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Direct measurements of CO2 flux below a spruce forest canopy
Agricultural and Forest Meteorology 126 (2004) 157–168 www.elsevier.com/locate/agrformet
Direct measurements of CO2 flux below a spruce forest canopy Jens-Arne Subke*, John D. Tenhunen Department of Plant Ecology, University of Bayreuth, D-95440 Bayreuth, Germany Received 2 December 2002; received in revised form 25 June 2004; accepted 29 June 2004
Abstract We applied the eddy covariance technique below the canopy of a mature Norway spruce (Picea abies) forest in SE Germany, with a dense ground cover of the grasses Calamagrostis villosa and Deschampsia flexuosa. Measurements were carried out during three field campaigns between May and November 1999. Data quality analysis led to the exclusion of around 61% of flux measurements. Chamber measurements of soil CO2 efflux at the same site showed that the micro-meteorological measurements systematically under-estimate the night-time CO2 efflux on average by about 41%. By correcting the eddy covariance flux on the basis of chamber measurements, flux measurements over a larger area could be obtained, and so it was possible to calculate the daytime gross assimilation flux for the ground vegetation averaged for each of four 4-week periods. Separation of fluxes according to grass species (by wind sectors) showed no detectable difference in CO2 flux, but scarcity of data following quality tests meant that comparisons were limited to selected periods only. Light response curves of the corrected flux values indicate different saturation values for the gross assimilation rates in each period, which reflect the phenological development of the grasses, as well as differences in environmental conditions. On the basis of mean day-time assimilation by ground vegetation, the contribution to the total gross assimilation of the stand was estimated as between 3 and 12% at different times of the year. About 30% of the carbon emitted annually as CO2 from the soil is estimated to be assimilated by the ground vegetation (ca. 170 g C m2 year1). # 2004 Elsevier B.V. All rights reserved. Keywords: Eddy covariance; Ground vegetation; Understorey gas exchange; Picea abies forest; Soil respiration
1. Introduction Photosynthesis and autotrophic respiration of the canopy dominate the above-ground CO2 exchange of a forest ecosystem. However, understorey- and groundvegetation may represent a considerable fraction of * Corresponding author. Present address: Stockholm Environment Institute, University of York, Heslington, York YO10 5DD, UK. Tel.: +44 1904 328639; fax: +44 1904 432898. E-mail address: [email protected] (J.-A. Subke).
the overall leaf area index (LAI), and therefore contribute significantly to the net gas exchange of a forest stand (e.g. Wedler et al., 1996; Black et al., 1996; More´n and Lindroth, 2000). These contributions are quite distinct from the ecophysiological processes in the crown space, owing to extreme differences in light regimes and different seasonal patterns in phenology. Eddy covariance (EC) measurements provide a micro-meteorological technique for quantifying the time-integrated flux of CO2 at a given height. For measurements of soil CO2 efflux, it has the great
0168-1923/$ – see front matter # 2004 Elsevier B.V. All rights reserved. doi:10.1016/j.agrformet.2004.06.007
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benefit that it does not disturb the natural soil system in the way that chamber measurements do, and that fluxes are sampled from a considerably larger area. However, especially underneath the forest canopy, the EC technique is limited to periods of favourable atmospheric conditions (owing to a set of assumptions underlying the measurement theory), and measurements using chambers are more commonly employed. The validity of below canopy EC measurements has been addressed in numerous studies, which show that this technique can be applied, but measuring uncertainty remains as a main concern (Baldocchi and Meyers, 1991; Baldocchi and Vogel, 1996; Baldocchi et al., 1997, 2000; Lamaud et al., 2001; Wilson and Meyers, 2001). Experimental results from either leaf cuvettes (Wedler et al., 1996) or ground chambers (Goulden and Crill, 1997; More´ n and Lindroth, 2000) indicate significant contributions of ground vegetation gas exchange in relation to the stand carbon fluxes. However, scaling up of this type of measurement to the stand level is difficult due to the large heterogeneity in important environmental parameters such as radiation, and the patchiness in understorey-vegetation distribution. Partitioning of the below canopy CO2 flux by combining EC and chamber measurements has been attempted in only a few studies (Black et al., 1996; Law et al., 1999; Janssens et al., 2001). The goal of this study was to apply EC measurements in conjunction with a continuous and automated chamber system for soil CO2 efflux to obtain assimilation estimates of the ground vegetation gas exchange. This approach with two complementary measuring techniques allows a correction of the relatively uncertain flux estimates of the eddy covariance measurements according to the chamber measurements. A comparison of day- and night-time EC fluxes can then be used to calculate the assimilation flux of carbon by the ground vegetation, and the total annual contribution to overall carbon balance of the ecosystem.
the Fichtelgebirge, a small mountain range in SE Germany (760 m above sea level). The forest plot has an area of about 7.5 ha, with a gentle south-west facing slope (<28). Forest thinning in previous decades have led to a low tree density (312 trees per hectare), while the canopy LAI is 6.0 m2 m2. The average height of the mature trees is around 26 m, with a mean crown-base height (=live canopy) of around 12.5 m. There is no forest understorey, and virtually no branches (live or dead) are present below 12.5 m, resulting in an open trunk space between ground cover and crown-base height. The dense ground cover is composed mainly of the grasses Calamagrostis villosa and Deschampsia flexuosa, with a maximum height of about 50 cm. EC measurements were carried out in three campaigns in 1999: from 12 May to 4 July, from 13 August to 24 September, and from 18 October to 17 November. The eddy covariance system was located between three extensive ground vegetation patches. To the North and East of the sensor (330–1158), ground vegetation was exclusively composed of C. villosa, a broad-leaved grass species, which re-grows annually from a substantial rhizome system to a height of about 40 cm. In the wind sectors between South-East and South West (115–2508), the ground vegetation is mainly composed of D. flexuosa, a perennial grass species that forms dense patches in which it grows to only about 10 cm in height. In the wind sector between 2508 and 3308, ground cover was a mixture of D. flexuosa and young spruce (P. abies) trees (2–4-yearold, ca. 20 cm high). LAI measurements of the ground vegetation were performed using the LAI2000 Plant Canopy Analyser (Li-Cor, Lincoln, NE, USA) and were verified by biomass sampling. The LAI of D. flexuosa patches was about 2 throughout the year, while C. villosa, did not start growing until mid-April (in 1999), and leaf development was complete in about June, when the LAI was around 2.5 in monospecific patches. Leaf senescence of C. villosa occurred from mid-October following a cold period, and was complete following frost and first snow on 12 November.
2. Methods 2.2. Data acquisition and processing 2.1. Site description The study site is a 112-year-old Norway spruce (Picea abies) forest located at 508080 N and 118520 E in
2.2.1. Measuring equipment Three-dimensional wind velocity was measured using a sonic anemometer (Solent R2, Gill Instru-
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ments, Lymington, UK; sampling frequency = 20.8 Hz) at 2 m above the forest floor. CO2 and water vapour concentrations were measured using an infrared gas analyser (Li-6262, Li-Cor, Lincoln, USA; sampling frequency = 10 Hz). Zero and Span calibrations of the gas analyser were carried out fortnightly. Sampling air was drawn from immediately below the anemometer (about 10 cm below sonic booms) at around 5 l min1, and filtered using PTFE filters (pore size = 1 mm; Gelman Sciences, Ann Arbor, USA) which were replaced fortnightly. The gas analyser, diaphragm pump and a laptop computer as well as a nitrogen cylinder for flushing of the reference gas cell were contained in two small field enclosures about 8 m due north of the sensors. Both enclosures were positioned close to tree trunks to minimise additional disturbance of the fetch of the EC system in this wind sector. 2.2.2. Data processing and corrections Raw data was stored in 30 min files for postprocessing using the software EdiSol (Version 0.36, J.M. Massheder, J.B. Moncrieff, University of Edinburgh, UK), and fluxes were calculated for 30 min intervals using the post-processing software EdiRe (Version 1.4.3.710, R. Clement, University of Edinburgh). A two axis coordinate rotation was performed to align the x-axis for flux computations with the threedimensional wind direction (i.e. v¯ and w¯ = 0). The time it takes for sample air to travel from the intake to the analyser results in a time lag between the signals from anemometer and gas analyser. Since no mass flow controller was used to maintain a constant flow rate from sampling inlet to the gas analyser, the lag time between wind signal and concentration measurement were calculated for each 30 min flux calculation. The cross correlations between vertical wind (w) and CO2 concentration (c) were checked for all 30 min measuring intervals and fluxes were excluded if lag values fell outside a plausible time range (between 2 and 4 s). The vertical CO2 flux was calculated from the covariance of w and c for 30 min intervals. We adhere to the convention of labelling fluxes as positive if they are directed upward, and negative for downward fluxes. The CO2 flux was corrected for fluctuations in air density due to variations in temperature and humidity, using the Webb corrections (Webb et al., 1980).
