Introduction
Methane (CH4) is the second-most important anthropogenic greenhouse gas,
accounting for ∼ 15 % of anthropogenic forcing to climate change
(Forster et al., 2007; IPCC, 2013; Rodhe, 1990). Therefore, an accurate
estimate of CH4 exchange between land and the atmosphere is fundamental
for understanding climate change (Bridgham et al., 2013; Nazaries et al.,
2013; Spahni et al., 2011). The ecosystem modeling approach has been one of
the most broadly used integrative tools for examining mechanistic processes,
quantifying the budget of CH4 flux across spatial and temporal scales
(Arah and Stephen, 1998; Riley et al., 2011; Walter et al., 1996; Zhuang et
al., 2004) and predicting future flux (Anisimov, 2007). Specifically, many
CH4 models have been developed to integrate data, improve process
understanding, quantify budgets, and project exchange with the atmosphere
under a changing climate (Cao et al., 1995; Grant, 1998; Huang et al., 1998a;
Potter, 1997). In addition, model sensitivity analyses help to design field
and laboratory experiments by identifying the most uncertain processes and
parameters in the models (Massman et al., 1997; Xu, 2010).
Based on the complexity of the CH4 processes represented, CH4
models fall into two broad categories: (1) empirical models that are used to
estimate and extrapolate measured methanogenesis, methanotrophy, or CH4
emission at plot, country, or continental scales (Christensen et al., 1996;
Eliseev et al., 2008; Mokhov et al., 2007; Wania et al., 2009, 2010); and
(2) process-based models that are used for prognostic understanding of
individual CH4 processes in response to multiple environmental drivers
and budget quantification (reviewed below). This separation emphasizes the
high-level model structure rather than the specific processes represented;
therefore, models with many processes represented with empirical functions
are still classified as process-based models if they represent many key
processes of CH4 production, oxidation, and transport. Although this
separation is rather arbitrary, it helps one to understand the
characteristics and purpose of models in a systems perspective.
Over the past decades, many empirical and process-based models have been
developed, for example, CASA (Potter, 1997), CH4MOD (Huang et al., 1998b),
CLM4Me (Riley et al., 2011), DAYCENT (Del Grosso et al., 2000), DLEM (Tian et
al., 2010; Xu and Tian, 2012), DNDC (Li, 2000), ecosys (Grant,
1998), HH (Cresto Aleina et al., 2015), MEM (Cao et al., 1995), and TEM
(Zhuang et al., 2004). However, recent analyses and model inter-comparisons
have shown that most of these models poorly reproduce regional- to
global-scale observations (Bohn and Lettenmaier, 2010; Bohn et al., 2015;
Melton et al., 2013; Wania et al., 2013). A comprehensive synthesis and
evaluation of the mechanisms incorporated into these models is lacking. This
review focuses on primary processes of CH4 cycling in the terrestrial
ecosystems and their representation in the models. The critical CH4
processes include substrate cycling, methanogenesis, methanotrophy, and
transport in the soil profile, and their environmental controls. Emphasis is
given to how these mechanisms were simulated in various models and how they
were categorized in terms of complexity and ecosystem function. The review
focuses on CH4 models developed for terrestrial ecosystems, which is
defined as ecosystems on land and wetlands with less than 2 m standing
water. This classification is used to distinguish them from pure aquatic
ecosystems and considering the important role of wetlands in CH4
cycling. Therefore, models for understanding reactions in bioreactors (Bhadra
et al., 1984; Pareek et al., 1999), mining plots (De Visscher and Van
Cleemput, 2003), aquatic ecosystems, and marine systems (Elliott et al.,
2011) were excluded. An early pioneering effort of multiplying wetland area
by average CH4 flux to estimate global CH4 budget was excluded from
this review as well (Matthews and Fungi, 1987). This review further excludes
the CH4 emission from biomass burning, termites, and ruminants, because
this paper primarily focuses on soil biogeochemical processes represented in
ecosystem models. The model names are determined by two criteria: (1) if the
model has been named in the original publication, it will be used to
represent the model; (2) if the model has not been named, the last name of
the first author will be used to name the model: for example, “Segers
model” or “Gong model”. In this paper we first provide an overview of the
range of processes that have been considered in CH4 models over the past
4 decades, and then further classify existing models as determined by the
range of processes considered. We finished with several suggested research
topics, which would be beneficial for better developing and applying CH4
models for either understanding CH4 cycling or quantifying CH4
budgets at various scales.
Terrestrial ecosystem models for CH4 cycling and the model
representation of three pathways of CH4 transport (models are in
alphabetical order; author's last name is used if the model name is not
available).
Model
Aerenchynma
Diffusion
Ebullition
References
Beckett model
Yes
Yes
No
Beckett et al. (2001)
Cartoon model
Yes
Yes
Yes
Arah and Stephen (1998); Arah and Kirk (2000)
CASA
Yes
Yes
Yes
Potter (1997); Potter et al. (1996)
CH4MOD
Yes
Yes
Yes
Huang et al. (1998b, 2004); Li et al. (2012)
Christensen model
No
No
No
Christensen et al. (1996)
CLASS
No
Yes
No
Curry (2007, 2009)
CLM4Me
Yes
Yes
Yes
Riley et al. (2011)
CLM-Microbe
Yes
Yes
Yes
Xu et al. (2014, 2015)
DAYCENT
No
Yes
No
Del Grosso et al. (2000, 2002, 2009)
Ding model
Yes
No
No
Ding and Wang (1996)
DLEM
Yes
Yes
Yes
Tian et al. (2010); Xu and Tian (2012)
DNDC
Yes
Yes
Yes
Li (2000)
DOS-TEM
Yes
Yes
Yes
Fan et al. (2013)
ecosys
No
Yes
Yes
Grant (1998, 2001)
Gong model
Yes
Yes
Yes
Gong et al. (2013)
HH model
Yes
Yes
Yes
Cresto Aleina et al. (2015)
IAP-RAS
No
No
No
Eliseev et al. (2008); Mokhov et al. (2007)
Kettunen model
Yes
Yes
Yes
Kettunen (2003)
Lovley model
No
No
No
Lovley and Klug (1986)
LPJ-Bern
Yes
Yes
Yes
Spahni et al. (2011)
LPJ-WHyMe
Yes
Yes
Yes
Wania et al. (2009, 2010)
LPJ-WSL
No
No
No
Hodson et al. (2011)
Martens model
Yes
Yes
Yes
Martens et al. (1998)
MEM
No
No
No
Cao et al. (1995, 1998)
MERES
Yes
Yes
No
Matthews et al. (2000)
Nouchi model
Yes
Yes
No
Hosono and Nouchi (1997); Nouchi et al. (1994)
ORCHIDEE
Yes
Yes
Yes
Ringeval et al. (2010, 2011)
Ridgwell model
No
Yes
No
Ridgwell et al. (1999)
SDGVM
No
No
No
Hopcroft et al. (2011)
Segers model
Yes
Yes
Yes
Segers and Kengen (1998); Segers and Leffelaar (2001a, b); Segers et al. (2001)
Tagesson model
No
No
No
Tagesson et al. (2013)
TCF
Yes
Yes
Yes
Watts et al. (2014)
TEM
Yes
Yes
Yes
Zhuang et al. (2004)
TRIPLEX-GHG
Yes
Yes
Yes
Zhu et al. (2014)
UW-VIC
Yes
Yes
Yes
Bohn and Lettenmaier (2010); Bohn et al. (2007)
van Bodegom model
Yes
Yes
Yes
van Bodegom et al. (2000, 2001)
VISIT
Yes
Yes
Yes
Inatomi et al. (2010); Ito and Inatomi (2012)
De Visscher model
No
Yes
No
De Visscher and Van Cleemput (2003)
Walter model
Yes
Yes
Yes
Walter and Heimann (2000); Walter et al. (1996)
Xu model
Yes
Yes
Yes
Xu et al. (2007)
Primary CH<inline-formula><mml:math display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">4</mml:mn></mml:msub></mml:math></inline-formula> processes
Biological CH4 production in sediments was first noted in the late 18th
century (Volta, 1777), and the microbial oxidation of CH4 was proposed
at the beginning of the 20th century (Söhngen, 1906). Since then,
CH4 cycling processes have been intensively studied and documented
(Christensen et al., 1996; Hakemian and Rosenzweig, 2007; Lai, 2009; Melloh
and Crill, 1996; Mer and Roger, 2001), and most have been described
mathematically and incorporated into ecosystem models (Table 1). Herein, we
do not attempt to review all CH4 processes, as a number of reviews have
been published on this topic (Barlett and Harriss, 1993; Blodau, 2002;
Bridgham et al., 2013; Cai, 2012; Chen et al., 2012; Conrad, 1995, 1996;
Hakemian and Rosenzweig, 2007; Higgins et al., 1981; Lai, 2009; Monechi et
al., 2007; Segers, 1998; Wahlen, 1993). Rather, we focus on primary CH4
processes in terrestrial ecosystems and their environmental controls from a
modeling perspective. In this context there exist three major methanogenesis
mechanisms, two CH4 methanotrophy mechanisms, and three aggregated
CH4 transport pathways in plants and soils. We note that most models do
not explicitly represent all of these transport pathways, and that the
relative importance of these pathways varies substantially in time, space,
and with ecosystem types. We also pay attention to several other modeling
features, including capability for plot- or regional-level simulations,
vertical representation of biogeochemical processes, and whether the model is
embedded in an Earth system model (ESM).
