Arctic tundra ecosystems are currently facing amplified rates of climate
warming. Since these ecosystems store significant amounts of soil organic
carbon, which can be mineralized to carbon dioxide (CO2) and methane
(CH4), rising temperatures may cause increasing greenhouse gas fluxes
to the atmosphere. To understand how net the ecosystem exchange (NEE) of
CO2 will respond to changing climatic and environmental conditions,
it is necessary to understand the individual responses of the processes
contributing to NEE. Therefore, this study aimed to partition NEE at the
soil–plant–atmosphere interface in an arctic tundra ecosystem and to
identify the main environmental drivers of these fluxes. NEE was partitioned
into gross primary productivity (GPP) and ecosystem respiration
(Reco) and further into autotrophic (RA) and
heterotrophic respiration (RH). The study examined CO2 flux
data collected during the growing season in 2015 using closed-chamber
measurements in a polygonal tundra landscape in the Lena River Delta,
northeastern Siberia. To capture the influence of soil hydrology on
CO2 fluxes, measurements were conducted at a water-saturated polygon
center and a well-drained polygon rim. These chamber-measured fluxes were
used to model NEE, GPP, Reco, RH, RA, and net
primary production (NPP) at the pedon scale (1–10 m) and to determine
cumulative growing season fluxes. Here, the response of in situ measured
RA and RH fluxes from permafrost-affected soils of the
polygonal tundra to hydrological conditions have been examined. Although
changes in the water table depth at the polygon center sites did not affect
CO2 fluxes from RH, rising water tables were linked to
reduced CO2 fluxes from RA. Furthermore, this work found
the polygonal tundra in the Lena River Delta to be a net sink for atmospheric
CO2 during the growing season. The NEE at the wet, depressed polygon
center was more than twice that at the drier polygon rim. These differences
between the two sites were caused by higher GPP fluxes due to a higher
vascular plant density and lower Reco fluxes due to oxygen
limitation under water-saturated conditions at the polygon center in
comparison to the rim. Hence, soil hydrological conditions were one of the
key drivers for the different CO2 fluxes across this highly
heterogeneous tundra landscape.
Introduction
An estimated 1000 Pg (petagrams) of organic carbon (OC) are stored in the upper
3 m of northern permafrost-affected soils (Hugelius et al., 2014). Given the
large amount of OC stored in these soils, the response of the arctic carbon
cycle to a changing climate is of global importance (McGuire et al., 2009).
Over thousands of years, carbon has been sequestered in permafrost-affected
soils and sediments due to cold conditions and poor drainage, resulting in
water saturation and slow organic matter decomposition. Currently, arctic
ecosystems are facing amplified warming (AMAP, 2017; Taylor et al., 2013),
which will lead to the longer and deeper thawing of permafrost-affected soils
(Romanovsky et al., 2010). On the one hand, the microbial decomposition of
newly available thawed permafrost organic matter releases carbon dioxide
(CO2) and methane (CH4) (e.g., Knoblauch et al., 2018, 2013;
Zimov et al., 2006a; Schuur et al., 2009; Grosse et al., 2011). On the other
hand, higher temperatures increase the assimilation of CO2 by tundra
vegetation due to a prolonged growing period and increased nutrient
availability in the deeper layers of thawed soils (e.g., Beermann et al.,
2017; Elmendorf et al., 2012; Salmon et al., 2016; Parmentier et al., 2011).
With an area of 3 million km2, more than half of the northern
high-latitude tundra ecosystems are situated in Russia (Walker et al., 2005).
To date, just a few studies on CO2 fluxes from the vast Russian
arctic tundra ecosystems are available (e.g., Parmentier et al., 2011;
Marushchak et al., 2013; Rößger et al., 2019; Kittler et al., 2016),
especially on the pedon scale (Kwon et al., 2016; Corradi et al., 2005;
Heikkinen et al., 2004; Zamolodchikov et al., 2000). Since tundra soils are
highly heterogeneous on the pedon scale in terms of temperature and moisture
(Aalto et al., 2013), measurements on this scale are required to determine
the response of individual CO2 fluxes to these parameters. To cover
this heterogeneity on the pedon scale, chamber measurements are more
appropriate than the eddy covariance (EC) method, which covers the next
larger scale, even though a downscaling EC approach for CO2 fluxes of
an arctic ecosystem was recently presented (Rößger et al., 2019). An
improved understanding of CO2 dynamics in permafrost-affected soils
is needed to improve estimates of future CO2 balances of the
highly heterogeneous arctic tundra
regions. Without developments in our understanding of the response of
CO2 dynamics in permafrost-affected ecosystems to changing climatic
conditions such as temperature and moisture, estimates of the carbon balance
of the circum-arctic tundra and its future response to changing climatic
conditions remain biased.
The net ecosystem exchange (NEE) of CO2 between the land surface and
the atmosphere is composed of the CO2 uptake by plants, termed gross
primary productivity (GPP), and the release of CO2 from soils and
plants, which is ecosystem respiration (Reco) (Chapin et al., 2006). The
latter can be further split into autotrophic respiration by plants
(RA) and heterotrophic respiration (RH) consisting of
microbial soil organic matter (SOM) decomposition. In this study the
atmospheric sign convention is used, whereby a positive NEE defines a net
release of CO2 from the soil to the atmosphere and a negative sign
defines a net uptake of CO2 from the atmosphere.
In order to partition NEE into its underlying fluxes, measurements of GPP,
Reco, RA, and RH are required. These individual
process-based fluxes governing the CO2 balance respond differently to
changing climatic conditions such as temperature and moisture. For instance,
it was shown that temperature changes in arctic soils could cause a
significant increase in the CO2 uptake via GPP (Shaver et al., 1998;
Oberbauer et al., 2007; Natali et al., 2012; Mauritz et al., 2017), which can
be, beside other factors, attributed to shifts in vegetation composition (Elmendorf
et al., 2012; Hudson et al., 2011) and increased nutrient availability
(Johnson et al., 2000; Salmon et al., 2016; Beermann et al., 2015).
Furthermore, the effect of drainage on GPP remains uncertain; some studies
found drainage of arctic soils to reduce GPP (Merbold et al., 2009; Chivers
et al., 2009; Kwon et al., 2016), while other studies found drainage to lead
to a slight increase in GPP (Olivas et al., 2010; Kittler et al., 2016). The
effect of increasing soil moisture on GPP differs between ecosystems (Mauritz
et al., 2017; Olivas et al., 2010; Chivers et al., 2009). As respiratory processes are
temperature sensitive (Mahecha et al., 2010), the release of CO2 by
Reco increases in response to soil warming across arctic ecosystems
(e.g., Hicks Pries et al., 2015; Oberbauer et al., 2007; Natali et al., 2015).
An increase in Reco was also observed as a result of drainage of
arctic soils and vice versa: a decrease with increasing water saturation
(Elberling et al., 2013; Mauritz et al., 2017; Chivers et al., 2009; e.g.,
Kwon et al., 2016; Olivas et al., 2010) was observed due to the presence or absence of
oxygen in drained soils (Hobbie et al., 2002). However, it was also shown
that Reco fluxes could increase with increasing water saturation
due to higher soil temperatures in water-saturated soils (Zona et al., 2012),
which highlights the interconnection of moisture and temperature in soils. In
general, higher soil temperatures lead to a higher increase in Reco
than GPP, which causes a reduction of the net CO2 uptake
(Parmentier et al., 2011; Oberbauer et al., 2007; Voigt et al., 2017; Mauritz
et al., 2017). Also, drainage of arctic soils causes a reduction of NEE (means
less negative values) due to a higher increase in Reco than GPP
(Merbold et al., 2009; Chivers et al., 2009; Kittler et al., 2016; Olivas et
al., 2010), while the effect of increasing water saturation of soils on NEE
differs between arctic ecosystems (Chivers et al., 2009; Mauritz et al.,
2017). Both soil temperature and moisture are predicted to change in
the future due to increased temperatures and precipitation in the pan-Arctic
(Christensen et al., 2013). As Reco and GPP respond differently to
temperature and moisture changes it is essential not only to focus on changes
to NEE, but also to gain a quantitative understanding of its components and their
individual responses to environmental and climatic changes to improve model
simulations of future CO2 fluxes. Therefore, partitioning approaches
for in situ measured CO2 fluxes are required.
