Article CC BY 4.0
refereed
published

The effect of relative humidity on eddy covariance latent heat flux measurements and its implication for partitioning into transpiration and evaporation

Affiliation
Department of Biogeochemical Integration, Max Planck Institute for Biogeochemistry, Jena, Germany
Zhang, Weijie;
Affiliation
Department of Biogeochemical Integration, Max Planck Institute for Biogeochemistry, Jena, Germany
Jung, Martin;
Affiliation
Department of Biogeochemical Integration, Max Planck Institute for Biogeochemistry, Jena, Germany
Migliavacca, Mirco;
Affiliation
CREAF, Cerdanyola del Vallès, Spain
Poyatos, Rafael;
Affiliation
Hydro-Climate Extremes Lab (H-CEL), Faculty of Bioscience Engineering, Ghent University, Ghent, Belgium
Miralles, Diego G.;
Affiliation
Department of Biogeochemical Integration, Max Planck Institute for Biogeochemistry, Jena, Germany
El-Madany, Tarek S.;
Affiliation
Climate Change Unit, Environmental Protection Agency of Aosta Valley, Aosta, Italy
Galvagno, Marta;
Affiliation
Fundación Centro de Estudios Ambientales del Mediterráneo, Paterna, Valencia, Spain
Carrara, Arnaud;
Affiliation
European Commission, Joint Research Centre, (VA), Ispra, Italy
Arriga, Nicola;
Affiliation
Technical University of Denmark, Department of Environment and Resource Management, Kgs, Lyngby, Denmark
Ibrom, Andreas;
Affiliation
Institute for Atmospheric and Earth System Research/Physics (INAR), Faculty of Science, P.O. Box 68, 00014 University of Helsinki, Helsinki, Finland
Mammarella, Ivan;
Affiliation
University of Tuscia - DIBAF, Viterbo, Italy
Papale, Dario;
Affiliation
College of Science and Engineering, James Cook University, Cairns, Australia
Cleverly, Jamie R.;
Affiliation
College of Science and Engineering, James Cook University, Cairns, Australia
Liddell, Michael;
Affiliation
Department of Ecology, University of Innsbruck, Innsbruck, Austria
Wohlfahrt, Georg;
Affiliation
Bioclimatology, University of Göttingen, Göttingen, Germany
Markwitz, Christian;
Affiliation
Technische Universität Dresden, Faculty of Environmental Sciences, Dresden, Germany
Mauder, Matthias;
Affiliation
Department of Geography, University of Zurich, Zurich, Switzerland
Paul-Limoges, Eugenie;
Affiliation
Agrosphere Institute, IBG-3, Forschungszentrum Jülich GmbH, Jülich, Germany
Schmidt, Marius;
Affiliation
Department of Environmental Systems Science, ETH Zurich, Zurich, Switzerland
Wolf, Sebastian;
GND
139033467
VIAF
95629877
Affiliation
Thünen Institute of Climate-Smart Agriculture, Braunschweig, Germany
Brümmer, Christian;
Affiliation
School of Earth, Environment and Society, McMaster University, Hamilton, Canada
Arain, M. Altaf;
Affiliation
National Research Council of Italy, Institute of Bioeconomy, Rome, Italy
Fares, Silvano;
Affiliation
Research Faculty of Agriculture, Hokkaido University, Sapporo, Japan
Kato, Tomomichi;
Affiliation
Department of Physical Geography and Ecosystem Science, Lund University, Sweden
Ardö, Jonas;
Affiliation
Global Change Research Group, San Diego State University, San Diego, United States
Oechel, Walter;
Affiliation
Department of Forests Ecosystems and Society, College of Forestry, Oregon State University, Corvallis, United States
Hanson, Chad;
Affiliation
Institute for Atmospheric and Earth System Research/Physics (INAR), Faculty of Science, P.O. Box 68, 00014 University of Helsinki, Helsinki, Finland
Korkiakoski, Mika;
Affiliation
Climate Sciences Department, Lawrence Berkeley National Laboratory, Berkeley, United States
Biraud, Sébastien;
Affiliation
Karlsruhe Institute of Technology (KIT), Institute of Meteorology and Climate Research (IMK), DepartmentAtmospheric Environmental Research (IFU), Garmisch-Partenkirchen, Germany
Steinbrecher, Rainer;
Affiliation
University of Nebraska, Lincoln, United States
Billesbach, Dave;
Affiliation
Faculty of Science and Technology, Free University of Bozen-Bolzano, Bolzano, Italy
Montagnani, Leonardo;
Affiliation
School of Earth and Environmental Sciences, The University of Queensland, Saint Lucia, Australia
Woodgate, William;
Affiliation
Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing, China
Shao, Changliang;
Affiliation
Department of Biogeochemical Integration, Max Planck Institute for Biogeochemistry, Jena, Germany
Carvalhais, Nuno;
Affiliation
Department of Biogeochemical Integration, Max Planck Institute for Biogeochemistry, Jena, Germany
Reichstein, Markus;
Affiliation
Department of Biogeochemical Integration, Max Planck Institute for Biogeochemistry, Jena, Germany
Nelson, Jacob A.

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.

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