BGBiogeosciencesBGBiogeosciences1726-4189Copernicus PublicationsGöttingen, Germany10.5194/bg-13-597-2016Methane dynamics in the subarctic tundra: combining stable isotope analyses,
plot- and ecosystem-scale flux measurementsMarushchakM. E.FriborgT.https://orcid.org/0000-0001-5633-6097BiasiC.HerbstM.JohanssonT.KiepeI.LiimatainenM.LindS. E.MartikainenP. J.VirtanenT.SoegaardH.ShurpaliN. J.narasinha.shurpali@uef.fihttps://orcid.org/0000-0003-1052-4396Department of Environmental and Biological Sciences, University of
Eastern Finland, PO Box 1627, 70211 Kuopio, FinlandDepartment of Geosciences and Natural Resource Management, University
of Copenhagen, Øster Voldgade 10, 1350 Copenhagen K, DenmarkDepartment of Environmental Sciences, University of Helsinki, P.O. Box
65, 00014 University of Helsinki, FinlandN. J. Shurpali (narasinha.shurpali@uef.fi)2February201613259760827July201526August20154January201620January2016This work is licensed under a Creative Commons Attribution 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/3.0/This article is available from https://bg.copernicus.org/articles/13/597/2016/bg-13-597-2016.htmlThe full text article is available as a PDF file from https://bg.copernicus.org/articles/13/597/2016/bg-13-597-2016.pdf
Methane (CH4) fluxes were investigated in a subarctic Russian tundra
site in a multi-approach study combining plot-scale data, ecosystem-scale
eddy covariance (EC) measurements, and a fine-resolution land cover
classification scheme for regional upscaling. The flux data as measured by
the two independent techniques resulted in a seasonal (May–October 2008)
cumulative CH4 emission of 2.4 (EC) and 3.7 g CH4 m-2 (manual
chambers) for the source area representative of the footprint of the EC
instruments. Upon upscaling for the entire study region of 98.6 km2,
the chamber measured flux data yielded a regional flux estimate of 6.7 g CH4 m-2 yr-1. Our upscaling efforts accounted for the large
spatial variability in the distribution of the various land cover types
(LCTs) predominant at our study site. Wetlands with emissions ranging from
34 to 53 g CH4 m-2 yr-1 were the most dominant
CH4-emitting surfaces. Emissions from thermokarst lakes were an order
of magnitude lower, while the rest of the landscape (mineral tundra) was a
weak sink for atmospheric methane. Vascular plant cover was a key factor in
explaining the spatial variability of CH4 emissions among wetland
types, as indicated by the positive correlation of emissions with the leaf
area index (LAI). As elucidated through a stable isotope analysis, the
dominant CH4 release pathway from wetlands to the atmosphere was
plant-mediated diffusion through aerenchyma, a process that discriminates
against 13C-CH4. The CH4 released to the atmosphere was
lighter than that in the surface porewater, and δ13C in the
emitted CH4 correlated negatively with the vascular plant cover (LAI).
The mean value of δ13C obtained here for the emitted CH4,
-68.2 ± 2.0 ‰, is within the range of values from
other wetlands, thus reinforcing the use of inverse modelling tools to better
constrain the CH4 budget. Based on the IPCC A1B emission scenario, a
temperature increase of 6.1 ∘C relative to the present day has
been predicted for the European Russian tundra by the end of the 21st
Century. A regional warming of this magnitude will have profound effects on
the permafrost distribution leading to considerable changes in the regional
landscape with a potential for an increase in the areal extent of
CH4-emitting wet surfaces.
Introduction
The Arctic tundra, underlain by permafrost, covers 9.2 million km2,
i.e., 8 % of the global land area, and the large carbon pools stored in
Arctic soils are extremely vulnerable to global warming (McGuire et al.,
2012). The Arctic region can greatly alter the atmospheric concentrations of
CO2 (carbon dioxide) and CH4 (methane) through feedback mechanisms
(Post et al., 2009). Inverse modelling results based on measurements of
concentrations and stable isotope composition of CH4 have already
proved the importance of high-latitude wetlands as global CH4 sources
(Riley et al., 2011). For example, the spike in the
global mean atmospheric CH4 concentration in 2007 has been attributed
to anomalously high summer temperatures experienced by these ecosystems
during that year (Dlugokencky et al., 2011). Despite
the large areal extent, the Russian tundra region is relatively less
explored as far as its biogeochemical functioning is concerned. According to
the latest estimates, this region is presently considered to be a net carbon
dioxide (CO2) sink and a source of atmospheric CH4. Although
CO2 flux represents the major component in the total C flow between
tundra ecosystems and the atmosphere, CH4 is equally important owing to
its 28 times higher global warming potential over a 100-year time horizon
(IPCC, 2014).
