Cross-evaluation of measurements of peatland methane emissions on microform and ecosystem scales using high-resolution landcover classification and source weight modelling

Forbrich, Inke; Kutzbach, Lars; Wille, Christian; Becker, Thomas; Wu, Jiabing; Wilmking, Martin

doi:10.1016/j.agrformet.2011.02.006

Cited by 62 publications

(64 citation statements)

References 39 publications

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“…Laine et al, 2006;Riutta et al, 2007;Forbrich et al, 2011), but such comparisons should take into account the potential artefacts of both chamber and eddy covariance measurements. In highly turbulent conditions, eddy covariance may measure a higher gas exchange rate than the biological flux due to enhanced gas transport from soil induced by pressure changes (Gu et al, 2005), while closed chambers miss the mass flow component and may measure smaller fluxes when deployed for a short period because of the transient reduction in concentration gradient, or in some cases, may measure larger fluxes due to pressure pumping as winds blow over the vent tube (Conen and Smith, 1998).…”

Section: Implications For Chamber Flux Measurementsmentioning

confidence: 99%

The effect of atmospheric turbulence and chamber deployment period on autochamber CO2 and CH4 flux measurements in an ombrotrophic peatland

et al. 2012

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Abstract. Accurate quantification of soil-atmosphere gas exchange is essential for understanding the magnitude and controls of greenhouse gas emissions. We used an automatic, closed, dynamic chamber system to measure the fluxes of CO 2 and CH 4 for several years at the ombrotrophic Mer Bleue peatland near Ottawa, Canada and found that atmospheric turbulence and chamber deployment period had a considerable influence on the observed flux rates. With a short deployment period of 2.5 min, CH 4 flux exhibited strong diel patterns and both CH 4 and nighttime CO 2 effluxes were highly and negatively correlated with ambient friction velocity as were the CO 2 concentration gradients in the top 20 cm of peat. This suggests winds were flushing the very porous and relatively dry near-surface peat layers and reducing the belowground gas concentration gradient, which then led to flux underestimations owing to a decrease in turbulence inside the headspace during chamber deployment compared to the ambient windy conditions. We found a 9 to 57 % underestimate of the net biological CH 4 flux at any time of day and a 13 to 21 % underestimate of nighttime CO 2 effluxes in highly turbulent conditions. Conversely, there was evidence of an overestimation of ∼ 100 % of net biological CH 4 and nighttime CO 2 fluxes in calm atmospheric conditions possibly due to enhanced near-surface gas concentration gradient by mixing of chamber headspace air by fans. These problems were resolved by extending the deployment period to 30 min. After 13 min of chamber closure, the flux rate of CH 4 and nighttime CO 2 became constant and were not affected by turbulence thereafter, yielding a reliable estimate of the net biological fluxes. The measurement biases we observed likely exist to some extent in all chamber flux measurements made on porous and aerated substrate, such as peatlands, organic soils in tundra and forests, and snowcovered surfaces, but would be difficult to detect unless high frequency, semi-continuous observations were made.

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Section: Implications For Chamber Flux Measurementsmentioning

confidence: 99%

The effect of atmospheric turbulence and chamber deployment period on autochamber CO2 and CH4 flux measurements in an ombrotrophic peatland

et al. 2012

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“…The vegetation variability is driven by environmental factors that can also have important direct controls on CH 4 emissions (e.g., hydrology [18]), which can further enhance the link between vegetation type and CH 4 flux. Therefore, the spatial heterogeneity of tundra vegetation communities can potentially explain the spatial variability in CH 4 fluxes [24], but can also make it difficult to fully quantify and understand localised differences in CH 4 emissions [25][26][27][28].…”

Section: Introductionmentioning

confidence: 99%

“…The inclusion of detailed vegetation community distribution (through integrating vegetation maps in footprint modelling) is likely to improve the accuracy of CH 4 emission predictions, as it will be possible to determine which plant communities CH 4 fluxes are coming from [15,28]. Remote sensing is a powerful tool for monitoring and mapping vegetation distribution and changes across large areas [36][37][38].…”

