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ARS Home » Pacific West Area » Boise, Idaho » Northwest Watershed Research Center » Research » Publications at this Location » Publication #207463

Title: Modeling surface winds in mountainous catchments as a function of topography and vegetation

item Marks, Daniel
item Winstral, Adam

Submitted to: Trans American Geophysical Union
Publication Type: Abstract Only
Publication Acceptance Date: 12/20/2006
Publication Date: 12/20/2006
Citation: Marks, D., and Winstral, A. 2006. Modeling surface winds in mountainous catchments as a function of topography and vegetation. EOS Transactions of the American Geophysical Union, 87(52) Fall Meeting Supplement, ABS C21B-1173.

Interpretive Summary:

Technical Abstract: In order to develop accurate distributed hydrological models, spatially accurate meteorological forcing fields are required. In mountainous basins, elevation and topography strongly influence temperature, precipitation, vapor pressure, and wind. At the watershed scale, temperature, precipitation, and vapor pressure are largely dependent on elevation and receive adequate representation via lapse rates. Wind speed and its consequent effects on the redistribution of snow however, is also strongly dependent on proximal upstream physiography and strong gradients are often present within defined elevation bands. This study used wind data from a unique set of wind monitoring sites located across a gradient of exposures within the 0.36 km2 Reynolds Mountain East (RME) research basin in southwestern Idaho, USA to examine relationships between wind speeds, upstream topography, and vegetation. Following a one-year calibration period an optimal relationship between topography, vegetation cover, and hourly averaged wind speeds was developed. Using a jack-knife procedure the topography-vegetation-wind speed relationship was then validated in successive years at RME. The developed procedure for spatially distributing wind speeds accurately simulated the observed wind gradients, had extrapolation capabilities such that end-members could be reasonably predicted when not available, and was computationally efficient.