BGBiogeosciencesBGBiogeosciences1726-4189Copernicus PublicationsGöttingen, Germany10.5194/bg-13-3945-2016Water level, vegetation composition, and plant productivity explain
greenhouse gas fluxes in temperate cutover fens after inundationMinkeMertenmerten.minke@ti.bund.deAugustinJürgenBurloAndreiYarmashukTatsianaChuvashovaHannaThieleAnnettFreibauerAnnetteTikhonovVitalijHoffmannMathiasThünen Institute of Climate–Smart Agriculture,
Braunschweig, GermanyInstitute for Landscape Biogeochemistry, ZALF e.V., Müncheberg,
GermanyScientific and Practical Centre of the National Academy of Sciences of
Belarus for Biological Resources, Minsk, BelarusInstitute for Nature Management of the National Academy of Sciences of
Belarus, Minsk, BelarusInstitute of Botany and Landscape Ecology, Ernst-Moritz-Arndt University, Greifswald,
GermanyMichael Succow Foundation, Greifswald, GermanyInstitute of Soil Landscape Research, ZALF e.V., Müncheberg,
GermanyMerten Minke (merten.minke@ti.bund.de)8July201613133945397010September201529October20152April20165June2016This 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/13/3945/2016/bg-13-3945-2016.htmlThe full text article is available as a PDF file from https://bg.copernicus.org/articles/13/3945/2016/bg-13-3945-2016.pdf
Peat extraction leaves a land surface with a strong relief of deep cutover
areas and higher ridges. Rewetting inundates the deep parts, while less
deeply extracted zones remain at or above the water level. In temperate fens
the flooded areas are colonized by helophytes such as Eriophorum angustifolium,
Carex spp., Typha latifolia or Phragmites australis dependent
on water depth. Reeds of Typha and Phragmites are reported as large sources of methane, but
data on net CO2 uptake are contradictory for Typha and rare for Phragmites. Here, we
analyze the effect of vegetation, water level and nutrient conditions on
greenhouse gas (GHG) emissions for representative vegetation types along
water level gradients at two rewetted cutover fens (mesotrophic and
eutrophic) in Belarus. Greenhouse gas emissions were measured campaign-wise
with manual chambers every 2 to 4 weeks for 2 years and interpolated
by modelling.
All sites had negligible nitrous oxide exchange rates. Most sites were
carbon sinks and small GHG sources. Methane emissions generally increased
with net ecosystem CO2 uptake. Mesotrophic small sedge reeds with water
table around the land surface were small GHG sources in the range of 2.3 to
4.2 t CO2 eq. ha-1 yr-1. Eutrophic tall sedge – Typha latifolia reeds on newly
formed floating mats were substantial net GHG emitters in the range of 25.1
to 39.1 t CO2 eq. ha-1 yr. They represent transient vegetation
stages. Phragmites reeds ranged between -1.7 to 4.2 t CO2 eq. ha-1 yr-1
with an overall mean GHG emission of 1.3 t CO2 eq. ha-1 yr-1.
The annual CO2 balance was best explained by vegetation biomass, which
includes the role of vegetation composition and species. Methane emissions
were obviously driven by biological activity of vegetation and soil
organisms.
Shallow flooding of cutover temperate fens is a suitable measure to arrive
at low GHG emissions. Phragmites australis establishment should be promoted in deeper flooded
areas and will lead to moderate, but variable GHG emissions or even
occasional sinks. The risk of large GHG emissions is higher for eutrophic
than mesotrophic peatlands. Nevertheless, flooding of eutrophic temperate
fens still represents a safe GHG mitigation option because even the hotspot
of our study, the floating tall sedge – Typha latifolia reeds, did not exceed the typical
range of GHG emissions from drained fen grasslands and the spatially
dominant Phragmites australis reed emitted by far less GHG than drained fens.
Introduction
Cutover peatlands represent about 10 percent of all drained peatlands
outside the tropics with the main share in the Nordic countries and Eastern
Europe (Joosten and Clarke, 2002). Since the 1990s restoration of cutaway
peatlands was conducted especially in Canada, Finland, Sweden and Ireland.
Similar projects in Eastern Europe started later, but already cover vast
areas. 42 000 ha of degraded peatlands were restored in Belarus since 2007
and about 80 000 ha since 2010 in the European part of Russia, aiming to
decrease GHG emissions from microbial peat oxidation and peat fire incidents
(Tanneberger and Wichtmann, 2011; Wetlands International, 2015).
A large proportion of the peatlands that have been rewetted or are available
for rewetting in Russia and Belarus are abandoned cutover fens (Minayeva et
al., 2009; Tanovitskaya and Kozulin, 2011). Rewetting of such sites creates
a mosaic of wet and flooded zones and elevated drier parts, and results in
rapid vegetation changes (Kozulin et al., 2010; Thiele et al., 2011). At
sites with the water level close to surface species like Eriophorum angustifolium, Carex vesicaria and Lythrum salicaria establish
within a few years, or, under more nutrient rich conditions, Calamagrostis canescens, Lysimachia thyrsiflora, Carex elata, and
Salix. At flooded areas with standing water depths of more than 20 cm mainly
Phragmites australis emerges, whereas water levels above 30 cm in the medium term only result in
the establishment of submerse and floating plants (Kozulin et al., 2010;
Thiele et al., 2011).
Studies from rewetted cutover boreal peatlands and temperate bogs show that
methane and carbon dioxide emissions are strongly related to water level,
vegetation, and meteorological conditions (Tuittila et al., 1999, 2000;
Drösler, 2005; Yli-Petäys et al., 2007; Soini et al., 2010;
Samaritani et al., 2011; Strack and Zuback, 2013; Wilson et al., 2013; Beyer
and Höper, 2015). For rewetting it is frequently recommended to raise the water
level throughout the year to close to the surface and to avoid inundation in
order to promote the establishment of peat forming vegetation and to prevent
high methane emissions (Drösler et al., 2008; Couwenberg et al., 2008,
2011; Joosten et al., 2012). Such conditions have been proven optimal for
bog restoration (Beyer and Höper, 2015), but their feasibility for fens has
been questioned (Koebsch et al., 2013; Zak et al., 2015). In practice, fens
are often rewetted by shallow flooding.
So far, complete GHG balances are not available for rewetted temperate
cutover fens. Such fens differ from those in the above cited studies in
particular by the massive establishment of Typha and Phragmites australis in shallow water, i.e. of
species that are potentially strong pathways of methane (Kim et al., 1998;
Brix et al., 2001; Whiting and Chanton, 2001; Kankaala et al., 2004;
Hendriks et al., 2007; Chu et al., 2015; Knox et al., 2015; Strachan et al.,
2015). Whereas earlier studies indicate that the radiative forcing of such
methane emissions may be compensated for by the simultaneous very strong net
CO2 uptake (Brix et al., 2001; Whiting and Chanton, 2001), recent
observations described Typha dominated wetlands as often only weak CO2 sinks
(Rocha and Goulden, 2008; Chu et al., 2015; Strachan et al., 2015; but cf.
Knox et al., 2015). Moreover, submerse and floating plants that are promoted
by deep flooding have much higher methane production potential than emergent
species (Kankaala et al., 2003; Zak et al., 2015).
This study aims to quantify GHG emissions from inundated temperate cutover
fens recolonized by wetland plants. We measured for 2 years the CO2,
CH4, and N2O emissions from Phragmites australis communities and other representative
vegetation types along water level gradients in a mesotrophic and a
eutrophic rewetted cutover fen in Belarus. We hypothesize the following.
All sites are net CO2 sinks: peat loss by oxidation has stopped after
rewetting. The net CO2 sink increases with nutrient status, the
productivity of the vegetation and peaks under shallow inundation.
Methane emissions increase with the productivity of the vegetation and peak
under shallow inundation.
The net GHG balance is near neutral when water levels are close to surface
because CH4 emissions are balanced by the net CO2 sink. The net
GHG balance turns into a source when sites are continuously flooded because
the global warming by CH4 emissions exceeds the net CO2 sink.
Materials and methodsStudy sites
Greenhouse gas fluxes were measured at two sites in Belarus (Fig. 1) with a
temperate continental climate with fully humid conditions and warm summers
(Dfb after Köppen, 1936; cf. Kottek et al., 2006). Both sites have been
subject to peat extraction, but differ with respect to time since rewetting,
water depth, peat characteristics and nutrient status, vegetation, and
regional climate.
Location of the study sites.
“Barcianicha” (54.10∘ N; 26.29∘ E) is located in
central Belarus on an alluvial plain between the rivers Al'šanka and
Zahodniaia (“western”) Biarėzina and predominantly fed by groundwater
discharge (Maksimenkov et al., 2006). In 1990 about 190 ha of Barcianicha
were drained and from 1992 to 1995 peat was extracted by milling over an
area of 150 ha to a remaining peat depth of about 80 cm. After abandonment
ditches were closed with earth dams and water level was raised on 60 % of
the area, allowing wetland species like Phragmites australis,
Carex rostrata and Eriophorum angustifolium to establish (Maksimenkov et
al., 2006). Strong water level amplitudes between summer and winter were
stabilized in 2007 by weirs and overflow dams. In 2010 most of the area had
water levels at or slightly above the surface throughout the year. Tall
reeds, dominated by Phragmites australis of up to 2 m height, covered the area. Three GHG
monitoring sites were installed along a water level gradient, including the
small sedge reeds Eriophorum angustifolium–Carex rostrata
(further indicated as BA Eriophorum–Carex) and Carex rostrata–Equisetum fluviatile (BA
Carex–Equisetum) at 15 m distance, and a
Phragmites australis–Carex rostrata reed (BA Phragmites–Carex) at another 30 m
distance (Appendix Table A1).
The second peatland, “Giel'cykaŭ Kašyl”' (52.38∘ N;
25.21∘ E), forms part of the “Bierastaviec” fen and is situated
on the left bank of Jasiel'da River. It belongs to the Ramsar site
“Sporaŭski zakaznik” and was drained in 1975 (Kadastrovyj spravochnik,
1979). More than 1 m of peat remained after peat extraction and
grassland was established. But as the area proved to be unsuited for hay
production, the pumping station was turned off in 1985. The area was flooded
by the Jasiel'da, which is connected with Giel'cykaŭ Kašyl' by a 300 m-long channel. During the vegetation period the area receives additional
water pumped out of an adjacent drained fen. Phragmites australis of 3 m height
dominates the area, which is flooded up to 1 m above the surface. A
30–80 m wide swampy zone along the edges is formed by Typha latifolia, T. angustifolia, and tussocks of
Carex elata and C. vesicaria floating on up to 1 m of water. GHG monitoring was performed in
the floating tall sedge reeds at two sites: a Carex elata–Lysimachia thyrsiflora site (GK
Carex–Lysimachia), and a Typha latifolia–Hydrocharis morsus–ranae
site (GK Typha–Hydrocharis; Table A1), at 3 m distance from each
other. The third Phragmites australis–Lemna trisulca site (GK Phragmites–Lemna) was situated 20 m from the first two sites
in the deeper inundated main area, separated from the swampy zone by a
flooded ditch.
