Introduction
Methane (CH4) contributes to about 20 % of global warming in terms
of radiative forcing, and its concentration in the atmosphere increased at a
rate of 0.5 ppb year-1 in 1999–2006; this rate rapidly increased to
6 ppb year-1 from 2007 to 2011 (IPCC, 2013). Although the total global
lake area accounts for approximately 3.7 % of the Earth's non-glaciated
land area (Verpoorter et al., 2014), CH4 emissions from global lakes
account for up to 14.9 % of natural CH4 emissions (IPCC, 2013).
However, this estimate has been associated with large uncertainties because
of the high spatial and temporal variations of CH4 emissions and the
insufficient multi-seasonal measurements of CH4 effluxes, especially in
tropical and subtropical lakes (Yang et al., 2011; Ortiz-Llorente and
Alvarez-Cobelas, 2012; Bastviken et al., 2015; Li and Bush, 2015).
CH4 effluxes in lakes feature high temporal variations (Käki et al.,
2001; Xing et al., 2004, 2005, 2006; Duan et al., 2005; Palma-Silva et al.,
2013). For example, previous studies found that the mean CH4 effluxes
over 1 day were 0.018 and 68.27 mmol m-2 day-1 (Xing et al.,
2004; Duan et al., 2005; Chen et al., 2007; Podgrajsek et al., 2014); even
larger variations were found on a seasonal scale (Xing et al., 2005, 2006;
Duan et al., 2005; Ortiz-Llorente and Alvarez-Cobelas, 2012; Wik et al.,
2014). These large variations in CH4 effluxes highlight the importance
of frequent and multi-seasonal measurements (Bastviken et al., 2008, 2015;
Chen et al., 2013). Unfortunately, most earlier studies on CH4 emissions
were based on short-term measurements, ranging from daily to seasonal scales,
and were conducted during the daytime (Xing et al., 2004, 2005; Duan et al.,
2005; Schrier-Uijl et al., 2011; Rõõm et al., 2014). To our
knowledge, multi-seasonal measurements of CH4 effluxes have only been
conducted in high-latitude lakes (Utsumi et al., 1998a, b; Huttunen et al.,
2003; Rõõm et al., 2014; Wik et al., 2014), and few studies on
tropical and subtropical lakes (Xing et al., 2005, 2006; Ortiz-Llorente and
Alvarez-Cobelas, 2012), especially large ones, had measurement durations
longer than 1 year.
The magnitude of CH4 emission mainly depends on the dynamic balance
between the microbial processes of CH4 production, oxidation, physical
transportation from the anaerobic zone to the atmosphere in lakes, and
regulation by multiple, interconnected physical, chemical, and biological
variables (Sun et al., 2012; Liu et al., 2013; Serrano-Silva et al., 2014;
Rasilo et al., 2015). CH4 production and oxidation are microbial
processes regulated by organic carbon loading, dissolved organic matter, lake
nutrient status, and N availability (Bridgham et al., 2013; Liu et al., 2013;
Hershey et al., 2014; Rasilo et al., 2015); temperature (Liikanen et al.,
2003; Marotta et al., 2014; Yvon-Durocher et al., 2014); lake depth and size
(Juutinen et al., 2009; Rasilo et al., 2015); pH, O2, NO32-,
Fe3+, and SO42- in the sediment and water column (van Bodegom
and Scholten, 2001; Schrier-Uijl et al., 2011; Bridgham et al., 2013); and
populations and potential activities of methanogens and methanotrophs
(Segers, 1998; van Bodegom and Scholten, 2001; Liu et al., 2015; Liu and Xu,
2016). CH4 transportation is driven by three major mechanisms, namely,
molecular diffusion, bubble ebullition, and plant-mediated transportation
(Bridgham et al., 2013; Chen et al., 2013; Zhu et al., 2016). These
mechanisms are affected by water stratification and seasonal overturns of the
water mass, which are determined by temperature (Palma-Silva et al., 2013;
Rõõm et al., 2014), wind-forced mixing (Wanninkhof, 1992; Palma-Silva
et al., 2013), water depth (Liu et al., 2013), boundary layer dynamics
(Poindexter et al., 2016; Anthony and Macintyre, 2016), hydrostatic pressure
(Chanton, 1989), and different vascular plants (Juutinen et al., 2009; Zhu et
al., 2016). Most studies examined CH4 emissions and their influencing
factors in small lakes because of their large contribution to the global
CH4 budget (Bastviken et al., 2004; Downing, 2010; Bartosiewicz et al.,
2015; Holgerson and Raymond, 2016). Although small lakes are a large source
of atmospheric CH4, CH4 emissions from large lakes were not
neglected due to their large areas (Bastviken et al., 2010; Rasilo et al.,
2015; Townsend-Small et al., 2016). However, few studies reported temporal
CH4 emissions and their key regulating factors on different temporal
scales in large lakes. Therefore, investigating the impacts of physical and
biological factors on temporal CH4 effluxes based on multi-seasonal
measurements in a large lake is also important to estimate lake CH4
emissions.
