Introduction
Boreal peatlands, covering a large fraction of the Northern Hemisphere, are
an important terrestrial carbon pool, whose size is estimated to be around
500 ± 100 Pg of organic carbon when integrated over the entire peat
depth (Yu, 2012). Photosynthesis and respiration of plant and microbial
communities regulate the size of this pool. However, peatlands are also prone
to rapid ecological changes related to climate, which modify the interaction
between hydrology, carbon cycle, vegetation cover, and microtopography.
Detailed knowledge of the processes governing the carbon exchange in northern
peatlands over the course of a growing season is limited, especially with
respect to the impact of relevant environmental variables.
While continuous and long-term time series of carbon dioxide (CO2) and
sensible and latent heat fluxes are already available from several boreal
peatland sites in Europe, measurements of this kind are rare in Siberia. The
closest permanent western Siberian bog installation is part of the Zotino Tall Tower
Observation Facility
(ZOTTO)
facility (Heimann et al., 2014), while other comparable stations are found in
European Russia (Ust Pojeg, Gazovic et al., 2010), Finland (Tervalamminsuo, Annalea Lohila,
personal communication, 2017;
Siikaneva bog site, Korrensalo et al., 2017), Sweden (Fäjemyr, Lund
et al., 2007), and Canada (Mer Bleue, Humphreys et al., 2014). This is
mainly due to the lack of developed measurement sites with the infrastructure
suitable for continuous monitoring of the ecosystem–atmosphere exchange
processes and general inaccessibility of key ecological zones and biomes. In
remote and large areas such as western Siberia, current estimates of greenhouse
gas exchange rates are largely uncertain because discontinuous and
short-term observations (static chamber technique) have often been used to
derive regional and long-term exchange rates (Golovatskaya and Dyukarev,
2008; Schneider et al., 2011; Glagolev et al., 2011; Sabrekov et al., 2013).
Currently, only about 10 eddy covariance (EC) flux tower sites are active in
Russia east of the Ural Mountains (Alekseychik et al., 2016). No prior publications of
eddy covariance fluxes from the western Siberian peatlands are known to the
authors. Previous studies utilizing the EC method in Boreal peatlands
elsewhere have shown the importance of temperature, solar radiation, and
water table depth in controlling the net ecosystem exchange (NEE) (Arneth et
al., 2002; Aurela, 2004; Lafleur et al., 2003; Friborg et al., 2003;
Humphreys et al., 2006). Most studies show that, during the growing season,
peatlands typically act as net sinks of CO2. However, during warm and
dry growing seasons the peatland sink strength is notably reduced and in some
cases leads to net CO2 losses (Bubier et al., 2003; Lafleur et al.,
2003).
In order to fill the western Siberian measurement gap, we have recently
established a new EC flux tower at the raised bog site at the Mukhrino field
station in Khanty–Mansi Autonomous Okrug (Russia). The Mukhrino field
station is officially part of the PEEX station network (Alekseychik et al.,
2016) and INTERACT (http://www.eu-interact.org/). The energy and carbon
dioxide flux data provided by the eddy covariance tower are unique for
the western Siberian middle taiga so far and the only set-up functioning in
western Siberia as of 2016 (within at least a radius of 1000 km). The aims of this study
are to present and analyse the new flux data tower with the related ancillary
measurements, to investigate the diurnal and seasonal variations in NEE and
energy fluxes, and to determine summertime budgets of energy and
CO2 of the ecosystem.
Materials and methods
Site description
The Mukhrino field station (60∘54′ N, 68∘42′ E) is
located at the eastern terrace of the Irtysh River 20 km south of the point
of confluence with the Ob River, in the middle taiga zone of the West
Siberian Lowland. The West Siberian Lowland is a geographical region of Russia
bordered by the Ural Mountains in the west and the Yenisey River in the east;
the region covers 2.75 × 106 km2 from 62–89∘ E
to 53–73∘ N. Paludification in the West Siberian Lowland started after
a climate warming 11 500 cal BP, with 55 % of the present C store accumulated by 6000 cal BP. The mires
have expanded very little during the past 3000 years (Turunen et al., 2001).
The middle taiga ecozone (59–62∘ N) covers an area of about
0.57 × 106 km2 in the central part of the West Siberian
Lowland; the region features flat terrain with elevations of 80 to
100 m a.s.l. (above sea level).
