Automated closed-chamber measurements of methane fluxes from intact leaves and trunk of Japanese cypress

Continuous in situ measurements of methane (CH4) fluxes from intact leaves and trunk of Japanese cypress (Chamaecyparis obtusa Sieb. et Zucc) were conducted in a temperate forest from August 2009 to August 2010. An automated closed-chamber system, which was used to evaluate CO2 exchange between the atmosphere and forest ecosystems, was coupled to a laser-based instrument to monitor CH4 concentrations. Temporal changes in CH4 concentrations from the foliage and trunk were measured at one-second intervals during chamber closure to determine CH4 fluxes between the leaf and trunk surfaces and the atmosphere. While recent studies have suggested that some plants emit CH4 under aerobic conditions, emission or uptake of CH4 in detectable amounts with our experimental system, by intact leaves or the trunk of C. obtusa, was not significantly observed throughout the measurement period

One year of continuous measurements of soil CH4 and CO2 fluxes in a Japanese cypress forest: Temporal and spatial variations associated with Asian monsoon rainfall

We examined the effects of Asian monsoon rainfall on CH[4] absorption of water-unsaturated forest soil. We conducted a 1 year continuous measurement of soil CH[4] and CO[2] fluxes with automated chamber systems in three plots with different soil characteristics and water content to investigate how temporal variations in CH[4] fluxes vary with the soil environment. CH[4] absorption was reduced by the “Baiu” summer rainfall event and peaked during the subsequent hot, dry period. Although CH[4] absorption and CO[2] emission typically increased as soil temperature increased, the temperature dependence of CH[4] varied more than that of CO[2], possibly due to the changing balance of activities between methanotrophs and methanogens occurring over a wide temperature range, which was strongly affected by soil water content. In short time intervals (30 min), the responses of CH[4] and CO[2] fluxes to rainfall were different for each plot. In a dry soil plot with a thick humus layer, both fluxes decreased abruptly at the peak of rainfall intensity. After rainfall, CO[2] emission increased quickly, while CH[4] absorption increased gradually. Release of accumulated CO[2] underground and restriction and recovery of CH[4] and CO[2] exchange between soil and air determined flux responses to rainfall. In a wet soil plot and a dry soil plot with a thinner humus layer, abrupt decreases in CH[4]fluxes were not observed. Consequently, the Asian monsoon rainfall strongly influenced temporal variations in CH[4] fluxes, and the differences in flux responses to environmental factors among plots caused large variability in annual budgets of CH[4] fluxes

Measurement of methane flux over an evergreen coniferous forest canopy using a relaxed eddy accumulation system with tuneable diode laser spectroscopy detection

Very few studies have conducted long-term observations of methane (CH4) flux over forest canopies. In this study, we continuously measured CH4 fluxes over an evergreen coniferous (Japanese cypress) forest canopy throughout 1 year, using a micrometeorological relaxed eddy accumulation (REA) system with tuneable diode laser spectroscopy (TDLS) detection. The Japanese cypress forest, which is a common forest type in warm-temperate Asian monsoon regions with a wet summer, switched seasonally between a sink and source of CH4 probably because of competition by methanogens and methanotrophs, which are both influenced by soil conditions (e.g., soil temperature and soil moisture). At hourly to daily timescales, the CH4 fluxes were sensitive to rainfall, probably because CH4 emission increased and/or absorption decreased during and after rainfall. The observed canopy-scale fluxes showed complex behaviours beyond those expected from previous plot-scale measurements and the CH4 fluxes changed from sink to source and vice versa

Methane exchange in a poorly-drained black spruce forest over permafrost observed using the eddy covariance technique

