|Marks, Danny - Danny|
Submitted to: Hydrological Processes
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 7/21/2008
Publication Date: 11/12/2008
Citation: Winstral, A.H., Marks, D.G., Gurney, R. 2009. An Efficient Method for Distributing Wind Speeds over Heterogeneous Terrain. Hydrological Processes, Volume 23(17):2526-2535.
Interpretive Summary: The need for an estimate of wind is critical to the effort to model the development and melting of the seasonal snow cover over complex topography. A simplified technique, based on terrain and vegetation parameters, uses limited measurements of wind, a digital representation of the terrain, and a statistical representation of the effect the terrain and vegetation canopy have on the wind field to generate the hourly wind fields needed to model the seasonal snow cover. The method is successfully tested over several sites in North America.
Technical Abstract: High spatial variability of wind over mountain landscapes can create strong gradients in mass and in energy fluxes at the scale of tens of meters. Variable winds are often cited as the cause of high heterogeneity in snow distribution in non-forested mountain locations. Distributed models capable of capturing the variability in these fluxes require a time-series of distributed wind data at a comparably fine spatial scale. Atmospheric and surface wind flow models in these regions have been limited by our ability to represent this complex processes in a computationally efficient manner. Simplified models based on terrain and vegetation parameters are not as explicit as more complex, fluid-flow models, but are computationally efficient for real time operational use. We developed and applied a simplified wind model based on analysis of upwind terrain to predict wind speeds across diverse topographies at three mountainous research locations. Each site was instrumented with a network of wind sensors to capture the full range of wind variability present. Differences in upwind topography were significantly related (p < 0.0001) to wind speed differences between sites. Wind speeds at each sensor location were modeled from each of the other intra-site locations as if data from only one sensor were available. The wind model explained 69% of the observed variance with a mean absolute prediction error of 0.8 m/sec, 19% of the observed-wind mean. These results were very encouraging given the inherent complexity and profound variability of processes determining wind patterns in these systems.