The region has a subarctic or boreal climate (Köppen–Geiger code Dfc)
with long cold winters, short warm summers, and frequent change in weather
conditions. The nearest meteorological station in Khanty–Mansiysk (20 km
N-E of the measurement site) gives an average monthly air temperature range
from -20 to 18 ∘C over the year, with a mean annual temperature
of -1.1 ∘C. The latter had increased by 0.4 ∘C from
1893–1935 to 1970–1999 (Bulatov, 2007). Median annual precipitation
is 520 mm, and evapotranspiration is 445 mm (Bulatov, 2007). Mean summer
precipitation is 208 mm, ranging from 74 to 354 mm over the period from
1934 to 2014. Permafrost in any form is absent. Peat soils freeze to a depth
of about 50 cm. Typically, snowmelt and river break-up start in the first
half of May. The mean duration of the snow cover period is 180 days (from 19 October
to 25 April) with an average March snow depth of 54 cm. The growing season lasts
for 98 days (Bulatov, 2007); the number of growing
degree days > 5 ∘C is from 900 to 1500 (median: 1250).
The excess water supply and flat terrain with poor drainage provide
favourable conditions for wetland formation in the region. Large wetland
systems commonly cover watersheds (34 % of the zonal area) and have a
convex dome with centres that are 3 to 6 m higher than the periphery. The
wetland subtypes here have strict spatial regularities. Ridge–hollow–lake
complexes (15 % of the total wetland area) represent central plateau
depressions with stagnant water. They consist of numerous small lakes up to
2 m deep with peat bottoms and waterlogged hollows. Different types of
ridge–hollow complexes dominate (42 %), covering the better-drained
gentle slopes. Pine bogs (28 %) are more frequent in drier areas where
the peat surface is typically 10–50 cm above the water table level. Poor
and rich fens (8 %) develop along the wetland edges and watercourses
where the nutrient availability is higher. Open bogs with mosaic dwarf
shrubs and sphagnum vegetation are widespread (5 %) at the periphery of
individual wetland bodies. Wooded swamps (2 %) surround the peatland
systems (Terentieva et al., 2016). Primary lakes of 100–2000 m in diameter
and up to 5 m depth with mineral bottom are widespread.
(a) Map showing the Mukhrino station location, (b) photo of the EC
tower facing southwest, (c) digital elevation map based on drone
survey, and (d) surface-type classification map. (d) Includes an eddy covariance footprint
overlay, with the isolines giving the 70 % cumulative EC source zone in
the three stability classes. Colour coding in (d) is as follows: dark
green is
ridges–hummocks, light green is lawns–hollows, and dark blue is ponds. The red
cross marks the location of the EC tower.
The Mukhrino field station (map, Fig. 1a) is located on the eastern edge of a
peatland 10 km × 5 km in size. The study site is considered to be
representative of raised bogs, a peatland type dominant not only in the west
(Masing et al., 2010) but also in the other parts of Siberia (Schulze et al.,
2015). The peat layer of up to 5 m in depth is composed of sphagnum with
minor contributions by other plants. The pH is 3.5–5 and electric conductivity
is from 0 to 200 µSm m-2 (Sabrekov et al., 2011). The rate of
peat accumulation at a nearby wetland site was 0.35 mm yr-1, while the
average dry bulk density of the peat was
92.7 g dm-3
with an average C peat content of 52.7 % (Turunen et al., 2001).
Pine bogs and ridge–hollow complexes are dominant within the boundaries of
the Mukhrino bog (Fig. 1b–d). The tree cover of ridges and pine bogs is
represented by stunted Pinus sylvestris. The dwarf shrub layer
consists of Ledum palustre, Andromeda polifolia,
Chamaedaphne calyculata, Vaccinium vitis-idaea,
Vaccinium uliginosum, and Oxycoccus palustris. Herbs are
represented by Rubus chamaemorus and a few tiny species of sundews
(Drosera anglica, D. intermedia, D. rotundifolia).
Carex limosa, Eriophorum vaginatum, Scheuchzeria palustris are widespread within oligotrophic hollows of ridge–hollow
complexes. The moss layer of raised bogs consists of sphagnum mosses such as
S. fuscum, S. lindbergii, S. balticum,
S. papillosum, S. angustifolium, S. magellanicum,
S. jensenii, etc. The area fractions of open water, hollows, and
ridges within a 200 m radius around the flux tower are 1, 67, and 32 %,
respectively (Fig. 1c–d).
Over the past years, the Mukhrino bog has been the focus of a large number
of studies ranging from surface–atmosphere gas exchange (Glagolev et al.,
2011) to geochemistry and physical, chemical, and biochemical properties of
peat (Stepanova and Pokrovsky, 2011; Szajdak et al., 2016), hydrology
(Bleuten and Filippov, 2008), and microbiology including mycology
(Filippova et al., 2015).
Measurements
Turbulent fluxes of momentum, sensible (H) and latent (LE) heat, and CO2
were measured between 1 May and 2 September 2015 with the EC technique. The EC system included a 3-D ultrasonic anemometer (Gill R3,
Gill Instruments Limited, UK), providing three wind velocity
components and the sonic temperature, and an open-path infrared gas analyser
(LI-7500, Li-cor Biosciences, USA) for the measurement of CO2 and water
vapour (H2O). The EC sensors were mounted on a tower at 4 m height
above the peat surface. The horizontal separation between the sonic
anemometer and the gas analyser was 15 cm. The open-path gas analyser was
connected to the analogue input of the sonic anemometer. The data were logged
on a mini-computer via serial cable at the sampling frequency of 10 Hz. The
eddy covariance tower coordinates are 60.89133∘ N,
68.67627∘ E.
