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

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

マレーシア・サラワク州における熱帯泥炭生態系のメタン収支

Tropical peatlands of Southeast Asia, widely distributed in Indonesia and Malaysia, are a globally important carbon reservoir, storing an enormous amount of soil organic carbon as peat. In recent decades, however, the peatlands have been threatened with rapid land cover changes, predominantly into industrial plantations of oil palm and pulpwood. Owing to the huge soil carbon stock, high groundwater level (GWL) and high temperature, tropical peatlands potentially function as a significant source of methane (CH4) to the atmosphere. However, chamber studies of soil CH4 flux have reported that CH4 emissions from tropical peat swamp ecosystems were negligible. On the other hand, recently, it was reported that some tree species growing in peat swamp forest emit considerable CH4 from their stems. Thus, ecosystem-scale flux measurement is essential to quantify the CH4 balance of tropical peat ecosystems. In this study, we measured ecosystem-scale CH4 flux continuously above three different tropical peat ecosystems in Sarawak, Malaysia for three years from February 2014 to January 2017. This is the first study applying the eddy covariance technique in tropical peat ecosystems. The three sites were different in disturbance; namely an undrained peat swamp forest (UF), a relatively disturbed secondary peat swamp forest (DF) and an oil palm plantation (OP) established on peat after deforestation. The objectives of this study were to: (1) quantify the net ecosystem exchange of CH4 (FCH4) of each site; (2) examine the responses of FCH4 to environmental factors; and (3) compare FCH4 among the three ecosystems and discuss the inter-site difference of CH4 balance. The FCH4 was determined half-hourly as the sum of eddy CH4 flux and CH4 storage change and summed up annually after gap filling. Daily mean FCH4 was positively correlated to GWL in UF and DF, in which GWL governed the production and oxidation of CH4 in peat. On the other hand, FCH4 was almost independent of GWL in OP, in which GWL was lowered by drainage. Monthly mean FCH4 was always positive even in drained OP, meaning CH4 sources. Mean annual CH4 emissions (± 1 SD) were 8.46 ± 0.51, 4.17 ± 0.69 and 2.19 ± 0.21 g C m–2 yr–1, respectively, in UF, DF and OP. There was a significant difference (P < 0.001) among the sites. The annual CH4 emission was highest in UF with the highest GWL and lowest in water-managed OP. The inter-site difference was explained considerably by GWL from a significant positive exponential relationship (P < 0.001). The ecosystem-scale CH4 emission from UF was lower than those from mid-latitude peat ecosystems, though it was much higher than soil CH4 emissions measured by the chamber technique in tropical peat swamp forests. The difference was probably due to CH4 emissions from tree stems, which were not measured in the soil chamber studies. A significant positive relationship was found between FCH4 and GWL on monthly and annual bases, including all data from the three sites. The positive relationship indicates that the conversion of a peat swamp forest to an oil palm plantation decreases CH4 emissions, because the land conversion accompanies drainage. However, the decrease of CH4 emissions would be insufficient to offset the increase of carbon dioxide emissions through oxidative peat decomposition. The oil palm plantation drained deep to –62 cm on average still functioned as a small CH4 source probably because of high CH4 emissions from ditches

マレーシア・サラワク州における熱帯泥炭生態系のメタン収支 [全文の要約]

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Soil carbon dioxide emissions due to oxidative peat decomposition in an oil palm plantation on tropical peat

Soil carbon dioxide (CO₂) efflux was measured continuously for two years using an automated chamber system in an oil palm plantation on tropical peat. This study investigated the factors controlling the CO₂ efflux and quantified the annual cumulative CO₂ emissions through soil respiration and heterotrophic respiration, which is equivalent to oxidative peat decomposition. Soil respiration was measured in close-to-tree ( 3 m, FT) plots, and heterotrophic respiration was measured in root-cut (RC) plots by a trenching method. The daily mean CO2 efflux values (mean ±1 standard deviation) were 2.80±2.18, 1.59±1.18, and 1.94±1.581 μmol m⁻² s⁻¹ in the CT, FT, and RC plots, respectively. Daily mean CO₂ efflux increased exponentially as the groundwater level or water-filled pore space decreased, indicating that oxidative peat decomposition and gas diffusion in the soil increased due to enhanced aeration resulting from lower groundwater levels. Mean annual gap-filled CO₂ emissions were 1.03 ± 0.53, 0.59 ± 0.26, and 0.69 ± 0.21 kg C m⁻² yr⁻¹ in the CT, FT, and RC plots, respectively. Soil CO₂ emissions were significantly higher in the CT plots (P < 0.05), but did not differ significantly between the FT and RC plots. This implies that root respiration was negligible in the FT plots. Heterotrophic respiration accounted for 66% of soil respiration. Annual CO₂ emissions through both soil and heterotrophic respiration were smaller than those of other oil palm plantations on tropical peat, possibly due to the higher groundwater levels, land compaction, and continuous measurement of soil CO₂ efflux in this study. Mean annual total subsidence was 1.55 to 1.62 cm yr⁻¹, of which oxidative peat decomposition accounted for 72 to 74%. In conclusion, water management to raise groundwater levels would mitigate soil CO₂ emissions from oil palm plantations on tropical peatland