Simulation of CO2 and CH4 emissions from peatlands and organic soils: an improvement of SOC pool initialization in LDNDC model

Advanced process-oriented models can reliably predict emissions of carbon containing greenhouse gases (CO₂ and CH₄) from both peatlands and arable soils. However, changes in land use, such as the conversion of peatlands into arable land or grassland following drainage, or the rewetting of drained peatlands, make it challenging for a single model to simulate soil processes and greenhouse gas emissions accurately across such different conditions.We evaluated the LandscapeDNDC (LDNDC) model (Kraus et al., 2015) by comparing its simulations with field measurements of soil properties and CO2 and CH4 exchange rates (Eickenscheidt et al., 2015; Hommeltenberg et al., 2015). The default distribution of soil organic carbon (SOC) into pools with different decomposability, which is typically initialized in models based on the C:N ratio of soil organic matter (SOM), was ineffective for soils with a high organic matter content (>10% organic C). To address this, we distributed SOC into particulate and mineral-associated fractions (POM and MAOM) based on the concept of Lavallee et al (2020). Furthermore, we divided the MAOM into fast- and slow-decomposable pools, according to the C:N ratio of total organic matter (OM). The initial fraction of POM in total OM was derived from the total C content using the function proposed by Rühlmann (2020). Incorporating these changes into LDNDC’s soil biochemistry module improved agreement with observations and resolved the problem of underestimating ecosystem respiration in drained peatlands used for grassland or crop production. The improved initialization of SOC pools in the LDNDC model should enable more precise simulation of soil C stocks and GHG emissions at regional level, where soils with a wide range of SOC content need to be considered simultaneously.ReferencesEickenscheidt, T., Heinichen, J., Drösler, M., 2015. The greenhouse gas balance of a drained fen peatland is mainly controlled by land-use rather than soil organic carbon content. Biogeosciences 12, 5161–5184. https://doi.org/10.5194/bg-12-5161-2015Hommeltenberg, J., Mauder, M., Drösler, M., Heidbach, K., Werle, P., Schmid, H.P., 2014. Ecosystem scale methane fluxes in a natural temperate bog-pine forest in southern Germany. Agricultural and Forest Meteorology 198–199, 273–284. https://doi.org/10.1016/j.agrformet.2014.08.017Kraus, D., Weller, S., Klatt, S., Haas, E., Wassmann, R., Kiese, R., Butterbach-Bahl, K., 2015. A new LandscapeDNDC biogeochemical module to predict CH4 and N2O emissions from lowland rice and upland cropping systems. Plant Soil 386, 125–149. https://doi.org/10.1007/s11104-014-2255-xLavallee, J.M., Soong, J.L., Cotrufo, M.F., 2020. Conceptualizing soil organic matter into particulate and mineral-associated forms to address global change in the 21st century. Global Change Biology 26, 261–273. https://doi.org/10.1111/gcb.14859Ruehlmann, J., 2020. Soil particle density as affected by soil texture and soil organic matter: 1. Partitioning of SOM in conceptional fractions and derivation of a variable SOC to SOM conversion factor. Geoderma 375, 114542. https://doi.org/10.1016/j.geoderma.2020.114542