Wetlands are the largest and most uncertain natural sources of atmospheric
methane (CH4). Several process-based models have been developed to
quantify the magnitude and estimate spatial and temporal variations in
CH4 emissions from global wetlands. Reliable models are required to
estimate global wetland CH4 emissions. This study aimed to test two
process-based models, CH4MODwetland and Terrestrial Ecosystem Model (TEM), against the CH4 flux
measurements of marsh, swamp, peatland and coastal wetland sites across the
world; specifically, model accuracy and generality were evaluated for
different wetland types and in different continents, and then the global
CH4 emissions from 2000 to 2010 were estimated. Both models showed
similar high correlations with the observed seasonal/annual total CH4
emissions, and the regression of the observed versus computed total
seasonal/annual CH4 emissions resulted in R2 values of 0.81 and
0.68 for CH4MODwetland and TEM, respectively. The
CH4MODwetland produced accurate predictions for marshes, peatlands,
swamps and coastal wetlands, with model efficiency (EF) values of 0.22,
0.52, 0.13 and 0.72, respectively. TEM produced good predictions for
peatlands and swamps, with EF values of 0.69 and 0.74, respectively, but it
could not accurately simulate marshes and coastal wetlands (EF <0).
There was a good correlation between the simulated CH4 fluxes and the
observed values on most continents. However, CH4MODwetland showed no
correlation with the observed values in South America and Africa. TEM
showed no correlation with the observations in Europe. The global CH4
emissions for the period 2000–2010 were estimated to be 105.31 ± 2.72 Tg yr-1 by CH4MODwetland and 134.31 ± 0.84 Tg yr-1 by
TEM. Both models simulated a similar spatial distribution of CH4
emissions globally and on different continents. Marshes contribute
36 %–39 % of global CH4 emissions. Lakes/rivers and swamps are the
second and third greatest contributors, respectively. Other wetland types
account for only approximately 20 % of global emissions. Based on the
model applicability, if we use the more accurate model, i.e., the one that
performs best as evidenced by a higher model efficiency and a lower model
bias, to estimate each continent and wetland type, we obtain a new assessment of
116.99–124.74 Tg yr-1 for the global CH4 emissions for the
period 2000–2010. Our results imply that performance at a global scale may
conceal model uncertainty. Efforts should be made to improve model accuracy
for different wetland types and regions, particularly hotspot regions, to
reduce the uncertainty in global assessments.
Introduction
Atmospheric methane (CH4) is the second most prevalent human-induced
greenhouse gas (GHG) after carbon dioxide (CO2). Its radiative forcing
effect is 28 times greater than that of CO2 on a 100-year horizon
(Myhre et al., 2013). The radiative forcing attributed to CH4 has been
re-evaluated by the Intergovernmental Panel on Climate Change (IPCC) Fifth
Assessment Report (AR5) and was reported to be almost twice as high as the
value reported in the Fourth Assessment Report (AR4), with values of 0.97 W m-2 versus 0.48 W m-2, respectively (Myhre et al., 2013). This
estimate considers that the emission of CH4 leads to an increase in
ozone production, stratospheric water vapor and CO2, which can affect
its own lifetime (Boucher et al., 2009; Myhre et al., 2013; Shindell et al.,
2012).
The growth rate of the atmospheric CH4 concentration has varied in
different historical periods. There was an exponential increase from
preindustrial times to the 1980s. The growth rate decreased after the 1980s
and was close to zero from 1999 to 2006; then, the growth rate resumed
strong growth in the period of 2007–2017 (Dlugokencky et al., 2009, 2016;
Nisbet et al., 2019). However, the causes that drive the variations in
growth rate remain unclear due to the uncertainties in estimating CH4
emissions and sinks (Ghosh et al., 2015; Saunois et al., 2016; Nisbet et
al., 2019; Dalsøren et al., 2016).
Integrated at the global scale, wetlands are the largest and most uncertain
source of CH4 emitted to the atmosphere (Kirschke et al., 2013;
Saunois et al., 2016). These emissions represent approximately 30 % of the
total CH4 input (Saunois et al., 2016). Bottom-up and top-down
approaches are popular methods for estimating global CH4 emissions from
natural wetlands. Top-down approaches are based on inverse models (e.g.,
Bousquet et al., 2006; Fraser et al., 2013; Meirink et al., 2008; Tsuruta et
al., 2017; Bruhwiler et al., 2014), which determine “optimal” surface fluxes
that best fit atmospheric CH4 observations given an atmospheric
transport model including chemistry, prior estimates of fluxes and their
uncertainties (Kirschke et al., 2013). Bottom-up approaches use
process-based models that describe the relationship between the
environmental factors and the processes of CH4 production, oxidation
and emission using mathematical equations (e.g., Li et al., 2010; Zhu et
al., 2013, 2014; Zhang et al., 2002; Walter and Heimann, 2000;
Tian et al., 2015; Riley et al., 2011; Meng et al., 2012; Zhuang et al.,
2006).
Recent studies related to the bottom-up approach have used an ensemble of
process-based models driven by the same climate forcing to estimate the
global CH4 emissions from natural wetlands. For example, the Wetland
and Wetland CH4 Intercomparison of Models Project (WETCHIMP) used 10
land surface models and estimated global CH4 emissions of 190 ± 76 Tg CH4 yr-1 for the 1993–2004 period (Melton et al., 2013).
In the following year, Kirschke et al. (2013) assessed a large emission
range of 142–287 Tg CH4 yr-1 from 1980 to 2010. Saunois et al. (2016) and Poulter et al. (2017) estimated global emissions of 153–227 Tg CH4 yr-1 for the decade 2003–2012 and 184 ± 22 Tg CH4 yr-1 for the decade 2000–2012 using ensemble process-based
models (Poulter et al., 2017). Saunois et al. (2016) suggested that
approximately 70 % of the uncertainty was due to model structures and
parameters.
Natural wetland ecosystems are greatly heterogeneous on a global scale.
Wetlands vary widely by continent with respect to area and type (Kingsford
et al., 2016; Keddy, 2010). Some wetland types have higher emissions, while
some emit less CH4; this difference is because the processes of
controls on CH4 cycling differ among wetland types (Bridgham et al.,
2013). For example, sedge-dominated marshes or fens often emit higher CH4
fluxes, because sedges can increase methanogenic substrates as part of their
plant productivity and promote CH4 transportation through their soft
aerenchyma and lacunae tissues (McEwing et al., 2015; Jitka et al., 2017;
Bhullar et al., 2013; Joabsson and Christensen, 2001; Kwon et al., 2017;
King et al., 2002; Chanton, 2005). Bog soils with anaerobic incubations emit
little CH4 due to the particularly high CO2:CH4 ratios of the
end products of anaerobic carbon (Bridgham et al., 1998; Galand et al.,
2010; Keller and Bridgham, 2007). Coastal wetlands with high salinity
usually emit less CH4 than other wetlands, because the sulfate in
seawater inhibits CH4 production (Bartlett et al., 1985; Delaune et
al., 1983; Li et al., 2016; Poffenbarger et al., 2011).
Model evaluation is a core part of model development and testing (Bennett et
al., 2013). Based on the model evaluation, the modeler must be confident
that the model will fulfill its purpose (Bennett et al., 2013; Rykiel,
1996). If applying process-based models for global-scale CH4
estimations, it is necessary to evaluate its performance in different
wetland types and regions. This process is also helpful for confirming the
source of uncertainties and improving the model. However, previous studies
have always focused on global assessments and have overlooked model
performance in different wetland types or regions, which may have induced
high uncertainties (Poulter et al., 2017; Saunois et al., 2016; Kirschke et
al., 2013; Melton et al., 2013). CH4MODwetland (Li et al., 2010) and
the Terrestrial Ecosystem Model (TEM) (Zhuang et al., 2004, 2007, 2013; Melillo et al.,
1993) are two established
process-based models that can be used to simulate regional and global
wetland CH4 emissions. Both models have been validated at specific
sites (Zhu et al., 2013; Li et al., 2010, 2017). However, we do
not have information on the accuracy and applicability of the models for
different wetland types and on different continents. The objectives of this
study were to comprehensively evaluate the model performances of
CH4MODwetland and TEM for different wetland types and on different
continents and then to use the models to estimate global CH4 emissions
from natural wetlands.
