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Elsevier, Agricultural and Forest Meteorology, (177), p. 24-34, 2013

DOI: 10.1016/j.agrformet.2013.04.002

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Canopy-structure effects on surface roughness parameters: Observations in a Great Lakes mixed-deciduous forest

Journal article published in 2013 by Kyle D. Maurer, Brady S. Hardiman ORCID, Christoph S. Vogel, Gil Bohrer
This paper is available in a repository.
This paper is available in a repository.

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Abstract

Over forested canopies, the physical structure of vegetation interacts with wind by exerting drag on the flow, thus generating turbulent mixing that is necessary for scalar transport. We use 11 years of above-canopy wind speed measurements from spatially and temporally heterogeneous forest environments to disentangle the effects of different features of changing canopy structure on the surface roughness parameters: displacement height (d), roughness length (z0), and the aerodynamic canopy height (ha). We find a significant increasing long-term trend of dormant-season (leaf-off) ha, which closely resembles the rate of biometrically derived vertical stem growth over years. We show that the values of d and z0 trade-off with higher d and shorter z0 when leaf area is high in the growing season. Using airborne lidar measurements and a footprint model for flux-source location detection, we show that these d and z0 trade-offs also correspond with the spatial differences between taller and shorter subplot patches.We show that incorporating seasonal-scale temporal heterogeneity of d and z0 into surface-flux and ecosystem models will improve their accuracy. However, incorporating simple empirical modifications to surface-structure roughness parameters due to inter-annual variation in canopy height and leaf area did not lead to improved modeling of frictional velocity within this study. Further investigation of structure–roughness relationships is needed to incorporate these aspects. Finally, this study proposes a meteorological-based method for estimating vertical stem growth in undisturbed forest environments by tracking ha over time.