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2.2.3. Data quality tests Eddy covariance measurements below the forest canopy are limited by the low atmospheric turbulence conditions and the complexity of the terrain upwind from the sensors. Low wind speed and wakes caused by tree trunks in the vicinity of the sensors may affect measurements of turbulent exchange, while the heterogeneous distribution of radiation penetrating the canopy may induce thermals near the forest floor, causing intermittent biases in the fluxes. Power spectra of w and c as well as the cospectrum of w0 c0 (Fig. 1) show high spectral densities for wavenumbers ranging from 0.001 to 0.1 m1. The decline of spectral density for higher wavenumbers was generally steeper than the typical values associated with the dispersal of turbulent energy in the inertial subrange (slopes indicated in Fig. 1), which suggests that the cascade of energy dispersal from larger to smaller eddies is shortcircuited below the canopy. Data quality tests used in above canopy EC measurements (e.g. Foken and Wichura, 1996) are not
Fig. 1. Typical power spectra (a) for CO2 (c; solid symbols) and vertical wind velocity (w; open symbols), and co-spectrum (b) for the covariance between w and c (w0 c0 ), exemplified for measurements on 19 June 1999, between 11:00 and 11:30 h. Spectral densities are multiplied by natural frequencies, and normalised by the respective variance or covariance. The natural frequency (x-axis) is divided by the mean wind speed (0.65 m s1) of the same period.
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readily applicable for below canopy conditions, since here the surface layer similarity theory, on which the tests are based, do not apply. For the acceptance of EC flux data in this study, we define critical values for wind statistical parameters and signal properties above or below which fluxes were not reliable by comparison of EC flux to chamber fluxes at night-time. The standard deviation of the CO2 concentration (sc) during a measuring interval was found to exhibit a strong influence over whether EC fluxes were consistent with chamber fluxes. High variation in CO2 concentration during the measuring interval presumably indicate periods of little turbulent mixing, and slow build-up or release of CO2 through advective processes, leading to invalid EC flux measurements. For sc > 2.25 ppm, EC fluxes were not consistent with chamber data, irrespective of wind statistical parameters. Fluxes below this threshold value were excluded, if the mean wind velocity (u) was below 0.3 m s1, u* was below 0.05 m s1, or if the drag coefficient (u*u1) exceeded 0.5. 2.3. CO2 storage correction Estimation of CO2 storage in the air column below the eddy covariance sensors require a detailed measurement of the CO2 concentration profile between the soil and the measuring height. Due to instrumental limitations, only CO2 concentrations measured at 2 m (eddy covariance system) and at 10 cm above the forest floor (soil chamber system, see below) were available. To obtain a simple estimate of the storage flux, mean values of 30 min intervals (corresponding to time intervals for eddy flux computations) from either height were averaged, and the storage flux calculated according to S¼
Dc z Dt
(1)
where Dc is the change in CO2 concentration over a time interval (Dt), and z is the height of the air column between sensor and forest floor, in this case 2 m. 2.4. CO2 flux components Soil respiration was measured at five locations surrounding the eddy covariance station using an automated open chamber system during the entire growing
season. Soil chambers consisted of a cylindrical steel collar (20 cm diameter, 8 cm high) which was inserted to a depth of about 1.5 cm into the forest floor, and a Perspex lid with a centrally mounted air inlet (see Subke et al., 2003 for a detailed description of the chamber system and measurements). All aboveground vegetation was removed within chambers by clipping grasses at the soil surface at the beginning of measurements and for any re-growth during the experiment, so that chamber data represent the soil CO2 efflux only. The chamber data led to the development of a soil CO2 efflux model, based on the soil temperature at 5 cm depth (T) and the volumetric soil water content (t) of the organic layer: FS ¼ 3:57 e
403ð1=56:021=ðTþ46:02ÞÞg
t 0:172 þ t
(2)
No significant influence of ground cover type on FS could be detected (Subke et al., 2003). For the purpose of this analysis, the chamber flux is assumed to be a reliable estimate of the soil CO2 efflux. Night-time EC fluxes (FE) were consistently lower than FS (Fig. 2), and the linear regression between both fluxes was used for correcting FE values: FC ¼
FE 0:588
(3)
The EC measurements integrate the CO2 flux from the soil and the CO2 gas exchange from the above ground plant parts of the ground vegetation (V): F C ¼ FS þ V
(4)
V, in turn, is the difference of two fluxes in opposite directions: V ¼ A þ RV
(5)
where A is the flux due to photosynthetic assimilation of CO2 into the vegetation (hence the negative sign), and RV is the respiratory flux from all above-ground parts of the ground vegetation. The correction of FE by linear regression against chamber fluxes at night-time means that RV is neglected (A is zero at night-time). Direct measurements of the respiration by aboveground plant parts was not possible, and for the interpretation of flux data, this respiration term is considered as part of the forest floor CO2 efflux.
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point gives a spatially representative estimate of the forest floor quantum flux density. The linear relationship for daily flux sums above and below the canopy (Fig. 3) is used to scale above canopy Qp in order to obtain the mean below canopy photon flux density.
3. Results 3.1. CO2 flux below the forest canopy
2.5. Light measurements
Excluding periods of instrumental failure, and after removal of signal spikes, a total of 5003 30-min fluxes were computed for the three measuring campaigns combined. Only about 39% of these (1975 data) passed all quality criteria. High variability in the CO2 signal and low friction velocity was found to be the most frequent reasons for data rejection (Table 1). For the correlation between FE and FS at night-time (Fig. 2), the correlation coefficient was found to be higher when FE fluxes were not corrected by adding the storage flux (R2 = 0.565 for EC data only and 0.414 for storage corrected fluxes), but the slope of the regression line was unchanged. No storage correction was therefore carried out prior to flux analysis. In order to estimate the average flux contribution of the ground vegetation, the valid measurements were averaged according to the hour of day at which they were taken, and from which ground cover type the
Due to instrumental limitations, it was not possible to sample the quantum flux near the forest floor at high spatial resolution, and only measurements at one point below the canopy were possible (quantum flux sensor LI-190SA, Li-Cor, Lincoln, NE, USA), installed at 0.8 m above the forest floor. To obtain an estimate of the mean photosynthetic quantum flux density (Qp) near the forest floor, daily flux sums obtained by the quantum sensor were compared to the solar radiation data above the canopy obtained at a meteorological tower located about 120 m from the EC system (CM14, Kipp & Zonen, Saskatoon, Canada). The shortwave radiation measured above the canopy was converted into photosynthetic quantum flux density by multiplication by a factor of 2.2 mmol s1 W1 (Jones, 1992). This comparison assumes that the occurrence of sun flecks at the forest floor is evenly distributed, so that summed over the day, the measurement at one
Fig. 3. Daily photosynthetic photon flux densities (Qp) measured above and below the canopy during all four measuring campaigns in 1999. The linear regression was used to scale above canopy measurements in order to obtain mean below canopy Qp.
Fig. 2. Thirty-minute CO2 flux averages measured by eddy covariance (FE), plotted against spatially averaged soil chamber flux (FS) during night-time, separated into ground cover species according to wind sectors (a: C. villosa, b: D. flexuosa, c: mixture D. flexuosa and young P. abies trees). The slopes of the regression lines are all significant (P < 0.0001), and do not differ significantly from each other. For the correction of all FE values, a regression of all data was used (y = 0.588x; R2 = 0.57, n = 338, P < 0.0001).
Day-time uptake of CO2 by the ground vegetation was estimated by subtracting FS from FC: A ¼ FC FS
(6)
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Table 1 Frequency of 30 min flux data failing quality tests for a total of 5003 data points Test criterion
Number of data failing test
sc < 2.25 ppm u > 0.3 m s1 u* > 0.05 m s1 u*u1 < 0.5 2 s < c lag < 4 s
1686 459 1620 129 886
Note that data frequently failed more than one quality test for a given 30 min interval, with a total of 1975 passing all tests. Signal spikes were removed prior to data quality tests. The parameter ‘‘c lag’’ is the signal lag between the wind and corresponding CO2 concentration measurement (see text).
