Articles | Volume 17, issue 8
https://doi.org/10.5194/tc-17-3435-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/tc-17-3435-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Brief communication: Comparison of in situ ephemeral snow depth measurements over a mixed-use temperate forest landscape
Holly Proulx
Department of Civil and Environmental Engineering, University of New Hampshire, Durham, NH 03824, USA
Jennifer M. Jacobs
CORRESPONDING AUTHOR
Department of Civil and Environmental Engineering, University of New Hampshire, Durham, NH 03824, USA
Earth Systems Research Center, Institute for the Study of Earth,
Oceans, and Space, University of New Hampshire, Durham, NH 03824, USA
Elizabeth A. Burakowski
Earth Systems Research Center, Institute for the Study of Earth,
Oceans, and Space, University of New Hampshire, Durham, NH 03824, USA
Eunsang Cho
Hydrological Sciences Laboratory, NASA Goddard Space Flight Center,
Greenbelt, MD 20771, USA
Earth System Science Interdisciplinary Center, University of Maryland, College Park, MD 20742, USA
Adam G. Hunsaker
Department of Civil and Environmental Engineering, University of New Hampshire, Durham, NH 03824, USA
Earth Systems Research Center, Institute for the Study of Earth,
Oceans, and Space, University of New Hampshire, Durham, NH 03824, USA
Franklin B. Sullivan
Earth Systems Research Center, Institute for the Study of Earth,
Oceans, and Space, University of New Hampshire, Durham, NH 03824, USA
Michael Palace
Earth Systems Research Center, Institute for the Study of Earth,
Oceans, and Space, University of New Hampshire, Durham, NH 03824, USA
Department of Earth Sciences, University of New Hampshire, Durham, NH 03824, USA
Cameron Wagner
Department of Civil and Environmental Engineering, University of New Hampshire, Durham, NH 03824, USA
Related authors
Holly Proulx, Jennifer M. Jacobs, Elizabeth A. Burakowski, Eunsang Cho, Adam G. Hunsaker, Franklin B. Sullivan, Michael Palace, and Cameron Wagner
The Cryosphere Discuss., https://doi.org/10.5194/tc-2022-7, https://doi.org/10.5194/tc-2022-7, 2022
Manuscript not accepted for further review
Short summary
Short summary
This study compares snow depth measurements from two manual instruments and an airborne platform in a field and forest. The manual instruments’ snow depths differed by 1 to 3 cm. The airborne measurements , which do not penetrate the leaf litter, were consistently shallower than either manual instrument. When combining airborne snow depth maps with manual density measurements, corrections may be required to create unbiased maps of snow properties.
Maxime Thomas, Thomas Moenaert, Julien Radoux, Baptiste Delhez, Eléonore du Bois d'Aische, Maëlle Villani, Catherine Hirst, Erik Lundin, François Jonard, Sébastien Lambot, Kristof Van Oost, Veerle Vanacker, Matthias B. Siewert, Carl-Magnus Mörth, Michael W. Palace, Ruth K. Varner, Franklin B. Sullivan, Christina Herrick, and Sophie Opfergelt
EGUsphere, https://doi.org/10.5194/egusphere-2025-3788, https://doi.org/10.5194/egusphere-2025-3788, 2025
This preprint is open for discussion and under review for The Cryosphere (TC).
Short summary
Short summary
This study examines the rate of permafrost degradation, in the form of the transition from intact well-drained palsa to fully thawed and inundated fen at the Stordalen mire, Abisko, Sweden. Across the 14 hectares of the palsa mire, we demonstrate a 5-fold acceleration of the degradation in 2019–2021 compared to previous periods (1970–2014) which might lead to a pool of 12 metric tons of organic carbon exposed annually for the topsoil (23 cm depth), and an increase of ~1.3%/year of GHG emissions.
