In the global methane budget, the largest natural source
is attributed to wetlands, which encompass all ecosystems composed of
waterlogged or inundated ground, capable of methane production. Among them,
northern peatlands that store large amounts of soil organic carbon have been
functioning, since the end of the last glaciation period, as long-term
sources of methane (CH
The atmospheric methane level estimated from ice cores analysis (Etheridge et al., 1998) and in situ measurements (Blake et al., 1982; Dlugokencky, 2021; Prinn et al., 2018) has nearly tripled since the preindustrial equilibrium value, i.e., from 680 ppb to reach a value of 1892 ppb in December 2020 (Dlugokencky, 2021; Saunois et al., 2020). This increase is consistent with the world population increase and industrialization, such as the increase in fossil fuel extraction and use, organic waste generation, and livestock numbers (Raynaud et al., 2003).
Methane is the second most important anthropogenic greenhouse gas (GHG)
after CO
The major pathway for methane production is via microbial processes, which is
limited by the availability of substrates (polymeric and monomeric compounds
derived from carbohydrates, fatty acids, amino acids, acetate, and hydrogen;
(Blodau, 2002; Le Mer and
Roger, 2001), the low oxygen content that is directly correlated with soil
water content, and soil temperature. After its production, CH
Since the late 1980s, many CH
A general presentation of ORCHIDEE-PCH
The ORCHIDEE land surface model is a dynamic global process-oriented model
that simulates carbon, water, and energy fluxes between the biosphere,
land surface geosphere, and atmosphere. The carbon scheme describes
photosynthesis, respiration, soil carbon cycle, and CO
A northern peatlands scheme has been recently integrated into the model
(ORCHIDEE-PEAT,
Largeron
et al., 2018; Qiu et al., 2018), which includes a peatland PFT (plant
functional type) with adapted biological parameters created to allow a
separate calculation of the water balance. This PFT is defined as a flood-tolerant C
The methane scheme in Fig. 1 delineates (1) methanogenesis of the three carbon pools, (2) methane and oxygen transport in the soil and snow layers, (3) transport of methane to the atmosphere by ebullition, (4) plant-mediated transport, and (5) methanotrophy by soil oxic conditions and root exudates.
Model diagram of methane cycling processes in ORCHIDEE-PCH
Each of these processes is constrained by soil temperature, soil water
content (
Methanogenesis in soil occurs when oxygen concentration is limited for
microorganisms and is considered for each type of soil carbon pool
(
The formation of methane bubbles in water-filled pores is determined by
In wetlands, some vascular plants have developed a strategy to carry oxygen down
to their root tips by employing aerenchyma tissue. These tissues are
air channels in which gas exchange depends on the gradient of gas
concentrations between the soil and the atmosphere. Oxygen is transferred
from the atmosphere to the roots and creates an aerobic zone around them in
which methane will be oxidized. The proportion of methane oxidized
(
The gas diffusion scheme features the diffusion of CH
The only source of oxygen considered is from the atmosphere and is
determined using atmospheric surface pressure, temperature, and an
atmospheric O
The model was evaluated on 14 peatland sites distributed on the Eurasian and
American continents in boreal and temperate northern regions (from
41 to 69
Site characteristics. Site identification includes the country
initials and a three-letter name for each site; locations of the
sites are provided by the country, latitude (Lat), and longitude (Lon)
values. Hydrological characteristics are distinguished by the type of
ecosystem: fen, bog, tundra, and marsh.
Each peatland site is a sub-grid area embedded in the 0.5
Simulation conditions and framework to constrain peatland soil carbon stocks.
List of parameters driving the methane production, oxidation, and
transport scheme in ORCHIDEE-PCH
The methane scheme revisited in ORCHIDEE-PCH
Two types of simulations are performed over the site-specific observation period defined in Table 1: a single site (SS) experiment for which parameters are optimized for each site and a multi-site (MS) experiment that aims at refining one set of parameters considering all sites together. The single site experiments are performed for 100 iterations and aim at finding the lowest cost function employing the model–data root mean square difference (RMSD). Prior conditions for the single site experiment are described and listed in Table 3. Initial parameter values and ranges were derived from the literature and expert knowledge, and parameter uncertainties are defined as 40 % of the prescribed ranges. Across sites, mean values of each parameter serve as prior conditions for the multi-site experiment. The latter was performed for 50 iterations and aims to evaluate methane emission uncertainties at hemispheric scale when only one set of parameters is employed.
