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Tree movements, wind loading, and wind speeds

Citation

Kamimura, Kana et al. (2022), Tree movements, wind loading, and wind speeds, Dryad, Dataset, https://doi.org/10.5061/dryad.r4xgxd2dj

Abstract

In the future with climate change, we expect more forest and tree damage due to the increasing strength and changing trajectories of tropical cyclones (TCs). However, to date, we have limited information to estimate likely damage levels and, importantly, nobody has ever measured exactly how forest trees behave mechanically during a TC. In 2018, a category-5 TC destroyed trees in our on-going research plots, in which we were measuring tree movement and wind speed in two different tree spacing plots. Interestingly, we found damaged trees in only the wider spaced plot. Here for the first time we present how trees dynamically respond to strong winds during a TC. Sustained strong winds obviously trigger the damage to trees and forests, but inter-tree spacing is also a key factor because the level of support from neighboring trees modifies the effective “stiffness” against the wind both at the single tree and whole forest stand level.

Methods

Study site

We established two research plots in Chiyoda Experimental Forests of the Forestry and Forest Product Research Institute (FFPRI) in Japan (36.184°N, 140.216°E; 42 m above sea level). In 2005, 2-year old genetically similar Cryptomeria japonica trees (full siblings) were planted in a compartment at 1.8 m spacing (3,000 trees/ha, 25 m × 22 m in size) (Mori et al. 2019). As a windbreak, 55 Cryptomeria japonica trees were also planted at the compartment edge; they were genetically unrelated to the subject trees. In November 2017, we thinned some trees in half of the compartment and created two plots with a 15 m × 6 m in size: P-100 (total 24 trees, unthinned since planting, 1.8 m tree spacing, 15.7 cm mean diameter at a breast height of 1.3 m (dbh) with 2.7 cm standard deviation (s.d.)) and P-50 (total 12 trees, thinned every other tree, 3.6 m mean tree spacing, 15.3 cm mean dbh with 3.3 cm s.d.). The means of dbh were not significantly different at 5% significant level (p = 0.7360) based on a two-sample t-test without assuming the equal variance between the plots. We also measured the tree heights of the cut trees in P-50: 13.5 m mean tree height with 1.2 m s.d. (n = 32). A buffer zone with a similar treatment surrounded each plot. When the plots were created, all trees were cut down in the other compartments located in a northeast and east direction. Our compartment was surrounded by an open field and a 3.5 m wide paved road (on the south to southwest side).

Data collection

We attached two aluminum strain-transducers (Moore et al. 2005) at 0.25 m height on 36 trees in the north and east direction (or the west direction when we could not attach the transducer in the east direction due to the stem shape). The transducers had one-active and one-dummy strain gauge (KFGS-5-120-C1-23, Kyowa Inc., Tokyo, Japan) to make reliable measurements of the strain values in the tree stem without becoming detached from the growing trees. The other half of the Wheatstone bridge was completed on a circuit board with balanced precision resistors, and the whole bridge was then attached to a PhidgetBridge 1046 (Phidgets Inc., Calgary, Canada), which supplied power to the strain gauges and converted the strain to voltage. We also attached an Inertial Measurement Unit (IMU, PhidgetSpatial 3/3/3 Basic: 1042, Phidgets Inc., Calgary, Canada) to 33 tree stems in the north direction at 6 m height, which was the height from the ground surface to the initially estimated crown base. We avoided the three smallest trees due to the risk of climbing up to 6 m. The IMU includes three sensors: accelerometer, gyroscope, and magnetometer. Because the IMU uses the magnetic field, we first obtained the calibration parameters based on the local magnetic field (PhidgetSpatial User’s guide, https://www.phidgets.com/?&prodid=32). The signals from the strain gauges and IMU were recorded at 10 Hz and logged by Raspberry Pi3 (Raspberry Pi Foundation, Cambridge, UK). We used the Linux “cron” utility in the Raspberry Pi3 to control the data-logging Python codes. Data-logging started running at 0 minutes and stopped at 59 minutes every hour and the gyroscope in the IMU was reset to zero at the start of each recording period to compensate for drift.

An ultrasonic anemometer (CYG-81000, R. M. Young Co., Traverse City, USA) was attached to the top of a mast (12.5 m height: about 1.3 m below than the mean tree height). The position was 12.6 m from the north edge and 10.8 m from the west edge of the compartment. It measured three orthogonal components of the wind: u, v and w, at 1 Hz frequency, which was logged by a GRAPHTEC data logger midi LOGGER GL240 (Graphtec Co., Yokohama, Japan). The anemometer fell off the mast at around 2:10, 1 October 2018, and we confirmed there was useful data until 1:50. We installed a 3-cup anemometer (S-WSB-M003, ONSET Co., Bourne, USA) at 11 m height on an electronic pylon tower (50 m north of the sonic anemometer), and recorded at 0.1 Hz.

Wind from tropical cyclone (typhoon Trami)

The Japan Meteorological Agency reported that a category-5 tropical cyclone, super typhoon Trami, made landfall in Japan at approximately 20:00, 30 September 2018, and tracked north over the main island and changed to a tropical storm by 12:00, 1 October 2018 (www.data.jma.go.jp, in Japanese). The 3-cup anemometer measured 29.71 ms-1 between 2:00 to 2:10 (the highest 10 minute mean wind speed), and the ultrasonic anemometer recorded 4.67 ms-1 between 1:40 and 1:50, 1 October 2018 (10 minute mean wind speed inside the canopy).