The published literature concludes that two processes dominate biological
CH4 production (Conrad, 1999; Krüger et al., 2001): acetoclastic
methanogenesis – CH4 production from acetate – and hydrogenotrophic
methanogenesis – CH4 production from hydrogen (H2) and carbon
dioxide (CO2). Acetoclastic and hydrogenotrophic methanogenesis account
for ∼ 50–90 and ∼ 10–43 % of global annual CH4
produced, respectively (Conrad and Klose, 1999; Kotsyurbenko et al., 2004;
Mer and Roger, 2001; Summons et al., 1998). Methylotrophic methanogenesis
(producing CH4 from methanol, methylamines, or dimethylsulfide) is
usually considered a minor contributor of CH4, but may be significant in
marine systems (Summons et al., 1998). The proportion of CH4 produced
via any of these pathways varies widely in time, space, and across ecosystem
types.
Methanotrophy occurs under aerobic (Gerard and Chanton, 1993) and anaerobic
(Smemo and Yavitt, 2011) conditions. These oxidative processes can occur in
several locations in soil and plants (Frenzel and Rudolph, 1998; Heilman and
Carlton, 2001, Ström et al., 2005) and using CH4 either produced in
the soil column or transported from the atmosphere (Mau et al., 2013). Large
variation in the relative magnitudes of these pathways as a percentage of
total methanotrophy has been observed: aerobic oxidation of CH4 in soil
contributes 1–90 % (King, 1996; Ström et al., 2005), anaerobic
oxidation of CH4 within the soil profile contributes 0.3–5 %
(Blazewicz et al., 2012; Murase and Kimura, 1996), oxidation of CH4
during transport in plant aerenchyma contributes < 1 % (Frenzel
and Karofeld, 2000; Frenzel and Rudolph, 1998), and oxidation of atmospheric
CH4 contributes ∼ 10–100 % (ranging from ∼ 10 % for
wetland to ∼ 100 % for upland) (Gulledge and Schimel, 1998a, b;
Topp and Pattey, 1997) to total methanotrophy in the ecosystem. CH4 is
transported from the soil profile to the atmosphere in typical open-water
wetlands by seven pathways that could be aggregated into three:
plant-mediated transport accounts for 12–98 % (Butterbach-Bahl et al.,
1997; Mer and Roger, 2001; Morrissey and Livingston, 1992), diffusion
accounts for ∼ 5 % for wetlands and > 90 % for
upland systems (Barber et al., 1988; Mer and Roger, 2001), and ebullition
accounts for 10–60 % (Chanton et al., 1989; Tokida et al., 2007) of the
CH4 produced in the soil that is emitted into the atmosphere. The
plant-mediated transport includes diffusive and advective (associated with
gas or liquid flow) transports; soil matrix transport includes soil gaseous
diffusion and advection and aqueous diffusion and advection. Because
diffusion normally dominates soil matrix transport, we only consider here the
model's representation of diffusion, consistent with other studies (Mer and
Roger, 2001; Bridgham et al., 2013).
Environmental factors affecting CH4 processes have many direct and
indirect controls. The dominant direct factors controlling methanogenesis and
methanotrophy in most ecosystems include oxygen availability, dissolved
organic carbon concentration, soil pH, soil temperature, soil moisture,
nitrate and other reducers, ferric iron, microbial community structure,
active microbial biomass, wind speed (Askaer et al., 2011), plant root
structure (Nouchi et al., 1990), etc. Indirect factors include soil texture
and mineralogy, vegetation, air temperature, soil fauna, nitrogen input,
irrigation, agricultural practices, sulfate reduction, and carbon quality
(Banger et al., 2012; Bridgham et al., 2013; Hanson and Hanson, 1996; Higgins
et al., 1981; Mer and Roger, 2001). The complicated effects induced by a few
key factors in CH4 processes have been mathematically described and
incorporated into many CH4 models, for example, direct factors such as
soil temperature, moisture, oxygen availability, soil pH, and soil redox
potential (Grant, 1998; Riley et al., 2011; Tian et al., 2010; Zhuang et al.,
2004). The indirect factors such as nitrogen input (Banger et al., 2012),
irrigation (Wassmann et al., 2000), and agricultural practices were not
reviewed in this study as their impacts are indirect and were modeled through
impacts on vegetation and hydrology (Li, 2000; Ren et al., 2011; Xu et al.,
2010).
Published CH4 models and modeling trends in terms of
applicability and mechanistic representation of CH4 cycling processes
over recent decades; the envisioned CH4 model capability.
![]()
Model representation of CH<inline-formula><mml:math display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">4</mml:mn></mml:msub></mml:math></inline-formula> processes
We reviewed 40 CH4 models (Fig. 1 and Table 1), which were developed for
a variety of purposes. The first CH4 model was published in 1986 by
Lovley and Klug (1986) to simulate methanogenesis in freshwater sediments,
and since then a number of CH4 models have been developed and applied at
numerous scales (Table 1). For example, Cao et al. (1995) developed the
Methane Emission Model (MEM) and applied it to quantify the global CH4
source in rice paddies and the sensitivity of the global CH4 budget's
response to climate change (Cao et al., 1995, 1998). Grant et al. (1998)
developed the ecosys model, which is currently the ecosystem-scale
model that most mechanistically represents the many kinetic processes and
microbial mechanisms for methanogenesis, methanotrophy, and CH4 emission
(Grant and Roulet, 2002). Riley et al. (2011) developed CLM4Me, a CH4
module for the Community Land Model, which is incorporated into the Community
Earth System Model. The family of LPJ models (LPJ-Bern, LPJ-WHyMe, LPJ-WSL)
was developed under the LPJ framework to simulate CH4 processes, but
with different modules for CH4 cycling; for example, LPJ-Bern and
LPJ-WHyMe incorporate the Walter CH4 module (Walter and Heimann, 2000;
Walter et al., 1996; Wania et al., 2009), while LPJ-WSL incorporates the
CH4 module from Christensen et al. (1996). The number of CH4 models
has steadily increased since the 1980s (Fig. 1): 1 in the 1980s, 11 in the
1990s, 14 in the 2000s, and 14 for 2010–2015. This increase in model
developments is driven by many factors, including a desire to understand the
contribution of CH4 processes to the regional CH4 budget (Fig. 1).