Methods and materials
The performance evaluation should clearly depend on the model objectives
(Bennett et al., 2013). The models considered in this study aim to estimate
the annual emissions from global wetlands. Therefore, the accuracy and
applicability of the model in simulating annual CH4 emissions for
different wetland types and continents are very important in a performance
evaluation. Several process-based models have been developed in recent
decades (Xu et al., 2016). Some models are simple semiempirical models that
focus on the biochemical processes of CH4 production, oxidation and
emission, e.g., Walter's model (Walter et al., 1996; Walter and Heimann,
2000), CASA (Potter, 1997) and CH4MODwetland (Li et al., 2010). This
kind of model requires simple inputs and parameters and is easily
extrapolated to a regional scale. Other models are based on more complex
land ecosystem models coupled to the CH4 processes module, such as
Community Land Model 4 Methane model (CLM4Me), Organising Carbon and Hydrology In Dynamic Ecosystems model (ORCHIDEE), Sheffield Dynamic Global Vegetation Model (SDGVM) and Terrestrial Ecosystem Model (TEM). These models describe complex ecosystem
processes and require more inputs and parameters. In this study, we chose
CH4MODwetland and TEM to compare the model performance of a simple
easy-to-run model and a sophisticated land ecosystem model. Moreover, both
models have been validated at the site scale, but no comprehensive accuracy
analysis in different continents or for various wetland types has been done
before. We collected CH4 flux measurements from 43 wetlands spanning
the main wetland types in the world from peer-reviewed literature (Table 1).
A set of statistical methods was used to comprehensively evaluate the
performance of CH4MODwetland and TEM in different wetland types and
on different continents. Finally, we extrapolated both models to estimate
the global CH4 emissions from 2000 to 2010.
Description of observation wetland sites. Observation period (YYYY.MM).
IDWetland name, continentLocationWetlandPlant speciesObservationReferencetypeperiod1Northeast Siberia, Russia, EU72∘22′ N, 126∘28′ EPeatlandaCarex spp., Limprichtia revolvens, Meesialongiseta1999.5–1999.9 2003.7–2004.7dWagner et al. (2003); Wille et al. (2008)2Northeast Siberia, Russia, EU71∘30′ N, 130∘00′ EPeatlandaEriophorum, Carex spp., Sphagnum spp., Salix spp.1993.7–1993.8Nakano et al. (2000)3Northeast Siberia, Russia, EU68∘30′ N, 161∘24′ EPeatlandaLarix, Alnus spp., Betula spp., Salix spp.1995.7–1995.8Nakano et al. (2000)4Northeast Siberia, Russia, EU70∘50′ N, 147∘29′ EPeatlandaBetula nana, Salix pulchra, dwarf shrubs, sedge, Sphagnum2008.7–2008.8d 2009.6–2009.8dParmentier et al. (2011)5Zackenberg, Greenland, EU74∘30′ N, 21∘00′ WPeatlandCassiope tetragona, Salix arctica1996.6–1996.8 1999.7–1999.8 2000.7–2000.8Christensen et al. (2000); Joabsson and Christensen (2001)6Abisko, Sweden, EU68∘22′ N, 19∘03′ EPeatlandaEriophorum angustifolium, Carex spp.1974.6–1974.9 2008–2009dSvensson and Rosswall (1984); Olefeldt et al. (2012)7Kaamanen, Finland, EU69∘08′ N, 27∘17′ EPeatlandShrubs, Carex spp., moss, etc.1998.4–1999.4dAurela et al. (2002)8Sanjiang Plain, China, ASe47∘35′ N, 133∘31′ EMarshCarex lasiocarpa, Deyeuxia angustifolia2002.6–2005.11Hao (2006); Song et al. (2008)9Ruoergai Plateau, China, AS32∘47′ N, 102∘32′ EPeatlandCarex muliensis, Carex meyeriana2001.4–2001.10Wang et al. (2002)10Wuliangsu Lake, China, ASe40∘47′–41∘03′ N, 108∘43′–108∘57′ EMarshPhragmites australis2003.4–2003.10Duan et al. (2005)11Haibei alpine marsh, China, AS37∘29′ N, 101∘12′ EMarshCarex allivescers2002.7–2002.9Hirota et al. (2004)12Zhalong wetland, China, AS46∘52′–47∘32′ N, 123∘47′–124∘37′ EMarshPhragmites australis2009.5–2009.10Huang et al. (2011)13Liao River delta, China, AS40∘40′–41∘25′ N, 121∘35′–122∘55′ ECoastalbPhragmites australis1997.4–1997.11Huang et al. (2005)14Chongming Island, China, ASe31∘15′ N, 121∘30′ ECoastalbScirpus2004.5–2004.12 2011.2–2011.12Li et al. (2014)15Guangzhou, China, AS23∘01′ N, 113∘46′ ECoastalcAegiceras corniculatum etc.2005.3–2005.12dKang et al. (2008)16Haikou, China, AS19∘51′ N, 110∘24′ ECoastalcBruguiera sexangula1996.1–1997.12dYe et al. (2000)17Sarawak, Malaysia, ASe2∘49′ N, 111∘51′ ESwampFlooded forestf2002.8–2003.7Melling et al. (2005)18Kalimantan, Indonesia, AS2∘20′ S, 113∘55′ ESwampShorea balangeran1994.9–1995.9Page et al. (1999); Jauhiainen et al. (2005)
Continued.
IDWetland name, continentLocationWetlandPlant speciesObservationReferencetypeperiod19Congo River basin, the Congo, AF4∘00′ S–0∘00′, 14∘00′–18∘00′ ESwampFlooded forestf1988dTathy et al. (1992)20Congo River basin, the Congo, AF0∘00′–4∘00′ N, 14∘00′–18∘00′ ESwampFlooded forestf1988dTathy et al. (1992)21Pantanal, Brazil, SA19∘30′ S, 57∘00′ WMarshPaspalum repens1998.1–1998.12Alvalá and Kirchhoff (2000); Melack et al. (2004)22Lago Calado, Brazil, SA3∘15′ S, 60∘34′ WSwampFlooded forestf1985dCrill et al. (1988)23Central Brazilian Amazon, SA5∘00′ S–0∘00′, 50∘00′–70∘00′ WSwampFlooded forestf1985dDevol et al. (1988)24Negro River basin, Brazil, SA0∘17′ S, 63∘34′ WSwampEmergent sedge, shrub, palm2005.1–2006.1dBelger et al. (2011)25Bethel, Alaska, USA, NA60∘45′ N, 161∘45′ WPeatlandaEmpetrum nigrum, Carex aquatilis,Sphagnum spp.1988.7–1988.8Bartlett et al. (1992)26Bethel, Alaska, USA, NA61∘5′ N, 162∘1′ WPeatlandaEmpetrum nigrum, Carex aquatilis,Sphagnum spp.1988.7–1988.8Fan et al. (1992)27Prudhoe Bay, Alaska, USA, NA70∘30′ N, 149∘00′ WPeatlandaSphagnum spp.1984dSebacher et al. (1986)28Alaska arboretum, USA, NA64∘52′ N, 147∘51′ WPeatlandaEriophorum vaginarum, Carex spp., Sphagnum spp.1987.6–1987.10 1988.6–1988.10 1989.6–1989.10Whalen and Reeburgh (1992)29Saskatchewan, Canada, NAe53∘57′ N, 105∘57′ WPeatlandBuckbean-Carex spp.1994.5–1994.9 1995.5–1995.10Suyker et al. (1996); Sellers et al. (1997)30Michigan, USA, NA42∘27′ N, 84∘01′ WPeatlandScheuchzeria palustris, Carex oligosperma1991.1–1993.12Shannon et al. (1996)31Toolik Lake, USA, NAe68∘38′ N, 149∘38′ WPeatlandaEriophorum, Carex spp.1990.6–1990.8 1992.6–1992.8 1993.5–1993.9Christensen (1993) Schimel et al. (1994, 1995)32Hudson Bay, Canada, NA51∘18′–51∘31′ N, 80∘28′–80∘38′ WPeatlandLarch, black spruce, Sphagnum spp.1990.6–1990.10Moore et al. (1994)33Quebec, Canada, NA54∘48′ N, 66∘49′ WPeatlandCarex spp.1989.6–1989.9Moore et al. (1990)34Mississippi, USA, NA34∘24′ N, 89∘50′ WMarshCarex hyalinolepis, Hydrocotyle umbellata, Festuca obtusa2005.5–2006.7Koh et al. (2009)35Sherman Island, USA, NA38∘2′ N, 121∘45′ WPeatlandHordeum murinum L., Lepidium latifolium L.2009.4–2011.4dHatala et al. (2012)36Marcell Experimental Forest,47∘30′ N, 93∘29′ WPeatlandCarex spp., sphagnum moss, Eriophorum2009–2010dOlson et al. (2013)USA, NAchamissonis, etc.