Ecosystem-scale methane (CH4) exchange was observed in a poorly-drained black spruce forest over permafrost in interior Alaska during the snow-free seasons of 2011–2013, using the eddy covariance technique. The magnitude of average CH4 exchange differed depending on wind direction, reflecting spatial variation in soil moisture condition around the observation tower, due to elevation change within the small catchment. In the drier upper position, the seasonal variation in CH4 emission was explained by the variation in soil water content only. In the wetter bottom, however, in addition to soil temperature and soil water content, seasonal thaw depth of frozen soil was also an important variable explaining the seasonal variation in CH4 exchange for this ecosystem. Total snow-free season (day of year 134–280) CH4 exchanges were 12.0 ± 1.0, 19.6 ± 3.0, and 36.6 ± 4.4 mmol m−2 season−1 for the drier upper position, moderately wet area, and wetter bottom of the catchment, respectively. Observed total season CH4 emission was nearly one order smaller than those reported in other northern wetlands, due probably to the relatively low ground water level and low soil temperature. The interannual variation of total snow-free season CH4 emission in the wetter bottom of the catchment was influenced by the amount of rainfall and thaw depth. On the other hand, in the drier upper position the amount of rainfall did not strongly affect the total season CH4 emission. Different responses of CH4 exchange to environmental conditions, depending on the position of a small catchment, should be considered when estimating the spatial variation in CH4 exchange accurately in ecosystems over permafrost.ArticleAGRICULTURAL AND FOREST METEOROLOGY. 214(0):157-168 (2015)journal articl

Insights into the mechanism of diurnal variations in methane emission from the stem surfaces of Alnus japonica

木の中にガスパイプライン？ --ガス漏れの場所を特定せよ！--. 京都大学プレスリリース. 2022-07-15.Recent studies have suggested that in certain environments, tree stems emit methane (CH₄). This study explored the mechanism of CH₄ emission from the stem surfaces of Alnus japonica in a riparian wetland. Stem CH₄ emission rates and sap flux were monitored year-round, and fine-root anatomy was investigated. CH₄ emission rates were estimated using a closed-chamber method. Sap flux was measured using Granier-type thermal dissipation probes. Root anatomy was studied using both optical and cryo-scanning electron microscopy. CH₄ emissions during the leafy season exhibited a diurnally changing component superimposed upon an underlying continuum in which the diurnal variation was in phase with sap flux. We propose a model in which stem CH₄ emission involves at least two processes: a sap flux-dependent component responsible for the diurnal changes, and a sap flux-independent component responsible for the background continuum. The contribution ratios of the two processes are season-dependent. The background continuum possibly resulted from the diffusive transport of gaseous CH₄ from the roots to the upper trunk. Root anatomy analysis indicated that the intercellular space of the cortex and empty xylem cells in fine roots could serve as a passageway for transport of gaseous CH₄

Upscaling Wetland Methane Emissions From the FLUXNET-CH4 Eddy Covariance Network (UpCH4 v1.0):Model Development, Network Assessment, and Budget Comparison

Wetlands are responsible for 20%–31% of global methane (CH4) emissions and account for a large source of uncertainty in the global CH4 budget. Data-driven upscaling of CH4 fluxes from eddy covariance measurements can provide new and independent bottom-up estimates of wetland CH4 emissions. Here, we develop a six-predictor random forest upscaling model (UpCH4), trained on 119 site-years of eddy covariance CH4 flux data from 43 freshwater wetland sites in the FLUXNET-CH4 Community Product. Network patterns in site-level annual means and mean seasonal cycles of CH4 fluxes were reproduced accurately in tundra, boreal, and temperate regions (Nash-Sutcliffe Efficiency ∼0.52–0.63 and 0.53). UpCH4 estimated annual global wetland CH4 emissions of 146 ± 43 TgCH4 y−1 for 2001–2018 which agrees closely with current bottom-up land surface models (102–181 TgCH4 y−1) and overlaps with top-down atmospheric inversion models (155–200 TgCH4 y−1). However, UpCH4 diverged from both types of models in the spatial pattern and seasonal dynamics of tropical wetland emissions. We conclude that upscaling of eddy covariance CH4 fluxes has the potential to produce realistic extra-tropical wetland CH4 emissions estimates which will improve with more flux data. To reduce uncertainty in upscaled estimates, researchers could prioritize new wetland flux sites along humid-to-arid tropical climate gradients, from major rainforest basins (Congo, Amazon, and SE Asia), into monsoon (Bangladesh and India) and savannah regions (African Sahel) and be paired with improved knowledge of wetland extent seasonal dynamics in these regions. The monthly wetland methane products gridded at 0.25° from UpCH4 are available via ORNL DAAC (https://doi.org/10.3334/ORNLDAAC/2253).</p

