Environmental controls on methane fluxes in a cool temperate bog

Agricultural and Forest Meteorology 281 (2020) 107852

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Environmental controls on methane fluxes in a cool temperate bog a,⁎

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c

a

a

Masahito Ueyama , Tomotsugu Yazaki , Takashi Hirano , Yusuke Futakuchi , Mikita Okamura a b c

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Graduate School of Life and Environmental Sciences, Osaka Prefecture University, 599-8531, Japan School of Agriculture, Meiji University, Kawasaki, 214-8571, Japan Research Faculty of Agriculture, Hokkaido University, Sapporo, 060-0808, Japan

ARTICLE INFO

ABSTRACT

Keywords: CH4 flux Eddy covariance Cool temperate bog Barometric pressure Vascular plants

Peatlands are important ecosystems for understanding global methane (CH4) and greenhouse gas budgets. We evaluated the CH4 emissions from a cool temperate bog in northern Japan and their responses to environmental drivers based on four years of quasi-continuous measurements with the eddy covariance method. The bog acted as a 13 to 19 g C m−2 yr−1 CH4 source. High annual CH4 emissions outweighed the annual carbon dioxide (CO2) sink strength (-223 to -79 g C m−2 yr−1), except in one year, resulting in a net greenhouse gas source at the annual timescale, although greater uncertainties in the annual CO2 budget (14%) than the CH4 budget (5%) caused uncertainties in the greenhouse gas budget. The seasonal variations in the CH4 fluxes were well explained by soil temperatures in the deep layers and the leaf area index of vascular plants. Pressure-driven CH4 emissions were measured at synoptic timescales, indicating high contributions of ebullition emissions. The episodic CH4 emissions during two typhoon events accounted for 5.1% of the annual fluxes in 2018. Nighttime CH4 emissions were greater than daytime emissions during the early growing season, possibly due to the hydrodynamic transport associated with soil temperature instability at night. The annual CH4 emissions were high when the dissolved oxygen concentration at 10 cm was low and the annual CO2 sink was great, suggesting the importance of anaerobic conditions and substrate availability, which facilitate CH4 production and restrict CH4 oxidation. The climate conditions in the study years were within the normal range; thus, further long-term measurements covering a wide range of climatic conditions will be required.

1. Introduction Methane (CH4) is the second most powerful greenhouse gas after carbon dioxide (CO2), and the global warming potential of CH4 is 34 times greater on mass basis than that of CO2 over a 100-year horizon (Intergovernmental Panel on Climate Change, 2013). The atmospheric CH4 concentration increased from 722 ppb in the preindustrial period to 1857.32 ppb in 2018 (www.esrl.noaa.gov/gmd/ccgg/trends_ch4/), playing an important role in a positive radiative forcing (Etminan et al., 2016). Wetlands annually emit 127 - 227 Tg CH4 yr−1 of CH4 at the global scale, which contributes approximately half of the natural CH4 emissions or one-quarter of the total CH4 emissions to the atmosphere (Saunois et al., 2016). The emission dynamics from natural wetlands are thought to influence the interannual variabilities in atmospheric CH4 concentrations (Bousquet et al., 2006). At the same time, spatiotemporal variations in the CH4 budget are difficult to quantify due to the complex dynamics, including variations in wetland fractions and biogeochemical cycles in wetlands (Bohn et al., 2015; Melton et al.,

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2013; Peltola et al., 2019). Consequently, the current state-of-the-art estimates of the terrestrial CH4 budget exhibit large discrepancies (Saunois et al., 2016), causing major uncertainties in climate projections. CH4 flux measurements have been conducted using chamber methods (Turetsky et al., 2014) and recently by the eddy covariance method (Baldocchi, 2014; Knox et al., 2019). Previous field measurements clarified that wetland CH4 emissions are sensitive to soil and water temperatures (Rinne et al., 2018; Yvon-Durocher et al., 2014), water table positions (Brown et al., 2014; Olefeldt et al., 2013; Rinne et al., 2018), inundated area fractions (Sachs et al., 2008), and vascular plant cover and productivity (McEwing et al., 2015; Turetsky et al., 2014; Whiting and Chanton, 1993). These environmental conditions determine methanogenic archaeal and methanotrophic activities, which are controlled by temperature, anaerobic/ aerobic conditions, and substrate availability (Bridgham et al., 2013; Conrad 2009). In addition to CH4 production and consumption, CH4 transport processes have been reported to be controlled by atmospheric turbulence (Sachs et al., 2008), barometric pressure (Nadeau et al.,

Corresponding author. E-mail address: [email protected] (M. Ueyama).

https://doi.org/10.1016/j.agrformet.2019.107852 Received 31 July 2019; Received in revised form 17 November 2019; Accepted 21 November 2019 0168-1923/ © 2019 Elsevier B.V. All rights reserved.

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2013; Tokida et al., 2005, 2007a, b), vegetation phenology (Marushchak et al., 2016), and diffusion in soil and water (Koebsch et al., 2015; Poindexter et al., 2016). These environmental conditions intricately control CH4 dynamics across different temporal and spatial scales, resulting in high spatiotemporal heterogeneity in CH4 fluxes (Knox et al., 2019; Koebsch et al., 2015; Turetsky et al., 2014). The lack of continuous observations hampers the accurate estimates of terrestrial CH4 budgets (Bohn et al., 2015), although eddy covariance measurements are increasing globally (Knox et al., 2019). Despite the high spatial heterogeneity of CH4 fluxes, only 200 sites for measuring CH4 fluxes were available as of 2018 (Knox et al., 2019), which is very limited compared with the more than 900 active sites for CO2, water vapor, and energy flux measurements (Chu et al., 2017). In addition to the lack of measurement sites, only a few measurements lasting more than five years are available (e.g., Rinne et al., 2018), resulting in a poor understanding of the interannual variabilities. Limited measurement periods make it difficult to determine how CH4 fluxes change with changes in environmental drivers, disturbance, and extreme events. Although winter CH4 emissions have been reported to be important (Zona et al., 2016), measurements are often conducted during only snow-free periods (Brown et al., 2014; Iwata et al., 2015; Nadeau et al., 2013; Sachs et al., 2008). The peatlands in northern Japan, which are located at the southern border of the northern peatland distribution, have suffered from perturbations from human activities and climate change (Fujita et al., 2009). Hokkaido Island, northern Japan, previously had 200,642 ha of peatlands, but more than 70% of these peatlands have been lost due to reclamation (Fujita et al., 2009). The remaining 150 peatlands are now susceptible to disturbances, including drainage and the associated expansion of vascular plants from the surrounding uplands, sedimentation, and eutrophication (Fujita et al., 2009). Furthermore, northern Japan currently experiences significant climatic changes, including increasing air temperature at a rate of 1.59 °C per century and changes in the precipitation pattern (Japan Meteorological Agency, 2017). Climate models have projected further changes in climate in this region by the end of this century; the projected changes are 4.7 ± 0.7 °C increases in the annual air temperatures and 136.3 ± 173.6 mm increases in the annual precipitation compared with those at the end of the twentieth century (Japan Meteorological Agency, 2019). These environmental changes should influence the remaining wetlands and their greenhouse gas budgets (Hirano et al., 2016; Nagata et al., 2005; Takakai et al., 2005) and will possibly aggravate the functions of peatlands in the future (Fujita et al., 2009). In this study, we evaluated CH4 emissions from a cool temperate bog in northern Japan and their responses to environmental drivers over four years (2015–2018) using quasi-continuous measurements based on the eddy covariance method. In particular, we present the variations in CH4 emissions at diurnal, seasonal, and interannual timescales and discuss how the CH4 fluxes can be explained by biotic factors (productivity and leaf area index of vascular vegetation) and abiotic factors (temperature, water conditions, and barometric pressure).