Colleen Mortimer, Lawrence Mudryk, Eunsang Cho, Chris Derksen, Mike Brady, and Carrie Vuyovich
The Cryosphere, 18, 5619–5639, https://doi.org/10.5194/tc-18-5619-2024, https://doi.org/10.5194/tc-18-5619-2024, 2024
Short summary
Short summary
Ground measurements of snow water equivalent (SWE) are vital for understanding the accuracy of large-scale estimates from satellites and climate models. We compare two types of measurements – snow courses and airborne gamma SWE estimates – and analyze how measurement type impacts the accuracy assessment of gridded SWE products. We use this analysis to produce a combined reference SWE dataset for North America, applicable for future gridded SWE product evaluations and other applications.
Annelise Waling, Adam Herrington, Katharine Duderstadt, Jack Dibb, and Elizabeth Burakowski
Weather Clim. Dynam., 5, 1117–1135, https://doi.org/10.5194/wcd-5-1117-2024, https://doi.org/10.5194/wcd-5-1117-2024, 2024
Short summary
Short summary
Atmospheric rivers (ARs) are channel-shaped features within the atmosphere that carry moisture from the mid-latitudes to the poles, bringing warm temperatures and moisture that can cause melt in the Arctic. We used variable-resolution grids to model ARs around the Greenland ice sheet and compared this output to uniform-resolution grids and reanalysis products. We found that the variable-resolution grids produced ARs and precipitation that were more similar to observation-based products.
Eunsang Cho, Megan Verfaillie, Jennifer M. Jacobs, Adam G. Hunsaker, Franklin B. Sullivan, Michael Palace, and Cameron Wagner
EGUsphere, https://doi.org/10.5194/egusphere-2024-1530, https://doi.org/10.5194/egusphere-2024-1530, 2024
Short summary
Short summary
Uncrewed Aerial Systems (UAS) lidar and structure-from-motion (SfM) photogrammetry are effective methods for mapping high-resolution snow depths. However, there are limited studies comparing their performance across different surface features and tracking spatial patterns of snowpack changes over time. Our study found that UAS lidar outperformed SfM photogrammetry. With limited wind effects, the snow spatial structure captured by UAS lidar remained temporally stable throughout the snow season.
Justin M. Pflug, Melissa L. Wrzesien, Sujay V. Kumar, Eunsang Cho, Kristi R. Arsenault, Paul R. Houser, and Carrie M. Vuyovich
Hydrol. Earth Syst. Sci., 28, 631–648, https://doi.org/10.5194/hess-28-631-2024, https://doi.org/10.5194/hess-28-631-2024, 2024
Short summary
Short summary
Estimates of 250 m of snow water equivalent in the western USA and Canada are improved by assimilating observations representative of a snow-focused satellite mission with a land surface model. Here, by including a gap-filling strategy, snow estimates could be improved in forested regions where remote sensing is challenging. This approach improved estimates of winter maximum snow water volume to within 4 %, on average, with persistent improvements to both spring snow and runoff in many regions.
Eunsang Cho, Yonghwan Kwon, Sujay V. Kumar, and Carrie M. Vuyovich
Hydrol. Earth Syst. Sci., 27, 4039–4056, https://doi.org/10.5194/hess-27-4039-2023, https://doi.org/10.5194/hess-27-4039-2023, 2023
Short summary
Short summary
An airborne gamma-ray remote-sensing technique provides reliable snow water equivalent (SWE) in a forested area where remote-sensing techniques (e.g., passive microwave) typically have large uncertainties. Here, we explore the utility of assimilating the gamma snow data into a land surface model to improve the modeled SWE estimates in the northeastern US. Results provide new insights into utilizing the gamma SWE data for enhanced land surface model simulations in forested environments.
Eunsang Cho, Carrie M. Vuyovich, Sujay V. Kumar, Melissa L. Wrzesien, and Rhae Sung Kim
The Cryosphere, 17, 3915–3931, https://doi.org/10.5194/tc-17-3915-2023, https://doi.org/10.5194/tc-17-3915-2023, 2023
Short summary
Short summary
As a future snow mission concept, active microwave sensors have the potential to measure snow water equivalent (SWE) in deep snowpack and forested environments. We used a modeling and data assimilation approach (a so-called observing system simulation experiment) to quantify the usefulness of active microwave-based SWE retrievals over western Colorado. We found that active microwave sensors with a mature retrieval algorithm can improve SWE simulations by about 20 % in the mountainous domain.