For each site, to minimize the discrepancy between observed and simulated
methane emissions, iterative single site simulations were performed.
Successive runs serve to ensure that the minimum reached is not a local
minimum. Results from the last minimization experience are reported in Table 4 (uncertainties in parameters at sites are in Table S1). As expected, most
optimized parameters fit within the initial range defined in Table 3 except
for four of the sites. One of these four sites, DE-Spw, is among the sites
that emits the lowest amount of methane (up to 7 mg m
Single site optimized values of methane scheme parameters for each peatland site. In parentheses are the prior parameter ranges which differ from the values in Table 3. Uncertainties for these ranges are specified in parentheses.
Discrepancies between observed and simulated methane
emissions are quantified by the root mean square difference (RMSD) approach.
Minimization efficiency of each test is indicated by the relationship
between the prior using default values and posterior RMSD as (1
Across sites,
Differences between observed and simulated methane fluxes employing initial
and optimized parameters are quantified by the RMSD prior (RMSD
Temporal distribution of methane at sites emitting less than 10 mg CH
In addition to the mismatch between observed and simulated methane emissions
during the observed period, Figs. 2, 3, 4, and 5 show the simulated water
table position, the amount of methane that is emitted by diffusion, plant
transport, and ebullition, the temporal methane concentration in the soil and in
the snow, and the depth at which the largest amount of methane is produced
together with the rate of production at that depth. These variables show the
consistency of the model regarding peatland functioning. US-Los and DE-Spw
emitted less than 10 mg CH
Other sites that emitted less than 150 mg CH
Temporal distribution of methane for sites emitting between 10 and
150 mg CH
Sites that emitted between 150 and 400 mg CH
Temporal distribution of methane for sites emitting between 150
and 400 mg CH
The highest simulated methane fluxes of 600 mg CH
Temporal distribution of methane for sites emitting more than 400 mg CH
For large-scale simulations only one set of parameters is needed for the
simulation of methane emissions to achieve the average of each
parameter value optimized on-site being commonly employed. Here, a multi-site
optimization has been performed for which prior values correspond to the
average values of each parameter obtained from the single site optimizations
described in Sect. 3.1. This multi-site optimization serves to assess to
what extent a multi-site optimization is more efficient than using average
values of parameters optimized on-site independently. Multi-site optimized
parameter values acquired by using average values of parameters defined at
each site and the initial ranges (Table 3) are shown in Table 6. Compared to
the prior values,
Multi-site prior and optimized values of methane scheme parameters. Parameter prior values are the average value of the parameters optimized at each site. Parameter descriptions and references are in Table 3.
In Table 7, RMSD
A multi-site optimization has also been performed employing extended ranges of parameter values that are enlarged to the maximum and minimum values obtained for the single site optimizations (Tables S4 to S6 and Fig. S9). Despite a different set of parameters being defined (Table S3), discrepancies between observed and simulated emissions (Tables S5 and S6 and Fig. S10) are similar to the ones obtained using default parameter ranges.
Discrepancies between observed and simulated methane
emissions are quantified by the root mean square difference (RMSD) approach.
Minimization efficiency of the multi-site optimization is indicated by the
relationship between the prior using average values of parameters optimized
by the single site optimization and posterior RMSD
Simulated and observed (gray line) methane emissions using single site (dashed dark line) and multi-site (solid dark line) optimized parameters.
