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ECN publication
Title:
Comparison of chamber and eddy covariance-based CO2 and CH4 emission estimates in a heterogeneous grass ecosystem on peat
 
Author(s):
Schrier, A.P.; Kroon, P.S.; Hensen, A.; Leffelaar, P.A. ; Berendse, F.; Veenendaal, E.M.
 
Published by: Publication date:
ECN Biomass, Coal and Environmental Research 18-8-2010
 
ECN report number: Document type:
ECN-W--10-033 Article (scientific)
 
Number of pages:
8  

Published in: Agricultural and Forest Meteorology (Elsevier), , 2010, Vol.150, p.825-831.

Abstract:
Fluxes of methane (CH4) and carbon dioxide (CO2) estimated by empirical models based on small-scale chamber measurements were compared to large-scale eddy covariance (EC) measurements for CH4 and to a combination of EC measurements and EC-based models for CO2. The experimental area was a flat peat meadow in the Netherlands with heterogeneous source strengths for both greenhouse gases. Two scenarios were used to assess the importance of stratifying the landscape into landscape elements before up-scaling the fluxes measured by chambers to landscape scale: one took the main landscape elements into account (field, ditch edge ditch), the other took only the field into account. Non-linear regression models were used to up-scale the chambermeasurements to field emission estimates. EC CO2 respiration consisted of measured night time EC fluxes and modeled day time fluxes using the Arrhenius model. EC CH4 flux estimate was based on daily averages and the remaining data gaps were filled by linear interpolation. The EC and chamber-based estimates agreed well when the three landscape elements were taken into account with 16.5% and 13.0% difference for CO2 respiration and CH4, respectively. However, both methods differed 31.0% and 55.1% for CO2 respiration and CH4 when only field emissions were taken into account when up-scaling chamber measurements to landscape scale. This emphasizes the importance of stratifying the landscape into landscape elements. The conclusion is that small-scale chamber measurements can be used to estimate fluxes of CO2 and CH4 at landscape scale if fluxes are scaled by different landscape elements.


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