Journal Article FZJ-2015-00513

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Uncertainty analysis of eddy covariance CO$_{2}$ flux measurements for different EC tower distances using an extended two-tower approach

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2014
Copernicus Katlenburg-Lindau [u.a.]

Biogeosciences discussions 11(8), 11943 - 11983 () [10.5194/bgd-11-11943-2014]

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Abstract: The use of eddy covariance CO2 flux measurements in data assimilation and other applications requires an estimate of the random uncertainty. In previous studies, the two-tower approach has yielded robust uncertainty estimates, but care must be taken to meet the often competing requirements of statistical independence (non-overlapping footprints) and ecosystem homogeneity when choosing an appropriate tower distance. The role of the tower distance was investigated with help of a roving station separated between 8 m and 34 km from a permanent EC grassland station. Random uncertainty was estimated for five separation distances with an extended two-tower approach which removed systematic differences of CO2 fluxes measured at two EC towers. This analysis was made for a dataset where (i) only similar weather conditions at the two sites were included and (ii) an unfiltered one. The extended approach, applied to weather-filtered data for separation distances of 95 m and 173 m gave uncertainty estimates in best correspondence with the independent reference method The introduced correction for systematic flux differences considerably reduced the overestimation of the two-tower based uncertainty of net CO2 flux measurements, e.g. caused by different environmental conditions at both EC towers. It is concluded that the extension of the two-tower approach can help to receive more reliable uncertainty estimates because systematic differences of measured CO2 fluxes which are not part of random error are filtered out.

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Contributing Institute(s):
  1. Agrosphäre (IBG-3)
Research Program(s):
  1. 246 - Modelling and Monitoring Terrestrial Systems: Methods and Technologies (POF2-246) (POF2-246)
  2. 255 - Terrestrial Systems: From Observation to Prediction (POF3-255) (POF3-255)

Appears in the scientific report 2014
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Medline ; Creative Commons Attribution CC BY 3.0 ; DOAJ ; OpenAccess ; NCBI Molecular Biology Database ; SCOPUS
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 Record created 2015-01-15, last modified 2022-09-30


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