Biological Systems Engineering



Weijie Zhang, Max Planck Institute for Biogeochemistry
Martin Jung, Max Planck Institute for Biogeochemistry
Mirco Migliavacca, Max Planck Institute for Biogeochemistry
Rafael Poyatos, CREAF - Centre de Recerca Ecològica i Aplicacions Forestals
Diego G. Miralles, Universiteit Gent
Tarek S. El-Madany, Max Planck Institute for Biogeochemistry
Marta Galvagno, Environmental Protection Agency of Aosta Valley
Arnaud Carrara, Fundacion Centro de Estudios Ambientales del Mediterraneo
Nicola Arriga, European Commission Joint Research Centre
Andreas Ibrom, Technical University of Denmark
Ivan Mammarella, Helsingin Yliopisto
Dario Papale, Università degli Studi della Tuscia Viterbo
Jamie R. Cleverly, James Cook University
Michael Liddell, James Cook University
Georg Wohlfahrt, Universität Innsbruck
Christian Markwitz, Georg-August-Universität Göttingen
Matthias Mauder, Technische Universität Dresden
Eugenie Paul-Limoges, Universität Zürich
Marius Schmidt, Forschungszentrum Jülich (FZJ)
Sebastian Wolf, ETH Zürich
Christian Brümmer, Johann Heinrich von Thünen Institute
M. Altaf Arain, McMaster University
Silvano Fares, Consiglio Nazionale delle Ricerche
Tomomichi Kato, Hokkaido University
Jonas Ardö, Institutionen för Naturgeografi och Ekosystemvetenskap, Lunds Universitet
Walter Oechel, San Diego State University
Chad Hanson, Oregon State University
Mika Korkiakoski, Helsingin Yliopisto
Sébastien Biraud, Lawrence Berkeley National Laboratory
Rainer Steinbrecher, Karlsruher Institut für Technologie
Dave Billesbach, University of Nebraska–Lincoln
Leonardo Montagnani, Free University of Bozen-Bolzano
William Woodgate, The University of Queensland
Changliang Shao, Chinese Academy of Agricultural Sciences
Nuno Carvalhais, Max Planck Institute for Biogeochemistry
Markus Reichstein, Max Planck Institute for Biogeochemistry
Jacob A. Nelson, Max Planck Institute for Biogeochemistry

Date of this Version



Agricultural and Forest Meteorology 330 (2023) 109305



This is an open access article under the CC BY license


While the eddy covariance (EC) technique is a well-established method for measuring water fluxes (i.e., evaporation or 'evapotranspiration’, ET), the measurement is susceptible to many uncertainties. One such issue is the potential underestimation of ET when relative humidity (RH) is high (>70%), due to low-pass filtering with some EC systems. Yet, this underestimation for different types of EC systems (e.g. open-path or closed-path sensors) has not been characterized for synthesis datasets such as the widely used FLUXNET2015 dataset. Here, we assess the RH-associated underestimation of latent heat fluxes (LE, or ET) from different EC systems for 163 sites in the FLUXNET2015 dataset. We found that the LE underestimation is most apparent during hours when RH is higher than 70%, predominantly observed at sites using closed-path EC systems, but the extent of the LE underestimation is highly site-specific. We then propose a machine learning based method to correct for this underestimation, and compare it to two energy balance closure based LE correction approaches (Bowen ratio correction, BRC, and attributing all errors to LE). Our correction increases LE by 189% for closed-path sites at high RH (>90%), while BRC increases LE by around 30% for all RH conditions. Additionally, we assess the influence of these corrections on ET-based transpiration (T) estimates using two different ET partitioning methods. Results show opposite responses (increasing vs. slightly decreasing T-to-ET ratios, T/ET) between the two methods when comparing T based on corrected and uncorrected LE. Overall, our results demonstrate the existence of a high RH bias in water fluxes in the FLUXNET2015 dataset and suggest that this bias is a pronounced source of uncertainty in ET measurements to be considered when estimating ecosystem T/ET and WUE.