Auxiliary parameters were measured and recorded by two automatic
meteorological stations located within 30 m from the EC tower. The measured
parameters include the soil temperature profiles at depths of 2, 5, 10, 20,
and 50 cm (thermocouple sensors), net radiation (Kipp & Zonen NR Lite
radiometer), incoming and reflected photosynthetically active radiation
(Li-cor LI-190SA Quantum Sensor), air temperature and relative humidity
(Rotronic HygroClip S3), and precipitation (HOBO Data Logging Rain Gauge
RG3-M). The soil temperature profiles were installed in ridge and hollow, with two
replicates in each microsite; the replicates were averaged for further uses.
Water table level was also measured in both types of microsites with two
Mini-Diver sensors (DI 501).
The station uses an autonomous power supply system consisting of solar panels
(4 kW in total) and a wind vane generator (3 kW). A combined charge
controller–invertor unit charges the batteries with the total capacity of
800 Ah and supplies up to 3 kW to the field station. In wintertime, a
2.5 kW petrol generator is additionally used.
Flux calculation
The post-field processing of EC raw data was performed with EddyUH software
(Mammarella et al., 2016). Fluxes of sensible and latent heat and
CO2 were calculated as the 30 min block-averaged covariances between
the scalars and the vertical wind velocity:
H=ρdcpw′Ta′‾LE=ρdLvMwMaw′χH2O′‾Fco2=ρdMaw′χCO2′‾,
in which ρd is the dry air density (kg m-3), cp the
specific heat capacity of dry air (J kg-1 K-1), Lv
the latent heat of vaporization for water (J kg-1), Ta the
air temperature (K), and Ma and Mw the molar masses of
dry air and water, respectively. The terms w′Ta′‾,
w′χH2O′‾, and w′χCO2′‾ are the covariances between w and
Ta and dry mole fractions of CO2 and H2O, respectively.
Data were de-spiked according to standard methods (Vickers and Mahrt, 1997);
thereafter, wind velocity components were rotated into a natural coordinate
system by performing a two-step rotation at each 30 min interval, setting the
x axis along the mean wind direction and zeroing the mean vertical wind
velocity. The time delay between the vertical wind speed w and the scalar
(CO2 or H2O) was derived for each 30 min interval by maximizing
the respective cross-correlation function, calculated in a very narrow window
(from -0.5 to 0.5 s). The fluxes were corrected for high- and low-frequency
losses that occur due to the limited frequency response of the EC system and
the finite time averaging period used for calculating the fluxes,
respectively. Correction was done according to Mammarella et al. (2009) by
using experimentally and theoretically determined co-spectral transfer
functions at high and low frequency. The estimated low-pass filter time
constant for CO2 and H2O was 0.05 s. The effect of this correction
is very small and is mainly caused by the separation between the open-path
analyser and the sonic anemometer. The high-frequency attenuation can be
clearly seen in the measured co-spectra (Fig. 2a). When performing the
spectral correction to the CO2 and H2O fluxes, the derived transfer
functions were used together with the site-specific co-spectral model, which
was estimated by a non-linear fit of the measured w′T′‾
co-spectrum. The normalized frequency of the co-spectral peak (nm) was
also estimated from the co-spectrum for each 30 min record, and the
site-specific stability dependence was established (Fig. 2b–c). In unstable
conditions (stability parameter z/L < 0) nm has a constant
value of 0.05, whereas in stable conditions an increase with atmospheric
stability is observed (z/L > 0). Before calculating the
sensible heat flux, the 30 min sonic temperature covariances are converted
to actual air temperature covariances following the approach of van Dijk et
al. (2004). LE and CO2 fluxes are corrected for air density fluctuations
(Webb et al., 1980). Finally, the Burba correction (Method 4 in Burba
et al., 2008) was applied to the CO2 and LE fluxes.
(a) Normalized frequency-weighted co-spectra of temperature, carbon
dioxide, and water vapour as functions of normalized frequency measured on
16 July 2015, 11:00–13:30 UTC + 5 (mean value of stability parameter (z-d)L-1=-0.023).
The two following subplots show the 30 min values of the
normalized peak frequency nm versus the stability parameter
(z-d)L-1 in unstable conditions (b) and stable conditions (c).
Ground heat flux (G) was calculated for the ridge and hollow microforms from
the peat temperature profile as heat storage change in the top 50 cm of soil
following the methodology described in Ochsener et al. (2007) and elsewhere:
G=∫050cmCv∂T∂tdz.
