Improving snow simulations through improved representations of vegetation conditions: insights from high resolution simulations over California
- 1University of Arizona (Tucson, United States of America)
Abstract
Most hydrological models underestimate snow water equivalent (SWE) in the mountainous western US. Key limitations may include inadequate representation of spatial variability due to coarse-resolution precipitation input and inadequate representations of snow-vegetation interactions. Vegetation affects snow dynamics through snow interception, throughfall/unloading, and energy transfer through the canopy, yet basin-scale studies on the vegetation effects are limited. To address this issue, we applied Noah-MP version 5.0 with a dynamic vegetation module over the Sacramento and San Joaquin River Basins in California at 1 km resolution. The model was driven by the Analysis of Record for Calibration (AORC) forcing at its native 1 km resolution and by the NLDAS-2 forcing downscaled from ∼ 12 to 1 km using the WRF-Hydro Meteorological Forcing Engine. We carried out eight model experiments driven by the two forcing datasets and two vegetation schemes over the two Basins. Compared to the 4 km PRISM and 1 km AORC, the downscaled 1 km NLDAS-2 forcing data shows higher precipitation and lower temperatures over the mountains, resulting in more snowfall and SWE, which is more consistent with in-situ SWE observations. Using the 1 km NLDAS-2 forcing, the default vegetation scheme that prescribes leaf area index (LAI) and vegetation cover fraction, produces excessive SWE on the ground, mainly because the strong canopy shading (too high LAI) reduces snowmelt, even though more snowfall is intercepted by the canopy. The dynamic vegetation module produces smaller LAI than the prescribed (more consistent with MODIS LAI) and thus leads to more shortwave radiation reaching the snow surface, thereby enhancing episodic melting during the accumulation season. Validation against in-situ snow data suggests that the use of dynamic vegetation model and the downscaled NLDAS-2 data perform the best. These findings highlight the importance of vegetation effects and downscaling of atmospheric forcing to improve the accuracy of snow modeling over the Southwest US.
Versions
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BibTeX
@article{gupta2026improving,
title = {{Improving snow simulations through improved representations of vegetation conditions: insights from high resolution simulations over California}},
author = {Aniket Gupta and Ali Behrangi and Mohammad Farmani and Patrick Broxton and Guo-Yue Niu},
journal = {The Cryosphere},
year = {2026},
month = oct,
doi = {10.5194/tc-20-5973-2026},
url = {https://tc.copernicus.org/articles/20/5973/2026/},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Improving snow simulations through improved representations of vegetation conditions: insights from high resolution simulations over California
AU - Aniket Gupta
AU - Ali Behrangi
AU - Mohammad Farmani
AU - Patrick Broxton
AU - Guo-Yue Niu
PY - 2026
DA - 2026/10/15
JO - The Cryosphere
DO - 10.5194/tc-20-5973-2026
UR - https://tc.copernicus.org/articles/20/5973/2026/
AB - Most hydrological models underestimate snow water equivalent (SWE) in the mountainous western US. Key limitations may include inadequate representation of spatial variability due to coarse-resolution precipitation input and inadequate representations of snow-vegetation interactions. Vegetation affects snow dynamics through snow interception, throughfall/unloading, and energy transfer through the canopy, yet basin-scale studies on the vegetation effects are limited. To address this issue, we applied Noah-MP version 5.0 with a dynamic vegetation module over the Sacramento and San Joaquin River Basins in California at 1 km resolution. The model was driven by the Analysis of Record for Calibration (AORC) forcing at its native 1 km resolution and by the NLDAS-2 forcing downscaled from ∼ 12 to 1 km using the WRF-Hydro Meteorological Forcing Engine. We carried out eight model experiments driven by the two forcing datasets and two vegetation schemes over the two Basins. Compared to the 4 km PRISM and 1 km AORC, the downscaled 1 km NLDAS-2 forcing data shows higher precipitation and lower temperatures over the mountains, resulting in more snowfall and SWE, which is more consistent with in-situ SWE observations. Using the 1 km NLDAS-2 forcing, the default vegetation scheme that prescribes leaf area index (LAI) and vegetation cover fraction, produces excessive SWE on the ground, mainly because the strong canopy shading (too high LAI) reduces snowmelt, even though more snowfall is intercepted by the canopy. The dynamic vegetation module produces smaller LAI than the prescribed (more consistent with MODIS LAI) and thus leads to more shortwave radiation reaching the snow surface, thereby enhancing episodic melting during the accumulation season. Validation against in-situ snow data suggests that the use of dynamic vegetation model and the downscaled NLDAS-2 data perform the best. These findings highlight the importance of vegetation effects and downscaling of atmospheric forcing to improve the accuracy of snow modeling over the Southwest US.
SN - 1994-0424
ER -