Articles | Volume 19, issue 1
https://doi.org/10.5194/tc-19-393-2025
© Author(s) 2025. 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-19-393-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Brief communication: Monitoring snow depth using small, cheap, and easy-to-deploy snow–ground interface temperature sensors
Claire L. Bachand
CORRESPONDING AUTHOR
Earth and Environmental Sciences, Los Alamos National Laboratory, Los Alamos, NM, USA
Department of Biology and Wildlife, University of Alaska Fairbanks, Fairbanks, AK, USA
Chen Wang
Earth and Environmental Sciences, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
Baptiste Dafflon
Earth and Environmental Sciences, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
Lauren N. Thomas
Earth and Environmental Sciences, Los Alamos National Laboratory, Los Alamos, NM, USA
Department of Geography, University of Colorado Boulder, Boulder, CO, USA
Ian Shirley
Earth and Environmental Sciences, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
Sarah Maebius
Earth and Environmental Sciences, Los Alamos National Laboratory, Los Alamos, NM, USA
Department of Civil and Environmental Engineering, Princeton University, Princeton, NJ, USA
Colleen M. Iversen
Environmental Sciences Division and Climate Change Science Institute, Oak Ridge National Laboratory, Oak Ridge, TN, USA
Katrina E. Bennett
Earth and Environmental Sciences, Los Alamos National Laboratory, Los Alamos, NM, USA
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Xiang Huang, Yu Zhang, Bo Gao, Charles J. Abolt, Ryan L. Crumley, Cansu Demir, Richard P. Fiorella, Bob Busey, Bob Bolton, Scott L. Painter, and Katrina E. Bennett
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Predicting hydrological runoff in Arctic permafrost regions is difficult due to limited observations and complex terrain. We used a detailed physics-based model to improve runoff estimates in a Earth system land model. Our method improved runoff accuracy and worked well across two different Arctic regions. This helps make climate models more reliable for understanding water flow in permafrost areas under a changing climate.
Nathan Alec Conroy, Jeffrey M. Heikoop, Emma Lathrop, Dea Musa, Brent D. Newman, Chonggang Xu, Rachael E. McCaully, Carli A. Arendt, Verity G. Salmon, Amy Breen, Vladimir Romanovsky, Katrina E. Bennett, Cathy J. Wilson, and Stan D. Wullschleger
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The Cryosphere Discuss., https://doi.org/10.5194/tc-2023-8, https://doi.org/10.5194/tc-2023-8, 2023
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Carlotta Brunetti, John Lamb, Stijn Wielandt, Sebastian Uhlemann, Ian Shirley, Patrick McClure, and Baptiste Dafflon
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Shuang Ma, Lifen Jiang, Rachel M. Wilson, Jeff P. Chanton, Scott Bridgham, Shuli Niu, Colleen M. Iversen, Avni Malhotra, Jiang Jiang, Xingjie Lu, Yuanyuan Huang, Jason Keller, Xiaofeng Xu, Daniel M. Ricciuto, Paul J. Hanson, and Yiqi Luo
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The Cryosphere, 16, 719–736, https://doi.org/10.5194/tc-16-719-2022, https://doi.org/10.5194/tc-16-719-2022, 2022
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Haruko M. Wainwright, Sebastian Uhlemann, Maya Franklin, Nicola Falco, Nicholas J. Bouskill, Michelle E. Newcomer, Baptiste Dafflon, Erica R. Siirila-Woodburn, Burke J. Minsley, Kenneth H. Williams, and Susan S. Hubbard
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Jiancong Chen, Baptiste Dafflon, Anh Phuong Tran, Nicola Falco, and Susan S. Hubbard
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We develop a hybrid model to estimate the spatial distribution of the thickness of the soil layer, which also provides estimations of soil transport and soil production rates. We apply this model to two examples of hillslopes in the East River watershed in Colorado and validate the model. The results show that the north-facing (NF) hillslope has a deeper soil layer than the south-facing (SF) hillslope and that the hybrid model provides better accuracy than a machine-learning model.
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Short summary
Temporally continuous snow depth estimates are important for understanding changing snow patterns and impacts on frozen ground in the Arctic. In this work, we developed an approach to predict snow depth from variability in snow–ground interface temperature using small temperature sensors that are cheap and easy to deploy. This new technique enables spatially distributed and temporally continuous snowpack monitoring that has not previously been possible.
Temporally continuous snow depth estimates are important for understanding changing snow...