skip to main content


Title: Snow interception modelling: Isolated observations have led to many land surface models lacking appropriate temperature sensitivities
Abstract

When formulating a hydrologic model, scientists rely on parameterizations of multiple processes based on field data, but literature review suggests that more frequently people select parameterizations that were included in pre‐existing models rather than re‐evaluating the underlying field experiments. Problems arise when limited field data exist, when “trusted” approaches do not get reevaluated, and when sensitivities fundamentally change in different environments. The physics and dynamics of snow interception by conifers is just such a case, and it is critical to simulation of the water budget and surface albedo. The most commonly used interception parameterization is based on data from four trees from one site, but results from this field study are not directly transferable to locations with relatively warmer winters, where the dominant processes differ dramatically. Here, we combine a literature review with model experiments to demonstrate needed improvements. Our results show that the choice of model form and parameters can vary the fraction of snow lost through interception by as much as 30%. In most simulations, the warming of mean winter temperatures from −7 to 0°C reduces the modelled fraction of snow under the canopy compared to the open, but the magnitude of simulated decrease varies from about 10% to 40%. The range of results is even larger when considering models that neglect the melting of in‐canopy snow in higher‐humidity environments where canopy sublimation plays less of a role. Thus, we recommend that all models represent canopy snowmelt and include representation of increased loading due to increased adhesion and cohesion when temperatures rise from −3 to 0°C. In addition to model improvements, field experiments across climates and forest types are needed to investigate how to best model the combination of dynamically changing forest cover and snow cover to better understand and predict changes to albedo and water supplies.

 
more » « less
Award ID(s):
1703663
NSF-PAR ID:
10375339
Author(s) / Creator(s):
 ;  ;  ;  ;  ;  
Publisher / Repository:
Wiley Blackwell (John Wiley & Sons)
Date Published:
Journal Name:
Hydrological Processes
Volume:
35
Issue:
7
ISSN:
0885-6087
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. Abstract

    In the Arctic, winter soil temperatures exert strong control over mean annual soil temperature and winter CO2emissions. In tundra ecosystems there is evidence that plant canopy influences on snow accumulation alter winter soil temperatures. By comparison, there has been relatively little research examining the impacts of heterogeneity in boreal forest cover on soil temperatures. Using seven years of data from six sites in northeastern Siberia that vary in stem density we show that snow-depth and forest canopy cover exert equally strong control on cumulative soil freezing degrees days (FDDsoil). Together snow depth and canopy cover explain approximately 75% of the variance in linear models of FDDsoiland freezingn-factors (nf; calculated as the quotient of FDDsoiland FDDair), across sites and years. Including variables related to air temperature, or antecedent soil temperatures does not substantially improve models. The observed increase in FDDsoilwith canopy cover suggests that canopy interception of snow or thermal conduction through trees may be important for winter soil temperature dynamics in forested ecosystems underlain by continuous permafrost. Our results imply that changes in Siberian larch forest cover that arise from climate warming or fire regime changes may have important impacts on winter soil temperature dynamics.

     
    more » « less
  2. Abstract

    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
  3. 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.

     
    more » « less
  4. 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.

     
    more » « less
  5. Improving high-resolution (meter-scale) mapping of snow-covered areas in complex and forested terrains is critical to understanding the responses of species and water systems to climate change. Commercial high-resolution imagery from Planet Labs, Inc. (Planet, San Francisco, CA, USA) can be used in environmental science, as it has both high spatial (0.7–3.0 m) and temporal (1–2 day) resolution. Deriving snow-covered areas from Planet imagery using traditional radiometric techniques have limitations due to the lack of a shortwave infrared band that is needed to fully exploit the difference in reflectance to discriminate between snow and clouds. However, recent work demonstrated that snow cover area (SCA) can be successfully mapped using only the PlanetScope 4-band (Red, Green, Blue and NIR) reflectance products and a machine learning (ML) approach based on convolutional neural networks (CNN). To evaluate how additional features improve the existing model performance, we: (1) build on previous work to augment a CNN model with additional input data including vegetation metrics (Normalized Difference Vegetation Index) and DEM-derived metrics (elevation, slope and aspect) to improve SCA mapping in forested and open terrain, (2) evaluate the model performance at two geographically diverse sites (Gunnison, Colorado, USA and Engadin, Switzerland), and (3) evaluate the model performance over different land-cover types. The best augmented model used the Normalized Difference Vegetation Index (NDVI) along with visible (red, green, and blue) and NIR bands, with an F-score of 0.89 (Gunnison) and 0.93 (Engadin) and was found to be 4% and 2% better than when using canopy height- and terrain-derived measures at Gunnison, respectively. The NDVI-based model improves not only upon the original band-only model’s ability to detect snow in forests, but also across other various land-cover types (gaps and canopy edges). We examined the model’s performance in forested areas using three forest canopy quantification metrics and found that augmented models can better identify snow in canopy edges and open areas but still underpredict snow cover under forest canopies. While the new features improve model performance over band-only options, the models still have challenges identifying the snow under trees in dense forests, with performance varying as a function of the geographic area. The improved high-resolution snow maps in forested environments can support studies involving climate change effects on mountain ecosystems and evaluations of hydrological impacts in snow-dominated river basins. 
    more » « less