Environmental controls on the photosynthesis and respiration of a boreal lichen woodland: a growing season of whole-ecosystem exchange measurements by eddy correlation

Fan, Song‐Miao; Goulden, Michael L.; Munger, J. William; Daube, Bruce C.; Bakwin, Peter S.; Wofsy, Steven C.; Amthor, Jeffrey S.; Fitzjarrald, David R.; Moore, Kathleen E.; Moore, Tim R.

doi:10.1007/bf00341356

Cited by 112 publications

(71 citation statements)

References 49 publications

(17 reference statements)

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“…Net Ecosystem Exchange was compared to daily average eddy correlation net CO2 flux data (methodology described in Fan et al 1995). There was general agreement throughout the year (Fig.…”

Section: Ecosystem Carbon Balance Resultsmentioning

confidence: 86%

Modelling temporal variability in the carbon balance of a spruce/moss boreal forest

Frolking

Goulden

Wofsy

et al. 1996

Global Change Biology

143

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“…Net Ecosystem Exchange was compared to daily average eddy correlation net CO2 flux data (methodology described in Fan et al 1995). There was general agreement throughout the year (Fig.…”

Section: Ecosystem Carbon Balance Resultsmentioning

confidence: 86%

Modelling temporal variability in the carbon balance of a spruce/moss boreal forest

Frolking

Goulden

Wofsy

et al. 1996

Global Change Biology

143

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“…Currently, an empirical correction is applied to compensate for the underestimation of nighttime flux measurements (Fan et al, 1995;Goulden et al, 1996;Falge et al, 2001;Gu et al, 2004). This correction is based on CO 2 flux measurements obtained during windy periods and on the friction velocity (u Ã ) that allows one to differentiate between a windy period and a non-turbulent period (light winds).…”

Section: Introductionmentioning

confidence: 99%

Comparing CO2 Storage and Advection Conditions at Night at Different Carboeuroflux Sites

Aubinet

Berbigier

Bernhofer

et al. 2005

Boundary-Layer Meteorol

176

147

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Abstract. Anemometer and CO 2 concentration data from temporary campaigns performed at six CARBOEUROFLUX forest sites were used to estimate the importance of non-turbulent fluxes in nighttime conditions. While storage was observed to be significant only during periods of both low turbulence and low advection, the advective fluxes strongly influence the nocturnal CO 2 balance, with the exception of almost flat and highly homogeneous sites. On the basis of the main factors determining the onset of advective fluxes, the 'advection velocity', which takes net radiation and local topography into account, was introduced as a criterion to characterise the conditions of storage enrichment/depletion. Comparative analyses of the six sites showed several common features of the advective fluxes but also some substantial differences. In particular, all sites where advection occurs show the onset of a boundary layer characterised by a downslope flow, negative vertical velocities and negative vertical CO 2 concentration gradients during nighttime. As a consequence, vertical advection was observed to be positive at all sites, which corresponds to a removal of CO 2 from the ecosystem. The main differences between sites are the distance from the ridge, which influences the boundarylayer depth, and the sign of the mean horizontal CO 2 concentration gradients, which is probably determined by the source/sink distribution. As a consequence, both positive and negative horizontal advective fluxes (corresponding respectively to CO 2 removal from the ecosystem and to CO 2 supply to the ecosystem) were observed. Conclusive results on the importance of non-turbulent components in the mass balance require, however, further experimental investigations at sites with different topographies, slopes, different land covers, which would allow a more comprehensive analysis of the processes underlying the occurrence of advective fluxes. The quantification of these processes would help to better quantify nocturnal CO 2 exchange rates.

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“…These empirical models typically use soil temperature (Fang et al 1998;Buchmann 2000;Janssens and Pilegaard 2003), soil moisture (Davidson et al 2000;Epron et al 2004;Sotta et al 2004) as well as their interaction (Tufekcioglu et al 2001;Lee et al 2002;Tang and Baldocchi, 2005) for large-scale SR estimates. For example, previous investigators proposed several different functions to describe the relationship between SR and soil temperature, including linear or sinusoidal regressions (Fan et al 1995;O'Connell et al 2003;Chimner 2004); exponential equations (Davidson et al 1998;Buchmann 2000;Sá nchez et al 2003;Reth et al 2004); Arrhenius equations (Lloyd and Taylor 1994;Thierron and Laudelout 1996); power models (Fang and Moncrieff 2001); and logistic models (Rodeghiero and Cescatti 2005). However, a number of field studies have clearly shown that none of these models appears to be consistently better than the others.…”

Section: Introductionmentioning

confidence: 99%

Soil temperature and biotic factors drive the seasonal variation of soil respiration in a maize (Zea mays L.) agricultural ecosystem

Han

Zhou

et al. 2006

Plant Soil

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The diurnal and seasonal variation of soil respiration (SR) and their driving environmental factors were studied in a maize ecosystem during the growing season 2005. The diurnal variation of SR showed asymmetric patterns, with the minimum occurring around early morning and the maximum around 13:00 h. SR fluctuated greatly during the growing season. The mean SR rate was 3.16 lmol CO 2 m -2 s -1 , with a maximum of 4.87 lmol CO 2 m -2 s -1 on July 28 and a minimum of 1.32 lmol CO 2 m -2 s -1 on May 4. During the diurnal variation of SR, there was a significant exponential relationship between SR and soil temperature (T) at 10 cm depth: SR ¼ ae bT . At a seasonal scale, the coefficient a and b fluctuated because the biomass (B) increased a, and the net primary productivity (NPP) of maize markedly increased b of the exponential equation. Based on this, we developed the equationto estimate the magnitude of SR and to simulate its temporal variation during the growth season of maize. Most of the temporal variability (93%) in SR could be explained by the variations in soil temperature, biomass and NPP of maize. This model clearly demonstrated that soil temperature, biomass and NPP of maize combined to drive the seasonal variation of SR during the growing season. However, only taking into account the influence of soil temperature on SR, an exponential equation over-or underestimated the magnitude of SR and resulted in an erroneous representation of the seasonal variation in SR. Our results highlighted the importance of biotic factors for the estimation of SR during the growing season. It is suggested that the models of SR on agricultural sites should not only take into account the influence of soil temperature, but also incorporate biotic factors as they affect SR during the growing season.

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Environmental controls on the photosynthesis and respiration of a boreal lichen woodland: a growing season of whole-ecosystem exchange measurements by eddy correlation

Cited by 112 publications

References 49 publications

Modelling temporal variability in the carbon balance of a spruce/moss boreal forest

Modelling temporal variability in the carbon balance of a spruce/moss boreal forest

Comparing CO2 Storage and Advection Conditions at Night at Different Carboeuroflux Sites

Soil temperature and biotic factors drive the seasonal variation of soil respiration in a maize (Zea mays L.) agricultural ecosystem

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