Site characteristics
Each site was split into three plots. Peat depth, stratigraphy and degree of
decomposition after Von Post (AG Boden, 2005) were determined for each site
using a chamber corer (50 cm long, 5 cm diameter). One mixed surface peat
sample (0–5 cm) from each plot was analysed for total carbon (C) and total
N (Vario EL III, Germany), and three samples per plot for pH (Hanna Combo HI
98130, calibrated with 7.01 and 4.01 buffer solution, stored in KCl
solution, HANNA instruments, USA). After the study, above-ground biomass was
harvested from all plots (Barcianicha, 29 October 2012; Giel'cykaŭ
Kašyl', 11 September 2012), oven dried at 60 ∘C till weight
constancy, and three mixed samples per plot were analysed for total C and N.
Vegetation cover of the 70 cm × 70 cm plots was assessed in
coverage classes after Peet et al. (1998). Nomenclature for vascular plants
and mosses follows Rothmaler (2002), and Abramov and Volkova (1998),
respectively. The nutrient status of the sites was estimated by plant
species groups as indicator (Koska et al., 2001).
Water levels were measured continuously (daily averages stored) with Mini-Diver data loggers (Eigenbrodt, Germany), installed in perforated tubes
(inner diameter 46 mm). One Mini-Diver was situated next to BA
Carex–Equisetum in Barcianicha, and another in the middle between the floating tall sedge
– Typha latifolia sites and GK Phragmites–Lemna in Giel'cykaŭ Kašyl'. Manual water level
measurements were conducted at each site in every second to third week.
Daily water levels relative to ground surface were calculated for every plot
by linear regression between the continuous automatic water level time
series and manually measured water levels. Because of strong peat
oscillation this approach did not work for the floating sites GK
Typha–Hydrocharis and GK Carex–Lysimachia. Photographic documentation (monthly during vegetation season,
one time per winter, WL estimation error < 5 cm) was used here
instead to reconstruct relative water levels for linear regression with
Mini-Diver records.
Measurement of greenhouse gas exchange
In order to account for typical small-scale differences between vegetation
types we applied a manual chamber approach to measure greenhouse gas
exchange. Each of the six GHG measurement sites was equipped with three
plastic collars of 70 cm × 70 cm, established in a row about 40 cm
apart from each other. Each collar represents one plot. The row was east–west oriented and the north side was the working side to minimize artificial
shading during measurements. Collars were inserted 15 cm deep into the peat
at Barcianicha. At Giel'cykaŭ Kašyl' because of the high water
level, collars were fixed on tubes orthogonally inserted into the peat and
anchored in the underlying sand. Measurements were conducted from
pre-installed boardwalks from August 2010 to August 2012.
CO2 exchange was measured with transparent chambers made of plexiglas
(88 % light transmission, ice packs for cooling, Drösler, 2005) and
opaque chambers made of grey ABS plastic covered with a white film. Both
were equipped with fans for air mixing and had an inner size 72.5 cm × 72.5 cm × 51.2 cm. Opaque and transparent extensions of
same area and 31.2 or 51.2 cm height with open tops were used to enlarge the
chambers to accommodate for tall plants. Chambers and extensions were sealed
airtight by closed cell rubber tubes attached to the bottom rims
(Drösler, 2005). Carbon dioxide concentrations were measured
continuously by circulating air in a closed loop between the chamber and an
infrared gas analyser (LI-820, LI-COR Biosciences, USA) and recorded every
5 s by a data logger (CR200 or CR1000, Campbell Scientific, USA).
Simultaneously, air temperature inside and outside the chamber, and PAR were
recorded automatically (“109” temperature probes protected by radiation
sheets, SKP215, Campbell Scientific, USA), while soil temperatures were
measured manually in 2, 5, and 10 cm depth once per chamber measurement with
Pro-DigiTemp insertion thermometers (Carl Roth, Germany). For CO2
measurements bright or hardly cloudy days were selected to capture the
complete PAR range from zero to solar noon. During the 1-day measurement
campaigns 8 to 10 transparent chamber measurements of 2–3 min were carried out on each plot from dawn until late afternoon.
Measurements were equally distributed over the daily range of PAR to
determine light response of gross primary production (GPP). A similar number
of opaque chamber measurements of 3–5 min were performed over the same
period to capture the temperature response of ecosystem respiration
(Reco). Measurement campaigns were repeated every third to fourth week
to account for seasonal changes in water table depth and plant development.
CH4 and N2O fluxes were measured once every second to third week
during the snow-free period and monthly during winter using non-air mixed
opaque chambers, of the same material as the other opaque chambers, but
shaped as a truncated pyramid (inner size at bottom 72.5 cm × 72.5 cm,
inner size at top 62.5 cm × 62.5 cm, height 51.2 cm). Four to
five air samples were taken from the chamber headspace during a 15–20 min
enclosure and subsequently analysed in the laboratory with a gas
chromatograph (Chromatec-Cristal 5000.2, Chromatec, Russia), using an
electron capture detector (ECD) for analysing N2O and a flame
ionization detector (FID) for CH4, and an auto-sampler (Loftfield,
Germany). From August 2010 to August 2012 a total of 36 CH4 and
N2O as well as 26 CO2-measuring campaigns were carried out at
every site.
Diurnal CH4 emission dynamics and the effect of chamber transparency
and headspace mixing were additionally studied at one plot per site by
frequent CH4 measurements for one to two summer days, using alternately
two (opaque and transparent, both with fan) or three (opaque and transparent
with and opaque without fan) chamber types (for details cf. Minke et al.,
2014).
Parameters for the development of flux models were recorded on site during
GHG-measurement campaigns, and monitored continuously by nearby climate
stations (BA: Višnieva, 5.6 km NW of Barcianicha, and GK: Z'dzitava,
6.3 km NE of Giel'cykaŭ Kašyl'). At the stations soil temperatures in 2
and 5 cm depth, and air temperature 20 cm above surface were measured with
“109” temperature probes (Campbell Scientific, USA). Photosynthetically
active radiation (PAR) was monitored using a SKP215 Quantum Sensor,
precipitation with the R.M. Young 52202 Tipping Bucket Rain Gauge (heated), atmospheric pressure
with CS100 Setra Barometric Sensor, and all data were recorded half-hourly
with CR200 data loggers (all devices from Campbell Scientific, USA).
Regression between site and climate station temperature data was
subsequently applied to derive continuous half-hourly time series for each
site. Due to technical problems with the rain gauges precipitation data were
received from Gidrometcentr, Belarus, from the weather stations in
Valožyn (15 km E of Barcianicha) and Pružany (54 km WNW of
Giel'cykaŭ Kašyl'). Data from both weather stations of Gidrometcentr
were also used to calculate 30 years (1979–2008) monthly averages of air
temperature and precipitation.
Calculation of flux rates, annual emission models, and
uncertaintiesCarbon dioxide
The net ecosystem exchange (NEE, the CO2 flux between the ecosystem and
the atmosphere) is the balance between CO2 inputs to the ecosystem by
gross primary production (GPP) and CO2 losses by ecosystem respiration
(Reco; Alm et al., 1997; Chapin III et al., 2002). A positive sign refers to
a flux from the ecosystem to the atmosphere, a negative sign to an ecosystem
sink (cf. Falge et al., 2001). Annual NEE rates were modelled for each plot
separately based on the plot and campaign specific relationships between
Reco and temperature, as well as between GPP and PAR.
Modelling NEE using the approach of Hoffmann et al. (2015) resulted in
surprisingly high annual net CO2 uptake rates of the Phragmites australis sites. To check
for possible impacts of the calculation routine on the result we used
alternatively the approach of Leiber-Sauheitl et al. (2014) and arrived at
slightly smaller CO2 sinks. Both approaches are reasonable, build on
the same assumptions but differ with respect to flux estimation, reference
temperature, GPP model and importance of the significance of the model fits,
as described in the following paragraphs. To avoid that modelled CO2
exchange rates would be biased by specific features of only one of the
approaches, both approaches were used to model annual CO2 exchange
rates and their means were taken as final estimates. However, for simplicity
we only present modelled CO2 time series derived by the Hoffmann
(H) approach.
Calculation of measured CO2 flux rates
Measured CO2 flux rates were calculated in both approaches as linear
CO2 concentration change in the chamber over time. Measurements were
discarded if PAR fluctuated by > ± 10 % (transparent chambers) and
chamber temperature > ± 0.75 K (transparent and opaque chambers) from
the mean of the selected flux calculation interval. In the H approach a
moving window of variable time was applied to adjust the starting point and
length of the regression sequence according to the regression quality. The
optimal flux length was selected in a second step, based on the minimum
Akaike information criterion (AIC) of the flux fit to the Reco or the
GPP functions. In the Leiber–Sauheitl (LS) approach a moving window of
constant length (1 min for all except for 2 min for opaque flux
measurements at Phragmites australis plots because of large chamber volumes and slow
concentration changes) was used to select the regression sequence with
maximum R2 and minimum variance. If maximum R2 resulted in different
fluxes than minimum variance (46 % of all flux measurements) the mean of
both was used as flux estimate.
Modelling of half-hourly CO2 exchange rates
In both approaches the Lloyd and Taylor (1994) equation (Eq. 1) was fitted
to Reco flux data against site temperatures for each plot and campaign.
Reco=Rref×expE0×1Tref-T0-1T-T0Reco= ecosystem respiration (mg CO2-C m-2 h-1),
Rref=Reco at reference temperature (mg CO2-C m-2 h-1),
E0= activation energy like parameter (K), Tref= reference
temperature (283.15 K), T0= temperature constant for the
start of biological processes: (227.13 K), T= soil or air temperature
during measurement of best fit with the dataset (K).
In the H approach Eq. (1) was fitted to Reco flux rates separately for
air temperature and soil temperatures and the final Reco parameter pairs
were selected out of all significant (p≤ 0.1) sets based on the lowest
AIC. If parameterization was not significant or failed, or if the daily
temperature amplitude was below 3 K, the average CO2 flux of the
measurement campaign was used. In the LS approach, one Reco fit per
plot and campaign was calculated against air temperatures. If
parameterization was impossible or the temperature amplitude was below 2 K,
the mean campaign Reco flux was used.
In a second step GPP fluxes were determined by subtracting modelled
Reco fluxes from temporally corresponding measured NEE flux rates. In the
H approach a rectangular hyperbola equation (Michaelis and Menten, 1913; Eq. 2)
was fitted to the relation between PAR and GPP flux rates to calibrate GPP
parameter sets of α (initial slope of the curve; light use
efficiency) and GPmax (rate of carbon fixation for infinite PAR).
GPP=α×PAR×GPmaxα×PAR+GPmax
GPP parameter pairs with lowest AIC were selected from each campaign out of
all significant regression parameters (p≤ 0.1). If the parameter
estimation failed, a non rectangular hyperbolic equation was fitted to the
data (Gilmanov et al., 2007). If this failed, too, an average parameter
approach was used. Assuming declining GPP fluxes when PAR drops from 500 to
0 µmol m-2 s-1α was set -0.01 and GPmax
estimated as the mean campaign GPP flux. In the LS approach the modified
Michaelis–Menten model of Falge et al. (2001; Eq. 3) was applied and GP2000
was calculated instead of GPmax, i.e. the rate of carbon fixation at
PAR of 2000 µmol m-2 s-1. Campaigns for which no GPP fit was
found were skipped.
