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

文献类型： 外文期刊

第一作者： Zhang, Weijie

作者： Zhang, Weijie;Jung, Martin;Migliavacca, Mirco;El-Madany, Tarek S.;Carvalhais, Nuno;Reichstein, Markus;Nelson, Jacob A.;Zhang, Weijie;Miralles, Diego G.;Migliavacca, Mirco;Arriga, Nicola;Poyatos, Rafael;Poyatos, Rafael;Galvagno, Marta;Carrara, Arnaud;Ibrom, Andreas;Mammarella, Ivan;Korkiakoski, Mika;Papale, Dario;Cleverly, Jamie R.;Liddell, Michael;Wohlfahrt, Georg;Markwitz, Christian;Mauder, Matthias;-Limoges, Eugenie Paul;Schmidt, Marius;Wolf, Sebastian;Bruemmer, Christian;Arain, M. Altaf;Kato, Tomomichi;Fares, Silvano;Kato, Tomomichi;Oechel, Walter;Ardo, Jonas;Oechel, Walter;Oechel, Walter;Oechel, Walter;Hanson, Chad;Biraud, Sebastien;Steinbrecher, Rainer;Billesbach, Dave;Montagnani, Leonardo;Woodgate, William;Woodgate, William;Shao, Changliang;Carvalhais, Nuno;Reichstein, Markus

作者机构：

关键词： Evapotranspiration; Energy balance closure; Latent energy; FLUXNET; Eddy covariance

期刊名称：AGRICULTURAL AND FOREST METEOROLOGY （ 影响因子：6.2； 五年影响因子：6.9 ）

ISSN： 0168-1923

年卷期： 2023 年 330 卷

页码：

收录情况： SCI

摘要： While the eddy covariance (EC) technique is a well-established method for measuring water fluxes (i.e., evap-oration 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 underesti-mation is highly site-specific. We then propose a machine learning based method to correct for this underesti-mation, 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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