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Intermittent snow depth observations can be leveraged with data assimilation (DA) to improve model estimates of snow water equivalent (SWE) at the point scale. A key consideration for scaling a DA system to the basin scale is its performance at locations with forest cover – where canopy-snow interactions affect snow accumulation and melt, yet are difficult to model and parameterize. We implement a particle filter (PF) assimilation technique to assimilate intermittent depth observations into the Flexible Snow Model (FSM2), and validate the output against snow density and SWE measurements across paired forest and open sites, at two locations with different climates and forest structures. Assimilation reduces depth error by 70-90%, density error by 5-30%, and SWE error by 50-70% at forest locations (relative to control model runs) and brings errors in-line with adjacent open sites. The PF correctly simulates the seasonal evolution of the snowpack under forest canopy, including cases where interception lowers SWE in the forest during accumulation, and shading reduces melt during the ablation season (relative to open sites). The snow model outputs are sensitive to canopy-related parameters, but DA reduces the range in depth and SWE estimates resulting from spatial variations or uncertainties in these parameters by more than 50%. The results demonstrate that the challenge of accurately measuring, estimating, or calibrating canopy-related parameters is reduced when snow depth observations are assimilated to improve SWE estimates.</p>
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Near-field variability of evaposublimation in a montane conifer forest
Methods that combine in-situ measurements, statistical methods, and model simulations with remotely sensed data provide a pathway for improving the robustness of surface flux products. For this research, we acquired eddy-covariance fluxes along a five-tower transect in a snowy montane forest over three consecutive winters to characterize near-field variability of the subcanopy environment. The novel experiment design enabled discriminating near-field evaposublimation sources. Boosted regression trees reveal that the predictive capacity of state variables change with season and storm cycle frequency. High rates of post-storm evaposublimation of canopy-intercepted snow at this site were constrained by short residence time of snow in the canopy due to throughfall and melt. The snow melt-out date for open vs. closed canopy conditions depended on total snowfall accumulation. Compared with low accumulation years, the snow melt-out date under the dense canopy during the high accumulation winter was later than for the open area, as shading became more important later in the season. The field experiments informed an environmental response function that was used to integrate ERA5-Land latent heat flux data at 20-km nominal resolution with USFS Tree Canopy Fraction data at 30-m resolution and showed near-field flux variability that was not resolved in model simulations. Previous evaposublimation results from experiments in alpine and subalpine environments do not directly translate to a montane forest due to differences in process rates.
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- Award ID(s):
- 1848019
- PAR ID:
- 10542627
- Publisher / Repository:
- Frontiers
- Date Published:
- Journal Name:
- Frontiers in Earth Science
- Volume:
- 11
- ISSN:
- 2296-6463
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3389/feart.2023.1249113