The total volumetric heat capacity, Cv, was calculated as the sum
of volumetric heat capacities of the solid, water, and air constituents,
weighted by their volume fractions in the soil matrix. The temperature
measurements at 5, 10, 20, and 50 cm depths were used here. Volumetric soil
water content profile was modelled as a function of water level (Yurova et
al., 2007; Weiss et al., 1998):
θ(z)=φ1+(a(-(WT-z)))b-1+1/b,
in which ϕ is the soil porosity and a, b are the empirical parameters. ϕ
was taken as 95 %, corresponding to the average of the representative
acrotelm and catotelm values (Granberg et al., 1999). Therefore, the fraction
of solid peat particles constituted the remaining 5 % of the volume. The variables a
and b were adopted from Yurova et al. (2007). The ridge and hollow
water table (WT)
measurements were used to model θ for two microsite types. Finally, a
footprint-representative estimate of G was obtained as an average of the
ridge and hollow fluxes weighted by the respective area fractions (68 %
and 32 %).
Flux quality criteria and footprint
In this study, we analysed the data in the period between 1 May and
31 August 2015 (122 days). A long gap in CO2 and H2O flux data, due
to infrared gas analyser (IRGA) malfunction, occurred between 25 July and 6 August 2015. Short gaps
during night-time amount to 73 % of the total night-time periods, being
mainly due to limited power availability, but also low turbulence conditions.
Night-time was defined as the periods with
PAR < 10 µmol m-2 s-1. Other instrumental
problems causing spikes in the measured CO2 and H2O signals (mainly
caused by rain) were eliminated by the despiking method as described in
Sect. 2.3 and by visual inspection of the raw data time series. The 30 min
time series containing more than five spikes were discarded from further
analysis, causing a loss of about 4 % of CO2 and H2O data and
13 % of sonic anemometer data. Only the 30 min records with friction
velocity (u∗) larger than 0.1 m s-1 and fluxes with
stationarity less than 1 (Foken and Wichura, 1996) were used in further
analyses. Finally, the overall data coverage for quality-controlled and
filtered CO2 and H and LE heat fluxes during the chosen period
was 28, 33, and 35 %, respectively. CO2 nocturnal data from August
were
affected by spikes and were thus excluded from analysis.
The flux footprint was estimated using the Kormann and Meixner (2001) model. In
the calculations, a value of 0.12 m (an average for May–August calculated
from sonic anemometer data assuming a logarithm wind profile in near-neutral
stability conditions) for the aerodynamic roughness length was used, whereas
wind speed, Obukhov length, and standard deviation of lateral wind velocity
component were acquired from the EC data. The average source area
contributing 70 % of the flux ranged from 89 m in unstable conditions up
to about 116 m in near-neutral and 202 m in stable conditions (Fig. 1d).
Energy flux gap filling
In order to avoid systematic bias in calculation of cumulative energy flux
values, the energy flux time series were gap filled. The soil heat flux
record was complete. As for its calculation, gap-filled peat temperature
series were used. The rest of the fluxes were individually gap filled
following Falge et al. (2001). First, the net radiation flux was gap filled
using the mean diurnal variation (MDV) and look-up tables for longer gaps and
with linear interpolation for shorter gaps. Next, LE and H were gap filled
using linear regression against Rn. The MDV and linear regression
models were calculated in a moving time window 10 days wide. The R2 of
the gap-filling models reached 0.68 for H, 0.81 for LE, and 0.89 for
Rn.
Partitioning and gap filling of net ecosystem exchange
The NEE measured by EC was partitioned into
ecosystem GPP and ecosystem respiration
(Re) and then gap filled following the next steps:
A NEE expression incorporating a Q10-type module for respiration and
a rectangular hyperbolic GPP module (Eq. 6) was fit to the data at
PAR < 300 µmol m-2 s-1.NEE=RrefQ10T0-Tref10︸(a)Re-PmaxPARk+PAR︸(b)GPP,in which T0 is the area-weighted average temperature of hollows and
hummocks at a 5 cm depth (∘C), Tref the reference
temperature of 12 ∘C, Rref the reference respiration
(µmol(CO2) m-2 s-1), and Q10 the temperature
sensitivity, PAR the photosynthetically active radiation (µmol m-2 s-1), Pmax the maximum photosynthesis (µmol(CO2) m-2 s-1), and k the value of PAR at 1/2Pmax (µmol m-2 s-1). All four parameters
(Q10, Rref, Pmax, and k) were evaluated by
fitting at this step. The resulting Q10= 1.99 (95 % CI [1.42;
2.57]) was then fixed for the whole study period.
The respiration module (a) of Eq. (6) was fit to the night-time data in a
30-day-wide moving time window, with Rref being evaluated at each
step. The window was shifted by 1-day steps.