In the Arctic, CH4 is mostly emitted from wetlands (Heikkinen et
al., 2004; Mastepanov et al., 2008) and from lakes and ponds
(Walter et al., 2008). On the whole, there is a general
consensus that the Arctic region is a moderate CH4 source (19 Tg yr-1; McGuire et al., 2012). However, this
estimate is fraught with uncertainty ranging from 8 and 29 Tg yr-1
(McGuire et al., 2012). This is attributed to the flux variability across
time and space that is poorly characterized as yet. Methane exchange is
unevenly distributed across the landscape (hot spots; Walter et al., 2006) and may occur during short
periods of time (Mastepanov et al., 2008). For
reliable estimates of CH4 balance, continuous measurements made across
all important constituent land cover types are required. The ideal way to
achieve this is to apply chamber and eddy covariance techniques in parallel.
Such an approach has, however, rarely been adopted in Arctic investigations
(Parmentier et al., 2011; Sachs et al., 2008). The eddy covariance method
allows continuous ecosystem-scale measurement of methane exchange, while
chamber techniques aid in a proper characterization of the inherent site
spatial variability. The two methods employed simultaneously help improve
the accuracy of the regional flux estimates, as has been shown for CO2
at this site (Marushchak et al., 2013).
In addition to increasing the accuracy of flux estimates, processes
underlying CH4 dynamics need to be better understood. This is important
for developing process-based biogeochemical models with an ability to
simulate present and future CH4 fluxes (Riley et al., 2011). In this
respect, stable isotope analyses of CH4 have been useful as they
provide valuable information on mechanisms of CH4 production, transport,
and oxidation (Chanton, 2005; Chanton et al., 2005; Popp et al., 1999). The
two dominant CH4 production pathways, hydrogenotrophic (CO2
reduction) and acetoclastic methanogenesis (acetate fermentation),
discriminate differently against 13C-isotope (Chanton et al., 2005).
Also, CH4 oxidation by methanotrophic bacteria favours the 12C-isotope,
leaving the residual CH4 enriched with 13C (Whiticar, 1999).
Isotopic fractionation of CH4 released from wetland to the atmosphere
depends on the mode of its transport. While ebullition and diffusion through the
air–water interface cause practically no fractionation, diffusive transport
through plant aerenchyma is slower for 13C-CH4, resulting in
lighter CH4 being emitted from the plants compared to that in sediment
porewater (Chanton et al., 2005). Knowledge on the isotopic composition of
CH4 from various ecosystem types is also important for top-down
modelling in which the aim is to determine the relative contribution of
different emission sources to the atmospheric CH4 content (Riley et
al., 2011). Data on isotopic composition of C in peatland CH4 emissions
are sparse, especially from the Russian tundra ecosystems
(Sapart et al., 2013). A more detailed
characterization of CH4 emissions is highly relevant to better
constrain global CH4 sinks and sources, particularly in view of the
growing emphasis on the role of northern peatlands in the global C cycle.
The present-day trends have revealed that the permafrost temperatures in the
discontinuous zone have risen by 2 ∘C and the southern boundary
of permafrost has retreated northwards in the Russian Arctic
(Romanovsky et al., 2010). Changes in permafrost extent and active
layer thickness affect vegetation composition (Christensen, 2004) and
CH4 flux (Johansson et al., 2006) from northern wetlands.
In a study on permafrost dynamics of the Russian tundra (Anisimov,
2007), permafrost temperatures have been projected to increase by 2–3 ∘C by 2050 with a 15–25 % increase in the active layer
thickness and a 25 % increase in the CH4 emissions from the northern
Russian wetlands. Such projections can be improved with a robust estimate of
the magnitude of CH4 fluxes, their spatial and temporal variability, and
underlying mechanisms.
Our aim here is to provide an estimate of CH4 fluxes as measured by two
independent measurement techniques in a subarctic Russian tundra region and
to deepen our understanding of the factors regulating methane exchange in
this environment. To investigate methane fluxes and underlying mechanisms in
the Russian Arctic at various scales (from plots to landscape), we used a
set of methodological tools including stable isotope investigations, EC- and
chamber-based flux measurement techniques and regional upscaling by a
fine-scale QuickBird satellite image-based land cover classification scheme.
We report here a full year of CH4 measurements by static chambers and
gas gradient methods. These methods were complemented by fluxes measured
using the EC technique from early spring to autumn in 2008. To the best of our knowledge, this is one
of the rare studies that employs multi-level approaches on CH4 dynamics over various temporal and spatial scales in the Russian Arctic
environment.
Land cover classification of the field site employing QuickBird
satellite imagery. The eddy covariance (EC) tower is indicated by a star, concentric lines drawn
around the tower at 100 m intervals represent the EC footprint area. Areal
coverage of fen and willow land cover types in different sectors from the EC
tower is shown in a table to the right of the figure, zero refers to north.
