In the U.S., a dedicated system of snow measurement stations and snowpack modeling products is available to estimate the snow water equivalent (SWE) throughout the winter season. In other regions of the world that depend on snowmelt for water resources, snow data can be scarce, and these regions are vulnerable to drought or flood conditions. Even in the U.S., water resource management is hampered by limited snow data in certain regions, as evident by the 2011 Missouri Basin flooding due in large part to the significant Plains snowpack. Satellite data could potentially provide important information in under-sampled areas. This study compared the daily AMSR-E and SSM/I SWE products over nine winter seasons to spatially distributed, modeled output SNODAS summed over 2100 watersheds in the conterminous U.S. Results show large areas where the passive microwave retrievals are highly correlated to the SNODAS data, particularly in the northern Great Plains and southern Rocky Mountain regions. However, the passive microwave SWE is significantly lower than SNODAS in heavily forested areas, and regions that typically receive a deep snowpack. The best correlations are associated with basins in which maximum annual SWE is less than 200 mm, and forest fraction is less than 20%. Even in many watersheds with poor correlations between the passive microwave data and SNODAS maximum annual SWE values, the overall pattern of accumulation and ablation did show good agreement and therefore may provide useful hydrologic information on melt timing and season length.
Abstract. The Snow Ensemble Uncertainty Project (SEUP) is an effort
to establish a baseline characterization of snow water equivalent (SWE)
uncertainty across North America with the goal of informing global snow
observational needs. An ensemble-based modeling approach, encompassing a
suite of current operational models is used to assess the uncertainty in
SWE and total snow storage (SWS) estimation over North America during the
2009–2017 period. The highest modeled SWE uncertainty is observed in
mountainous regions, likely due to the relatively deep snow, forcing
uncertainties, and variability between the different models in resolving the
snow processes over complex terrain. This highlights a need for
high-resolution observations in mountains to capture the high spatial SWE
variability. The greatest SWS is found in Tundra regions where, even though
the spatiotemporal variability in modeled SWE is low, there is considerable
uncertainty in the SWS estimates due to the large areal extent over which
those estimates are spread. This highlights the need for high accuracy in
snow estimations across the Tundra. In midlatitude boreal forests, large
uncertainties in both SWE and SWS indicate that vegetation–snow impacts are
a critical area where focused improvements to modeled snow estimation
efforts need to be made. Finally, the SEUP results indicate that SWE
uncertainty is driving runoff uncertainty, and measurements may be beneficial
in reducing uncertainty in SWE and runoff, during the melt season at high
latitudes (e.g., Tundra and Taiga regions) and in the western mountain
regions, whereas observations at (or near) peak SWE accumulation are more
helpful over the midlatitudes.
Shugong (2021) Snow Ensemble Uncertainty Project (SEUP): quantification of snow water equivalent uncertainty across North America via ensemble land surface modeling. The Cryosphere, 15 (2). pp. 771-791.
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