|Paper title||High-resolution estimation of methane emissions from boreal and arctic wetlands using satellites|
|Form of presentation||Poster|
Methane is the world's second most important anthropogenic greenhouse gas. It is rising as a result of a variety of factors, including agriculture (e.g., livestock and rice production) and energy generation (mining and use of fuel). Some natural processes, such as the release of methane from natural wetlands, have also changed as a result of human intervention and climate change.
An important uncertainty in the modelling of methane emissions from natural wetlands is the wetland area. It is difficult to model because of several factors, including its spatial heterogeneity on a large range of scales. As we demonstrate using simulations spanning a large range in resolution, getting the spatiotemporal covariance between the variables that drive methane emissions right is critical for accurate emission quantification. This is done using a high-resolution wetland map (100x100m²) and soil carbon map (250x250m²) of the Fenno-Scandinavian Peninsula, in combination with a highly simplified CH₄ emission model that is coarsened in six steps from 0.005° to 1°.
We find a strong relation between wetland emissions and resolution (up to 12 times higher CH₄ emissions for high resolution compared to low resolution), which is sensitive, however, to the sub-grid treatment of the wetland fraction.
As soil moisture is likely to have strong controlling effects on temporal and spatial variability in CH₄ emissions from wetlands. We try to improve CH₄ emissions using high-resolution remote sensing soil moisture datasets, in comparison to modelled soil moisture datasets obtained from the global hydrological model PCR-GLOBWB (PCRG). FluxNet CH₄ observations for 9 selected sites spread over the northern hemisphere were used to validate our simplified model results over the period between 2015 and 2019. As we will show, realistic estimates can be obtained using a highly simplified representation of CH₄ emissions at a high resolution, which is a promising step for minimizing the significant uncertainties for the modelling of CH₄ emissions at local and regional scales.