fluxes originated (i.e. according to wind sector). We divided the data set of the first (and longest) measuring campaign into two sub-sets (12 May–11 June, and 12 June–4 July), to improve the temporal representation of the below canopy flux throughout the growing season. Scarcity of data following the quality tests meant that fluxes had to be averaged over the entire length of each measuring period to obtain consistent trends representative for the respective periods. Data sets were not gap-filled, resulting in variable numbers of measurements being aggregated for a given daytime hour and ground cover type. Since wind from south and westerly directions dominated during the first and second measuring period, fluxes of C. villosa are underrepresented during many of the averaging periods. Comparison to the daily flux trends in the three sectors indicates that, at least for those hours over which sufficient flux data were available, no significant differences between ground cover types were apparent (Fig. 4). Given the similarity in the magnitude of the assimilation flux for the different groundcover types (Fig. 4), we aggregated flux data from all wind directions in order to estimate the average daily flux sums. The soil CO2 efflux shows considerably less variation over the course of an average day (amplitude of daily flux curve in Fig. 5), as well as between different days (error bars in Fig. 5) than EC fluxes during the same period. Both the magnitude of the day-time assimilation flux, and the length of the period over which photosynthetic assimilation could be detected show a pronounced seasonal trend. Average daily flux sums for the ground vegetation carbon assimilation were calculated by summing up all assimilation flux
averages (i.e. the difference between curves in Fig. 5) obtained during daylight hours, to yield the mean daily carbon (C) assimilation for each period. Daylight hours were defined as those hourly classes with an average incoming short-wave radiation of over 20 W m2 above the canopy throughout the length of a data set. These were: May–July (5–19 h), August/ September (7–18 h), October/November (8–17 h). For comparison with other ecosystem flux components, the stand gross photosynthetic flux (i.e. leaf assimilation per unit ground area) was calculated using the model PROXEL (Reichstein, 2001), which had been parameterised for water and carbon fluxes from the same site (Subke et al., 2003). Flux sums were calculated for the corresponding periods of below canopy EC measurements and aggregated for the same daylight hours as the ground vegetation. In this configuration, the LAI of the ground vegetation is included in the total stand LAI, so that the calculated flux integrates the photosynthesis of both canopy and ground vegetation. The soil CO2 efflux was calculated for each of the four periods (using Eq. (2)) and averaged to give the daily mean CO2 efflux (i.e. over 24 h) for each of the periods. Values for both stand photosynthesis and soil CO2 efflux were calculated in hourly time steps. Table 2 gives the different flux sums for the four periods and indicates the total below-canopy flux (excluding above-ground respiration) as well as the fraction of the stand photosynthetic uptake due to the ground vegetation. 3.2. Light response of ground vegetation Light response curves for each of the four measuring periods were obtained using a following hyperbolic regression function: A¼
aQp þc Qp þ b
(7)
where a, b, and c are fitted parameters. Light saturation occurred from around 60 mmol m2 s1 with differences in the assimilation values at saturation for the four periods (Fig. 6). The approximate assimilation value under saturating light (Asat) was calculated using Eq. (7) with the fitted parameters for each of the four periods, and Qp = 100 mmol m2 s1 (Table 3).
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Fig. 4. Assimilation flux (A) averaged for corresponding day-time hours over each of the four periods in 1999, and separated into the ground vegetation types C. villosa (filled squares), D. flexuosa (filled triangles), and mixed D. flexuosa/P. abies (open circles). Error bars indicate 1 S.E., and only averages for day-time hours with n > 3 are presented.
Fig. 5. CO2 flux below the canopy measured by eddy covariance (FC, open symbols) and by soil chambers (FS, solid symbols) averaged for corresponding day-time hours for each of the four periods in 1999. Error bars indicate 1 S.E., and only averages with n > 5 are presented.
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Table 2 CO2 flux sums for ground vegetation gross assimilation, soil CO2 efflux and canopy gross photosynthetic uptake measured in 1999 Period
Ground vegetation day-time gross assimilation (a)
(b)
12 12 13 18
0.61 1.17 0.79 0.12
2.12 2.49 2.84 0.97
May–11 June June–4 July August–24 September October–17 November
0.05 0.04 0.03 0.02
Soil efflux
Canopy day-time gross assimilation (c)
0.01 0.01 0.01 0.01
7.99 8.63 7.92 3.70
Below-canopy flux (a + b)
2.61 1.90 2.69 1.83
1.51 1.32 2.05 0.84
Fraction ground vegetation a/(a + c)
0.05 0.04 0.03 0.02
0.07 0.12 0.09 0.03
All values are means standard deviations in g C m2 day1. A negative sign indicates a flux directed from the atmosphere into the vegetation, and a positive sign a flux from the vegetation (or soil) into the atmosphere.
4. Discussion The results of our study confirm the problematic conditions for eddy covariance measurements below a closed canopy reported in previous work (Baldocchi and Meyers, 1991; Baldocchi and Vogel, 1996; Black et al., 1996; Law et al., 1999; Lamaud et al., 2001). Especially night-time measurements are affected by insufficient atmospheric mixing, which leads to a violation to the assumptions underlying the eddy covariance theory. The criteria used to discriminate
between ‘‘valid’’ and ‘‘invalid’’ are critical to the interpretation of measured fluxes, and differ between published studies. Janssens et al. (2000), for example, only include day-time data for their comparison of eddy covariance and chamber measurements due to the uncertainty of night-time data. As the data quality tests in our study showed, a sizeable portion of the day-time measurements suffer from insufficient turbulence, so that a discrimination between valid and invalid measurements based on the turbulence characteristics during a given measuring interval is a more
Fig. 6. Photosynthetic assimilation (A) by the ground vegetation in response to mean below canopy radiation (Qp), as measured during the four periods in 1999. Light response curves were fitted to 30 min fluxes, using Eq. (7), and fitted parameters and statistics are shown in Table 3.
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Table 3 Fitted parameters and statistics for light response curves of 30 min fluxes (see Eq. (7), Fig. 6), and approximate assimilation flux values under saturating light at the forest floor (Asat; calculated for Qp = 100 mmol m2 s1), together with mean air temperatures (T) and soil water contents (t) for the four measuring periods in 1999 12 May–11 June
12 June–4 July
13 August–24 September
18 October–17 November
Fit parameters and statistics a b c R2 x2
2.25 0.59 45.3 27.8 0.079 0.091 0.32 0.605
2.96 0.18 27.0 6.3 0.090 0.102 0.59 0.55
2.09 0.22 16.9 7.6 0.290 0.142 0.36 0.81
1.09 0.45 29.8 26.1 0.058 0.044 0.17 0.20
Measuring period averages Asat (mmol m2 s1) T (air) (8C) t (m3 m3)
1.63 9.4 0.28
2.42 10.9 0.25
2.08 12.3 0.22
0.89 4.6 0.27
sensible approach than to simply divide into day- and night-time measurements. However, turbulence at our measuring location was generally low with u* exceeding 0.2 m s1 in only about 2% of measuring periods. Measurements made under low turbulence have been reported to under-estimate respiration fluxes (Matteucci et al., 2000; Janssens et al., 2000), or even result in negative fluxes at night-time (Black et al., 1996). The reasons put forward to account for this under-estimation include horizontal advection of CO2 (Black et al., 1996), cold-air drainage, a temporal exchange pattern that shifts towards high-frequency eddies, for which the measuring set-up is inadequate, or vertical wind fluctuations too small to be detected by the anemometer (Goulden et al., 1996). The mean vertical wind velocity (w) ¯ measured throughout all campaigns showed a daily trend with peak velocities of +0.015 and 0.015 m s1 at 1:00 and 14:00 h, respectively, indicating that transport of CO2 through horizontal advection is likely at the site. However, there was no consistent trend in the ratio of EC/ chamber flux in relation to w, ¯ so that no correction of EC fluxes on the basis of w ¯ was possible. Storage of CO2 may also account for a reduction in flux during calm periods, and ‘‘plumes’’ of CO2 rich air have been reported by several researchers (e.g. Grace et al., 1996; Greco and Baldocchi, 1996). Data from periods of CO2 concentration build-up or release had been filtered by the threshold value of 2.25 ppm for sc, and the simple storage correction performed in this study did not significantly change the relationship between night-time EC and chamber fluxes. The small magnitude of the storage term during periods of
‘‘valid’’ measurements is not likely to account for an under-estimation of about 41% found by the comparison between EC and chamber flux measurements at night. The data quality tests we applied are purely empirical and the use of the same rules at a different site may be questionable. The ratio of under-estimation by EC measurements showed consistency for a wide range of wind speed and friction velocities, resulting in extremely low critical values of data acceptance for these parameters. For example, the slope for a linear regression between FC and FS ranged only from 0.58 to 0.62 for u* classes between 0.2 and 0.05 m s1, before dropping to lower values (and poorer coefficients of correlation) at lower u* values. Similarly, light response curves were consistent throughout the same range of u* values. Since no single parameter, or combination of parameters, could be identified that could account for the divergence of flux measurements, no correction on the basis of meteorological conditions was possible, and the scaling of EC fluxes on the basis of the nighttime comparison with chamber fluxes (Fig. 2) was the best workable option to make flux measurements obtained by either systems comparable. The use of soil chamber measurements as a reference for night-time EC flux has to account for measuring errors associated with chamber measurements. The soil chambers have been designed to minimise measuring artefacts resulting from soil disturbance and pressure differences between chamber space and ambient atmosphere, commonly found for open flow-through chamber designs. A recent comparison of chamber designs against the known CO2 flux from
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an experimental tank showed that over a range of efflux rates and soil moisture conditions, this particular chamber gives reliable estimates of the actual soil CO2 efflux (Pumpanen et al., in press). Compared to the variability of the night-time EC flux measurements, chambers thus produce relatively reliable efflux values, which for the purpose of this study is accepted as the true soil CO2 efflux. By correcting the night-time fluxes obtained by eddy covariance against those measured with the continuous soil chamber system, it was possible to reduce the otherwise considerable error due to the conditions that limit micrometeorological flux measurements at the site. For the extrapolation of this calibration to day-time measurements, it is assumed that there is no selective systematic error, e.g. where errors occur during day-time or night-time only. Following these approximate error corrections, it is difficult to estimate the absolute value of the measuring certainty. By grouping data into hourly classes, remaining random errors would diminish with increasing numbers pffiffiffiffi of measurements in each category according to 1= N , where N is the number of data points (Moncrieff et al., 1996). The contribution of ground vegetation photosynthesis to the stand photosynthesis could be estimated as between 3 and 12% for the different periods (Table 2). The seasonality of these flux contributions is most likely to be a result of temperature and soil moisture differences between the four periods (Table 3). For C. villosa, the difference in LAI throughout the season would also mean a difference in assimilation potential per unit ground area. However, due to the scarcity of valid flux data from the wind sector containing this grass species, no seasonal trend due to LAI could be separated within the flux results (compare Fig. 4). The very low assimilation flux during the last campaign is limited to only a few daylight hours, and is most likely to be due to the perennial D. flexuosa, and the P. abies seedlings only, since leaf senescence of C. villosa started from mid-October. Up-scaled results of leaf cuvette measurements on the ground vegetation in a pine forest in SW Germany indicate a contribution of 20% to the total NPP (Wedler et al., 1996). The LAI of the ground vegetation in that particular study is greater than 50% of the canopy LAI (1.54 and 2.8, respectively), which differs markedly from conditions at our study site (LAIs of 2.2 for the mean ground cover and 6.0 for the canopy).