with drainage via the open ditches surrounding the mire. The site within the flux footprint was characterized as the original bog, where graminoids (Moliniopsis japonica), sedges (e.g., Carex omiana, Carex middendorffii, Rhynchospora alba, Rhynchospora fauriei, and Eriophorum vaginatum), and sparse woody plants (Vaccinium oxycoccos, Myrica gale, and Ilex crenata) were distributed among Sphagnum mosses (Sphagnum papillosum) (Nagata et al., 2005; Takakai et al., 2005). Since the site was on a well-developed bog, the vegetation was relatively homogeneous within the flux footprint. The maximum plant height was 59 ± 19 cm for grasses (mean ± standard deviation among ten locations) and 43 ± 7 cm for woody plants (mean ± standard deviation among five locations). The maximum leaf area index (LAI) measured by a plant canopy analyzer was 2.2 ± 0.4 m2 m−2 (mean ± standard deviation from 2015 to 2018), ranging from 1.8 - 2.7 m2 m−2. The thickness of peat was approximately 4 to 5 m, which began to accumulate on the alluvial plain approximately 2000 years ago (Fujita, 2007; Fujita et al., 2009). The carbon contents in the top 5 cm and at a depth of ca. 30 cm were 33% and 32%, respectively, and the nitrogen contents were 1.71% and 1.46%, respectively. The pH values of the peat-pore water at depths of 5 cm and 30 cm were 4.94 and 5.05, respectively. The dry bulk density was 0.065 ± 0.013 Mg m−3 (mean ± standard deviation; Takakai et al., 2005). The rubbed fiber contents at 0–25 cm and 25–50 cm were 44% and 21%, respectively, whereas the pyrophosphate color index was 4 (Kondo et al., 1997). These conditions indicate that the peat was classified as hemic soil or mucky-peat (Lynn et al., 1974). Based on phylogenetic analysis of the peat, the predominant methanotrophs were Methylomonas, whereas the predominant methanogenic archaea were Methanoregula (Narihiro et al., 2011). The climate of the site was classified as Dfb according to the Köeppen classification. Based on the weather station data from the Japan Meteorological Agency (4.5 km north of this site), the annual mean air temperature was 7.2 ± 0.6 °C, and the annual precipitation was 1170 ± 181 mm yr−1 between 1981 and 2018 (mean ± standard deviation; Table 1). Approximately 19% of the precipitation fell as snow, where precipitation was assumed to be snow when the daily mean air temperature was negative. In terms of the air temperature during the snow-free period (April - October) and annual precipitation, the years from 2015 to 2018 experienced a normal climate; those climate variables were within 1 standard deviation (σ) of the long-term mean. 2.2. Measurements The turbulent fluxes of sensible heat, latent heat, CO2, and CH4 were measured using an eddy covariance system installed at 2.3 m above the ground using pipe scaffolding. The flux tower was located within the center of the original bog area. Wind velocities and air temperature were measured using a sonic anemometer (CSAT3, Campbell Scientific Inc., USA). CO2 and water vapor densities were measured using an open-path gas analyzer (EC150, Campbell Scientific Inc., USA) beginning in September 2013. CH4 density was measured using an open-path gas analyzer (LI-7700, Li-Cor, USA) beginning in April 2015. The turbulent fluctuations were recorded at 10 Hz using a data logger (CR6, Campbell Scientific Inc., USA). Ancillary environmental variables were also measured. The air temperature and relative humidity were measured at 1.95 m and 1.63 m using temperature and humidity sensors (HMP155, Vaisala, Finland) in ventilated shields, respectively. The barometric pressure was measured with a barometer (PTB210, Vaisala, Finland). The wind speed and direction were measured using a wind monitor (5103, R. M. Young, USA). The downward and upward shortwave radiations were measured using a radiometer (CNR1, Kipp & Zonen, Netherlands). The downward photosynthetic photon flux density (PPFD) was measured using a quantum sensor (CPR-P AR-01, Climatec, Japan). Other sensors (SE-SQ110, Apogee, USA) for measuring downward and upward PPFDs

2. Field measurements 2.1. Study site The study site was established at a cool temperate bog, Bibai Mire, in Hokkaido, northern Japan (registered as JP-Bby in the European Fluxes Database Cluster; 43°19′22.82′’N, 141°48′38.51′’E, 16 m above sea level). The Bibai Mire is one of the remaining mires of the Ishikari Mire, which originally had an area of 55,000 ha but decreased to 119 ha after agricultural development (Fujita, 2007). The Bibai Mire was a 22 ha conserved mire, but only 2 ha was preserved with the original bog vegetation intact. The other areas were invaded by dwarf bamboo (Sasa palmata), which mainly occurred because of the drying associated 2

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Table 1 Summary of the climate conditions of annual mean air temperature (TAVE), growing degree days (GDD), annual precipitation, and annual rainfall; and the soil and vegetation conditions during the snow-free periods for the dissolved O2 concentration at 10 cm, water table position (WTP), soil temperature at 30 cm (Ts30), annual maximum leaf area index (LAI), maximum snow depth, and length of snow free period. Precipitation and rainfall data were obtained from a nearby weather station at Bibai of the Japan Meteorological Agency. The base temperature for the GDD was 5 °C. The long-term meteorological conditions (mean and standard deviation) were based on data from the nearby weather station from 1981 to 2018, where the snow-free period was calculated for the period from 1982 to 2018. Year