Materials and methodsSite description
This study was conducted in the subarctic tundra of the Komi Republic,
Northeast European Russia. The measurement site is located near the village of
Seida (67∘03′21′′ N, 62∘56′45′ E, 100 m a.s.l.) and
situated in the discontinuous permafrost zone just above the northern
treeline. Based on the long-term climatic data from the Vorkuta station
(67∘48′ N, 64∘01′ E, 172 m a. s. l.) for the 1977–2006
period, the mean annual temperature in the region is -5.6 ∘C,
with January being the coldest month (mean temperature -20.4 ∘C)
and July the warmest one (mean temperature 13.0 ∘C), and an annual
precipitation of 501 mm. A more detailed description of the site as well as
information on permafrost and carbon storage of the tundra soils of this
region can be found in Hugelius et al. (2011), Marushchak et al. (2011, 2013) and Biasi et al. (2013).
A high-resolution QuickBird satellite image (Fig. 1) was used to map the
distribution of the various land cover types (LCTs) of the study area of
98.6 km2 (Hugelius et al., 2011; Marushchak et al., 2013). In terms of
areal coverage, the tundra heath (58 %) and tundra bog vegetation (24 %) found especially on permanently frozen peat plateaus are the dominant
ecosystem types in the region followed by willow stands (9 %), and
various fen ecosystems (6 %). The peat plateaus are spotted by
unvegetated, patterned ground features – referred to hereafter as bare peat
circles – which have been studied by Repo et al. (2009) as they were
found to emit large amounts of N2O to the atmosphere. The willow
stands are typically 0.5–1.5 m in height and grow on low-lying areas with
waterlogged soils. The dominating plant species besides various Salix species are
Carex aquatilis Wahl., Betula nana L., Eriophorum russeolum Fries and Comarum palustre L. Fens are found on littoral areas of thermokarst
lakes and on the edges of the frozen peat plateau peatlands. They are
mesotrophic and can be divided according to the dominant vascular plant
species into Eriophorum fens (dominating vascular plant species Eriophorum russeolum Fries) and Carex fens
(dominating vascular plants Carex aquatilis Wahl. and Comarum palustre L.). Sphagnum species dominate the ground
layer and form a dense mat floating on the water together with vascular
plant roots. Small lakes, mostly of thermokarst origin, cover a minor part
of the landscape (1 %).
Instrumental setup and methodologyPlot-scale CH4 flux measurements at terrestrial land cover
types
Ten land cover types, each with three replicate measuring plots, were
established for the determination of CH4 fluxes from the soil surface,
three of them on water-logged wetlands, three on peat plateau and four on
upland tundra. Fluxes of CH4 were determined using the methodology
described in detail for nitrous oxide fluxes by Marushchak et al. (2011).
Fluxes were measured by the static chamber technique 11–16 times during the
snow-free season from early July to mid-October 2007 and 16–21 times during
the snow-free season from late May–early July until the beginning of October
2008. In addition, CH4 fluxes were measured 2–5 times per plot during
the snow cover period in January–June with a snow-gradient method (Merbold
et al., 2013). The CH4 concentrations in the collected gas samples were
analysed within three months from sampling using a gas chromatograph
equipped with a flame ionization detector (Agilent 6890N, Agilent
Technologies Deutschland, Böblingen, Germany). A leakage test with a
high CH4 concentration (15 ppm) showed that the reduction in gas
concentration in the sample vials over 2 months was less than 1 % (data
not shown). Flux calculation and the criteria to accept or reject fluxes for
further analysis are described in Marushchak et al. (2011). Water table
level (WT), active layer depth (AL), soil temperature at 2 and 25 cm depths
and vascular leaf area index (LAI; only in 2008) were monitored at different
land cover types by manual and continuous measurements as described by
Marushchak et al. (2011, 2013). The adjustment of moss surface to water
table fluctuations on willow and fen microsites was monitored in 2008 by a
measuring pole pushed through the peat profile down to the mineral soil.
Lake methane emission measurements
Release of CH4 by diffusion and ebullition pathways was studied in
three thermokarst lakes from July to August 2007 (11 samplings between days
191–239) and from June to October 2008 (19 samplings between days 182–276).
The area of the studied lakes was 0.03–3 ha with the maximum depth ranging
from 2.0 to 2.6 m and surface water pH from 4.6 to 5.5. Diffusive CH4
flux was calculated from CH4 concentration in the surface water and
local wind speed using the thin boundary layer (TBL) model (Liss
and Slater, 1974). Surface water samples were collected during daytime (8 a.m.–19 p.m.). The determination of CH4 concentration with a headspace
method and flux calculation were carried out as described in
Repo et al. (2007). Linear interpolation was used to
obtain daily CH4 concentrations. The hourly averaged wind speed
measured at 2 m, normalized to 10 m using a logarithmic wind profile, was
used to calculate hourly flux rates. Ebullitive CH4 flux was monitored
with permanently installed, submerged funnel gas collectors (Repo et al.,
2007). Each lake had 6–7 replicate gas collectors (diameter 0.35 m), which were
sampled concurrently with surface water sampling. Gas samples were stored
and analysed as described above.