The authors calculate an average accumulation of 1.1 g C m2 day1 over 1 week in May with high incident above-canopy radiation. In a boreal Norway spruce forest, More´ n and Lindroth (2000) arrive at an estimate of about 1 g C m2 day1 for 7 days in June, by scaling measurements from a large chamber up to the forest stand (canopy LAI = 5.6, but no ground cover LAI stated). These daily flux estimates compare well with the findings in this study (daily averages between 0.6 and 1.2 g C m2 day1 for measuring periods from May to September; Table 2). Although both approaches (i.e. scaling up from isolated chamber measurements versus integrating fluxes over larger ground patches) suffer from errors related to heterogeneity of conditions and underlying assumptions that are not always satisfied, this similarity encourages further investigations of understorey flux contributions across forest types. The method used to calculate average incident radiation at the forest floor ignores the occurrence of sun flecks. The carbon gained by understorey species may however depend on the frequency of sun flecks, since leaf gas exchange measurements indicate that between 30 and 65% of the understorey assimilation in moist redwood forests (Pfitsch and Pearcey, 1989), and 2–27% in temperate deciduous forests (Schulze, 1972; Koizumi and Oshima, 1993) is due to the utilisation of sun flecks. A more accurate determination of the light conditions in the respective wind sectors for each 30 min flux value would in all likelihood have reduced the scatter in Fig. 6. Nonetheless, the magnitude of the average photosynthetic response can be estimated from the presented data. The maximum assimilation rate for saturating light at the forest floor shows a clear seasonal trend reflecting the changes in photosynthetically active leaf area and environmental conditions (Fig. 6, Table 3). Averaging variable parameters such as temperature and soil water content over periods of about 4–6 weeks masks periodic occurrences of extreme values, which may affect the ground vegetation gas exchange markedly. During early September, for example, the soil water content of the organic soil layer dropped below 0.15 m3 m3 for over a week (Subke et al., 2003), resulting in a significant decrease in the soil CO2 efflux over that period. The photosynthetic activity of the ground vegetation is likely to have been affected
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by this short period of drought also, owing to its relatively shallow root system. Interpolating between the average amounts of carbon assimilation over the measuring periods to the entire vegetation period can give a rough estimate of the gross assimilation flux of the ground vegetation. It can be assumed that assimilation started at around mid-April (when the average daily temperature rose to above 5 8C; the last ground penetrating frost in 1999 occurred on 21 April), at about the time when the annual leaf growth of C. villosa started. In autumn, the first frost was recorded for 16 October, but mean daily air temperatures stayed at around 5 8C until early November. Interpolating the measured flux results over the year by assuming an assimilation flux of zero before 15 April, and after 15 November, the annual carbon uptake by the ground vegetation was estimated to be around 170 g C m2. Considerable amounts of carbon assimilated by the ground vegetation are likely to be respired by the plants (leaf and root respiration), while about 85 g C m2 are found in the standing biomass (32 g C m2 above ground, and 53 g C m2 below ground). On an annual basis, gross uptake by the ground vegetation represents about 28% of the carbon respired from the soil (ca. 590 g C m2 year1 in 1999; Subke et al., 2003), which includes heterotrophic as well as autotrophic belowground respiration by trees and the ground vegetation.
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Measurements above the canopy integrate over all flux contributions (assimilation as well as respiration) within the stand, and individual flux terms have to be deduced from regression functions. Information on the relative contributions from the different components through direct measurements are necessary to validate such regressions, and this study demonstrates that despite problematic atmospheric conditions below the canopy, information on the physiological functioning of ground cover vegetation can be obtained if eddy covariance is used in combination with chamber measurements.
Acknowledgements The authors would like to thank J. Gerchau (Bayreuth Institute for Terrestrial Ecosystem Research, Bayreuth, Germany) for supplying above-canopy radiation data, B. Wichura and T. Foken (University Bayreuth, Germany) for help with the processing of eddy covariance data, as well as T. Gru¨ nwald and C. Bernhofer (University Dresden, Germany) for comments on an early version of the manuscript. This work was funded by the German Ministry for Education and Research (BMBF), grant no. PT BEO-51-0339476C.
References 5. Conclusion The results presented here indicate a contribution by ground cover vegetation of up to about 12% to the total stand assimilation flux in the summer months in a Norway spruce forest with high leaf area index, with about 30% of the carbon respired from the forest soil being cycled through the ground vegetation. Ecosystem models for coniferous stands generally assume constant LAI throughout the year. However, in stands with ground vegetation, the overall LAI changes with the phonological development of the ground species, affecting the seasonal gas exchange pattern. Depending on the relative contribution of the ground vegetation to the overall LAI, this component may be significant for an accurate description of C dynamics within a forest stand, and has to be accounted for in ecosystem models.
Baldocchi, D.D., Meyers, T.P., 1991. Trace gas exchange above the floor of a deciduous forest. I. Evaporation and CO2 efflux. J. Geophys. Res. Atmos. 96, 7271–7285. Baldocchi, D.D., Vogel, C.A., 1996. A comparative study of water vapor, energy and CO2 flux densities above and below a temperate broad-leave and a boreal pine forest. Tree Physiol. 16, 5–16. Baldocchi, D.D., Vogel, C.A., Hall, B., 1997. Seasonal variation of carbon dioxide exchange rates above and below a boreal jack pine forest canopy. Agric. For. Meteorol. 83, 147–170. Baldocchi, D.D., Law, B.E., Anthoni, P.M., 2000. On measuring and modelling energy fluxes above the floor of a homogeneous and heterogeneous conifer forest. Agric. For. Meteorol. 102, 187–206. Black, T.A., Den Hartog, G., Neumann, H.H., Blanken, P.D., Yang, P.C., Russell, C., Nesic, Z., Lee, X., Chen, S.C., Staebler, R., Novak, M.D., 1996. Annual cycles of water vapour and carbon dioxide fluxes in and above a boreal aspen forest. Global Change Biol. 2, 219–229. Foken, T., Wichura, B., 1996. Tools for quality assessment of surface-based flux measurements. Agric. For. Meteorol. 78, 83–105.