Sensitivity analyses were previously performed to assess methane emission
model responsiveness to parameter values
(Meng
et al., 2012; Riley et al., 2011; Spahni et al., 2011; Wania et al., 2009;
Zhu et al., 2014). These studies
(van
Huissteden et al., 2009; Riley et al., 2011) suggested that temperature
dependency of methanogenesis is the most influential parameter affecting
methane production, whereas methane emissions are mostly sensitive to
oxidation and plant transport. Indeed, in large-scale models such as CLM4Me,
LPJ-GUESS, LPX-Bern, CNRM, and ORCHIDEE
(Potter,
1997; Riley et al., 2011; Khvorostyanov et al., 2008b; Wania et al., 2009,
2010; Zhu et al., 2014; Morel et al., 2019) methane production results from
anoxic decomposition of soil organic matter, the rate of which is constrained by the
soil oxic and anoxic decomposition ratio (
After methanogenesis, methane is mobilized in pores and ultimately emitted to the atmosphere or is oxidized by methanotrophs depending on whether methane travels along the anoxic or the oxic parts of the soil. In large-scale models, methanotrophy is formulated employing a Michaelis–Menten or a first-order kinetic framework based on soil methane and oxygen content (Morel et al., 2019). These formulations are then driven by the oxidation rate, the values of which vary from a few hours to days. In the present work, we employed the first-order kinetic formulation of Khvorostyanov et al. (2008a) that is driven by methane and oxygen content. Optimization of the oxidation rate leads to values that are spread over the full range of 1 to 5 per day. This is consistent with the review paper of Smith et al. (2003), highlighting the fact that methanotrophy is more sensitive to soil moisture than soil temperature and that there is a direct link between methane oxidation rate and gas diffusivity. Thus, the optimization of the oxidation rate results from the balance between model inputs and outputs that are respectively available methane and oxygen substrates as well as methane fluxes, which explain this large variability in oxidation rate. In addition, in our model, snow is considered in the diffusion scheme, which in part controls diffusivity of oxygen from the atmosphere to the ground in winter (e.g., Fig. 2c).
Methane emissions mediated by vascular plants result from series of
processes that include (1) the diffusion and advective transport of methane
and oxygen in aerenchyma tissues, (2) autotrophic respiration of a fraction of
oxygen transiting in aerenchyma of vascular plants
(Colmer,
2003; Nielsen et al., 2017), (3) methane production by microbial
decomposition of plant exudates, and (4) methane oxidation by exudates and by
remaining oxygen at the root level brought through aerenchyma that increase
methanotroph activities. Modeling these processes requires (1)
understanding and quantifying them
(Kaiser
et al., 2017; Raivonen et al., 2017; Riley et al., 2011; Wania, 2007) as well as
(2) evaluating the average density of vascular plants that are capable of
significant gas transport across ecosystems. While a significant number of
studies provide insight on gas exchanges through vascular plants, densities
of vascular plants with aerenchyma in peatlands are poorly characterized. In
the most recent models, formulations of various complexity were used to
simulate vegetation-mediated gas transport considering mainly CH
When methane is significantly produced in the soil, the accumulation of
methane in the water-saturated pores involves the formation of methane-rich
bubbles that will migrate in the soil layers and eventually deliver methane
to the atmosphere. This flux of methane is commonly prompted in land surface
models by the amount of methane that is no longer soluble in saturated
water-filled pores. This excess amount is defined here from the mixing ratio
(mrx
Soil and litter organic carbon and plant exudates are recognized to be the
main substrates for methanogenesis
(Chang
et al., 2019; Riley et al., 2011; Whalen, 2005). Recent work of
Hopple et al. (2019) demonstrates
that dissolved organic carbon (DOC) also significantly contributes to anoxic
decomposition in peatlands. Some field studies suggested that high-latitude
methanogenesis can be substrate-limited
(Chang
et al., 2019; Riley et al., 2011; Whalen, 2005). In large-scale models, soil
organic carbon (SOC) is considered to be the primary source of methane; however, in
order to increase the rate of methanogenesis, labile organic matter, such as
litter carbon and plant exudates, is directly combined with soil carbon,
bypassing oxic decomposition processes to account for them as substrates for the
methane production scheme (Morel et al., 2019; Khvonostyanov et al., 2008b). In
the present study, SOC is the only substrate for methanogenesis for which
total soil carbon stock and maximum peat depth have been adjusted to
observation data at each site (Table 2). Simulation results show that at
sites that emitted more than 400 mg CH
Integrated simulated soil organic carbon content of peatland sites up to 0.75 m depth.
Sensitivity of methane fluxes to model parameters was evaluated by comparing
annual methane emissions obtained by employing single site (SS) and
multi-site (MS) optimized parameters. Table 9 reports annual observed and
simulated methane fluxes as well as the contributions among the three types of
methane transport, i.e., diffusion, ebullition, and plant-mediated.
Considering all 14 sites, average annual methane emissions for the observed
values are 18
Yearly methane emissions defined from the observed data (Obs) as well as simulations employing optimized parameters obtained by the single site optimization (SSO) and multi-site optimization (MSO). The methane fluxes combine methane emitted by diffusion, plant-mediated transport, and ebullition.