The study site on Samoylov Island, Lena River Delta,
northeastern Siberia (72∘22′ N, 126∘28′ E). Satellite
images: (a) NASA, 2002; (b) Boike et al., 2012;
(c) Boike et al., 2015.
The release of CO2 from soils by Reco is the largest efflux
of carbon from terrestrial ecosystems to the atmosphere (Mahecha et al.,
2010). Autotrophic respiration can be separated into aboveground plant
respiration and belowground plant respiration (i.e., respiration of roots).
Heterotrophic respiration is associated with the decomposition of SOM by
heterotrophic soil organisms. To date, only a few estimates of RH
fluxes from arctic tundra ecosystems over the growing season have been
published (Nobrega and Grogan, 2008; Biasi et al., 2014; Segal and Sullivan,
2014), with data lacking for ecosystems such as the polygonal tundra. Warming
of the arctic soils will influence RH fluxes both directly and
indirectly: rising soil temperatures will increase SOM decomposition, but
will also cause permafrost thaw, exposing previously frozen SOM to microbial
decomposition (Schuur et al., 2011; Dorrepaal et al., 2009). This
decomposition could substantially reduce carbon storage in arctic tundra
ecosystems, as gross ecosystem productivity has been found to be less
temperature sensitive than Reco in these ecosystems (Grogan and
Chapin, 2000; Dorrepaal et al., 2009). Warming could also reduce soil
moisture (Suseela et al., 2012) and increase RA due to increasing
aboveground biomass (Natali et al., 2012), which could lead to a lower
contribution of RH to Reco (Hicks Pries et al., 2015;
Chen et al., 2016). Furthermore, changes in soil moisture are known to affect
microbial activity in soils directly with decreasing activity during times of
high and low soil moisture and an optimum at moderate soil moisture
conditions (Moyano et al., 2013). The increase in RA and
RH fluxes due to warming might be compensated for by higher net
primary production (Hicks Pries et al., 2013), but whether this compensation
is valid for the entire growing season and across highly
heterogeneous arctic ecosystems on the pedon scale remains uncertain. Furthermore, it remains
uncertain how RA fluxes will respond to changing hydrological
regimes as the impact of this parameter on RA fluxes has never
been analyzed in tundra regions.
As changes in soil temperature and moisture can significantly alter the
individual fluxes contributing to NEE, this study aims to improve the current
understanding of CO2 flux dynamics in permafrost-affected ecosystems
by (i) partitioning NEE into individual flux components (photosynthesis,
ecosystem respiration, and autotrophic and heterotrophic respiration) at the
pedon scale of the polygonal tundra and (ii) gaining insights into the
response of these individual fluxes to different environmental parameters.
Therefore, closed-chamber measurements were conducted at two sites in the
polygonal tundra in northeastern Siberia over an almost complete growing
season. Finally, a CO2 budget for a nearly complete vegetation period
is determined for the two sites using data-calibrated flux models. These
models were based on the time-sensitive bulk flux partitioning model by
Runkle et al. (2013), which has been used in different arctic ecosystems
(Helbig et al., 2017; Zona et al., 2014).
Study site
The investigation area is located on Samoylov Island in the southern central
Lena River Delta, northeastern Siberia (72∘22′ N,
126∘28′ E; Fig. 1). The Lena River forms the largest delta in
the Arctic, which can be geomorphologically divided into river terraces of
different ages and floodplain levels (Schwamborn et al., 2002). The delta is
located in the continuous permafrost zone with permafrost extending to depths
of 300 to 500 m (Yershov, 1998) and relatively low mean annual soil
temperatures of -7.8 ∘C at 1.7 m of depth compared to other arctic
tundra sites (Boike et al., 2013). The study site has an arctic continental
climate characterized by low temperatures and low precipitation. The mean
annual air temperature between 1998 and 2011 was -12.5∘C, and
mean annual precipitation between 1981 and 2011 was 321 mm (Pogoda i Klimat,
2016), while summer rainfall is 125 mm, ranging from 52 to 199 mm (Boike et
al., 2013). Polar day lasts from 7 May until 8 August, and polar night lasts
from 15 November to 28 January. Snowmelt usually starts in the first half of
June, and the growing season usually spans from around mid-June until
mid-September.
The study site is covered by ice-wedge polygonal tundra on a Late Holocene
river terrace with elevations from 10 to 16 m above sea level on the eastern
part of Samoylov Island. The development of polygonal structures has created
depressed polygon centers surrounded by elevated polygon rims with elevation
differences of about 0.5 m. Underlying permafrost prevents drainage in
polygon centers, resulting in water-saturated soils, anoxic soil conditions at
shallow depths, and significant amounts of soil organic carbon of around
33 kg m-2 in the uppermost meter (Zubrzycki et al., 2013). In
contrast, due to oxic conditions in the topsoil, the elevated polygon rim
soils have accumulated less soil organic carbon of around 19 kg m-2
(Zubrzycki et al., 2013). A land cover classification based on Landsat
satellite imagery revealed that if excluding large thermokarst lakes the
polygonal tundra on Samoylov Island consists of 65 % dry tundra,
19 % wet tundra, and 16 % small water bodies including small
ponds overgrown by vascular plants (Muster et al., 2012).
In this study, two different sites were investigated: (i) a wet–depressed
polygon center (wet tundra) and (ii) its surrounding elevated polygon rim
(dry tundra, 72∘22′26 N 126∘29′49 E). These sites were
located within the footprint area of an eddy covariance (EC) system in which
NEE of CO2 was measured (Holl et al., 2019; Kutzbach et al., 2007b;
Wille et al., 2008; Runkle et al., 2013). The maximum active layer depth
(ALD) at the study site was deeper at the polygon center (40 cm) than at the
polygon rim (30 cm). The soils at the polygon centers were classified as
Histic or Reductaquic Cryosols (IUSS Working Group WRB, 2014) with a
water table close to the soil surface. Polygon rim soils were characterized
by cryoturbation and therefore classified as Turbic Glacic Cryosols (IUSS
Working Group WRB, 2014) with a water table just a few centimeters above the
permafrost table. Total organic carbon (TOC) contents above 10 % were
found in the surface horizon above the cryoturbated horizons of the polygon
rim, while high TOC contents were found at the polygon center throughout the
active layer (Zubrzycki et al., 2013). Vegetation on polygon rims is
dominated by mosses (Hylocomium splendens, Polytrichum
spp., Rhytidium rugosum), some small vascular plants (Dryas punctata and Astragalus frigidus), and lichens (Peltigera
spp.) and can be classified as non-tussock sedge, dwarf-shrub, moss tundra
(Walker et al., 2005). The vegetation of the polygon centers was dominated by
the hydrophilic sedge Carex aquatilis, which have in general much
higher growth forms than at the rim, and mosses (Drepanocladus revolvens, Meesia triqueta, Scorpidium scorpioides) and
was classified as sedge, moss, dwarf-shrub wetland (Walker et al., 2005).
