|Surface shear velocity estimates in a sparsely vegetated rangeland landscape
|Year of Publication
|Zhang, P, Edwards, BL, Webb, NP, Ziegler, NP, Van Zee, JW, Wheeler, B, Okin, GS, Gillies, J
|AGU 2023 Fall Meeting: Wide. Open. Science.
|San Francisco, California
|landscape, rangeland, sparsely, surface shear, vegetated, velocity estimates
Accurate estimates of shear velocity at the soil surface are critical to understanding aeolian processes in vegetated landscapes—where momentum from the wind is partitioned among plants, other roughness elements, and erodible sediments. Shear velocities estimated from wind speed profiles extending above the canopy are not ideal for characterizing conditions at the sediment bed because they include the influence of vegetation on the wind field. Sonic anemometers have been used to estimate shear velocity on largely unvegetated, homogenous surfaces or for eddy covariance applications above the canopy. Shear velocity estimates from sonic anemometers, however, are susceptible to errors from instrument tilt, slope, and complex terrain or roughness. Raw u, v, and w wind vectors are typically reoriented to wind streamlines using one of three methods, double rotation, triple rotation, or planar fit, each of which has limitations depending on meteorological conditions and site complexity. Another recent approach uses invariants of the Reynolds stress tensor to estimate shear velocity without the need for tilt corrections. Here, we evaluate shear velocity estimates from the four methods for sonic anemometers deployed adjacent to a honey mesquite (Prosopis glandulosa Torr.) shrub from October 2022 to July 2023 and compare with estimates from collocated Irwin sensors deployed flush with the soil surface. Shear velocities estimated using the Reynolds stress tensor method were in general slightly larger over the study period (~6–7%) and exhibited the most variability among methods, but results confirm the suitability of the method for aeolian studies in sparsely vegetated landscapes. Shear velocity also varied significantly with wind direction and position relative to the shrub. We map shear velocity estimates from anemometers and Irwin sensors by wind speed and direction. Results suggest including the lateral influence of vegetation could improve drag partition estimates.