Submitted to: Trans American Geophysical Union
Publication Type: Abstract Only
Publication Acceptance Date: 8/20/2001
Publication Date: 8/20/2001
Citation: Prasad, R., Tarboton, D., Liston, G., Luce, C., and Seyfried, M.S. 2001. Testing a blowing snow model against distributed snow measurements at Upper Sheep Creek, Idaho, United States of America. Water Resources Research, v.37, p. 2825-2829. Interpretive Summary:
Technical Abstract: In this paper a physically-based numerical snow transport model (SnowTran-3D) was used to simulated snow drifting over a 30 m grid, and was compared to detailed snow water equivalence surveys on three dates within a small 0.25 km2 subwatershed., Upper Sheep Creek. Two precipitation scenarios and two vegetation scenarios were used to carry out four snow transport model runs in order to: (1) evaluate the blowing snow model, (2) evaluate the sensitivity of the snow transport model to precipitation and vegetation inputs, and (3) evaluate the linearity of snow accumulation patterns and the utility of the drift factor concept in distributed snow modeling. Spatial comparison methods consisted of (1) pointwise comparisons of measured and modeled snow, (2) visual comparisons of the spatial maps, (3) comparisons of the basinwide average, (4) comparisons of zonal averages in accumulation and scour zones, and (5) comparisons of distribution functions. We found that the basin average modeled snow water equivalence was in reasonable agreement with observations, and that visually the spatial pattern of snow accumulation was well represented except for a small pattern shift. In spite of the overall model success, pointwise comparisons between the modeled and observed snow water equivalence maps displayed significant errors. Observation-based drift factors were obtained from calibration using measured snow water equivalence maps and a physically-based snow melt model. The distributions of SnowTran-3D modeled drift factors from two precipitation scenarios on three dates were compared with the distribution of observation-based drift factors to evaluate the assumption of linearity.