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Quantification of uncertainties introduced by data-processing procedures of sap flow measurements using the cut-tree method on a large mature tree

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Mar 19, 2020 version files 9.06 MB

Abstract

Motivation: Sap flow sensors are crucial instruments to understand whole-tree water use. The lack of direct calibration of the available methods on large trees and the application of several data-processing procedures may jeopardize our understanding of water uptake dynamics by increasing the uncertainties around sensor-based estimates. We directly compared the heat ratio method (HRM) sap flow measurements to water uptake measured gravimetrically using the cut-tree method on a large mature aspen tree to quantify those uncertainties for ten consecutive days.

Dataset: In this dataset, we provide sap flux density (ten-minutes intervals; g.cm-2.hr-1; corrected for wounding and sapwood thermal diffusivity) obtained from four HRM sap flow sensors installed at 2.5 m high on the focus tree (20 m tall, 60 years old trembling aspen in the boreal mixedwood region of Alberta) between July 18th and August 22nd 2017. We present the code and data (weather data from neighboring weather station) used to calculate whole-tree sap flux (L.hr-1) from each of the individual sensors using different methods of radial integration of sap flux density across the sapwood area estimated via different calculations, as well as different zero-flow corrections used. The cut-tree procedure was applied to the focus tree, and gravimetric measurements of water uptake (ten-minutes intervals) were made using a recording scale. We directly compared the different estimates of hourly, daily and cumulative sap flows obtained with gravimetric measurement of water uptake. We present the code providing the statistical analysis and results reported in the associated publication (Merlin, M., Solarik, K.A., Landhäusser, S.M. Quantification of uncertainties introduced by data-processing procedures of sap flow measurements using the cut-tree method on a large mature tree. 2020. Agricultural and Forest Meteorology, http://dx.doi.org/10.1016/j.agrformet.2020.107926)