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@PHDTHESIS{Reitz:969289,
author = {Reitz, Oliver},
othercontributors = {Leuchner, Michael and Bendix, Jörg},
title = {{A}ssessment and prediction of carbon dioxide fluxes with
eddy covariance and machine learning techniques},
school = {Rheinisch-Westfälische Technische Hochschule Aachen},
type = {Dissertation},
address = {Aachen},
publisher = {RWTH Aachen University},
reportid = {RWTH-2023-09084},
pages = {1 Online-Ressource : Illustrationen, Diagramme, Karten},
year = {2023},
note = {Veröffentlicht auf dem Publikationsserver der RWTH Aachen
University 2024; Dissertation, Rheinisch-Westfälische
Technische Hochschule Aachen, 2023},
abstract = {Land use and land cover changes and the terrestrial carbon
sink are two important components of the global carbon
budget. Several methodological approaches exist to measure
fluxes of CO2 and other greenhouse gases between ecosystems
and the atmosphere. With an accurate quantification of these
fluxes, it is possible to compare carbon source and sink
strengths between different land covers and to evaluate
environmental influences on these terms. Out of those
methods, the eddy covariance technique has the advantage of
providing direct and quasi-continuous turbulent flux
observations at the ecosystem scale. However, to compare
eddy covariance data to, e.g., top-down methods and to
achieve spatially gapless data sets, these point
measurements with a relatively small footprint require a
spatial upscaling with statistical methods such as machine
learning and ancillary remote sensing data. Another issue
with eddy covariance data sets is the underrepresentation of
certain ecosystem types and climatic regions. Recently
disturbed ecosystems belong to this group, but usually also
exhibit non ideal characteristics for eddy covariance
measurements such as abrupt surface changes and
heterogeneous regrowth. Therefore, it is important to assess
the uncertainty of eddy covariance measurements for
disturbed ecosystems in regard to different choices of
measurement design and processing and thus to improve the
interpretability of such measurements. On the other hand, a
changing climate can also enforce a reduced sink strength on
ecosystems through, e.g., heat and drought. In this way,
eddy covariance derived data on CO2 uptake in combination
with other environmental measurements and advanced
statistical analyses can reveal limiting conditions for
photosynthesis and thus a reduced efficiency to use light
for CO2 assimilation. In this dissertation, these three
issues, i) spatial upscaling of eddy covariance data, ii)
methodological uncertainties of obtaining flux data at
disturbed sites, and iii) environmental impacts on
ecosystem-scale photosynthesis, are addressed within the
TERENO Eifel/Lower Rhine Valley Observatory, which comprises
the Rur catchment, mostly located in western Germany. In a
first study, eddy covariance CO2 flux data from different
land covers within the Rur catchment were upscaled to the
whole catchment area using a random forest machine learning
model incorporating MODIS remote sensing and COSMO-REA6
reanalysis data. For this task, state-of-the-art predictor
variable selection methods for machine learning models were
evaluated. Results of this studys how that combining eddy
covariance flux data with remote sensing products and
reanalysis data is a feasible way to upscale CO2 flux
information to the regional scale at a relatively high
spatial resolution (250 m) and across various land covers.
The study further indicates that averaging multiple model
runs in the feature selection process can improve these
results. Although an R² of 0.41 is in the range of other
studies using a spatial cross validation scheme, this value
reveals that there is still room for improvement. Main
limitations of the analysis include a low prediction
performance on high magnitude fluxes as a narrower range was
predicted than observed, and the fact that differences
between land cover classes were also narrower in the
upscaled product than between eddy covariance stations. The
further analyses were confined to a subregion within the Rur
catchment, the Wüstebach site in the northern Eifel low
mountain range. The site encompasses the Wüstebach
headwater region and is mostly composed of a planted spruce
forest but also contains a deforested area of 8.6 ha with
unmanaged regrowth. This fast-growing vegetation requires a
regular adjustment of the eddy covariance measurement height
in order to ensure a stable flux source area in the long run
and to prevent high spectral losses. In a second study, CO2
and H2O fluxes were hence measured over the deforested area
with eddy covariance systems in two different heights and
were processed with five different spectral corrections. In
this way, the uncertainty from measurement height and choice
of spectral correction was assessed, and insights were
gained in the trade-offs that must be considered at a site
with non-ideal characteristics. For the deforested site,
results show that at the lower height spectral corrections
were higher and had a higher standard deviation among
methods compared to the upper height for both CO2 and H2O
fluxes. The average standard deviation between heights was
even higher than between spectral corrections at the same
height $(24.8\%$ of CO2 flux; $9.7\%$ of H2O flux).
Furthermore, the energy balance closure was on average about
$9\%$ better for the upper system than for the lower system.
On the other hand, the modelled footprints of both heights
did not match the average footprint of the previous years at
the lower height. Hence, the study indicates a difficulty of
achieving a stable flux source area over longer time periods
for fast growing vegetation but also emphasizes the
importance of a carefully adjusted measurement height.
Although the study improved the interpretability of flux
measurements for a disturbed site, its main limitation
comprises the difficulty to apply one of the common
footprint models to estimate the flux source area for this
site with complex flow, especially over the forest edges. A
third study concerned the Wüstebach spruce forest. For this
site gross primary productivity derived from eddy covariance
CO2 flux data was combined with measurements of green canopy
absorbed photosynthetically active radiation (APARg), sap
flow, and other meteorological and plant physiological data.
In this way, water-limiting conditions for photosynthesis
and the light use efficiency of a spruce forest were
evaluated. In addition, the importance of environmental
variables for the prediction of gross primary productivity
was assessed with state-of-the-art machine learning variable
importance measures. In this study, data from the 2021
growing season was analyzed, for which the light use
efficiency of green parts of the forest was on average 4.0
± $2.3\%$ and showed a unimodal relation to air temperature
with a maximum around 15 °C. For modelling gross primary
productivity with tree based machine learning models, canopy
chlorophyll content likely as a seasonal variable for
photosynthetic capacity and APARg likely as a diurnal
variable for energy supply were the most important
variables. On days with high vapor pressure deficit,
tree-scale sap flow and ecosystem-scale gross primary
productivity both shifted to a clockwise hysteretic response
to APARg. It is demonstrated that the onset of such a
clockwise hysteretic pattern of sap flow to APARg can be a
useful indicator of afternoon stomatal closure related to
water-limiting conditions. However, the main limitation of
this case study is its limited extent, as just one
comparatively cool and wet growing season at a single site
with a single dominant tree species, Picea abies, was
investigated. Overall, this dissertation highlights the use
of direct flux measurements and machine learning methods for
both the evaluation of land cover changes and the impact of
changing environmental conditions on the CO2 source and sink
strengths of terrestrial ecosystems.},
cin = {551520 / 530000},
ddc = {550},
cid = {$I:(DE-82)551520_20140620$ / $I:(DE-82)530000_20140620$},
pnm = {SFUoA002 - Comparative Study of Radiative and Carbon Fluxes
at Three Ecosystems in Germany, Canada and Costa Rica
(ComRadE) (EXS-SF-SFUoA002) / Exploratory Research Space:
Seed Fund (2) als Anschubfinanzierung zur Erforschung neuer
interdisziplinärer Ideen / Excellence Strategy},
pid = {G:(DE-82)EXS-SF-SFUoA002 / G:(DE-82)EXS-SF / G:(DE-82)EXS},
typ = {PUB:(DE-HGF)11},
doi = {10.18154/RWTH-2023-09084},
url = {https://publications.rwth-aachen.de/record/969289},
}
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