Articles | Volume 20, issue 6
https://doi.org/10.5194/tc-20-3467-2026
© Author(s) 2026. 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-20-3467-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
17 Jun 2026
Research article |
| 17 Jun 2026
| 17 Jun 2026
Airborne lidar and machine learning reveal decreased snow depth in burned forests
Arielle Koshkin
CORRESPONDING AUTHOR
Hydrologic Science and Engineering, Colorado School of Mines, Golden, CO 80401, USA
Institute for Arctic and Alpine Research, University of Colorado, Boulder, CO 80309, USA
Adrienne M. Marshall
Hydrologic Science and Engineering, Colorado School of Mines, Golden, CO 80401, USA
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Surface hoar crystals grow on top of snowpacks overnight. Little work has focused on climatic conditions leading to surface hoar. We use data from three field campaigns in the Colorado Rockies to investigate. We show that surface hoar events decline as the climate warms, and that there may be 14 % fewer events per year in the future. There is still work needed to reconcile measured amounts of surface hoar crystals, models, and the relationship with turbulent air.
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Surface hoar crystals grow on top of snowpacks overnight. Little work has focused on climatic conditions leading to surface hoar. We use data from three field campaigns in the Colorado Rockies to investigate. We show that surface hoar events decline as the climate warms, and that there may be 14 % fewer events per year in the future. There is still work needed to reconcile measured amounts of surface hoar crystals, models, and the relationship with turbulent air.
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We assessed the U.S. National Hydrologic Model's ability to simulate several components of the water cycle using multiple datasets of environmental variables. We find that the model's accuracy in streamflow simulation is positively and negatively impacted by the additional constraints, and more model parameters are identified as important. Our results inform operational hydrologic modeling by illuminating the complexities of using the continually expanding suite of data products.
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Snow water equivalent (SWE) is a critical water resource to many regions globally. Estimating SWE remains a challenge in hydrology highlighting the need for consistent evaluation frameworks. This study applied a standard approach for SWE evaluation across a range of datasets in the western US, using the Airborne Snow Observatory (ASO) SWE dataset as the reference observational dataset. We outline and demonstrate an example of a community evaluation protocol using datasets in this study.
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Short summary
Wildfires are burning higher in elevation and changing how snow accumulates and melts, disrupting the magnitude and timing of streamflow. Using machine learning and high resolution snow maps, we found that burned forests hold less snow compared to unburned forests, especially in spring, at higher elevations, and on south-facing slopes. These results show how fire reshapes mountain snowpacks, with important implications for water resources in a warming climate.
Wildfires are burning higher in elevation and changing how snow accumulates and melts,...
