The effect of relative humidity on eddy covariance latent heat flux measurements and its implication for partitioning into transpiration and evaporation
(2023) In Agricultural and Forest Meteorology 330.- Abstract
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... (More)
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.
(Less)
- author
- Zhang, Weijie
; Jung, Martin
; Migliavacca, Mirco
; Poyatos, Rafael
; Miralles, Diego G.
; El-Madany, Tarek S.
; Galvagno, Marta
; Carrara, Arnaud
; Arriga, Nicola
; Ibrom, Andreas
; Mammarella, Ivan
; Papale, Dario
; Cleverly, Jamie R.
; Liddell, Michael
; Wohlfahrt, Georg
; Markwitz, Christian
; Mauder, Matthias
; Paul-Limoges, Eugenie
; Schmidt, Marius
; Wolf, Sebastian
; Brümmer, Christian
; Arain, M. Altaf
; Fares, Silvano
; Kato, Tomomichi
; Ardö, Jonas
LU
; Oechel, Walter
; Hanson, Chad
; Korkiakoski, Mika
; Biraud, Sébastien
; Steinbrecher, Rainer
; Billesbach, Dave
; Montagnani, Leonardo
; Woodgate, William
; Shao, Changliang
; Carvalhais, Nuno
; Reichstein, Markus
and Nelson, Jacob A.
(Less) - organization
- publishing date
- 2023-03-01
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Eddy covariance, Energy balance closure, Evapotranspiration, FLUXNET, Latent energy
- in
- Agricultural and Forest Meteorology
- volume
- 330
- article number
- 109305
- publisher
- Elsevier
- external identifiers
-
- scopus:85145820336
- ISSN
- 0168-1923
- DOI
- 10.1016/j.agrformet.2022.109305
- language
- English
- LU publication?
- yes
- additional info
- Funding Information: We thank associated PIs for confirming the sensor types and spectral corrections. RP acknowledges support from the Spanish State Research Agency ( DATAFORUSE , RTI2018–095297-J-I00 ) and the Alexander von Humboldt Foundation (Germany). AC thanks project ELEMENTAL (CGL 2017–83538-C3–3-R, MINECO-FEDER). WW is supported by an Australian Research Council DECRA Fellowship ( DE190101182 ). DP thanks for the support of the ENVRI-FAIR H2020 project ( GA 824068 ). This work used eddy covariance data acquired and shared by the FLUXNET community, including these networks: AmeriFlux, AfriFlux, AsiaFlux, CarboAfrica, CarboEuropeIP, CarboItaly, CarboMont, ChinaFlux, Fluxnet-Canada, GreenGrass, ICOS, KoFlux, LBA, NECC, OzFlux-TERN, TCOS-Siberia, TERENO, and USCCC. The ERA-Interim reanalysis data are provided by ECMWF and processed by LSCE. The FLUXNET eddy covariance data processing and harmonization were carried out by the European Fluxes Database Cluster, the AmeriFlux Management Project (supported by the U.S. Department of Energy's Office of Science under Contract No. DE-AC02–05CH11231 ), and Fluxdata project of FLUXNET , with the support of CDIAC and ICOS Ecosystem Thematic Center, and the TERN OzFlux , ChinaFlux , and AsiaFlux offices . Funding Information: We thank associated PIs for confirming the sensor types and spectral corrections. RP acknowledges support from the Spanish State Research Agency (DATAFORUSE, RTI2018–095297-J-I00) and the Alexander von Humboldt Foundation (Germany). AC thanks project ELEMENTAL (CGL 2017–83538-C3–3-R, MINECO-FEDER). WW is supported by an Australian Research Council DECRA Fellowship (DE190101182). DP thanks for the support of the ENVRI-FAIR H2020 project (GA 824068). This work used eddy covariance data acquired and shared by the FLUXNET community, including these networks: AmeriFlux, AfriFlux, AsiaFlux, CarboAfrica, CarboEuropeIP, CarboItaly, CarboMont, ChinaFlux, Fluxnet-Canada, GreenGrass, ICOS, KoFlux, LBA, NECC, OzFlux-TERN, TCOS-Siberia, TERENO, and USCCC. The ERA-Interim reanalysis data are provided by ECMWF and processed by LSCE. The FLUXNET eddy covariance data processing and harmonization were carried out by the European Fluxes Database Cluster, the AmeriFlux Management Project (supported by the U.S. Department of Energy's Office of Science under Contract No. DE-AC02–05CH11231), and Fluxdata project of FLUXNET, with the support of CDIAC and ICOS Ecosystem Thematic Center, and the TERN OzFlux, ChinaFlux, and AsiaFlux offices. Publisher Copyright: © 2022
- id
- 093ace63-f4f3-4749-b18e-d8b9a699b686
- date added to LUP
- 2024-01-12 12:54:00
- date last changed
- 2024-01-12 15:31:52
@article{093ace63-f4f3-4749-b18e-d8b9a699b686,
abstract = {{<p>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.</p>}},
author = {{Zhang, Weijie and Jung, Martin and Migliavacca, Mirco and Poyatos, Rafael and Miralles, Diego G. and El-Madany, Tarek S. and Galvagno, Marta and Carrara, Arnaud and Arriga, Nicola and Ibrom, Andreas and Mammarella, Ivan and Papale, Dario and Cleverly, Jamie R. and Liddell, Michael and Wohlfahrt, Georg and Markwitz, Christian and Mauder, Matthias and Paul-Limoges, Eugenie and Schmidt, Marius and Wolf, Sebastian and Brümmer, Christian and Arain, M. Altaf and Fares, Silvano and Kato, Tomomichi and Ardö, Jonas and Oechel, Walter and Hanson, Chad and Korkiakoski, Mika and Biraud, Sébastien and Steinbrecher, Rainer and Billesbach, Dave and Montagnani, Leonardo and Woodgate, William and Shao, Changliang and Carvalhais, Nuno and Reichstein, Markus and Nelson, Jacob A.}},
issn = {{0168-1923}},
keywords = {{Eddy covariance; Energy balance closure; Evapotranspiration; FLUXNET; Latent energy}},
language = {{eng}},
month = {{03}},
publisher = {{Elsevier}},
series = {{Agricultural and Forest Meteorology}},
title = {{The effect of relative humidity on eddy covariance latent heat flux measurements and its implication for partitioning into transpiration and evaporation}},
url = {{http://dx.doi.org/10.1016/j.agrformet.2022.109305}},
doi = {{10.1016/j.agrformet.2022.109305}},
volume = {{330}},
year = {{2023}},
}