TAVE °C

GDD °C days

Precipitation mm yr−1

Rainfall mm yr−1

O2 % WTP cm Ts30 °C LAI max m2 m−2 Max. Snow Depth*3 cm Snow Free period days

2015 2016 2017 2018 Mean Long-term

7.7 6.8 6.8 7.2 7.1 7.2 ± 0.6

2022 2047 2005 2047 2030 2106 ± 119

1103 1392 1226 1266 1247 1170 ± 181

1001 1162 1049 1047 1064 949 ± 178

7.5 3.0 1.6 4.8 4.2 –

−0.8 0.5 −1.1 −7.5 −2.2 –

14.8 16.2 14.7 14.6 15.1 –

2.0 1.8 2.7 2.4 2.2 –

± ± ± ± ±

1.1*1 0.9*1 1.2*1 1.1*1 0.4⁎2

89 137 67 129 106 130 ± 29*4

237 197 224 223 220 226 ± 12

*1 The plus/minus sign denotes the standard deviation of the LAI at ten locations. ⁎2 The plus/minus sign denotes the standard deviation of the LAI for each year. *3 Maximum snow depth was calculated as "November to April" from a preceding year. *4 Statistics for snow depth were calculated for the period from 2010 to 2018, because no data were available from 1982 to 2009.

were installed in March 2018. The rainfall was measured using a tipping bucket (52,202, R. M. Young, USA) without heating. Snow depth was measured using a rugged acoustic distance sensor (SR50A, Campbell Scientific Inc., USA). The volumetric soil water contents at a depth of 5 cm were measured at two locations using time-domain reflectometry sensors (CS616, Campbell Scientific Inc., USA). The soil temperatures at depths of 0.5, 5.5, 10.5, 20.5, and 30.5 cm were measured at two locations using a thermocouple with the reference junction compensation measured by a thermistor (T109, Campbell Scientific Inc., USA). The thermocouples were mounted on a polyvinyl chloride (PVC) pipe that was inserted vertically into the peat soil to measure the soil temperature at the target depth. The depths of the soil temperature measurements were deepened to 15, 20, 25, 35, and 45 cm in November 2015, partly because of the growth of Sphagnum mosses on the peat surface and because the PVC pipe was weighed down by a heavy snow load. Thus, one of the two profiles was relocated to the original depth. The ground heat fluxes were measured at a depth of 10 cm using two soil heat plates (HFP01SC, Hukseflux, Netherlands). Dissolved oxygen (O2) contents at depths of 10 cm and 30 cm were measured as the gas-liquid equilibrium O2 content using a galvanic battery (KE-25, GS-Yuasa, Japan) installed in a semipermeable membrane. Another galvanic battery was installed at a depth of 5 cm in May 2015. The environmental variables were sampled at 10-second intervals, and 10-minute means were recorded using a data logger (CR1000, Campbell Scientific Inc., USA) with a multiplexer (AM16/32, Campbell Scientific Inc., USA). A time-lapse camera (TLC2000, Brinno, Taiwan) was installed to take photographs showing a distant prospect of the flux footprint at one-hour intervals during the daytime. The LAI was measured using a plant canopy analyzer (LAI-2000, Li-Cor, USA or LAI2200, Li-Cor, USA) approximately once a month during the snow-free period at ten locations.

method in Helbig et al. (2016). High-frequency attenuations were corrected based on the method in Massman (2000). The net ecosystem exchange (NEE) of CO2 was calculated as the sum of the measured CO2 flux and the rate of change in CO2 storage, which was based on the CO2 concentrations measured by the eddy covariance sensor. Low-quality data, such as those collected under nonstationary or rainy conditions, were removed (Ueyama et al., 2012). The nighttime flux under calm conditions was eliminated using the friction velocity (u*) threshold based on Papale et al. (2006), where the u*-thresholds were determined to be 0.15 m s − 1. CH4 fluxes were eliminated when the residual signal strength indicator of the analyzer was less than 15. The flux data were also eliminated when the 80% flux footprint exceeded the boundary of the mire according to an analytical footprint model (Kormann and Meixner, 2000). After quality control, the data availability (percentage of the analyzed data per year) was 6.7 ± 3.1% for CO2 and 2.8 ± 1.8% for CH4 in the winter (mean ± standard deviation for the four years), which was defined as the period when the daily air temperature was less than 0 °C; the data availability was 28.0 ± 6.1% for CO2 and 23.5 ± 5.1% for CH4 in the nonwinter seasons and 21.4 ± 3.7% for CO2 and 17.2 ± 3.4% for CH4 annually. NEE was partitioned into gross primary productivity (GPP) and ecosystem respiration (RE) using the Flux Analysis Tool program version 2 (Ueyama et al., 2012; http://atmenv.envi.osakafu-u.ac.jp/staff/ ueyama/softwares/). Nighttime NEE was used for parameterizing the exponential function (Lloyd and Taylor, 1994) to air temperature with a 48-day moving window. NEE was also parameterized using a nonrectangular hyperbola function each day with a 15-day moving window. Robust parameters of the respiration and nonrectangular hyperbola models were estimated as an ensemble mean of 100 bootstrap samples for each day (Ueyama et al., 2014). GPP was calculated by subtracting NEE from RE. Data gaps for the CO2 flux, sensible heat flux and latent heat flux were filled using the Flux Analysis Tool program (Ueyama et al., 2012). First, small data gaps for periods less than two hours were filled using linear interpolation. Second, the look-up-table (LUT) method was applied using PPFD, air temperature, and vapor pressure deficit (VPD) data. If VPD data were not available, the LUT was created using PPFD and air temperature data. If neither VPD nor air temperature data were available, the LUT was created using only PPFD data. Third, the remaining gaps for the sensible and latent heat fluxes were filled using the mean diurnal variation (MDV). The LUT and MDV were created as an ensemble mean of 100 bootstrap samples for each day with a 15-day moving window. Finally, for the CO2 flux, when the LUT method was not available, a nonlinear regression method was applied. This condition occurred when the data gap was longer than 15 days, and we used the mean seasonality of the parameters for the respiration and nonrectangular hyperbola models from other years.

