15 research outputs found

    Environmental and vegetation controls on the spatial variability of CH4 emission from wet-sedge and tussock tundra ecosystems in the Arctic

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    Aims Despite multiple studies investigating the environmental controls on CH4 fluxes from arctic tundra ecosystems, the high spatial variability of CH4 emissions is not fully understood. This makes the upscaling of CH4 fluxes from plot to regional scale, particularly challenging. The goal of this study is to refine our knowledge of the spatial variability and controls on CH4 emission from tundra ecosystems. Methods CH4 fluxes were measured in four sites across a variety of wet-sedge and tussock tundra ecosystems in Alaska using chambers and a Los Gatos CO2 and CH4 gas analyser. Results All sites were found to be sources of CH4, with northern sites (in Barrow) showing similar CH4 emission rates to the southernmost site (ca. 300 km south, Ivotuk). Gross primary productivity (GPP), water level and soil temperature were the most important environmental controls on CH4 emission. Greater vascular plant cover was linked with higher CH4 emission, but this increased emission with increased vascular plant cover was much higher (86 %) in the drier sites, than the wettest sites (30 %), suggesting that transport and/or substrate availability were crucial limiting factors for CH4 emission in these tundra ecosystems. Conclusions Overall, this study provides an increased understanding of the fine scale spatial controls on CH4 flux, in particular the key role that plant cover and GPP play in enhancing CH4 emissions from tundra soils

    Improving a plot-scale methane emission model and its performance at a northeastern Siberian tundra site

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    In order to better address the feedbacks between climate and wetland methane (CH<sub>4</sub>) emissions, we tested several mechanistic improvements to the wetland CH<sub>4</sub> emission model Peatland-VU with a longer Arctic data set than any other model: (1) inclusion of an improved hydrological module, (2) incorporation of a gross primary productivity (GPP) module, and (3) a more realistic soil-freezing scheme. <br><br> A long time series of field measurements (2003–2010) from a tundra site in northeastern Siberia is used to validate the model, and the generalized likelihood uncertainty estimation (GLUE) methodology is used to test the sensitivity of model parameters. <br><br> Peatland-VU is able to capture both the annual magnitude and seasonal variations of the CH<sub>4</sub> flux, water table position, and soil thermal properties. However, detailed daily variations are difficult to evaluate due to data limitation. Improvements due to the inclusion of a GPP module are less than anticipated, although this component is likely to become more important at larger spatial scales because the module can accommodate the variations in vegetation traits better than at plot scale. <br><br> Sensitivity experiments suggest that the methane production rate factor, the methane plant oxidation parameter, the reference temperature for temperature-dependent decomposition, and the methane plant transport rate factor are the most important parameters affecting the data fit, regardless of vegetation type. Both wet and dry vegetation cover are sensitive to the minimum water table level; the former is also sensitive to the runoff threshold and open water correction factor, and the latter to the subsurface water evaporation and evapotranspiration correction factors. <br><br> These results shed light on model parameterization and future improvement of CH<sub>4</sub> modelling. However, high spatial variability of CH<sub>4</sub> emissions within similar vegetation/soil units and data quality prove to impose severe limits on model testing and improvement

    Evaluation of a plot-scale methane emission model using eddy covariance observations and footprint modelling

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    Most plot-scale methane emission models – of which many have been developed in the recent past – are validated using data collected with the closed-chamber technique. This method, however, suffers from a low spatial representativeness and a poor temporal resolution. Also, during a chamber-flux measurement the air within a chamber is separated from the ambient atmosphere, which negates the influence of wind on emissions
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