EGU General Assembly 2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

How can process-based modelling improve tropical peat greenhouse gas emission factors?

Erin Swails1, Kristell Hergoualc'h1, Jia Deng2, and Steve Frolking2
Erin Swails et al.
  • 1Center for International Forestry Research, Indonesia
  • 2Earth Systems Research Center, Institute for the Study of Earth, Oceans and Space, University of New Hampshire, USA

Degradation, conversion and drainage of tropical peatlands generate sizeable emissions of greenhouse gases (GHG). Current IPCC default emission factors (EF) for drained tropical peatlands are based on a very limited number of observations, thereby resulting in large uncertainties in emissions estimates. Impacts of disturbance on peat GHG emissions in undrained tropical peatlands can also be substantial but are not well characterized and not considered by IPCC guidelines. Research is critically needed to support development of more accurate EF for national GHG accounting for both drained and undrained degraded tropical peatlands. To explore the potential of process-based modelling to refine tropical peat EF, we used the DeNitrification DeComposition (DNDC) model to simulate peat GHG emissions and biogeophysical variables in oil palm plantations and undrained primary and secondary peat swamp forests of Central Kalimantan, Indonesia.

The simulated magnitude of C inputs (litterfall and root mortality) and dynamics of annual heterotrophic respiration and peat decomposition N2O fluxes in oil palm plantations were generally consistent with field observations. The modelled onsite oil palm peat CO2 EF was lower than the IPCC default (11 Mg CO2-C ha-1 yr-1) and decreased from 7.7 ± 0.4 Mg C ha-1 yr-1 in the first decade to 3.0 ± 0.2 and 1.8 ± 0.3 Mg C ha-1 yr-1 in the second and third decades of the rotation. The modelled N2O EF from peat decomposition was higher than the IPCC default (1.2 kg N ha-1 yr-1) and increased from 3.5 ± 0.3 kg N ha-1 yr-1 in the first decade to 4.6 ± 0.5 kg N ha-1 yr-1 in the following ones. Modelled fertilizer-induced N2O emissions were minimal and much less than 1.6% of N inputs indicated by the IPCC EF in wet climates regardless of soil type. Temporal variations in oil palm EF were strongly linked to soil C:N ratio and mineral N content for CO2 and fertilizer-induced N2O emissions, and to precipitation, water table level, and soil NH4+ content for peat decomposition N2O emissions. These results suggest that current IPCC EF for oil palm on organic soil could over-estimate onsite CO2 emissions and underestimate peat decomposition N2O emissions and that decadal-scale temporal variation in emissions should be considered for further improvement of EF. Simulations allowed the generation of oil palm EF disaggregated by plantation age and emission source (decomposition, fertilizer-induced), a practical and useful application for GHG inventories in tropical peatlands.

In unconverted land uses, the GHG budget (Mg CO2-equivalent ha-1 yr-1) was ten times higher in the secondary forest (10.2 ± 4.5) than in the primary forests (0.9 ± 3.9) on the account of a larger peat C budget and N2O emission rate. Preliminary modelling results suggest increased peat C outputs from heterotrophic respiration and decreased C inputs from litterfall and root mortality in secondary forest compared to primary forest. Our study highlights the disastrous atmospheric impact associated with not only conversion to oil palm but also forest degradation in tropical peatlands and stresses the need to investigate GHG fluxes in disturbed undrained lands.

How to cite: Swails, E., Hergoualc'h, K., Deng, J., and Frolking, S.: How can process-based modelling improve tropical peat greenhouse gas emission factors?, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5145,, 2022.