Using GNSS-based vegetation optical depth, tree sway motion, and eddy covariance to examine evaporation of canopy-intercepted rainfall in a subalpine forest

Burns, Sean P.; Humphrey, Vincent; Gutmann, Ethan D.; Raleigh, Mark S.; Bowling, David R.; Blanken, Peter D.

doi:https://doi.org/10.5194/bg-22-5741-2025

Articles | Volume 22, issue 20

https://doi.org/10.5194/bg-22-5741-2025

© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/bg-22-5741-2025

© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 22, issue 20

Research article

|

21 Oct 2025

Research article |

| 21 Oct 2025

Using GNSS-based vegetation optical depth, tree sway motion, and eddy covariance to examine evaporation of canopy-intercepted rainfall in a subalpine forest

Sean P. Burns, Vincent Humphrey, Ethan D. Gutmann, Mark S. Raleigh, David R. Bowling, and Peter D. Blanken

Supplement

https://doi.org/10.5194/bg-22-5741-2025-supplement

Data sets

US-NR1 AmeriFlux Site Data Peter Blanken, Sean Burns, Russ Monson, Dave Bowling, and Andrew Turnipseed https://doi.org/10.17190/AMF/1246088

Multi-year measurements of tree motion from an accelerometer on a fir tree near Niwot Ridge, Colorado Mark Raleigh https://zenodo.org/records/5149307

US-NR1 AmeriFlux Site Supplemental Data Sean P. Burns, Peter D. Blanken, and Russell K. Monson https://doi.org/10.15485/1671825

GNSS-based Vegetation Optical Depth, Tree Sway, and Evapotranspiration data from the Niwot Ridge Subalpine Forest (US-NR1) AmeriFlux site Burns S. P., Humphrey V., Raleigh M. S., Bowling D. R., Gutmann E. D., and Blanken P. D. https://doi.org/10.15485/2574352

Model code and software

Code to Process Tree Sway Frequency from Accelerometer Data Mark S. Raleigh https://github.com/truewind/accelerometer_tree_sway/

Python Toolkit for Deriving Vegetation Optical Depth (VOD) from Pairs of GNSS Receivers Vincent Humphrey https://github.com/vincenthumphrey/gnssvod

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Short summary

We compared two techniques that are affected by the amount of liquid water in a forest canopy. One technique relies on remote sensing (a pair of GPSs) and the other uses tree motion generated by the wind. Though completely different, these two techniques show strikingly similar changes when rain falls on an evergreen forest. We combine these measurements with eddy covariance fluxes of water vapor to provide insight into the evaporation of canopy-intercepted precipitation.