In the same time window, GPP was calculated as the residual of measured
NEE and respiration model, after which the GPP module (b) of Eq. (6) was
fit to produce the values of Pmax and k.
The daily window values of Rref, Pmax, and k were then
smoothed out with the spline interpolation procedure and rescaled down to the
30 min resolution of the original data. Figure 3 shows the resulting
parameter time series.
Finally, the NEE model (NEEmod, equal to the Re
model minus GPP model) was calculated at a 30 min resolution and used to
fill the gaps in the measured NEE.
Time series of the environmental variables: (a) air and
peat temperatures, (b) PAR
albedo, (c) PAR, (d) relative
humidity, (e) precipitation, (f) water table
depth, (g) wind speed, and (h) wind direction. The grey
dots are 30 min measurements, while the lines represent daily averages,
except in (b) in which the dots are midday
(10:00–16:00 UTC + 5) medians of PAR albedo
and in (e) in which a bold line shows the cumulative precipitation.
In (a), the presented peat temperatures are area-weighted averages
of two hollow and two ridge measurement locations.
Results and discussion
Environmental conditions
Weather in Mukhrino during the summer season of 2015 was unusual for the
regional climate. Spring was early and warm: the average air temperatures in
May and June were 4.1 ∘C, or 3.4 ∘C higher than the
long-term average (Fig. 3a). It caused an unusually early and rapid snowmelt
in April and the beginning of May; the last patches of snow melted by 3 May,
while three temperature profiles out of four indicated freezing until 3 May
at 5 cm depth and until 6 May at 20 cm depth. In contrast to the climatic
average, June was the warmest month, with an average air temperature of
18 ∘C and a maximum value of 32 ∘C. However, the rest of
summer was greatly affected by the cool fronts that brought precipitation and
cloudiness. For this reason, the average July and August values sunk below
the average by 2.7 and 1.7 ∘C, respectively. Maximum soil
temperature at a 20 cm depth (19 ∘C) was observed every June for the last decade, while soil temperature at
50 cm had two maxima of 16 ∘C in beginning of July and in the first
week of August (Fig. 3a).
Photosynthetically active radiation was at its maximum in May–June, slightly
decreasing in July and August (Fig. 3c). The maximum midday value of
1463 µmol m-2 s-1 was registered in the middle of June.
Precipitation considerably differed from the 81-year reference period (not
shown). It was 2.7 times higher in June–July because of three heavy rainfall
periods (4–9 June, 2–5, 18–20 July 2015). The total cumulative
precipitation of the study period (May–August) was 405 mm, or 45 %
higher compared to the reference period, and 325 mm in May–July (Fig. 3e).
The frequent precipitation helped to sustain high relative humidity (d).
Accordingly, the water table depth (WTD) changes followed the intensity and
frequency of precipitation, decreasing slowly during dry periods and rapidly
increasing in heavy rain (Fig. 3f). There was snow on the ground until
3 May and after 30 September. In fact, the end of the snowmelt can be seen as a
steep reduction in PAR albedo at the beginning of May (Fig. 4b; the hollow
albedo starts at 0.12 in early May, but the axis is limited at 0.08 for
clarity). Otherwise, PAR albedo follows the typical trends, being higher in
the hollows than in the ridges and showing downward peaks during the
precipitation events.
Time series of the 30 min average surface fluxes measured with
the eddy covariance system: (a) net ecosystem exchange, (b) net radiation, (c) sensible
heat, (d) latent heat, and (e) ground heat flux.
The prevailing wind direction was from the south-south-west
(150–260∘, 45 % of cases), in which the proportions of open
water, hollows, and ridges within the 200 m radius are 1, 67, and 32 %,
respectively. Similar proportions hold for the entire area within a 200 m
radius around the mast.
Monthly averages of air temperature (Ta), soil
temperature at 5 cm depth (Tp), photosynthetic active radiation (PAR),
cumulative precipitation (mm), net radiation (Rn), ground heat
flux (G), sensible heat flux (H), latent heat flux (LE), Bowen ratio (ß),
energy balance residual (Res = Rn - H - LE - G), energy balance
closure (EBC = (H + LE + G) / (Rn)), and gap-filled NEE.