GPP=α×PAR×GP2000GP2000+α×PAR-GP20002000×PAR
Based on the GPP parameter pairs and continuously monitored PAR data, GPP
was modelled in both approaches for each plot at a temporal resolution of 30
min. NEE was subsequently calculated as the difference between GPP and
Reco.
Uncertainty, accuracy, and variability
Model performance for the interpolation between the measurement campaigns
was estimated for the H approach by leave-one-out cross-validation. Stepwise
one measurement campaign was left out after the other and the modelled
Reco and NEE fluxes were compared with the measured fluxes in the left
out campaigns. Model performance was assessed by the Nash–Sutcliffe
efficiency (NSE, Moriasi et al., 2007).
The random error of the annual CO2 balances was calculated for the
H approach using the R-script Version 1.1 of Hoffmann et al. (2015).
Campaign specific confidence intervals (p= 0.01) were determined for the
temperature models, as well as for the Reco and GPP parameter pairs by
bootstrapping. Subsequently 100 samples were taken randomly from the
confidence intervals and used to compute Reco, GPP, and NEE models. The
random error of the CO2 models calculated with the H approach
represents the model uncertainty at the days of the measurement campaigns,
but not of the interpolation. As indicated by the differences between the H
and LS approaches, the uncertainty of the annual balances is larger. To
arrive at more realistic error estimates we accounted for the random error
and for the difference between both approaches and defined the confidence
intervals as the difference between the annual sums of both approaches plus
2 times the annual random error calculated for the H approach.
Methane and nitrous oxideCalculation of fluxes
Methane fluxes were estimated with the R package “flux 0.2–1” (Jurasinski
et al., 2012) using linear regression. For normalized root mean square error
(NRMSE) < 0.2 the flux with the largest number of concentration
measurements was preferred. If NRMSE ≥ 0.2 a set of fluxes was
estimated using the maximum number up to at least three concentration
measurements. Subsequently the flux with the lowest NRMSE was selected.
Fluxes were accepted if NRMSE < 0.4, R2≥ 0.8 and n≥ 3. This was
the case in 639 out of 686 methane flux measurements, with 477 accepted
fluxes based on n≥ 4.
Nitrous oxide flux rates and their standard deviations were calculated with
linear regression using the same air samples as accepted for CH4 flux
calculation.
Modelling of emissions
Methane fluxes correlated with some environmental factors. This allowed to
develop a univariate nonlinear regression model for daily methane fluxes.
The relatively small number of observations did not allow any multivariate
approaches. First, the relation between environmental factors (air
temperature, soil temperature, water level, air pressure, PAR, GPP,
Reco, NEE) and measured CH4 fluxes was tested for each plot using
non-parametric Spearman's correlation to identify the strongest driving
parameter. Second, several published nonlinear regression models (Eqs. 1, 4,
5) were fitted to the relation between methane emissions and the driver and
the optimal model was selected based on the AIC.
The strongest Spearman's ρ correlations were found between methane
fluxes and instantaneous on site soil temperature (median ρ for 2 years and all 18 plots = 0.85, n= 36), followed by half-hourly and daily
Reco (both 0.83), half-hourly GPP (-0.80; both modelled with the
H approach), and on site air temperature (0.75). Mean daily site specific
soil temperatures, calculated by linear regression between site measurements
and climate station data, also correlated well with methane fluxes (median ρ per plot and year = 0.85) and had a strong covariance with other factors.
Water level did not correlate significantly with methane emissions at any
plot, possibly because it was always close to or above the surface.
Therefore mean daily soil temperature was chosen as the single driving
factor for modelling methane emission.
The temperature dependency of methane production and emission was previously
described by the Arrhenius function or its logarithmic form (Conrad et al.,
1987; Schütz et al., 1990; Daulat and Clymo, 1998; Kim et al., 1998):
F=A×e-ER×T,
where F= flux rate of CH4 (mg CH4-C m-2 h-1), A= Arrhenius
parameter (mg CH4-C m-2 h-1), E= apparent
activation energy (J mol-1), R= gas constant
(8.314 J mol-1 K-1), and
T= soil temperature (K).
Also an exponential function or its logarithmic form has been widely applied
to calculate methane emission in relation to temperature (Dise et al.,
1993; Saarnio et al., 1997; Kettunen et al., 2000; Tuittila et al., 2000;
Laine et al., 2007; Rinne et al., 2007; Wilson et al., 2009):
F=a×eb×T,
where F= flux rate of CH4 (mg CH4-C m-2 h-1), a= flux
rate at T= 0 ∘C (mg CH4-C m-2 h-1),
b= coefficient (∘C-1), and T= soil temperature (∘C).
The third tested function was the Lloyd and Taylor (1994) equation for soil
respiration (Eq. 1, Sect. 2.4.1).
We used the AIC to select from Eqs. (1), (4), and (5) the one that best
fitted to our data set. The differences were small but the AIC of the Lloyd
and Taylor equation (Eq. 1) was the smallest for 33 out of 36 fits (fits for
2 years and 18 plots) and was therefore chosen to model methane emissions
for all plots and years.
As N2O fluxes did not correlate with recorded environmental factors
annual emissions were estimated by linear interpolation between
measurements.
Uncertainty, accuracy, and variability
Model performance was tested by leave-one-out cross-validation.
Errors of modelled annual methane emissions were calculated using Monte
Carlo simulation in four steps. We included the uncertainty of the
temperature transfer from the climate station to the site, the uncertainty
of the measured flux rates, and the parameter uncertainty of the Lloyd and
Taylor fits. Temperature uncertainty was quantified by 1000 times repeated
bootstrapped re-sampling of site and station temperatures with the same
indices. Second, a set of 1000 normally distributed flux values was
generated for every flux measurement based on the calculated CH4 flux
rates and their standard deviation. Third, each of the 1000 soil temperature
data set was paired with one of the 1000 flux data sets and 1000 Lloyd and
Taylor fits (Eq. 1) were performed. Fourth, from each of the Lloyd and
Taylor fits 1000 bootstrap parameter samples were created using bootstrap of
the residuals (Efron, 1979; Leiber-Sauheitl et al., 2014). More than 99 %
of the bootstrap fits were successful what resulted in more than 990 000
parameter pairs per plot and year. Finally, 1000 Lloyd and Taylor fits were
randomly sampled from the parameter pairs, combined with the 1000 soil
temperature data set and used to calculate 1000 methane models per plot and
year. For each time point and the annual sums 95 and 5 % quantiles
were calculated to construct confidence intervals of the time series and
annual gas balances. As the CH4 model fits include the temperature and
methane flux uncertainties over the entire year, the 90 % confidence
intervals do to some extent also account for the interpolation between
measurement days.
Uncertainties of annual N2O fluxes were calculated solely based on
estimates and standard deviations of the measured fluxes. 1000 normally
distributed values of each flux were generated and linearly interpolated.
This resulted in 1000 annual emission estimates per plot and year, but the
calculated 90 % confidence intervals represent only the uncertainties of
the measured fluxes.
Statistical analyses
Correlations between annual balances of CH4 and CO2 with site
factors were tested using the non-parametric Spearman's ρ.
Differences of daytime methane fluxes among chamber types were analysed
using either the Mann–Whitney test or the Kruskal–Wallis test with the
post-hoc nonparametric Tukey-type multiple comparison procedure developed by
Nemenyi (Zar, 1999).
ResultsSite conditions
Most of the residual peat at both peatlands was very slightly to moderately
decomposed radicel peat (Table 1). Surface peat was eutrophic and acid at
both study sites, but less decomposed at Barcianicha as compared to
Giel'cykaŭ Kašyl' (Table 1). Barcianicha had 27 to 76 cm thick
layers of brown moss peat about 40 cm below surface, while for
Giel'cykaŭ Kašyl' notable amounts of Phragmites macrofossils were found in the
upper 100 to 140 cm of the profile.
Given are means ± standard deviations, n= 3 plots
a Summer = June–August, winter = December–February, b harvest at
Barcianicha 29 October 2012, and at Giel'čykaŭ Kašyl' 9 November 2012,
c pH (KCL) mean of three samples, d total carbon and nitrogen
content, one sample, e von Post peat decomposition scale: H3 very
slightly, H4 slightly, H5 moderately, H6 moderately highly, H8 very highly
decomposed peat.
Vegetation was homogeneous within sites types at Barcianicha and varied
little between years (Table A1). Vegetation indicated mesotrophic conditions
at Barcianicha (Koska et al., 2001). At Giel'cykaŭ Kašyl' species
cover was homogeneous among plots and years for GK Phragmites–Lemna (Table A1). The
floating sites GK Carex–Lysimachia and GK Typha–Hydrocharis constituted a strongly interweaved fine mosaic
of sedge tussocks and cattail and shared many species. Vegetation indicated
eutrophic conditions for Giel'cykaŭ Kašyl' (Koska et al., 2001).
Mean annual temperature at Barcianicha was 6.5 ∘C in the first
and 6.9 ∘C in the second measurement year, close to the long term
mean (6.4 ∘C, 1979–2008). Annual precipitation in the first year
(740 mm) was higher than the long-term mean (665 mm) due to heavy summer
rains (Fig. 2a), but lower (633 mm) in the second.
Cumulative monthly precipitation (bars) and average monthly air
temperatures (dots) for Barcianicha (a) and Giel'čykaŭ Kašyl' (b).
Actual temperatures (black) were measured in (a) Višnieva, 5.6 km NW
of Barcianicha, and (b) Z'dzitava, 6.3 km NE of Giel'čykaŭ
Kašyl'. Actual precipitation data (black) and 30 year averages
(1979–2008) of temperatures and precipitation (grey) are from meteorological
stations of “Gidrometcentr” in (a) Valožyn, 15 km E of Barcianicha, and
(b) Pružany, 54 km WNW of Giel'čykaŭ Kašyl'.
Giel'cykaŭ Kašyl' (Fig. 2b) was generally warmer and drier than
Barcianicha (long-term mean 7.3 ∘C and 594 mm, respectively,
1979–2008). Also here the first year was wetter (804 mm) and the second
year drier (500 mm) and annual temperatures were close to the long term mean
(7.3 ∘C in the first year; 7.9 ∘C in the second
year).
Annual water levels relative to the surface at Barcianicha decreased in the
order BA Phragmites–Carex (14 ± 2 cm above surface),
BA Carex–Equisetum and BA Eriophorum–Carex (at surface; Table 1).
Differences among plots within sites were small (Fig. 4c, d, and e).
Annual values for both years were the same (Table 1). Summer and winter
median water levels were very similar, despite temporal fluctuations of up
to 18 cm.
Diurnal variation of methane emissions, measured with different
chamber types, and outside PAR, at BA Eriophorum–Carex (plot I, 18 July 2012),
BA Carex–Equisetum (plot III, 16 September 2012), BA
Phragmites–Carex (plot II, 8 August 2012), GK
Typha–Hydrocharis and GK Carex–Lysimachia (both plot I,
12 July 2012 and 13 July 2012), and GK Phragmites–Lemna (plot II, 21
September 2011). Data of BA Phragmites–Carex and GK
Phragmites–Lemna are from Minke et al. (2014).