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Goulden, M.L., Crill, P.M., 1997. Automated measurements of CO2 exchange at the moss surface of a black spruce forest. Tree Physiol. 17, 537–542. Goulden, M.L., Munger, J.W., Fan, S.-F., Daube, B.C., Wofsy, S.C., 1996. Measurements of carbon sequestration by long-term eddy covariance: methods and a critical evaluation of accuracy. Global Change Biol. 2, 169–182. Grace, J., Lloyd, J., McIntyre, J., Miranda, A.C., Meir, P., Miranda, H.S., 1996. The use of eddy covariance to infer the net carbon dioxide uptake of Brazilian rain forest. Global Change Biol. 2, 209–217. Greco, S., Baldocchi, D.D., 1996. Seasonal variations of CO2 and water vapour exchange rates over a temperate deciduous forest. Global Change Biol. 2, 183–197. Janssens, I.A., Kowalski, A.S., Longdoz, B., Ceulemans, R., 2000. Assessing forest soil CO2 efflux: an in situ comparison of four techniques. Tree Physiol. 20, 23–32. Janssens, I.A., Kowalski, A.S., Ceulemans, R., 2001. Forest floor CO2 fluxes estimated by eddy covariance and chamber-based model. Agric. For. Meteorol. 106, 61–69. Jones, H.G., 1992. Plants and Microclimate, 2nd ed. Cambridge University Press, Cambridge, UK. Koizumi, H., Oshima, Y., 1993. Light environment and carbon gain of understory herbs associated with sunflecks in a warm temperate deciduous forest in Japan. Ecol. Res. 8, 135–142. Lamaud, E., Oge´ e, J., Brunet, Y., Berbigier, P., 2001. Validation of eddy flux measurements above the understorey of a pine forest. Agric. For. Meteorol. 106, 187–203. Law, B.E., Baldocchi, D.D., Anthoni, P.M., 1999. Below-canopy and soil CO2 fluxes in a ponderosa pine forest. Agric. For. Meteorol. 94, 171–188. Matteucci, G., Dore, S., Stivanello, S., Rebmann, C., Buchmann, N., 2000. Soil respiration in beech and spruce forests in Europe: trends, controlling factors, annual budgets and implications for the ecosystem carbon balance.In: Schulze, E.-D. (Ed.), 2000. Carbon and Nitrogen Cycling in European Forest Ecosystems;
Ecological Studies, vol. 142. Springer Verlag, Heidelberg, Germany, pp. 217–236. Moncrieff, J., Malhi, Y., Leuning, R., 1996. The propagation of errors in long-term measurements of land-atmosphere fluxes of carbon and water. Global Change Biol. 2, 231–240. More´ n, A.-S., Lindroth, A., 2000. CO2 exchange at the floor of a boreal forest. Agric. For. Meteorol. 101, 1–14. Pfitsch, W.A., Pearcey, R.W., 1989. Daily carbon gain by Adenocaulon bicolor (Asteraceae), a redwood forest under-story herb, in relation to its light environment. Oecologia 80, 465–470. Pumpanen, J., Kolari, P., Ilvesniemi H., Minkkinen K., Vesala, T., Niinisto¨ , S., Lohila, A., Larmola, T., Morero, M., Pihlatie, M., Janssens, I., Curiel, J., Gru¨ nzweig, J.M., Reth, S., Subke, J.A., Savage, K., Kutsch, W., Østreng, G., Ziegler, W., Anthoni, P., Lindroth, A., Hari, P., in press. Comparison of different chamber techniques for measuring soil CO2 efflux. Agric. For. Meteorol. Reichstein, M., 2001. Drought effects on carbon and water exchange in three Mediterranean ecosystems. Doctorate Thesis. Univer¨ kologie, 89, sity of Bayreuth, Germany. Bayreuther Forum O ¨ K, Bayreuth, Germany. BITO Schulze, E.-D., 1972. Die Wirkung von Licht und Temperatur auf den CO2 Gaswechsel verschiedener Lebensformen aus der Krautschicht eines montanen Buchenwaldes. Oecologia 9, 223–234. Subke, J.-A., Reichstein, M., Tenhunen, J.D., 2003. Explaining temporal variation in soil CO2 efflux in a mature spruce forest in SE Germany. Soil Biol. Biochem. 35, 1467–1483. Webb, E., Pearman, G., Leuning, R., 1980. Correction of flux measurements for density effects due to heat and water vapour transfer. Quart. J. R. Meteor. Soc. 106, 85–100. Wedler, M., Geyer, R., Heindl, B., Hahn, S., Tenhunen, J.D., 1996. Leaf-level gas exchange and scaling-up of forest understorey carbon fixation rates with a ‘‘patch scale’’ canopy model. Theor. Appl. Climatol. 53, 145–156. Wilson, K.B., Meyers, T.P., 2001. The spatial variability of energy and carbon dioxide fluxes at the floor of a deciduous forest. Boundary Layer Meteorol. 98, 443–473.
Direct measurements of CO2 flux below a spruce forest canopy Jens-Arne Subke*, John D. Tenhunen Department of Plant Ecology, University of Bayreuth, D-95440 Bayreuth, Germany Received 2 December 2002; received in revised form 25 June 2004; accepted 29 June 2004
Abstract We applied the eddy covariance technique below the canopy of a mature Norway spruce (Picea abies) forest in SE Germany, with a dense ground cover of the grasses Calamagrostis villosa and Deschampsia flexuosa. Measurements were carried out during three field campaigns between May and November 1999. Data quality analysis led to the exclusion of around 61% of flux measurements. Chamber measurements of soil CO2 efflux at the same site showed that the micro-meteorological measurements systematically under-estimate the night-time CO2 efflux on average by about 41%. By correcting the eddy covariance flux on the basis of chamber measurements, flux measurements over a larger area could be obtained, and so it was possible to calculate the daytime gross assimilation flux for the ground vegetation averaged for each of four 4-week periods. Separation of fluxes according to grass species (by wind sectors) showed no detectable difference in CO2 flux, but scarcity of data following quality tests meant that comparisons were limited to selected periods only. Light response curves of the corrected flux values indicate different saturation values for the gross assimilation rates in each period, which reflect the phenological development of the grasses, as well as differences in environmental conditions. On the basis of mean day-time assimilation by ground vegetation, the contribution to the total gross assimilation of the stand was estimated as between 3 and 12% at different times of the year. About 30% of the carbon emitted annually as CO2 from the soil is estimated to be assimilated by the ground vegetation (ca. 170 g C m2 year1). # 2004 Elsevier B.V. All rights reserved. Keywords: Eddy covariance; Ground vegetation; Understorey gas exchange; Picea abies forest; Soil respiration
1. Introduction Photosynthesis and autotrophic respiration of the canopy dominate the above-ground CO2 exchange of a forest ecosystem. However, understorey- and groundvegetation may represent a considerable fraction of * Corresponding author. Present address: Stockholm Environment Institute, University of York, Heslington, York YO10 5DD, UK. Tel.: +44 1904 328639; fax: +44 1904 432898. E-mail address: [email protected] (J.-A. Subke).
the overall leaf area index (LAI), and therefore contribute significantly to the net gas exchange of a forest stand (e.g. Wedler et al., 1996; Black et al., 1996; More´n and Lindroth, 2000). These contributions are quite distinct from the ecophysiological processes in the crown space, owing to extreme differences in light regimes and different seasonal patterns in phenology. Eddy covariance (EC) measurements provide a micro-meteorological technique for quantifying the time-integrated flux of CO2 at a given height. For measurements of soil CO2 efflux, it has the great
0168-1923/$ – see front matter # 2004 Elsevier B.V. All rights reserved. doi:10.1016/j.agrformet.2004.06.007
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benefit that it does not disturb the natural soil system in the way that chamber measurements do, and that fluxes are sampled from a considerably larger area. However, especially underneath the forest canopy, the EC technique is limited to periods of favourable atmospheric conditions (owing to a set of assumptions underlying the measurement theory), and measurements using chambers are more commonly employed. The validity of below canopy EC measurements has been addressed in numerous studies, which show that this technique can be applied, but measuring uncertainty remains as a main concern (Baldocchi and Meyers, 1991; Baldocchi and Vogel, 1996; Baldocchi et al., 1997, 2000; Lamaud et al., 2001; Wilson and Meyers, 2001). Experimental results from either leaf cuvettes (Wedler et al., 1996) or ground chambers (Goulden and Crill, 1997; More´ n and Lindroth, 2000) indicate significant contributions of ground vegetation gas exchange in relation to the stand carbon fluxes. However, scaling up of this type of measurement to the stand level is difficult due to the large heterogeneity in important environmental parameters such as radiation, and the patchiness in understorey-vegetation distribution. Partitioning of the below canopy CO2 flux by combining EC and chamber measurements has been attempted in only a few studies (Black et al., 1996; Law et al., 1999; Janssens et al., 2001). The goal of this study was to apply EC measurements in conjunction with a continuous and automated chamber system for soil CO2 efflux to obtain assimilation estimates of the ground vegetation gas exchange. This approach with two complementary measuring techniques allows a correction of the relatively uncertain flux estimates of the eddy covariance measurements according to the chamber measurements. A comparison of day- and night-time EC fluxes can then be used to calculate the assimilation flux of carbon by the ground vegetation, and the total annual contribution to overall carbon balance of the ecosystem.
the Fichtelgebirge, a small mountain range in SE Germany (760 m above sea level). The forest plot has an area of about 7.5 ha, with a gentle south-west facing slope (<28). Forest thinning in previous decades have led to a low tree density (312 trees per hectare), while the canopy LAI is 6.0 m2 m2. The average height of the mature trees is around 26 m, with a mean crown-base height (=live canopy) of around 12.5 m. There is no forest understorey, and virtually no branches (live or dead) are present below 12.5 m, resulting in an open trunk space between ground cover and crown-base height. The dense ground cover is composed mainly of the grasses Calamagrostis villosa and Deschampsia flexuosa, with a maximum height of about 50 cm. EC measurements were carried out in three campaigns in 1999: from 12 May to 4 July, from 13 August to 24 September, and from 18 October to 17 November. The eddy covariance system was located between three extensive ground vegetation patches. To the North and East of the sensor (330–1158), ground vegetation was exclusively composed of C. villosa, a broad-leaved grass species, which re-grows annually from a substantial rhizome system to a height of about 40 cm. In the wind sectors between South-East and South West (115–2508), the ground vegetation is mainly composed of D. flexuosa, a perennial grass species that forms dense patches in which it grows to only about 10 cm in height. In the wind sector between 2508 and 3308, ground cover was a mixture of D. flexuosa and young spruce (P. abies) trees (2–4-yearold, ca. 20 cm high). LAI measurements of the ground vegetation were performed using the LAI2000 Plant Canopy Analyser (Li-Cor, Lincoln, NE, USA) and were verified by biomass sampling. The LAI of D. flexuosa patches was about 2 throughout the year, while C. villosa, did not start growing until mid-April (in 1999), and leaf development was complete in about June, when the LAI was around 2.5 in monospecific patches. Leaf senescence of C. villosa occurred from mid-October following a cold period, and was complete following frost and first snow on 12 November.
