|Prasad, R - UTAH STATE UNIVERSITY|
|Tarboton, D - UTAH STATE UNIVERSITY|
|Liston, Glen - COLORADO STATE UNIVERSITY|
|Luce, Charles - US FOREST SERVICE|
Submitted to: Water Resources Research
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: October 6, 2000
Publication Date: N/A
Interpretive Summary: In regions where the vegetative cover is low relative to the depth of snow accumulation, wind may greatly influence the distribution of snow cover. Where there is significant topography, snow often accumulates in lee positions. Melt dynamics of these accumulation drifts is very different from those where the snow cover is uniform, as is generally assumed when snowmelt runoff is estimated for potential power generation and irrigation. The effects of drifting can be accounted for the drifts can be described. We tested a model designed to simulate snow drift formation by comparing simulated and measured snow depths and water contents in a snow drift in the Reynolds Creek experimental watershed. We found that the model simulated the drift shape and snow distribution in the basin reasonably well, but that the actual position of the drift was shifted somewhat. This may not be a problem for many simulation questions. We also found that we could reasonably extrapolate results for a single year to wetter or drier years.
Technical Abstract: In this paper a physically based snow transport model was used to simulate snow drifting over a 30 m grid and was compared to detailed snow water equivalence (SWE) surveys on three dates within a small 0.25 sq. km subwatershed, Upper Sheep Creek. We found that the basin average modeled SWE was in reasonable agreement with observations and that visually the spatial pattern of snow accumulation was well represented except for a pattern shift. Pointwise comparisons between the modeled and observed SWE maps displayed significant errors. The distribution function of the modeled drift factors from two precipitation scenarios on three dates were compared with the distribution function of the observation-based drift factors obtained previously by calibration to evaluate the assumption of linearity. We found only a 14% reduction in explained variance in the distribution function of drift factors for a 69% increase in precipitation, suggesting that the simplification provided by the use of drift factor distributions will result in errors that are tolerable in many cases.