Methane emissions and origin in tree stems in an upland forest

Author(s):  
Josep Barba ◽  
Rafael Poyatos ◽  
Margaret Capooci ◽  
Rodrigo Vargas

<p>Trees can exchange methane (CH4) with the atmosphere through their stems. However, the magnitudes, patterns, drivers and origin of these emissions as well as the biogeochemical pathways that might result in net CH4 production or uptake are still poorly understood. One of the most important constraints is the limited information on the spatial and temporal variability of these emissions. Manual measurements are useful for measuring spatial variability of stem emissions (both within and between trees), but their low temporal frequency hinders our understanding of temporal patterns. In contrast, high-frequency measurements capture temporal variability, but instrumentation cost and complex technical logistics preclude high number of spatial replicates. In this study we combined manual and automated measurements of tree stem emissions in 18 different bitternut hickory trees (Carya cordiformis) in an upland forest during one growing season. Methane emissions were measured at two stem heights (75 and 150 cm) in three trees every 30 min, whereas the other 15 trees were measured once every two weeks at three different stem heights (50, 110 and 170 cm). Additionally, sap flow, soil temperature, soil water content, ground water level, and CH4 concentrations in the heartwood and in the soil profile were measured. Finally, we performed incubations of stem cores to test its potential for producing CH4. All trees were net sources of methane during the experiment, but some of them showed sporadic capture of CH4. High-frequency measurements revealed large temporal variability of stem emissions even within hours. Trees showed a seasonal trend of CH4 emissions partially explained by sap flow, soil moisture and temperature, but the pattern and the magnitudes were not consistent between and within trees. Even when a larger number of trees were studied (15 trees with manual measurements every two weeks), no consistent spatial pattern emerged among trees or with stem height, with emissions differing up to two orders of magnitude among trees. We found high CH4 concentrations in the heartwood of the trees (up to 75,000 ppm), no relevant concentrations in the soil profile (<6 ppm in all cases), and methanogenic capacity in all trees (stem cores were able to produce CH4 in laboratory incubations), supporting the interpretation that CH4 emitted by treestems was likely produced in the heartwood of the trees rather than being produced in soils and transported by the roots. Our results provide evidence on the potential origin of CH4 emitted by tree stems, but also indicate that the spatial and temporal patterns of stem emissions should be better described in order to assess the role of trees in local-to-global CH4 budgets.</p>

1996 ◽  
Vol 16 (3) ◽  
pp. 212-218 ◽  
Author(s):  
Darrell R. Jackson ◽  
Kevin L. Williams ◽  
Kevin B. Briggs

2020 ◽  
Vol 186 ◽  
pp. 116319 ◽  
Author(s):  
L. Liu ◽  
Z.J. Yang ◽  
K. Delwiche ◽  
L.H. Long ◽  
J. Liu ◽  
...  

2018 ◽  
Vol 181 (1) ◽  
pp. 7-11 ◽  
Author(s):  
Hermann F. Jungkunst ◽  
Katharina H. E. Meurer ◽  
Gerald Jurasinski ◽  
Engelbert Niehaus ◽  
Anke Günther

2021 ◽  
Author(s):  
Sarah Waldo ◽  
Jake J. Beaulieu ◽  
William Barnett ◽  
David A. Balz ◽  
Michael J. Vanni ◽  
...  

Abstract. Waters impounded behind dams (i.e. reservoirs) are important sources of greenhouses gases, especially methane (CH4), but their contribution is not well constrained due to high spatial and temporal variability, limitations in monitoring methods to characterize hot spot and hot moment emissions, and the limited number of studies that investigate diurnal, seasonal, and interannual patterns in emissions. In this study, we investigate the temporal patterns and biophysical drivers of CH4 emissions from Acton Lake, a small eutrophic reservoir, using a combination of methods: eddy covariance monitoring, continuous warm-season ebullition measurements, spatial emission surveys, and measurements of key drivers of CH4 production and emission. We used an artificial neural network to gap-fill the eddy covariance time series and to explore the relative importance of biophysical drivers on the inter-annual timescale. Acton Lake had cumulative areal emission rates of 40.6 ± 5.9 and 71.4 ±  4.2 g CH4 m−2 in 2017 and 2018, respectively, or 97.4 ± 14 and 171 ± 10 Mg CH4 in 2017 and 2018 across the whole 2.4 km2 area of the lake. The main difference between years was a period of elevated emissions lasting less than two weeks in the spring of 2018, which contributed 17 % of the total annual emissions, and was likely due to favourable sediment temperature and algal carbon substrate availability in 2018 compared to 2017. CH4 emissions only displayed diurnal patterns 18.5 % of the monitoring period, suggesting that factors that do not follow a diurnal pattern (e.g. substrate availability) may be driving emissions. Combining spatially extensive measurements with temporally continuous monitoring enabled us to quantify aspects of the spatial and temporal variability in CH4 emission. We found that the relationships between CH4 emissions and sediment T depended on location within the reservoir and observed a clear spatio-temporal offset in maximum CH4 emissions as a function of reservoir depth. These findings suggest a strong spatial pattern in CH4 biogeochemistry within this relatively small (2.4 km2) reservoir. In addressing the need for a better understanding of GHG emissions from reservoirs, there is a trade-off in intensive measurement of one water body versus short-term and/or spatially limited measurements in many water bodies. The insights from multi-year, continuous, spatially extensive studies like this one can be used to inform both the study design and emission upscaling from spatially or temporally limited results, specifically the importance of trophic status and intra-lake variability in assumptions about upscaling CH4 emissions.


2015 ◽  
Vol 123 (3) ◽  
pp. 349-362 ◽  
Author(s):  
Kazuhiko Terazawa ◽  
Kenji Yamada ◽  
Yasuyuki Ohno ◽  
Tadashi Sakata ◽  
Shigehiro Ishizuka

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