2. Methods 2.2. Data acquisition and processing 2.1. Site description The study site is a 112-year-old Norway spruce (Picea abies) forest located at 508080 N and 118520 E in
2.2.1. Measuring equipment Three-dimensional wind velocity was measured using a sonic anemometer (Solent R2, Gill Instru-
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ments, Lymington, UK; sampling frequency = 20.8 Hz) at 2 m above the forest floor. CO2 and water vapour concentrations were measured using an infrared gas analyser (Li-6262, Li-Cor, Lincoln, USA; sampling frequency = 10 Hz). Zero and Span calibrations of the gas analyser were carried out fortnightly. Sampling air was drawn from immediately below the anemometer (about 10 cm below sonic booms) at around 5 l min1, and filtered using PTFE filters (pore size = 1 mm; Gelman Sciences, Ann Arbor, USA) which were replaced fortnightly. The gas analyser, diaphragm pump and a laptop computer as well as a nitrogen cylinder for flushing of the reference gas cell were contained in two small field enclosures about 8 m due north of the sensors. Both enclosures were positioned close to tree trunks to minimise additional disturbance of the fetch of the EC system in this wind sector. 2.2.2. Data processing and corrections Raw data was stored in 30 min files for postprocessing using the software EdiSol (Version 0.36, J.M. Massheder, J.B. Moncrieff, University of Edinburgh, UK), and fluxes were calculated for 30 min intervals using the post-processing software EdiRe (Version 1.4.3.710, R. Clement, University of Edinburgh). A two axis coordinate rotation was performed to align the x-axis for flux computations with the threedimensional wind direction (i.e. v¯ and w¯ = 0). The time it takes for sample air to travel from the intake to the analyser results in a time lag between the signals from anemometer and gas analyser. Since no mass flow controller was used to maintain a constant flow rate from sampling inlet to the gas analyser, the lag time between wind signal and concentration measurement were calculated for each 30 min flux calculation. The cross correlations between vertical wind (w) and CO2 concentration (c) were checked for all 30 min measuring intervals and fluxes were excluded if lag values fell outside a plausible time range (between 2 and 4 s). The vertical CO2 flux was calculated from the covariance of w and c for 30 min intervals. We adhere to the convention of labelling fluxes as positive if they are directed upward, and negative for downward fluxes. The CO2 flux was corrected for fluctuations in air density due to variations in temperature and humidity, using the Webb corrections (Webb et al., 1980).
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2.2.3. Data quality tests Eddy covariance measurements below the forest canopy are limited by the low atmospheric turbulence conditions and the complexity of the terrain upwind from the sensors. Low wind speed and wakes caused by tree trunks in the vicinity of the sensors may affect measurements of turbulent exchange, while the heterogeneous distribution of radiation penetrating the canopy may induce thermals near the forest floor, causing intermittent biases in the fluxes. Power spectra of w and c as well as the cospectrum of w0 c0 (Fig. 1) show high spectral densities for wavenumbers ranging from 0.001 to 0.1 m1. The decline of spectral density for higher wavenumbers was generally steeper than the typical values associated with the dispersal of turbulent energy in the inertial subrange (slopes indicated in Fig. 1), which suggests that the cascade of energy dispersal from larger to smaller eddies is shortcircuited below the canopy. Data quality tests used in above canopy EC measurements (e.g. Foken and Wichura, 1996) are not
Fig. 1. Typical power spectra (a) for CO2 (c; solid symbols) and vertical wind velocity (w; open symbols), and co-spectrum (b) for the covariance between w and c (w0 c0 ), exemplified for measurements on 19 June 1999, between 11:00 and 11:30 h. Spectral densities are multiplied by natural frequencies, and normalised by the respective variance or covariance. The natural frequency (x-axis) is divided by the mean wind speed (0.65 m s1) of the same period.
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readily applicable for below canopy conditions, since here the surface layer similarity theory, on which the tests are based, do not apply. For the acceptance of EC flux data in this study, we define critical values for wind statistical parameters and signal properties above or below which fluxes were not reliable by comparison of EC flux to chamber fluxes at night-time. The standard deviation of the CO2 concentration (sc) during a measuring interval was found to exhibit a strong influence over whether EC fluxes were consistent with chamber fluxes. High variation in CO2 concentration during the measuring interval presumably indicate periods of little turbulent mixing, and slow build-up or release of CO2 through advective processes, leading to invalid EC flux measurements. For sc > 2.25 ppm, EC fluxes were not consistent with chamber data, irrespective of wind statistical parameters. Fluxes below this threshold value were excluded, if the mean wind velocity (u) was below 0.3 m s1, u* was below 0.05 m s1, or if the drag coefficient (u*u1) exceeded 0.5. 2.3. CO2 storage correction Estimation of CO2 storage in the air column below the eddy covariance sensors require a detailed measurement of the CO2 concentration profile between the soil and the measuring height. Due to instrumental limitations, only CO2 concentrations measured at 2 m (eddy covariance system) and at 10 cm above the forest floor (soil chamber system, see below) were available. To obtain a simple estimate of the storage flux, mean values of 30 min intervals (corresponding to time intervals for eddy flux computations) from either height were averaged, and the storage flux calculated according to S¼
Dc z Dt
(1)
where Dc is the change in CO2 concentration over a time interval (Dt), and z is the height of the air column between sensor and forest floor, in this case 2 m. 2.4. CO2 flux components Soil respiration was measured at five locations surrounding the eddy covariance station using an automated open chamber system during the entire growing
season. Soil chambers consisted of a cylindrical steel collar (20 cm diameter, 8 cm high) which was inserted to a depth of about 1.5 cm into the forest floor, and a Perspex lid with a centrally mounted air inlet (see Subke et al., 2003 for a detailed description of the chamber system and measurements). All aboveground vegetation was removed within chambers by clipping grasses at the soil surface at the beginning of measurements and for any re-growth during the experiment, so that chamber data represent the soil CO2 efflux only. The chamber data led to the development of a soil CO2 efflux model, based on the soil temperature at 5 cm depth (T) and the volumetric soil water content (t) of the organic layer: FS ¼ 3:57 e
403ð1=56:021=ðTþ46:02ÞÞg
t 0:172 þ t
(2)
No significant influence of ground cover type on FS could be detected (Subke et al., 2003). For the purpose of this analysis, the chamber flux is assumed to be a reliable estimate of the soil CO2 efflux. Night-time EC fluxes (FE) were consistently lower than FS (Fig. 2), and the linear regression between both fluxes was used for correcting FE values: FC ¼
FE 0:588
(3)
The EC measurements integrate the CO2 flux from the soil and the CO2 gas exchange from the above ground plant parts of the ground vegetation (V): F C ¼ FS þ V
(4)
V, in turn, is the difference of two fluxes in opposite directions: V ¼ A þ RV
(5)
where A is the flux due to photosynthetic assimilation of CO2 into the vegetation (hence the negative sign), and RV is the respiratory flux from all above-ground parts of the ground vegetation. The correction of FE by linear regression against chamber fluxes at night-time means that RV is neglected (A is zero at night-time). Direct measurements of the respiration by aboveground plant parts was not possible, and for the interpretation of flux data, this respiration term is considered as part of the forest floor CO2 efflux.
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point gives a spatially representative estimate of the forest floor quantum flux density. The linear relationship for daily flux sums above and below the canopy (Fig. 3) is used to scale above canopy Qp in order to obtain the mean below canopy photon flux density.
3. Results 3.1. CO2 flux below the forest canopy
2.5. Light measurements
Excluding periods of instrumental failure, and after removal of signal spikes, a total of 5003 30-min fluxes were computed for the three measuring campaigns combined. Only about 39% of these (1975 data) passed all quality criteria. High variability in the CO2 signal and low friction velocity was found to be the most frequent reasons for data rejection (Table 1). For the correlation between FE and FS at night-time (Fig. 2), the correlation coefficient was found to be higher when FE fluxes were not corrected by adding the storage flux (R2 = 0.565 for EC data only and 0.414 for storage corrected fluxes), but the slope of the regression line was unchanged. No storage correction was therefore carried out prior to flux analysis. In order to estimate the average flux contribution of the ground vegetation, the valid measurements were averaged according to the hour of day at which they were taken, and from which ground cover type the
Due to instrumental limitations, it was not possible to sample the quantum flux near the forest floor at high spatial resolution, and only measurements at one point below the canopy were possible (quantum flux sensor LI-190SA, Li-Cor, Lincoln, NE, USA), installed at 0.8 m above the forest floor. To obtain an estimate of the mean photosynthetic quantum flux density (Qp) near the forest floor, daily flux sums obtained by the quantum sensor were compared to the solar radiation data above the canopy obtained at a meteorological tower located about 120 m from the EC system (CM14, Kipp & Zonen, Saskatoon, Canada). The shortwave radiation measured above the canopy was converted into photosynthetic quantum flux density by multiplication by a factor of 2.2 mmol s1 W1 (Jones, 1992). This comparison assumes that the occurrence of sun flecks at the forest floor is evenly distributed, so that summed over the day, the measurement at one
Fig. 3. Daily photosynthetic photon flux densities (Qp) measured above and below the canopy during all four measuring campaigns in 1999. The linear regression was used to scale above canopy measurements in order to obtain mean below canopy Qp.