Ta
Tp
PAR
P
WTD
Rn
G
H
LE
ß
Res
EBC
NEEgapf
[∘C]
[∘C]
[µmol
[mm]
[cm]
[Wm-2]
[Wm-2]
[Wm-2]
[Wm-2]
[–]
[Wm-2]
[–]
[gC m-2]
m-2 s-1]
May
11.1
11.8
405
12
-17
126
8
23
73
0.32
22
0.87
-35
June
17.9
19.6
411
121
-16
133
3
30
96
0.27
5
1.04
-72
July
15.2
18.0
318
191
-11
88
1
15
69
0.27
3
1.01
-79
August
12.6
15.2
295
80
-13
62
-4
13
61
0.26
-8
1.27
-16
May–August
14.3
16.1
359
405
-14
102
2
20
74
0.28
6
0.99
-202
Surface energy exchange
Time series of the surface energy fluxes and their monthly diurnal courses
are shown in Figs. 4 and 5, respectively. The midday net radiation
(Rn) averaged 397 and 364 W m-2 in May and June,
respectively, reflecting the large amounts of incoming solar radiation
(Fig. 3c). However, the high post-snowmelt water levels and undeveloped
vascular vegetation in May resulted in low albedo, somewhat lowering
Rn. Conversely, the midday values in the second part of
the summer are clearly lower, being 275 and 211 W m-2 in July and
August, respectively. In fact, as later in the summer the developed ground
vegetation attains a higher reflectivity, this increases the surface albedo
and decreases Rn. In addition, the frequent overcast conditions
(16 days in July and 17 in August) further reduced incoming solar radiation
in late summer (Table 1). The soil gained heat throughout most of the studied
period, but the average flux is very small, ranging from 8 W m-2 in
May to 1 W m-2 in July. The ground starts to cool down in August with
G equalling -4 W m-2. Most of the available energy is released as
latent heat flux, whose monthly average value is between 61 and
96 W m-2, while sensible heat fluxes are more than 3 times lower,
ranging from 30 W m-2 in July to 13 W m-2 in August (Table 1).
Monthly average diurnal courses of the energy balance components.
The time shown on the x axis is local winter time (UTC + 5).
There is a marked seasonal change in LE, which starts to increase rapidly in
May due to high Rn values, reaching the daily mean peak in July
(239 W m-2), and then decreases in July, reaching the minimum value of about 140 W m-2 at
noon in August (Fig. 5 and Table 1).
Although a seasonal change in H is also observed, it is characterized by a
smaller amplitude. Monthly mean values of Bowen ratio (ß) are rather low
(around 0.3), showing no significant seasonal variation (Table 1). However,
the sequence of rainy and dry periods caused short-term variations in ß
of
between 0.15 and 0.6 on a timescale of about 2 weeks. Turbulent heat fluxes
(H and LE) show a diurnal variation typical of land ecosystems, being in
phase with Rn (Fig. 5). The dominance of LE (with respect to H)
for northern wetlands has already been reported. Using a flux tower at a
western Siberia bog site located close to Plotnikovo (56∘51′ N,
82∘50′ E), Shimoyama et al. (2003) show values of ß ranging
from 0.57 in the early growing season to 0.78 in the peak growing season.
Similar values were found by Aurela et al. (2015) in a Finnish wetland
(ß = 0.78, Lompolojänkkä) and at the wetland site Degerö
in Sweden (ß = 0.83, Peichl et al., 2013). However, lower values of
ß, more similar to the one in our study, were observed in other northern
wetlands (Runkle et al., 2014; Wu et al., 2010; Eugster et al., 2000). Most
probably, the difference can be explained by difference in water table
depths. One has to also account for the unusually wet conditions in 2015,
which must have enhanced LE to a certain extent. High water availability
supported high LE and G in May and June. The subsequent reduction in G could
have been partly related to higher ground shading by aboveground biomass.
The energy balance closure (EBC) for the whole period is around 1.07 (Fig. 6)
when adopted as a slope of LE + H + G vs. Rn or 0.99
when calculated as a ratio of the corresponding cumulative fluxes. The slight
excess on the side of H + LE + G might be the product of
uncertainty in the modelled peat water content (Eq. 5), which may in turn
have affected G. Monthly estimates of the EBC and the residual (Res) are
reported in Table 1. The EBC (ratio method) ranges from 0.87 in May, when the
difference between available energy and the turbulent fluxes is
22 W m-2, to 1.27 in August, when Res is -8 W m-2. The summer
months (June and July) show the best values of EBC and Res. The observed
values are in line with those from other wetland studies (Kurbatova et al.,
2002; Peichl et al., 2013; Runkle et al., 2014). In their FLUXNET site-based
energy balance closure study, Stoy et al. (2013) reported an average value of
0.76 for the wetland site category, highlighting the relevance of including
the heat storage term in sites with high water table depth. Shimoyama et
al. (2003) obtained a better EBC value (0.9 vs. 0.82 in July), estimating the
soil heat flux in the bog from an area-averaged value of soil thermal
parameter (instead of using a point value), to some extent accounting for the
surface heterogeneity and the presence of microtopography. Our use of
area-weighted average temperature profiles and individual water level
measurements in the ridge and hollow microsites has resulted in a similarly
high EBC, implying that the main component of spatial heterogeneity must have
been captured.