Temporal extrapolation of plot-scale CH4 fluxes
The temperature dependence of CH4 flux was used to produce daily
CH4 exchange rates during the snow-free period for the land cover types
with large CH4 fluxes: willow stands, Carex fen and Eriophorum fen. Regression
functions based on air temperature and peat temperatures at 2 and 25 cm were
tested, and the best fit was obtained with temperature at 25 cm. Addition of
a water table term improved the model fit in 2007 (helped explain 20 %
additional variation in the flux data) and resulted in a more realistic
seasonal pattern, so the following function was used:
CH4flux(gCH4-Cm-2d-1)=α×β(T-10)/10×exp(γ×WT),
where T is the soil temperature at 25 cm (∘C) and WT is the water
table level (cm). Model parameters were estimated for each measurement plot
individually using the SPSS 14.0 statistical software. The regression
functions explained 85 % of the overall variability in fluxes across the
different vegetation types (Table 1; Fig. 2).
For the remaining terrestrial plots with low emissions and for lakes,
CH4 fluxes were integrated over time using linear interpolation for the
days between the measurements as described by Marushchak et al. (2011).
Linear interpolation was also used for willows and fens for the snow period
when the water table levels were not monitored. The annual fluxes were
calculated for the period from 6 October 2007 until the termination of
measurements on 5 October 2008.
Summary of the empirical models used to generate the seasonal
CH4 flux estimates for the different wetland land cover types at the
Seida study site. These seasonal estimates are based on chamber fluxes
measured during the snow-free period. Models were fitted separately for each
measurement plot.
SiteYearαβγR2Carex fen (n=3)20070.11–0.262.1–7.4-0.06–0.010.18–0.8420080.15–0.207.4–22.3-0.02–0.040.78–0.83Eriophorum fen (n=3)20070.04–0.113.3–3.7-0.09–0.040.20–0.8220080.06–0.095.3–35.3-0.01–0.090.62–0.66Willow stand (n=3)20070.14–0.221.6–5.10.02–0.050.77–0.8720080.30–0.322.3–9.3-0.01–0.070.79–0.94
A comparison of observed and predicted CH4 fluxes (n=254)
measured using chambers during 2007–2008 from different tundra wetland types
in the Seida study site. The solid line represents the linear least-squares
fit of the data. Statistics from the linear regression analysis are also
shown in the figure.
Isotope analysis of emitted and porewater CH4
The δ13C-values of the emitted and porewater CH4 were
determined during summer 2007 and 2008 from the three surface types with
high water table and, thus, with a potential for high CH4 release:
willow stands, Carex and Eriophorum fens. Gas samples were collected biweekly in 2007 from
mid-July until late August (total five times) and twice in 2008, in late June
and in early August. Five gas samples were collected for the isotopic
analysis during the time of the chamber closure and injected into 35 mL
glass vials (Wheaton) topped with rubber septa and prefilled with N2
gas. Porewater at 5 and 30 cm depths was sampled from permanently installed
gas collectors made out of perforated plastic tubes following
Maljanen et al. (2003). A water sample of
30 mL was taken in a 60 mL syringe, a similar volume of synthetic
CH4-free air was added and the syringe was then shaken for 2 min, after
which the gas phase was transferred to a glass vial (Labco Exetainer)
prefilled with pure N2. The 5 cm gas collector was occasionally above
water table level, in which case poregas was sampled and transferred
directly into a vial. Additionally, porewater was sampled from 4 to 5 depths
extending down to 40–60 cm in June and August 2008 with a steel probe
connected to a syringe. Also ambient air samples were collected for isotopic
analysis.
Isotope analyses of CH4 were done at the laboratories of the University of
Eastern Finland by gas chromatography isotope ratio mass spectrometry
(GC-IRMS; Thermo Finnigan Delta XP, Germany) equipped with a
preconcentration unit (Precon, Thermo Scientific, Germany; Dorodnikov et al., 2013). If needed, samples were diluted. Values
are expressed as δ13C relative to VPDB (Vienna Pee Dee
Belemnite Standard) using a standard gas with known 13C values.
The standard error of five repeated measurements of isotope standard was less
than 0.5 ‰ for CH4. Methane concentrations of all
samples were separately analysed by gas chromatography (Hewlett Packard
5890A) equipped with a flame ionization detector (FID) for CH4
(Mörsky et al., 2008). The Keeling plot method
(Pataki et al., 2003) was used to determine the δ13C-value of emitted CH4. According to this method, the δ13C value of the emitted CH4 is obtained by plotting the measured
δ13C values against the inverse of CH4 concentrations,
where the intercept of the linear equation with the y axis is the δ13C value of the emitted CH4.
EC measurements
The landscape-scale CH4 fluxes were measured by the EC technique during
the period from mid-May to early October 2008. The CO2 fluxes, measured
simultaneously with the CH4 flux data presented in this paper, have
already been reported in Marushchak et al. (2013) and Kiepe et al. (2013).