Fig. 2. Thirty-minute CO2 flux averages measured by eddy covariance (FE), plotted against spatially averaged soil chamber flux (FS) during night-time, separated into ground cover species according to wind sectors (a: C. villosa, b: D. flexuosa, c: mixture D. flexuosa and young P. abies trees). The slopes of the regression lines are all significant (P < 0.0001), and do not differ significantly from each other. For the correction of all FE values, a regression of all data was used (y = 0.588x; R2 = 0.57, n = 338, P < 0.0001).
Day-time uptake of CO2 by the ground vegetation was estimated by subtracting FS from FC: A ¼ FC FS
(6)
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Table 1 Frequency of 30 min flux data failing quality tests for a total of 5003 data points Test criterion
Number of data failing test
sc < 2.25 ppm u > 0.3 m s1 u* > 0.05 m s1 u*u1 < 0.5 2 s < c lag < 4 s
1686 459 1620 129 886
Note that data frequently failed more than one quality test for a given 30 min interval, with a total of 1975 passing all tests. Signal spikes were removed prior to data quality tests. The parameter ‘‘c lag’’ is the signal lag between the wind and corresponding CO2 concentration measurement (see text).
fluxes originated (i.e. according to wind sector). We divided the data set of the first (and longest) measuring campaign into two sub-sets (12 May–11 June, and 12 June–4 July), to improve the temporal representation of the below canopy flux throughout the growing season. Scarcity of data following the quality tests meant that fluxes had to be averaged over the entire length of each measuring period to obtain consistent trends representative for the respective periods. Data sets were not gap-filled, resulting in variable numbers of measurements being aggregated for a given daytime hour and ground cover type. Since wind from south and westerly directions dominated during the first and second measuring period, fluxes of C. villosa are underrepresented during many of the averaging periods. Comparison to the daily flux trends in the three sectors indicates that, at least for those hours over which sufficient flux data were available, no significant differences between ground cover types were apparent (Fig. 4). Given the similarity in the magnitude of the assimilation flux for the different groundcover types (Fig. 4), we aggregated flux data from all wind directions in order to estimate the average daily flux sums. The soil CO2 efflux shows considerably less variation over the course of an average day (amplitude of daily flux curve in Fig. 5), as well as between different days (error bars in Fig. 5) than EC fluxes during the same period. Both the magnitude of the day-time assimilation flux, and the length of the period over which photosynthetic assimilation could be detected show a pronounced seasonal trend. Average daily flux sums for the ground vegetation carbon assimilation were calculated by summing up all assimilation flux
averages (i.e. the difference between curves in Fig. 5) obtained during daylight hours, to yield the mean daily carbon (C) assimilation for each period. Daylight hours were defined as those hourly classes with an average incoming short-wave radiation of over 20 W m2 above the canopy throughout the length of a data set. These were: May–July (5–19 h), August/ September (7–18 h), October/November (8–17 h). For comparison with other ecosystem flux components, the stand gross photosynthetic flux (i.e. leaf assimilation per unit ground area) was calculated using the model PROXEL (Reichstein, 2001), which had been parameterised for water and carbon fluxes from the same site (Subke et al., 2003). Flux sums were calculated for the corresponding periods of below canopy EC measurements and aggregated for the same daylight hours as the ground vegetation. In this configuration, the LAI of the ground vegetation is included in the total stand LAI, so that the calculated flux integrates the photosynthesis of both canopy and ground vegetation. The soil CO2 efflux was calculated for each of the four periods (using Eq. (2)) and averaged to give the daily mean CO2 efflux (i.e. over 24 h) for each of the periods. Values for both stand photosynthesis and soil CO2 efflux were calculated in hourly time steps. Table 2 gives the different flux sums for the four periods and indicates the total below-canopy flux (excluding above-ground respiration) as well as the fraction of the stand photosynthetic uptake due to the ground vegetation. 3.2. Light response of ground vegetation Light response curves for each of the four measuring periods were obtained using a following hyperbolic regression function: A¼
aQp þc Qp þ b
(7)
where a, b, and c are fitted parameters. Light saturation occurred from around 60 mmol m2 s1 with differences in the assimilation values at saturation for the four periods (Fig. 6). The approximate assimilation value under saturating light (Asat) was calculated using Eq. (7) with the fitted parameters for each of the four periods, and Qp = 100 mmol m2 s1 (Table 3).
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Fig. 4. Assimilation flux (A) averaged for corresponding day-time hours over each of the four periods in 1999, and separated into the ground vegetation types C. villosa (filled squares), D. flexuosa (filled triangles), and mixed D. flexuosa/P. abies (open circles). Error bars indicate 1 S.E., and only averages for day-time hours with n > 3 are presented.
Fig. 5. CO2 flux below the canopy measured by eddy covariance (FC, open symbols) and by soil chambers (FS, solid symbols) averaged for corresponding day-time hours for each of the four periods in 1999. Error bars indicate 1 S.E., and only averages with n > 5 are presented.
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Table 2 CO2 flux sums for ground vegetation gross assimilation, soil CO2 efflux and canopy gross photosynthetic uptake measured in 1999 Period
Ground vegetation day-time gross assimilation (a)
(b)
12 12 13 18
0.61 1.17 0.79 0.12
2.12 2.49 2.84 0.97
May–11 June June–4 July August–24 September October–17 November
0.05 0.04 0.03 0.02
Soil efflux
Canopy day-time gross assimilation (c)
0.01 0.01 0.01 0.01
7.99 8.63 7.92 3.70
Below-canopy flux (a + b)
2.61 1.90 2.69 1.83
1.51 1.32 2.05 0.84
Fraction ground vegetation a/(a + c)
0.05 0.04 0.03 0.02
0.07 0.12 0.09 0.03
All values are means standard deviations in g C m2 day1. A negative sign indicates a flux directed from the atmosphere into the vegetation, and a positive sign a flux from the vegetation (or soil) into the atmosphere.
4. Discussion The results of our study confirm the problematic conditions for eddy covariance measurements below a closed canopy reported in previous work (Baldocchi and Meyers, 1991; Baldocchi and Vogel, 1996; Black et al., 1996; Law et al., 1999; Lamaud et al., 2001). Especially night-time measurements are affected by insufficient atmospheric mixing, which leads to a violation to the assumptions underlying the eddy covariance theory. The criteria used to discriminate
between ‘‘valid’’ and ‘‘invalid’’ are critical to the interpretation of measured fluxes, and differ between published studies. Janssens et al. (2000), for example, only include day-time data for their comparison of eddy covariance and chamber measurements due to the uncertainty of night-time data. As the data quality tests in our study showed, a sizeable portion of the day-time measurements suffer from insufficient turbulence, so that a discrimination between valid and invalid measurements based on the turbulence characteristics during a given measuring interval is a more
Fig. 6. Photosynthetic assimilation (A) by the ground vegetation in response to mean below canopy radiation (Qp), as measured during the four periods in 1999. Light response curves were fitted to 30 min fluxes, using Eq. (7), and fitted parameters and statistics are shown in Table 3.