For instance, Lovley's model was built to understand the CH4 production
and sulfate reduction in freshwater sediment (Lovley and Klug, 1986); while
all models published in the 2010s are applicable for CH4 budget
quantification, particularly at regional scale. This rapid increase in
CH4 model development indicates a growing effort to analyze CH4
cycling and quantify CH4 budgets across spatial scales. Meanwhile, the
key mechanisms represented in the models have increased at a slower pace
(Fig. 2). The most important changes are representation of vertically
resolved processes within the soil and regional model simulation. For
example, the percentage of the newly developed models with vertically
resolved CH4 biogeochemistry has increased from 54 % before 2000 to
∼ 79 % in the most recent decade (2010–2015). The proportion of
models with regional simulation capability (producing a spatial map of
CH4 fluxes with inputs of spatial map of driving forces) has doubled
from ∼ 50 % before the 2010s to almost 100 % afterwards
(Fig. 2).
Key mechanisms/features of CH4 processes and their
representations in CH4 models.
Key mechanisms
Models
Methanogenesis
Cartoon model, CASA, CH4MOD, Christensen model, CLM4Me, CLM-Microbe, Ding model, DLEM, DNDC, DOS-TEM, ecosys, Gong model, IAP-RAS, Kettunen model, Lovley model, LPJ-Brn, LPJ-WHyMe, LPJ-WSL, Martens model, MEM, MERES, ORCHIDEE, SDGVM, Segers model, TCF, TEM, TRIPLEX-GHG, UW-VIC, van Bodegom's model, VISIT, Walter model, Xu's model
Methanotrophy
Cartoon model, CASA, CLASS, CLM4Me, CLM- Microbe, DAYCENT, DLEM, DNDC, DOS-TEM, ecosys, Gong model, Kettunen model, LPJ-Bern, LPJ-WHyMe, Martens model, MEM, MERES, ORCHIDEE, Ridgwells model, SDGVM, Segers model, TCF, TEM, TRIPLEX-GHG, UW-VIC, van Bodegom's model, VISIT, De Visscher model, Walter model, Xu model
Anaerobic oxidation of CH4
CLM-Microbe, Martens model
Substrate (acetate/DOC)
CH4MOD, CLM-Microbe, DLEM, DNDC, ecosys, Gong model, Kettunen model, Lovley model, Martens model, MEM, MERES, SDGVM, Segers model, TCF, van Bodegom model, Xu model
Microbial functional groups
CLM-Microbe, ecosys, Segers model
CH4 storage in soil profile
Beckett model, Cartoon model, CLM4Me, CLM-Microbe, ecosys, Kettunen model, Martens model, MERES, Nouchi model, ORCHIDEE, Segers model, UW-VIC, van Bodegom model, VISIT, De Visscher model, Walter model
O2 availability for CH4 oxidation
Beckett model, Cartoon model, CLM4Me, CLM-Microbe, ecosys, Kettunen model, MERES, Segers model, van Bodegom model, De Visscher model, Xu model
Iron biogeochemistry
van Bodegom model
Sulfate biogeochemistry
Lovley model, Martens model, van Bodegom model
Frozen trapped CH4
None
Embedded in the Earth system model
CLASS, CLM4Me, CLM-Microbe, IAP-RAS, ORCHIDEE, SDGVM
Vertical resolved biogeochemistry
Beckett model, Cartoon model, CLASS, CLM4Me, CLM-Microbe, DNDC, DOS-TEM, ecosys, Gong model, HH model, IAP-RAS, Kettunen model, Lovley model, LPJ-Bern, LPJ-WHyMe, LPJ-WSL, Martens model, MERES, ORCHIDEE, Ridgwell model, SDGVM, Seger model, TRIPLEX-GHG, UW-VIC, VISIT, De Visscher model, Walter model, Xu model
Regional-scale, capacity for up-scaling
CASA, CH4MOD, Christensen model, CLASS, CLM4Me, CLM-Microbe, DAYCENT, DLEM, ecosys, Gong model, HH model, IAP-RAS, LPJ-Bern, LPJ-WHyMe, LPJ-WSL, Martens model, MEM, MERES, ORCHIDEE, Ridgwell model, SDGVM, Tagesson model, TCF, TEM, TRIPLEX-GHG, UW-VIC, VISIT, Walter model
Percentage of CH4 models with consideration of some key
CH4 mechanisms. The percentage was calculated as the number of models
considering each mechanism divided by the total number of published models in
each time period.
![]()
The majority of these models were designed to simulate land-surface exchange
in saturated ecosystems (primarily natural wetlands and rice paddies) (Huang
et al., 1998b; Li, 2000; Walter et al., 1996) (Table 1). Not all of the
models explicitly represented the belowground mechanistic processes for
CH4 production and consumption and the primary carbon biogeochemical
processes (Christensen et al., 1996; Ding and Wang, 1996). The
land–atmosphere CH4 exchange is a net balance of many processes,
including production, oxidation, and transport, which are represented in
models with different complexities (Table 2). Some models are quite
complicated, while some are relatively simple. The obvious tradeoff in
modeling CH4 cycling is to represent mechanisms as accurately as
possible while managing complexity (Evans et al., 2013), and ensuring that
additional complexity enhances predictability (Tang and Zhuang, 2008).
CH<inline-formula><mml:math display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">4</mml:mn></mml:msub></mml:math></inline-formula> model classification
Based on a cluster analysis that considers model characteristics including
acetoclastic methanogenesis, hydrogenotrophic methanogenesis, methanotrophy,
different CH4 transport pathways, multiple soil layers, and oxygen
availability, current CH4 models can be classified into three groups
(Figs. 3 and 4). The first group of CH4 models uses a very simple
framework for land-surface CH4 flux, and most were developed before the
2000s (Christensen's model, CASA, etc.) (Fig. 4a). These models treated
land-surface CH4 flux as an empirical function and link it to
environmental controls or soil organic carbon. This group of models ignored
the mechanistic processes of methanogenesis, methanotrophy, and CH4
transport. The second group of CH4 models considers processes in a
relatively simple manner (one or two primary CH4 transport pathways,
methanogenesis as a function of DOC (dissolved organic carbon), oxidation of
atmospheric CH4, etc.); however, the methanogenesis and methanotrophy
mechanisms are still not mechanistically represented (Fig. 4b). For example,
DLEM simulates CH4 production with a Michaelis–Menten equation with DOC
concentration as substrate (Tian et al., 2010); Walter's model simulates
CH4 production with a simple multiplier between substrate availability
and environmental scalars and CH4 oxidation with a Michaelis–Menten
equation (Walter et al., 1996). The third group of CH4 models explicitly
simulates the processes for methanogenesis, methanotrophy, and CH4
transport as well as their environmental controls, which allows comprehensive
investigation of physical, chemical, or biological processes' contribution to
land-surface CH4 flux (Fig. 4c). Of the models in the third group, none
fully represents all these processes (although some have most of the features
described); for example, the ecosys model is one of the few models
to represent most of the CH4 cycling processes shown in Fig. 4c,
although it has not been embedded in an Earth system model.
Cluster analysis showing three groups of CH4 models based on
model characteristics (lines with the same color indicate CH4 models in
the same group; green lines represent a relatively simple model structure,
red lines represent relatively mechanistic models, and blue lines represent
mechanistic models).
![]()
The mathematical equations used to describe the CH4 processes
used in representative models (PCH4 is the CH4 production
rate; OxidCH4 is the CH4 oxidation rate;
TCH4 is the CH4 transport rate; DCH4 is
the CH4 diffusion rate; some parameters may have been changed from the
original publication to keep relative consistentcy in this table).