Fluctuations in the vertical wind speed were measured at a height of 2.75 m
above the ground using a three-dimensional sonic anemometer (R3, Gill
Instruments Ltd, UK). A quantum cascade laser (QCL) spectrometer was used
for CH4 concentration measurements (Aerodyne Inc., USA). The CH4
fluxes were calculated and corrected for theoretical separation between
instruments and attenuation of the CH4 signal in the intake tube using
the software package, AltEddy version 3.5 (Alterra, University of
Wageningen, The Netherlands). Methane fluxes were further corrected for
simultaneous flux of H2O (Webb et al., 1980). Further
data processing and quality control followed the standard methodology of
Aubinet et al. (1999) and Foken et al. (2005). Calculation of the source
area for the flux measurements followed the principles described in Soegaard
et al. (2000) and Marushchak et al. (2013), where LCTs were based on a
QuickBird satellite image classification.
ResultsClimatic conditions during the study period
Plot-scale measurements of CH4 fluxes on terrestrial sites and lakes
were made primarily during the 2007 and 2008 growing seasons and less
frequently during the cold season in between (Fig. 3a and b). A detailed
discussion of weather conditions during the study period can be found in
Marushchak et al. (2011). In brief, mid-summer temperatures were higher than
the long-term averages during both years, and July was hotter in 2007 (17.9 ∘C) than in 2008 (15.8 ∘C). The amount of
precipitation received during the two growing seasons was comparable to the
long-term regional precipitation. In 2008, a period from mid-May through
early October was covered by simultaneous plot-scale and EC measurements
(Fig. 3c). In the beginning of this measurement campaign, there was still a
90 % snow cover and soil temperatures were below the freezing point. By
early October, the diurnal average air temperatures had again dropped close
to zero and the maximum active layer thickness varied from 41 cm to greater
than 120 cm depending on the land cover type.
Seasonal distribution of daily values of chamber and EC measured
CH4 fluxes during the period from July 2007 to October 2008. Top panel:
raw data as measured by the chamber method at dominant terrestrial land
cover types (LCTs); Middle panel: daily mean fluxes measured using chambers
at terrestrial LCTs and lakes interpolated over the study period; Bottom
panel: a comparison of the daily fluxes measured using EC technique with the
plot-scale chamber data integrated over the EC footprint area and over the
QuickBird map area for the whole study region of 98.6 km2.
Spatial variability in CH4 fluxes
Based on the plot-scale measurements, wetland sites (willow stands and fens)
were the emitters of high amounts of CH4 to the atmosphere throughout
the snow-free season (Fig. 3a). The CH4 fluxes increased in the order:
Eriophorum fen < Carex fen < willow stands, with LAI of vascular plants
explaining 88 % of the differences in fluxes among the sites (Fig. 4).
The annual CH4 emissions from these wetland types were 11 ± 4.5,
37 ± 17, and 53 ± 8 g CH4 m-2, respectively (the
standard deviations indicate the flux variability associated with replicate
measurements). At willow and Carex fen sites, the floating Sphagnum mat followed the
fluctuations in the ground water level. This dampened the amplitude of the
water table level variation relative to the moss surface. While the absolute
amplitude of the water table level at the fen sites in 2008 was 23 cm, this
was reduced to about 10 cm relative to the moss surface as a result of the
surface adjustment. Consequently, the fen sites remained submerged 5–10 cm
below the water level even during the driest part of the growing season in
July 2008. The willow LCT did not have a floating moss layer but the mean
water table was still maintained close to the moss surface. The CH4
fluxes from these sites showed a strong exponential dependence on soil
temperature at the individual plot level. Moreover, a strong exponential
relationship between CH4 flux and soil temperature was also
corroborated by EC measurements made on the landscape level (Fig. 5). The
drier peatland habitats – the tundra bog and bare peat circles – were smaller
CH4 sources (0.2 ± 0.2 and
0.7 ± 1.1 g CH4 m-2 yr-1, respectively). The upland tundra types were at times small sinks
for atmospheric methane during the season. When accumulated over the entire
season, they were close to being neutral, with the CH4 emissions ranging
from -0.03 to 0.13 g CH4 m-2.
Correlation between cumulative seasonal CH4 fluxes with (a) mean vascular LAI,
(b)δ13C of CH4 flux recorded in
different wetland LCTs during the 2008 growing season.
The annual CH4 emissions from the thermokarst lakes varied from 2.1 to
5.3 g CH4 m-2 (mean 4.2 g CH4 m-2), and thus were
lower than for wetland sites (Fig. 3b). The magnitude of total CH4
emissions as well as the importance of diffusion vs. ebullition pathways
varied strongly among the lakes. The contribution of the ebullitive flux
ranged from 5 to 94 % and was the highest in the biggest of the lakes
with the most intensive thermokarst processes occurring. The highest
diffusive flux was observed in the smallest lake with the least open water
area. The seasonal mean CH4 concentration in the surface water was
0.3–3.7 µmol L-1 in 2007 and 0.4–8.0 µmol L-1 in
2008.