Field survey

After the passage of typhoon Trami, we surveyed all trees in the compartment (26 October to 21 November 2018) and found eight damaged trees including seven uprooted trees (leaning on neighboring trees) only in the area that had been thinned in the previous year. One tree, shaped like a two-tine fork, was broken at the connection between the two separate stems at 4.25 m height. We categorized trees as damaged if the minimum ratio of its angle to the ground (these calculations are explained in the following section) was less than 83% between 6:00 and 7:00, 1 October 2018 (low wind speed period). For our on-going research project, we conducted tree-pulling experiments on 37 undamaged trees in the unthinned side of the research compartment including P-100 in 2019. Using the averaged stem angles (recorded at 0.25 and 3 m height) at the maximum TM from the 32 uprooted trees, we determined the threshold angle for damage, which was the ratio of a mean lean angle of 14.9 degrees (4.8 degrees s.d.) to the vertical rest position (assumed as 90 degrees). Stem angles were based on assuming the tree stems were always straight without any curvature due to the wind force.

We cut and logged all P-50 trees (including the windbreak trees), and measured dbh, tree height, crown width and length, and stem diameter at 0.25 m height. We did not cut the P-100 trees and only measured dbh and stem diameter at 0.25 m height to continue measuring tree oscillations in the following year. We could not measure the single tree heights in P-100 from inside the plot because of the high stem density, so we measured the tree heights of three trees from outside the P-100 plot using a Vertex (HAGLÖF SWEDEN Co., Långsele, Sweden) in November 2018. We estimated the single tree heights in P-100 using the mean tree height of the three trees in 2018, single tree heights measured in November 2019 and mean tree heights of the genetically related trees planted in other compartments (next to our compartment) measured in November 2017 (Mori et al. 2019). Assuming more sunlight reaching P-100 due to the absence of the thinned area and windbreak trees in the growing season in 2019, we estimated 1/3 growth rate from 2017 to 2018 and 2/3 from 2018 to 2019. The estimated mean tree height in 2018 showed a good agreement with the measured mean tree height of the three trees. Despite some technical problems during the TC, in total tree movement and turning moment data from 29 trees were successfully obtained: 20 trees in P-100 and 9 trees (including 3 damaged trees) in P-50; 12 trees had both the strain gauge and IMU data. Some hourly data sets were missing due to the power conditions during the TC. The mean dbhs of the two plots were not significantly different at the 0.05 significance level (p=0.8852).

Data processing

The strain gauge data were filtered and calibrated. First, we filtered the strain gauge output voltages using a high-pass Butterworth filter method with a 10 minute cut-off frequency (Duperat et al. 2021) and inserted 10-minute dummy data in the first and last row of the data to avoid over-fitting. The filtered data were then transformed to turning moments (Nm) at the stem base using the conversion parameters, which we had previously obtained in field experiments. We pulled the trees with a small non-damaging amount (March and May 2018) or compressed the attached transducers (November 2018) to find the linear relationship between the strain and turning moment.

We rotated the IMU outputs to correct their installed x, y, and z-position, which were confirmed by IMU behavior tests in the laboratory (August 2020). The 3-sensor data (9-axis) were then fused using the 3-sensor orientation method, which calculates quaternions of every data set to reduce errors and noise from the local magnetic conditions. The Euler ZYX convention then computed x, y, and z coordinates at the given height. In this paper, we chose the center of gravity (mass) of the crown (assumed as 6 m + crown length/2) to describe the tree oscillation. Since the trees had already begun oscillating at the beginning of the study period (20:00 on 30 September 2018), we used outputs from 1:00-2:00, 28 September 2018, with no measured wind outside the plots, as the reference data to calculate the tree vertical rest positions. The stem angles to the ground were estimated by comparing the reference x, y, and z coordinates and those during the TC. The detailed calculation procedure is found in the documentation of the MATLAB Sensor Fusion and Tracking Toolbox (MathWorks Inc., Natick, USA; https://uk.mathworks.com/products/sensor-fusion-and-tracking.html).

The u, v, and w wind components, measured by the ultrasonic anemometer (20:00 on 30 September 2018 to 1:50 on 1 October 2018), were rotated to reduce bias from the changeable wind conditions during a strong wind. We used the double rotation method in the coordinate rotation methods (Golzio et al. 2019) to obtain the best value of the vertical component (w) (Finnigan 2004). The process was based on 10 minute averages because of the lack of stationarity in the wind conditions during the typhoon. We calculated the instantaneous momentum flux (u’w’) using the second rotated streamline vector u2 and vertical vector w2.

Usage Notes

This dataset includes four compressed folders: the data of the strain gauges ("TM_Filtered.zip") and Inertial Measurement Unit ("IMU_Original.zip" & "Coordinates.zip") measured between 20:00, 30 September to 7:00 on 1 October 2018 and the data of wind inside and outside the plots ("Wind.zip"). Detailed information can be found in the "README.txt" file in each folder. "Trees_2018.xlsx" provides the tree dimensions in 2018 (after the typhoon), which were used for the analysis. 

Funding

Japan Society for the Promotion of Science, Award: 17K07836

Japan Society for the Promotion of Science, Award: 20H03024