Poyang Hu, a subtropical lake, is the largest freshwater lake in China, but
its multi-seasonal CH4 emissions have not been adequately measured. In
our previous study, we explored the spatial variations of CH4
efflux over the lake with 44 sampling locations (Liu et al., 2013). In
addition, we also found that microbial biomass and community structure influenced CH4 efflux from Poyang Hu greatly (Liu and Xu, 2016). In this study,
we measured the CH4 efflux at three sites which we chose on the basis of
our previous result over the course of 4 years in Poyang Hu to (1) examine
the multi-seasonal mean CH4 efflux; (2) explore the CH4 efflux
dynamics, including diel and seasonal variations; and (3) quantify the
relationships between the CH4 efflux and environmental factors and
identify the possible factors driving CH4 effluxes on different temporal
scales.
Materials and methods
Site description
Poyang Hu (28∘22′–29∘45′ N,
115∘47′–116∘45′ E) is located in southern China in
Jiangxi Province, with a surface area of 3283 km2 and a total catchment
area of 162 000 km2, which is separated from the northern and southern
parts by the Songmen Mountain. Poyang Hu receives water input from five main
tributaries, namely, the Raohe, Xinjiang, Fuhe, Ganjiang, and Xiu Shui. The climate is humid subtropical with a mean annual
temperature of 17.5 ∘C and an annual precipitation of 1680 mm (Ye
et al., 2011). Vegetation in the lake is composed of macrophytes, including
Carex sp. (Carex cinerascens Kükenth. and
Carexargyi Levl. et Vant.) and Artemisia selengensis in the
hydrophyte zone and the main submerged aquatic macrophytes, including
Ceratophyllum demersum, Potamogeton malaianus,
Potamogeton crispus, and Hydrilla verticillata (Wang et
al., 2011).
This study was conducted near the Poyang Lake Laboratory of the Wetland
Ecosystem Research Station (operated by the Chinese Academy of Sciences),
which is located in the northern subbasin of Poyang Hu in Xingzi County,
Jiangxi Province (Fig. 1). The five tributaries flow into the lake in the
southeast of Xingzi County, which then joins with the Yangtze River. The
water level fluctuated dramatically from 7.78 to 18.57 m above sea level
(Wu Song station) between the wet (April to September) and dry seasons (October to
March) during the study period because of rainfall and Three Gorge
management. Poyang Hu is not stratified (Zhu and Zhang, 1997), with mean and
maximum depths of 8 and 23 m, respectively. The mean concentrations of total
nitrogen (TN), total phosphorous (TP), suspended solids (SSs), and
chlorophyll a (Chl a) in Poyang Hu were 3.45, 0.11, 39.98, and
9.04 mg L-1, respectively (Yao et al., 2015).
Location of sampling sites in Poyang Hu.
CH4 efflux measurements
The CH4 efflux was measured using floating chambers, including both
ebullition and diffusive fluxes (Bastviken et al., 2004, 2010). The floating
chamber was constructed using a PVC pipe 100 cm in length and 20 cm in
diameter with Styrofoam floats attached to the sides. The floating chambers
were inserted 80 cm into the water and 20 cm above the water surface to
minimize the perturbation of the surface water flow to the pressure inside
the chambers. We tested the chamber system with different insertion depths in
the laboratory and field and found that the current depth of about 80 cm
could effectively prevent the impacts of the surrounding Styrofoam floats
while maintaining the chamber balance in moderate winds. A similar design of
floating chambers was used in previous studies (Lorke et al., 2015; Zhao et
al., 2015). Earlier studies found that floating chambers should be situated at
the water surface in a flowing-water system to minimize the “drag” effect
of flowing water on chamber pressure (Bastviken et al., 2010; Vachon and
Prairie, 2013; McGinnis et al., 2015). However, the water in Poyang Hu did
not have an apparent directional flow except for some waves during the
measurement period. So the insertion depth was deeper than those of previous
studies to avoid the impact of waves in Poyang Hu on the chamber body in
the current study. A detailed description of the floating-chamber system can
be found in Liu et al. (2013). So we measured the total CH4 efflux
including both ebullition and diffusive effluxes and cannot differentiate
between ebullitive and diffusive fluxes by means of our chamber.