Identifying dominant environmental predictors of freshwater wetland methane fluxes across diurnal to seasonal time scales

While wetlands are the largest natural source of methane (CH4) to the atmosphere, they represent a large source of uncertainty in the global CH4 budget due to the complex biogeochemical controls on CH4 dynamics. Here we present, to our knowledge, the first multi-site synthesis of how predictors of CH4 fluxes (FCH4) in freshwater wetlands vary across wetland types at diel, multiday (synoptic), and seasonal time scales. We used several statistical approaches (correlation analysis, generalized additive modeling, mutual information, and random forests) in a wavelet-based multi-resolution framework to assess the importance of environmental predictors, nonlinearities and lags on FCH4 across 23 eddy covariance sites. Seasonally, soil and air temperature were dominant predictors of FCH4 at sites with smaller seasonal variation in water table depth (WTD). In contrast, WTD was the dominant predictor for wetlands with smaller variations in temperature (e.g., seasonal tropical/subtropical wetlands). Changes in seasonal FCH4 lagged fluctuations in WTD by similar to 17 +/- 11 days, and lagged air and soil temperature by median values of 8 +/- 16 and 5 +/- 15 days, respectively. Temperature and WTD were also dominant predictors at the multiday scale. Atmospheric pressure (PA) was another important multiday scale predictor for peat-dominated sites, with drops in PA coinciding with synchronous releases of CH4. At the diel scale, synchronous relationships with latent heat flux and vapor pressure deficit suggest that physical processes controlling evaporation and boundary layer mixing exert similar controls on CH4 volatilization, and suggest the influence of pressurized ventilation in aerenchymatous vegetation. In addition, 1- to 4-h lagged relationships with ecosystem photosynthesis indicate recent carbon substrates, such as root exudates, may also control FCH4. By addressing issues of scale, asynchrony, and nonlinearity, this work improves understanding of the predictors and timing of wetland FCH4 that can inform future studies and models, and help constrain wetland CH4 emissions.Peer reviewe

Upscaling Wetland Methane Emissions From the FLUXNET-CH4 Eddy Covariance Network (UpCH4 v1.0): Model Development, Network Assessment, and Budget Comparison

Wetlands are responsible for 20%-31% of global methane (CH4) emissions and account for a large source of uncertainty in the global CH4 budget. Data-driven upscaling of CH4 fluxes from eddy covariance measurements can provide new and independent bottom-up estimates of wetland CH4 emissions. Here, we develop a six-predictor random forest upscaling model (UpCH4), trained on 119 site-years of eddy covariance CH4 flux data from 43 freshwater wetland sites in the FLUXNET-CH4 Community Product. Network patterns in site-level annual means and mean seasonal cycles of CH4 fluxes were reproduced accurately in tundra, boreal, and temperate regions (Nash-Sutcliffe Efficiency similar to 0.52-0.63 and 0.53). UpCH(4) estimated annual global wetland CH4 emissions of 146 +/- 43 TgCH4 y(-1) for 2001-2018 which agrees closely with current bottom-up land surface models (102-181 TgCH4 y(-1)) and overlaps with top-down atmospheric inversion models (155-200 TgCH4 y -1). However, UpCH4 diverged from both types of models in the spatial pattern and seasonal dynamics of tropical wetland emissions. We conclude that upscaling of eddy covariance CH4 fluxes has the potential to produce realistic extra-tropical wetland CH4 emissions estimates which will improve with more flux data. To reduce uncertainty in upscaled estimates, researchers could prioritize new wetland flux sites along humid-to-arid tropical climate gradients, from major rainforest basins (Congo, Amazon, and SE Asia), into monsoon (Bangladesh and India) and savannah regions (African Sahel) and be paired with improved knowledge of wetland extent seasonal dynamics in these regions. The monthly wetland methane products gridded at 0.25 degrees from UpCH4 are available via ORNL DAAC (https://doi.org/10.3334/ ORNLDAAC/2253).Plain Language Summary Wetlands account for a large share of global methane emissions to the atmosphere, but current estimates vary widely in magnitude (similar to 30% uncertainty on annual global emissions) and spatial distribution, with diverging predictions for tropical rice growing (e.g., Bengal basin), rainforest (e.g., Amazon basin), and floodplain savannah (e.g., Sudd) regions. Wetland methane model estimates could be improved by increased use of land surface methane flux data. Upscaling approaches use flux data collected across globally distributed measurement networks in a machine learning framework to extrapolate fluxes in space and time. Here, we train and evaluate a methane upscaling model (UpCH4) and use it to generate monthly, globally gridded wetland methane emissions estimates for 2001-2018. The UpCH4 model uses only six predictor variables among which temperature is dominant. Global annual methane emissions estimates and associated uncertainty ranges from upscaling fall within state-of-the-art model ensemble estimates from the Global Carbon Project (GCP) methane budget. In some tropical regions, the spatial pattern of UpCH4 emissions diverged from GCP predictions, however, inclusion of flux measurements from additional ground-based sites, together with refined maps of tropical wetlands extent, could reduce these prediction uncertainties