MethodsMeteorological data
Meteorological variables were recorded at 30 min intervals at the nearby EC
system and adjacent meteorological station 40 m southwest of the study site.
Data collected were air temperature (MP103A; ROTRONIC AG, Switzerland), air
pressure (RPT410F; Druck Messtechnik GmbH, Germany), and photosynthetically
active radiation (PAR; wavelength: 400–700 nm; QS2, Delta-T Devices Ltd.,
UK), as well as the incoming and reflected components of shortwave and
longwave radiation, respectively (CNR 1; Kipp&Zonen, the Netherlands). The
radiative surface temperature (Tsurf; in Kelvin, K) was calculated
as
Tsurf=L↑Bεσ1/4,
where L↑B is the upward infrared radiation (W m-2),
σ is the Stefan–Boltzmann constant (W m-2 K-4), and the
dimensionless emissivity ε was assumed to be 0.98 after Wilber
et al. (1999). Furthermore, soil temperature (Tsoil) was measured
at 2 cm of soil depth in intervals of 30 min at an adjacent polygon rim and
center.
Soil sampling and vegetation indices
Undisturbed soil samples were taken from the active layer at the polygon rim
using steel rings (diameter 6 cm). At the water-saturated polygon center, an
undisturbed soil monolith was taken from the active layer using a spade and
subsequently subsampled into four soil layers based on the degradation status
of the organic matter. Coarse roots were removed, and soil samples were
homogenized for analysis of soil water content (mass difference between wet
and dried (105 ∘C) soil samples) and pH (CG820; Schott AG, Mainz,
Germany). Total carbon and nitrogen (N) contents (VarioMAX cube; Elementar
Analysesysteme GmbH, Hanau, Germany), as well as total organic carbon
and total inorganic carbon contents (TIC; liquiTOC II, Elementar
Analysesysteme GmbH, Hanau, Germany), were determined from dried
(105 ∘C for more than 24 h) and milled soil samples. To analyze
vegetation indices, gridded quadrats of 10 cm × 10 cm were placed
over the collars, and a visual identification of the plant species present as
well as their abundance (% surface cover) was conducted in four grid
squares.
Net ecosystem exchange and ecosystem respiration
A total of eight PVC frames (50 cm × 50 cm), four at each site,
were installed in July 2014 in preparation for NEE and Reco flux
measurements with closed chambers the following year. The frames were
equipped with a U-shaped frame filled with water to avoid gas exchange
between the chamber headspace and ambient air. The chamber
(50 cm × 50 cm × 50 cm) used for NEE and Reco
flux measurements was made of clear acrylic glass (Plexiglas SunActive GS;
Evonik Industries AG, Germany). The chamber was equipped with a fan for
continuous mixing of headspace air (axial fan, 12V/DC; Conrad Electronic SE,
Germany). Furthermore, a PAR sensor (SKP212; Skye Instruments Ltd., UK) and a
temperature probe (107 Thermistor probe; Campbell Scientific Ltd., USA) were
installed inside the chamber. Including the volume inside the chamber frames,
the chamber enclosed a volume of 124–143 L. For Reco
measurements, the chamber was covered with an opaque material. Boardwalks
were installed at both sites to avoid disturbance. The volumetric soil water
content (VWC) was measured with a GS3 sensor (Decagon Devices, Inc., USA)
during each measurement directly beside the chamber frame at a depth of 5 cm.
A diver (Schlumberger Ltd., USA) was installed at the polygon center to
measure water table (WT) depth every 15 min. To prevent pressure-induced gas
release during chamber closure (Christiansen et al., 2011), two holes (3 cm
in diameter) at the top of the chamber were left open while placing the
chamber on the frames and then closed for measurements. Soil temperatures
between the surface and the frozen ground in 5 cm intervals and thaw depth
were measured daily at both sites. For each chamber flux measurement,
CO2 concentrations in the chamber headspace were continuously
measured with a gas analyzer (UGGA 30-p; Los Gatos Research, USA). The
chamber headspace air was pumped in a closed loop via transparent
polyurethane tubes (inner diameter 4 mm, each 10 m length) through the
analyzer with a flow rate of 200 mL min-1. The CO2
concentration was logged (CR800 series; Campbell Scientific Ltd., USA)
together with PAR as well as soil and air temperature at a frequency of
1 Hz. Each chamber closure period was restricted to 120 s to minimize
warming inside the chamber relative to the ambient temperature.
Chamber measurements were conducted from 11 July until 22 September 2015, at
least every third day between 06:00 and 21:00 (local time), apart from the
period 2–9 and 17–24 August. Two consecutive measurements were performed at
each frame: first, NEE (n=679) was measured with the transparent chamber,
followed by an Reco measurement (n=679) with the dark chamber
shortly after. The four frames of one site were measured consecutively before
moving to the other site. GPP fluxes were calculated from the sum of the
measured Reco and NEE fluxes.
Heterotrophic respiration
For RH measurements the root-trenching method was applied at both
sites. It is challenging to separate belowground respiration fluxes into
autotrophic and heterotrophic components because roots and microorganisms are
closely linked within the rhizosphere (Hanson et al., 2000). There are a wide
range of methods for partitioning Reco (Subke et al., 2006;
Kuzyakov, 2006), each with its associated advantages and disadvantages. Root
trenching, for example, despite some disturbance on the plant–soil interface,
can give accurate estimates of the rates of RA and RH
(Diaz-Pines et al., 2010) and produces similar results as a non-disturbing
14C partitioning approach in an arctic tundra ecosystem (Biasi et al.,
2014) and a partitioning approach based on 13C (Chemidlin
Prévost-Bouré et al., 2009). In this study, by inserting PVC frames
below the main rooting zone at 20 cm deep into the soil, lateral roots were
cut off. All living plant biomass including living moss tissue inside the
frames was removed carefully in 2014. To prevent regrowth, the living plant
biomass was removed periodically over the measurement period. This removal
causes the die-off of roots, and in a period of days after the disturbance
RH equals NEE. A total of eight frames, four at each site, were
prepared for RH measurements. RH fluxes (n=662) were
measured during the same periods and with the same closure period as NEE and
Reco measurements on unaltered plots.
To test if RH fluxes are biased due to the additional decomposition
of residual roots, four additional PVC frames (two per site) were installed
in 2015 following the sampling and preparation protocol of 2014. A total of
302 RH flux measurements were made on these newly installed plots.
The difference between the mean RH fluxes of each single plot
trenched in 2014 and those trenched in 2015 were analyzed using a Student's
t test.
RA fluxes at the unaltered sites were calculated by subtracting the
mean RH fluxes measured at the trenched sites from the mean of the
Reco fluxes at the unaltered sites of the same day. The calculated
RA fluxes were summed with the calculated GPP fluxes to estimate the
net primary productivity (NPP) fluxes.
Flux calculation
CO2 fluxes (µg CO2 m-2 s-1) were
calculated using MATLAB® R2015a (The
MathWorks Inc., Natick, MA, 2000) with a routine that uses different
regression models to describe the change in the chamber headspace CO2
concentration over time and conducts statistical analysis to aid model
selection (Eckhardt and Kutzbach, 2016; Kutzbach et al., 2007a).
Due to possible perturbations while placing the chamber on the frame, the
first 30 s of each 2 min measurement period were discarded and the
remaining 90 data points were used for flux calculations. The precision of
the gas analyzer with 1 s signal filtering is <0.3 ppm for CO2
according to the manufacturer. The root mean square error (RMSE) did not
exceed this value under the typical performance of chamber measurements and the
fitting of the linear and nonlinear regression models. Higher RMSE values
indicated failed model fitting or disturbed chamber measurements. Therefore,
if RMSE exceeded 0.3 ppm, the concentration-over-time curve was
reinspected. Variation of PAR during chamber measurements due to shifts in
cloud cover leads to irregular CO2 concentration time series and
perturbation of the calculated CO2 fluxes (Schneider et al., 2012).
