Predicting Global Patterns of Tree Water Use: An Ensemble Learning Approach Using SAPFLUXNET

Understanding global patterns of tree water use is crucial for predicting forest resilience and ecosystem responses under climate change. Despite its importance, a high-resolution global assessment of tree water-use where observations are not available remains lacking. This study aims to create a global map of tree water use using machine learning approaches applied to sap flow measurements. Here we leverage the SAPFLUXNET database, a global repository of standardized sap velocity measurements, combined with remote sensing, meteorological, and tree characteristics data utilizing machine learning techniques to estimate global gridded sap velocity. We employ an ensemble learning approach with two distinct setups: site-level and plant-level setup. The site-level setup aggregates plant measurements at each location and incorporates site-level predictors, while the plant-level setup utilizes individual measurements with both site and plant-level variables. For each setup, we implement four machine learning algorithms: Support Vector Machine, Random Forest, XGBoost, Artificial Neural Networks and Long Short-Term Memory. To optimize predictor selection and prevent model complexity, we employ the Guided Hybrid Genetic Algorithm. The final ensemble estimate will be derived as the median of all predictions. This analysis yields two major outcomes. First, the ensemble learning approach produces a daily global dataset of sap velocity at 0.1 ° from 2000-2018 and reveal global patterns of tree water use, highlighting systematic variations across biomes and their relationship to environmental gradients. Second, our methodology identified the relative importance of predictors, and dominant climatic controls across different ecosystems. These findings will advance our understanding of forest ecosystem responses to environmental change and support more accurate predictions of forest resilience under future climate scenarios.

Key words: tree water-use, machine learning, SAPFLUXNET, climate change