Section: Introductionmentioning

confidence: 99%

Upscaling CH4 Fluxes Using High-Resolution Imagery in Arctic Tundra Ecosystems

et al. 2017

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Abstract:Arctic tundra ecosystems are a major source of methane (CH 4 ), the variability of which is affected by local environmental and climatic factors, such as water table depth, microtopography, and the spatial heterogeneity of the vegetation communities present. There is a disconnect between the measurement scales for CH 4 fluxes, which can be measured with chambers at one-meter resolution and eddy covariance towers at 100-1000 m, whereas model estimates are typically made at thẽ 100 km scale. Therefore, it is critical to upscale site level measurements to the larger scale for model comparison. As vegetation has a critical role in explaining the variability of CH 4 fluxes across the tundra landscape, we tested whether remotely-sensed maps of vegetation could be used to upscale fluxes to larger scales. The objectives of this study are to compare four different methods for mapping and two methods for upscaling plot-level CH 4 emissions to the measurements from EC towers. We show that linear discriminant analysis (LDA) provides the most accurate representation of the tundra vegetation within the EC tower footprints (classification accuracies of between 65% and 88%). The upscaled CH 4 emissions using the areal fraction of the vegetation communities showed a positive correlation (between 0.57 and 0.81) with EC tower measurements, irrespective of the mapping method. The area-weighted footprint model outperformed the simple area-weighted method, achieving a correlation of 0.88 when using the vegetation map produced with the LDA classifier. These results suggest that the high spatial heterogeneity of the tundra vegetation has a strong impact on the flux, and variation indicates the potential impact of environmental or climatic parameters on the fluxes. Nonetheless, assimilating remotely-sensed vegetation maps of tundra in a footprint model was successful in upscaling fluxes across scales.

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“…This method applied a gap-filling model that includes the attenuating effect of atmospheric stability on flux measurements, where methane production was related to soil temperature and water table level. Recently Forbrich et al (2011) tested various models where peat temperatures at various depths, water table level, barometric pressure and friction velocity were integrated in order to gap-fill their time series. Furthermore, large uncertainties in applied methods do still exist with no common protocol on missing data recovery of CH 4 eddy covariance flux data.…”

Section: S Dengel Et Al: Testing the Applicability Of Neural Networmentioning

confidence: 99%

Testing the applicability of neural networks as a gap-filling method using CH4 flux data from high latitude wetlands

et al. 2013

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Abstract. Since the advancement in CH 4 gas analyser technology and its applicability to eddy covariance flux measurements, monitoring of CH 4 emissions is becoming more widespread. In order to accurately determine the greenhouse gas balance, high quality gap-free data is required. Currently there is still no consensus on CH 4 gap-filling methods, and methods applied are still study-dependent and often carried out on low resolution, daily data.In the current study, we applied artificial neural networks to six distinctively different CH 4 time series from high latitudes, explain the method and test its functionality. We discuss the applicability of neural networks in CH 4 flux studies, the advantages and disadvantages of this method, and what information we were able to extract from such models.Three different approaches were tested by including drivers such as air and soil temperature, barometric air pressure, solar radiation, wind direction (indicator of source location) and in addition the lagged effect of water table depth and precipitation. In keeping with the principle of parsimony, we included up to five of these variables traditionally measured at CH 4 flux measurement sites. Fuzzy sets were included representing the seasonal change and time of day. High Pearson correlation coefficients (r) of up to 0.97 achieved in the final analysis are indicative for the high performance of neural networks and their applicability as a gapfilling method for CH 4 flux data time series. This novel approach which we show to be appropriate for CH 4 fluxes is a step towards standardising CH 4 gap-filling protocols.

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Cross-evaluation of measurements of peatland methane emissions on microform and ecosystem scales using high-resolution landcover classification and source weight modelling

Cited by 62 publications

References 39 publications

The effect of atmospheric turbulence and chamber deployment period on autochamber CO<sub>2</sub> and CH<sub>4</sub> flux measurements in an ombrotrophic peatland

The effect of atmospheric turbulence and chamber deployment period on autochamber CO<sub>2</sub> and CH<sub>4</sub> flux measurements in an ombrotrophic peatland

Upscaling CH4 Fluxes Using High-Resolution Imagery in Arctic Tundra Ecosystems

Testing the applicability of neural networks as a gap-filling method using CH<sub>4</sub> flux data from high latitude wetlands

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Cross-evaluation of measurements of peatland methane emissions on microform and ecosystem scales using high-resolution landcover classification and source weight modelling

Cited by 62 publications

References 39 publications

The effect of atmospheric turbulence and chamber deployment period on autochamber CO&lt;sub&gt;2&lt;/sub&gt; and CH&lt;sub&gt;4&lt;/sub&gt; flux measurements in an ombrotrophic peatland

The effect of atmospheric turbulence and chamber deployment period on autochamber CO&lt;sub&gt;2&lt;/sub&gt; and CH&lt;sub&gt;4&lt;/sub&gt; flux measurements in an ombrotrophic peatland

Upscaling CH4 Fluxes Using High-Resolution Imagery in Arctic Tundra Ecosystems

Testing the applicability of neural networks as a gap-filling method using CH&lt;sub&gt;4&lt;/sub&gt; flux data from high latitude wetlands

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The effect of atmospheric turbulence and chamber deployment period on autochamber CO<sub>2</sub> and CH<sub>4</sub> flux measurements in an ombrotrophic peatland

The effect of atmospheric turbulence and chamber deployment period on autochamber CO<sub>2</sub> and CH<sub>4</sub> flux measurements in an ombrotrophic peatland

Testing the applicability of neural networks as a gap-filling method using CH<sub>4</sub> flux data from high latitude wetlands