Isotopic signature of C-CH4 in emission and
porewater
The δ13C of CH4 flux did not show much variability among
the wetland types, years, or sampling dates (Table 2, Fig. 6). The bulk
average ±SD of δ13C in CH4 emitted across peatland
types and years was -68.2 ± 2.0 ‰. During the
June sampling in 2008, the CH4 released from Eriophorum fen was remarkably
heavier than during the other samplings or at other wetland types, which
resulted in a high mean annual δ13C-CH4 value. Methane
emitted from wetlands was lighter (δ13C more negative) than the
porewater CH4 at 5 and 30 cm depth (Fig. 6). In most of the cases,
δ13C of porewater CH4 at 5 cm (-52.3 ± 6.6 ‰) was heavier than that at 30 cm (-60.7 ± 2.8 ‰). The more detailed profile samplings in 2008
revealed an overall trend of decreasing δ13C values with depth
(Fig. 7). Porewater CH4 in the rhizosphere (0–20 cm) was enriched with
13C compared to deeper depths. Also, CH4 released to the atmosphere
was lighter than that at any depth in the peat profile, except for the June
sampling at Eriophorum fen. A negative linear correlation was found between δ13C in CH4 emission and vascular LAI across the wetland plots
(Fig. 4, the higher the LAI, the lighter the CH4 emitted; P < 0.0001).
Isotopic signature (13C) of δ13C in CH4
emission of various tundra wetland types and magnitude of CH4 flux
during isotope sampling. Data are growing season means ±SD, n=3.
Dependence of CH4 flux measured using EC technique from June
until early October at fen surfaces on soil temperature. The solid line
represents the Q10 function fitted to the data using a nonlinear least-squares fit (x is soil temperature, y is daily mean methane flux, a
represents the base methane flux rate and b is the Q10 coefficient).
The statistics generated from the regression analysis are also presented in
the graph.
Landscape-scale and regional CH4 balance
The fluxes of various land cover types were spatially extrapolated over the
EC footprint area and further over the whole study region of 98.6 km2
using the data on the land cover classification. When the plot-scale
measurements were scaled up to the EC footprint area, the CH4 flux
estimate (3.7 g CH4 m-2) obtained was larger than the estimate by
the EC technique (2.4 g CH4 m-2 for the whole EC measuring
campaign, Fig. 3c). An LAI map produced for the area based on the QuickBird
image (see Marushchak et al., 2013) showed that the fen plots selected for the chamber measurements had on
average higher LAI (1.2) than the fens in the region (0.7). If the linear
relationship between CH4 flux and LAI presented in Fig. 4 is used to
correct the CH4 fluxes from fens to account for the lower LAI in the
landscape, the CH4 estimate was reduced to 2.8 g CH4 m-2.
This is close to the estimate based on EC measurements (Table 3).
The regional CH4 emission, without LAI correction for fen fluxes, was
5.6 g CH4 m-2 for the EC measurement period from May through
September and 6.7 g CH4 m-2 for the whole year. Contribution of
the non-growing season to this annual CH4 flux was 30 %. The higher
emission compared to the EC footprint area can be explained by higher
coverage of wetlands in the whole study region (willow coverage 8.7 % vs.
1.6 %). In the EC footprint area there were more tundra bog (39 %),
fen (10 %), and lakes (9%) and less tundra heath (41 %) and willows
(2 %) than in the whole QuickBird area.
Methane balance of the eddy covariance (EC) tower footprint area
and the whole study region of 98.6 km2 based on EC and area-integrated
plot-scale measurements. For the EC tower footprint area, chamber fluxes
were corrected to account for the higher LAI on fen measurement plots (see
text for more details on why such a correction was necessary).
CH4 balance, g CH4 m-2EC measuring campaignAnnual2008 (days 139–279)(days 280/2007–279/2008)EC footprint area EC2.4not determinedPlot-scale measurements, with LAI correction2.8 ± 1.23.4 ± 1.6Plot-scale measurements, without LAI correction3.7 ± 1.54.4 ± 2.1Study region of 98.6 km2Plot-scale measurements, without LAI correction5.6 ± 1.36.7 ± 1.8
Mean and standard deviations in δ13C of emitted
CH4 (red) and of CH4 contained in porewater samples collected
from 5 cm (green) and 30 cm depths (yellow) of wetlands at the Seida site
during 2007 and 2008 growing seasons from (a) willow stands, (b)Carex fens and
(c)Eriophorum fens.
Discussion
In comparison with other studies of the Russian Arctic tundra, the
landscape-scale CH4 emissions estimated in the present study are
relatively low. Methane flux values presented here are comparable to those
measured during June to mid-September in the Lena River Delta (Sachs et
al., 2008; Wille et al., 2008). A seasonal (May–September) emission of 2.4 g CH4 m-2 as measured by EC technique is less than what has been
reported for northeastern Siberia – e.g. Corradi et al., 2005 (16 g CH4 m-2) and Van Der Molen et al., 2007 (4 g CH4 m-2).