Energy balance plot. The
sum of latent and sensible heat fluxes and soil heat flux is plotted against
net radiation flux. A linear function is fit to the data and shown together
with a 1:1 line. The mean ratio of LE + H + G to Rn is
0.99.
Carbon exchange
The time series of Pmax, k, quantum yield α, and
Rref presented in Fig. 7 reveal notable trends. Rref
drops between May and early June, possibly related to rainy weather spells,
but then increases rapidly by August, probably in response to the increasing
soil temperatures, availability of substrate, and plant productivity.
Pmax has a broad peak in June–July, which is probably governed
by the vascular leaf area index, with a possible contribution of the
acclimation, as the air temperatures were closer to optimal in that period
(see Fig. 10 below). k had a maximum in June, with a gentle reduction
later towards August. This evidence of higher photosynthetic activity at low light
in late summer may again point to acclimation. In turn, the Pmax
and k evolutions result in a May–August upward trend in α.
However, one could speculate that this behaviour is due to the seasonality of
plant functional group activity to an extent. A Finnish boreal fen study of
Korrensalo et al. (2017) found a wide seasonal variation in the contributions
of moss and vascular species to the ecosystem-scale photosynthesis. The moss
photosynthesis (gC m-2 d-1) declined steadily throughout summer,
while various vascular species were most active in June, July, or August; it
was also common for k of many species to have a peak in June and/or become
reduced throughout the growing season, in line with the findings of the
current study.
Time series of the CO2 flux model parameters. The dots are the
daily values evaluated in a moving time window, and the solid line is a
spline interpolant, while the shaded area shows the 95 % confidence
interval, which is calculated at each time window step. The interpolated
parameter lines stretch to the beginning of May and end of August, showing
the constant values used at the edges of the study period.
(a) Seasonal variation in the gap-filled NEE; the grey dots
correspond to 30 min averages and the black line corresponds to the daily averages. The
major front passage events are marked with green. (b) Measured NEE and PAR
during the third frontal event. (c) Cumulative gap-filled NEE,
Re,
and GPP.
Mean diurnal course of NEE and its components for individual
months during the study period. The time on the x axis corresponds to local
winter time (UTC + 5).
The time series of gap-filled NEE is shown in Fig. 8a. The largest flux
amplitudes are found during June and July when 30 min values range between
about -10 and 5 µmol m-2 s-1. In May, the measured
NEE has a narrower amplitude because of lower temperature and PAR values.
Monthly differences in the NEE amplitude can be clearly seen in Fig. 9, in
which the mean diurnal course is plotted for each month. The modelled NEE
(Eq. 6) closely follows the measured NEE. As expected, the largest net carbon
uptake was observed in June and July (-72 and -79 gC m-2,
respectively), while in May it amounted to -35 gC m-2.
Unfortunately, poor data coverage was achieved in August, making the
corresponding monthly cumulative value somewhat more uncertain, although it
seems to have been much lower (-16 gC m-2). Overall, the bog site
acted as a net CO2 sink in the analysed period. The cumulative
gap-filled NEE of May–August was -202 gC m-2, which breaks down
into 157 gC m-2 of Re and 359 gC m-2 of GPP. For
the period of May–July with the best data coverage, the corresponding values
were -186, 91, and 277 gC m-2. The Mukhrino May–August GPP falls
between 224 and 243 gC m-2 observed over the same period by two
Canadian bogs and 466–539 gC m-2 in Mer Bleue (Humphreys et
al., 2014). However, in terms of net summer uptake, the Mukhrino was among
the highest estimates. For example, Friborg et al. (2003) reported an average
July CO2 uptake of -7545 mg m-2 d-1 (which corresponds to
a cumulative sum of -64 gC m-2) measured with EC at the Bakchar bog
close to Plotnikovo village in western Siberia. This value is very close to
the Mukhrino NEE sum of June (Table 1). Lower CO2 uptake is reported for
the Zotino bog, where growing season (May–October) cumulative NEEs range
between -43 and -60 gC m-2 in different years, with a maximum
daily mean NEE of about -2 µmol m-2 s-1 measured in
July 2000 (Arneth et al., 2002). Lower NEEs in the range of -50 to
-90 gC m-2 yr-1 were shown for several peatlands of northern
European Russia and Siberia (Dolman et al., 2012). Daily net carbon uptakes
ranging between -1 and -2.8 gC m-2 d-1 were measured during
summer in other northern peatlands in Canada (Humphreys et al., 2006);
compare with the June–August average of -1.8 gC m-2 d-1 in
Mukhrino.
The effect of weather conditions
The year 2015 was characterized by diverse weather, starting with an early
and warm spring and continuing rainy and overcast days. Carbon uptake became
significantly limited during the passage of five cold fronts that occurred in
June–August (see Fig. 8), four of which were associated with ample
precipitation. During those short periods, uptake plunged to very low values,
with the ecosystem even becoming CO2 neutral or a small source during
some periods. A closer look at one such period is provided by Fig. 8b.