Mean daily air temperature (a) and mean daily PAR (b) at
Višnieva (a, b) and Z'dzitava (l, m), mean daily water table positions,
mean daily measured (points) and modelled (lines) CH4 fluxes, and mean
daily modelled (H approach) GPP, and Reco(i, j, k) of Barcianicha (c–k)
and Giel'čykaŭ Kašyl' sites (n–v).
Water tables at GK Phragmites–Lemna (Giel'cykaǔ Kašyl') were about 1 m in the
first year, and dropped to about 70 cm above surface in the second year
(Table 1). At the close by floating tall sedge – Typha latifolia reed sites water levels
were only about 10 cm above the surface and the drop from the first to the
second year was small, both because of the oscillating peat surface. Summer
water levels were lower than winter levels, but never dropped significantly
below surface (Table 1, Fig. 4n and o). Annual water levels varied more
among plots than at Barcianicha, with a maximum of 11 cm at GK
Phragmites–Lemna (Fig. 4p).
Above-ground biomass harvested in autumn 2012 at Barcianicha (Table 1) was
largest for BA Phragmites–Carex (296 ± 79 g C m-2), lower for BA
Eriophorum–Carex (117 ± 34 g C m-2), and smallest for BA
Carex–Equisetum (55 ± 22 g C m-2). Biomass harvests of GK
Typha–Hydrocharis and GK Carex–Lysimachia were similar to BA
Phragmites–Carex, but that of GK Phragmites–Lemna were 2 times larger (586 ± 121 g C m-2,
Table 1). The doubled biomass production of Phragmites australis at Giel'cykaŭ Kašyl'
compared to Barcianicha supports the nutrient rich conditions, probably
resulting from different water supply (river and grassland drainage water
for GK, groundwater for BA) and different land use history (after peat
extraction temporary grassland before rewetting of GK, rewetting directly
after peat extraction of BA).
Carbon dioxide emissions
Model performance tested for the H approach was good for both years and all
site types and plots. Cross-validation resulted in a median NSE of 0.78
(range from 0.38 to 0.90) for the Reco models and of 0.76 (0.21 to 0.91)
for the NEE models.
All sites of Barcianicha were net CO2 sinks in the first year. NEE was
-528 (90 % confidence interval -933, -194) g CO2-C m-2 yr-1
for BA Phragmites–Carex, -86 (-130, -38) g CO2-C m-2 yr-1 for
BA Eriophorum–Carex and -88 (-114, -68) g CO2-C m-2 yr-1 for
Carex–Equisetum (Fig. 5, Table 2). In the second year, resulting from increased
Reco and decreased GPP, the net CO2 uptake decreased. NEE of BA
Phragmites–Carex dropped to -329 (-431, -220) g CO2-C m-2 yr-1, BA
Eriophorum–Carex became CO2 neutral and BA Carex–Equisetum turned into a small source of 24 (-6,
55) g CO2-C m-2 yr-1.
Annual fluxes of CO2, CH4, and Carbon (C balance = NEE
+ CH4 emissions) with 90 % confidence intervals.
SiteYearRecoGPPNEECH4 emissionsC balance(g CO2–C m-2 yr-1)(g CO2–C m-2 yr-1)(g CO2–C m-2 yr-1)(g CH4–C m-2 yr-1)(g C m-2 yr-1)BA Eriophorum–Carex1364 (339 to 396)-449 (-512 to -407)-86 (-130 to -38)10 (9 to 13)-75 (-114 to -30)2406 (368 to 458)-413(-449 to -376)-7(-49 to 21)11 (10 to 14)4 (-35 to 30)BA Carex–Equisetum1232 (196 to 262)-320(-361 to -279)-88(-114 to -68)17 (13 to 22)-71(-92 to -56)2327 (282 to 371)-302(-334 to -281)24 (-6 to 55)13 (9 to 16)37 (8 to 66)BA Phragmites–Carex1614 (478 to 737)-1141(-1595 to -888)-528(-933 to -194)42 (28 to 58)-486(-873 to -156)2706 (568 to 842)-1035(-1134 to -949)-329(-431 to -220)36 (22 to 52)-293(-377 to -205)GK Typha–Hydrocharis1921 (841 to 982)-771(-842 to -665)151 (41 to 300)60 (47 to 77)210 (111 to 360)2973 (818 to 1156)-1086(-1476 to -862)-113(-418 to 66)68 (52 to 92)-45(-343 to 142)GK Carex–Lysimachia11105 (1007 to 1207)-940(-1081 to -774)166 (66 to 252)86 (63 to 121)252 (145 to 356)21270 (1221 to 1362)-1054(-1243 to -789)216 (48 to 470)85 (59 to 142)301 (137 to 552)GK Phragmites–Lemna1936 (733 to 1200)-1547(-1726 to -1386)-611(-819 to -450)100 (48 to 147)-516(-747 to -349)21092 (937 to 1210)-2267(-2733 to -1843)-1175(-1567 to -690)101 (61 to 177)-1074(-1453 to -565)
Uncertainties on the site level include the uncertainties of the
plot models and the spatial heterogeneity. They were calculated by pooling
the plot specific annual models derived by error calculation. Different
CO2 balances of the H approach and the LS approach were accounted for by
adding the differences randomly to 50 % of the respective annual values
derived by error calculation with the H approach. To derive uncertainties of
C balances the annual models of NEE and CH4 derived by plot-wise error
calculation were summarized and combined site-wise.
Annual CO2 (NEE, Reco, GPP), CH4 and N2O fluxes
at Barcianicha (a, c, e) and Giel'čykaŭ Kašyl' (b, d, f).
Uncertainties for CO2 fluxes are 50 % of the difference between both
modelling approaches plus the 90 % confidence intervals of the H approach.
Uncertainties for CH4 represent 90 % confidence intervals of the
models, but for N2O only 90 % CI of the measured N2O fluxes.
Light grey = 1st year, darker grey = 2nd year. Plots are
ordered I, II, III.
Both, sinks and sources were larger at the Giel'cykaŭ Kašyl' sites.
NEE of GK Phragmites–Lemna was -611 (-819, -450)
g CO2-C m-2 yr-1 in the first and, despite of increasing
Reco fluxes, -1175 (-1567,
-690) g CO2-C m-2 yr-1 in the second year. The high values
were attributed to extremely high annual GPP reaching -2267 (-2733,
-1843) g CO2-C m-2 yr-1 in the second year, equivalent to
twice the Reco fluxes (Fig. 5, Table 3). At the other Giel'cykaŭ
Kašyl' sites Reco and GPP also increased from the first to the
second year, but Reco and GPP largely balanced each other. GK
Typha–Hydrocharis consequently varied between a source of 151 (41, 300) g CO2-C m-2 yr-1
in the first and a sink of -113 (-418, 66) g CO2-C m-2 yr-1
in the second year. GK Carex–Lysimachia was a net CO2
source in both years, releasing 166 (66, 252) g CO2-C m-2 yr-1
in the first and 216 (48, 470) g CO2-C m-2 yr-1
in the second year.
Scatter plots of annual NEE, Reco, GPP, CH4 emissions,
median annual water levels (both years for all plots, n= 36), and above-ground biomass carbon (second year for all plots, n= 18). Spearman's
ρ significant at `P≤ 0.05; *P≤ 0.01; **P≤ 0.001; ***P≤ 0.0001. Spearman's ρ in brackets
without GK Typha–Hydrocharis and GK Carex–Lysimachia (n= 30 for correlations among water levels and fluxes; n= 15
for correlations among biomass and fluxes). Small symbols indicate first
year, large symbols second year.
On average the net CO2 sink at Barcianicha decreased in the second year
by 130 g CO2-C m-2 yr-1 or 56 % but increased at
Giel'cykaŭ Kašyl' by 259 g CO2-C m-2 yr-1 or
263 % compared to the first year.
Small-scale spatial variability of annual NEE fluxes was largest for GK
Phragmites–Lemna (187 ± 153 g CO2-C m-2 yr-1 and mean ± standard
deviation of the absolute differences between annual plot emissions and
annual site emissions, n= 6; Table 3; Fig. 5). The absolute value of within-site spatial
variability of NEE exchange rates was lower for BA Phragmites–Carex, GK
Carex–Lysimachia and GK Typha–Hydrocharis and small
for BA Eriophorum–Carex and BA Carex–Equisetum
(16 ± 13 and 9 ± 5 g CO2-C m-2 yr-1; Table 3, Fig. 5). The order of sites changes,
when within site variability of NEE is related to annual site NEE fluxes.
Relative variability was the same for BA Carex–Equisetum and GK Phragmites–Lemna (19 ± 12 and
20 ± 11 %, respectively, Table 3). This is related to the importance
of the annual flux magnitude as illustrated by BA Eriophorum–Carex in the second year that
resulted from an annual site NEE of -7 g CO2-C m-2 yr-1 and
an absolute within site spatial variability of 11 g CO2-C m-2
yr-1 in a relative variability of 152 %.
Small-scale spatial variability of net CO2 and CH4
emissions.
Given are means ± standard deviations, n= 6.
a absolute differences between annual plot emissions and annual site
emissions.
b Absolute differences between annual plot emissions and annual site
emissions in percentages of absolute values of annual site emissions.
Methane emissionsDiurnal variability of methane emissions and impact of chamber
types
Opaque and transparent chambers slightly differed in the development of air
temperature and relative humidity of the headspace during the measurements.
Despite cooling temperature rose more in transparent (up to
3 ± 0.5 ∘C, mean ± standard error; Table A2) than in opaque
chambers (up to 1.4 ± 0.2 ∘C). Due to cooling, however,
relative humidity increased less in transparent (up to 18.1 ± 3.7 %)
than in opaque chambers (up to 14.8 ± 2.3 %). Differences were
significant at few measurement days only (Table A2).
Pronounced diurnal methane emission dynamics were observed for BA
Phragmites–Carex and GK Phragmites–Lemna, much stronger than for any other site (Fig. 3). Significantly
different methane emissions between opaque and transparent chambers,
however, were only found for GK Typha–Hydrocharis and GK Carex–Lysimachia (Table A2). Measurements with
transparent chambers resulted here in 20 and 10 % higher emission
estimates than with opaque chambers with fan. Also for BA
Eriophorum–Carex I measurements with transparent chambers produced 9 % higher flux rates
than opaque chambers, but the difference was not significant (Fig. 3, Table A2).
At all other sites the flux rates measured with transparent and opaque
chambers with fan agreed within 2 %. The chamber intercomparison suggests
a potential reduction of convective gas transport in Typha latifolia by shading with the
regularly applied opaque chambers without fan. Consequently, the measured
growing season fluxes from GK Typha–Hydrocharis and GK Carex–Lysimachia were corrected upwards by 20 % as
Typha latifolia was present at all plots except for GK Carex-Lysimachia I in 2012 where the diurnal chamber
intercomparison took place (Table A2). Fluxes from the other sites were not
corrected because chamber effects were not significant.
