Articles | Volume 20, issue 10
https://doi.org/10.5194/tc-20-5973-2026
© 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-20-5973-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
15 Oct 2026
Research article |
| 15 Oct 2026
| 15 Oct 2026
Improving snow simulations through improved representations of vegetation conditions: insights from high resolution simulations over California
Aniket Gupta
CORRESPONDING AUTHOR
Department of Hydrology and Atmospheric Sciences, The University of Arizona, Tucson, AZ, United States
Ali Behrangi
Department of Hydrology and Atmospheric Sciences, The University of Arizona, Tucson, AZ, United States
Mohammad Farmani
Department of Hydrology and Atmospheric Sciences, The University of Arizona, Tucson, AZ, United States
Patrick Broxton
School of Natural Resources and the Environment, The University of Arizona, Tucson, AZ, United States
Guo-Yue Niu
CORRESPONDING AUTHOR
Department of Hydrology and Atmospheric Sciences, The University of Arizona, Tucson, AZ, United States
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Short summary
Most hydrological models tend to underestimate snow over the mountainous southwestern US. This may result from uncertainties in precipitation forcing and/or inadequate representation of snow–vegetation interactions, particularly canopy interception and shading. Through model experiments, we demonstrate how downscaled meteorological forcing and improved vegetation representation can improve snow simulations over the southwestern US.
Most hydrological models tend to underestimate snow over the mountainous southwestern US. This...