Examples of calculating the slope of total effluxes, including
diffusive and ebullitive effluxes. All the concentrations are presented in
original (volumetric parts per million units). White circles represent the
CH4 concentrations at different sampling times. Grey circles represent
the adjusted concentration. Black trend lines represent the data used for the
total efflux calculation. The different letters in the figure panels mean
different occurrence times for ebullition: no ebullition (a),
occurrence of ebullition at 20 min (b), 40 min (c), and
60 min (d).
We collected a gas sample (ambient concentration) immediately after the
chamber was closed and three other samples at a 20 min interval for 1 h.
The gas was extracted into a 12 mL evacuated glass vial by a 2 mL syringe
needle with an air pump, which enhanced the pressure in the vial to 3 bars.
Subsequently, the gas samples were transported immediately to a laboratory
for CH4 concentration analysis. The CH4 concentration was measured
using a gas chromatograph (GC) equipped
with a flame ionization detector (GC7890A, Agilent Technologies, Inc., Santa
Clara, CA, USA). We used nitrogen (N2) as the carrier gas, which ran at
a flow rate of 30 mL min-1. We calibrated the gas chromatograph for
every four samples with a calibration gas of 2.03 ppm at 99.92 %
precision (China National Research Center for Certified Reference Materials,
China). The oven and detector temperatures of the GC were set to 55 and
250 ∘C, respectively.
Calculation of the CH4 efflux was based on the CH4 concentration of
the four samples using a linear regression model, which was calculated on the
basis of the slope of the concentration change during the whole period when
the chamber was closed. Data quality control was conducted following the
method of Rasilo et al. (2015) before the regression models were fitted. As a
result, most of the models performed satisfactorily, with a coefficient of
determination (R2) greater than 0.95. In case of ebullition, the
CH4 concentration inside the chamber would deviate from the normal
trend. Most of the CH4 concentrations measured immediately after the
ebullition point slightly decreased mainly because of the CH4 diffusion
back to water when the CH4 concentration inside the chamber space
increased suddenly through bubbling. To include the ebullition-induced CH4
emissions, we only used two measured concentrations: the first measurement
(ambient concentration) and an ebullition-adjusted concentration that was
obtained by adding the diffusion-induced concentration increment when
ebullition occurred (Fig. 2). Specifically, when ebullition occurred during
the first 20 min, we obtained the ebullition-adjusted concentration by
summing up concentration on 20 min and the 2-fold incremental concentration
derived from concentration between the third and fourth sampling times.
When the ebullition occurred at the third sampling, we summed up the
concentration at 40 min and the incremental concentration between the first
and second sampling times. When the ebullition occurred at the fourth
sampling, we used the first and fourth sampling concentrations directly to
calculate the slope of the total efflux.
Samplings took place at a monthly interval from January 2011 to December 2014
at three sites in Poyang Hu (Fig. 1): site A (Luoxingdun:
29∘3′29′′ N, 116∘16′49′′ E), site B (Mantianxing:
29∘34′25′′ N, 116∘13′29′′ E), and site C
(Huoyanshan: 29∘39′0′′ N, 116∘16′11′′ E). The mean
water depth at our sampling sites was 3 m. The sampling sites lacked aquatic
plants. Our previous study examined the spatial pattern of the CH4
efflux from the lake (Liu et al., 2013). Therefore, we focused on the
multi-seasonal dynamics of CH4 efflux from the current study. At each
site, four chambers were placed approximately 10 m away from a small boat to
minimize disturbance. Measurements were conducted from the early morning to
the late afternoon with about six cycles of measurements for each chamber,
except for days when the diel-cycle measurements were taken. We conducted
four 24 h measurements at the three sites to examine the diel variations of
CH4 effluxes: 24–25 July 2011, 5–6 September 2012,
13–14 January 2013, and 14–15 January 2015. These measurements were
conducted every 2 h from 08:00 to 08:00 CST (GMT + 8) the next day, providing 12 cycles of measurements for each
chamber per 24 h.
