The motion of trees in the wind: a data synthesis
- 1Plant Sciences, University of Cambridge, CB2 3EA, UK
- 2Department of Mathematics, Imperial College London, UK
- 3Department of Wind Energy, Technical University of Denmark, Frederiksborgvej 399, Roskilde, 4000, Denmark
- 4Department of Natural Resources, University of Connecticut, Mansfield, CT 06269, USA
- 5Hydrology and Quantitative Water Management Group, Wageningen University, Wageningen, The Netherlands
- 6Department of Wood and Forest Sciences, Laval University, Quebec, G1V 0A6, Canada
- 7Bavarian State Institute of Forestry (LWF), Hans-Carl-von-Carlowitz-Platz 1, D-85354 Freising
- 8Baruch Institute of Coastal Ecology and Forest Science, Clemson University, PO Box 596, Georgetown, SC 29442, USA
- 9Centre de recherche sur les matériaux renouvelables, Département des sciences du bois et de la forêt, Université Laval, Québec, QC G1V 0A6, Canada
- 10Department of Environmental Conservation, University of Massachusetts, Amherst, MA 01003, USA
- 11Civil and Environmental Engineering, University of Wisconsin Madison, Madison, WI
- 12School of Ecosystem and Forest Sciences, Faculty of Science, University of Melbourne, Melbourne, Australia
- 13Centre for Urban Greenery and Ecology, National Parks Board, 259569 Singapore
- 14Timberlands Ltd., Rotorua 3010, New Zealand
- 15Environmental Meteorology, University of Freiburg, Germany
- 16Argus Electronics gmbh, Erich-Schlesinger-Str. 49d, 18059 Rostock
- 17College of Forest Resources and Environmental Science, Michigan Technological University, Houghton, MI 49931 USA
- 18Renewable Resources Dept, University of Alberta, USA
- 19Oregon State University, Corvallis, OR 97331, USA
- 20Department of Botany, University of British Columbia, Canada
- 21Department of Civil and Environmental Engineering, Western University, Canada
- 22Department of Environmental Horticulture, IFAS, University of Florida
- 23Gulf Coast Research and Education Center, 14625 County Road 672, Wimauma, FL 33598, United States
- 24The Morton Arboretum, Lisle, IL 60532, USA
- 25College of Earth, Ocean, and Atmospheric Sciences, Oregon State University, Corvallis OR 97331 USA
- 26Oregon Research Electronics, Tangent, OR 97389, USA
- 27Institut Européen de la Forêt Cultivée, 69 route d’Arcachon, 33612, Cestas, France
Abstract. 1. Interactions between wind and trees control energy exchanges between the atmosphere and forest canopies. This energy exchange can lead to the widespread damage of trees and wind is a key disturbance agent in many of the world’s forests. However, most research on this topic has focused on conifer plantations, where risk management is economically important, rather than broadleaf forests, which dominate the forest carbon cycle. This study brings together all available tree motion time-series data to systematically evaluate the factors influencing tree responses to wind loading, including data from both broadleaf and coniferous trees in forests and open environments.
2. We found that the two most descriptive features of tree motion were: (a) the fundamental frequency, which is a measure of the speed at which a tree sways and is strongly related to tree height, and (b) the slope of the power spectrum, which is related to the efficiency of energy transfer from wind to trees. Intriguingly, the slope of the power spectrum was found to remain constant from medium to high wind speeds for all trees in this study. This suggests that, contrary to some predictions, damping or amplification mechanisms do not change dramatically at high wind speeds and therefore wind damage risk is related, relatively simply, to wind speed.
3. Conifers from forests were distinct from broadleaves in terms of their response to wind loading. Specifically, the fundamental frequency of forest conifers was related to their size according to the cantilever beam model (i.e. vertically distributed mass), whereas broadleaves were better approximated by the simple pendulum model (i.e. dominated by the crown). Forest conifers also had a steeper slope of the power spectrum. We interpret these finding as being strongly related to tree architecture, i.e. conifers generally have a simple shape due to their apical dominance, whereas broadleaves exhibit a much wider range of architectures with more dominant crowns.
Toby D. Jackson et al.
Toby D. Jackson et al.
Singapore tree motion data https://doi.org/10.7910/DVN/FHJBYG
Montmorency tree motion data https://doi.org/10.5683/SP2/WZIKSR
Manaus tree motion data https://doi.org/10.4121/uuid:c9974180-aa9b-40b4-8dbb-06d5b1fce693
Wytham Woods tree motion data https://doi.org/10.5285/533d87d3-48c1-4c6e-9f2f-fda273ab45bc
Danum Valley tree motion data https://doi.org/10.5285/657f420e-f956-4c33-b7d6-98c7a18aa07a
Model code and software
Tree Motion - code and summary data to reproduce the results https://doi.org/10.5281/zenodo.4265811
Toby D. Jackson et al.
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