Disentangling biotic and abiotic drivers of CO2 flux in a drained German peatland using eddy covariance flux measurements and modelling techniques

Natural undisturbed peat lands act as a net carbon sink while drainage and subsequent aeration of the peat layers leads to oxidation of the organic material and the release of greenhouse gases. In Germany emissions from peatlands make up over 7 % of the country’s total annual carbon emissions. However, continuous observations with a high temporal resolution in German peatlands are still rather sparse. Due to the heterogeneity of peatland ecosystem characteristics and their relations to GHG fluxes, it is a major challenge to understand and model emissions across the wider category of peatlands. Some of the frequently used models (for example in interpolation of chamber-based measurements) are straightforward and easy to implement but leave potentially valuable information aside. In this study we use high-frequency (10Hz) CO₂ and H₂O exchange measured with eddy covariance at a drained bog, along with a suite of meteorological, hydrological and phenological measurements to disentangle the roles of biotic and abiotic variables in peatland CO₂ emissions. The site is a highly drained but intact bog with a peat body of roughly 3 m and a groundwater depth of around 60 cm, located in northwestern Germany. Preliminary results collected with manual chambers show that methane fluxes are negligible. First, we test the model performance of the commonly used rectangular hyperbolic light response curve for GPP. We then extend this model by including a greenness index derived from a time series of daily Phenocam images, allowing us to evaluate the impact of biotic drivers and their seasonality. Similarly, for R_eco we test the performance of the classical exponential Lloyd & Taylor model and modify it by accounting for the underlying hysteresis observed in the response of respiration to soil temperature changes, by including hydrological drivers such as soil moisture, precipitation and water table depth. Our results advance our capacity for understanding and predicting how peatland ecosystems respond and contribute to changes in the Earth´s future climate.