Continued.
IDWetland name, continentLocationWetlandPlant speciesObservationReferencetypeperiod37Mer Bleue peatland, Canada, NA45∘41′ N, 75∘48′ WPeatlandChamaedaphne calyculata, Ledumgroenlandicum, etc.1999–2010dMoore et al. (2011)38Sag riverside, Alaska, NA69∘30′ N, 148∘13′ WPeatlandaVascular plant, moss and a few short shrubs1996.6–1996.9dHarazono et al. (2006)39Happy Valley, Alaska, NA69∘10′ N, 148∘51′ WPeatlandaSphagnum moss, sedge1995.6–1995.9dHarazono et al. (2006)40Churchill Manitoba, Canada, NA58∘40′ N, 93∘50′ WPeatlandCarex aquatilis, Eriophorum spp., etc.2008–2010dHanis et al. (2013)41Northern Alaska, USA, NA71∘17′ N, 156∘36′ WPeatlandMoss, Carex aquatilis, Eriophorumvaginatum, etc.2007.6–2007.8dZona et al. (2009)42Alberta, Canada, NA54∘57′ N, 112∘28′ WPeatlandPicea mariana, Larix laricina, shrub, etc.2007.5–2007.9dLong et al. (2010)43Great Dismal Swamp, USA, NA35∘54′ N, 76∘09′ ESwampTaxodium distichum, Nyssa sylvatica, etc.2007.7–2009.6dMorse et al. (2012)
a Tundra.
b Tidal marsh.
c Mangrove.
d We used the reported average yearly CH4 flux of the
experimental year or period from the literature.
e Wetland sites used for calibration.
f These swamp sites do not have plant speciesinformation in the
literature.
Model overviewCH4MODwetland
The CH4MODwetland model is a process-based biogeophysical model used
to simulate the processes of CH4 production, oxidation and emission
from natural wetlands (Li et al., 2010). The model was established based on
CH4MOD, which is used to predict CH4 emissions from rice paddies (Huang
et al., 1997). In CH4MODwetland, we focused on the
differences in the supply of methanogenic substrates between natural
wetlands and rice paddies. Methanogenic substrates are derived from root
exudates, the decomposition of plant litter and soil organic matter. The
methane production rates were determined based on the methanogenic
substrates and the influence of environmental factors, including soil
temperature, soil texture and soil redox potential. Additionally, we
incorporated the influence of salinity on CH4 production to improve the
model performance for coastal wetlands (Li et al., 2016). Inputs to the
CH4MODwetland model include the daily air and soil temperature, water table
depth, annual aboveground net primary productivity (ANPP), soil sand
fraction, soil organic matter, bulk density, and soil salinity. The outputs
are the daily and annual CH4 production and emissions. We used the
TOPMODEL hydrological model to simulate the water table depth as the
inputs of CH4MODwetland (Bohn et al., 2007; Li et al., 2015,
2019a; Zhu et al., 2013; Beven and Kirkby, 1979).
The main parameters that must be calibrated in CH4MODwetland include
the vegetation index (VI), which was used to quantify the different capacities
for producing root exudates of the various plant species; the fraction of
plant-mediated transport available (Tveg); the fraction of CH4
oxidized during plant-mediated transport (Pox); the proportion of
belowground net primary productivity (BNPP) to the total net primary
productivity (NPP) (fr); the fraction of nonstructural component in plant
litter (FN) (Table S1 in the Supplement); and the empirical constant of the influence of
salinity. The model parametrization and main parameters are described in
Sect. S1 in the Supplement.
TEM
TEM is another process-based biogeochemical model that couples carbon,
nitrogen, water and heat processes in terrestrial ecosystems to simulate
ecosystem carbon and nitrogen dynamics (Melillo et al., 1993; Zhuang et al.,
2007, 2013). The methane dynamics module was first coupled
within TEM by Zhuang et al. (2004) to explicitly simulate the process of
methane production (methanogenesis), oxidation (methanotrophy) and transport
between the soil and the atmosphere. Methane production is assumed to occur
only in saturated zones and is regulated by organic substrate, soil thermal
conditions, soil pH, and soil redox potentials; methane oxidation, which
occurs in the unsaturated zone, depends on the soil methane and oxygen
concentrations, temperature, moisture and redox potential. Methane transport
is described by three pathways in TEM: (1) diffusion through the soil
profile, (2) plant-aided transport and (3) ebullition. TEM has also
been coupled with TOPMODEL (Zhu et al., 2013). The model calibration of the
TEM is well documented in Sect. S2 and Table S2.
Site information and data sourcesSite information
We collected data from 43 wetland sites across the world (Table 1). The wetland sites
included 6 marsh sites, 25 peatland sites, 8 swamp sites and 4 coastal
wetland sites. Among the wetland sites, 7 sites are distributed in Europe
(EU), 11 sites are distributed in Asia (AS), 2 sites are distributed in
Africa (AF), 4 sites are distributed in South America (SA) and 19 sites are
distributed in North America (NA). The observations were from the late 1980s
to the 2010s. The observation periods covered either a growing season or a
whole year (Table 1). We calculated the total amount of CH4 emissions
during the growing season or the whole year as the observed seasonal/annual
CH4 emissions. For most of the wetland sites, the total amount of
seasonal/annual CH4 emissions during the observation period was
calculated by summing the daily observations. Gaps in the CH4 emission measurements were filled by linear interpolation between two
adjacent days of observations. For a few wetland sites, the observed
seasonal/annual CH4 emissions were directly obtained from the
literature. More details about the location, vegetation and observation
periods are described in Table 1.
Wetland map
The global wetland distributions of different wetland types were based on
the Global Lakes and Wetlands Database (GLWD-3; https://www.worldwildlife.org/publications/global-lakes-and-wetlands-database-lakes-and-wetlands-grid-level-3, last access: 10 October 2019) (Lehner and Döll, 2004)
(Fig. 1). According to GLWD-3, the wetland types include (1) lakes, (2) reservoirs,
(3) rivers (we combined lakes, reservoirs and rivers as a single wetland
type, hereafter referred to as lakes/rivers), (4) freshwater marsh and
floodplain (hereafter referred to as marsh), (5) swamp forest and flooded
forest (hereafter referred to as swamp), (6) coastal wetland, (7) saline
wetland (we combined coastal wetland and saline wetland as a single wetland
type, hereafter referred to as coastal wetland), (8) bog, fen and mire
(hereafter referred to as peatland), (9) intermittent wetland and (10) no-specific wetland. All of the observed sites (Table 1) are distributed on
the wetland map (Fig. 1).
Wetland site distribution (Table 1) and global wetland maps of
GLWD-3 (Lehner and Döll, 2004).
The global wetland area (excluding rivers) was estimated by the “Global
Review of Wetland Resources and Priorities for Wetland Inventory (GRoWI)”
as 530–570 Mha (Spiers, 1999). We used an average value, as the wetland
area excluded rivers in this study. The global wetland area of rivers was
based on GLWD-3. Therefore, we assumed that the global wetland area was 584 Mha, which represented the wetland area for the period from 2000 to 2010.
The cartography-based GLWD-3 data provide a global distribution of natural
wetlands at a 30 s resolution. Then, we aggregated the merged map up to
0.5∘×0.5∘ (latitude × longitude)
grids. The wetland area (excluding rivers) in each pixel was adjusted by the
ratio of the global wetland area estimated by GRoWI and by GLWD-3.
Driver data
The input climate data for the models include the daily air temperature,
precipitation, cloudiness and vapor pressure. The historical daily climate
data were developed from the latest monthly datasets of the Climatic
Research Unit (CRU TS 3.10) of the University of East Anglia in the United
Kingdom (Harris et al., 2014).
