3. Simulations using E3SM land surface model
5. Future work
1. Introduction
2 .Field measurements
4. Regional SIF
5. Future work
Biophysical Processes and Feedback Mechanisms Controlling the Methane Budget
of an Amazonian PeatlandFenghui Yuan1, Timothy J. Griffis1*, Daniel T. Roman2, Jeffrey D. Wood3, Angela Lafuente4, Erik Lilleskov2, Hinsby Cadillo-Quiroz5,
Randall Kolka2, Jhon Ever Rengifo6, Lizardo Fachin6, Rod Chimner4, Craig Wayson2, Kristell Hergoualc’h7, Daniel M. Ricciuto8
1University of Minnesota; 2USDA Forest Service; 3University of Missouri; 4Michigan Technological University; 5Arizona State University; 6Instituto de Investigaciones de la Amazonia Peruana, Peru; 7Center for International Forest Research, Indonesia; 8Oak Ridge National Laboratory; *Correspondence: [email protected] (PI)
• Eddy covariance system for
measuring carbon and energy
fluxes have been established and
maintained for nearly 3 years.
• In 2021, automatic 16-
chamber-based system for
measuring soil and trunk CO2
and CH4 emissions has been
successfully installed based upon
microtopography and tree
species.
FIGURE 2. Location of PE-QFR AmeriFlux site. Also shown is
the global distribution of peatlands. (Xu et al. 2018).
FIGURE 1. Experimental and modeling approach illustrating the links among
experimental process studies and model development, validation and forecasting.
FIGURE 6. Comparing diel patterns of
modeled and observed CO2 and CH4 fluxes
for wet and dry seasons in 2019.
• Established in 2017 (AmeriFlux ID: PE-QFR)• Location: Quistococha, Peru (73º19′ 08.1" W; 3º50′ 03.9" S)
• Elevation: 104 m
• IGBP classification: Permanent Wetland
• Dominant vegetation: Mauritia flexuosa L.f. (21.3m height)
• Soil peat layer thickness: 1.92–2.45 m
• MAT=27.2 ºC
• MAP=2753.2 mm
References:[1] Griffis T.J. et al. 2020. Hydrometeorological sensitivities of net ecosystem carbon dioxide and methane
exchange of an Amazonian palm swamp peatland. Agricultural and Forest Meteorology, 295:108167.
[2] Ricciuto D.M., et al. 2021. An integrative model for soil biogeochemistry and methane processes: I.
model structure and sensitivity analysis. JGR-Biogeosciences. (In press)
[3] Yuan F., et al. 2021 An integrative model for soil biogeochemistry and methane processes : II. warming
and elevated CO2 effects on peatland CH4 emissions. JGR-Biogeosciences. (In press)
[4] Haren J., et al. 2021. A versatile gas flux chamber reveals high tree stem CH4 emissions in Amazonian
peatland. Agricultural and Forest Meteorology, 307:108504.
[5] Xu J., et al. 2018 PEATMAP: Refining estimates of global peatland distribution based on a meta-
analysis. CATENA 160: 134–140.
Acknowledgments: This work is supported by the U.S. Department of Energy, Office of Science, Office of Biological and Environmental Research, Terrestrial Ecosystem Science Program (DE-SC0020167). The data used have been submitted to the AmeriFlux program and available at: https://ameriflux.lbl.gov/sites/siteinfo/PE-QFR.
PROJECT SUMMARY
FIGURE 3. Trunk chamber gas
concentration traces for flux
measurements.
Automatic chamber-based system
• The model is derived from ELM-
SPRUCE. (Ricciuto et al. 2021)
• After parameter optimization and
several modifications of key
algorithms with observed data, the
ELM model can capture the seasonal
and diurnal CO2 and CH4 fluxes
overall, although there is still
considerable room for improvement.
• The new algorithms are mainly:
(1) soil hydraulic property function
involving an updated water retention
curve with empirical tropical soil
data; (2) dynamic vcmax25 function
according to seasonal phenological
and physiological changes of tropical
species;(3) water stress scalar
function in CH4 processes which is
improved as a dynamic function of
water table level.
• Tropical peatlands are a major CH4
source, but large uncertainties persist in
their source estimates and significant
knowledge gaps still exist in their process
understanding because of a lack of CH4
observations in the tropics.
• We aim to address these questions by
providing much needed data on CH4
fluxes in tropical peatlands and
developing and testing the Energy
Exascale Earth System Model (E3SM)
land surface component (ELM) for
tropical regions which has been
successfully applied in a boreal forest
peatland (ELM-SPRUCE version).
• Installation of air sample profile system for Lagrangian
analysis of CH4 and CO2 exchange.
• Evaluate flux partitioning and resolve vertical source/sink
distribution based on chamber and profile observations.
• Improve our understanding of the response mechanisms of
GPP to wet/dry seasons in tropical peatlands based on deeper
SIF analyses.
• Finalize the parameter optimizations and function
improvements of ELM before the end of 2021, and then
simulate the climate sensitivities of carbon fluxes to different
scenarios for this tropical peatland.
FIGURE 4. Workflow of observations‐
based off-line modeling optimization.
FIGURE 5. Comparing seasonal patterns
of modeled and observed CO2 and CH4
fluxes for wet and dry seasons in 2019.
FIGURE 7: Taylor diagrams showing correlations coefficients, standard deviations and RMSEs between daily observations and ELM simulations in wet and dry seasons.
Table 1. Parameters for sensitivity analysis.
PE-QFR
FIGURE 9: Spatial and temporal variation of sun induced
fluorescence (SIF) in the Pastaza-Marañón foreland basin.
FIGURE 10: Mean SIF of different
land cover classes within the Pastaza-
Marañón foreland basin.
• Regional patterns of photosynthesis
are being investigated using SIF and
other remote sensing products.
FIGURE 8: Sensitivity analysis for model responses of CO2 and CH4 fluxes, GPP and ecosystem respiration (ER) to 20 parameters in Table 1.
Parameter Definition Parameter DefinitionP1 bdnr Bulk denitrification rate P11 r_mort Mortality rate
P2 br_mrBase rate for maintenance
respiration (MR)P12 stem_leaf New stem carbon per new leaf carbon
P3 decomp_depth_efoldinge-folding depth for
decompositionP13 k_dom
Decomposition rate of dissolved
organic matter
P4 flnr Fraction of leaf N in RuBisco P14 m_dAceProdACmax Maximum rate of acetic acid
production from available carbon
P5 froot_leaf Fine root to leaf allocation ratio P15 m_dACMinQ10Temperature sensitivity of available
carbon mineralization
P6 frootcn Fine root C:N ratio P16 m_dGrowRAceMethanogensGrowth rate of acetoclastic
methanogens
P7 grperc Growth respiration fraction P17 m_dH2ProdAcemaxMaximum reaction rate of conversion
of H2 and CO2 to acetic acid
P8 ksomfacDecomposition fraction of soil
organic matterP18 m_dYAceMethanogens
Growth efficiency of acetoclastic
methanogens
P9 leafcn Leaf carbon/nitrogen (C:N) ratio P19 m_dKCH4OxidCH4Half-saturation coefficient of CH4
oxidation for CH4 concentration
P10 q10_mr Temperature sensitivity for MR P20 Nfix Nitrogen fixation rate