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Table 3 Fitted parameters and statistics for light response curves of 30 min fluxes (see Eq. (7), Fig. 6), and approximate assimilation flux values under saturating light at the forest floor (Asat; calculated for Qp = 100 mmol m2 s1), together with mean air temperatures (T) and soil water contents (t) for the four measuring periods in 1999 12 May–11 June
12 June–4 July
13 August–24 September
18 October–17 November
Fit parameters and statistics a b c R2 x2
2.25 0.59 45.3 27.8 0.079 0.091 0.32 0.605
2.96 0.18 27.0 6.3 0.090 0.102 0.59 0.55
2.09 0.22 16.9 7.6 0.290 0.142 0.36 0.81
1.09 0.45 29.8 26.1 0.058 0.044 0.17 0.20
Measuring period averages Asat (mmol m2 s1) T (air) (8C) t (m3 m3)
1.63 9.4 0.28
2.42 10.9 0.25
2.08 12.3 0.22
0.89 4.6 0.27
sensible approach than to simply divide into day- and night-time measurements. However, turbulence at our measuring location was generally low with u* exceeding 0.2 m s1 in only about 2% of measuring periods. Measurements made under low turbulence have been reported to under-estimate respiration fluxes (Matteucci et al., 2000; Janssens et al., 2000), or even result in negative fluxes at night-time (Black et al., 1996). The reasons put forward to account for this under-estimation include horizontal advection of CO2 (Black et al., 1996), cold-air drainage, a temporal exchange pattern that shifts towards high-frequency eddies, for which the measuring set-up is inadequate, or vertical wind fluctuations too small to be detected by the anemometer (Goulden et al., 1996). The mean vertical wind velocity (w) ¯ measured throughout all campaigns showed a daily trend with peak velocities of +0.015 and 0.015 m s1 at 1:00 and 14:00 h, respectively, indicating that transport of CO2 through horizontal advection is likely at the site. However, there was no consistent trend in the ratio of EC/ chamber flux in relation to w, ¯ so that no correction of EC fluxes on the basis of w ¯ was possible. Storage of CO2 may also account for a reduction in flux during calm periods, and ‘‘plumes’’ of CO2 rich air have been reported by several researchers (e.g. Grace et al., 1996; Greco and Baldocchi, 1996). Data from periods of CO2 concentration build-up or release had been filtered by the threshold value of 2.25 ppm for sc, and the simple storage correction performed in this study did not significantly change the relationship between night-time EC and chamber fluxes. The small magnitude of the storage term during periods of
‘‘valid’’ measurements is not likely to account for an under-estimation of about 41% found by the comparison between EC and chamber flux measurements at night. The data quality tests we applied are purely empirical and the use of the same rules at a different site may be questionable. The ratio of under-estimation by EC measurements showed consistency for a wide range of wind speed and friction velocities, resulting in extremely low critical values of data acceptance for these parameters. For example, the slope for a linear regression between FC and FS ranged only from 0.58 to 0.62 for u* classes between 0.2 and 0.05 m s1, before dropping to lower values (and poorer coefficients of correlation) at lower u* values. Similarly, light response curves were consistent throughout the same range of u* values. Since no single parameter, or combination of parameters, could be identified that could account for the divergence of flux measurements, no correction on the basis of meteorological conditions was possible, and the scaling of EC fluxes on the basis of the nighttime comparison with chamber fluxes (Fig. 2) was the best workable option to make flux measurements obtained by either systems comparable. The use of soil chamber measurements as a reference for night-time EC flux has to account for measuring errors associated with chamber measurements. The soil chambers have been designed to minimise measuring artefacts resulting from soil disturbance and pressure differences between chamber space and ambient atmosphere, commonly found for open flow-through chamber designs. A recent comparison of chamber designs against the known CO2 flux from
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an experimental tank showed that over a range of efflux rates and soil moisture conditions, this particular chamber gives reliable estimates of the actual soil CO2 efflux (Pumpanen et al., in press). Compared to the variability of the night-time EC flux measurements, chambers thus produce relatively reliable efflux values, which for the purpose of this study is accepted as the true soil CO2 efflux. By correcting the night-time fluxes obtained by eddy covariance against those measured with the continuous soil chamber system, it was possible to reduce the otherwise considerable error due to the conditions that limit micrometeorological flux measurements at the site. For the extrapolation of this calibration to day-time measurements, it is assumed that there is no selective systematic error, e.g. where errors occur during day-time or night-time only. Following these approximate error corrections, it is difficult to estimate the absolute value of the measuring certainty. By grouping data into hourly classes, remaining random errors would diminish with increasing numbers pffiffiffiffi of measurements in each category according to 1= N , where N is the number of data points (Moncrieff et al., 1996). The contribution of ground vegetation photosynthesis to the stand photosynthesis could be estimated as between 3 and 12% for the different periods (Table 2). The seasonality of these flux contributions is most likely to be a result of temperature and soil moisture differences between the four periods (Table 3). For C. villosa, the difference in LAI throughout the season would also mean a difference in assimilation potential per unit ground area. However, due to the scarcity of valid flux data from the wind sector containing this grass species, no seasonal trend due to LAI could be separated within the flux results (compare Fig. 4). The very low assimilation flux during the last campaign is limited to only a few daylight hours, and is most likely to be due to the perennial D. flexuosa, and the P. abies seedlings only, since leaf senescence of C. villosa started from mid-October. Up-scaled results of leaf cuvette measurements on the ground vegetation in a pine forest in SW Germany indicate a contribution of 20% to the total NPP (Wedler et al., 1996). The LAI of the ground vegetation in that particular study is greater than 50% of the canopy LAI (1.54 and 2.8, respectively), which differs markedly from conditions at our study site (LAIs of 2.2 for the mean ground cover and 6.0 for the canopy).
The authors calculate an average accumulation of 1.1 g C m2 day1 over 1 week in May with high incident above-canopy radiation. In a boreal Norway spruce forest, More´ n and Lindroth (2000) arrive at an estimate of about 1 g C m2 day1 for 7 days in June, by scaling measurements from a large chamber up to the forest stand (canopy LAI = 5.6, but no ground cover LAI stated). These daily flux estimates compare well with the findings in this study (daily averages between 0.6 and 1.2 g C m2 day1 for measuring periods from May to September; Table 2). Although both approaches (i.e. scaling up from isolated chamber measurements versus integrating fluxes over larger ground patches) suffer from errors related to heterogeneity of conditions and underlying assumptions that are not always satisfied, this similarity encourages further investigations of understorey flux contributions across forest types. The method used to calculate average incident radiation at the forest floor ignores the occurrence of sun flecks. The carbon gained by understorey species may however depend on the frequency of sun flecks, since leaf gas exchange measurements indicate that between 30 and 65% of the understorey assimilation in moist redwood forests (Pfitsch and Pearcey, 1989), and 2–27% in temperate deciduous forests (Schulze, 1972; Koizumi and Oshima, 1993) is due to the utilisation of sun flecks. A more accurate determination of the light conditions in the respective wind sectors for each 30 min flux value would in all likelihood have reduced the scatter in Fig. 6. Nonetheless, the magnitude of the average photosynthetic response can be estimated from the presented data. The maximum assimilation rate for saturating light at the forest floor shows a clear seasonal trend reflecting the changes in photosynthetically active leaf area and environmental conditions (Fig. 6, Table 3). Averaging variable parameters such as temperature and soil water content over periods of about 4–6 weeks masks periodic occurrences of extreme values, which may affect the ground vegetation gas exchange markedly. During early September, for example, the soil water content of the organic soil layer dropped below 0.15 m3 m3 for over a week (Subke et al., 2003), resulting in a significant decrease in the soil CO2 efflux over that period. The photosynthetic activity of the ground vegetation is likely to have been affected
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by this short period of drought also, owing to its relatively shallow root system. Interpolating between the average amounts of carbon assimilation over the measuring periods to the entire vegetation period can give a rough estimate of the gross assimilation flux of the ground vegetation. It can be assumed that assimilation started at around mid-April (when the average daily temperature rose to above 5 8C; the last ground penetrating frost in 1999 occurred on 21 April), at about the time when the annual leaf growth of C. villosa started. In autumn, the first frost was recorded for 16 October, but mean daily air temperatures stayed at around 5 8C until early November. Interpolating the measured flux results over the year by assuming an assimilation flux of zero before 15 April, and after 15 November, the annual carbon uptake by the ground vegetation was estimated to be around 170 g C m2. Considerable amounts of carbon assimilated by the ground vegetation are likely to be respired by the plants (leaf and root respiration), while about 85 g C m2 are found in the standing biomass (32 g C m2 above ground, and 53 g C m2 below ground). On an annual basis, gross uptake by the ground vegetation represents about 28% of the carbon respired from the soil (ca. 590 g C m2 year1 in 1999; Subke et al., 2003), which includes heterotrophic as well as autotrophic belowground respiration by trees and the ground vegetation.
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Measurements above the canopy integrate over all flux contributions (assimilation as well as respiration) within the stand, and individual flux terms have to be deduced from regression functions. Information on the relative contributions from the different components through direct measurements are necessary to validate such regressions, and this study demonstrates that despite problematic atmospheric conditions below the canopy, information on the physiological functioning of ground cover vegetation can be obtained if eddy covariance is used in combination with chamber measurements.
Acknowledgements The authors would like to thank J. Gerchau (Bayreuth Institute for Terrestrial Ecosystem Research, Bayreuth, Germany) for supplying above-canopy radiation data, B. Wichura and T. Foken (University Bayreuth, Germany) for help with the processing of eddy covariance data, as well as T. Gru¨ nwald and C. Bernhofer (University Dresden, Germany) for comments on an early version of the manuscript. This work was funded by the German Ministry for Education and Research (BMBF), grant no. PT BEO-51-0339476C.
References 5. Conclusion The results presented here indicate a contribution by ground cover vegetation of up to about 12% to the total stand assimilation flux in the summer months in a Norway spruce forest with high leaf area index, with about 30% of the carbon respired from the forest soil being cycled through the ground vegetation. Ecosystem models for coniferous stands generally assume constant LAI throughout the year. However, in stands with ground vegetation, the overall LAI changes with the phonological development of the ground species, affecting the seasonal gas exchange pattern. Depending on the relative contribution of the ground vegetation to the overall LAI, this component may be significant for an accurate description of C dynamics within a forest stand, and has to be accounted for in ecosystem models.
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