Rewetting can effectively reduce greenhouse gas (GHG) emissions from drained peatlands. Reliable emissions estimation approaches are needed for accounting of such reductions and for evaluating the potential in terms of carbon credits. Annual mean water level and vegetation are reliable and widely used proxies for emissions estimation. However, indications of water level based on plant species (e.g. Ellenberg Indicator Values) are qualitative with large variances, and there are insufficient high-quality flux measurement data to support the direct use of vegetation as a proxy for GHG fluxes. Here we combine vegetation and water level proxies to estimate emissions, by using bioindication of vegetation communities for water level together with the linear correlation between annual mean water level and GHG fluxes. This approach is demonstrated in the Drentsche Aa brook valley in The Netherlands, where peatlands were rewetted to restore rich fen vegetation. Biodiversity of the landscape was monitored by repeated vegetation mapping before and after rewetting, which enables the estimation of emissions reduction as a co-benefit. Mean annual water level values are assigned to mapped vegetation types using existing data on water level dynamics from measurements on corresponding plant communities. GHG emissions are estimated using linear regression models of gas fluxes against mean annual water levels. This approach provides spatially explicit and quantitative estimation of mean annual water levels and GHG fluxes. When combined with information on spatial patterns and variances, the resulting estimations can promote recognition of the carbon co-benefits of biodiversity restoration while facilitating more site-specific optimisation of management practices.

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