Discrepancies between the observation data and the SSO and MSO simulations are
displayed in Fig. 6. At sites that emitted the largest amount of methane
e.i. PL-Wet, RU-Che, and US-Wpt, SSO and MSO simulations were underestimated
up to 46 and 53 g CH
Difference in annual methane emissions defined between the observed data (Obs) and simulations employing optimized parameters obtained by the single site optimization (SSO) and by multi-site optimization (MSO).
Average methane emissions estimated from these 14 sites can be utilized to
roughly calculate emissions from peatlands located northern of 30
The methane model developed by
Khvorostyanov et al. (2008a) has
been modified to encompass northern peatlands and permafrost features
embedded in the most recent version of ORCHIDEE-PEAT v2.0. This modified
version, ORCHIDEE-PCH
Single site optimization results highlighted the fact that the depth of the highest
methane production fluctuates between 20 cm during the warmer season and 75 cm
during the cold season. This demonstrates the sensitivity of methanogenesis
to soil temperature and provides insight on the extent to which
methanogenesis takes place in the soil layers. This also serves to
identify sites that are substrate-limited and to emphasize the need for
global-scale models to consider dissolved organic matter as a source of
methane substrate. Indeed, in some site simulation studies prescribed
methane substrate originating from litter decomposition or plant exudates
was added to soil organic content in order to balance out the lack of
labile substrate. In the scheme of ORCHIDEE-PCH
Optimization of parameters simultaneously employing methane emissions from
all 14 sites produce a reduction in the rate of methanotrophy and in
methane transport in the soil by ebullition, promoting methane oxidation at
the root level and transport of methane by vascular plants. These involve a
large overestimation of sites emitting small amounts of methane.
Nonetheless, on average methane emissions simulated employing the multi-site
optimization approach are only overestimated by about 5 g CH
The source code (
The optimization tool is available through a dedicated website for data
assimilation with ORCHIDEE (
Measured eddy covariance fluxes and related meteorological data can be obtained from the SNO-T (
The supplement related to this article is available online at:
ES revised and modified the implementation of the methane module in
ORCHIDEE-PEAT, performed model optimization simulations in ORCHIDEE-PCH
The contact author has declared that neither they nor their co-authors have any competing interests.
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
The modeling work was supported by the European Union's Horizon 2020 Project CRESCENDO under contract 641816 and Labex VOLTAIRE ANR-10-LABX-100-01. The authors acknowledge the support of staff at each site. Research at US-Los was supported by the AmeriFlux Network Management Project under contract no. 7544821 to the ChEAS core site cluster. Funding for the measurements in Biebrza National Park was provided by the Polish National Science Centre under projects UMO-2015/17/B/ST10/02187 and UMO-2020/37/B/ST10/01219. US-Bog was supported by the United States National Science Foundation: NSF OPP 1107892, 1503912, 1936712. Lawrence B. Flanagan acknowledges funding from the Natural Sciences and Engineering Research Council of Canada (NSERC), the FLUXNET-Canada Network (NSERC, the Canadian Foundation for Climate and Atmospheric Sciences – CFCAS, and BIOCAP Canada), and the Canadian Carbon Program (CFCAS). Fieldwork at FR-Lag was funded as part of the Labex VOLTAIRE and the PIVOTS project of the Région Centre – Val de Loire (ARD 2020 program and CPER 2015–2020) in the framework of the French Peatland Observatory, SNO Tourbières, endorsed by CNRS-INSU. Research work at DE-Hmm was funded by the Deutsche Forschungsgemeinschaft under Germany's Excellence Strategy – EXC 177 “CliSAP – Integrated Climate System Analysis and Prediction” – contributing to the Center for Earth System Research and Sustainability (CEN) of Universität Hamburg. The work at PL-Wet is based on use of Large Research Infrastructure CzeCOS supported by the Ministry of Education, Youth and Sports of CR within the CzeCOS program under grant number LM2018123. Natalia Kowalska acknowledges the support by SustES – Adaptation strategies for sustainable ecosystem services and food security under adverse environmental conditions (CZ.02.1.01/0.0/0.0/16_019/0000797).
This research has been supported by Horizon 2020 (CRESCENDO (grant no. 641816)) and Labex VOLTAIRE (grant no. ANR-10-LABX-100-01).
This paper was edited by Carlos Sierra and reviewed by two anonymous referees.