These perturbed concentration time series show distinct autocorrelation of
the residuals of the regression models and were filtered out by using a
threshold for residual autocorrelation indicated by the Durbin–Watson test
(Durbin and Watson, 1950). The flux curve was reinspected if the RMSE
exceeded 0.3 ppm or showed a distinct autocorrelation to see if
irregularities could be removed by adjusting the size of the flux calculation
window. If irregularities could be removed by adjusting the size of the flux
calculation window, the flux curve was recalculated; if not, the
measurement was discarded. Overall, about 3 % (n=47) of the CO2
flux measurements (NEE, Reco and RH measurements) were
discarded from the dataset because they did not meet the abovementioned
quality criteria.
Studies have shown that CO2 fluxes calculated with linear regression
models can be seriously biased (Kutzbach et al., 2007a), while nonlinear
regression models significantly improve flux calculations (Pihlatie et al.,
2013). However, we found that the temporal evolution of CO2
concentration in the chamber was best modeled with a linear regression
model, as determined by the Akaike information criterion corrected
for small samples sizes (AICc) (Burnham and Anderson, 2004). This
is in good agreement with other studies, which have shown that in some cases
a linear regression model can produce a better CO2 flux estimate for
a nonlinear concentration-over-time curve than a nonlinear regression model
(Koskinen et al., 2014; Görres et al., 2014).
Modeling <inline-formula><mml:math id="M151" display="inline"><mml:mrow class="chem"><mml:msub><mml:mi mathvariant="normal">CO</mml:mi><mml:mn mathvariant="normal">2</mml:mn></mml:msub></mml:mrow></mml:math></inline-formula> fluxes at the pedon scale
Different numerical models were fitted to the measured Reco and
RH fluxes and to the calculated GPP fluxes to quantify seasonal
GPP, Reco, and RH fluxes. To calibrate the models, these
were fitted to the GPP, Reco, and RH fluxes. The
resulting fitting parameters were used to reproduce the fluxes over the
complete measurement period. Model calibration was done by applying a 15 d
moving window over the measurement period moving in 1 d intervals. If
fewer than eight chamber measurements were performed during these 15 d, the
moving window was extended to 19 d. Subsequently, the modeled fluxes for
each measurement plot were averaged for each site. CO2 fluxes from
each of the four measurement plots were used separately for model calibration
and the summed fluxes were used to analyze differences between both sites
using a Student's t test.
The empirical Q10 model (van't Hoff, 1898) was fitted to the measured
Reco and RH fluxes:
Reco,H=Rbase×Q10Ta,surf,soil-Trefγ,
where the (variable) fit parameter Rbase is the basal respiration
at the reference temperature Tref (15 ∘C). The reference
temperature and γ (10 ∘C) were held constant according to
Mahecha et al. (2010). Q10 was a fit parameter describing the ecosystem
sensitivity of respiration to a 10 ∘C change in temperature. For
this study a fixed Q10 value of 1.52 was used, which represents the
seasonal mean value of the bulk partitioning model for the CO2 fluxes
in the EC footprint area (Runkle et al., 2013). Air temperature
(Ta), surface temperature (Tsurf), and soil temperature
(Tsoil) measured at a depth of 2 cm were tested as input
variables.
The model calibration was done with MATLAB®
R2015a (The MathWorks Inc., Natick, MA, 2000). The model parameters were
estimated by nonlinear least-squares regression fitting (nlinfit function),
and the uncertainty of the parameters was determined by calculating the
95 % confidence intervals using the nlparci function. The selection of
the best-performing temperature as an input variable for the Reco and
RH model was based on comparing the Radj2 of the model
runs with different temperatures as an input variable. The selected input
variable was chosen for all measurement plots of the same site.
To estimate GPP, the measured Reco fluxes were subtracted from the
measured NEE for each measurement plot. The rectangular hyperbola function
was fitted to the calculated GPP fluxes as a function of PAR (in
µmol m-2 s-1):
GPP=-Pmax×α×PARPmax+α×PAR,
where the (variable) fit parameter Pmax was the maximum canopy
photosynthetic potential (hypothetical GPP at infinite PAR). The values for
the initial canopy quantum efficiency α (in
µg m-2 s-1/µmol m-2 s-1; initial
slope of the GPP model at PAR = 0) were obtained from modeling the
CO2 fluxes with EC data (Holl et al., 2018). From the determined
values when α was held variable, a function was formulated that
accounts for the seasonality of α with specific values for each day
of the growing season using the following function:
α=b×exp-absx-cd2×e2+f,
where b=0.042, c=209.5, d=2, e=25.51, f=0.008, and x is the day of the year
2015. Afterwards, these values (variable on daily basis) were used for both
sites to reproduce GPP fluxes from chamber measurements over the complete
measurement period.
Although the transmissivity of the chamber material was high, with >90 %
for wavelengths between 380 and 780 nm (Evonik, 2015), it caused a reduction
in the amount of incoming radiation reaching the surface, which could be further reduced
based on the sun elevation. During the complete measurement period, the PAR
values measured inside the chamber were on average 20 % lower than the
PAR values measured outside the chamber (data not shown). Therefore, GPP
modeling was conducted in two steps. First, the GPP model was calibrated
using PAR values measured inside the chamber; secondly, the reproduction
of GPP fluxes over the growing season was carried out using PAR values
measured outside the chamber. Without this two-step calibration the GPP
fluxes would have been underestimated.
Meteorological conditions from mid-July to end of September.
(a) Half-hourly air temperature measured at 2 m of height at the eddy
covariance tower and surface temperature; (b) soil temperatures
measured at 2 cm of depth at the polygon rim and center; (c) water table
relative to the soil surface measured at the polygon center and volumetric
water content measured at the polygon rim; (d) daily measured thaw
depth at the polygon rim and center; (e) daily precipitation
measured at the eddy covariance station; (f) photosynthetically
active radiation (PAR) measured half-hourly at the eddy covariance tower.
The NEEs for both sites were calculated as the sum of the modeled GPP and
Reco fluxes. The RA fluxes were calculated as the difference of
the modeled Reco and RH fluxes. Furthermore, NPP was calculated
from the sum of RA and GPP fluxes.
As both sites are within the footprint of an EC station, which determines
CO2 fluxes on a larger spatial scale (100 to 1000 m), the resulting
NEE from the modeling approach was compared with NEE of the same period
obtained from EC measurements reported by Holl et al. (2019). For this
upscaling, the resulting NEEs from the chamber model were weighted
(NEEchamber) based on the half-hourly relative contributions of
the surface classes defined by Muster et al. (2012) to the EC footprint using
the following equation:
NEEchamber=NEEC×Coverwet+NEER×Coverdry,
where NEEC and NEER are the modeled half-hourly
chamber NEE for the polygon center and rim, respectively, and Coverwet
and Coverdry are the relative contribution of the surface classes
polygon center and rim, respectively, to the EC footprint as given in Holl et
al. (2019).
ResultsMeteorological data, environmental conditions, and soil
characteristics
The mean daily air temperature over the study period ranged from 23 to
-2∘C (Fig. 2a). The average air temperature in August 2015
(9 ∘C) was similar to the long-term mean air temperature for the
period 1998–2011 (Boike et al., 2013). Compared to the long-term mean, it
was about 1 ∘C colder during July (9 ∘C), whereas September
was around 2 ∘C warmer than the reference period (3 ∘C).