3. Data analyses The turbulent fluxes were calculated using the Flux Calculator program version 2 (Ueyama et al., 2012; http://atmenv.envi.osakafu-u. ac.jp/staff/ueyama/softwares/). The half-hourly covariance of the vertical wind velocity and the scalar quantities of air temperature, water vapor density, CO2 density, and CH4 density were calculated using the eddy covariance method. Before calculating the covariance, spike data were removed from the raw data. We applied the relevant corrections, including double rotation of the coordinates, conversion from sonic temperature to dry-bulb temperature, and density fluctuation corrections for the open-path sensors, and a spectroscopic effect for the open-path CH4 analyzer (McDermitt et al., 2011). The attenuation of the fluctuations in CO2 density due to the use of a slow temperature sensor in the EC150 during processing was corrected based on the 3

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Data gaps in the half-hourly CH4 fluxes were filled using random forest regression. The input features for random forest regression were the soil temperatures at the different depths, dissolved O2 concentrations at a depth of 10 cm, water table position, barometric pressure, NEE, GPP, and RE. We applied 5 × 2 cross-validation with a random search to tune the hyper-parameters using the training data (Ueyama et al., 2018), which were a random selection of 80% of the total data. The regressions were trained with the training data, and were tested with the test data, which were 20% of the total data and were not used for the training processes. We repeated these processes 20 times to obtain regressions that had different hyper-parameters determined from the 20 different initial parameters and then calculated the mean from the 20 different CH4 fluxes to fill the data gaps. We used a scikit-learn library (version 0.19.1) for the random forest regression in Python 3.6.6. The CH4 fluxes predicted by the random forests were highly correlated with the observed fluxes (R2 = 0.91 ± 0.02 for the test data and R2 = 0.97 ± 0.01 for the training data; ± denotes one standard deviation from the 20 different regressions). Canopy greenness was quantified using red (R), green (G), and blue (B) digital numbers from the time-lapse camera. We calculated the green ratio (GR = G / (R + G + B); Harazono et al., 2009) for a region of interest targeting the bog vegetation. The daily median was calculated from the hourly data during the daytime. The sensitivity of fluxes (RE and CH4 flux) to soil temperatures was quantified based on a Q10 equation. The value of Q10 was defined as

Q10

F = R0

5 cm showed that this soil layer was more aerobic than the deeper soil layers, where the dissolved O2 concentration was high even when the soil water table position was high. The dissolved O2 concentration at 10 cm showed that the dropping water table position changed the conditions in this soil layer from anoxic to aerobic. The periodic drop in the water table position prevented the anoxic conditions in this soil layer in 2015 due to decreased rainfall (Table 1). The O2 concentration at 10 cm generally lagged approximately several days after changes in the water table position (Fig. 1) because of the time required for O2 diffusion in drying conditions and O2 consumption in wetting conditions. The dissolved O2 concentration at 30 cm showed that the deep soil was generally under anoxic conditions, except early in 2015. This result was probably because low rainfall occurred in 2014 (1063 mm yr−1 in 2014). In the snow period, the water table was above the soil surface because the peat was compressed by the heavy snow load (Yazaki and Yabe, 2012). The seasonal variations in the GR and LAI indicated the phenology of the ecosystem (Fig. 1d). The GR started to increase from May 20 to 23 approximately one month after snow disappearance, which was generally observed from April 5 to 18 (Fig. 1a). After the start of the increase, the GR immediately reached the annual peak in early June to late July and then gradually declined. The annual peak of LAI ranged from 1.8 to 2.7 m2 m−2 (Table 1), which was observed when the GR started to decline. 4.2. Diurnal and seasonal variations in fluxes

1 Ts

CO2 fluxes showed clear seasonal variations (Fig. 2). A daily CO2 sink was observed immediately after snow melt (Fig. 2d). The peak of the daily CO2 sink was observed in mid-June in 2015, 2016, and 2018, but this peak occurred in mid-July in 2017. Daytime uptake during the peak period was greater in 2016 and 2017 than in the other years (Fig. 2a). Seasonal variations in the CH4 flux were delayed compared with those in the CO2 fluxes (Fig. 2c, d). The seasonality in the CH4 flux was approximately one month later than that for GPP (R2 = 0.84 with a delay of 34 days based on the cross-correlation analysis) and RE (R2 = 0.81 with a delay of 27 days), but the seasonality was delayed by more than one and a half months compared with that in the NEE (R2 = 0.66 with a delay of 50 days). The peak CH4 emissions were observed from August to September. Immediately after the snow melt, high CH4 emissions were observed (Fig. 3). The magnitudes of the CH4 emission were 1.2~2.5 times greater than those immediately before snow melt, when the seven-day mean CH4 fluxes for immediately before and after snow disappearance were compared. In 2016, high CH4 emissions were observed during the snow-melting season, and emissions then declined after the snow completely disappeared. In other years, we could not measure such a decline after the high CH4 emissions, because snow melt induced dusty conditions; thus, the lenses of the analyzer were not clean. CH4 fluxes showed distinct diurnal variations during all periods except the snow period from January to March (Fig. 4). From July to December, the CH4 fluxes peaked during the daytime, which was comparable to the variations in soil temperature at 2 cm and GPP. In contrast, great CH4 fluxes were observed at night from April to June, which were inversely correlated with the diurnal variations in the soil temperature at 2 cm and GPP. Interestingly, the soil temperature gradient between 2 cm and 20 cm indicates stable stratified conditions during the daytime but unstable convective conditions at night from April to September, which could accelerate diffusion at night.

(1)

where F is the CH4 flux or RE, R0 is the flux at 0 °C, and Ts is the soil temperature. Since Q10 changes with depth at which the soil temperature was measured, we refer to the value as an apparent Q10 in this study. To estimate uncertainties in the annual fluxes, we compared the annual fluxes to those processed by the FLUXNET-CH4 project (Knox et al., 2019). The FLUXNET-CH4 project used the same input data for half-hourly fluxes, but the steps of filtering, gap-filling and partitioning were standardized for the global dataset and thus differed from our approaches (described above). To ensure consistent processing, FLUXNET-CH4 did not use the water table position and soil temperature as input variables to fill the gaps in CH4 flux data because those data were not available for all sites. We compared the annual fluxes in our approaches to those by the FLUXNET-CH4 dataset, whose CH4 and CO2 fluxes were filled using artificial neural networks, and partitioned CO2 fluxes were based on a nighttime approach. Further details on the processes used by the FLUXNET-CH4 project are available in Knox et al. (2019). 4. Results 4.1. Environmental conditions The soil temperatures showed clear seasonal variations (Fig. 1a). The daily mean soil temperatures peaked in August, ranging from 27 °C at 2 cm to 22 °C at 50 cm. In general, the soil was not frozen even during the winter due to thermal insulation by the deep snow, although the air temperature often dropped below −10 °C. Snow accumulated less in the winter from 2017 to 2018 than in the other years. Consequently, the soil temperatures at 2 cm and 5 cm dropped to −0.5 °C and −0.1 °C, respectively, from January to March 2018. The number of growing degree days (GDD; 5 °C of the base temperature) was highest in 2018, whereas the soil temperature at 30 cm was highest in 2016 (Table 1), which probably occurred because a high water table position accelerated heat conduction in 2016. The water table position and dissolved O2 concentrations showed consistent variations (Fig. 1b, c). The dissolved O2 concentration at

4.3. Environmental drivers LAI explained approximately 60% of the variation in the measured CH4 flux (p < 0.01; Fig. 5). Since LAI and CH4 fluxes exhibited similar pattern on the seasonal timescale (Figs. 1 and 2), the examined 4