GHG balances based on the global warming potentials of CO2,
CH4 and N2O for a time horizon of 100 yr (GWP100 of
CO2= 1, of CH4= 28 and of N2O = 265 CO2–equivalents, Myhre
et al., 2013) with 90 % confidence intervals.
SiteYearCO2 balanceCH4 balanceN2O balanceGHG balance(t CO2 eq. ha-1 yr-1)(t CO2 eq. ha-1 yr-1)(t CO2 eq. ha-1 yr-1)(t CO2 eq. ha-1 yr-1)BA Eriophorum–Carex1-3.1(-4.8 to -1.4)3.8 (2.9 to 5.0)-0.1(-0.8 to 0.8)0.5 (-1.4 to 3.1)2-0.3(-1.8 to 0.8)4.2 (3.6 to 5.1)0.2 (-0.2 to 0.8)4.1 (2.3 to 6.0)BA Carex–Equisetum1-3.2(-3.2 to -2.5)6.4 (5.0 to 8.0)-0.1(-0.7 to 0.5)3.1 (1.9 to 5.0)20.9 (-0.2 to 2.1)4.7 (3.2 to 6.1)-0.3(-0.9 to 0.2)5.3 (3.3 to 7.3)BA Phragmites–Carex1-19.4(-34.2 to -7.1)15.6 (10.4 to 21.6)-0.3(-2.9 to 3.0)-4.1(-16.9 to 11.9)2-12.1(-15.8 to -8.1)13.3 (8.4 to 19.4)-0.6(-3.6 to 2.0)0.7 (-6.5 to 6.6)GK Typha–Hydrocharis15.5 (1.5 to 11.0)22.3 (17.4 to 28.6)0.6 (-1.7 to 2.7)28.5 (21.5 to 38.9)2-4.2(-15.3 to 2.4)25.5 (19.3 to 34.4)0.4 (-0.7 to 1.5)21.7 (7.6 to 36.1)GK Carex–Lysimachia16.1 (2.4 to 9.2)32.3 (23.6 to 45.5)-0.1(-2.1 to 1.8)38.2 (27.8 to 53.7)27.9 (1.8 to 17.2)31.6 (22.2 to 53.1)0.4 (-0.8 to 1.9)39.9 (25.8 to 60.7)GK Phragmites–Lemna1-22.4(-30.0 to -16.5)35.7 (18.0 to 54.7)0.6 (-2.4 to 3.8)13.9 (-10.6 to 36.0)2-43.1(-57.5 to -25.3)37.7 (22.9 to 66.2)0.0 (-3.5 to 3.4)-5.4(-29.2 to 40.0)
Confidence intervals include the uncertainties of the plot models
and the spatial heterogeneity. To derive uncertainties of GHG balances the
annual models of CO2 (NEE), CH4 and N2O derived by plot-wise
error calculation were summarized and combined site-wise.
Estimation of net primary production (NPP), heterotrophic (Rh)
and autotrophic respiration (Ra) from the Phragmites australis
sites.
a Green above-ground biomass (AGB) present at end of the
first measuring year was estimated for each GHG–plot from biomass harvest at
three to four sample plots (40 cm × 40 cm) close to collars
accordingly to the share of green vs. dead culms. At the end of the second
year green AGB of the plots was calculated from the plot harvest (Table 1)
accordingly to the share of green vs. dead culms.
b Green AGB was assumed to equal above-ground net primary production (AG
NPP), although this may underestimate NPP by about 10 % (Westlake, 1982).
Reported below-ground net primary production (BG NPP) to AG NPP ratios range
from 0.34–2.58 (Westlake, 1982; Scarton et al., 1999; Soetaert et al.,
2004; Asaeda et al., 2006). We used the estimate of 1.4 from reeds in North
Jutland (Schierup, 1978; cited in Westlake, 1982) for BA
Phragmites–Carex and a lower ratio (1.2) for GK
Phragmites–Lemna, because below-ground biomass allocation of
Phragmites australis was found to be proportionally less in deep (70
or 75 cm), compared to shallow (20 or 5 cm) water (Vretare et al., 2001).
c Net primary production (NPP) = AG NPP plus BG NPP
d Heterotrophic respiration (Rh)= NPP minus [NEE]
e Autotrophic respiration (Ra)= [GPP] minus NPP.
Annual methane emissions
The Lloyd–Taylor methane models performed well for all sites except for the
second year of BA Phragmites–Carex and GK
Phragmites–Lemna. NSE for all but the Phragmites australis sites ranged between
0.38 and 0.85 (median 0.58). Models of the Phragmites australis sites were acceptable in the
first year (median NSE 0.37, range 0.05 to 0.82) but performed worse in the
second year (median 0.01, range -0.25 to 0.24), where models did not
adequately capture the seasonal course of methane emissions at three out of
six Phragmites plots. Models of GK
Phragmites–Lemna III and BA Phragmites–Carex III did not explain the high
emissions in August 2011 (Figs. 4h and s). Both mentioned models and the model of BA
Phragmites–Lemna I overestimated emissions in spring and early summer 2012. Annual
emissions were calculated alternatively by linear interpolation for the
second year of BA Phragmites–Lemna I and III and GK
Phragmites–Lemna III. The resulting flux rates of 25,
28, and 118 g CH4-C m-2 yr-1 lie within the 90 %
confidence intervals of the temperature driven Lloyd–Taylor methane model
(30, 32, and 139 g CH4-C m-2 yr-1; Table A3). The
Lloyd–Taylor models were therefore accepted despite of negative NSE.
GK Phragmites–Lemna had the highest methane emissions of all sites, estimated to 100
(90 % confidence interval 48, 147) and 101 (61, 177) g CH4-C m-2 yr-1 in the first and second year, respectively, (Table 2).
GK Carex–Lysimachia released less methane. GK Typha–Hydrocharis was the smallest source among the studied
sites at Giel'cykaŭ Kašyl' with 60 (47, 77) and 68 (52, 92) g CH4-C m-2 yr-1,
but still larger than the Barcianicha
sites.
BA Phragmites–Carex emitted 42 (28, 58) in the first and 36
(22, 52) g CH4-C m-2 yr-1 in the second year. BA Carex–Equisetum was a much smaller methane
source, but the absolute lowest annual methane emissions were found at BA
Eriophorum–Carex being 10 (9, 13) in the first and 11 (10, 14) g CH4-C m-2 yr-1 in the second year (Table 2).
Methane emissions of all sites hardly differed between years (Table 2). They
decreased in the second year at Barcianicha by on average 3 g CH4-C m-2 yr-1
or 14 % but increased at Giel'cykaŭ Kašyl' by 4
g CH4-C m-2 yr-1 or 5 % compared to the first year.
Absolute and relative small-scale variability of methane emissions tended to
increase with annual methane emission height (Fig. 5, Table 3).
Nitrous oxide emissions
Emissions of N2O from all plots were around zero (Fig. 5e and f).
Maximum plot emissions were around 0.5 g N2O-N m-2 yr-1,
but were usually compensated for by similar large uptakes in a neighbour
plot or the other year. The overlap of the 90 % confidence of all sites,
plots and years indicates that N2O emissions were not significantly
different among them.
Correlations between annual GHG emissions and site parameters
GHG emissions were only weakly related to surface peat characteristics.
Spearman's ρ of the correlation between annual methane emissions and
C / N ratio was -0.50* and between annual net CO2 exchange and pH
0.40' `P≤ 0.05; *P≤ 0.01, n= 36, i.e. correlation of peat
characteristics of 18 plots with annual fluxes of these plots of two GHG
measuring years).
Median annual water level was not correlated with Reco, but with NEE and
CH4 emissions and most strongly with GPP (Fig. 6). Correlations of
water levels with Reco, GPP, NEE and CH4,
were highly significant when the floating sites GK
Typha–Hydrocharis and GK Carex–Lysimachia were excluded from the analysis
(Fig. 6, ρ in brackets). Correlations of water level with NEE and CH4
were also strong for Barcianicha alone (ρ=-0.60**, 0.85***,
respectively, **P≤ 0.001; ***P≤ 0.0001, n= 18).
Total above-ground biomass carbon harvested after the second measuring year
strongly correlated with the second year annual balances of CH4,
Reco and GPP, but not with NEE (Fig. 6). Without the floating tall sedge
– Typha latifolia sites correlations between biomass and balances of Reco and GPP were
stronger and the correlation between biomass and NEE became highly
significant. When only Barcianicha was analysed, correlation between biomass
and methane emissions were not significant, but correlations between
biomass and Reco, GPP, and NEE were strong (ρ= 0.98***,
-0.98***, -0.95**, respectively, n= 9).
Annual CH4 emissions did not correlate with annual NEE, but strongly
with Reco and GPP (Fig. 6). Excluding GK Typha–Hydrocharis
and GK Carex–Lysimachia resulted in highly
significant correlation between methane and NEE (Fig. 6, ρ=-0.83,
P < 0.0001, n= 30). For Barcianicha alone correlation between NEE and CH4
emissions was also significant (ρ=-0.67, P= 0.0028, n= 18).
As expected, within-site variation of Reco and absolute GPP generally
scaled with biomass (Fig. 6). Methane emissions increased among plots of BA
Phragmites–Carex with increasing absolute GPP and Reco and all three fluxes were
positively correlated with above-ground biomass. A positive correlation
between biomass and methane also occurred for GK Carex–Lysimachia, while at GK
Phragmites–Lemna methane emissions tended to decrease with increasing net CO2 uptake
(Fig. 6).
Carbon and GHG-balances of sites
Both Phragmites sites were surprisingly strong carbon sinks (Table 2) but also methane
sources and had only low net GHG emissions with an overall mean of 1.3 t CO2 eq. ha-1 yr. The 2-year average GHG balances of the
shallowly flooded, mesotrophic site BA Phragmites–Carex and the deeply inundated,
eutrophic site GK Phragmites–Lemna were -1.7 (90% confidence interval -15.0, 10.2)
and 4.2 (-26.8, 37.7) t CO2 eq. ha-1 yr-1, respectively. The
mesotrophic small sedge reeds BA Eriophorum–Carex and BA Carex–Equisetum with water tables around the
land surface were weak carbon sinks and methane sources (Table 2). Both
sites were small net GHG emitters of 2.3 (-1.0, 5.6) and 4.2 (2.1, 6.8)
t CO2 eq. ha-1 yr-1, respectively. The eutrophic, floating tall
sedge – Typha latifolia reeds were, despite of shallow relative water depths, strong
methane sources and in most years also net CO2 emitters. GK
Typha–Hydrocharis was a substantial GHG source of 25.1 (9.5, 37.9) t CO2 eq. ha-1 yr-1 and
GK Carex–Lysimachia even emitted 39.1 (26.6, 58.0) t CO2 eq. ha-1 yr-1.
The role of N2O exchange was negligible for the GHG-balances of all
sites.
DiscussionAnnual CO2 and methane balances
Contrary to our hypothesis (i) only three sites were stable net CO2
sinks, two sites switched between sink and source and one site was a net
CO2 source in both years. Surprisingly, both eutrophic tall sedge –
Typha latifolia reeds on newly formed floating mats were net CO2 sources over the 2-year period although the mats suggest a net carbon accumulation since
rewetting.