The total precipitation from mid-July to the end of September 2015 was
78 mm,
which is below the mean precipitation of 96±48 mm between 2003 and
2010 (Boike et al., 2013).
From mid-July to the end of September 2015, soil temperatures at 2 cm of depth
at the polygon rim showed a higher diurnal variability than at the center.
The highest soil temperatures were measured in mid-July and at the beginning
of August. At the end of September, the temperatures became slightly negative
(Fig. 2b). At the polygon rim, the thaw depth increased from the beginning of
the campaign in mid-July until mid-September to reach a maximum depth of 36 cm. Maximum thaw depth was reached at the polygon center much earlier in the
season (mid-July) and remained relatively constant until mid-September. The
water table depth at the polygon center was tightly coupled to rainfall. The
VWC at 5 cm of soil depth was on average 30 % at the polygon rim, with
highest values observed after rainfall events (Fig. 2c). The daily averaged
PAR values showed a strong seasonality with decreasing daily mean values
towards the end of the season, although there was a period at the end of July
with rather low daily averaged PAR values.
Chamber-measured NEE, Reco, and RH fluxes, as well
as calculated GPP, NPP, and RA fluxes. The error bars denote the
standard deviation of the four replicate measurements at each site.
(a) Fluxes of NEE (n= 83), Reco (n= 85), and
RH (n= 85) at the polygon center; (b) calculated
fluxes of GPP (n= 83), NPP (n= 83), and RA (n= 85) at
the polygon center; (c) measured fluxes of NEE (n= 83),
Reco (n= 85), and RH (n= 85) at the polygon rim;
(d) calculated fluxes of GPP (n= 83), NPP (n= 83), and
RA (n= 85) at the polygon rim.
The total soil organic carbon content was lower at the polygon rim
(2 %–12 %) than at the polygon center (10 %–20 %) and
showed a decrease with depth, which was more pronounced at the polygon rim.
The estimated SOC stocks within 30 cm of depth were about 11 kg m-2 and
about 21 kg m-2 at the polygon rim and center, respectively. The total
inorganic carbon content was 0.2 % at both sites in each soil depth.
In general, the CO2 uptake (NEE) at the polygon center was higher
(with more negative values) than at the rim (Fig. 3). In September both
sites acted as small net CO2 sources. The standard error of the flux
calculation was around 3.5 and
2.3 µg CO2 m-2 s-1 for the polygon center and
rim, respectively, and decreased slightly towards the end of the season. In
contrast to the NEE, the measured Reco fluxes were on average
higher at the rim compared to the center. The highest ecosystem respiration
fluxes of the rim and center were measured at beginning of August, when the air
temperature exceeded 20 ∘C.
In general, the release of CO2 by RH was higher at the
polygon rim than at the center and showed no seasonality (Fig. 3). An
increase in RH fluxes after periodical re-clipping of the
vegetation was not observed. Comparing RH fluxes from measurement
plots that were trenched in 2014 with those trenched in 2015 revealed no
significant differences (t test, p>0.05) between the years of
root trenching (data not shown).
Due to a period with rather low daily averaged PAR at the end of July, the
uptake was partly lower as at the beginning of the measurement period at both
sites. After reaching peak net CO2 uptake at the beginning of August,
the uptake decreased until the end of September. This seasonality was more
pronounced at the polygon center than at the polygon rim. Interestingly,
towards September the net CO2 uptake at the polygon rim exhibited an
increase for a period of about 1 week, before it decreased again towards
the end of September. Reco fluxes showed a similar but less
distinct seasonal pattern, and the peak of the highest Reco fluxes
was in mid-August. In contrast, RH fluxes showed no seasonal trend
at the polygon center, while at the polygon rim the RH fluxes were
also highest when Reco and NEE reached their maxima.
Relationships between water table fluctuations and
(a)Reco fluxes, (b)RH fluxes,
(c)RA fluxes, and (d) GPP fluxes during the
period July–August at the polygon center. Negative values on the x axis
indicate a water table below the soil surface.
Fitting parameters of the CO2 flux models. The values are
given with the standard deviation of the model results from the single
measurement plots (light grey error bars) and the confidence intervals
(95 %) of the fitting parameters (dark grey error bars).
As GPP, NPP, and RA fluxes were calculated from the measured NEE,
Reco, and RH fluxes, these fluxes show similar patterns of
seasonality. The highest GPP and NPP fluxes were observed during the
vegetation maximum, with a more pronounced seasonality at the polygon center
compared to the rim. In general, RA fluxes were within the same
range at both sites, which is in contrast to the calculated GPP fluxes that
were almost twice as high at the polygon center as at the rim.
Interestingly, the Reco fluxes were linearly correlated with WT
fluctuations from the beginning of July until the end of August (Fig. 4d). In
contrast, neither a trend of higher RH fluxes during times of high
WT nor a trend of lower RH fluxes during times of low WT was observed.
Instead, the RA fluxes showed a significant correlation (R2=0.71; p<0.05) with WT fluctuations.
Modeled and measured CO2 fluxes at the polygon center in
µg CO2 m-2 s-1. Measured fluxes are available
for NEE (a), Reco(b), and
RH(c). NEE model fluxes were calculated from modeled GPP
(e) minus modeled Reco, RA model
fluxes (d) from modeled Reco minus modeled RH,
and NPP model fluxes (f) from modeled GPP minus modeled
RA. Note the different scales of the axes.
The fitting parameter of the GPP model (Eq. 3), Pmax, showed strong
spatial and temporal variability (Fig. 5b). The α values (Eq. 4) used
for the GPP model showed a high temporal variability with a mean of 1.47±0.62. This value increased sharply towards the peak vegetation period at the
end of July and decreased thereafter until the end of the growing season. The
Pmax values showed a strong temporal variability (high standard
deviation) at the polygon center (mean: 250.7±101.9µg CO2 m-2 s-1). Considerable
differences in Pmax were also observed between the polygon rim and
the center. The average Pmax at the polygon rim (135.4±37.2µg CO2 m-2 s-1) was substantially lower
than at the polygon center (250.7±101.9µg CO2 m-2 s-1). As with the measured
NEE, Pmax values displayed an increase at the polygon rim towards the
end of September. The fitting parameter of the Reco and
RH model (Eq. 2), Rbase, also showed strong spatial and
temporal variability (Fig. 5d). In general, Rbase was higher at the
polygon rim. The averaged Rbase values for the RH model
fit differed substantially between sites with 14.6±2.1µg CO2 m-2 s-1 at the polygon center and
29.0±2.9µg CO2 m-2 s-1 at the polygon
rim.
Modeled and measured CO2 fluxes at the polygon rim in
µg CO2 m-2 s-1. Measured fluxes are available
for NEE (a), Reco(b), and
RH(c). NEE model fluxes were calculated from modeled GPP
(e) minus modeled Reco, RA model fluxes
(d) from modeled Reco minus modeled RH, and NPP
model fluxes (f) from modeled GPP minus modeled RA. Note
the different scales of the axes.
Polygon center Reco fluxes were best modeled using surface
temperature as an explanatory variable (Radj2=0.70), while for
the polygon rim the soil temperature showed the best fitting
(Radj2=0.46). In contrast to the Reco fluxes, the
polygon center RH fluxes were best modeled when the air
temperature was used as an explanatory variable (Radj2=0.55). At
the polygon rim, using the soil temperature as an explanatory variable showed
the best fitting (Radj2=0.45) when modeling RH
fluxes. Differences in the goodness of the fits for the Reco flux
model were small. The Radj2 of the GPP model was 0.82 for the
polygon center and 0.45 for the polygon rim.