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Fig. 1. Daily mean soil temperatures and snow depth (a), water table position (b), dissolved O2 concentration (c), and green ratio (GR) measured by a time lapse camera and leaf area index (LAI) measured by a plant canopy analyzer (d). Soil temperatures sensors at depths of 2, 5, 10, and 20 cm were reinstalled in November 2015, because the sensors subsided due to the growth of Sphagnum mosses and a heavy snow load. The vertical bars for LAI represent one standard deviation of LAI measured among ten locations.

relationship indicates a strong coupling between CH4 flux and LAI at the seasonal timescale. To evaluate the statistical power of this relationship during the mid-growing season, a relationship was examined when the LAI was greater than 1.0 m2 m−2. The correlations become weak (R2 = 0.17; p = 0.01), but still showed that CH4 fluxes were correlated with LAI within the peak season. During the peak seasons of the CH4 fluxes (Fig. 2c), high CH4 fluxes were observed when the barometric pressure dropped (Fig. 6). The pressure-mediated high CH4 fluxes were notable during typhoon events in 2018 (Fig. 6b), when the barometric pressure sharply decreased due to the approaching typhoon Jebi on September 5 and typhoon Trami on October 1. During the typhoon events, high CH4 fluxes greater than 400 nmol m−2 s−1 were observed. The episodic fluxes were approximately three times higher than the seven-day mean immediately before and after the typhoon events. CH4 fluxes increased as the soil temperatures increased (Fig. 7a). The relationships between CH4 fluxes and soil temperature exhibited hysteresis as the CH4 fluxes were higher later in the year (August to December) than earlier in the year (January to July) at the same soil temperatures. The temperature sensitivity, Q10, to soil temperature at 30 cm was higher earlier in the season (2.9; R2 = 0.58) than later in the season (2.4; R2 = 0.81). Consequently, the increased CH4 fluxes later in the season were attributed to the high base rate (R0 = 21.6 nmol m−2 s−1; R0 is defined as the base rate of flux at 0 °C) compared the rate earlier in the season (R0 = 5.7 nmol m−2 s−1). The hysteresis was in contrast to that for RE, as the RE was estimated to be higher earlier in

the season than in the later season (Fig. 7b). The apparent Q10 of RE was higher than that for CH4 fluxes for both earlier in the season (Q10= 4.5, R0 = 0.35 μmol m−2 s−1, R2 = 0.82) and later in the season (Q10= 4.0, R0 = 0.28 μmol m−2 s−1, R2 = 0.84). In general, the apparent Q10 for CH4 fluxes was greater for the deeper soil temperatures than the shallower soil temperatures, and the correlations between soil temperatures and CH4 fluxes were strengthened as the deeper soil temperatures were examined to a depth of 30 cm (Fig. 8). When soil temperatures were sufficiently high (more than 18 °C), CH4 fluxes linearly increased as the dissolved O2 concentration at 10 cm decreased (Fig. 9). The slope and intercept of the linear regression were −2.6 ± 0.26 nmol m−2 s−1 per % and 93.4 ± 2.1 nmol m−2 s−1, respectively, when the water table position was greater than −4 cm ( ± sign denotes the 95% confidence interval). When the water table position was lower than −8 cm, the slope and intercept of the linear regression were −3.0 ± 0.17 nmol m−2 s−1 per % and 121.1 ± 1.5 nmol m−2 s−1, respectively. This result indicates that CH4 fluxes were significantly higher at the low water table positions than at the high water table positions at similar dissolved O2 concentrations. 4.4. Annual budget and interannual variation The bog acted as a net annual source of CH4 and had global warming potential (GWP) for all years except 2016 (Table 2), where the GWP for the CO2 and CH4 budgets was calculated assuming that CH4 had a GWP that was 34 times greater than that of CO2 over a 100-year 5

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Fig. 2. Half-hourly net ecosystem exchange (NEE) (a), gross primary productivity (GPP) and ecosystem respiration (RE) (b), CH4 flux (c), and their daily mean (d). The black dots in (a) and (c) represent measured values, whereas the gray dots represent gap-filled values. The dots in (d) represent the daily mean, and the lines represent the seven-day moving mean.

horizon (IPCC, 2013). The annual CH4 emissions ranged from 13.0 to 19.0 g C m−2 yr−1. The bog acted as a net annual CO2 sink, ranging from −223 to −79 g C m−2 yr−1. Since the source of CH4 was generally greater than the CO2 sink, the bog represented −3 g CO2-eq m−2 yr−1 of the small GWP sink to 395 g CO2-eq m−2 yr−1 of the GWP source. Since the growing-season water table position was highest in 2016 (Table 1), the strongest annual CO2 sink was observed, resulting

in the annual GWP sink. The annual carbon emissions due to the CH4 flux were 12.8 ± 4.1% of the stored carbon as NEE or 1.9 ± 0.3% of the stored carbon as GPP (Table 2), which were equivalent to 2.3 ± 0.4% of the annual RE. The interannual variations in the CH4 fluxes tended to be associated with the carbon input, dissolved O2 concentration and water conditions, whereas the variations in CO2 fluxes were associated with water,

Fig. 3. Daily moving mean of half-hourly non-gap-filled CH4 flux and snow depth data during a period when the snow disappeared in spring. 6

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Fig. 4. Mean diurnal variations in CH4 flux, gross primary productivity (GPP), soil temperatures at depths of 2 cm and 20 cm for periods from January to March, April to June, July to September, and October to December. The shadows represent the 95% interval of the variation.

low mean O2 concentrations at a depth of 10 cm during the snow-free period (R2 = 0.70). The correlations with the annual CO2 fluxes were higher than those with water table position (R2 = 0.23) and annual maximum LAI (R2 = 0.00). The correlation between the annual NEE and the environmental variables was associated with the response of GPP rather than RE because high annual mean temperatures decreased the annual GPP (R2 = 0.73) rather than the annual RE (R2 = 0.00). The interannual variations in the GWP were mostly associated with the CO2 flux rather than CH4 flux (Table 2). The annual GWP was more strongly correlated with NEE (R2 = 0.76) than CH4 fluxes (R2 = 0.20). This result occurred because the interannual variations in the annual CH4 emissions were smaller (cv = 16%; cv is the coefficient of variance) than the variations in the CO2 sink (cv = 44%). The fraction of CH4 emissions during the snow period (145 ± 17 days) was, on average, 17.2 ± 3.5%, ranging from 12% to 20% (Table 2). The fraction was not related to the length of the snow period (R2 = 0.01) or the soil temperatures under snow (R2 = 0.23). The uncertainties associated with post-processing were greater for the CO2 flux than CH4 flux (Table 2). The different methods for gapfilling and flux partitioning induced relative uncertainties of 14% in NEE, 9% in GPP, and 14% in RE at the annual timescale, whereas those for CH4 fluxes were 5%. Although both methods used a nighttime approach for partitioning the CO2 flux, our estimates of RE were greater than those in the FLUXNET-CH4 database, resulting in a weaker annual CO2 sink. The uncertainties in GWP emissions were mostly caused by the high uncertainties in CO2 flux rather than CH4 flux.