For all site years with a net CO2 sink we can argue in line with
hypothesis (i) that peat loss by oxidation has stopped after rewetting. We
suggest that also in site years with a net CO2 source the CO2 loss
originated from decaying plant material rather than from peat. All source
sites were fully water-saturated throughout the year and had substantial
methane emissions, indicating fully anaerobic conditions. We suggest that
the CO2 originated from accumulated plant litter or from high stress
related plant respiration as the sites where CO2 sources occurred were
characterized by transitional vegetation stages (see below).
The CO2 and methane balances of the mesotrophic small sedge reeds at
Barcianicha agree with the literature. Eutrophic tall sedge – Typha latifolia reeds on
newly formed floating mats have not been studied before but results
generally agree with literature from eutrophic mineral reed ecosystems. The
Phragmites reeds also agree with literature with regard to the methane emissions, but
have an exceptionally strong CO2 sink. In the following details are
discussed for the three site groups.
Annual methane emissions from BA Eriophorum–Carex and BA Carex–Equisetum were of the same magnitude as
from similar small sedge reeds in two rewetted cutover Atlantic bogs (Wilson
et al., 2009, 2013). Net uptake and net release of CO2, however, was
smaller for BA Eriophorum–Carex and BA Carex–Equisetum as compared to the mentioned Irish sites (Wilson
et al., 2007, 2013; Table 6), perhaps partly resulting from lower
productivity.
Net annual CO2 and CH4 emissions from temperate wetlands
with vegetation comparable to Barcianicha' and Giel'čykaŭ Kašyl'.
Location, climateaSite description, methodbDominant plant speciesStudy yearswater levelc (cm above surface)NEEd (g CO2–C m-2 yr-1)CH4 emissionsd (g CH4–C m-2 yr-1)ReferenceOweninny bog, Ireland, 54.12∘ N, 9.58∘ W (Cfb)cutover blanket bog with oligotrophic, acid peat, rewetted 2003 (ch)Eriophorum angustifolium2009 to 20117 ± 1-348 ± 2225.3 ± 0.1Wilson et al. (2013)Turraun, Ireland, 53.28∘ N, 7.75∘ W (Cfb)Cutover bog with slightly acidic peat and calcareous subsoil, rewetted 1991 (ch)E. angustifolium – Carexrostrata2002 to 20035, -6.3163, 4083.2, 2.4Wilson et al. (2007, 2009)Typha latifolia2002 to 20037, 0.3266, 45129.1, 21.6Trebel valley mire complex, NE Germany, 54.10∘ N, 12.73∘ E (Cfb)Former fen grassland, rewetted 1997 (ch)Phragmites australisT. latifolia2011/12 to 2012/13-9,-19 6, -4-83,68-43, 9411, 110, 3Günther et al. (2014)C. acutiformis5, -3-3, 8147, 3Mokré Louky, Czech Republic, 49.02∘ N, 14.77∘ E (Cfb)eutrophicated sedge fen (ec)C. acuta2006 to 2008-20 to 10-199 ± 66Dušek et al. (2012)Vejlerne Nature Reserve, Denmark, 56.93∘ N, 9.05∘ E (Cfb)brackish wetland (ch, 10 occasions, two years)P. australissummer - to winter +-55248Brix et al. (2001)Horstermeer, Netherlands, 52.14∘ N, 5.04∘ E (Cfb)land along the ditches of a former fen grassland, rewetted about 1995 (ch)P. australis – T. latifolia2006-2 to 587.6Hendriks et al. (2007)Newport News Swamp, Virginia, USA, 37∘ N, 76.5∘ W (Cfb) Florida, USA, 30.5∘ N, 84.25∘ W (Cfa)freshwater marsh, 20 cm organic layer (ch)lake shore (ch)T. latifoliaT. latifolia1992/931992 to 19935 to 205 to 20-896 -978, -113981.651.6, 72.0Whiting and Chanton, 2001San Joaquin Freshwater Marsh, California, USA, 33.66∘ N, 117.85∘ W (Csb)freshwater marsh (ec)T. latifolia1999 to 03summer - to winter +136 ± 363Rocha andGoulden (2008)Sacramento–San Joaquin Delta, California, USA, 1st site: 38.05∘ N, 121.77∘ W, 2nd site: 38.11∘ N, 121.65∘ W (Csa)former fen pasture, rewetted 2010 (ec) former agricultural fen, rewetted 1997 (ec)T. spp., Schoenoplectusacutus2012/1310726-368 -39753 38.7Knox et al. (2015)Mer Bleue, Ontario, Canada, 45.4∘ N, 75.5∘ W (Dfb)freshwater marsh (ec – NEE, ch – CH4)T. angustifolia2005 to 2009at surface-224±54127 ± 19Strachan et al. (2015)Ballards Marsh, Nebraska, USA, 42.87∘ N, 100.55∘ W (Dfa)freshwater marsh, 10 to 30 cm litter (ec)P. australis199440 to 6060Kim et al. (1998)Winous Point, Lake Erie, Ohio, USA, 41.47∘ N, 83∘ W (Dfa)freshwater marsh, 20 cm organic layer (ec)T. angustifolia – Nymphaea odorata2011 to 1320 to 6065 ± 9250.8 ± 6.9Chu et al. (2015)Lake Vesijärvi, S Finland, 61.08∘ N, 25.50∘ E (Dfc)inundated peatland on the shore of an eutrophic lake (ch)P. australisP. australis1997 to 1999 1997 to 199910 to 2030 to 7033 ± 13.5 122.3 ± 56.5Kankaala et al. (2004)Loch Vale watershed, Colorado, USA, 40.29∘ N, 105.66∘ W (Dfc)pristine sedge fen (ch)C. aquatilis1996 to 98water saturated81 ± 431.2 ± 2.1Wickland et al. (2001)Panjin Wetland, Liaoning Province, NE China, 41.13∘ N, 121.90∘ E (Dwa)freshwater tidal wetland with silty clay (ec)P. australis2005vol. SWC 3 to 46 %-65Zhou et al. (2009)
a Climate type after Köppen and Geiger (Kottek et al.,
2006): Cfb – warm temperate, fully humid, warm summer; Cfa – warm
temperate, fully humid, hot summer; Csb – warm temperate with dry and warm
summer; Csa – warm temperate with dry and hot summer; Dfb – snow climate,
fully humid, warm summer; Dfa – snow climate, fully humid, hot summer; Dfc
– snow climate, fully humid, cool summer and cold winter; Dwa – snow
climate with dry winter and hot summer.
b ch – chamber method, ec – eddy covariance method.
c Annual water level (listed for 1 or 2 years, but given as
mean ± standard deviation when done for 3 or more years) or water level range
(water level of dry to water level of wet season).
d annual NEE and methane emissions, listed for 1 or 2 years, but
given as mean ± standard deviation when done for 3 or more years.
Methane emissions from BA Phragmites–Carex compared well to the shallow water inner reed
zone (33 g CH4-C m-2 yr-1) and that from GK
Phragmites-Lemna to the deep water outer reed zone (122 g CH4-C m-2 yr-1) of
lake Lake Vesijärvi in southern Finland (Table 6; Kankaala et al.,
2004). Methane emissions from a Phragmites australis dominated, shallowly inundated marsh in
north-central Nebraska, USA (60 g CH4-C m-2 yr-1; Kim et
al., 1998) as well as from wet Phragmites australis stands in a rewetted Dutch fen (88 g
CH4-C m-2 yr-1; Hendriks et al., 2007) were between both
Phragmites reeds of the present study. Annual NEE fluxes of both Phragmites australis sites were more than
10 times higher than at a freshwater tidal reed wetland in NE China, though
above-ground biomass was comparable (Zhou et al., 2009). The differences
result from smaller ratios of Reco to GPP in the present (0.58 ± 0.09,
n= 4) compared to the tidal reed study (0.95) and can be explained by
permanent inundation of BA Phragmites–Carex and GK
Phragmites–Lemna, and consequently low heterotrophic
respiration, while the soil of the tidal reed wetland was periodically
aerated. The importance of water level was also evident for a Phragmites australis site in a
rewetted former grassland fen in NE Germany that sequestrated
83 g CO2-C m-2 yr-1 and emitted 11 g CH4-C m-2 yr-1
in an exceptionally wet year (WL at surface) but released 68 g CO2-C m-2 yr-1 and only 1 g CH4-C m-2 yr-1
in a typical year (WL below surface; Günther et al., 2014).
Annual methane and CO2 fluxes from floating tall sedge – Typha latifolia reeds are
not reported in the literature. Methane emissions from GK
Typha–Hydrocharis and GK Carex–Lysimachia
were higher compared to a pristine, water saturated sedge fen
(dominated by Carex aquatilis) in the southern Rocky Mountains
(30 to 34 g CH4-C m-2 yr-1; Table 6; Wickland et al., 2001) or to Carex acutiformis and Typha latifolia sites during the
wet year in the above mentioned rewetted fen grassland (47 and 10 g CH4-C m-2 yr-1,
respectively; Günther et al., 2014).
They were comparable to temperate Typha latifolia (82 g CH4-C m-2 yr-1;
Whiting and Chanton, 2001) and T. angustifolia marshes (51 g CH4-C m-2 yr-1,
Chu et al., 2015; 127 g CH4-C m-2 yr-1, Strachan
et al., 2015). The constantly high water levels made us expect a net
CO2 uptake at GK Typha–Hydrocharis and GK
Carex–Lysimachia, as was found for Typha latifolia and T. angustifolia marshes (Whiting and
Chanton, 2001; Strachan et al., 2015), for a water saturated temperate sedge
fen in the Czech Republic (Dušek et al., 2012), and in the wet year for
Carex acutiformis and Typha latifolia (Günther et al., 2014). However, in contrast to our first hypothesis the
sites GK Typha–Hydrocharis and GK Carex–Lysimachia were net CO2 sources. Similar, a wet sedge fen in
the southern Rocky Mountains (Wickland et al., 2001) and a water saturated
Typha angustifolia marsh (Chu et al., 2015) were found to be CO2 sources (Table 6). Chu et
al. (2015) explain their findings by abnormal climatic conditions. As
climatic conditions during the first year of the present study were similar
to the long term average, other factors, like reduced GPP because of shading
from old standing leaves (Rocha et al., 2008) may have been important, as
there was much dry biomass present. Also the high water levels and their
strong fluctuations may have imposed stress on the vegetation (Dušek et
al., 2012), as indicated by changes in the cover of the dominant species
between years (Table A1) and the early aging of the sedges. High Reco
fluxes from the floating tall sedge – Typha latifolia reeds could be the result of
increased maintenance respiration because of environmental stress (Chapin et
al., 2002) combined with high heterotrophic respiration from decomposing
dead plant material which formed the main part of the sedge tussocks
(estimated from photographic documentation). This indicates that the plant
communities were not well adapted to the present conditions and represent a
transient development stage.
Robustness of annual GHG balancesMethane
Overall, our measurement design and data treatment produces annual methane
balances at the high end of the expected real fluxes.