The emissions reported in this study are also lower compared to the work of
Jackowicz-Korczyński et al. (2010) in subarctic Scandinavia (25 g CH4 m-2). Emission rates reported here are similar to the rates
measured by Friborg (2003) during a summer season at a high Arctic fen site
in NE Greenland. Overall, CH4 emissions from this site are relatively
low, owing to a low coverage of high-emitting wetlands (less than 20 %).
Nevertheless, it is evident from our chamber measurements that the wet parts
of the tundra ecosystem in the Seida area emit CH4 at a rate equivalent
or higher than what has been reported for similar tundra habitats in Russia
(e.g. Heikkinen et al., 2004).
The area-integrated chamber measurements presented here show higher fluxes
than those measured by the EC technique (Table 3). This could be attributed
to the disparity in the distribution of different land cover types within
and outside the EC tower footprint and to the variability associated with
the fluxes among various surface types as measured by the chambers. The high
variability among the surface types accounts for the difference in the
estimates by the two techniques. For example, the fen plots measured with
chambers had higher LAI than the fens in the region in general. Based on the
relationship between CH4 flux and LAI, when we corrected the chamber
CH4 flux estimate for such a LAI variation, the CH4 estimates
based on the two independent methods agreed with each other. Without this
correction, the chamber-based seasonal CH4 flux was higher than the
EC-based estimate (2.4 g CH4 m-2).
To characterize the CH4 released from the fens and willow stands, we
measured the δ13C values of CH4 in porewater and surface
emissions. The overall mean δ13C value of CH4 released to
the atmosphere was -68.2 ‰. This value is within the
range of values reported for wetlands from the Arctic including Siberia
(McCalley et al., 2014; Sriskantharajah et al., 2012). The δ13C value of CH4 from wetlands worldwide is -59 ± 6 ‰ (McCalley et al., 2014). Generally, the isotope
signal of CH4 from wetlands appears to be rather constant and
sufficiently distinct from other large sources, e.g. biomass burning
(Monteil et al., 2011), supporting the use of isotopes to
better constrain sources and sinks of atmospheric CH4 by inverse
modelling.
We have shown here that the CH4 emitted from the surface is
substantially lighter than the porewater methane. The 13C depletion in
the CH4 emission combined with rhizospheric enrichment of
13C-CH4 suggests that a large part of the emitted CH4 is
transported from peat to the atmosphere via plant aerenchyma, the gas
exchange system of aquatic plants. Diffusion through air-filled aerenchyma
causes fractionation against the heavier 13C-isotope, thus depleting the
δ13C of CH4 released from plants to the atmosphere
(Chanton et al., 2005). This in turn leaves rhizospheric
CH4 enriched with δ13C. Accordingly, we observed less
negative δ13C of porewater CH4 in the rhizosphere than at
greater depths, where it presumably was unaffected by fractionation due to
plant-mediated transport. Besides passive plant-mediated transport, another
process that causes δ13C depletion of CH4 emissions
relative to porewater CH4 is CH4 oxidation during diffusion
through the peat column. However, the importance of oxidation is likely
minor in these wetlands with such high water tables. Similar observations of
depleted CH4 in surface emissions compared to porewater were made by
others (Popp et al., 1999). It has been generally argued that
plant-mediated transport accounts for a large share of CH4 emissions in
wetlands inhabited by vascular plants (Kutzbach et al., 2004; Riley et
al., 2011; Van Der Nat et al., 1998).
Profiles of porewater δ13C-CH4 of wetlands at
the Seida site in June (red) and August (green) 2008. Yellow and blue
vertical lines represent δ13C of CH4 emitted during June
and August, respectively.
The assumption that plants play a role in the release of CH4 from these
sites is further supported by the negative correlation between δ13C of emitted CH4 and LAI. The depletion of 13C-CH4
with increasing LAI cannot be driven by the influence of plant-derived C
supply for methanogens (Riley et al., 2011). On the contrary, this would
have lead to a positive correlation: the acetate fermentation pathway that
relies on input of labile C compounds produces more enriched CH4 than
CO2 reduction (Whiticar, 1999). The observation that plants
mediate CH4 release is important in the context of climate change. This
implies that a significant part of the CH4 produced in the soil profile
bypasses the oxic soil zones, thus confounding the effect of water table
variations.
Temperature records from the nearby Vorkuta station (75 km north of the
field site) show that the average air temperature in the region rose by 0.9 ∘C from 1980–1999 to 2000–2008 (P. Kuhry, personal communication, 2010). A
climate scenario for the northern part of the Komi Republic, the region
within which the study site is situated, was developed as part of the
CARBO-North project using the IPCC-SRES emission scenario A1B, which
predicts a global warming of 2.8 ∘C by 2100 relative to today
(Stendel et al., 2011). For the northern part of the Komi
Republic, a temperature increase of nearly 7 ∘C relative to the
average over the period 1980–1999 is predicted by 2100. The clear Q10-type
temperature response of CH4 flux found in this study (Fig. 5) suggests
that warming of this magnitude could lead to a substantial increase of
CH4 emission. A crucial point in the assumption is that the water table
remains within a range favourable for CH4 production despite the
increases in evapotranspiration, which can be expected due to the higher
temperatures. The floating peatland surface in fens typical of this area
adjusts to fluctuations in the water table. This implies that the fen types
might remain water-logged, even if other tundra habitats would get drier.
