An evaluation of the flux-gradient and the eddy covariance method to measure CH4, CO2, and H2O fluxes from small ponds

Zhao, Jing; Zhang, Mi; Xiao, Wei; Wang, Wei; Zhang, Zhen; Zhou, Yu; Xiao, Qitao; Cao, Zheng-da; Jin, Xu; Zhang, Xiufang; Liu, Shoudong; Lee, Xuhui

doi:10.1016/j.agrformet.2019.05.032

Cited by 31 publications

(22 citation statements)

References 46 publications

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“…Therefore, including the terrestrial‐aquatic DOC flux is necessary to improve estimates of broad‐scale ecosystem carbon dynamics; however, this flux is often excluded or not well represented in many carbon budget estimation methods (Wei, 2020). For example, eddy‐covariance measurements have the ability to capture carbon exchange between the atmosphere and aquatic ecosystems within the tower footprint (Zhao et al., 2019). Atmospheric inversion models are a type of top‐down method that estimates vertical, terrestrial‐atmosphere carbon exchange including outgassing from aquatic ecosystems within the modeling domain (Schuh et al., 2019).…”

Section: Introductionmentioning

confidence: 99%

“…measurements have the ability to capture carbon exchange between the atmosphere and aquatic ecosystems within the tower footprint (Zhao et al, 2019). Atmospheric inversion models are a type of top-down method that estimates vertical, terrestrial-atmosphere carbon exchange including outgassing from aquatic ecosystems within the modeling domain (Schuh et al, 2019).…”

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confidence: 99%

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Identifying Key Environmental Factors Explaining Temporal Patterns of DOC Export From Watersheds in the Conterminous United States

et al. 2021

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An accurate accounting of the terrestrial carbon cycle, along with an advanced understanding of its underlying mechanisms, is required to predict future climate change and ecosystem response (Chapin et al., 2006). Carbon budget estimation and scaling efforts have typically focused on comparing "bottom-up" estimates of carbon stock change from field inventories and biospheric process models with "top-down" estimates of land-atmosphere carbon exchange from inversion modeling frameworks (Battin et al., 2009;. Increasingly recognized, however, is the important role that the lateral transfer of carbon through the aquatic system plays in the overall dynamics of the terrestrial ecosystem (Chapin et al., 2006), and that these "sideways" fluxes must be better characterized in order to balance the carbon budget and reconcile differences between top-down and bottom-up estimates (Webb et al., 2019). Current carbon budget estimation and scaling approaches may only implicitly include these lateral fluxes or ignore them altogether, thus making it difficult or impossible to compare among estimates (Hayes et al., 2018). This lateral carbon flux from soils to inland waters occurs in a few main forms, including dissolved organic carbon (DOC), dissolved inorganic carbon and particulate organic carbon (Battin et al., 2009). DOC is primarily formed by the incomplete decomposition of soil organic carbon, or is exuded by plants (Zarnetske et al., 2018), which can be moved from the soil to inland waters via surface runoff as well as subsurface lateral flow and serves as a key component of the lateral, terrestrial-aquatic carbon flux (Tank et al., 2018). In aquatic ecosystems, DOC can be subject to sedimentation in water bodies or be biotically or abiotically oxidized into CO 2 and released to the atmosphere, with the remaining DOC exported to the ocean (Bai et al., 2020;Battin et al., 2009). Therefore, including the terrestrial-aquatic DOC flux is necessary to improve estimates of broad-scale ecosystem carbon dynamics; however, this flux is often excluded or not well represented in many carbon budget estimation methods (Wei, 2020). For example, eddy-covariance

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Section: Introductionmentioning

confidence: 99%

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confidence: 99%

Identifying Key Environmental Factors Explaining Temporal Patterns of DOC Export From Watersheds in the Conterminous United States

et al. 2021

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“…The main input variables of the model include measurement height, roughness length, friction velocity, Obukhov length, the standard deviation of the cross‐wind component, wind direction, mean wind speed and the boundary layer height. Apart from the boundary layer height, other parameters were measured by the EC system (Zhao et al, 2019). The boundary layer height was provided by the Global Data Assimilation System of the U.S. National Oceanic and Atmospheric Administration (https://ready.arl.noaa.gov/gdas1.php).…”

Section: Methodsmentioning

confidence: 99%

Characteristics of turbulence over the semi‐fixed desert area north of Xinjiang, China

