Modeling the impact of drainage on peatland CO₂ and CH₄ fluxes and its underlying drivers

Abstract. Peatland drying is an important process affecting greenhouse gas (GHG) emissions. Ditching for forest drainage has been standard forest management practice in the Nordic countries in the past centuries, and drying increasingly occurs also from climate change induced drought. Previously published meta-analyses from literature suggest that typically, drainage increases CO₂ emissions by enhancing oxic decomposition in aerated upper layers while suppressing CH₄ emissions. However, the data do not elucidate short term variations of GHG fluxes during drainage and usually only regress GHG emissions as a function of the annual mean water table. Here we developed a new parameterization of drainage in a land surface model that represents peat processes and fluxes of CO₂ and CH₄, by adding a machine-learning module to predict the daily water table depths from simulated soil moisture in the upper soil layers and a ditch which receives drainage water. Because peatland pre-drainage GHG emissions differ between sites and influence subsequent changes from drainage, the simulations are performed for virtual drainage applied to a collection of 10 pristine sites at which the model parameters are calibrated against observed GHG fluxes. Different drainage intensities are simulated by prescribing lower water table depths from the ditch depth, from 5 to 50 cm below the initial water surface. The resulting GHG flux changes across sites are compared with meta-analysis data from northern sites and show realistic results with a reduced CO₂ sink and reduced CH₄ emissions. Additional comparison with continuous flux data collected in the UK for different sites associated with increasing drainage levels also shows good model performances. Overall, using GWP100 to compare the effect of CH₄ vs. CO₂ flux changes, our simulation results suggest only very small net GHG emission changes when CH₄ is expressed in CO₂-equivalents units using GWP100, when peatland is drained for 50 years, yet with differences between sites. Over time during 50 years of drainage, the emission factors of CO₂ flux decrease because of exhaustion of labile soil organic substrate for decomposition and the reduction of CH₄ emissions is amplified, also because of less material for anoxic decomposition. The sensitivities of CO₂ flux changes to increased water table depth changes are primarily controlled by initial CO₂ and CH₄ fluxes, initial soil carbon content, peat vegetation community, air temperature and initial water table depth. The influence of peat vegetation on the GHG flux sensitivities in the model occurs via differing lability of soil organic carbon pools, with moss-dominated sites having a lower sensitivity due to their longer peat turnover time. Our calibrated process-oriented model simulations of the sensitivities of GHG flux changes to water table depth can be emulated by linear regression models, which are simple and could be used in decision support tools and GHG regional budgets accounting.