The soil properties needed by the CH4MODwetland model include soil
texture (percentage of sand in the soil), bulk density, soil organic carbon
content, soil temperature and soil moisture. The additional information
needed by TEM includes the percentage of silt and clay in the soil, soil
pH, and site elevation. The soil texture data were derived from the soil map
of the Food and Agriculture Organization (FAO) (FAO, 2012). The soil
organic carbon content and the reference bulk density of wetland soils were
retrieved from the Harmonized World Soil Database (HWSD) (FAO, 2008)
by masking the HWSD with the Global Lakes and Wetlands Database (GLWD) (Lehner and Döll, 2004). The daily soil temperature data were
estimated by TEM from spatially interpolated climate data. The daily
soil moisture driving CH4MODwetland coupled with TOPMODEL was developed
from the monthly dataset (http://www.cpc.ncep.noaa.gov/soilmst/leaky_glb.htm. last access: 10 October 2019) by
temporal linear interpolation (Fan and van den Dool, 2004). The soil pH was
also derived from the global soil property dataset of the International
Geosphere-Biosphere Programme (IGBP) (Carter and Scholes, 2000).
The vegetation map of the IGBP was referenced to specify the vegetation
parameters for CH4MODwetland (Table S1) and TEM. The map was
derived from the IGBP Data and Information System (DIS) DISCover
Database (Belward et al., 1999; Loveland et al., 2000). The 1 km × 1 km DISCover dataset was reclassified into the TEM vegetation
classification scheme and then aggregated into 0.5∘×0.5∘ grids. The annual ANPP used to drive CH4MODwetland was
from the output of TEM.
For CH4MODwetland, a high-resolution topographic wetness index dataset
(Marthews et al., 2015) was used to calculate the changes in the water
table. Global salinity data were obtained from the World Ocean Atlas 2009
(Antonov et al., 2010). We also used 1 km × 1 km global elevation
data derived from the Shuttle Radar Topography Mission (SRTM) (Farr
et al., 2007). The above data were resampled to 0.5∘×0.5∘ grids to match the resolution of the other input data.
Model evaluation
We compared the observed seasonal/annual CH4 emissions from the wetland
sites (Table 1) and the simulated CH4 emissions at the 0.5∘×0.5∘ grid scale for the same period (described in Sect. 2.4). The statistics include the determination coefficient (R2), the
root-mean-square error (RMSE), the mean deviation (RMD), the model
efficiency (EF) and the coefficient of determination (CD) were used to
evaluate model performance on a global scale, a continental scale and for
each wetland type. Because of the limited number of sites in Africa and
South America, we combined the two continents together.
Two simulations with the same RMSE values may not be considered equivalent,
because the distribution of the error among the sources may not be the same
(Allen and Raktoe, 1981). We further analyzed the source of the model errors
by decomposing it into three components: the mean bias from the modeling
procedure (UM), the errors due to regression (UR) and the errors
due to random disturbances (UE) (Allen and Raktoe, 1981). The detailed
description and the equations used to calculate these statistics are
described in Sect. S3.
Model extrapolation
CH4MODwetland and TEM were used to simulate the CH4 emissions from global wetlands at a spatial resolution of 0.5∘×0.5∘. We established spatially explicit data for
climate, soils, vegetation, land use and other environmental inputs at a
0.5∘×0.5∘ spatial resolution to facilitate the
models at the global scale. Both process-based models were conducted for the
period of 1980–2010 in each pixel to simulate the temporal spatial
variations in CH4 fluxes. In this study, we focused only on the total
CH4 emissions for the period 2000–2010, because we assumed that the
wetland map represented the distribution of natural wetlands during this
period. The total CH4 emissions from the natural wetlands, excluding
the lakes/rivers in each pixel, were calculated as the product of the
CH4 fluxes and the gridded wetland area. To make an overall
global or continental CH4 emissions assessment, we evaluated the CH4
emissions from lakes/rivers using the IPCC Tier 1 method based on the
CH4 emissions factor (IPCC, 1996) and the area of lakes/rivers in each
pixel.
We aggregated the gridded values and obtained the annual mean CH4
emissions from each wetland type and each continent by CH4MODwetland
combined with the IPCC Tier1 method (hereafter referred to as Method A) and
TEM combined with the IPCC Tier1 method (hereafter referred to as Method B). In addition to the two global assessments Method A and Method B, we made
two other assessments of global CH4 emissions by choosing the more
accurate model (Method C and Method D). Based on the model performance
evaluation (Sect. 2.3), we found a more accurate model for each wetland type
and each continent. In the Method C approach, we chose the CH4
emissions from each continent simulated by the more accurate model. In
Oceania, we used the average simulated result by CH4MODwetland and the
TEM, because there was no wetland site on this continent (Table 1). We summed
the CH4 emissions from all continents and made an assessment of the
global CH4 emissions. In the Method D approach, we chose the CH4
emissions from marsh, peatland, swamp and coastal wetlands simulated by the
more accurate model. The CH4 emissions are from intermittent wetlands and
nonspecific wetlands (no-specific wetlands), which were used as the average result by CH4MODwetland
and TEM. The CH4 emissions from lakes/rivers were based on the IPCC
Tier 1 method. We summed the CH4 emissions from all wetland types and
assessed the global CH4 emissions.
ResultsModel evaluationModel evaluation for global wetland sites
Figure 2 shows the correlation of the modeled versus observed total amount of
seasonal/annual CH4 emissions by CH4MODwetland (Fig. 2a) and the
TEM (Fig. 2b). The regression of the observed versus computed total
seasonal/annual CH4 emissions by CH4MODwetland (Fig. 2a) resulted
in an R2 of 0.81, with a slope of 1.17 and an intercept of -1.93 g m-2 (n=58, p<0.001). The regression of the observed versus
computed total seasonal/annual CH4 emissions by TEM (Fig. 2b)
resulted in an R2 of 0.68, with a slope of 0.74 and an intercept of
4.77 g m-2 (n=58, p<0.001). These results indicated that the
variations in the CH4 emissions between sites and in different years
could be delineated by both process-based models.
Regression of simulated against observed total amount of
seasonal/annual CH4 emissions from global wetland sites by
CH4MODwetland(a) and TEM (b). The horizontal bars are the standard
errors from the sampling replicates at the wetland site. The red line is the
regression line of simulated vs. observed between modeled and observed
values. The blue line is the prediction correspondence. The dashed line is
the 1 : 1 line.
Model performance for CH4MODwetland and TEM for different continents and wetland types.
Wetland typeCH4MODwetlandTEM nor continentR2RMSERMDEFCDUMURUER2RMSERMDEFCDUMURUENorth America0.8275.37-1.960.570.490.040.610.390.8056.22-2.860.761.590.000.030.9728Asia0.9455.79-12.640.930.960.280.020.700.2672.561.710.321.930.000.030.9711Europe0.3562.69-32.600.151.130.270.030.69NS161.3329.39-4.650.340.030.840.1313South America/AfricaNS57.3239.52-0.800.670.480.070.450.5929.3313.130.532.220.130.040.836Marsh0.7529.440.520.220.370.000.730.27NS39.76-18.77-0.420.950.220.170.618Peatland0.8382.26-10.40.570.490.020.610.380.7069.457.960.691.140.010.030.9639Swamp0.5074.2843.070.130.540.340.190.470.7640.7619.020.741.270.220.030.757Coastal wetland0.8055.46-26.970.722.090.240.300.47NS188.26101.00-2.260.350.290.420.294Global0.8167.004.280.650.590.000.450.540.6863.584.630.681.460.010.010.9858
NS represents no significant correlation.
The statistics of the model performance of seasonal/annual CH4
emissions (Table 2) indicated that both process-based models had the
capability to simulate seasonal/annual CH4 emissions from natural
wetlands on a global scale (EF =0.65 for CH4MODwetland and EF =0.68
for TEM). However, a discrepancy still existed between the simulated and
observed seasonal/annual CH4 emissions (RMSE =67.00 % for
CH4MODwetland and RMSE =63.58 % for TEM). For
CH4MODwetland, the source of the errors was mainly from the regression
error and random error, while for TEM the errors were mainly due to
random disturbances (Table 2). Both models slightly overestimated the
seasonal/annual CH4 emissions on a global scale, with RMD values of
∼4 % (Table 2).
