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Process-based modelling of peat greenhouse gas emissions in Indonesian peatlands
1. Process-based modelling of greenhouse
gas emissions from oil palm plantations
in an Indonesian peatland
Erin Swails, Kristell Hergoualc’h, Jia Deng, Steve Frolking
Annual Meeting of the Society of Wetland Scientists, 1 – 10 June 2021
2. Tropical peatlands
• Extremely high soil C stocks –
total 350 Gt over 110 Mha
(Gumbricht et al 2017)
• Poor drainage → soil
waterlogging and SOC
accumulation
• Global extent - Southeast
Asia, Africa, South and
Central America
Planas Clarke et al. 2020
4. GHG emissions from Indonesian peatlands
Deforestation Burning Oil Palm Plantation
Drainage
Peat Forest
b
• Land-use conversion increases GHG emissions from peat
soils (Leifeld et al 2019, Hergoualc’h & Verchot 2014)
5. Peat GHG emissions
• OP peat emissions primarily released as CO2 (Swails et al 2021)
(Drösler et al 2014)
6. Data for estimation of peat GHG emissions
• 2013 IPCC supplement on wetlands: 1st
detailed GHG emission factors for tropical
peatlands
• EF based on data from Southeast Asia
(Hergoualc’h & Verchot 2014)
• Limited data for OP EF primarily based on
young plantations
• Process-based models can extrapolate point-
based measurements to larger regions over
extended time periods
7. Can DNDC simulate tropical peat GHG emissions?
• DeNitrification DeComposition (DNDC): 1-D process-based
model of C and N biogeochemistry
• Adapted for agricultural ecosystems, forests, and wetlands
8. Test site: Field measurements
• Permanent plots in smallholder oil palm plantations
(n=3) on peat
• Three years monthly measurements
o Peat GHG flux (total soil respiration, CH4, N2O)
o Controlling factors (water table, soil moisture + temp)
• Heterotrophic respiration estimated w/ site-specific
partitioning ratios
9. Modelling GHG fluxes and biogeophysical drivers
• Initialization – on-site measurements and literature-
based values
• Forcing
o Daily weather data from airport
o Management: planting, fertilization, harvest
• Calibration – vegetation growth, hydrology, SOC sub-
pools
• Testing – annual average peat GHG fluxes (total and
heterotrophic soil respiration, N2O, CH4)
• 30-year model runs – CP/FP simulations in each plot
• Up-scale outputs to plot level w/ spatial ratios
10. Calibration results: OP standing biomass,
litterfall, root mortality
• DNDC vegetation outputs fell within range of values
reported in the literature
12. Calibration results: Soil water-filled pore space
• DNDC adequately simulated fluctuations and micro-
spatial variation in soil WFPS
OP1 OP2 OP3
13. Calibration results: Soil temperature
• DNDC did not simulate the influence of vegetation
shading on soil temperature
OP1 OP2 OP3
closed canopy open canopy
intermediate
14. Calibration results: SOC sub-pools
• DNDC simulated the magnitude of total soil respiration
well overall
OP1 OP2 OP3
15. Model test results: Total and heterotrophic soil
respiration
RMSD = 3.0 Mg CO2-C ha-1 yr-1 RMSD = 4.0 Mg CO2-C ha-1 yr-1
Observed mean annual SR kg CO2-C ha-1 d-1
Predicted
mean
annual
SR
kg
CO
2
-C
ha
-1
d
-1
Observed mean annual SRh kg CO2-C ha-1 d-1
Predicted
mean
annual
SRh
kg
CO
2
-C
ha
-1
d
-1
16. What are long-term net CO2 emissions from peat
decomposition in OP plantations?
• Modelled net peat CO2 emissions declined
over time
IPCC
DNDC: yr 0 - 10
DNDC: yr 10 - 20
DNDC: yr 20 - 30
heterotrophic respiration
root mortality
litterfall
17. Takeaways and next steps
• Total soil respiration and biogeophysical
drivers were simulated reasonably well in
model runs, but..
o Heterotrophic respiration in our young OP
plantations was overestimated
• Model simulations indicate IPCC default
overestimates net CO2 emissions from peat
decomposition in older OP plantations
18. Takeaways and next steps
• Improve simulation of soil temperature
• Refine SOC sub-pool partitioning and spatial
representation of near/far conditions in OP
model simulations
• Use DNDC to investigate relationships between
GHG fluxes and potential proxies
• Model peat GHG emissions in forest