CH4 processes
Equations
Ecological description
Model examples
CH4 substrate and CH4 production
1
PCH4=f(TW)
A function of temperature (T) and moisture (W)
Christensen model, IAP-RAS, DAYCENT
2a
PCH4=r×HR×f(TW)
A portion of heterotrophic respiration, affected by temperature (T) and moisture (W)
LPJ family, CLM4Me, Ding model, MERES, TRIPLEX-GHG
2b
PCH4=r×SOM×f(TW)
A portion of soil organic matter (SOM), affected by temperature (T) and moisture (W); Walter's model uses indirect association with NPP
CH4MOD, DOS-Tem, Gong model, HH model, Walter model
3
PCH4=V×DOCKDOC+DOC×f(TW)
A portion of dissolved organic carbon (DOC), affected by temperature (T) and moisture (W)
MEM, DLEM
4
PCH4=f(DOCAcetate,CO2)×f(TW)
Mechanistic processes for CH4 production are considered, affected by temperature (T) and moisture (W)
Kettunen model, Segers model, van Bodegoms model, and ecosys
CH4 oxidation
5
OxidCH4=V×CH4KCH4+CH4×f(TW)
Oxidation as a function of CH4 concentration and temperature and moisture
DLEM, TRIPLEX-GHG, VISIT
6
OxidCH4=V×CH4KCH4+CH4O2KO2+O2×f(TW)
Oxidation as a function of CH4 and O2 concentration, temperature and moisture
Cartoon model, CLM4Me, CLM-Microbe, Kettunen model
CH4 transport
7
TCH4=V×([CH4]-[CH4]‾)
V is the parameter for distance, diffusion coefficient, etc.; [CH4] is the concentration of CH4 in the soil/water profile (dissolvability for DLEM, 0 for DNDC); and [CH4]‾ is the threshold of CH4 concentration above which CH4 will be transported to the atmosphere via either of the three transport pathways
DLEM, DNDC, Walter model
8a
A=Cz-CarLz/D+rapTρr
Aerenchyma transport
CLM4Me
8b
Moves to first unsaturated layer and then released to gaseous phase
Ebullition
CLM4Me
8c
DCH4=D×ΔCH4Δz
Diffusion of CH4 was simulated following Fick's law; CLM4Me separates aqueous and gaseous diffusion
CLM4Me, CLM-Microbe, ecosys, Ridgwell model, TRIPLEX-GHG; Sergers model
Temperature effects
9
fT=a×T+b fT=a×T2+b×T+c fT=b×e0.2424×T
Linear regression on temperature or degree days; DNDC simulate temperature impact on production not on oxidation
DAYCENT, DNDC, IAP-RAS, LPJ family
10
fT=Q10(T-Tref)10
Q10 equations; Tref is the reference temperature
CH4MOD, CLM-Microbe, CLM4Me, DLEM, VISIT, Kettunen model
11a
VT=V0×exp(ΔER1T0-1T)
Arrhenius equation
Cartoon model, Ding model
11b
fT=Ts×exp(A-HaR×Ts)1+expHdl-S×TsR×Ts+exp(S×Ts-HdhR×Ts)
Modified Arrhenius equation; Ts is soil temperature at K; A is the parameter for fT= 1.0 at Ts= 303.16 K; Ha is the energy of activation (J mol-1); R is universal gas constant (J mol-1 K-1); Hdl and Hdh are energy of low and high temperature deactivation (J mol-1)
ecosys
Moisture effects on methanogenesis and methanotrophy
12
No moisture effect is simulated, rather inundation area is simulated
No equation, while a temporal and spatial variation of inundation and saturation impacts
CASA
13
Fϑ=e-P/Pc
Water stress for oxidation, where P is soil moisture and Pc = -2.4 × 105 mm
CLM4Me
14
fSM=1,φ>0.2Mpa1-log10φ-log10(0.2)log10100-log10(0.2)β,0.2>=φ>=100Mpa0,φ>100Mpa
β is an arbitrary constant, ϕ is the soil water potential
CLASS
15
fprodSM=SM-SMfcSMsat-SMfc2×0.368×e(SM-SMfcSMsat-SMfc) foxidSM=1-fprod(SM)
Different impacts on CH4 production and consumption; SM: soil moisture; SMfc: field capacity; SMsat: saturation soil moisture
DLEM
16
fSM=(MV-Mmin)×(M-Mmax)(MV-Mmin)×(MV-Mmax)-(MV-Mopt)2
Bell-shape curve
TEM
pH effects
17a
fpH=(pH-pHmin)×(pH-pHmax)(pH-pHmin)×(pH-pHmax)-(pH-pHopt)2
Bell-shape curve
CLM-Microbe, MEM, TEM,
17b
fpH=10-0.2335×pH2+2.7727×pH-8.6
Bell-shape curve
CLM4Me
17c
fpH=0pH≤4orpH≥101.021+1 000 000×e-2.5×pH 4>pH> 71.021+1 000 000×e-2.5×14-pH 7>pH>10
Bell-shape curve
DLEM
Methanogenesis
Models make use of four types of modeling frameworks (Table 3) to relate
methanogenesis to substrate requirements. Similar to Eqs. (1)–(4) in
Table 3, there are four model algorithms to represent methanogenesis:
(1) empirical association between methanogenesis and environmental condition,
including temperature and water table; (2) empirical correlation of
methanogenesis with biological variables (particularly heterotrophic
respiration and soil organic matter); (3) methanogenesis as a function of DOC
concentration; and (4) a suite of mechanistic processes simulated for
methanogenesis.
Representation of the substrate for methanogenesis may be a key aspect of
simulating CH4 cycling in terrestrial ecosystems (Bellisario et al.,
1999); however, more than half of the models examined do not explicitly
simulate substrates for methanogenesis. We note, however, that explicit
representation of substrates and their effects on methanogenesis requires
additional model parameters, and therefore degrees of freedom in the model,
which can lead to increased equifinality (Tang and Zhuang, 2008). The
optimum complexity level for methanogenesis and consumption models remains
to be determined.
Three types of models with key mechanisms for CH4 production
and oxidation (SOM: soil organic matter; NPP: net primary production;
DOC: dissolved organic carbon; Oatm: oxidation of atmospheric
CH4; P: plant-mediated transport; D: diffusion transport; E:
ebullition transport; Oxid: oxidation; Otrans: oxidation
of CH4 during transport).
![]()
The first model algorithm correlates methanogenesis with environmental
factors and ignores substrate production and its influence on methanogenesis
(Eq. 1) (Table 3). This group includes Christensen's model (Christensen et
al., 1996), which simulates the net flux of CH4 based on fraction of
saturated soil column and soil temperature, and the IAP-RAS model (Mokhov et
al., 2007), which calculates methanogenesis as an empirical equation of soil
temperature. This group has a role in site-specific interpolation of
observations for scaling over time at a given site, but does not explicitly
represent carbon or acetate substrate. The second model algorithm directly
links methanogenesis with heterotrophic respiration or soil organic matter
content, but does not explicitly represent carbon or acetate substrate
availability (Eq. 2); examples are the LPJ model family (Hodson et al., 2011;
Spahni et al., 2011; Wania et al., 2009, 2010) and CLM4Me (Riley et al.,
2011). The third model algorithm simulates dissolved organic carbon (DOC) or
different pools of soil organic carbon, which are treated as a substrate pool
influencing CH4 production (Eq. 3); examples are the MEM (Cao et al.,
1995, 1998) and DLEM (Tian et al., 2010). The fourth model algorithm
considers the primary substrates for methanogenesis, that is, acetate and
single-carbon compounds (Eq. 4); examples are Kettunen's model (Kettunen,
2003), Segers' model (Segers and Kengen, 1998; Segers and Leffelaar, 2001a,
b; Segers et al., 2001), van Bodegom's model (van Bodegom et al., 2000,
2001), and the ecosys model (Grant, 1998).
Methanogenesis is a fundamental process for CH4 cycling, and the
majority of models simulate methanogenesis in either implicit or explicit
ways (Tables 2 and 3). For example, 32 models (i.e., Cartoon model, CASA,
CH4MOD, Christensen model, CLM4Me, Ding model, DLEM, DNDC, DOS-TEM, ecosys,
Gong model, HH model, IAP-RAS, Kettunen model, Lovley model, LPJ-Brn,
LPJ-WHyMe, LPJ-WSL, Martens model, MEM, MERES, ORCHIDEE, SDGVM, Segers model,
TCF, TEM, TRIPLEX-GHG, UW-VIC, van Bodegom model, VISIT, Walter model, and Xu
model) simulate methanogenesis as one individual process. As a comparison,
only 3 out of 40 CH4 models reviewed explicitly simulate two
methanogenesis pathways (acetoclastic methanogenesis and hydrogenotrophic
methanogenesis) (Table 3). As mentioned earlier, it is well recognized that
there are two dominant methanogenesis pathways, and their relative
combination changes significantly across environmental gradients, for
example, along the soil profile (Falz et al., 1999) and across landscape
types (McCalley et al., 2014). This lack of representation of two
methanogenesis mechanisms might have caused dramatic bias in simulating
CH4 flux temporally and spatially and needs to be addressed in future
model improvements.
