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@ARTICLE{Klosterhalfen:861629,
author = {Klosterhalfen, Anne and Graf, Alexander and Brüggemann,
Nicolas and Drüe, Clemens and Esser, Odilia and
González-Dugo, María P. and Heinemann, Günther and
Jacobs, Cor M. J. and Mauder, Matthias and Moene, Arnold F.
and Ney, Patrizia and Pütz, Thomas and Rebmann, Corinna and
Ramos Rodríguez, Mario and Scanlon, Todd M. and Schmidt,
Marius and Steinbrecher, Rainer and Thomas, Christoph K. and
Valler, Veronika and Zeeman, Matthias J. and Vereecken,
Harry},
title = {{S}ource partitioning of {H}2{O} and {CO}2 fluxes based on
high-frequency eddy covariance data: a comparison between
study sites},
journal = {Biogeosciences},
volume = {16},
number = {6},
issn = {1726-4189},
address = {Katlenburg-Lindau [u.a.]},
publisher = {Copernicus},
reportid = {FZJ-2019-02075},
pages = {1111 - 1132},
year = {2019},
abstract = {For an assessment of the roles of soil and vegetation in
the climate system, a further understanding of the
fluxcomponents of H2O and CO2 (e.g., transpiration, soil
respiration) and their interaction with physical conditions
andphysiological functioning of plants and ecosystems is
necessary. To obtain magnitudes of these flux components, we
appliedsource partitioning approaches after Scanlon and
Kustas (2010; SK10) and after Thomas et al. (2008; TH08)
tohigh-frequency eddy covariance measurements of 12 study
sites covering different ecosystems (croplands,
grasslands,and forests) in different climatic regions. Both
partitioning methods are based on higher-order statistics of
the H2O andCO2 fluctuations, but proceed differently to
estimate transpiration, evaporation, net primary production,
and soil respiration.We compared and evaluated the
partitioning results obtained with SK10 and TH08, including
slight modificationsof both approaches. Further, we analyzed
the interrelations among the performance of the partitioning
methods, turbulencecharacteristics, and site characteristics
(such as plant cover type, canopy height, canopy density,
and measurement height).We were able to identify
characteristics of a data set that are prerequisites for
adequate performance of the $partitioningmethods.\\SK10$ had
the tendency to overestimate and TH08 to underestimate soil
flux components. For both methods, the partitioningof CO2
fluxes was less robust than for H2O fluxes. Results derived
with SK10 showed relatively large dependencieson estimated
water use efficiency (WUE) at the leaf level, which is a
required input. Measurements of outgoinglongwave radiation
used for the estimation of foliage temperature (used in WUE)
could slightly increase the qualityof the partitioning
results. A modification of the TH08 approach, by applying a
cluster analysis for the conditionalsampling of
respiration–evaporation events, performed satisfactorily,
but did not result in significant advantages comparedto the
original method versions developed by Thomas et al. (2008).
The performance of each partitioning approachwas dependent
on meteorological conditions, plant development, canopy
height, canopy density, and measurementheight. Foremost, the
performance of SK10 correlated negatively with the ratio
between measurement height and canopyheight. The performance
of TH08 was more dependent on canopy height and leaf area
index. In general, all site characteristicsthat increase
dissimilarities between scalars appeared to enhance
partitioning performance for SK10 and TH08.},
cin = {IBG-3},
ddc = {550},
cid = {I:(DE-Juel1)IBG-3-20101118},
pnm = {255 - Terrestrial Systems: From Observation to Prediction
(POF3-255) / IDAS-GHG - Instrumental and Data-driven
Approaches to Source-Partitioning of Greenhouse Gas Fluxes:
Comparison, Combination, Advancement (BMBF-01LN1313A) /
TERENO - Terrestrial Environmental Observatories
(TERENO-2008)},
pid = {G:(DE-HGF)POF3-255 / G:(DE-Juel1)BMBF-01LN1313A /
G:(DE-HGF)TERENO-2008},
typ = {PUB:(DE-HGF)16},
UT = {WOS:000461618700002},
doi = {10.5194/bg-16-1111-2019},
url = {https://juser.fz-juelich.de/record/861629},
}