The modeled GPP, Reco, and RH fluxes were used to
calculate the NEE, RA, and NPP fluxes. All fluxes showed similar
seasonal patterns as fluxes from chamber measurements. The comparison between
modeled and measured fluxes showed highly significant correlation (R2=0.39–0.88, p<0.001; Figs. 6 and 7). However, the fluxes at the polygon
rim tended to be underestimated by the model if the respiration fluxes were
high and the other fluxes were low (close to zero or positive NEE). A similar
trend was observed for the respiration fluxes from the polygon center.
Furthermore, NEE, GPP, and NPP fluxes seem to be generally underestimated by
the flux models. However, this offset was to be expected due to the use of
different PAR values for flux calculation (see Sect. 3.6).
Means and range of the modeled fluxes in
µg CO2 m-2 s-1.
Polygon centerPolygon rim(µg CO2 m-2 s-1)(in µg CO2 m-2 s-1)NEEmean-68±12-26±19range-288±53 to 54±2-117±60 to 49±10GPPmean-98±10-61±17rangeup to -342±53up to -163±57Recomean29±1135±9range12±3 to 69±721±3 to 77±14RHmean11±321±5range6±1 to 27±214±4 to 46±13RAmean19±1114±5range1±3 to 55±45±5 to 32±19NPPmean-85±12-49±20rangeup to -300±53up to -142±57
Integrated CO2 fluxes at the polygon rim and center. The
values were calculated from the model results and are given in
g CO2 m-2. In total, both sites acted as a net CO2
sink during the growing season. NEE: net ecosystem exchange;
GPP: gross primary productivity; Reco: ecosystem respiration;
RH: heterotrophic respiration; RA: autotrophic
respiration; NPP: net primary productivity; WT: water table;
TD: thaw depth.
Integrated fluxes
Based on the modeled chamber CO2 fluxes, time-integrated CO2
fluxes were calculated for the period between mid-July and the end of
September 2015 (Table 1, Fig. 8). The integrated GPP flux at the polygon
center was significantly (t test, p<0.01) higher than at the polygon
rim. In contrast, the integrated RH fluxes at the polygon rim were
almost double those at the polygon center (p<0.001). This trend was also
observed for Reco fluxes, although here the difference was not as
large as seen for RH fluxes and was not significant (p>0.05). Furthermore, the flux differences in RA between
the sites were rather small. Much higher GPP fluxes in association with lower
RH and similar RA fluxes led to an integrated NEE, which
was more than twice as high at the polygon center (-68±12µg CO2 m-1 s-1) as at the rim (-26±19µg CO2 m-1 s-1) and led to an almost twice
as high NPP at the center as at the rim. The upscaled NEE from modeled
chamber data correlated highly significantly (R2=0.77, p<0.001)
with modeled NEE from EC data (Fig. 9). However, the upscaled NEE from
modeled chamber data tended to underestimate the highest uptake and release
by NEE in comparison to modeled NEE from EC data.
Comparison of chamber and half-hourly averaged EC NEE. The chamber
NEE was calculated based on the contribution of each surface class to the EC
footprint (Eq. 5).
Discussion
This study presented NEE, GPP, NPP, Reco, RH,
and RA fluxes obtained from direct measurements and modeling
approaches for dry and wet sites of the polygonal tundra. The RH
fluxes were higher at the polygon rim compared to the center due to drier
soil conditions at the rim. RA fluxes from both sites were similar,
although the vascular plant cover at the center was higher, probably due to
water-saturated conditions at the center. In addition, the integrated
Reco fluxes at the rim were higher than at the center due to
higher RH and similar RA fluxes at both sites. The mean
GPP fluxes are much higher at the center compared to the rim due to
differences in vegetation between the sites. Together with RA
fluxes that are within the same range between the sites, the differences in
GPP lead to an NPP almost 2 times higher at the center compared to the rim.
In sum, both the water-saturated polygon center and the non-saturated polygon
rim acted as net sinks for atmospheric CO2 for the period from mid-July to
the end of September 2015. However, the CO2 sink strength differed
substantially between wet and dry tundra, which can be related to the
different hydrological conditions and vegetation composition
Comparison of daily averaged CO2 fluxes from different
polygonal tundra sites, which are similar in vegetation and soil composition
to our study site. All listed fluxes were measured with the closed-chamber
technique.
LocationTundraPeriodNEEGPPRecoRef.type(g C m-2 d-1)(g C m-2 d-1)(g C m-2 d-1)Lena River Delta, RU (72∘ N,127∘ E)pol. rimpol. centerJul–Sep 2015-0.6±0.4-1.6±0.3-1.4±0.4-2.3±0.20.8±0.20.7±0.1aBarrow, US (71∘ N, 157∘ W)pol. rim pol. centerJun–Aug 2005-0.1±0.5-0.2±0.2-3.7±0.2-3.1±0.13.6±0.32.9±0.1bpol. rim pol. centerJun–Aug 2006-0.7±0.2-0.8±0.2-3.1±0.3-2.3±0.22.4±0.21.5±0.2Barrow, US (71∘ N, 157∘ W)pol. centerJun–Aug 19920.04±0.05-0.8±0.10.8±0.1cBarrow, US (71∘ N, 157∘ W)pol. centerJul–Aug 20080.1±0.8-3.9±1.83.9±1.8dBarrow, US (71∘ N, 157∘ W)pol. centerJul–Aug 20100.5±0.8-1.7±0.82.1±1.2eDaring Lake, CA(65∘ N, 111∘ W)dry heath wet sedgeJun–Sep 2004-0.01±0.1-0.9±0.1-1.7±0.3-1.7±0.11.8±0.20.8±0.1fCherskii, RU (68∘ N, 161∘ E)Carex shrubJul–Aug 2013 Jul–Aug 2014-0.5±0.1-2.2±0.2-2.5±0.1-6.2±0.12.0±0.14.0±0.2gVorkuta, RU (67∘ N, 63∘ E)sedge bogJun–Aug 1996-1.0±0.2-3.2±0.42.2±0.3hVorkuta, RU (67∘ N, 63∘ E)wet tundra dry tundraJun–Sep 2001 Jun–Sep 2001-1.1j1.2j-1.9j-1.9j0.9j3.2jiPrudhoe Bay, US(70∘ N, 149∘ W)wet tundraJun–Aug 1994-0.6±0.4-5.2±0.64.6±0.3kLena River Delta, RU(72∘ N, 127∘ E)dry tundraJun–Sep 2014 Jun–Sep 2015-0.9±3.0-0.7±2.6-3.6±3.4-2.7±3.22.7±0.91.9±1.0lwet tundraJun–Sep 2014 Jun–Sep 2015-0.4±1.9-0.7±2.4-2.3±2.3-2.9±2.71.9±0.72.2±0.7
a This study; b Olivas et al. (2011);
c Oechel et al. (1995); d Lara and Tweedie (2014);
e Lara et al. (2012); f Nobrega and Grogan (2008);
g Kwon et al. (2016); h Zamolodchikov et al. (2000);
i Heikkinen et al. (2004); j standard deviation not
specified; k Vourlitis et al. (2000); l Rößger et
al. (2019).
To the best of our knowledge, CO2 fluxes from polygon rim and center
sites have been reported only from Barrow, Alaska (Table 2). The daily
averaged net CO2 uptake at the polygon center from this study is
twice as high as reported from any other study concerning CO2 fluxes
from polygonal tundra. Only the study by Olivas et al. (2011) reported the polygonal
tundra to be a net sink, while other studies (Oechel et al., 1995; Lara et
al., 2012; Lara and Tweedie, 2014) reported the polygonal tundra to be a net
source of CO2 over the growing season. The GPP fluxes from the
polygon center from this study exceed the GPP fluxes from Barrow reported by
Oechel et al. (1995) and Lara et al. (2012), but they are distinctly lower
than those reported by Olivas et al. (2011) and Lara and Tweedie (2014). In
terms of respiration, the Reco fluxes from this study at both sites
are lower compared to the reported Reco fluxes from the polygonal
tundra at Barrow. However, the interannual variability of reported
CO2 fluxes from Barrow is rather high, which could also be caused by
different vegetation and soil composition between the sites at Barrow.
