Local modeling

Modeling how snow distribution depends on terrain, elevation, and vegetation will improve Earth system models

Comprehensive data from multiple seasons of field research in Arctic Alaska will help address uncertainties in Earth system and climate change models regarding snow cover in the region and its impacts on water and the environment.

“Snow cover and its distribution affect not only the Arctic but also global energy budgets, and so how it changes is critically important to understanding how the future global climate will change,” said Katrina Bennett, main author of the article in The Cryosphere. Bennett is a principal investigator at Los Alamos National Laboratory for the Department of Energy’s Arctic Next Generation Ecosystem Experiment project. “Our statistical model fills gaps in understanding the spatial distribution of snow.”

The research found that spatial distribution is most strongly dependent on vegetation, elevation, and landscape features, such as stream banks and banks – areas of topographic variability where shrubs grow and the snow accumulates.

Based on random forest machine learning, the statistical model characterizes the spatial pattern of late winter snow distribution and identifies key factors controlling the spatial distribution. The model also predicts snow distribution for local study sites and can be generalized to the entire region.

Bennett said the analysis will be useful for validating physics-based hydrological models of permafrost, such as the Advanced Earth Simulator being developed at Los Alamos. The work will also help validate and provide a better representation of snow redistribution in the land surface model in the Department of Energy’s Energetic Exascale Earth System Model.

“Ultimately, this will increase our understanding of the changing hydrology, topography, and vegetation dynamics in the Arctic and sub-Arctic,” Bennett said.

The seasons under the snow

The multi-institutional research team, which included members from Los Alamos, University of Alaska Fairbanks, Lawrence Berkeley National Laboratory, Oak Ridge National Laboratory and University of Wisconsin-Madison, carried out snow surveys in the spring of 2017-2019 at two small sites. on the Seward Peninsula.

“We would like to thank Mary’s Igloo, Sitnasuak and Council Native Corporation for their guidance and allowing us to conduct our research on their traditional lands,” Bennett said.

Fieldwork focused on collecting measurements of snow depth and density at the end of winter to calculate the amount of water contained in the snowpack. These measurements better characterize the impacts of snow cover on water and temperature than snow depth measurements.

To create a snow distribution model, the team estimated landscape factors for topography, vegetation, and wind, then quantified their impacts on snow distribution using three statistical models.

Funding: Department of Energy Office of Science, Office of Biological and Environmental Research through the Next Generation Ecosystem Experiment (NGEE) Arctic Project.

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Material provided by DOE/Los Alamos National Laboratory. Note: Content may be edited for style and length.