Environmental variables
Various environmental variables were also measured in the lake sediment,
surface water, and atmosphere at each individual site and then averaged them when
we used them. We collected surface water and sediment samples (0–15 cm) using a
plexiglass water grab and a stainless-steel sediment sampler (3 cm in
diameter) after obtaining gas samples. The water and sediment samples were
immediately stored in plastic bottles and bags, respectively. Then, all the
samples were stored in ice coolers and transported to a laboratory for
analysis within 1 week. In addition, we measured the wind speed at about
1.5 m above the water surface using a portable anemometer (Testo 410-1,
Testo, Germany) and the surface sediment (0–15 cm) temperature using a
mercury thermometer. We used a multi-parametric probe (556 MPS, YSI, USA) to
measure the water quality factors in situ, such as electrical conductivity
and dissolved oxygen (DO) content, at each sampling site from June 2013 to
June 2014. The water levels in the lake were obtained from the Xingzi
Hydrological Station, about 20 km from our sampling sites.
In the laboratory, the pH values of the water and sediment samples were
measured using a pH meter (Delta 320, Mettler–Toledo, Switzerland). Chemical
oxygen demand (COD) was measured using the spectrophotometric detection
method based on the Griess reaction (Jirka and Carter, 1975; Yao et al., 2015).
Chl a concentration was measured via spectrophotometry (Rasilo et al.,
2015; Yao et al., 2015), which was extracted in 90 % ethanol and then
analyzed spectrophotometrically at 750 and 665 nm in accordance with
ISO 10260 (1992). The SS level in the lake water was measured by a
gravimetric procedure, where the solids from the water sample were filtered,
dried, and weighed to determine the total nonfilterable residue of the
sample (Fishman and Friedman, 1989). TP concentration was measured using the
molybdenum blue method after persulfate digestion (Karl and Tien, 1992; Yao
et al., 2015). In addition, the nitrate (NO3-), ammonium
(NH4+), TN, and dissolved organic carbon (DOC) contents in the water
were measured using a total carbon and nitrogen analyzer using filtered water
(Shimadzu TOC-VCSH + TN module, Shimadzu, Japan). The sediment TN and
organic carbon contents after total sediment acidification with HCl 1N were
determined using a vario MAX CN element analyzer (NA Series 2, CE
Instruments, Germany). The values of measured environmental variables in our
study were given in Table S1 in the Supplement.
Considering the different sampling periods, we divided the environmental
variables into three groups (Table S1). The first group included sediment
temperature, sediment total nitrogen content, water level, DOC content in the
water, pH in the sediment, NH4+ and NO3- concentrations in
the water and sediment, sediment organic carbon content, the ratio of carbon
and nitrogen, and the mean daily wind speed over a 48-month period. The
second group included TN, TP, COD, and Chl a contents in the water, which
were sampled between June 2011 and December 2014. We sampled the third group
variables from June 2013 to June 2014, including DO content, conductivity,
and pH in the water.
Data analysis
We averaged the CH4 effluxes of the three sites to minimize the effect
of the spatial variation of CH4 efflux on the temporal dynamics of the
efflux. One-way ANOVA followed by a post-hoc Tukey's test and paired t test
were used to analyze the seasonal differences in the CH4 effluxes. We
employed stepwise multiple regressions to identify the environmental factors
driving the CH4 effluxes on different temporal scales. We also used
regression and correlation analyses to determine the relationships between
independent variables and CH4 effluxes. In addition, we considered each
study site as a random effect in linear mixed effects models in order to take
into account CH4 efflux variations among three sites when we
investigated seasonal and diurnal variations as well as the relationships
between CH4 efflux and environmental variables. We used the van't Hoff
equation to calculate the temperature sensitivity (Q10=e10b, where
b is the exponent of the exponential function between CH4 efflux and
sediment temperature) of CH4 efflux (Xu and Qi, 2001; Wei et al., 2015).
All statistical analyses were performed using the SPSS 17.0 statistical
software (SPSS Inc., Chicago, IL, USA), and graphs were created using the
Sigma Plot 11.0 program (Systat Software Inc., San Jose, CA, USA).
Discussion
CH4 effluxes in Poyang Hu
The mean CH4 emission in Poyang Hu was moderately higher than that in
other large lakes of more than 1 km2 in the world. The mean CH4
emission (0.54 mmol m-2 day-1) was within the reported range of
approximately 0.022–5.85 mmol m-2 day-1 in boreal and
temperate lakes over 1 km2 but was lower than diffusive effluxes in
subtropical lakes and total effluxes (including diffusion and ebullition) in
tropical lakes (Table 4). In addition, the mean CH4 emission in Poyang
Hu was comparable to the diffusive effluxes in tropical lakes (Table 4).