A comparison of the CO2 fluxes from the wet and dry site from this
study with other wet and dry sites of the arctic tundra revealed rather low
photosynthesis and respiration rates from the polygonal tundra on Samoylov
Island (Table 2). The Reco fluxes from this study at both sites are
the lowest compared to other sites, and the GPP fluxes of the polygon rim from
this study are at the lower end compared to other dry sites, while the GPP
fluxes of the polygon center are between the fluxes from other wet sites.
Only one study from a Carex shrub site in Cherskii reported higher
NEE (Kwon et al., 2016) compared to the polygon center from this study. Both
the moderate GPP and low Reco fluxes at the polygon center lead to
rather high net CO2 uptake compared to other arctic tundra sites.
The rather moderate GPP and low Reco fluxes of the polygonal tundra
on Samoylov Island compared to other arctic sites might be due to differences
in vegetation composition, organic matter contents, low nutrient
availability, or low temperatures and radiation at the study site. The
polygonal tundra on Samoylov Island is considered an ecosystem with rather
moderate GPP due to its low vascular plant cover with a maximum leaf coverage
of 0.3 (Kutzbach et al., 2007b). Mosses, which have a high coverage (>0.9), were dominant at both sites and have a much lower photosynthetic
capacity than vascular plants (Brown et al., 1980). In general, the
photosynthesis of vascular plants and respiration fluxes are lowered due to the low nutrient
availability in arctic tundra ecosystems (Shaver et al., 1998). A low
nutrient availability is typical for most tundra soils due to water-saturated
conditions and low soil temperatures (Johnson et al., 2000). These conditions
cause low microbial decomposition rates (Hobbie et al., 2002), which in turn
result in a low supply of bioavailable nutrients (Beermann et al., 2015).
However, following Sanders et al. (2010) the nitrogen turnover rates of the
soils found at the study site can be estimated as rather low compared to
other arctic tundra sites. Additionally, the long-term average net radiation
at the study site (June to August, 1999–2011) was 85 W m-2
(1999–2011), which is lower than values reported from other arctic tundra
sites in Alaska and Greenland (Boike et al., 2013; e.g., Wendler and Eaton,
1990; Oechel et al., 2014; Soegaard et al., 2001; Lynch et al., 1999). These
factors might explain the comparatively low Reco and moderate GPP
fluxes at the polygon rim and center compared to other arctic tundra sites.
The differences observed in GPP between the polygon rim and center can be
related to the vascular plant coverage. The polygon center had a much higher
abundance of sedges, while the rim was moss dominated, and the sparsely spread
vascular plants had shorter and fewer leaves. Therefore, the photosynthetic
capacity is higher at the polygon center than at the rim, resulting in the
center having a higher GPP. Additionally, limited water availability due to
the elevation of the polygon rim caused moisture runoff, with a drier or
desiccated moss layer, which may have contributed to a lower GPP (Olivas et
al., 2011). On the other hand, Olivas et al. (2011) found GPP fluxes to be
higher at a polygon rim than at a polygon center in the Alaskan coastal
plains. They related low GPP fluxes at the polygon center to the submersion of
the moss layer and vascular plants. At the polygon center of the current
study, the WT was frequently below the soil surface so that the submersion of
erect vascular plants was not regularly observed, and most of the moss
layer itself was not submerged. This difference in GPP between the Alaskan
study sites (Olivas et al., 2011) and those presented in this study reveals
the important influence, beside the vegetation composition, of water level
and its fluctuations throughout the season on CO2 fluxes.
Differences in respiration fluxes between the wet and dry sites can be
related to different soil conditions. The cold and waterlogged conditions,
typical for the polygon centers, reduced the decomposition of SOM due to oxygen
limitation, causing low microbial activity and therefore low RH
(Hobbie et al., 2002; Walz et al., 2017). Furthermore, moisture runoff at
the rim created drier conditions in the topsoil, which increased soil oxygen
availability and subsequently enhanced RH and Reco
(Oechel et al., 1998). In addition, the stronger diurnal amplitude of the
soil temperature at the polygon rim compared to the center led to higher
daily soil temperatures. Both the increased temperatures and oxygen supply at
the polygon rim relative to the center enhance microbial decomposition,
causing higher RH fluxes to be observed at the polygon rim. As
such, the low CO2 uptake (NEE) at the rim is caused not only by low
GPP, but also by higher Reco fluxes compared to the center. The
higher NEE at the polygon center compared to the rim is mainly driven by
substantially higher GPP and lower RH fluxes, which are due to
differences in vascular plant cover, temperature, and hydrology. This finding
is in good agreement with Nobrega and Grogan (2008), who compared a wet sedge,
dry heath, and mesic birch site and found that the highest CO2 uptake
at the wet sedge site was due to limited Reco associated with
waterlogged conditions.
Measurements of CO2 fluxes at the polygon rim showed an increase in
net CO2 uptake throughout September, whereas at the polygon center
the NEE appeared to continuously decrease (lower net uptake of CO2).
This increase in late-season NEE at the polygon rim cannot be explained by
rising PAR or temperature, but it may be related to the photosynthetic activity
of mosses. At the study site, Kutzbach et al. (2007b) considered September as
the period during which moss photosynthesis dominates GPP. During this time of the
growing season, mosses can still assimilate substantial amounts of
CO2 because they tend to reach light saturation at lower irradiance
(Harley et al., 1989). The photosynthetic activity of mosses declines rapidly
when they face desiccation because they cannot actively control their tissue
water content (Turetsky et al., 2012). Additionally, it has been shown that
mosses face light stress during times of high PAR (Murray et al., 1993). This
light stress causes delayed senescence and more late-season photosynthesis
(Zona et al., 2011). On Samoylov, the photosynthetic activity on the
moss-dominated polygon rim is expected to be low during warm and dry periods,
such as those seen at the beginning of September 2015, and during times of
high PAR. In contrast, with continuous rainfall, dew formation, and the lower
PAR observed in mid-September, the mosses on the polygon rim are likely to
have resumed their metabolic activity, which led to increasing NEE at the
rim. These findings are in good agreement with Olivas et al. (2011), who
reported the highest contribution of mosses to GPP at the beginning and end
of the growing season.
Partitioning respiration fluxes in arctic tundra ecosystems
To date, only a few studies have estimated RH fluxes from arctic
tundra ecosystems over a growing season under in situ conditions (Nobrega and
Grogan, 2008; Biasi et al., 2014). Surprisingly, the differences in
RH flux estimates reported in the literature and those presented in
this study were rather low. Differences in RH fluxes measured with
the trenching method may result from differences in the time between
trenching and the start of the measurements. Nobrega and Grogan (2008), for
example,
started their RH measurements 1 d after clipping, while
measurements in this study and that of Biasi et al. (2014) started about
1 year after treatment. Therefore, although these studies employed a similar
partitioning approach for seasonal estimates of RH fluxes, any
comparison must be made with caution. The few RH flux estimates in
the literature from other arctic tundra sites were higher than the
RH values from the Lena River Delta (0.5±0.1 and 0.3±0.02 g C m-2 d-1 at the polygon rim and center, respectively).