Model evaluation for different continents
We further analyzed the model predictions by CH4MODwetland and TEM
among different continents (Fig. 3, Table 2). There was a good correlation
between the simulated seasonal/annual CH4 emissions and the observed
values on most of the continents by the two models. The R2 varied
between 0.35 (Fig. 3e) and 0.94 (Fig. 3c) for CH4MODwetland and between
0.26 (Fig. 3d) and 0.80 (Fig. 3h) for TEM. The CH4MODwetland model
yielded more accurate predictions in Asia and North America, with EFs of
0.93 and 0.57, respectively (Fig. 3b and a, Table 2), than in South America
and Africa (EF <0 in Table 2) (Fig. 3g). TEM yielded more
accurate predictions in North America and South America/Africa than
CH4MODwetland, with EF values of 0.76 and 0.53, but performed poorly in
Europe (EF <0 in Table 2). CH4MODwetland underestimated the
observed emissions (RMD =-12.64 %) in Asia and Europe (RMD =-29.91 %) (Table 2). TEM overestimated the CH4 emissions in South
America/Africa (RMD =15.31 %) and slightly underestimated the CH4
emissions in North America (RMD =-2.86 %) (Table 1). Random error was the
main contributor to the model errors in Asia and Europe in
CH4MODwetland and in Asia, North America and South America/Africa in
TEM (Table 2). However, the regression error contributed most to the
model errors in North America in CH4MODwetland (Table 2).
Regression of simulated against observed total amount of
seasonal/annual CH4 emissions from North American wetland sites by
CH4MODwetland(a) and TEM (b), from Asian wetland sites by CH4MODwetland(c) and TEM (d), from European wetland sites by CH4MODwetland(e) and TEM (f), and from South American and African wetland sites by CH4MODwetland(g) and TEM (h). The horizontal bars are the standard errors from the sampling replicates at the wetland site. The blue line is the prediction correspondence. The dashed line is the 1 : 1 line.
Model evaluation for different wetland types
Figure 4 shows the regressions of the simulated values against the observed
total amount of seasonal/annual CH4 emissions from the different
wetland types. Regression analysis indicated that both models showed good
performance in modeling seasonal/annual CH4 emissions from the peatland
sites (Fig. 4c and d). TEM showed a better model efficiency and a lower
RMSE and RMD than the CH4MODwetland (Table 2) for peatland. For the
other wetland types, CH4MODwetland showed good performance in
simulating the seasonal/annual CH4 emissions from coastal wetlands (EF =0.72), followed by marshes (EF =0.22) and swamps (EF =0.13) (Table 2). TEM showed poor performance for the marsh sites (EF =-0.42) and
coastal wetlands (EF =-2.26) (Table 2); however, it showed good
performance for the swamp sites (EF =0.74). There was no significant
correlation (p>0.05) between the modeled and observed
seasonal/annual CH4 emissions from the marsh sites (Fig. 4b) and
coastal wetland sites (Fig. 4h).
Regressions of simulated against observed total amount of
seasonal/annual CH4 emissions from marsh sites by CH4MODwetland(a) and TEM (b), from peatland sites by CH4MODwetland(c) and the
TEM (d), from swamp sites by CH4MODwetland(e) and TEM (f), and from coastal wetland sites by CH4MODwetland(g) and TEM (h). The
horizontal bars are the standard errors from the sampling replicates at the
wetland site. The blue line is the prediction correspondence. The dashed
line is the 1 : 1 line.
The errors by CH4MODwetland were mainly due to the regression error for
marsh and peatland (Table 2). For coastal wetlands, the model bias
contributed 24 %, the regression error contributed 30 %, and the random
error contributed 47 % to the model errors (Table 2). The errors by the
TEM were mainly due to the random error in peatland and swamps (Table 2).
Global CH4 emissions from natural wetlandsSpatial pattern of global CH4 emissions
The distribution of the simulated annual mean CH4 fluxes and total
CH4 emissions for the period 2000–2010 showed similar patterns in
CH4MODwetland and TEM (Fig. 5). The simulated latitudinal
contributions of CH4 fluxes were consistent between the two models
(Sect. S4, Fig. 5a and b). Large emissions were found in
South America, southern Africa, and near the border of Canada and the United
States (Fig. 5c and d). The latitudinal sums of CH4 emissions
indicated that the strongest contribution came from the tropical zone (Fig. 5c and 5d). The latitudinal band of 10–0∘ S contributed
22.77 and 23.23 Tg yr-1CH4 in CH4MODwetland
and TEM, which accounted for 22 % and 18 % of the global emissions,
respectively. A secondary large peak was simulated in the 40–50∘ N
latitudinal band, with values of 14.64 and 16.66 Tg yr-1CH4 according to CH4MODwetland and TEM, respectively.
Generally, both models simulated a common decline in CH4 emissions from
lower latitudes to higher latitudes (Fig. 5c and d). The largest peak in
CH4 emissions was modeled in the 60–50∘ W meridional band,
with values of 11.63 Tg yr-1 in CH4MODwetland (Fig. 5c) and 13.83 Tg yr-1 in TEM (Fig. 5d). This peak corresponded to the longitudes
of the Amazon in South America. Both models simulated secondary peaks in the
30–40∘ E meridional band (Fig. 5c and d), which corresponded to
the longitudes of the Congo in Africa.
Spatial pattern of annual mean CH4 fluxes for 2000–2010,
with latitudinal and longitudinal distributions of annual mean CH4
fluxes by CH4MODwetland(a) and TEM (b). Spatial pattern of annual mean CH4 emissions for 2000–2010, with latitudinal and longitudinal distributions of annual mean CH4 emissions by CH4MODwetland(c) and TEM (d). The CH4 fluxes and emissions are aggregated in steps
of 10∘.
CH4 emissions from different continents and wetland types
Table 3 provides an overview of the CH4 emissions from different
continents and wetland types simulated by CH4MODwetland and TEM. A
comparison of simulated CH4 fluxes from different continents by
CH4MODwetland and TEM showed that the three highest fluxes were
modeled in South America, Africa and Asia (Table 3). TEM simulated
higher CH4 fluxes in Europe than in North America, but the
CH4MODwetland simulations showed the opposite. For Oceania, the two
models simulated similar fluxes.
CH4 simulations by CH4MODwetland and TEM for different continents and wetland types. All units are Tg CH4 yr-1±1σ, where the standard deviation represents the interannual variation in the model estimates.
∗ The IPCC Tier 1 method was used to estimate the CH4 emissions from lakes and rivers. The CH4 emission factor was from IPCC (1996).
Both models simulated the same sequence of CH4 fluxes: swamp, marsh,
intermittent wetland, no-specific wetland, coastal wetland and peatland
(Table 3). The simulated annual mean CH4 fluxes from intermittent
wetlands were almost equivalent in both models. For other wetland types,
TEM simulated higher CH4 fluxes than the CH4MODwetland model
(Table 3). Both models simulated peak emissions in summer and lower
emissions in winter for all wetland types except swamps (Fig. S1). Since
large areas of swamps are distributed in the Southern Hemisphere (Fig. 1), higher and
lower CH4 emissions were simulated during March to May and June to
August, respectively (Fig. S1).
The global CH4 emissions simulated by TEM were 29 Tg yr-1
higher than those simulated by CH4MODwetland (Table 3). This difference
depended on the differences in the CH4 fluxes and on the wetland area.
The simulated results showed that half of this difference was attributed to
marshes. South America contributed 30 % to this difference, because the
simulated CH4 fluxes differed greatly between TEM and
CH4MODwetland (Table 3).
The two models simulated similar spatial distributions of the CH4
emissions among different wetland types and continents (Table 3). Marshes
emit higher CH4 fluxes and have the largest area. Thus, marshes were
the greatest contributor to global CH4 emissions and contributed
36 %–39 % to global CH4 emissions (Table 3). Lakes/rivers and
swamps were the second and third contributors, respectively (Table 3). The
CH4 emissions from peatlands, coastal wetlands, intermittent wetlands
and no-specific wetlands accounted for only approximately 20 % of the
global emissions (Table 3).
Although North America accounted for 36 % of the global wetland area, it
contributed only 22 %–23 % to global emissions (Table 3). In contrast,
the wetland area in South America accounted for 15 % of the global area
and contributed 25 %–26 % to global CH4 emissions. Asia and
Africa also accounted for approximately 20 % of global emissions. The
lowest area and emissions were found in Oceania (Table 3).
Global CH4 estimations
The global CH4 emissions for the period 2000–2010 were estimated to be
105.31 ± 2.72 Tg yr-1 by Method A and 134.31 ± 0.84 Tg yr-1 by Method B. Based on the evaluation of model performance (Table 2), CH4MODwetland yielded the most accurate predictions for Asia and
Europe, and TEM yielded the most accurate predictions for North America
and South America/Africa. Using this combination, the global CH4
emissions were estimated to be 124.74 ± 1.22 Tg by Method C.
Similarly, in Method D, CH4MODwetland was used for simulations in
marshes and coastal wetlands, and TEM was used for simulations in
peatlands and swamps; as a result, the global wetland CH4 emissions
were estimated to be 116.99 ± 2.23 Tg.
