tedesco, M., Reichle, R., Low, A., Markus, T., and Foster, J. L.: Dynamic Approaches for Snow Depth Retrieval From Spaceborne Microwave Brightness Temperature, IEEE T. Geosci. Remote, 48, 1955–1967, https://doi.org/10.1109/TGRS.2009.2036910, 2010.
Tiuri, M. E., Sihvola, A. H., Nyfors, E. G., and Hallikaiken, M. T.: The Complex Dielectric Constant of Snow at Microwave Frequencies, IEEE J. Oceanic Eng., 9, 377–382, https://doi.org/10.1109/JOE.1984.1145645, 1984.
Treichler, D. and Kääb, A.: Snow depth from ICESat laser altimetry — A test study in southern Norway, Remote Sens. Environ., 191, 389–401, https://doi.org/10.1016/j.rse.2017.01.022, 2017.
Trujillo, E., Ramírez, J. A., and Elder, K. J.: Scaling properties and spatial organization of snow depth fields in sub-alpine forest and alpine tundra, Hydrol. Process., 23, 1575–1590, https://doi.org/10.1002/hyp.7270, 2009.
Tsang, L., Durand, M., Derksen, C., Barros, A. P., Kang, D.-H., Lievens, H., Marshall, H.-P., Zhu, J., Johnson, J., King, J., Lemmetyinen, J., Sandells, M., Rutter, N., Siqueira, P., Nolin, A., Osmanoglu, B., Vuyovich, C., Kim, E., Taylor, D., Merkouriadi, I., Brucker, L., Navari, M., Dumont, M., Kelly, R., Kim, R. S., Liao, T.-H., Borah, F., and Xu, X.: Review article: Global monitoring of snow water equivalent using high-frequency radar remote sensing, The Cryosphere, 16, 3531–3573, https://doi.org/10.5194/tc-16-3531-2022, 2022.
US Census Bureau: Cartographic Boundary Files, US Census Bureau [data set], https://www.census.gov/geographies/mapping-files/time-series/geo/cartographic-boundary.html (last access: 7 June 2024), 2020.
Valence, E., Baraer, M., Rosa, E., Barbecot, F., and Monty, C.: Drone-based ground-penetrating radar (GPR) application to snow hydrology, The Cryosphere, 16, 3843–3860, https://doi.org/10.5194/tc-16-3843-2022, 2022.
Vecherin, S., Meyer, A., Quinn, B., Letcher, T., and Parker, M.: Simulation of Snow Texture for Autonomous Vehicle Numerical Modeling, National Defense Industrial Association, http://gvsets.ndia-mich.org/publication.php?documentID=928 (last access: 7 June 2024), 2022.
Vuyovich, C. M., Marshall, H., Elder, K., Hiemstra, C., Brucker, L., and McCormick, M.: SnowEx20 Grand Mesa Intensive Observation Period Snow Pit Measurements, Version 1, NASA National Snow and Ice Data Center Distributed Active Archive Center [data set], https://doi.org/10.5067/DUD2VZEVBJ7S, 2021.
Wang, Z., Bovik, A. C., Sheikh, H. R., and Simoncelli, E. P.: Image quality assessment: From error visibility to structural similarity, IEEE T. Image Process., 13, 600–612, https://doi.org/10.1109/TIP.2003.819861, 2004.
Webb, R. W.: SnowEx20 Grand Mesa IOP UNM 800 and 1600 MHz MALA GPR, Version 1, NASA National Snow and Ice Data Center Distributed Active Archive Center [data set], https://doi.org/10.5067/WE9GI1GVMQF6, 2021.
Webb, R. W., Marziliano, A., McGrath, D., Bonnell, R., Meehan, T. G., Vuyovich, C., and Marshall, H.-P.: In Situ Determination of Dry and Wet Snow Permittivity: Improving Equations for Low Frequency Radar Applications, Remote Sens.-Basel, 13, 4617, https://doi.org/10.3390/rs13224617, 2021.
Wetlaufer, K., Hendrikx, J., and Marshall, L.: Spatial heterogeneity of snow density and its influence on snow water equivalence estimates in a large mountainous basin, Hydrology, 3, 3, https://doi.org/10.3390/hydrology3010003, 2016.
Wharton, R. P., Hazen, G. A., Rau, R. N., and Best, D. L.: Advancements In Electromagnetic Propagation Logging, in: Proceedings Society of Petroleum Engineers Rocky Mountain Regional Meeting, 14–16 May 1980, Casper, Wyoming, Society of Petroleum Engineers, https://doi.org/10.2118/9041-MS, 1980.
Winstral, A., Elder, K., and Davis, R. E.: Spatial Snow Modeling of Wind-Redistributed Snow Using Terrain-Based Parameters, J. Hydrometeorol., 3, 524–538, https://doi.org/10.1175/1525-7541(2002)003<0524:SSMOWR>2.0.CO;2, 2002.
Wong, J., Han, L., Bancroft, J. C., and Stewart, R. R.: Automatic time-picking of first arrivals on noisy microseismic data, in: Proceedings Canadian Society of Exploration Geophysics Meeting, 13–15 October 2009, Olympic Park, Calgary, Canada, pp. 1–6, 2009.
Yildiz, S., Akyurek, Z., and Binley, A.: Quantifying snow water equivalent using terrestrial ground penetrating radar and unmanned aerial vehicle photogrammetry, Hydrol. Process., 35, 1–15, https://doi.org/10.1002/hyp.14190, 2021.
Yilmaz, Ö.: Seismic Data Analysis, Society of Exploration Geophysicists, Tulsa, OK, ISBN 978-1-56080-094-1, https://doi.org/10.1190/1.9781560801580, 2001.
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...