Michaelis–Menten-like equations, widely used for simulating CH4
production and oxidation, consider substrates limiting factors (Segers and
Kengen, 1998). A few CH4 models in the third category of methanogenesis
models (linking methanogenesis with a substrate) use the
Michaelis–Menten-like equation to compute methanogenesis and methanotrophy
rates (Eqs. 3, 5, and 6). For example, DLEM simulates methanogenesis as a
function of DOC concentration and other environmental controls, and
Michaelis–Menten-like functions were used to compute methanogenesis on the
basis of DOC as a substrate.
Methanotrophy
Methanotrophy is another important process for simulating the
land–atmosphere exchange of CH4 (Table 2). Aerobic and anaerobic
methanotrophy occurs in different locations in the soil profile, and affects
both methanogenesis in the profile and CH4 diffusing in from the
atmosphere. For example, the oxidation of atmospheric CH4, rhizosphere
and bulk soil oxidation, and oxidation during CH4 transport from soil to
the atmosphere have been measured and modeled (Tables 1 and 2). Anaerobic
CH4 oxidation has been measured (Blazewicz et al., 2012) and has been
proposed to be incorporated into ecosystem models (Gauthier et al., 2015).
It has been confirmed that the aerobic oxidation of CH4 produced in the
soil profile and aerobic oxidation of atmospheric CH4 play a major role
in CH4 consumption in the system, and that anaerobic oxidation of
CH4 is a minor contributor. Currently, no models explicitly simulate
the anaerobic oxidation of CH4 in soil, although a few recent studies
highlighted the importance of this process (Blazewicz et al., 2012; Caldwell
et al., 2008; Conrad, 2009; Smemo and Yavitt, 2011; Valentine and Reeburgh,
2000). The key reasons for this omission are that the process has not been
mathematically described, the key parameters are uncertain (Gauthier et al.,
2015), and the biochemical mechanism is not fully understood.
Methanotrophy has been simulated with dual Monod Michaelis–Menten-like
equations with CH4 and oxygen as limiting factors (Table 3). Recent work
has shown that the Michaelis–Menten approach may be inaccurate when
representing multi-substrate, multi-consumer networks, and that a new
approach (called equilibrium chemistry approximation, ECA) can ameliorate
this problem (Tang and Riley, 2013, 2014; Zhu et al., 2016).
Although the ECA approach has not been applied for simulations of CH4
emissions, CH4 dynamics are inherently multi-consumer, including
transformations associated with methanogens, heterotrophs, ebullition,
advection, diffusion, and aerenchyma transport, even if only one substrate is
considered.
CH<inline-formula><mml:math display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">4</mml:mn></mml:msub></mml:math></inline-formula> within the soil/water profile
CH4 produced in the soil profile or below the water table is not
transported immediately into the atmosphere. The time required for CH4
to migrate from a deep soil profile to the atmosphere ranges from minutes to
days (depending on temperature, water, soil texture, and emissivity of plant
roots), or even a season if the surface is frozen. The majority of current
CH4 models assume that CH4 transport to the atmosphere occurs
immediately after CH4 is produced, and a portion is oxidized (Tian et
al., 2010; Fan et al., 2013); for models simulating CH4 flux over
minutes to days, the lack of modeled transport may produce unrealistic
simulations.
Some models do simulate CH4 dynamics within the soil and water profile
(e.g., ecosys, CLM4Me), which produces a lag between methanogenesis and emission,
allowing for oxidation to be explicitly represented during transport, and is
valuable for simulating the seasonality of CH4 flux (Table 2). For
example, the recently observed CH4 burst in the spring season in some
field experiments confirms that the storage of CH4 produced in winter
can produce a strong emission outburst (Song et al., 2012). Without
understanding the mechanism of CH4 storage beneath the soil surface,
this phenomenon will be difficult to simulate. In most of the models
considering CH4 storage, the CH4 is treated as a simple gas pool,
under the water table, which will be transported to the atmosphere through
several transport pathways.
CH<inline-formula><mml:math display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">4</mml:mn></mml:msub></mml:math></inline-formula> transport from soil to the atmosphere
The transport of CH4 produced and stored in the soil column is the
bottleneck for CH4 leaving the system; therefore, this process is an
important control on the instantaneous land-surface CH4 flux. Several
important pathways of CH4 transport to the atmosphere are identified:
plant-mediated diffusive and advective transport, aqueous and gaseous
diffusion, and ebullition (Beckett et al., 2001; Chanton, 2005; Mer and
Roger, 2001; Whiting and Chanton, 1996). Model simulation of these transport
pathways uses direct control of simulated land-surface CH4 flux, with
CH4 transport simulation considered in a manner similar to Eq. (7)
(Table 3).
The majority (83 %) of the current models simulate at least one transport
pathway. Specifically, 70 % of the models simulate CH4 transport via
aerenchyma, 80 % simulate gaseous diffusive transport, and 60 %
simulate ebullition transport (Table 1). More than 50 % of models
simulated these three transport pathways. Some models simulate explicitly the
aqueous and gaseous diffusion of CH4 (Riley et al., 2011), while most
models do not simulate advective transport. Many models simulate diffusion
and plant-mediated transport in very simple ways. For model improvement in
this area, three issues remain as challenges.
Most models treat transport implicitly; for example, the diffusion processes
are treated simply as an excessive release of CH4 when its concentration
exceeds a threshold (Tian et al., 2010). This treatment prevents the model
from simulating the lag between methanogenesis and its final release into the
atmosphere, which has been confirmed to be the key mechanism for hot moments
and hotspots of CH4 flux (Song et al., 2012) and for oxidation during
transport.
The parameters for plant species capable of transporting gas (i.e.,
aerenchyma) are poorly constrained (Riley et al., 2011), although plant-mediated
transport has been identified as the dominant pathway for CH4 emission
in some natural wetlands (Aulakh et al., 2000; Colmer, 2003).
Simultaneously representing aqueous and gaseous phases of CH4 is one
potentially important issue for simulating CH4 transport from soil to
the atmosphere (Tang and Riley, 2014). However, these processes are only
explicitly represented in a few extant CH4 models (Riley et al., 2011;
Grant et al., 1998).
Temperature dependence of CH4 processes in various models
(blank indicates the Q10 function is not used; all temperatures are
expressed as ∘C; 273.15 was used for unit conversion).
Model
Q10
Reference temperature (∘C)
Note
Sources
CASA
Based on a linear equation with temperature
Potter (1997)
DAYCENT
Linear equation y=0.209×T+0.845
Del Grosso et al. (2000)
LPJ family LPJ-Bern LPJ-WHyMe LPJ-WSL
Linear function was used for temperature impacts on diffusion.
Hodson et al. (2011); Spahni et al. (2011); Wania (2007)
Christensen's model
2
2
For temperature > 0, the temperature impact is set to zero when < 0.
Christensen and Cox (1995)
CH4MOD
3
30
T=30 for 30 < T ≤ 40
Huang et al. (1998b)
CLM4Me
2 for production, 1.9 for oxidation
22
Parameters for baseline simulation
Riley et al. (2011)
CLM-Microbe
1.5
13.5
Xu et al. (2015)
DLEM
2.5
30
For a temperature range of [-5, 30]; temperature impact is set to zero when < -5 or > 30.
