Journal Article

FZJ-2015-05180

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Uncertainty analysis of eddy covariance CO2 flux measurements for different EC tower distances using an extended two-tower approach

Post, H. (Corresponding author)FZJ* ; Hendricks-Franssen, H.-J.FZJ* ; Graf, A.FZJ* ; Schmidt, M.FZJ* ; Vereecken, H.FZJ*

2015
Copernicus Katlenburg-Lindau [u.a.]

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Please use a persistent id in citations: http://hdl.handle.net/2128/9026  doi:10.5194/bg-12-1205-2015

Abstract: The use of eddy covariance (EC) CO2 flux measurements in data assimilation and other applications requires an estimate of the random uncertainty. In previous studies, the (classical) 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 the classical two-tower approach and an extended approach which removed systematic differences of CO2 fluxes measured at two EC towers. This analysis was made for a data set 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 and 173 m gave uncertainty estimates in best correspondence with an 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 and decreased the sensitivity of results to tower distance. We therefore conclude that corrections for systematic flux differences (e.g., caused by different environmental conditions at both EC towers) can help to apply the two-tower approach to more site pairs with less ideal conditions.

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Contributing Institute(s):

1. Agrosphäre (IBG-3)

Research Program(s):

1. 255 - Terrestrial Systems: From Observation to Prediction (POF3-255) (POF3-255)

Appears in the scientific report 2015

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