Fig. 5. Relationships of daily CH4 flux to leaf area index (LAI). The line represents linear regressions, and the shadows represent the prediction interval (p = 0.05). LAI was measured by a plant canopy analyzer (LAI-2000 or LAI2200, Li-Cor, USA), and the CH4 flux is the gap-filled daily mean for a day when LAI was measured.

5. Discussion

temperature, and O2 conditions, although the limited measurement years (n = 4) did not allow for the determination of statistical significance. The high annual CH4 emissions were associated with a large annual CO2 sink (R2 = 0.53), low O2 concentrations at 10 cm (R2 = 0.89), and high annual precipitation (R2 = 0.52). In 2015, a low precipitation induced low water table and high surface O2 concentration, resulting in low annual CH4 emissions. The large annual CO2 sink was explained by the high annual precipitation (R2 = 0.71), high rainfall (R2 = 0.80), low mean annual air temperatures (R2 = 0.78) or

5.1. Annual CH4 budget The annual CH4 emissions measured at the bog (Table 2) were within the range observed in other temperate wetlands. Knox et al. (2019) synthesized global eddy covariance data from 60 sites and summarized that the annual CH4 emissions exhibited a median of 27.4 g C m−2 yr−1 (10.0~47.3 g C m−2 yr−1) according to 47 siteyear data points from temperate wetlands. The median value was greater than the annual emissions in Bibai (Table 2), probably because 7

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Fig. 6. Daily moving mean of the half-hourly non-gap-filled CH4 flux and barometric pressure data during the period when the CH4 flux showed the seasonal peak (a) and during a period when two typhoons passed near the site (b). The annotations in (b) represent the name of the typhoon.

(6.7 nmol m−2 s−1; Rinne et al., 2018) because the deep snowpack restricted peat freezing even near the peat surface during the winter at the Bibai site (Fig. 1). High CH4 emissions outweighed the annual CO2 sink except 2016, resulting in a net GWP source at the annual timescale (Table 1). This finding was consistent with previous measurements in northern wetlands (Friborg et al., 2003; Helbig et al., 2017; Olson et al., 2013; Rinne et al., 2018). In contrast, the interannual variability in the GWP was mostly associated with CO2 fluxes rather than CH4 emissions because the annual CH4 emissions were more stable than the CO2 sink. This finding was also consistent with the results of a previous study (Rinne et al., 2018). According to this previous study, the coefficient of variance for NEE (73%) was greater than that for CH4 emissions (24%). Interestingly, both our study and the previous study showed a negative relationship between the annual NEE and CH4 emissions, suggesting that these two fluxes are linked at the annual timescale. The fractional carbon emitted as CH4 to CO2 fixation/decomposition (Table 2) was comparable to that reported in previous studies. The measured fractions were similar to those for a boreal fen: approximately 10% of NEE and 2.7% of GPP (Rinne et al., 2018). Using pulse-labeling of a 14C tracer, King et al. (2002) estimated that approximately 2–3% of the carbon fixed by photosynthesis was immediately emitted as CH4 in

this bog was located near the northern edge of the temperate biome. Among multiple northern wetlands, the spatial variabilities in the annual CH4 emissions are known to be explained by the water table position during the growing season (Olefeldt et al., 2013; Turetsky et al., 2014). Compared to the synthesis studies, the annual emissions were lower than those expected at the observed water table position, probably because periodic fluctuations in the water table position prevented anaerobic conditions (Fig. 1). The annual emissions were slightly greater than those measured by the chamber method (13.2 g C m−2 yr−1; Nagata et al., 2005) at the same site in 2002 and 2003. The slightly higher emissions in our study could be explained by the decreased precipitation in the summer of 2002 (634 mm from April to October) compared with that during our study period (863 ± 89 mm), although a direct comparison was generally difficult due to the different spatial and temporal sampling footprints. The CH4 emissions during the snow period contributed considerably to the annual CH4 emissions (Table 2). The measured range of the contributions was similar to that found in previous studies. The coldseason CH4 emissions were previously reported to be ~20% of the annual budget in arctic and boreal wetlands (Rinne et al., 2018; Zona et al., 2016). The averaged CH4 emissions during the snow period (26.4 nmol m−2 s−1) were greater than those measured at a boreal fen

Fig. 7. Relationships of half-hourly non-gapfilled CH4 flux (a) and ecosystem respiration (RE) (b) to soil temperature at 30 cm. The relationship was separately examined for early in the year (January to July; green dots) and late in the year (August to December; orange dots). The relationships were examined when the barometric pressure was higher than 980 hPa.

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Fig. 8. Temperature sensitivity (apparent Q10; a) and determination coefficients (R2; b) for the exponential relationships between CH4 flux and soil temperatures at different depths under the snow-free conditions. The values are separately shown for early in the year (January to July; white dots) and late in the year (August to December; black dots). The relationships were examined when the barometric pressure was higher than 980 hPa for the period from 2016 to 2017.

Arctic tundra ecosystems. Cao et al. (1996) summarized that 3–25% of decomposed organic carbon was transformed to CH4. Assuming that autotrophic respiration was approximately half of GPP (Waring and Running, 1998), the estimated CH4 fraction ranged from 3.9–7.3% of the decomposition in this study. Whiting and Chanton (1993) reported that approximately 3% of the carbon fixed as NEE was emitted as CH4, which was a lower fraction than found in our study (Table 2). To estimate the net ecosystem carbon budget (NECB), measurements of additional components are required. Dissolved organic carbon (DOC) discharges from peatland are known to be an important component of the carbon budget (Yu, 2012). Yu (2012) reviewed the NECB in five northern peatlands, and found that the DOC discharges ranged from 3.2 to 25.4 g C m−2 yr−1 and were on average 15% of the NEE. Fraser et al. (2001) also reviewed the DOC discharged from 13 wetlands, which ranged from 1 to 48 g C m−2 yr−1. Hydrologic export of dissolved CO2 and CH4 might be an important in the carbon budget, although those components were generally small (Flessa et al., 2008; Pulliam, 1993). Based on the peat that accumulated above a layer that contained volcanic ash from the eruption of Mt. Tarumae in 1739 and exhibited the peat bulk density and carbon content (Kondo et al., 1997; Kawamoto et al., 2009), the long-term NECB was approximately 30~40 g C m−2 yr−1 in this bog. The measured carbon budget (130 g C m−2 yr−1; Table 2) was more than three times greater than this value; thus, DOC discharges could be an important component of the NECB in this bog. Productivity was possibly stimulated by CO2 fertilization, nitrogen deposition, and climate change (Thornton et al., 2007), which could also be a reason for the stronger carbon sink than in the longterm NECB.