DiscussionGenerality of CH4MODwetland and TEM
A lack of correspondence between the model output and observations could be
partly due to the observed flux data, e.g., the inevitable gap-filling of
missing data points to determine the seasonal/annual total emissions (Kramer
et al., 2002). The results showed differences between the observed and
simulated CH4 emissions by both CH4MODwetland and TEM on a
global scale (Fig. 2) and a continental scale (Fig. 3) and for different wetland
types (Fig. 4). The reliability of the observed flux data is not under
discussion in this study. We evaluated only the model accuracy and
applicability across different wetland types and continents.
On a global scale, both models fulfilled the criteria of sufficient accuracy
for the ability to predict CH4 fluxes (Table 2). However, this fuzzy
analysis may miss some real model performance. For the model applicability
on different continents, CH4MODwetland performed best in Asia, followed
by North America and Europe. It performed poorly in South America/Africa,
where swamps are more common (Table 2). TEM performed best in North
America, followed by South America/Africa and Asia. It performed poorly in
Europe (Table 2). Each continent has different main wetland types; thus, the
model applicability for different continents depended on its applicability
for different types. CH4MODwetland is suitable for marshes, peatlands
and coastal wetlands, but it cannot be applied in swamps (Table 2). This
limitation may be because in CH4MODwetland only a semiempirical
logistic model is used to simulate plant growth (Li et al., 2010). This
characteristic may induce large uncertainties in simulating the growth of
forests in swamps (Table 1). However, TEM uses the carbon and nitrogen
dynamics module (CNDM) to describe the effects of photosynthesis,
respiration, decomposition and nutrient cycling on NPP (Melillo et al.,
1993). Compared with CH4MODwetland, TEM performed well in
simulating NPP in various vegetation types (Melillo et al., 1993). According
to the model evaluation, TEM was suitable for swamps and peatlands but
had large uncertainties in marshes and coastal wetlands (Table 2). This
pattern may be because TEM focuses on two major wetland types: boreal
tundra and forest wetland (Zhuang et al., 2004). The biochemical processes
in TEM may be suitable for peatlands (tundra) and swamps (forest
wetland) but not suitable for marshes. For coastal wetlands, TEM did not
consider the inhibition of salinity on CH4 production (Poffenbarger et
al., 2011; Bartlett et al., 1987) and greatly overestimated the CH4
fluxes (Table 2). CH4MODwetland introduced the influence of salinity on
CH4 production and had good performance for coastal wetlands (Table 2).
Reducing uncertainties in global estimations
The estimates of global wetland CH4 emissions had large ranges in
previous studies (Zhu et al., 2015). The estimates by process-based models
ranged from 92 Tg yr-1 (Cao et al., 1996) to 297 Tg yr-1 (Gedney
et al., 2004) during the period of 1980–2012. Recently, an ensemble of
process-based models driven by the same climatic data has commonly been used
to estimate global wetland CH4 emissions (Melton et al., 2013; Kirschke
et al., 2013; Poulter et al., 2017; Saunois et al., 2016). However, the
uncertainties in the model mean estimation range from 12 % (Poulter et
al., 2017) to 40 % (Melton et al., 2013). The uncertainty mainly comes
from the wetland distribution and model structure and parameters (Saunois et
al., 2016). Estimating accurate wetland extent and its seasonal and annual
variations is a major challenge in present studies. The global estimations
of wetland area ranged from 4.3 to 12.9 Mha during the period of 1990
to 2005 (Melton et al., 2013). The wetland extent of 9.2 Mha from the GLWD
excluded water bodies, and this value was ∼40 % higher than
the wetland area used in this study. That is, this difference was the main
reason for the lower global estimations determined in this study than those
reported in previous works (Zhu et al., 2015; Melton et al., 2013; Poulter
et al., 2017; Saunois et al., 2016). Improving the accuracy of wetland
extent and temporal variations is important in reducing uncertainties in
global wetland CH4 estimations.
In addition to wetland area, the model structure and parameters accounted
for ∼70 % of the total uncertainties (Saunois et al.,
2016). The results of the accuracy analysis showed that for
CH4MODwetland regression bias accounted for 61 % of the model errors
in peatland and mean bias accounted for 22 % of the RMSE in swamp; for
TEM, mean bias and regression bias accounted for 29 % and 42 %,
respectively, of the model errors in coastal wetland (Table 2). This result
indicated that there were still uncertainties in the modeling procedure,
e.g., in the model mechanism or in parameterization (Zhang et al., 2017;
Allen and Raktoe, 1981). In the existing process-based models, which are not
limited to CH4MODwetland and TEM, some important procedures should
be focused on to reduce the bias due to the model mechanism. For example,
the mechanism of the freeze–thaw cycle is important in process-based models
(Wei and Wang, 2017) because of the large contribution of CH4 released during the nongrowing season in some frozen regions (Friborg et
al., 1997; Huttunen et al., 2003; Mastepanov et al., 2008; Zona et al.,
2016). In addition, quantifying CH4 ebullition is important but
difficult due to the uncertainty in estimates of CH4 emissions from
peatlands (Stanley et al., 2019). Moreover, although the importance of
plants in CH4 biogeochemical processes has been reported in many
studies, better modeling and characterization of plant community structure
is needed (Bridgham et al., 2013). Finally, most of the present
process-based models do not have the ability to simulate CH4 exchange
from water bodies, such as lakes, rivers and reservoirs, although such water
bodies contribute significantly to the global budget (Deemer et al., 2016).
The use of the IPCC Tier method inevitably induces large uncertainties in
the global estimates. The above mechanisms should be incorporated into
existing process-based models to reduce the uncertainties in the current
assessment.
The observational data that are related to processes of and controls on CH4
production, consumption and transport also limit the model calibration and
validation. The flux data of 43 wetland sites used for model performance in
this study are quite limited and do not represent all climatic, soil,
hydrologic and vegetation conditions across global natural wetlands (Table 1). The observations in this study used both the chamber method and the eddy
covariance method (Aubinet et al., 2012), which are widely used for CH4
observations (Table 1). There are differences in measuring CH4 fluxes
between the two methods (Chaichana et al., 2018). The eddy covariance method
may underestimate the fluxes (Twine et al., 2000; Sachs et al., 2010), while
the chamber method may overestimate the fluxes (Werle and Kormann, 2001).
These differences may introduce uncertainties to model calibration and
validation. Furthermore, both process-based models were evaluated on an
annual basis rather than on a daily scale. The validation of seasonal
variation was not performed in this study, partly because we cannot obtain
the daily step data for some of the sites. Fine temporal validation against
more flux datasets, especially fluxes by eddy covariance experiments, and
intermediate variables that control the CH4 process are necessary in
future studies (Wei and Wang, 2017).
Conclusion
Two process-based models, CH4MODwetland and TEM, were used to
simulate annual CH4 emissions from different wetland types and
continents, and their performances were evaluated. Model validation showed
that both models could simulate variations between different wetland sites
and years. The statistical analysis of model performance showed that
CH4MODwetland was capable of simulating CH4 emissions from
marshes, peatlands, swamps and coastal wetlands, while TEM was capable
of simulating CH4 emissions from peatlands and swamps (model efficiency
>0). CH4MODwetland performed well in Asia, Europe and North
America, while TEM performed well in North America, Asia, South America
and Africa. The models were then used to estimate global wetland CH4 emissions. The CH4 simulations of both models had good agreement in
terms of the latitudinal and meridional bands. The global CH4 emissions
for the period 2000–2010 were estimated to be 105.31 ± 2.72 Tg yr-1 by CH4MODwetland and 134.31 ± 0.84 Tg yr-1 by the
TEM. If we used a more accurate model to estimate each continent and wetland
type based on the models' generality, the estimated global CH4
emissions would be 116.99–124.74 Tg yr-1 for the period 2000–2010. The
uncertainty in global wetland CH4 assessments by the process-based
model approach comes from the inaccuracy of the wetland mapping area, the
modeling procedure and the observational limitations. Future research on
accurately mapping wetlands, improving model mechanisms and parametrization,
and using more observations to evaluate model performance would improve
global estimations.
Code and data availability
TEM and CH4MODwetland model code and model datasets (input data
and model results) are available at 10.5281/zenodo.3537621 (Li et al., 2019b).
The supplement related to this article is available online at: https://doi.org/10.5194/gmd-13-3769-2020-supplement.