The pronounced diurnal methane emission dynamics from BA
Phragmites–Carex and GK Phragmites–Lemna with 5-fold flux increases from morning to midday result from
active air transport in Phragmites australis aerenchyma in the growing season driven by sun light
(Armstrong and Armstrong, 1991; Brix et al., 1992; Armstrong et al., 1996).
In contrast to other studies (Van der Nat and Middelburg, 2000; Günther
et al., 2013) we did not find a significant impact of chamber transparency
on measured methane emission rates, maybe because enclosed plants were
connected by rhizomes with culms outside the chamber. Such connection seems
to allow for pressure propagation and continuation of unrestrained
convective gas flow (Juutinen et al., 2004; Minke et al., 2014).
Consequently the application of opaque chambers has not biased annual
emission estimates from the Phragmites australis sites.
Day-to-day variability and seasonal variation of average daily emissions
from Phragmites australis stands are controlled by sediment temperature (Kim et al., 1998;
Kankaala et al., 2004), which supports our decision to use soil temperature
for modelling methane emissions. However, a single measurement at any time
during daylight does not represent the daily emission average. For the
monitored days (Fig. 3) most measurements between 9.00 and 18.00 Moscow standard time resulted
in equal or higher estimates as compared to the 24 h mean. Our daylight
measurements during the growing period slightly overestimate the daily
methane flux rates. In summary, our approach tended to overestimate the real
emissions at the Phragmites australis sites.
GK Typha–Hydrocharis showed less pronounced diurnal methane emission dynamics (Fig. 3).
Unlike Phragmites, Typha latifolia reacted on shading. The reduction of emissions by opaque chambers
agrees with other studies of Typha latifolia (Chanton et al., 1993; Whiting and Chanton,
1996). Similar to Phragmites australis, green parts of Typha latifolia pressurize during daylight which drives
convective gas transport and accelerates methane efflux (Brix et al., 1992;
Whiting and Chanton, 1996). Obviously, Typha latifolia plants are less connected than
Phragmites and cannot compensate for small-scale shading during chamber deployment.
Our transparent/opaque ratios of measured methane flux rates of 1.2 agrees
with previous studies for Typha latifolia (1.1 – Whiting and Chanton, 1996; 1.3 –
Günther et al., 2013). However, we do not know the variability of the
ratio under different weather conditions. We applied the correction factor
1.2 for total daily methane emissions during the growing season, although
chamber transparency only matters during daylight. Estimated annual
emissions will consequently be at the high end of real emissions from the
site.
Methane measurements were significantly affected by shading at the floating
Carexelata plot, but not at the small sedge plots dominated by Carex rostrata and Eriophorum angustifolium. Gas transport in
sedges is driven only by diffusion (Armstrong, 1979; King et al., 1998).
Existing studies were ambiguous regarding the effect of shading by chambers.
Shading reduced methane emissions from Carex aquatilis (Morrissey et al., 1993) and Carex allivescers (Hirota
et al., 2004), but not from Carex limosa and Carex rostrata (Whiting and Chanton, 1992), Carex acutiformis (Günther et
al., 2013) and Eriophorum angustifolium (Joabsson et al., 1999; Whiting and Chanton, 1992).
Carbon dioxide
The two approaches used to model CO2 exchange rates resulted in very
similar annual balances. Plot-wise annual Reco calculated with the
H approach was on average 5 ± 5 % (mean ± standard deviation,
n= 36) below the LS approach, while GPP sink was 1 ± 3% (n= 36)
higher. Resulting annual net CO2 uptake was consequently on average
stronger for the H approach than for the LS approach. The mean difference of
NEE between both approaches was 43 ± 41 g CO2-C m-2 yr-1 (n= 36). This indicates that measured fluxes and general
modelling assumptions, i.e. the temperature relation of Reco and PAR
relation of GPP were robust towards differences in flux calculation and
model parameterization. Also the good results of the cross validations of
the models of the H approach at all sites indicate a high robustness of the
results.
The net annual CO2 sink of the Phragmites australis sites was surprisingly large, especially
at GK Phragmites–Lemna. The first year NEE of this site agreed with the estimate of Brix
et al. (2001; Table 6) but the second year uptake was twice as high. To test
for plausibility we roughly estimated the carbon flux partitioning in the
ecosystem from independent data. Based on dry weight of green above-ground
biomass assessed at the end of the growing seasons 2011 and 2012 and on
published ratios between above-ground and below-ground biomass production we
estimated the net annual primary production (NPP, g C m-2 yr-1) of
the Phragmites australis sites during both GHG measurement periods (Table 5). Using NPP, NEE,
and GPP we estimated heterotrophic and autotrophic respiration (Rh and
Ra, Table 5) and evaluated their meaningfulness. As expected because of
inundation, heterotrophic respiration was low, ranging between 77 and 114 g CO2-C m-2 yr-1. The ratios of heterotrophic respiration to
methane emissions (CO2-C / CH4-C) were 2.2 and 2.3 in the first
and second year, respectively, for BA Phragmites–Carex and closer, 1.0 and 1.1, for GK
Phragmites–Lemna. Similar ratios were found in incubation experiments for organic bottom
sediments and the upper peat layer of a flooded former fen grassland
(Hahn-Schöfl et al., 2011). Calculated autotrophic respiration was half
of GPP, but differed considerably between years (43 to 61 %). This range
is plausible given the uncertainty of the underlying estimates (especially
of NPP), as the efficiency of converting GPP to NPP is generally assumed to
be relatively constant (cf. Chapin et al., 2002). In summary, the carbon
partitioning test was plausible and supports the exceptional net CO2
uptake in the Phragmites sites. Such uptake may be explained by strong rhizome
formation in a relatively young reed ecosystem but may not represent a
long-term equilibrium.
Controls of annual GHG emissions
Reality proved more complex than our hypotheses. We studied transient
vegetation development stages after fen rewetting, which may not necessarily
be generalized to equilibrium stages. The findings related to the hypotheses
are as follows.
The annual CO2 balance was best explained by vegetation biomass, which
includes the role of vegetation composition and species. Phragmites reeds were by far
the most productive ecosystems at both studied peatlands. The nutrient
status affected productivity, but species effects dominated the CO2
balance. Inundation depth had no systematic effect on the annual CO2
balance.
Methane emissions were site specific. They increased with productivity and
correlated strongly with Reco fluxes. Methane was obviously most driven
by biological activity of vegetation and soil organisms. Continuously
inundated sites tended to have higher methane emissions than sites where
water levels remained near the land surface.
Under mesotrophic conditions rewetting leads to stable small net GHG sources
or even sinks because methane emissions are largely balanced by the net
CO2 sink. Under eutrophic conditions, rewetted fens remain net GHG
sources in most cases. Vegetation types can be sinks or sources of CO2
and emit substantial amounts of methane so that rewetting effects on the GHG
balance remain difficult to predict.
We reject, however, that the CO2 sink and methane emissions peak under
shallow inundation. In contrast, the various vegetation types with shallow
water showed strongly diverging CO2, methane and GHG balances in a
small water level range.
The high GHG emissions from the floating tall sedge – Typha latifolia reeds are comparable
to deep-drained temperate fen grassland (26 t CO2 eq. ha-1 yr-1
– Drösler et al., 2014; 65 t CO2 eq. ha-1 yr-1
– Eickenscheidt et al., 2015). In contrast to the other sites of the
present study, important targets of peatland rewetting, i.e. restoration of
the carbon sink function and reduction of GHG emissions have not been
achieved for GK Typha–Hydrocharis and GK Carex–Lysimachia.
In the following we discuss the background for the revision of the
hypotheses, reasons for the differences among sites and the individual
drivers of the GHG fluxes.
Water table
In a meta-analysis Blain et al. (2014) found that methane emissions from
boreal and temperate, undrained and rewetted peatlands tend to increase but
the CO2 sink to decrease along a water level gradient from 30 cm below
to 20 cm above surface. The water level in our study ranged from 3 cm below
to 104 cm above surface with most sites within 10 cm water table range. The
diverse vegetation types with roughly similar water table had widely
diverging CO2, methane and GHG balances that we cannot confirm any
trend. In drained peatlands water table position defines the depth of the
aerobic zone and consequently the rate of peat oxidation (Blain et al.,
2014; Couwenberg et al., 2011). The sites of the present study, however,
were permanently water saturated and water levels affect CO2 fluxes
most likely indirectly via other controlling factors, for example vegetation
composition. Methane emissions under flooded conditions are hardly affected
by water table position (Blain et al., 2014). When aerenchymous plants are
abundant, as in the present study, they dominate the gas exchange and
methane bypasses the oxygenated water column (Whiting and Chanton, 1992;
Chanton and Whiting, 1995). In analogy to CO2, water level has affected
methane emissions of the studied sites mainly indirectly by vegetation
composition and the type and abundance of aerenchymous plants.
Near-zero nitrous oxide emissions at all sites agree with other studies from
rewetted fens with permanent water saturation (Hendriks et al., 2007; Wilson
et al., 2013).
Nutrient conditions
The different nutrient status of the studied peatlands cannot be explained
by surface peat properties, which were both eutrophic, but by water supply
(river and grassland drainage water for Giel'cykaŭ Kašyl',
groundwater for Barcianicha). Eutrophic conditions supported the
establishment of more productive plant species at Giel'cykaŭ Kašyl'
compared to the mesotrophic Barcianicha, higher productivity of Phragmites australis and higher
microbial activity indicated by higher Reco and methane fluxes. This is
in line with Blain et al. (2014) who found that methane and CO2
emissions are higher from rich temperate rewetted fens as compared to poor
fens and bogs. Our results indicate that rich temperate rewetted fens may be
further subdivided into mesotrophic and eutrophic to account for
significantly different methane emissions.
Vegetation and plant productivity
Plant productivity was the main control of CO2 fluxes, as indicated by
the strong correlation between biomass and NEE for all sites except GK
Typha–Hydrocharis and GK Carex–Lysimachia (Fig. 6). However, also differences of methane emissions within
sites increased with above-ground biomass and GPP (Fig. 6), and were larger
in Giel'cykaŭ Kašyl' compared to Barcianicha, and in both peatlands
for the Phragmites australis sites (Table 3). This is most likely due to control of vegetation
and plant productivity on methane emissions, as indicated by the highly
significant correlation between methane emissions and biomass, and can be
explained by supply of organic material and by plant mediated gas exchange
(Whiting and Chanton, 1993; Chanton et al., 1995; Bellisario et al., 1999;
Whalen, 2005).
Fresh organic substrates were rather limited at Barcianicha, as indicated by
the thin layer of litter and many bare peat patches. More emissions can be
expected when more litter accumulates (Waddington and Day, 2007). Plant
litter was more abundant at Giel'cykaŭ Kašyl', certainly because of
higher plant productivity, but also because of a longer period since
rewetting and deeper inundation. This may explain why a strong correlation
between NEE and methane emissions was found at Barcianicha, but not at
Giel'cykaŭ Kašyl'. Methane production did not only depend on actual
primary production, especially in the floating tall sedge – Typha latifolia reeds of
Giel'cykaŭ Kašyl'. Methane emissions from GK Typha–Hydrocharis and GK
Carex–Lysimachia were high and, similar to the large Reco fluxes, at least partly
fuelled by old litter. Also allochthonous carbon can not be excluded as a
substrate for methane production at Giel'cykaŭ Kašyl' (Chu et al.,
2015), for example from floating plants like Lemna trisulca that form detritus with a much
higher methane production potential compared to Phragmites australis litter (Kankaala et al.,
2003).