Gap-filling eddy covariance methane fluxes : Comparison of machine learning model predictions and uncertainties at FLUXNET-CH4 wetlands

Time series of wetland methane fluxes measured by eddy covariance require gap-filling to estimate daily, seasonal, and annual emissions. Gap-filling methane fluxes is challenging because of high variability and complex responses to multiple drivers. To date, there is no widely established gap-filling standard for wetland methane fluxes, with regards both to the best model algorithms and predictors. This study synthesizes results of different gap-filling methods systematically applied at 17 wetland sites spanning boreal to tropical regions and including all major wetland classes and two rice paddies. Procedures are proposed for: 1) creating realistic artificial gap scenarios, 2) training and evaluating gap-filling models without overstating performance, and 3) predicting halfhourly methane fluxes and annual emissions with realistic uncertainty estimates. Performance is compared between a conventional method (marginal distribution sampling) and four machine learning algorithms. The conventional method achieved similar median performance as the machine learning models but was worse than the best machine learning models and relatively insensitive to predictor choices. Of the machine learning models, decision tree algorithms performed the best in cross-validation experiments, even with a baseline predictor set, and artificial neural networks showed comparable performance when using all predictors. Soil temperature was frequently the most important predictor whilst water table depth was important at sites with substantial water table fluctuations, highlighting the value of data on wetland soil conditions. Raw gap-filling uncertainties from the machine learning models were underestimated and we propose a method to calibrate uncertainties to observations. The python code for model development, evaluation, and uncertainty estimation is publicly available. This study outlines a modular and robust machine learning workflow and makes recommendations for, and evaluates an improved baseline of, methane gap-filling models that can be implemented in multi-site syntheses or standardized products from regional and global flux networks (e.g., FLUXNET).Peer reviewe

Methane exchange in a poorly-drained black spruce forest over permafrost observed using the eddy covariance technique

Ecosystem-scale methane (CH4) exchange was observed in a poorly-drained black spruce forest over permafrost in interior Alaska during the snow-free seasons of 2011–2013, using the eddy covariance technique. The magnitude of average CH4 exchange differed depending on wind direction, reflecting spatial variation in soil moisture condition around the observation tower, due to elevation change within the small catchment. In the drier upper position, the seasonal variation in CH4 emission was explained by the variation in soil water content only. In the wetter bottom, however, in addition to soil temperature and soil water content, seasonal thaw depth of frozen soil was also an important variable explaining the seasonal variation in CH4 exchange for this ecosystem. Total snow-free season (day of year 134–280) CH4 exchanges were 12.0 ± 1.0, 19.6 ± 3.0, and 36.6 ± 4.4 mmol m−2 season−1 for the drier upper position, moderately wet area, and wetter bottom of the catchment, respectively. Observed total season CH4 emission was nearly one order smaller than those reported in other northern wetlands, due probably to the relatively low ground water level and low soil temperature. The interannual variation of total snow-free season CH4 emission in the wetter bottom of the catchment was influenced by the amount of rainfall and thaw depth. On the other hand, in the drier upper position the amount of rainfall did not strongly affect the total season CH4 emission. Different responses of CH4 exchange to environmental conditions, depending on the position of a small catchment, should be considered when estimating the spatial variation in CH4 exchange accurately in ecosystems over permafrost.ArticleAGRICULTURAL AND FOREST METEOROLOGY. 214(0):157-168 (2015)journal articl