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Evaluation of the Snow Cover in the Soil, Vegetation, and Snow (SVS) Land Surface Model

Evaluation of the Snow Cover in the Soil, Vegetation, and Snow (SVS) Land Surface Model AbstractThe Soil, Vegetation, and Snow (SVS) land surface model was recently developed at Environment and Climate Change Canada (ECCC) for operational numerical weather prediction and hydrological forecasting. This study examined the performance of the snow scheme in the SVS model over multiple years at 10 well-instrumented sites from the Earth System Model–Snow Model Intercomparison Project (ESM-SnowMIP), which covers alpine, maritime, and taiga climates. The SVS snow scheme is a simple single-layer snowpack scheme that uses the force–restore method. Stand-alone, point-scale verification tests showed that the model is able to realistically reproduce the main characteristics of the snow cover at these sites, namely, snow water equivalent, density, snow depth, surface temperature, and albedo. SVS accurately simulated snow water equivalent, density, and snow depth at open sites, but exhibited lower performance for subcanopy snowpacks (forested sites). The lower performance was attributed mainly to the limitations of the compaction scheme and the absence of a snow interception scheme. At open sites, the SVS snow surface temperatures were well represented but exhibited a cold bias, which was due to poor representation at night. SVS produced a reasonably accurate representation of snow albedo, but there was a tendency to overestimate late winter albedo. Sensitivity tests suggested improvements associated with the snow melting formulation in SVS. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Hydrometeorology American Meteorological Society

Evaluation of the Snow Cover in the Soil, Vegetation, and Snow (SVS) Land Surface Model

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Publisher
American Meteorological Society
Copyright
Copyright © American Meteorological Society
ISSN
1525-7541
eISSN
1525-7541
DOI
10.1175/JHM-D-20-0249.1
Publisher site
See Article on Publisher Site

Abstract

AbstractThe Soil, Vegetation, and Snow (SVS) land surface model was recently developed at Environment and Climate Change Canada (ECCC) for operational numerical weather prediction and hydrological forecasting. This study examined the performance of the snow scheme in the SVS model over multiple years at 10 well-instrumented sites from the Earth System Model–Snow Model Intercomparison Project (ESM-SnowMIP), which covers alpine, maritime, and taiga climates. The SVS snow scheme is a simple single-layer snowpack scheme that uses the force–restore method. Stand-alone, point-scale verification tests showed that the model is able to realistically reproduce the main characteristics of the snow cover at these sites, namely, snow water equivalent, density, snow depth, surface temperature, and albedo. SVS accurately simulated snow water equivalent, density, and snow depth at open sites, but exhibited lower performance for subcanopy snowpacks (forested sites). The lower performance was attributed mainly to the limitations of the compaction scheme and the absence of a snow interception scheme. At open sites, the SVS snow surface temperatures were well represented but exhibited a cold bias, which was due to poor representation at night. SVS produced a reasonably accurate representation of snow albedo, but there was a tendency to overestimate late winter albedo. Sensitivity tests suggested improvements associated with the snow melting formulation in SVS.

Journal

Journal of HydrometeorologyAmerican Meteorological Society

Published: Jun 7, 2021

References