Author contributions
TL and LinY pondered the rationale of the method. TL and YL developed and performed the model simulations. WS, QZ, WZ,
GW, ZQ, LijY, HL and RZ performed the data collection and processing. TL prepared the article with contributions from all coauthors.
Competing interests
The authors declare that they have no conflict of interest.
Acknowledgements
This work was jointly supported by the National Natural Science Foundation of China (grant nos. 41775159, 91937302, and 31000234) and the National Key Scientific and Technological Infrastructure project “Earth System Science Numerical Simulator Facility” (EarthLab). We acknowledge data support from the National Earth System Science Data Center, National Science & Technology Infrastructure of China (http://www.geodata.cn, last access: August 2019).
Financial support
This research has been supported by the National Natural Science Foundation of China (grant nos. 41775159, 91937302, and 31000234) and the National Key Scientific and Technological Infrastructure project “Earth System Science Numerical Simulator Facility” (EarthLab).
Review statement
This paper was edited by Jason Williams and reviewed by two anonymous referees.
References
Allen, O. B. and Raktoe, B. L.: Accuracy analysis with special reference to
the prediction of grassland yield, Biom. J., 23, 371–388, 1981.Alvalá, P. C. and Kirchhoff, V. W. J. H.: Methane fluxes from the
Pantanal floodplain in Brazil: seasonal variation, in: Non-CO2
greenhouse gases: Scientific understanding, control and implementation:
Proceedings of the Second International Symposium, 8–10 September 1999, Noordwijkerhout, the Netherlands, edited by: van Ham, J., Baede, A. P. M.,
Meyer, L. A., and Ybema, R., Springer Netherlands, Dordrecht, the Netherlands, 95–99,
2000
Antonov, J. I., Seidov, D., Boyer, T. P., Locarnini, R. A., Mishonov,
A. V., Garcia, H. E., Baranova, O. K., Zweng, M. M., and Johnson, D. R.,
World Ocean Atlas 2009, Volume 2: Salinity, in: NOAA Atlas NESDIS 69, edited
by: Levitus, S., U.S. Government Printing Office, USA, 2010.
Aubinet, M., Vesala, T., and Papale, D.: Eddy covariance: a practical guide
to measurement and data analysis, Springer Science & Business Media, Dordrecht, the Netherlands,
2012.Aurela, M., Laurila, T., and Tuovinen, J. P.: Annual CO2 balance of a
subarctic fen in northern Europe: importance of the wintertime efflux, J.
Geophys. Res.-Atmos., 107, 4607, 10.1029/2002JD002055, 2002.
Bartlett, K. B., Harriss, R., and Sebacher, D.: Methane flux from coastal salt
marshes, J. Geophys. Res.-Atmos., 90, 5710–5720, 1985.Bartlett, K. B., Bartlett, D. S., Harriss, R. C., and Sebacher, D. I.:
Methane emissions along a salt marsh salinity gradient, Biogeochemistry, 4,
183–202, 10.1007/bf02187365, 1987.Bartlett, K. B., Crill, P., Sass, R., Harriss, R., and Dise, N.: Methane
emissions from tundra environments in the Yukon-Kuskokwim Delta, Alaska, J.
Geophys. Res., 97, 16645–16660, 10.1029/91JD00610, 1992.Belger, L., Forsberg, B. R., and Melack, J. M.: Carbon dioxide and methane
emissions from interfluvial wetlands in the upper Negro River basin, Brazil,
Biogeochemistry, 105, 171–183, 10.1007/s10533-010-9536-0, 2011.
Belward, A. S., Estes, J. E., and Kline, K. D.: The IGBP-DIS global 1-km
land-cover data set DISCover: A project overview, Photogramm. Eng. Remote
Sens., 65, 1013–1020, 1999.Bennett, N. D., Croke, B. F. W., Guariso, G., Guillaume, J. H. A., Hamilton,
S. H., Jakeman, A. J., Marsili-Libelli, S., Newham, L. T. H., Norton, J. P.,
Perrin, C., Pierce, S. A., Robson, B., Seppelt, R., Voinov, A. A., Fath, B.
D., and Andreassian, V.: Characterising performance of environmental models,
Environ. Model. Softw., 40, 1–20,
10.1016/j.envsoft.2012.09.011, 2013.
Beven, K., and Kirkby, M. J.: A physically based, variable contributing area
model of basin hydrology, Hydrol. Sci. B., 24, 43–69, 1979.Bhullar, G. S., Iravani, M., Edwards, P. J., and Venterink, H. O.: Methane
transport and emissions from soil as affected by water table and vascular
plants, BMC Ecol., 13, 32, 10.1186/1472-6785-13-32, 2013.Bohn, T., Lettenmaier, D., Sathulur, K., Bowling, L., Podest, E., McDonald,
K., and Friborg, T.: Methane emissions from western Siberian wetlands:
heterogeneity and sensitivity to climate change, Environ. Res. Lett., 2,
045015, 10.1088/1748-9326/2/4/045015, 2007.Boucher, O., Friedlingstein, P., Collins, B., and Shine, K. P.: The indirect
global warming potential and global temperature change potential due to
methane oxidation, Environ. Res. Lett., 4, 044007, 10.1088/1748-9326/4/4/044007, 2009.
Bousquet, P., Ciais, P., Miller, J., Dlugokencky, E., Hauglustaine, D.,
Prigent, C., Van der Werf, G., Peylin, P., Brunke, E.-G., and Carouge, C.:
Contribution of anthropogenic and natural sources to atmospheric methane
variability, Nature, 443, 439–443, 2006.
Bridgham, S., Updegraff, K., and Pastor, J.: Carbon, Nitrogen, and
Phosphorus Mineralization in Northern Wetlands, Ecology, 79, 1545–1561,
1998.
Bridgham, S. D., Cadillo-Quiroz, H., Keller, J. K., and Zhuang, Q.: Methane
emissions from wetlands: biogeochemical, microbial, and modeling
perspectives from local to global scales, Glob. Change Biol., 19,
1325–1346, 2013.Bruhwiler, L., Dlugokencky, E., Masarie, K., Ishizawa, M., Andrews, A., Miller, J., Sweeney, C., Tans, P., and Worthy, D.: CarbonTracker-CH4: an assimilation system for estimating emissions of atmospheric methane, Atmos. Chem. Phys., 14, 8269–8293, 10.5194/acp-14-8269-2014, 2014.
Cao, M., Marshall, S., and Gregson, K.: Global carbon exchange and methane
emissions from natural wetlands: Application of a process-based model, J.
Geophys. Res.-Atmos., 101, 14399–14414, 1996.
Carter, A. J. and Scholes, R. J.: Spatial global database of soil
properties., IGBP Global Soil Data Task CD-ROM, International
Geosphere-Biosphere Programme Data Information Systems, Toulouse, France, 2000.Chaichana, N., Bellingrath-Kimura, S., Komiya, S., Fujii, Y., Noborio, K.,
Dietrich, O., and Pakoktom, T.: Comparison of closed chamber and eddy
covariance methods to improve the understanding of methane fluxes from rice
paddy fields in Japan, Atmosphere, 9, 356, 10.3390/atmos9090356, 2018.
Chanton, J. P.: The effect of gas transport on the isotope signature of
methane in wetlands, Org. Geochem., 36, 753–768, 2005.Christensen, T. R.: Methane emission from Arctic tundra, Biogeochemistry,
21, 117–139, 10.1007/BF00000874, 1993.Christensen, T. R., Friborg, T., Sommerkorn, M., Kaplan, J., Illeris, L.,
Soegaard, H., Nordstroem, C., and Jonasson, S.: Trace gas exchange in a
high-Arctic valley: 1. Variationsin CO2 and CH4 flux between
tundra vegetation types, Global Biogeochem. Cy., 14, 701–713, 2000.Crill, P. M., Bartlett, K. B., Wilson, J. O., Sebacher, D. I., Harriss, R.
