Sap Flow Analyzer: A tool to standardize sap flow estimation and scaling to whole‐tree water use using the HFD method

Abstract

Abstract Sap flow measurements are fundamental to understanding water use in trees and could aid in predicting climate change effects on forest function. Deriving knowledge from such measurements requires empirical calibrations and upscaling methods to translate thermometric recordings to tree water use. Here, we developed a user‐friendly open‐source application, the Sap Flow Analyzer (SFA), which estimates sap flow rates and tree water use from the heat field deformation (HFD) instruments. The SFA incorporates four key features to ensure maximum accuracy and reproducibility of sap flow estimates: diagnosis diagrams to assess data patterns visually, regression models implemented to increase accuracy when estimating K (the main HFD parameter), three approaches to upscale sap flow rates to whole‐tree water use and visualization of the input parameters' uncertainty. Thirteen participants were given three raw datasets and assigned data processing tasks using the SFA user guide, from estimating sapwood depth to scaling sap flow rates to whole‐tree water use to assess the reproducibility and applicability of the SFA. Participants' results were reasonably consistent and independent of their background in using the SFA, R, or HFD method. The results showed lower variability for high flow rates (SD: mean 1% vs. 10%). K estimates and sapwood depth differentiation were the primary sources of variability, which in turn was mainly caused by the user's chosen scaling method. The SFA provides an easy way to visualize and process sap flow and tree water use data from HFD measurements. It is the first free and open software tool for HFD users. The ability to trace analysis steps ensures reproducibility, increasing transparency and consistency in data processing. Developing tools such as the SFA and masked trials are essential for more precise workflows and improved quality and comparability of HFD sap flow datasets.