The zone of floating mats will most likely continue for many years to emit
large amounts of methane and only a shift towards Phragmites australis dominated plant
communities with larger CO2 sink potentials seems to allow for
reduction of GHG emissions. Such a shift may not be unlikely, because
Phragmites australis is growing on most of the area of Giel'cykaŭ Kašyl' and has been
abundant at GK Typha–Hydrocharis and GK Carex–Lysimachia in former times, as indicated by macrofossils in
the peat profile (Table 1).
Conclusions
The eutrophic peatland Giel'cykaŭ Kašyl' with deep standing water
had a large carbon sink potential, but also a high risk of local net
CO2 losses. The site varied spatially and temporally between being a
small net GHG sink and a large GHG source because of high methane emissions.
The mesotrophic peatland Barcianicha with shallow, constant water levels, in
contrast, constituted a smaller but more stable carbon sink and only a small
GHG source. Both net CO2 uptake and methane emissions were strongly
linked to vegetation and plant productivity, which in turn were related to
water level and nutrient conditions. Emission variability increased with
productivity of sites. This implies that the formulation of robust emission
factors for high-productive vegetation types and mire ecosystems requires
more long-term and spatially resolved GHG emission studies than for
low-productive ones.
Unexpectedly high carbon losses and GHG emissions from the floating tall
sedge – Typha latifolia reeds of Giel'cykaŭ Kašyl' were most likely caused by
vegetation suffering from high and strongly fluctuating water levels. The
exact sources of these high emissions, as well as the duration and
successional pathway of the supposed transitional phase require further
investigation.
Our study indicates that permanent, shallow inundation of cutover temperate
fens is a suitable measure to arrive at low GHG emissions. Phragmites australis establishment
should be promoted in deeper flooded areas and will lead to comparably
moderate, but variable GHG emissions or even occasional sinks. The study
supports previous findings for rewetted peatlands that the risk of high GHG
emissions is higher for eutrophic than mesotrophic peatlands. In spite of
the possible high emissions in some vegetation types or years, flooding of
eutrophic fens still represents a safe GHG mitigation option for temperate
fens because even the hotspot of our study, the eutrophic floating mats, did
not exceed typical GHG emissions from drained fen grasslands and the
spatially dominant flooded Phragmites australis reed emitted by far less GHG than drained fens.
Plant species cover of GHG measuring plots in summer 2010 and 2012.
Vegetation types of sites studied in Barcianicha:
Eriophorum angustifolium–Carex rostrata–reed (BA
Eriophorum–Carex), Carex rostrata–Equisetum fluviatile–reed (BA Carex–Equisetum), Phragmites australis–Carex rostrata–reed (BA Phragmites–Carex), and
Giel'čykaŭ Kašyl': Carex elata–Lysimachia thyrsiflora–reed (GK Carex–Lysimachia), Typha latifolia–Hydrocharis morsus–ranae–reed (GK Typha–Hydrocharis),
Phragmites australis–Lemna trisulca–reed (GK
Phragmites–Lemna). Plant cover scale according to Peet et al. (1998):
Class 1 = very few individuals, 2 = cover of 0–1 %, 3 = 1–2 %,
4 = 2–5 %, 5 = 5–10 %, 6 = 10–25 %, 7 = 25–50 %,
8 = 50–75 %, 9 = 75–95 %, 10 > = 95 %.
Species not exceeding cover class 2 are only shown if they meet class 2 in
more than two relevés.
Mean ± SD error of daytime (PAR > 2 µmol m-2 s-1)
CH4 flux rates, PAR, Tin, and RHin by plot
and chamber type (DF = opaque mixed chamber, TF = transparent mixed
chamber, D = not mixed opaque chamber). Values with same letter superscript
do not differ significantly at P < 0.05 (Mann–Whitney or
Kruskal–Wallis test; post-hoc non-parametric Nemenyi test), data of
BA Phragmites-Carex II and GK Phragmites-Lemna II from
Minke et al. (2014).
Annual fluxes of CO2, CH4, and N2O with confidence
intervals
SiteYearPlotRecoGPPNEECH4 emissionsN2O emissions(g CO2-C m-2 yr-1)(g CO2-C m-2 yr-1)(g CO2-C m-2 yr-1)(g CH4-C m-2 yr-1)(mg N2O-N m-2 yr-1)BA1thI378 (359 to 398)-496 (-514 to -478)-118 (-132 to -104)11 (9 to 14)-80 (-189 to 21)Eriophorum-CarexII358 (338 to 378)-441 (-449 to -433)-83 (-102 to -63)10 (8 to 12)-89 (-213 to 49)III355 (338 to 372)-411 (-415 to -406)-56 (-75 to -37)10 (8 to 13)79 (-65 to 245)2ndI436 (413 to 459)-444 (-451 to -437)-8 (-35 to 19)12 (10 to 14)32 (-67 to 130)II391 (367 to 414)-413 (-421 to -406)-23 (-51 to 6)11 (9 to 12)39 (-38 to 115)III390 (379 to 401)-381 (-387 to -375)9 (-5 to 23)11 (10 to 14)95 (-75 to 284)BA1thI210 (195 to 226)-287 (-296 to -278)-77 (-87 to -66)15 (13 to 18)-40 (-148 to 71)Carex-EquisetumII245 (227 to 263)-350 (-362 to -338)-105 (-115 to -95)19 (16 to 23)-21 (-132 to 85)III241 (226 to 255)-322 (-334 to -310)-82 (-88 to -76)17 (14 to 21)-23 (-203 to 168)2ndI303 (280 to 326)-286 (-292 to -280)17 (-9 to 43)10 (8 to 13)-28 (-110 to 56)II353 (334 to 372)-331 (-335 to -327)22 (2 to 43)14 (13 to 19)-84 (-150 to -12)III323 (300 to 347)-290 (-295 to -284)34 (10 to 57)14 (12 to 16)-113 (-296 to 79)BA1thI498 (473 to 522)-967 (-999 to -935)-469 (-517 to -421)32 (26 to 39)-515 (-833 to -226)Phragmites-CarexII693 (646 to 741)-1555 (-1600 to -1509)-861 (-942 to -780)46 (34 to 57)356 (-246 to -982)III650 (594 to 705)-902 (-921 to -884)-253 (-318 to -188)48 (36 to 61)-75 (-487 to 335)2ndI615 (562 to 669)-963 (-980 to -947)-348 (-410 to -285)30 (21 to 35)-63 (-977 to 849)II769 (691 to 848)-1122 (-1136 to -1108)-353 (-437 to -269)45 (36 to 57)-466 (-943 to 849)III732 (680 to 785)-1018 (-1052 to -984)-286 (-360 to -212)32 (24 to 42)87 (-174 to 374)GK1thI877 (836 to 918)-801 (-813 to -790)76 (36 to 116)59 (49 to 73)95 (-673 to 886)Typha-HydrocharisII923 (912 to 934)-831 (-844 to -817)92 (74 to 111)59 (47 to 73)130 (-279 to 533)III963 (942 to 984)-680 (-697 to -663)284 (263 to 304)61 (44 to 83)220 (-52 to 515)2ndI1104 (1046 to 1161)-1446 (-1480 to -1412)-342 (-424 to -261)63 (51 to 75)151 (-124 to 449)II827 (816 to 838)-870 (-881 to -859)-43 (-60 to -27)65 (50 to 82)74 (-223 to 372)III988 (972 to 1005)-943 (-967 to -919)46 (20 to 72)77 (59 to 103)76 (-111 to 257)GK1thI1124 (1090 to 1158)-962 (-989 to -934)162 (135 to 189)86 (74 to 100)-137 (-677 to 419)Carex-LysimachiaII1167 (1124 to 1211)-1065 (-1084 to -1047)102 (60 to 144)72 (59 to 86)162 (-160 to 505)III1024 (1005 to 1044)-792 (-814 to -770)233 (206 to 259)101 (75 to 140)-91 (-358 to 160)2ndI1246 (1224 to 1268)-811 (-837 to -785)435 (395 to 475)84 (65 to 121)100 (-140 to 346)II1331 (1296 to 1367)-1205 (-1248 to -1162)126 (56 to 196)67 (56 to 82)-56 (-220 to 88)III1233 (1219 to 1246)-1146 (-1188 to -1104)87 (42 to 132)102 (76 to 162)229 (-128 to 599)GK1thI921 (892 to 949)-1446 (-1511 to -1380)-525 (-607 to -443)113 (88 to 139)58 (-524 to 684)Phragmites-LemnaII767 (729 to 804)-1516 (-1568 to -1465)-750 (-827 to -673)61 (43 to 83)-101 (-783 to 548)III1121 (1037 to 1206)-1680 (- 1737 to -1623)-559 (-623 to -495)112 (73 to 164)468 (-256 to 1176)2ndI1170 (1122 to 1219)-2678 (-2745 to -2611)-1507 (-1584 to -1431)87 (65 to 113)99 (-652 to 872)II970 (929 to 1012)-2235 (-2362 to -2108)-1265 (-1381 to -1149)77 (57 to 110)-437 (-1017 to 140)III1135 (1062 to 1208)-1887 (-1939 to -1836)-752 (-825 to -679)139 (86 to 202)330 (-253 to 937)
Uncertainties of CO2 balances on the plot level were
calculated as 50 % of the difference between the H approach and the
LS approach plus the 90 % CI's of the H approach. Plot level uncertainties
for CH4 represent the 90 % confidence intervals (CI's) of the models,
but for N2O only the 90 % CI's of the measured N2O fluxes.
Acknowledgements
This study was funded by the KfW Entwicklungsbank in the framework of the
International Climate Initiative of the German Federal Ministry for the
Environment, Nature Conservation and Nuclear Safety (BMU) under BMU project
Reference No.: II. C. 53, and by the Centre for International Migration and
Development (CIM) and the Royal Society for the Protection of Birds (RSPB).
We thank APB – BirdLife Belarus and the National Academy of Sciences of
Belarus for creating ideal research conditions, Hans Joosten for support in
designing the study and for commenting the manuscript, Nadzeya
Liashchynskaya, Hanna Grabenberger, Aleksandr Novik, Nikolaj Belovezhkin,
Konstantin Timokhov and Aleksandr Pavlyuchenko for help in the field, Sergej
Zui for construction and maintenance of measuring equipment, Vyacheslav
Rakovich for showing us Barcianicha, Petr Boldovskij, Vadim Protasevich and
the students and teachers of the school of Z'dzitava for warm welcome,
logistical support and information on land use history, Michel Bechthold for
advice on evaluation of the hydrological data, Roland Fuß, Katharina
Leiber-Sauheitl, and Thomas Leppelt for consultations on statistical issues,
and two
anonymous referees for constructive remarks on the manuscript.
Edited by: P. Stoy
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