Tian et al. (2010)
Kettunenn's model
4.0 for production, 2.0 for oxidation
10
Standard Q10 function
Kettunen (2003)
ORCHIDEE
Abisko site, 2.6; Michigan site, 3.2; Panama site, 1.2
Mean annual temperature
Q10 function with different parameters across biomes
Ringeval et al. (2010)
TEM
Alpine tundra: wetland, 3.5; upland, 0.8. Wet tundra: wetland, 2.2; upland, 1.1. Boreal forest: wetland, 1.9; upland, 1.5
Alpine tundra: wetland, -3.0; upland, 8.0. Wet tundra: wetland, -5.5; upland, 8.0. Boreal forest: wetland, 1.0; upland, 7.0
Q10 function with different parameters across biomes
Zhuang et al. (2004)
TRIPLEX-GHG
1.7–16 for production, 1.4–2.4 for oxidation
25 for optimal, 45 for highest temperature
Modified Q10 equation
Zhu et al. (2014)
VISIT
Mean annual temperature
Ito and Inatomi (2012)
Walter's model
2
Ombrotrophic bog, 12; poor fen, 6.5; oligotrophic pine fen, 3.5; Arctic tundra, 0; swamp, 27
Q10 function with different parameters across biomes
Walter and Heimann (2000)
Cartoon model
10
Arrhenius equation
Arah and Stephen (1998)
ecosys
30
Modified Arrhenius equation
Grant et al. (1993)
Environmental controls on CH<inline-formula><mml:math display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">4</mml:mn></mml:msub></mml:math></inline-formula> processes
Although a suite of environmental factors affects various CH4 processes,
many of these factors are not explicitly simulated in many models. These
factors include soil temperature, soil moisture, substrate, soil pH, soil
redox potential, and oxygen availability. Many other factors not incorporated
into the models could indirectly affect CH4 cycling. For example,
nitrogen fertilizer affects methanogenesis through its stimulating impacts on
ecosystem productivity, which in turn affects DOC, soil moisture and soil
temperature (Xu et al., 2010). The CLM4Me model simulates permafrost and its
effects on CH4 dynamics, and has a simple relationship for soil pH
impacts on methanogenesis (Riley et al., 2011). Wania et al. (2013) reviewed
a number of active CH4 models for their representation of CH4
production area. In this review, we specifically focus on temperature,
moisture, and pH because these factors directly affect CH4 processes in
all environments, and they have been explicitly simulated in many of the
models.
Three types of mathematical functions have been used to simulate the
temperature dependence of CH4 processes: (1) linear functions of air or
soil temperature (Eq. 9 in Table 3), (2) the Q10 function (Eq. 10 in
Table 4), and (3) the Arrhenius type function (Eq. 11 in Table 3). Of these
three model representations of temperature dependence, the Q10 equation
is the most common mathematical description. However, the parameters for
these empirical functions vary widely across the models (Table 4). Actual
temperature responses may diverge significantly from the models at low
temperatures, close to the freezing point of water, and high temperatures,
close to the denaturation point of enzymes.
Soil moisture is an important factor controlling CH4 processes, because
water limits O2 diffusion from the air through the soil column and
because microbes can become stressed at low matric potential. CH4 is
produced typically under conditions with a low reduction potential, which is
normally associated with long-term inundation. Although methanogenesis occurs
solely under reducing conditions (methanogenesis within plant biomass under
aerobic condition has never been simulated, although it has been reported in
experiments; Keppler et al., 2006), methanotrophy occurs under drier, aerobic
conditions. A low water content can also limit microbial activity in frozen
soils or soils with high osmolarity (Watanabe and Ito, 2008). Therefore, soil
moisture has different impacts on different CH4 processes. Four types of
model representation are used to simulate moisture effects on CH4
processes (Eqs. 13–16 in Table 3).
Methanogenesis occurs only in the saturated zone and an exponential function
for soil moisture is used to control methanotrophy (e.g., CLM4Me).
Linear function for moisture impacts (e.g., CLASS use linear function for
moisture impact on methanotrophy) (Curry, 2007).
Reciprocal responsive curves for moisture impacts on methanogenesis and
methanotrophy (e.g., DLEM) (Tian et al., 2010).
A bell-shaped curve for methanogenesis (e.g., TEM uses a function similar to
Eq. (16) for moisture impacts) (Zhuang et al., 2004).
Soil pH has been included in a number of CH4 models (Cao et al., 1995;
Zhuang et al., 2004). Methanogens and methanotrophs depend on proton and
sodium ion translocation for energy conservation; thus, they are directly
affected by pH. The pH impacts on CH4 processes are simulated as a
bell-shaped curve although the mathematical functions used to describe pH
impacts are different (Eqs. 17a, b, and c). Moreover, even when the same
functions were used in different models, they were associated with different
parameter values, indicating slightly different response functions; for
example, the MEM model sets pHmin (minimum pH value for CH4
processes being active), pHopt (optimal pH value for CH4
processes being most active), and pHmax (minimum pH value for
CH4 processes being active) values of 5.5, 7.5, and 9 (Cao et al.,
1995). This set of parameter values was adopted in the TEM model (Zhuang et
al., 2004), whereas the DLEM model uses values of 4, 7, and 10 (Tian et al.,
2010). The CLM4Me model uses a different function while keeping the impact
curve at the same shape, but its peak has an optimal pH of 6.2 (Meng et al.,
2012). It should be noted that while pH has been confirmed to significantly
affect CH4 production (Xu et al., 2015), the simulation of pH dynamics
caused by organic acid in soils remains a key challenge for the incorporation
of this phenomenon.
For the other environmental factors, model representation is still in its
infancy; however, several models consider oxygen availability as an electron
acceptor for methanotrophy (e.g., Beckett model, Cartoon model, CLM4Me,
ecosys, Kettunen model, MERES, Segers model, van Bodegom model, De
Visscher model, and Xu model). In addition, only a few models simulate the
impacts of the electron acceptor (nitrate, sulfate, etc.) on CH4
processes (Table 2). For example, the van Bodegom model simulates iron
biogeochemistry, and the Lovley model, Marten model, and van Bodegom model
all simulate sulfate as the electron acceptor and its impacts on
methanogenesis and methanotrophy (Lovley and Klug, 1986; Martens et al.,
1998; van Bodegom et al., 2001). Explicitly representing these processes
enables future coupling of CH4 cycling to processes that are regionally
significant, such as iron reduction on the Alaskan North Slope (Miller et
al., 2015). These models have the potential advantage of more accurately
simulating biogeochemical processes of carbon and ions, although large
uncertainties still exist because of the lack of data for constraining model
parameters.
CH<inline-formula><mml:math display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">4</mml:mn></mml:msub></mml:math></inline-formula> implementation in ESMs
The importance of CH4 flux in simulating climate dynamics has been well
recognized (IPCC, 2013; Ringeval et al., 2011), yet few ESMs have implemented
a CH4 module (Ringeval et al., 2011; Riley et al., 2011; Xu et al.,
2014; Hopcroft et al., 2011; Eliseev et al., 2008). While these models have
been claimed to be coupled within ESMs, truly fully coupled simulations
within ESMs to evaluate CH4 dynamic impacts on global climate systems
are rare (Eliseev et al., 2008; Hopcroft et al., 2011). For example, the
SDGVM has been coupled within the Fast Met Office UK Universities Simulator
(FAMOUS), a coupled general circulation model, to study the association
between terrestrial CH4 fluxes with rapid climate fluctuation during the
last glacial period (Hopcroft et al., 2011). The IAP-RAP model was used to
simulate terrestrial CH4 flux and its contributions to atmospheric
CH4 concentrations and, further on, climate change. The quasi-coupling
between ORCHIDEE_WET with an ocean–atmosphere general circulation model was
used to theoretically evaluate terrestrial CH4 dynamics on climate
systems (Ringeval et al., 2011). The CLM application within the CESM
framework has both CLM4Me and CLM-Microbe modules for CH4 dynamics, but
none of them have been applied for a fully coupled simulation to evaluate
CH4-climate feedback. It should be a key research effort for the CLM
community in the next 5 years to complete this coupling. All previous coupled
ESM simulations have concluded that changes in terrestrial CH4 flux have
small impacts on climate change, while they also pointed out that large
uncertainties exist. Given the importance of CH4 as a greenhouse gas and
uncertainties in current ESMs in simulating permafrost carbon and CH4
flux, more efforts should be invested to implement the CH4 module in
ESMs and further evaluate the CH4-climate feedback under different
climate scenarios.