Additionally, the isotope data suggest that CH4 is released largely via
plant aerenchyma thereby escaping its oxidation, implying relatively minor
effects of water table fluctuations. Moreover, the growth of willow stands
in the study area has been reported to be higher owing to warmer
temperatures (Forbes et al., 2010). Enhanced willow stand
productivity may further lead to increased CH4 emissions, also
evidenced by the fact that plants control the net CH4 release.
Based on the positive correlation between LAI and CH4 flux, we estimate
that a 50 % increase in the LAI would favour enhanced release of CH4
to the extent of nearly 35 % from tundra wetlands in the study region.
While less uncertainty is associated with the direct effects of temperature
increase on the methanogenic processes, a high degree of uncertainty does
exist with respect to consequences of temperature increases on the
geomorphological changes of the studied tundra landscape and their possible
impact on vegetation. In addition to the direct enhancement of CH4
fluxes by higher temperatures, warming of 6.1 ∘C by 2100 relative
to the present day will evidently cause thawing of permafrost and result in
landscape changes in the study region. Our measurements of active layer
thickness over the season reveal that the seasonal active layer is deepest
in the wettest (low-lying) parts of the tundra, which are characterized by
lakes, fens, and willow wetlands. A possible consequence of the predicted
warming could be that these wetland cover types become more prevalent in the
future. Based on our results, willow stands and fen sites are the strongest
emitters of CH4. Any landscape change leading to the formation and
expansion of such wetland types owing to permafrost thaw would further
increase CH4 escape, thus providing a strong positive feedback to
climate change in the region.
Concluding remarks
Arctic tundra ecosystems are among the world's fastest warming biomes. These
ecosystems, underlain by permafrost, are extremely vulnerable to the impacts
of anthropogenic climate change. They have been a huge store for organic C
since the last glaciation in the area. The current warming Arctic trend
poses a threat to these ecosystems as their soil temperature is likely to
rise above 0 ∘C leading subsequently to the thawing of the
underlying permafrost. While the fact that these ecosystems are fast
undergoing changes has been established with a fair degree of certainty
based on field data, how these ecosystems will respond to the future climate
is still uncertain. Therefore, with a view to understanding the future
ecosystem responses better, regional studies aiming at a proper
characterization of the atmosphere–biosphere greenhouse gas (GHG) exchange
in the Arctic have been launched. To that end, the work presented in this
paper serves to provide the much-needed seasonal and annual methane flux
estimates from Northeast European Russia, a region not yet well
represented in the Arctic studies. Flux data on other GHGs (CO2 and
N2O) from this study site have already been reported in earlier
publications (Repo et al., 2009; Marushchak et al., 2011,
2013).
Owing to the spatially heterogeneous nature of the studied ecosystem
(Virtanen and Ek, 2014), this study segregated the site into several major
land cover types employing a fine-scale land cover classification scheme.
Chamber techniques were used to measure CH4 fluxes during 2007 and 2008
growing seasons from replicate plots on 10 different LCTs. These data were
useful in characterizing the inherent variability in methane CH4 flux
at the studied site. To complement these plot-scale measurements, the EC
technique was also used to characterize this ecosystem's CH4 source
strength. Employing empirical modelling and vascular leaf area data, the
up-scaled plot-scale data agreed well with the seasonal CH4 flux
estimates obtained using the EC technique. Soil temperature, water table
level, and leaf area were found to be the major factors controlling CH4
release to the atmosphere. Growing season δ13C-CH4
isotopic analyses confirmed the important role of plants in transferring
methane to the atmosphere. The data and process-level information generated
in this study are useful in the biogeochemical modelling of C and N dynamics in
Arctic ecosystems.
Acknowledgements
This research was funded by the Danish Council for Independent Research
Natural Sciences (FNU) (Reference number: 645-06-0493) and the EU Sixth
Framework Programme Global Change and Ecosystems (CARBO-North, project
contract number 036993). Petr Ievlev, Simo Jokinen, Igor Marushchak,
Aleksander Novakovsky, Irina Samarina, Vladimir Shchanov, and Tatiana Trubnikova are acknowledged for the contribution to determination of plot-scale fluxes and Thomas Grelle, Daniel Grube Pedersen Rasmus Jensen, and
Anders Bjørk for their technical support of the EC measurements. M. E. Marushchak received personal funding from the Finnish Graduate School of
Forest Sciences. Maarit Liimatainen received funding from Olvi Foundation
for her contribution in this work. Further, we thank P. Kuhry and M. Stendel
for their contribution to the IPCC model scenarios for the Komi region.
Edited by: T. R. Christensen
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