However, the mean CH4 efflux from Poyang Hu was only higher than those
in other lakes over 100 km2 (except the Võrtsjärv Lake). The
lower CH4 emissions in our study may be attributed to the low
concentration of carbon substrates in the water and sediments in Poyang Hu.
The DOC concentration in Poyang Hu was merely 3.3 mg L-1, which was
much lower than that of the 5.8 mg L-1 in Biandantang Lake and
7.4 mg L-1 in Donghu Lake, which are two subtropical lakes in China
(Xing et al., 2005, 2006). Poyang Hu also has a lower organic carbon
content in its sediments than most other lakes. The average organic carbon
content in the sediments in Poyang Hu was 0.89 %, which was much lower
than that of 30.76 % averaged over five temperate lakes (Schrier-Uijl et
al., 2011) and slightly higher than that of nearly 0.75 % in tropical
lakes in the Pantanal region (Bastviken et al., 2010).
CH4 effluxes in summer
The CH4 effluxes in Poyang Hu were substantially greater in summer
than in the other seasons, accounting for more than 63 % of the annual
total emissions. This finding suggests that summer is the critical season in
managing the CH4 emissions from Poyang Hu. The high effluxes in summer
may be attributed to the higher temperature, higher substrate availability,
and greater temperature sensitivity during this season than the other
seasons.
The high summer CH4 effluxes may due to high temperature in summer. The
CH4 effluxes were highly correlated with the sediment temperature
through an exponential function in our study. During the study period, the
mean (June–August) air temperature in summer was 28.5 ∘C, whereas
that in winter was only 5.9 ∘C. Our results confirmed the findings
of previous studies that lake CH4 effluxes are driven by temperature
(Bastviken et al., 2008; Marinho et al., 2009; Palma-Silva et al., 2013;
Rõõm et al., 2014). This is supported by the fact that a warm
temperature provides a high optimal temperature for methanogen growth
(Nozhevnikova et al., 2007; Rooney-Varga et al., 2007; Duc et al., 2010) and
the proportion of hydrogenotrophic methanogenesis (Borrel et al., 2011;
Marotta et al., 2014). The high summer CH4 effluxes might also be
because of the ample substrate supply in this season because the
decomposition rate of new organic matter was much faster than that of old
organic matter (Davidson and Janssens, 2006; Gudasz et al., 2010). In the
present study, CH4 efflux positively correlated with the Chl a content
(Table 2) that was not correlated with other environmental factors (Table S4)
and acted as an indicator of primary production. Earlier studies discovered a
high amount of labile organic matter, including allochthonous inputs of
terrestrial organic matter, during the summer flooding and autochthonous
production within-lake by phytoplankton and benthic algae in summer (Crump et
al., 2003; Xing et al., 2005, 2006; Bade et al., 2007). Previous studies
showed that fresh organic carbon from dead algae stimulates CH4
emissions in lakes (Huttunen et al., 2002; Xing et al., 2005) because the
degradation of dead alga and algal exudates are the precursors for CH4
production (Ferrón et al., 2012; Xiao et al., 2015; Liang et al., 2016).
However, we did not find any correlation between the CH4 efflux and DOC
content in the water (p>0.05). The algal bloom in summer probably masked
the DOC effect on stimulating CH4 production. Earlier studies
demonstrated that 70–80 % of DOC molecules in lakes are recalcitrant
carbon (Tranvik and Kokalj, 1998; Wetzel, 2001).
The high summer CH4 effluxes were also driven by the greater temperature
sensitivity during summer. The apparent Q10 value in Poyang Hu was
2.04 in summer, which was much greater than the value of 1.67 in the other
seasons (Fig. 6). This finding is inconsistent with previous studies in
terrestrial and freshwater ecosystems (Davidson and Janssens, 2006; Gudasz et
al., 2010; Yvon-Durocher et al., 2014), where the Q10 values decreased
apparently with the increase in temperature (Xu and Qi, 2001; Chen et al.,
2010; Corkrey et al., 2012; Schipper et al., 2014). However, our result was
supported by a recent finding that the temperature sensitivities (Q10)
of CH4 effluxes from lake sediments are greater in the tropics than in
boreal regions (Marotta et al., 2014). We speculate that the temperature
effect on Q10 was confounded by other factors, such as water level and
substrate availability. The addition of a large amount of fresh carbon from
summer floods could dramatically boost CH4 production and thus the
apparent Q10 values during summer.