Higher growing season RH fluxes than found in this study
(0.8–1.8 g C m-2 d-1) have been measured at a mesic birch and
dry heath site at Daring Lake in Canada (Nobrega and Grogan, 2008) and at a
bare peat site (1.0 g C m-2 d-1) in the subarctic tundra at
Seida, Russia (Biasi et al., 2014). Both sites contained substantially higher
amounts of SOC in the organic-rich layer than the soil at the polygon rim and
were well-aerated compared to the soil at the polygon center, both of which
likely caused a higher organic matter decomposition rate and could explain
the higher RH fluxes than found at the polygonal tundra sites.
Similar RH fluxes to those reported in our study were measured at a
wet sedge site in Daring Lake (0.4 g C m-2 d-1) (Nobrega and
Grogan, 2008), where soil and environmental conditions like WT, ALD, soil
temperature, vegetation, and SOC were similar to the Samoylov sites and
vegetated peat sites in Seida (0.4–0.6 g C m-2 d-1) (Biasi et
al., 2014). Despite these differences, the average contributions of
RH to Reco of 42 % at the center and 60 % at the
rim are in good agreement with those observed at Seida (37 %–64 %)
and Daring Lake (44 %–64 %). Similar contributions have also been
determined from arctic tussock tundra sites, where RH makes up
approximately 40 % of growing season Reco (Segal and Sullivan,
2014; Nowinski et al., 2010), and from a moist acidic tussock tundra site
(Hicks Pries et al., 2013). In contrast to these results, in a subarctic
peatland, Dorrepaal et al. (2009) report a substantially higher contribution
of RH to Reco of about 70 %. The different
contribution of RH to Reco at the polygon rim and center
on Samoylov Island can be related to differences in vascular plant coverage
and moisture conditions between these sites. The higher GPP at the center
relative to the rim also caused higher rates of RA, in turn
lowering the contribution of RH to Reco. Additionally,
anoxic soil conditions due to standing water, which characterized the polygon
center, reduced SOM decomposition rates. Furthermore, Moyano et al. (2013)
and Nobrega and Grogan (2008) have shown that consistently moderate moisture
conditions, as at the polygon rim, promote microbial activity and therefore
enable higher RH rates than at the center.
At the polygon center, the WT significantly correlated with Reco
and RA fluxes, but no correlation between RH fluxes and
WT was found. In contrast to this, none of the determined respiration fluxes
(Reco, RH, RA) correlated with VWC at the
polygon rim, which might be due to a rather low range of VWC
(28 %–34 %). The RA fluxes may be negatively affected by
high WT due to the submersion of the moss layer and part-wise vascular leaves, as
submersion can lead to plant stress, reducing productivity and nutrient
turnover (Gebauer et al., 1995). However, if RA fluxes were
reduced due to low photosynthetic activity, we would expect a correlation
between GPP and RA fluxes, as observed at the polygon rim (R2=0.48, p<0.05) but not at the center (R2=0.01, p>0.05).
Instead, only half as much CO2 is released by RA at the
center compared to the rim at similar GPP fluxes, as the GPP : RA
ratio indicates (10.5 vs. 5.1 for the polygon center and rim, respectively).
It is likely that RA is reduced due to water-saturated soils, as
shown previously for Reco fluxes in the Arctic (e.g., Christensen et
al., 1998), perhaps due to slow diffusion under water-saturated conditions
(Frank et al., 1996). Furthermore, it might be possible that RH
fluxes are not affected by water table fluctuations as the decomposition of
SOM could take place in deeper layers. This finding is in contrast to a set
of studies that attributed correlations between Reco fluxes and WT
fluctuations solely to the impact of oxygen availability on RH
fluxes (Juszczak et al., 2013; Chimner and Cooper, 2003; Dorrepaal et al.,
2009) or an observed impact of moisture conditions on RH fluxes
across multiple peatland ecosystems (Estop-Aragonés et al., 2018), while
another study has shown no effect between water table fluctuations and
Reco fluxes (Chivers et al., 2009). However, the partitioning
approach used in this study showed that RH fluxes are not
responding to water table fluctuations. Instead the CO2 release by
RA is correlated with water table fluctuations. These findings show
the importance of hydrologic conditions for Reco fluxes and the
need for partitioning approaches to understand the response of individual
Reco fluxes to changing hydrologic conditions.
To determine the impact of hydrological conditions and temperature on
RH and RA fluxes, it would be useful to perform both
warming and wetting experiments in situ. So far, although a number of studies
have determined the temperature response of NEE, GPP, and Reco
fluxes in arctic ecosystems with warming experiments (e.g., Natali et al.,
2011; Frey et al., 2008; Voigt et al., 2017), much less research has focused
on the response of RA and RH fluxes to increasing
temperature (Hicks Pries et al., 2015). Wetting experiments in arctic tundra
ecosystems to determine the individual response of RA and
RH fluxes to changing hydrological conditions are also lacking. As
climate change will likely lead to strong changes in the hydrological regimes
of Siberian tundra regions (Zimov et al., 2006b; Merbold et al., 2009), the
responses of respiration fluxes to altered hydrological conditions should be
addressed in future studies.
Conclusion
The contributions of GPP, Reco, RH, and RA
fluxes to NEE in a drained (rim) and water-saturated (center) site in the
arctic polygonal tundra of northeast Siberia have been quantified in this
study. Both investigated sites acted as CO2 sinks during the
measurement period from mid-July to the end of September 2015. The polygon center was
a considerably stronger CO2 sink than the polygon rim. The main
drivers behind these differences in CO2 fluxes at the pedon scale
were the higher GPP at the polygon center and lower RH
fluxes at the polygon center. The substantial differences in NEE between the
dry and wet tundra sites highlight the importance of pedon-scale measurements
for reliable estimates of CO2 surface–atmosphere fluxes from arctic
tundra sites and the important role of soil moisture conditions in
CO2 fluxes. Hereby, it was shown that RA fluxes respond to
water table changes, with a low release of CO2 by RA fluxes
during times of a high water table. Therefore, future studies on CO2
fluxes from arctic tundra ecosystems should focus on the role of hydrological
conditions as a driver of these fluxes.
Data availability
All datasets shown are available at
10.1594/PANGAEA.898876 (last access: 4 April 2019; Eckhardt et al.,
2019).
Author contributions
TE, CK, LK, and EMP designed the
study. GS and TE performed the chamber measurements and laboratory analysis.
DH and TE performed the visualization of flux comparisons. TE wrote the paper
with contributions from all authors.
Competing interests
The authors declare that they have no conflict of
interest.
Acknowledgements
We would like to thank the
members of the joint Russian–German field campaigns LENA 2014 and LENA 2015,
especially Mikhail N. Gregoriev (Permafrost Institute, Yakutsk, Russia),
Waldemar Schneider, and Günter Stoof (Alfred Wegener Institute for Polar
and Marine Research, Potsdam, Germany), and the crew of the Russian research
station Samoylov for logistical as well as technical support. We are grateful
to Josefine Walz and Mercedes Molina Gámez for valuable help with chamber
measurements and Norman Roessger for intensive support on model development
(all Institute of Soil Science, Universität Hamburg). This work was
supported by the German Ministry of Education and Research
(CarboPerm Project, BMBF grant no. 03G0836A; KoPf Project, BMBF grant
no. 03F0764A). The German coauthors received additional support from the Cluster of
Excellence CliSAP (EXC177) at the University of Hamburg funded by the German
Research Foundation (DFG). We are also grateful for the reviews of
Albertus J. Dolman and two anonymous reviewers and the comments of the editor
Lutz Merbold on a previous version of this paper.
Review statement
This paper was edited by Lutz Merbold and reviewed by
Albertus J. (Han) Dolman and two anonymous referees.
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