5.2. Temperature response The soil temperatures in the deep layers were the most important driver among the examined environmental drivers at the seasonal timescale (Figs. 5-9). Deep soil temperatures were strongly correlated with CH4 fluxes (Fig. 8), suggesting that CH4 was more actively produced in the deep layer than in the shallow layer. In this bog, the top 20 cm from the surface was susceptible to water table fluctuations (Fig. 1b); thus, this layer occasionally experienced aerobic conditions (Fig. 1c). In contrast, the deep soil layer at 30 cm was generally anoxic (Fig. 1c), which could be suitable for producing CH4 and restricting CH4 consumption. This finding was consistent with the results of previous experiments in this bog, showing that bubble-formed CH4 (Tokida et al., 2007a) and dissolved CH4 (Tokida et al., 2005, 2007b) were high at depths below 30 cm. High correlations with soil temperatures in deep peat layers were also reported in northern peatlands (Brown et al., 2014; Long et al., 2010), and the co-varying effects on other environmental variables have been discussed, such as seasonal variations in phenology and productivity. The temperature sensitivity changed in the early and late growing seasons (Figs. 7, 8). In the early season, the estimated apparent Q10 values in the deep layers (Fig. 8a) was similar to that observed from a global synthesis (Yvon-Durocher et al., 2014). YvonDurocher et al. (2014) reported a similar temperature response in CH4 fluxes from microbial (Q10 = 3.2~4.2) to ecosystem (Q10 = 3.3~4.5) scales. The high Q10 in the early growing season and their consistency with the global study suggest that the temperature-limited production Fig. 9. Relationship between half-hourly CH4 flux and dissolved O2 concentration, where different colored plots represent different water table positions. The gray line represents the linear regression when the water table positions were higher than −4 cm (R2= 0.30; p < 0.01), whereas the black line represents the linear regression when the water table positions were lower than −8 cm (R2= 0.31; p < 0.01). The relationship was examined when low soil temperatures were not limiting CH4 fluxes; the soil temperature at 40 cm was higher than 18 °C.

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80.3 81.1 88.0 81.7 82.8

rate regulated CH4 fluxes more in the early growing season than in the later season. In contrast, in the late growing season, the CH4 fluxes slowly decreased despite a relatively fast decrease in soil temperatures, causing the low Q10 values in the late growing season. The low Q10 values with high fluxes (Fig. 7) suggest that transport of stored CH4 in the subsurface peat was more important than the temperature-limited production later in the season. The low correlations in the early season (Fig. 8b) indicate that other effects, such as oxidation and transport processes, co-limited CH4 fluxes.

(1.9) (2.6) (2.7) (–)

5.3. Role of vegetation Plant phenology and/or productivity were the second most important drivers that explained the seasonal variations in CH4 fluxes (Fig. 5). The correlation between CH4 fluxes and LAI suggests the importance of plant-mediated transport by vascular vegetation and substrate availability from root exudates (Marushchak et al., 2016; McEwing et al., 2015; Whiting and Chanton, 1993). Since the peat surface layer tended to be aerobic throughout the growing season (Fig. 1c), bypassing this layer was important for emitting CH4 into the atmosphere. Compared to the direct correlation between LAI and CH4 fluxes (Fig. 5), the seasonal variations in CH4 fluxes were delayed behind NEE and GPP, suggesting that time was required to break the substrates from NEE and GPP because the methanogenic archaeal community uses well-decomposed carbon (Bridgham et al., 2013). The increases in substrates associated with root mortality could also contribute to the high CH4 fluxes during the late growing season (Watanabe et al., 1999).

(1.6) (1.9) (2.2) (–) 1.6 1.7 2.0 2.2 1.9 16.3 (10.9) 8.1 (6.5) 10.8 (10.9) 16.0 (–) 12.8

1.7 2.0 2.4 2.5 2.3

FCH4_gs_fraction % FCH4/RE % (g C g−1C−1) FCH4/GPP % (g C g−1C−1) FCH4/NEE % (g C g−1C−1)

5.4. Pressure-driven CH4 flux

(710) (904) (862) (–)

742 715 794 695 736

(604) (644) (691) (–)

13.0 17.9 19.0 17.6 16.9

(11.6) (16.9) (18.8) (–)

297 (136) −3 (−186) 215 (222) 395 (–) 226

The measured pressure-driven CH4 fluxes (Fig. 6) suggested high contributions of the ebullition flux to the ecosystem-scale CH4 flux. In this bog, high CH4 fluxes following decreasing barometric pressures were previously observed with manual chamber measurements (Tokida et al., 2005, 2007a). Based on the chamber measurements, the ebullition fluxes were estimated to be up to 67% to 95% of the total CH4 fluxes, as the barometric pressure decreased (Tokida et al., 2005). The measured sensitivity to dropping pressures was similar to that found in previous chamber measurements. This result indicates that the ebullition flux was important to the CH4 exchange in this bog at not only the small scale, such as the chamber footprint, but also the ecosystem scale. Pressure-driven CH4 fluxes were often observed at the ecosystem scale (Nadeau et al., 2013; Sachs et al., 2008), but a clear relationship similar to that observed in this study was rarely reported. The clear sensitivity to the dropping barometric pressures could be caused by the extensive CH4 stored in the peat layer in this bog. Tokida et al. (2005; 2007a) reported that bubble-formed CH4 was stored in peat columns at depths below 30 cm. This finding suggests that transport was the most important process regulating CH4 emissions rather than production at less than weekly timescales. Ramirez et al. (2015) demonstrated that a small difference in peat porosity determined the characteristics of the ebullition fluxes (steady or spiked emissions); peat with lower porosity trapped bubbles more effectively and exhibited more spiked CH4 emissions. The compaction of peat by the heavy snow load (Yazaki and Yabe, 2012) could be the reason for the pressure-driven spiked CH4 emissions at this site. The extreme events due to typhoons induced episodic high CH4 emissions (Fig. 6b), but the contribution of the episodic emissions was small (5.1% of the annual CH4 emissions over four days). This result was probably because the bubble transport was not the regulating process of the CH4 flux at the annual timescale, although the ebullition fluxes were an important transport process. The produced CH4 could be transported by other processes through plant aerenchyma and/or diffusion once CH4 was produced and not oxidized in the peat if extreme events did not occur.