Timescale dependence of wind, substrate availability, and
temperature effects on CH4 effluxes
In this study, the effects of wind, substrate availability, and sediment
temperature on CH4 effluxes were highly timescale dependent. The
CH4 effluxes measured at bihourly intervals positively correlated with
wind speed in both simple and multiple regressions (Fig. 7a–d, Table 2) but
showed no correlation (p>0.05) when the diurnal or seasonal average
CH4 efflux and wind speed were applied (Fig. 7e–f). The effect of wind
on CH4 effluxes was mainly through its effects on the transport, air
pressure, and storage of CH4 from the bottom to the surface water (Abril
et al., 2005; Hahm et al., 2006; Guérin et al., 2007). Gas diffusion in
water is sensitive to pressure changes at the water–air interface (Paganelli
et al., 1975; Massmann and Farrier, 1992; Striegl et al., 2001; Nachshon et
al., 2012). High wind speed mechanically induces turbulences through friction
in the water and brings CH4-rich water from the bottom to the surface in
lakes (Wanninkhof, 1992; Palma-Silva et al., 2013; Xiao et al., 2013). The
CH4 efflux rapidly decreases or even becomes negative (indicating
CH4 absorption) to compensate for the deficits in the water profile
caused by earlier winds when the wind declines or comes to a halt. Our
results also confirmed that the CH4 efflux sharply declined to a
negative value after strong wind events (Fig. 4). This wind effect only
worked on short timescales, such as a bihourly one, when temperature only slightly
changed and other biological processes, such as microbial community
variation, were relatively stable. On a longer temporal scale, such as a seasonal scale as observed in the current study, the wind effect disappeared
because the wind-stimulated CH4 effluxes and the post-wind (or
between-gust) negative effluxes (absorptions) were compensated for. Our results
suggest that wind exerts minor effects on CH4 effluxes on large temporal
scales when temperature, water level, and substrate availability dominate.
Our results also suggest that caution must be taken when one applies the
empirical wind-speed-driven models developed based on short-term measurements
to estimate CH4 effluxes over long periods, such as months or years.
Meanwhile, the CH4 effluxes measured at monthly intervals positively
correlated with sediment temperature (Fig. 6), but the correlation
disappeared when applied at bihourly intervals (p>0.05). The lack of
correlation between the CH4 efflux and sediment temperature as measured
on a bihourly scale within 1 day can be explained by the small variation of
sediment temperature within 1 day, ranging from 0.95 to 1.85 ∘C.
Other factors, such as wind and atmospheric pressure, might shadow the weak
temperature effect within 1 day. Instead, we found a high correlation between
the bihourly measured CH4 effluxes and sediment temperature during the
diel measurement period from 14 to 15 January 2015 (r=0.88, p<0.0001).
Further analyses showed that this temperature effect might be apparent and
mainly caused by wind speed because the bihourly measured CH4 effluxes
and wind speed were highly correlated only from 14 to 15 January 2015 and not
on the other days (r=0.90, p<0.0001). However, sediment temperature
became the dominant factor on a seasonal scale when the temperature ranged
from about 4.4 ∘C in winter to 30.8 ∘C in summer (Fig. 3).
The sediment temperature and CH4 effluxes averaged over the diurnal
period significantly correlated in the 4-year study period (Fig. 6). Our
results suggest that the short-term CH4 efflux from Poyang Hu was
regulated by wind speed, but the multi-seasonal CH4 efflux was
ultimately controlled by sediment temperature and other biological (e.g.,
microbial activity) and biochemical (e.g., sediment carbon and nitrogen
content) processes. Therefore, understanding and modeling the dynamics of
CH4 effluxes on lake surfaces require the multi-seasonal measurements of
effluxes and related biotic and abiotic factors in lake water and sediments.
Finally, substrate availability, such as sediment TN content, TP, and Chl a
contents in the water, also influenced CH4 effluxes on a seasonal scale
in the current study (Tables 2, 3). However, the effects disappeared when
applied at bihourly intervals because the substrate did not change
significantly within 1 day.
In addition to the abovementioned factors, the DO concentration in the water
influenced the CH4 effluxes in the multivariate regression analysis. Specifically, the CH4 efflux closely correlated with the DO concentration in
the water (Table 2). This close correlation can be explained by the aerobic
CH4 oxidation in the water. Our result was supported by the previous
finding that a high DO concentration in the water results in low CH4
emission (Rõõm et al., 2014; McNicol and Silver, 2015; Yang et al.,
2015).