Characterization of the soil CO2 production and its carbon isotope composition in forest soil layers using the flux-gradient approach
Introduction
One of the key questions in climate change research relates to the future dynamics of soil CO2 efflux (Fs). Soil is the terrestrial ecosystem component that emits the largest CO2 efflux (Ryan and Law, 2005). In 2008, the global Fs was estimated at 98 ± 12 Pg C yr−1 (15 times greater than fossil fuel combustion; Denman et al., 2007) and its increase between 1989 and 2008 at 0.1 Pg C yr−1 (Bond-Lamberty and Thomson, 2010). Because a small change in Fs magnitude can significantly affect atmospheric CO2 concentration (Kirschbaum, 2006, Raich and Schlesinger, 1992) and because Fs is sensitive to climate (Davidson et al., 2006a, Kirschbaum, 2006, Luo and Zhou, 2006, Fang and Moncrieff, 2001, Schlesinger and Andrews, 2000), Fs studies have received much attention in recent years. The future response of Fs to climate change is far from being clear, however, because of the complexity of processes involved: multiple sources, and various and variable driving factors (Moyes et al., 2010, Ryan and Law, 2005). To disentangle this complex flux, it is necessary to focus on its underlying processes.
The use of C stable isotopes (12C and 13C) has become a powerful research tool for understanding these processes (Risk et al., 2012, Nickerson and Risk, 2009a, Nickerson and Risk, 2009b, Subke et al., 2009, Ekblad and Högberg, 2001, Ehleringer et al., 2000), especially by allowing the identification of Fs components and drivers (Moyes et al., 2010, Ngao et al., 2005, Ekblad and Högberg, 2001, Ehleringer et al., 2000). The variations in the Fs isotopic composition (δ13Fs) can indeed provide insights into the type of sources involved and the link between photosynthesis and respiration (Risk et al., 2012, Nickerson and Risk, 2009b, Subke et al., 2009, Bowling et al., 2008, Ekblad and Högberg, 2001).
The CO2 released by the soil surface (efflux, Fs) and its carbon isotope signature (13Fs) are the result of two main processes: the production of 12CO2 and 13CO2 by the soil (P) and their transport from the place of production up to the atmosphere (Fang and Moncrieff, 1999). Fs and δ13Fs are regularly assumed to represent the real behavior of CO2 sources (P) and their isotopic signature (δ13P) but physical processes (rain events, wind pumping, atmospheric pressure fluctuations) affecting the transport can be responsible for a discrepancy between Fs & Pand δ13Fs & δ13P (Fassbinder et al., 2012, Risk et al., 2012, Gamnitzer et al., 2011, Phillips et al., 2010). Since transport processes can play a significant role and lead to non steady state conditions, it is important to remove their impact in order to identify and describe the real behavior of P and δ13P and their drivers (Parent et al., 2013, Brüggemann et al., 2011).
Soil CO2 production results mainly from two components: an autotrophic component, feeding on carbon supplied by roots (root and rhizosphere), and a heterotrophic component, feeding on soil organic matter (saprophytic microorganisms). Both component contributions to total CO2 production vary with time and location (horizontally and vertically). These components can also differ in their natural isotopic abundance, which sometimes allows partitioning (Plain et al., 2009, Subke et al., 2009, Kodama et al., 2008, Formánek and Ambus, 2004). Temporal variability (intra- and inter-day) in δ13Fs measurements (assumed to be δ13P) has been reported (Risk et al., 2012, Bahn et al., 2009, Marron et al., 2009, Kodama et al., 2008) and attributed to autotrophic component δ13P variations (δ13Pa) (Risk et al., 2012). The variability in δ13Pa is often correlated with previous but recent atmospheric humidity, in a similar way to how photosynthetic discrimination responds to humidity (Phillips et al., 2010, Bowling et al., 2008, Knohl et al., 2005, McDowell et al., 2005, Ekblad and Högberg, 2001). Actually, the photosynthates become more enriched in 13C during dry periods. Such correlations allow the source-to-sink transport time-lag to be determined (Wingate et al., 2010, Plain et al., 2009, Ekblad et al., 2005) excepted during rainy periods and when there is a strong demand for carbon allocation to above ground biomass (Wingate et al., 2010).
CO2 transport is driven mainly by diffusion (Pumpanen et al., 2008, Davidson et al., 2006b, Hirano, 2005, Amundson et al., 1998), but turbulence-induced transport has also been highlighted in the topsoil (Bowling and Massman, 2011, Maier et al., 2010, Flechard et al., 2007, Takle et al., 2004). Isotopic discrimination occurs during the diffusion, as 12CO2 diffuses faster than 13CO2 (Amundson et al., 1998, Cerling et al., 1991). As a result, in non-steady state conditions, transport processes could induce differences between P and Fs or between δ13P and δ13Fs (Nickerson and Risk, 2009b, Moyes et al., 2010). In these cases, the δ13Fs might not adequately represent the behavior of δ13P.
A wide variety of methods, each with their own pros and cons, have been used to assess P and δ13P. They can be divided into two categories: surface and subsurface approaches.
