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TY  - THES
AU  - Reitz, Oliver
TI  - Assessment and prediction of carbon dioxide fluxes with eddy covariance and machine learning techniques
PB  - Rheinisch-Westfälische Technische Hochschule Aachen
VL  - Dissertation
CY  - Aachen
M1  - RWTH-2023-09084
SP  - 1 Online-Ressource : Illustrationen, Diagramme, Karten
PY  - 2023
N1  - Veröffentlicht auf dem Publikationsserver der RWTH Aachen University 2024
N1  - Dissertation, Rheinisch-Westfälische Technische Hochschule Aachen, 2023
AB  - 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
LB  - PUB:(DE-HGF)11
DO  - DOI:10.18154/RWTH-2023-09084
UR  - https://publications.rwth-aachen.de/record/969289
ER  -