Modelling CO₂ and CH₄ emissions from drained peatlands with grass cultivation by the BASGRA-BGC model

Abstract Cultivated peatlands under drainage practices contribute significant carbon losses from agricultural sector in the Nordic countries. In this research, we developed the BASGRA-BGC model coupled with hydrological, soil carbon decomposition and methane modules to simulate the dynamic of water table level (WTL), carbon dioxide (CO₂) and methane (CH₄) emissions for cultivated peatlands. The field measurements from four experimental sites in Finland, Denmark and Norway were used to validate the predictive skills of this novel model under different WTL management practices, climatic conditions and soil properties. Compared with daily observations, the model performed well in terms of RMSE (Root Mean Square Error; 0.06–0.11 m, 1.22–2.43 gC/m²/day, and 0.002–0.330 kgC/ha/day for WTL, CO₂ and CH₄, respectively), NRMSE (Normalized Root Mean Square Error; 10.3–18.3%, 13.0–18.6%, 15.3–21.9%) and Pearson's r (Pearson correlation coefficient; 0.60–0.91, 0.76–0.88, 0.33–0.80). The daily/seasonal variabilities were therefore captured and the aggregated results corresponded well with annual estimations. We further provided an example on the model's potential use in improving the WTL management to mitigate CO₂ and CH₄ emissions while maintaining grass production. At all study sites, the simulated WTLs and carbon decomposition rates showed a significant negative correlation. Therefore, controlling WTL could effectively reduce carbon losses. However, given the highly diverse carbon decomposition rates within individual WTLs, adding indicators (e.g. soil moisture and peat quality) would improve our capacity to assess the effectiveness of specific mitigation practices such as WTL control and rewetting

The full annual carbon balance of a subtropical coniferous plantation is highly sensitive to autumn precipitation

Abstract Deep understanding of the effects of precipitation on carbon budgets is essential to assess the carbon balance accurately and can help predict potential variation within the global change context. Therefore, we addressed this issue by analyzing twelve years (2003–2014) of observations of carbon fluxes and their corresponding temperature and precipitation data in a subtropical coniferous plantation at the Qianyanzhou (QYZ) site, southern China. During the observation years, this coniferous ecosystem experienced four cold springs whose effects on the carbon budgets were relatively clear based on previous studies. To unravel the effects of temperature and precipitation, the effects of autumn precipitation were examined by grouping the data into two pools based on whether the years experienced cold springs. The results indicated that precipitation in autumn can accelerate the gross primary productivity (GPP) of the following year. Meanwhile, divergent effects of precipitation on ecosystem respiration (Re) were found. Autumn precipitation was found to enhance Re in normal years but the same regulation was not found in the cold-spring years. These results suggested that for long-term predictions of carbon balance in global climate change projections, the effects of precipitation must be considered to better constrain the uncertainties associated with the estimation