Mamtimin

Wang

Sayit³

et al. 2021

Earth Surf Processes Landf

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As the largest fixed and semi-fixed desert in China, the Gurbantünggüt Desert undergoes a long period of snow cover in the winter and the rapid growth of ephemeral plants in the spring, presenting obvious seasonal changes in the underlying desert surface type, which can lead to variation in the turbulence of the nearsurface boundary layer turbulence over the desert. In this study, gradient tower data and eddy covariance data from 2017 were analysed to investigate the turbulence characteristics of the different surface boundary layers in the hinterland of the Gurbantünggüt Desert. The results indicate that stable atmospheric conditions in

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“…The aerodynamic gradient method was chosen for compatibility with a concurrent atmospheric mercury flux study (Obrist et al, 2017). This method has been shown to have greater variability compared to the more widely used eddy covariance method over diel timescales (Muller et al, 2009), though with reasonable agreement over longer timescales, with the caveat that the concentration gradients are precisely quantified using high-precision gas analysers (Zhao et al, 2019;Karlsson, 2017;Fritsche et al, 2008). Turbulent characteristics were measured using a Metek USA-1 sonic anemometer (Metek GmbH, Elmshorn, Germany), positioned 2.36 m above the tundra soil.…”

Section: Instrumentationmentioning

confidence: 99%

“…Modelled forecasting of Arctic methane fluxes is typically undertaken using air temperature data, due to its relative ease of measurement and prediction, and the assumption that air temperature is closely linked to soil temperature (Riley et al, 2011;Koven et al, 2013;Zhu et al, 2014). An analysis of a 29-year record at Barrow, Alaska, however, showed no correlation between increasing air temperature and methane concentration anomaly (Sweeney et al, 2016), suggesting that air temperature is an inadequate variable for predicting methane fluxes.…”

Section: Implications For Arctic Methane Fluxesmentioning

confidence: 99%

Environmental controls on ecosystem-scale cold-season methane and carbon dioxide fluxes in an Arctic tundra ecosystem

et al. 2020

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Abstract. Understanding the processes that influence and control carbon cycling in Arctic tundra ecosystems is essential for making accurate predictions about what role these ecosystems will play in potential future climate change scenarios. Particularly, air–surface fluxes of methane and carbon dioxide are of interest as recent observations suggest that the vast stores of soil carbon found in the Arctic tundra are becoming more available to release to the atmosphere in the form of these greenhouse gases. Further, harsh wintertime conditions and complex logistics have limited the number of year-round and cold-season studies and hence too our understanding of carbon cycle processes during these periods. We present here a two-year micrometeorological data set of methane and carbon dioxide fluxes, along with supporting soil pore gas profiles, that provide near-continuous data throughout the active summer and cold winter seasons. Net emission of methane and carbon dioxide in one of the study years totalled 3.7 and 89 g C m−2 a−1 respectively, with cold-season methane emission representing 54 % of the annual total. In the other year, net emission totals of methane and carbon dioxide were 4.9 and 485 g C m−2 a−1 respectively, with cold-season methane emission here representing 82 % of the annual total – a larger proportion than has been previously reported in the Arctic tundra. Regression tree analysis suggests that, due to relatively warmer air temperatures and deeper snow depths, deeper soil horizons – where most microbial methanogenic activity takes place – remained warm enough to maintain efficient methane production whilst surface soil temperatures were simultaneously cold enough to limit microbial methanotrophic activity. These results provide valuable insight into how a changing Arctic climate may impact methane emission, and highlight a need to focus on soil temperatures throughout the entire active soil profile, rather than rely on air temperature as a proxy for modelling temperature–methane flux dynamics.

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An evaluation of the flux-gradient and the eddy covariance method to measure CH4, CO2, and H2O fluxes from small ponds

Cited by 31 publications

References 46 publications

Identifying Key Environmental Factors Explaining Temporal Patterns of DOC Export From Watersheds in the Conterminous United States

Identifying Key Environmental Factors Explaining Temporal Patterns of DOC Export From Watersheds in the Conterminous United States

Characteristics of turbulence over the semi‐fixed desert area north of Xinjiang, China

Environmental controls on ecosystem-scale cold-season methane and carbon dioxide fluxes in an Arctic tundra ecosystem

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