Excluding the rain-free front in late June, each event brought about 100 mm
of precipitation, causing WT raises of 8–11 cm (Fig. 3d, e). However, no
dependency of surface exchange on WT was found. The regular and ample
precipitation helped sustain water level at a nearly constant level, which
was about -60 cm in ridges, while many hollows stayed inundated (Fig. 3f).
In a landscape dominated by ridges (in terms of green biomass), drawdown in
WT leads to its decoupling from the hydrological state of surface peat (Price
et al., 2003), and, therefore, all vegetation functions including
photosynthesis. Sustaining a high water level must prevent water stress in the
hummock vegetation, which constitutes a significant fraction of green biomass
in Mukhrino. In such exceptionally wet conditions as in 2015, the top peat
must have stayed moisturized most of the time, meaning that water
availability was not the limiting factor for CO2 uptake. At the same
time, the hollows stayed largely inundated and as such probably made a
smaller contribution to photosynthesis than hummocks.
The overcast conditions during front passage also resulted in temperature
drops by up to 13 ∘C. This obviously limited respiration, but also
restricted photosynthesis as the optimum growth temperature seems to be close
to 30 ∘C (Fig. 10b).
(a) Surface conductance versus vapour pressure deficit
demonstrating a well-known relationship. The dashed line is a fit (the
function is specified in the insert). (b) Normalized NEE (NEEnorm= NEE / NEEmod) versus air temperature for June–July 2015;
NEEnorm saturates approaching 25 ∘C.
High relative humidity, and thus lower vapour pressure deficit (VPD), must have partly
compensated for the lower CO2 uptake during the fronts by promoting
higher stomatal conductance (gs). In terms of the parameters
g1 and m (Fig. 10a), the response of gs to VPD was similar
to that in the southern Swedish bog Fäjemyr, northern Swedish fen
Degerö and southern Finnish fen Siikaneva (Alekseychik et al.,
unpublished data; Peichl et al., 2013). Mean bulk surface resistance
(rs, the reciprocal of conductance) of 74 s m-1 is
somewhat lower than approximately 85 s m-1 reported for the wetlands
ecosystem class by Kasurinen et al. (2014). As mentioned above, the Bowen
ratio was stable throughout the summer (∼ 0.3), but the weekly mean
values varied between 0.15 and 0.6 in close correlation with the
precipitation and cloudiness pattern.
Nevertheless, the favourable conditions of May and early June allowed for
apparent rapid accumulation of green biomass. Also, between the cold fronts,
air temperatures did occasionally exceed 25 ∘C,
promoting photosynthesis. This behaviour reflects the temperature control on
GPP that is common for the whole Boreal region (Reichstein et al., 2007). As
a result, the early spring and sustained wetness for the rest of the year
seem to have outweighed the GPP restriction during the front passages and
eventually led to an unusually high cumulative CO2 uptake.
No flux variation with wind direction was found, in consistency with the
similarity of ridge–hollow fractions in different wind direction sectors
(Fig. 1c, d).
Conclusions
This study provides the results of direct and continuous measurements of
surface energy balance components and CO2 flux at the Mukhrino bog site
in the western Siberian middle taiga. The turbulent fluxes measured by the EC
technique over May–August 2015 form a pioneering dataset of its kind for the
region.
The observed magnitudes and diurnal course of sensible and latent heat
fluxes were generally in agreement with previous bog studies. The latent
heat flux was about 3 times larger than the sensible heat, and the
monthly mean Bowen ratio did not show any significant seasonal variation.
However, short-term variations related to heavy rainfall events were
observed. In terms of monthly averages, May and June were characterized with
the highest available energy.
Carbon dioxide exchange was typical of a raised bog, with net CO2 sink
being rather high (202 gC m-2 for May–August) but within the range of
previous observations (IPCC, 2014, 2013). The remarkably wet weather of 2015
ensured high moisture availability and thus promoted high photosynthesis
during the sunny periods. However, the rainy and cool conditions during the
passage of several fronts limited photosynthesis so that the ecosystem
temporarily turned into a net CO2 source. The peak in carbon uptake
caused
the maximum available energy to lag by 1 month, falling in June–July, probably
being modulated by the course of vascular plant leaf area development.
The sharp seasonality of the photosynthesis and respiration model parameters
pointed at an ensemble of effects, including the variability in green
biomass, relative importance of plant functional groups, and acclimation.
Complementary chamber and plant-scale studies will help disentangle those
effects.
The Mukhrino station was established for the purpose of long-term monitoring
of ecosystem functioning and greenhouse gas exchange and continued its
operation in 2016. Obtaining a measurement record over several years with
varying weather is instrumental for determining the typical budgets of
the ecosystem, unaffected by untypical weather, which was the case in 2015.