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

Budishchev, A.; Mi, Y.; Huissteden, J. van; Marchesini, Luca Belelli; Schaepman‐Strub, Gabriela; Parmentier, Frans‐Jan W.; Fratini, Gerardo; Gallagher, A.; Maximov, Trofim C.; Dolman, A. J.

doi:10.5194/bg-11-4651-2014

Cited by 33 publications

(41 citation statements)

References 64 publications

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“…This is an important function for process-based models for simulating CH 4 emissions from wetlands with varying vegetation compositions. The high spatial variability of CH 4 emissions is often observed in boreal and arctic wetlands dominated by complex plant communities (Budishchev et al, 2014;Kutzbach et al, 2004;Morrissey and Livingston, 1992;Sebacher et al, 1986). Previous observations have shown that the CH 4 fluxes were lower at the moss site than the Carex site in the arctic tundra (Christensen, 1993).…”

Section: Importance Of Plant Community Composition In Ch 4 Simulationsmentioning

confidence: 98%

Importance of vegetation classes in modeling CH4 emissions from boreal and subarctic wetlands in Finland

Raivonen

Alekseychik

et al. 2016

Science of The Total Environment

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• CH 4 emission from Finnish wetlands was simulated by CH4MOD wetland .• We recalibrated the vegetation parameters of graminoids, shrubs and Sphagnum.• Simulated CH 4 variations agreed well with the eddy-covariance observations. • Annual CH 4 emissions were reasonably simulated in different years and sites.• Parameterization of different vegetation process was essential in long-term CH 4 estimates. Boreal/arctic wetlands are dominated by diverse plant species, which vary in their contribution to CH 4 production, oxidation and transport processes. Earlier studies have often lumped the processes all together, which may induce large uncertainties into the results. We present a novel model, which includes three vegetation classes and can be used to simulate CH 4 emissions from boreal and arctic treeless wetlands. The model is based on an earlier biogeophysical model, CH4MOD wetland . We grouped the vegetation as graminoids, shrubs and Sphagnum and recalibrated the vegetation parameters according to their different CH 4 production, oxidation and transport capacities. Then, we used eddy-covariance-based CH 4 flux observations from a boreal (Siikaneva) and a subarctic fen , which was consistent with the observations (7-22 g m −2 yr −1 G R A P H I C A L A B S T R A C T a b s t r a c t a r t i c l e i n f o). However, there are some discrepancies between the simulated and observed daily CH 4 fluxes for the Siikaneva site (RMSE = 50.0%) and the Lompolojänkkä site (RMSE = 47.9%). Model sensitivity analysis showed that increasing the proportion of the graminoids would significantly increase the CH 4 emission levels. Our study demonstrated that the parameterization of the different vegetation processes was important in estimating long-term wetland CH 4 emissions.

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Section: Importance Of Plant Community Composition In Ch 4 Simulationsmentioning

confidence: 98%

Importance of vegetation classes in modeling CH4 emissions from boreal and subarctic wetlands in Finland

Raivonen

Alekseychik

et al. 2016

Science of The Total Environment

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“…Several methods have been developed to upscale CH 4 fluxes, which often rely on the areal fraction of the vegetation community distribution around the EC tower [30]. The area-weighted average method uses the areal fraction of a vegetation community within a given area and multiplies it by CH 4 emissions derived from chamber measurements [33,81,82].…”

Section: Upscaling Ch 4 Emissionsmentioning

confidence: 99%

“…Eddy covariance (EC) and chambers are the most common methods to measure CH 4 fluxes in the field, and both give useful information [5,9,10]), but can also result in inaccurate emission predictions [29,30]. EC towers measure trace gas fluxes with a high temporal resolution across a footprint with radii of roughly 100-300 m for a tower 1-3 m tall [31].…”

Section: Introductionmentioning

confidence: 99%

“…This can potentially bias or overestimate the emission estimates if there is variation in the landcover within the footprint and, therefore, emission rates from different vegetation types are not considered when upscaling the CH 4 flux measurements across a larger area or a longer time period [33]. When the vegetation community found within the footprint of the EC tower is heterogeneous and 'anisotropic' (not the same in each direction of the tower [30]), it can have a significant impact on the measured flux. Many studies do not take into account vegetation variation within an EC tower footprint [34,35].…”

Section: Introductionmentioning

confidence: 99%

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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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“…Some of the models also simulate the O 2 transport and the simulated O 2 concentrations affect the CH 4 processes. These models have been used in multiple studies (e.g., van Huissteden, 2009, 2011;Khvorostianov et al, 2008;Ringeval et al, 2011;Melton et al, 2013;Budishchev et al, 2014;Cresto Aleina et al, 2015;Grant et al, 2015), and some are referred to in the assessment report of the Intergovernmental Panel on Climate Change (IPCC; Ciais et al, 2013). These models have different approaches in simulating the production of CH 4 , ranging from separating distinct heterotrophic microbial communities (Grant and Roulet, 2002) to taking a constant fraction of the simulated heterotrophic soil respiration (Riley et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

HIMMELI v1.0: HelsinkI Model of MEthane buiLd-up and emIssion for peatlands

et al. 2017

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Abstract. Wetlands are one of the most significant natural sources of methane (CH 4 ) to the atmosphere. They emit CH 4 because decomposition of soil organic matter in waterlogged anoxic conditions produces CH 4 , in addition to carbon dioxide (CO 2 ). Production of CH 4 and how much of it escapes to the atmosphere depend on a multitude of environmental drivers. Models simulating the processes leading to CH 4 emissions are thus needed for upscaling observations to estimate present CH 4 emissions and for producing scenarios of future atmospheric CH 4 concentrations. Aiming at a CH 4 model that can be added to models describing peatland carbon cycling, we composed a model called HIMMELI that describes CH 4 build-up in and emissions from peatland soils. It is not a full peatland carbon cycle model but it requires the rate of anoxic soil respiration as input. Driven by soil temperature, leaf area index (LAI) of aerenchymatous peatland vegetation, and water table depth (WTD), it simulates the concentrations and transport of CH 4 , CO 2 , and oxygen (O 2 ) in a layered one-dimensional peat column. Here, we present the HIMMELI model structure and results of tests on the model sensitivity to the input data and to the description of the peat column (peat depth and layer thickness), and demonstrate that HIMMELI outputs realistic fluxes by comparing modeled and measured fluxes at two peatland sites. As HIMMELI describes only the CH 4 -related processes, not the full carbon cycle, our analysis revealed mechanisms and dependencies that may remain hidden when testing CH 4 models connected to complete peatland carbon models, which is usually the case. Our results indicated that (1) the model is flexible and robust and thus suitable for different environments; (2) the simulated CH 4 emissions largely depend on the prescribed rate of anoxic respiration; (3) the sensitivity of Published by Copernicus Publications on behalf of the European Geosciences Union. 4666M. Raivonen et al.: HelsinkI Model of MEthane buiLd-up and emIssion for peatlands the total CH 4 emission to other input variables is mainly mediated via the concentrations of dissolved gases, in particular, the O 2 concentrations that affect the CH 4 production and oxidation rates; (4) with given input respiration, the peat column description does not significantly affect the simulated CH 4 emissions in this model version.

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

Cited by 33 publications

References 64 publications

Importance of vegetation classes in modeling CH4 emissions from boreal and subarctic wetlands in Finland

Importance of vegetation classes in modeling CH4 emissions from boreal and subarctic wetlands in Finland

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

HIMMELI v1.0: HelsinkI Model of MEthane buiLd-up and emIssion for peatlands

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