Summary
Through the 4 decades of modeling CH4 cycling in terrestrial ecosystems,
consensus has been reached on several fronts. First, CH4 cycling
includes a suite of complicated processes, and both the simple and complex
models are able to estimate land-surface CH4 flux to a certain level of
confidence, although models of different complexity do provide different
results (Tang et al., 2010). Second, although a number of CH4 models
have been developed, several gaps remain that need new model representations
(e.g., dynamic linkage between inundation dynamics and the CH4 module
(Melton et al., 2013), and anaerobic oxidation of CH4; Gauthier et al.,
2015).
Two recent CH4 model–model inter-comparison projects raised several
important points (Bohn et al., 2015; Melton et al., 2013): (1) the
distribution of the inundation area is important for accurately simulating
global CH4 emissions, but was poorly represented in CH4 models;
(2) the modeled response of land-surface CH4 emission to elevated
CO2 is likely biased as a number of global change factors were missing,
which indicates the need for modeling with multiple global environmental
factors; and (3) the need for comparison with high-frequency observational
data is identified as an important task for future model–model
inter-comparison. These lessons will be helpful for, and likely addressed
during, model improvements and applications of more mechanistic CH4
models.
Although the primary individual CH4 processes have been studied and
quantified at a certain level of confidence, only a few modeling studies have
reported these individual processes as previously discussed. For example,
three pathways of CH4 transports were represented in Kettunen (2003) and
Walter et al. (1996), but none of those modeled results have been evaluated
against observational results for those individual processes. One reason is
that measurements rarely distinguish between individual processes; another
reason is that the majority of CH4 models do not explicitly represent
all processes (Table 2). However, a number of studies report significant
shifts in the processes contributing to the surface CH4 flux along
environmental gradients or across biomes (Conrad, 2009; Krumholz et al.,
1995; McCalley et al., 2014). Projecting CH4 fluxes into future changing
climate conditions requires not only accurate simulations of CH4
processes, but also shifts among the various processes. In addition, CO2
flux has been evaluated within the Earth system modeling framework, but only
a few studies have evaluated the CH4 flux and its contribution to
climate dynamics. Given the much higher warming potential and relatively
faster rate of increase in atmospheric CH4, fully coupled simulations
are needed to represent the feedbacks between terrestrial CH4 exchanges
and climate. We note that a few recent studies reported a relatively small
climate warming–methane feedback from global wetlands and permafrost (Gao et
al., 2013; Gedney et al., 2004; Riley et al., 2011). A fully mechanistic
CH4 model that accounts for all the important features is critically
needed. In addition, a modeling framework to integrate multiple sources of
data, such as microbial community structure and functional activities,
ecosystem-level measurements, and global-scale satellite measurements of gas
concentration and flux is needed with these mechanistic CH4 models.
Needs for mechanistic CH<inline-formula><mml:math display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">4</mml:mn></mml:msub></mml:math></inline-formula> models
During the last few years, the scientific community has continued to improve
and optimize models to better simulate methanogenesis, methanotrophy,
CH4 transport, and their environmental and biological controls (Xu et
al., 2015; Zhu et al., 2014). A number of emerging tasks have been
identified, and progress in these directions is expected. First, linking
genomic data with large-scale CH4 flux measurements will be an
important, while challenging, task for the entire community; for example,
some work has been carried out in this direction (De Haas et al., 2011;
Larsen et al., 2012). An effort has been initialized to develop a new
microbial functional group-based CH4 model, which has the advantages of
linking genomic information for each individual process with the four
microbial functional groups (Xu et al., 2015). Second, data–data and
model–model comparisons are another important effort for model comparison
and improvement. One ongoing encouraging feature that all recently developed
CH4 models possess is the capability for regional simulations as well as
the possibility to be run at the site level (Riley et al., 2011; Zhu et
al., 2014).
Third, microbial processes need to be considered for incorporation into
ecosystem models for simulating carbon cycling and CH4 processes (DeLong
et al., 2011; Xu et al., 2014). Although a few models explicitly simulate the
microbial mechanisms of CH4 cycling (Arah and Stephen, 1998; Grant,
1998; Li, 2000; Segers and Kengen, 1998), none of them have been used for
regional- or global-scale estimation of microbial contributions to the
CH4 budget. A reasonable experimental design and a well-validated
microbial functional group-based CH4 model should be combined to enhance
our capability to apply models to estimate a regional CH4 budget and to
investigate the combination of microbial and environmental contributions to
the land-surface CH4 flux (DeLong et al., 2011). Fourth, incorporating
well-validated CH4 modules into Earth system modeling frameworks will
allow a fully coupled simulation that provides a holistic understanding of
the CH4 processes, with its connections to many other processes and
mechanisms in the atmosphere. Several recently developed models fall into the
framework of Earth system models (Riley et al., 2011; Ringeval et al., 2010),
which provide a foundation for this application in a relatively easy way.
This effort will likely contribute not only to the CH4 modeling
community, but also to the entire global change science community (Koven et
al., 2011). Iron and sulfate biogeochemistry has so far been modeled
implicitly by only a few models (Table 2), as mechanisms are as yet poorly
understood, and there is a paucity of data. Accordingly, these processes have
not been incorporated into recently developed models, and a more explicit
inclusion, based on improved biogeochemistry understanding, will hopefully be
achieved in the long term.
Key features of future mechanistic CH4 models with a full
representation of primary CH4 processes in the terrestrial ecosystems.
The data assimilation system and model benchmarking system are also shown as
auxiliary components of the future CH4 models.
![]()
Based on the above-mentioned needs and model features as well as the
mechanisms for the CH4 models, the next generation of CH4 models
will likely include several important features (Fig. 5). The models should
(1) be embedded in an Earth system model, (2) consider the vertical
distribution of thermal, hydrological, and biogeochemical transport and
processes, (3) represent mechanistic processes for microbial CH4
production, consumption, and transport, and (4) support data assimilation and
a model benchmarking system as auxiliary components.
Challenges in developing mechanistic CH<inline-formula><mml:math display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">4</mml:mn></mml:msub></mml:math></inline-formula> models
Knowledge gaps
Modeling CH4 cycling is a dynamic process. As new mechanisms are
identified, the modeling community should ensure that the mechanisms are well
studied and mathematically described, as has occurred over the past decades
(Conrad, 1989; McCalley et al., 2014; Schütz et al., 1989; Xu et al.,
2015). However, a number of knowledge gaps need to be filled before a full
modeling framework of CH4 processes within terrestrial ecosystems can be
achieved. The first gap is either confirmation or rejection of a few recently
observed CH4 mechanisms; these mechanisms need to be fully vetted before
being considered for incorporation into a model. One well-known mechanism
still under debate is aerobic CH4 production within plant tissue
(Beerling et al., 2008; Keppler et al., 2006). Since its first report in 2006
(Keppler et al., 2006), a few studies have confirmed the mechanism in
multiple plant species (Wang et al., 2007). While its existence in nature is
still under debate (Dueck et al., 2007), this mechanism will likely not be
incorporated into an ecosystem model before solid evidence is presented and
consensus is reached. The second new mechanism is CH4 production by
fungi (Lenhart et al., 2012). More field- or lab-based experiments are needed
to investigate this mechanism and its contribution to the global CH4
budget, probably through a data–model integration approach. Third, the
aerobic production of CH4 from the cleavage of methylphosphonate has
been demonstrated in marine systems (Karl et al., 2008), but the significance
of this process in terrestrial systems is unknown. Fourth, the large CH4
emissions from rivers and small ponds are still not fully understood
(Holgerson and Raymond, 2016; Martinson et al., 2010), which will likely be a
direction for future model improvement.