Eunsang Cho, Carrie M. Vuyovich, Sujay V. Kumar, Melissa L. Wrzesien, Rhae Sung Kim, and Jennifer M. Jacobs
Hydrol. Earth Syst. Sci., 26, 5721–5735, https://doi.org/10.5194/hess-26-5721-2022, https://doi.org/10.5194/hess-26-5721-2022, 2022
Short summary
Short summary
While land surface models are a common approach for estimating macroscale snow water equivalent (SWE), the SWE accuracy is often limited by uncertainties in model physics and forcing inputs. In this study, we found large underestimations of modeled SWE compared to observations. Precipitation forcings and melting physics limitations dominantly contribute to the SWE underestimations. Results provide insights into prioritizing strategies to improve the SWE simulations for hydrologic applications.
Holly Proulx, Jennifer M. Jacobs, Elizabeth A. Burakowski, Eunsang Cho, Adam G. Hunsaker, Franklin B. Sullivan, Michael Palace, and Cameron Wagner
The Cryosphere Discuss., https://doi.org/10.5194/tc-2022-7, https://doi.org/10.5194/tc-2022-7, 2022
Manuscript not accepted for further review
Short summary
Short summary
This study compares snow depth measurements from two manual instruments and an airborne platform in a field and forest. The manual instruments’ snow depths differed by 1 to 3 cm. The airborne measurements , which do not penetrate the leaf litter, were consistently shallower than either manual instrument. When combining airborne snow depth maps with manual density measurements, corrections may be required to create unbiased maps of snow properties.
Jennifer M. Jacobs, Adam G. Hunsaker, Franklin B. Sullivan, Michael Palace, Elizabeth A. Burakowski, Christina Herrick, and Eunsang Cho
The Cryosphere, 15, 1485–1500, https://doi.org/10.5194/tc-15-1485-2021, https://doi.org/10.5194/tc-15-1485-2021, 2021
Short summary
Short summary
This pilot study describes a proof of concept for using lidar on an unpiloted aerial vehicle to map shallow snowpack (< 20 cm) depth in open terrain and forests. The 1 m2 resolution snow depth map, generated by subtracting snow-off from snow-on lidar-derived digital terrain models, consistently had 0.5 to 1 cm precision in the field, with a considerable reduction in accuracy in the forest. Performance depends on the point cloud density and the ground surface variability and vegetation.
Cited articles
Berezovskaya, S. and D. L. Kane: Measuring snow water equivalent for
hydrological applications: part 1, accuracy of observations, in: Proceedings of
the 16th International Northern Research Basins Symposium and Workshop, 27 August–2 September, 2007,
Petrozavodsk, Russia, 29–37, 2007.
Bongio, M., Arslan, A. N., Tanis, C. M., and De Michele, C.: Snow depth time series retrieval by time-lapse photography: Finnish and Italian case studies, The Cryosphere, 15, 369–387, https://doi.org/10.5194/tc-15-369-2021, 2021.
Clyde, G. D.: Circular No. 99 – Utah Snow Sampler and Scales for Measuring Water Content of Snow, UAES Circulars, Paper 90, https://digitalcommons.usu.edu/uaes_circulars/90 (last access: 22 August 2023), 1932.
Derry, J., Kane, D., Lilly, M., and Toniolo, H.: Snow-course measurement
methods, North Slope, Alaska, University of Alaska Fairbanks, Water and
Environmental Research Center, Report INE/WERC, 15, http://www.arctic-transportation.org/doc/ADOT_NS_RPT0807_Final.pdf (last access: 22 August 2023), 2009.
Dixon, D. and Boon, S.: Comparison of the SnowHydro snow sampler with existing snow tube designs, Hydrol. Process., 26, 2555–2562, 2012.