The first category consists of Fs and δ13Fs measurements using either soil surface chambers or micrometeorological techniques (i.e. isotope flux ratio method and eddy covariance). The soil surface chambers are either static (non-steady-state) or dynamic (steady-state) (Nickerson and Risk, 2009a). In order to determine δ13Fs using a static chamber, CO2 and δ13CO2 are sampled several times within the chamber over a given deployment period and the data are then used to construct a Keeling plot (Nickerson and Risk, 2009a, Ohlsson et al., 2005, Keeling, 1958). This method is often criticized because it disturbs the natural CO2 and δ13CO2 gradient (Nickerson and Risk, 2009a). Dynamic chambers are more complex, but they seem to be better suited to directly measuring δ13Fs because they retain the natural soil-atmosphere CO2 gradient near its steady-state condition (Nickerson et al., 2013, Bahn et al., 2009, Marron et al., 2009, Subke et al., 2009). The micrometeorological techniques also preserve the natural CO2 and isotope gradient (Griffis et al., 2005) and present the advantage to measure the mean fluxes integrated over a footprint area larger than the surface intercepted by the chambers. However, they produce poor quality data when the level of atmospheric turbulence is too weak excluding low friction velocity events and application below dense canopy forest (Santos et al., 2012)
In addition, the surface approach has the inconvenience of neglecting transport-related effects and confusing Fs and δ13Fs with P and δ13P (Gamnitzer et al., 2011, Phillips et al., 2010). It also treats the soil as a black box from which no information about the vertical distribution of CO2 sources can be obtained, but this is a critical factor in understanding soil carbon dynamics (Davidson et al., 2006b, Jassal et al., 2004, Hirano et al., 2003, Tang et al., 2003). Nevertheless, the possibility of multiplying the number of soil chambers opens up the possibility of relatively wide spatial coverage (Risk et al., 2008).
The second category consists of the soil CO2 concentration profile and its isotopic composition (δ13CO2) follow up. It has the advantage of directly quantifying production rather than efflux. The P and δ13P can be deduced using a Keeling plot approach (Moyes et al., 2010) or a Flux-Gradient Approach (FGA) (de Jong, 1972). The Keeling plot approach assumes that the CO2 from the entire soil profile would reflect a mixture of only two constant sources (atmospheric CO2 and respired CO2), neglecting spatial and temporal fluctuations in δ13P (this assumption of neglect is, however, controversial; Risk et al., 2012, Phillips et al., 2010, Bahn et al., 2009). In addition, the Keeling plot approach does not give information about the vertical distribution of CO2 sources. The FGA, using Fick's first law (Pumpanen et al., 2008, Davidson et al., 2006b, Tang et al., 2003), assumes purely diffusive soil CO2 transport, which might not be the case when atmospheric turbulence is too high. The advantages of the FGA are that it takes account of the main transport process and of the vertical distribution of P, δ13P and their temporal variation. Nevertheless, the FGA requires good estimates of the effective soil gas diffusion coefficient and does not cover a wide spatial area (depending on the experimental device).
In this study, the FGA that was used successfully for CO2 (Pumpanen et al., 2008, Davidson et al., 2006b, Tang et al., 2003) was combined with high frequency measurements of δ13CO2, Fs and δ13Fs with the main objectives to determine the vertical distribution of P and δ13P in a forest site and to relate the temporal variation in P intensity and δ13P to climatic variables. So far as we know, this is the first study to be conducted using the FGA with C stable isotope data.
Section snippets
Determination of CO2 production profile
The vertical profile of soil CO2 production (P, μmol CO2 m−3 s−1) was deduced from soil CO2 concentration profiles using the mass balance equation of CO2 in a diffusive one-dimensional medium (Pumpanen et al., 2008, Davidson et al., 2006b, Hirano, 2005, Jassal et al., 2005, Fang and Moncrieff, 1999):where ɛ is the air-filled porosity (m3 m−3), [CO2] is the CO2 concentration (μmol CO2 m−3), t is the time (s), z is the depth (m), Ds is the effective soil diffusion coefficient (m2
Time-averaged vertical profile
The total amount of CO2 produced over the entire soil profile (Ol, Ah, AhC and C horizons) and averaged over the entire study period (n = 890 half-hours) amounted to 5.40 μmol CO2 m−2 s−1 (±0.042 μmol CO2 m−2 s−1), which is equal to the averaged Fs measured during the same period, suggesting that CO2 storage in soil air has no influence when a period of several days is considered (Parent et al., 2013).
The long-term averaged vertical distribution of CO2 production is represented in Fig. 2a. The relative
Conclusion
In this study, the FGA was combined with soil CO2 isotopic signature high frequency measurements in order to determine the vertical distribution of CO2 production (P) and the isotopic composition of CO2 produced (δ13P) in a Scots pine forest and to relate the temporal variation of P and δ13P to climatic variables.
The FGA gave consistent and promising results, except for the Ol horizon where CO2 production estimates were biased due to an incorrect description of CO2 transport in this layer that
Acknowledgments
Stéphanie Goffin acknowledges the support of the Belgium National Fund for Scientific Research (FNRS), within the framework of her thesis (FRIA). The authors are grateful to B. Clerc, P. Courtois and J.M Gioria of the UMR1137 at INRA Nancy for the installation and maintenance of the experimental set-up during summer 2010. The authors wish to thank the reviewers for their comments.
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2017, Atmospheric EnvironmentCitation Excerpt :Heavier values for the isotopic ratio of the source in the soil are characteristic of drier conditions, whereas lighter values correspond to higher soil moisture (Fig. 12). The results reported here are consistent with many previous studies, in which a heavier isotopic source value is observed during drier and warmer seasons compared to the colder and wetter months of the year (Ekberg et al., 2007; Marron et al., 2009; Goffin et al., 2014). Other studies have related the variations in the δ13C of ecosystem and soil respiration to air humidity (Ekblad and Högberg, 2001; Bowling et al., 2002; Ekblad et al., 2005).