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.
Understanding how the presence of a forest canopy influences the underlying snowpack is critical to making accurate model predictions of bulk snow density and snow water equivalent (SWE). To investigate the relative importance of forest processes on snow density and SWE, we applied the SUMMA model at three sites representing diverse snow climates in Colorado (USA), Oregon (USA), and Alberta (Canada) for 5 years. First, control simulations were run for open and forest sites. Comparisons to observations showed the uncalibrated model with NLDAS‐2 forcing performed reasonably. Then, experiments were completed to isolate how forest processes affected modelled snowpack density and SWE, including: (1) mass reduction due to interception loss, (2) changes in the phase and amount of water delivered from the canopy to the underlying snow, (3) varying new snow density from reduced wind speed, and (4) modification of incoming longwave and shortwave radiation. Delivery effects (2) increased forest snowpack density relative to open areas, often more than 30%. Mass effects (1) and wind effects (3) decreased forest snowpack density, but generally by less than 6%. The radiation experiment (4) yielded negligible to positive effects (i.e., 0%–10%) on snowpack density. Delivery effects on density were greatest at the warmest times in the season and at the warmest site (Oregon): higher temperatures increased interception and melted intercepted snow, which then dripped to the underlying snowpack. In contrast, mass effects and radiation effects were shown to have the greatest impact on forest‐to‐open SWE differences, yielding differences greater than 30%. The study highlights the importance of delivery effects in models and the need for new types of observations to characterize how canopies influence the flux of water to the snow surface.
more » « less- Award ID(s):
- 1761441
- NSF-PAR ID:
- 10392366
- Publisher / Repository:
- Wiley Blackwell (John Wiley & Sons)
- Date Published:
- Journal Name:
- Hydrological Processes
- Volume:
- 36
- Issue:
- 1
- ISSN:
- 0885-6087
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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