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, 1998.
Farnes, P. E., Goodison, B. E., Peterson, N. R., and Richards, R. P.:
Metrication of manual snow sampling equipment, Final report Western Snow
Conference, 19–21, https://westernsnowconference.org/sites/westernsnowconference.org/PDFs/1982Farnes.pdf (last access: 22 August 2023), 1983.
Gandahl, R.: Determination of the depth of soil freezing with a new
frost meter, Rapport, 20, 3–15, 1957 (in Swedish).
Goodison, B., Glynn, J., Harvey, K., and Slater, J.: Snow surveying in Canada: A perspective, Can. Water Resour. J., 12, 27–42, 1987.
Kaspari, M. and Yanoviak, S. P.: Biogeography of litter depth in
tropical forests: evaluating the phosphorus growth rate hypothesis,
Funct. Ecol., 22, 919–923, 2008.
Kinar, N. and Pomeroy, J.: Measurement of the physical properties of
the snowpack, Rev. Geophys., 53, 481–544, 2015.
Kopp, M., Tuo, Y., and Disse, M.: Fully automated snow depth
measurements from time-lapse images applying a convolutional neural network,
Sci. Total Environ., 697, 134213, https://doi.org/10.1016/j.scitotenv.2019.134213, 2019.
Leppänen, L., Kontu, A., Hannula, H.-R., Sjöblom, H., and
Pulliainen, J.: Sodankylä manual snow survey program, Geosci.
Instrum. Meth., 5, 163–179, 2016.
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.
López-Moreno, J. I., Leppänen, L., Luks, B., Holko, L., Picard, G., Sanmiguel-Vallelado, A., Alonso-González, E., Finger, D. C., and Arslan, A. N.: Intercomparison of
measurements of bulk snow density and water equivalent of snow cover with
snow core samplers: Instrumental bias and variability induced by observers,
Hydrol. Process., 34, 3120–3133, https://doi.org/10.1002/hyp.13785, 2020.
Perron, C. J., Bennett, K., and Lee, T. D.: Forest stewardship plan:
Thompson farm, NH, University of New Hampshire, Ossipee Mountain Land Company, West Ossipee,
https://universitysystemnh.sharepoint.com/teams/COLSASocialMedia/Shared Documents/Forms/AllItems.aspx?id=%2Fteams%2FCOLSASocialMedia%2FShared Documents%2FWebsite%2Fthompson%2Dfarm%2Dplan%2Epdf&parent=%2Fteams%2FCOLSASocialMedia%2FShared%20Documents%2FWebsite&p=true&ga=1 (last access: 16 August 2023), 2004.
Pirazzini, R., Leppänen, L., Picard, G., Lopez-Moreno, J. I., Marty, C., Macelloni, G., Kontu, A., Von Lerber, A., Tanis, C. M., Schneebeli, M., and De Rosnay, P.: European in-situ snow measurements: practices and purposes, Sensors, 18, 1–51, https://doi.org/10.3390/s18072016, 2018.
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, 2017.
Sturm, M. and Holmgren, J.: An automatic snow depth probe for field
validation campaigns, Water Resour. Res., 54, 9695–9701, 2018.
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, 2010.
Toose, P., King, J., Silis, A., and Derksen, C.: TVCSnow 2017–2018 tundra snow depth probe measurements (Version 1), Zenodo [data set], https://doi.org/10.5281/zenodo.4021328, 2020.
Walker, B., Wilcox, E. J., and Marsh, P.: Accuracy assessment of late winter snow depth mapping for tundra environments using Structure-from-Motion photogrammetry, Arctic Sci., 7, 588–604,
https://doi.org/10.1139/as-2020-0006, 2020.
Short summary
This study compares snow depth measurements from two manual instruments in a field and forest. Snow depths measured using a magnaprobe were typically 1 to 3 cm deeper than those measured using a snow tube. These differences were greater in the forest than in the field.
This study compares snow depth measurements from two manual instruments in a field and forest....