−79 (−107) −223 (−260) −176 (−172) −110 (–) −147 2015 2016 2017 2018 Mean

821 938 970 805 883

GWP g CO2-eq m−2 yr−1 FCH4 g C m−2 yr−1 RE g C m−2 yr−1 GPP g C m−2 yr−1 NEE g C m−2 yr−1 Year

Table 2 Summary of the annual net ecosystem exchange (NEE), gross primary productivity (GPP), ecosystem respiration (RE), CH4 flux (FCH4), global warming potential (GWP), ratio of the annual CH4 flux to NEE, GPP, and RE, and the fraction of the CH4 flux during a snow-free period to the annual CH4 flux (FCH4_gs_fraction). Values outside parentheses are obtained with our post-processing methodology, including site-specific drivers for gapfilling (shown in Section 3), whereas values in the parenthesis are from FLUXNET-CH4 database (Knox et al., 2019) using a standardized methodology with a limited set of drivers.

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5.5. Effect of water table and O2 concentration

This study clarified the variations and controlling factors of the CH4 fluxes at diurnal, seasonal, and interannual timescales under normal weather conditions, although careful considerations are required due to the short measurement period of four years. According to the long-term climate record measured near the site, the climate conditions during the measurement years were within ± 1σ of the variability in GDD, annual precipitation, and rainfall over the past 30 years. This result indicates that further long-term measurements during abnormal weather conditions will be required to understand the responses of CH4 fluxes to a wide range of climate conditions. This understanding is particularly important because the study area will potentially suffer from 4.6 ± 0.7 °C increases in the summer temperatures and 136.3 ± 173.6 mm increases in the annual precipitation by the end of this century (Japan Meteorological Agency, 2019). The predicted high temperatures could increase evapotranspiration (Hirano et al., 2016), decrease the water table position, and potentially change greenhouse gas fluxes, whereas an increase in precipitation might alleviate the temperature-induced drought conditions.

Anaerobic conditions at 10 cm were an important driver of the CH4 flux (Fig. 9). This result is true because CH4 is produced under anoxic conditions by methanogenic archaea and is oxidized under aerobic conditions by methanotrophs (Bridgham et al., 2013; Conrad 2009). Interestingly, CH4 fluxes were high when the water table position was low, if the dissolved O2 concentration was similar (Fig. 9). This result differed from the results of previous studies that showed that a high water table promoted CH4 emissions (Moore and Roulet, 1993; Rinne et al., 2018). The discrepancy was probably because the water table position was not an index that could explain the anaerobic conditions. This discrepancy was negligible at weekly or longer timescales, but the time lag between the changes in the water table position and O2 concentrations was significant at short timescales, such as several days (Fig. 1). Due to the short-term hysteresis, the dissolved O2 concentration was low when the water table position was declining. This change potentially contributed to the higher CH4 fluxes standardized to the dissolved O2 concentration because the emissions of stored CH4 were accelerated in response to a decline in the water table position (Moore and Roulet, 1993). Brown et al. (2014) reported that the highest CH4 emissions were observed when the water table position dropped below 40–55 cm rather than when it was near the surface. They discussed that the optimal depth was similar to the mean water table position during the growing season, when favorable conditions might be available for methanogenesis. This phenomenon could not be the reason for our observations because the mean water table position during the growing season was close to the surface.

6. Conclusions Based on four years of quasi-continuous measurements, we identified the environmental controls on the CH4 flux at a cool temperate bog at diurnal, seasonal, and interannual timescales. The examined controls were soil temperatures, LAI, dissolved O2 concentration, water table position, and barometric pressure, and the results suggest that oxidation and transport processes (i.e., plant aerenchyma, ebullition, and diffusion) played important roles in particular periods or specific timescales, in addition to CH4 production. The important processes should be further verified with independent experiments (e.g., incubation, isotope, and chamber experiments) and be quantified by integrating process-based models (Ma et al., 2017; Susiluoto et al., 2018). Future trajectories of CH4 fluxes should be constrained with additional long-term data that cover multiple decades and include abnormal weather condition because this study represented only a four-year period with almost normal weather conditions. Finally, long-term measurements at the cool temperate bog could be helpful for a better understanding of the CH4 fluxes at northern peatland because the study area is located at the southern boundary of the northern wetlands. The data from this cool temperate bog will help the gaps in the data on the full range of CH4 fluxes across the northern latitudes, and potentially improve the regional estimates of the CH4 budget (Peltola et al., 2019).

5.6. Nighttime emissions by soil temperature instability Nighttime CH4 emissions were greater than daytime emissions during the early growing season (Fig. 4). The diurnal variation could be explained by the enhanced diffusion by hydrodynamic transport due to soil temperature instability at night (Koebsch et al., 2015; Poindexter et al., 2016). During the early growing season, plant-mediated CH4 transport could be small due to the low LAI (Fig. 1d); thus, produced CH4 could not be as effectively transported during the daytime in the early growing season compared with that in the late growing season. Koebsch et al. (2015) also reported that hydrodynamic transport was the major driver of CH4 transfer at the diurnal timescale in early spring based on the eddy covariance data from an inundated fen with continuous wavelet analysis. The current and previous studies clearly indicate that continuous measurements covering the diurnal cycle are important for quantifying the CH4 emissions from wetlands.

Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

5.7. Interannual variation The prevalence of anaerobic conditions and strong CO2 sinks tended to induce high CH4 emissions at the annual timescale (Table 2). We could not isolate the two drivers because the annual NEE and O2 concentration also exhibited a strong negative correlation. The rainfall and water table position during the growing season exhibited no correlation with the annual CH4 emissions, suggesting that the water conditions alone could not explain the anaerobic conditions that favored CH4 production. Nonetheless, since anaerobic conditions were only measured at a high water table position (Fig. 1), the water conditions may be the most important driver of annual CH4 emissions. Based on a global synthesis (Knox et al., 2019), the water table position was found to be an important driver of the CH4 emissions from wetlands whose water table positions were near or below the peat surface. Our results could support these findings at the global scale. High air temperatures potentially decreased CH4 emissions, because the annual CO2 sink and GPP decreased with increasing air temperature, suggesting that hightemperature-induced desiccations reduced productivity, substrate availability for methanogenesis, and CH4 emissions.

Acknowledgments This study was partially supported by the JSPS KAKENHI (26701002). We thank Dr. Y. Kominami, Dr. S. Ohkubo and Dr. O. Nagata of the Hokkaido Agricultural Research Center, and Mr. Y. Sakamoto of the Taiyo Keiki for helping with the field observations. We thank anonymous reviewers and editor for constructive comments. The data are available from the author upon request and are also available in the European Fluxes Database Cluster. We thank the FLUXNET-CH4 project for providing the dataset for JP-Bby. The program codes for the Flux Calculator and Flux Analysis Tool are available at the author's website (http://atmenv.envi.osakafu-u.ac.jp/staff/ueyama/softwares/ ). References Baldocchi, D., 2014. Measuring fluxes of trace gases and energy between ecosystems and

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