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August 21, 2025Open Access

Improving Snow Simulations through Improved Representations of Vegetation Conditions: Insights from High Resolution Simulations over California

AGAniket GuptaUniversity of Arizona ABAli BehrangiUniversity of Arizona MFMohammad FarmaniUniversity of Arizona

Key Points

Dynamic vegetation models enhance accuracy of snow water equivalent in California's basins, showing improved results.
Compared to previous models, the new approach with 1 km resolution captures more snowfall and better aligns with observations.
Using downscaled NLDAS-2 data improves snowfall estimates, highlighting the role of precise precipitation in snow modeling.
The findings emphasize the need for high-resolution vegetation data to optimize snow simulations in mountainous areas.

Abstract

Abstract. Most hydrological models underestimate snow water equivalent (SWE) in the mountainous western US. Key limitations may be 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 the Noah-MP version 5.0 with a dynamic vegetation module to the Sacramento and San Joaquin River Basins in California at 1 km resolution driven by forcing from Analysis of Record for Calibration (AORC) and NLDAS-2, downscaled 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 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 with prescribed leaf area index (LAI) and vegetation cover fraction produces too much SWE on the ground due mainly to the strong canopy shading effect despite more snow intercepted by the canopy. The dynamic vegetation module produces smaller LAI than the prescribed (more consistent with MODIS data) 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 performs the best. These findings highlight the importance of vegetation effects and downscaling of atmospheric forcing to increase the accuracy of snow modeling over the Southwest US.

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