Another knowledge gap is the missing comprehensive understanding of spatial
and temporal variations in CH4 flux; particularly, the “hot spots” and
“hot moments” of observed CH4 flux are still not completely understood
(Becker et al., 2008; Mastepanov et al., 2008; Song et al., 2012). The
traditional static chamber method of measuring CH4 emissions could
underestimate the CH4 flux because sparse sampling is unlikely to detect
these foci or pulses of unusually high emissions. Better methods are also
needed to measure CH4 cycling during the shoulder seasons in the Arctic
and subarctic when fluxes may be most variable (Zona et al., 2016). These
knowledge gaps are key hurdles for CH4 model development efforts. No
model has yet been tested for simulating hotspots or hot moments over large
spatial or long temporal scales. However, the high range (usually of factor
1–10) of the observed CH4 flux might cause regional budgets to vary
substantially (Song et al., 2012); therefore, mechanistic model
representations of these mechanisms are highly needed.
Modeling challenges
Better simulation of CH4 cycling in terrestrial ecosystems requires
improvement in the model structure to represent mechanistic CH4
processes. First is the challenge to simulate the vertical profile of soil
biogeochemical processes and validate such models with observational results.
Although some models have a capability for vertical distribution of carbon
and nitrogen (Koven et al., 2013; Tang et al. 2013; Mau et al., 2013), a
better framework for CH4 and extension to cover the majority of CH4
models are needed. This vertical distribution of biogeochemistry is necessary
for simulating the vertical distribution of CH4 processes and CH4
transport through the soil profile before reaching the atmosphere. A second
challenge is incorporating tracer capability. Isotopic tracers (13C,
14C) have been widely used for quantifying the carbon flow and
partitioning among individual CH4 processes (Conrad, 2005; Conrad and
Claus, 2005), but for ecosystem models this capability has not been
represented even though it is very important to understanding CH4
processes and integrating field observational data. A third challenge is to
simulate microbial functional groups. Microbial processes are carried out by
different functional groups of microbes (Lenhart et al., 2012; McCalley et
al., 2014). Therefore, model comparison with individual processes requires
representing the microbial population sizes (or active biomass) for specific
functional groups (Tveit et al., 2015). This goal has proved more difficult
than representing plant functional types or traits in models, because not all
microbial taxonomic groups have ecologically coherent functions (Philippot et
al., 2010). A fourth challenge is to simulate the lateral transport of
dissolved and particulate biogeochemical variables that are necessary to
better simulate the storage and transport of CH4 within heterogeneous
landscapes (Weller et al., 1995). A fifth challenge is modeling CH4 flux
across spatial scales. Although a few studies have been used to demonstrate
the approach for simulating CH4 budget at plot scale and eddy covariance
domain scale (Zhang et al., 2012), a mechanistic framework to link CH4
processes at distinct scales is still lacking, while highly valuable.
Finally, a sixth challenge is accurate simulation of CH4 within
human-managed ecosystems. Human management practices are always hard to
simulate and predict, and their impacts on CH4 processes are challenging
(Li et al., 2005).
Data needs
First, a comprehensive data set of field measurements of CH4 fluxes
across various landscape types is needed to effectively validate the CH4
models. Although a number of data sets have been compiled (Aronson and
Helliker, 2010; Chen et al., 2012; Liu and Greaver, 2009; Mosier et al.,
1997; Yvon-Durocher et al., 2014), some landscape types are still not fully
covered. Meanwhile, high-frequency field observational data are also needed,
particularly long-term observational data in some less-studied ecosystems;
for example, Arctic tundra ecosystems have been considered an important
contributor to the global CH4 budget in the changing climate (IPCC,
2013; Koven et al., 2011); however, a long-term data set of CH4 flux is
lacking. It is well known that inter-annual variation of climate may turn an
ecosystem from a CH4 sink to a CH4 source (Nauta et al., 2015;
Shoemaker et al., 2014); therefore, a long-term observational data set that
covers these temporal shifts in CH4 flux and its associated ecosystem
information would improve our understanding of the processes and our
representation of them in CH4 models. Second, microbial community shifts
and their role in CH4 processes are important, although information is
incomplete for model representation of this mechanism (McCalley et al., 2014;
Schimel and Gulledge, 1998). Although a number of studies have reported the
microbial community structure and its potential association with changes in
CH4 processes (Schimel, 1995; Wagner et al., 2005), none of this progress has
been documented in a mathematical manner suitable for a modeling
representation.
Third, a comprehensive data set of all primary CH4 processes within an
individual ecosystem would be valuable for model optimization and validation.
Although some data sets exist, no study has investigated all primary
individual CH4 processes within the same plot over the long term. Given
the substantial spatial heterogeneity of CH4 processes, this lack of
process representation may cause bias in CH4 simulations at a regional
scale. It should be noted that land-surface net CH4 flux is a measurable
ecosystem-level process, whereas many individual CH4 processes are
difficult to accurately measure. Therefore, designing field- or lab-based
experiments suitable for measuring these processes is a fundamental need. For
example, the anaerobic oxidation of CH4 has been identified as a
critical process for some ecosystem types, but no comprehensive data set on
it is available for model development or improvement.
Last but not least, high-quality spatial data as driving forces and
validation data for CH4 models are critical for model development as
well (Melton et al., 2013; Wania et al., 2013). Spatial distribution and
dynamics of wetland areas probably are the most important data need for
CH4 models (Wania et al., 2013). Spatial distributions of soil
temperature, moisture, and texture are fundamental information because they
serve as direct or indirect environmental control on CH4 processes. The
recently launched Soil Moisture Active Passive (SMAP) satellite could be used
as an important data source of soil moisture for driving CH4 models
(Entekhabi et al., 2010). It has been identified that soil texture and pH are
important for simulating CH4 processes (Xu et al., 2015). In addition,
the atmospheric CH4 concentration data from satellites could be used as
an important benchmark for model validation purposes, for example, the
Scanning Imaging Absorption spectrometer for Atmospheric ChartographY
(SCIAMACHY) (Frankenberg et al., 2005) and the Greenhouse gas Observing
SATellite (GOSAT) (Yokota et al., 2009).
Data–model integration
Model development and data collection are two important but historically
independent scientific approaches; the integration between model development
and data collection is much stronger for advancing science (De Kauwe et al.,
2014; Luo et al., 2012; Peng et al., 2011). Although data–model integration
is recognized as very important for understanding and predicting CH4
processes and some progress has been made, integrating experiments and models
presents multiple challenges, particularly because (1) the methods for
integrating data with the models are not well developed for CH4 cycling;
(2) the metrics for evaluating data–model integration are not consistent in
the scientific community; and (3) regular communication between data
scientists and modelers on various aspects of CH4 processes and their
model representation is lacking.
Methods for data–model integration have been recently created, for example,
Kalman filter (Gao et al., 2011), Bayesian (Ogle and Barber, 2008; Ricciuto
et al., 2008; Schleip et al., 2009; Van Oijen et al., 2005), and Markov chain
Monte Carlo (Casella and Robert, 2005). However, no studies have evaluated
these methods for integrating CH4 data with models. In addition, the
metric for evaluating the data–model integration is still not well
developed. A very helpful strategy for data–model integration is to solicit
timely input from modelers when designing a field experiment. A good example
of this is US Department of Energy-sponsored project Next Generation
Ecosystem Experiments – Arctic (http://ngee-arctic.ornl.gov), which
was planned with inputs from field scientists, data scientists, and modelers.
Another successful example is the US DOE-sponsored project, Spruce and
Peatland Responses Under Climatic and Environmental Change (SPRUCE)
(mnspruce.ornl.gov), in which the experiment design for data–model
integration created an opportunity for modeling needs to be adopted by the
field scientists. A modeling framework that focuses on model parameterization
and validation ability is under development at Oak Ridge National Laboratory;
building a model optimization algorithm into an ESM framework will enable
more effective parameterization of newly developed CH4 modules within
CLM at site, regional, and global scales (Ricciuto et al., personal
communication, 16 December 2015).