C., Melack, J. M., MacIntyre, S., Lesack, L., and Smith-Morrill, L.:
Tropospheric methane from an Amazonian floodplain lake, J. Geophys. Res.-Atmos., 93, 1564–1570, 10.1029/JD093iD02p01564, 1988.Dalsøren, S. B., Myhre, C. L., Myhre, G., Gomez-Pelaez, A. J., Søvde, O. A., Isaksen, I. S. A., Weiss, R. F., and Harth, C. M.: Atmospheric methane evolution the last 40 years, Atmos. Chem. Phys., 16, 3099–3126, 10.5194/acp-16-3099-2016, 2016.Deemer, B. R., Harrison, J. A., Li, S., Beaulieu, J. J., DelSontro, T.,
Barros, N., Bezerra-Neto, J. F., Powers, S. M., dos Santos, M. A., and Vonk,
J. A.: Greenhouse gas emissions from reservoir water surfaces: A new global
synthesis, BioScience, 66, 949–964, 10.1093/biosci/biw117, 2016.Delaune, R. D., Smith, C. J., and Patrick Jr., W. H.: Methane release from Gulf
coast wetlands, Tellus B, 35B, 8–15, 10.1111/j.1600-0889.1983.tb00002.x,
1983.Devol, A. H., Richey, J. E., Clark, W. A., King, S. L., and Martinelli, L.
A.: Methane emissions to the troposphere from the Amazon floodplain, J.
Geophys. Res.-Atmos., 93, 1583–1592, 10.1029/JD093iD02p01583, 1988.Dlugokencky, E. J.: NOAA/ESRL, available at: http://www.esrl.noaa.gov/gmd/ccgg/trends_ch4/, last access: 18 July
2016.Dlugokencky, E. J., Bruhwiler, L., White, J., Emmons, L., Novelli, P. C.,
Montzka, S. A., Masarie, K. A., Lang, P. M., Crotwell, A., and Miller, J.
B.: Observational constraints on recent increases in the atmospheric
CH4 burden, Geophys. Res. Lett., 36, L18803, 10.1029/2009GL039780, 2009.
Duan, X., Wang, X., Mu, Y., and Ouyang, Z.: Seasonal and diurnal variations
in methane emissions from Wuliangsu Lake in arid regions of China, Atmos.
Environ., 39, 4479–4487, 2005.Fan, S. M., Wofsy, S. C., Bakwin, P. S., Jacob, D. J., Anderson, S. M.,
Kebabian, P. L., McManus, J. B., Kolb, C. E., and Fitzjarrald, D. R.:
Micrometeorological measurements of CH4 and CO2 exchange between
the atmosphere and subarctic tundra, J. Geophys. Res.-Atmos., 97,
16627–16643, 10.1029/91jd02531, 1992.Fan, Y. and van den Dool, H.: Climate Prediction Center global monthly soil
moisture data set at 0.5 resolution for 1948 to present, J. Geophys. Res.-Atmos., 109, D10102, 10.1029/2003JD004345, 2004.
FAO/IIASA/ISRIC/ISS-CAS/JRC: Harmonized World Soil Database, version 1.0,
FAO, Rome, Italy and IIASA, Laxenburg, Austria, 42 pp., 2008.
FAO/IIASA/ISRIC/ISS-CAS/JRC: Harmonized World Soil Database, version 1.2,
FAO and IIASA, Rome, Italy and Laxenburg, Austria, 43 pp., 2012.Farr, T. G., Rosen, P. A., Caro, E., Crippen, R., Duren, R., Hensley, S.,
Kobrick, M., Paller, M., Rodriguez, E., Roth, L., Seal, D., Shaffer, S.,
Shimada, J., Umland, J., Werner, M., Oskin, M., Burbank, D., and Alsdorf,
D.: The shuttle radar topography mission, Rev. Geophys., 45, RG2004, 10.1029/2005rg000183, 2007.Fraser, A., Palmer, P. I., Feng, L., Boesch, H., Cogan, A., Parker, R., Dlugokencky, E. J., Fraser, P. J., Krummel, P. B., Langenfelds, R. L., O'Doherty, S., Prinn, R. G., Steele, L. P., van der Schoot, M., and Weiss, R. F.: Estimating regional methane surface fluxes: the relative importance of surface and GOSAT mole fraction measurements, Atmos. Chem. Phys., 13, 5697–5713, 10.5194/acp-13-5697-2013, 2013.Friborg, T., Christensen, T., and Søgaard, H.: Rapid response of
greenhouse gas emission to early spring thaw in a subarctic mire as shown by
micrometeorological techniques, Geophys. Res. Lett., 24, 3061–3064, 10.1029/97GL03024, 1997.Galand, P. E., Yrjälä, K., and Conrad, R.: Stable carbon isotope fractionation during methanogenesis in three boreal peatland ecosystems, Biogeosciences, 7, 3893–3900, 10.5194/bg-7-3893-2010, 2010.Gedney, N., Cox, P., and Huntingford, C.: Climate feedback from wetland
methane emissions, Geophys. Res. Lett., 31, L20503, 10.1029/2004GL020919, 2004.Ghosh, A., Patra, P. K., Ishijima, K., Umezawa, T., Ito, A., Etheridge, D. M., Sugawara, S., Kawamura, K., Miller, J. B., Dlugokencky, E. J., Krummel, P. B., Fraser, P. J., Steele, L. P., Langenfelds, R. L., Trudinger, C. M., White, J. W. C., Vaughn, B., Saeki, T., Aoki, S., and Nakazawa, T.: Variations in global methane sources and sinks during 1910–2010, Atmos. Chem. Phys., 15, 2595–2612, 10.5194/acp-15-2595-2015, 2015.Hanis, K. L., Tenuta, M., Amiro, B. D., and Papakyriakou, T. N.: Seasonal dynamics of methane emissions from a subarctic fen in the Hudson Bay Lowlands, Biogeosciences, 10, 4465–4479, 10.5194/bg-10-4465-2013, 2013.
Hao, Q. J.: Effect of land-use change on greenhouse gases emissions in
freshwater marshes in the Sanjiang Plain, PhD Dissertation, Institute of
Atmospheric Physics, Chinese Academy of Sciences, Beijing, China, 2006.
Harazono, Y., Mano, M., Miyata, A., Yoshimoto, M., Zulueta, R., Vourlitis,
G., Kwon, H., and Oechel, W.: Temporal and spatial differences of methane
flux at arctic tundra in Alaska, Mem. Natl. Inst. Polar Res., 59, 79–95,
2006.
Harris, I., Jones, P., Osborn, T., and Lister, D.: Updated high-resolution
grids of monthly climatic observations – the CRU TS3. 10 Dataset, Int. J.
Climatol., 34, 623–642, 2014.Hatala, J. A., Detto, M., Sonnentag, O., Deverel, S. J., Verfaillie, J., and
Baldocchi, D. D.: Greenhouse gas (CO2, CH4, H2O) fluxes from
drained and flooded agricultural peatlands in the Sacramento-San Joaquin
Delta, Agr., Ecosyst. Environ., 150, 1–18, 2012.
Hirota, M., Tang, Y., Hu, Q., Hirata, S., Kato, T., Mo, W., Cao, G., and
Mariko, S.: Methane emissions from different vegetation zones in a
Qinghai-Tibetan Plateau wetland, Soil Biol. Biochem., 36, 737–748, 2004.Huang, G., Li, X., Hu, Y., Shi, Y., and Xiao, D.: Methane (CH4)
emission from a natural wetland of northern China, J. Environ. Sci. Health,
40, 1227–1238, 2005.
Huang, P. Y., Yu, H. X., Chai, L. H., Chai, F. Y., and Zhang, W. F.: Methane
emission flux of Zhalong Phragmites Australis wetlands in growth season,
Chin. J. Appl. Ecol., 22, 1219–1224, 2011.Huang, Y. A. O., Sass, R., and Fisher, F.: Methane emission from Texas rice
paddy soils. 1. Quantitative multi-year dependence of CH4 emission on
soil, cultivar and grain yield, Glob. Change Biol., 3, 479–489, 1997.Huttunen, J. T., Alm, J., Saarijärvi, E., Lappalainen, K. M., Silvola,
J., and Martikainen, P. J.: Contribution of winter to the annual CH4
emission from a eutrophied boreal lake, Chemosphere, 50, 247–250, 2003.
IPCC: Revised 1996 IPCC Guidelines for National Greenhouse Gas Inventories:
Reference Manual, Paris, France, 1996.
Jauhiainen, J., Takahashi, H., Heikkinen, J. E., Martikainen, P. J., and
Vasander, H.: Carbon fluxes from a tropical peat swamp forest floor, Glob.
Change Biol., 11, 1788–1797, 2005.Jitka, V., Jiří, D., Stanislav, S., Lenka, M., and Hana, Č.:
Effect of hummock-forming vegetation on methane emissions from a temperate
sedge-grass marsh, Wetlands, 37, 675–686, 10.1007/s13157-017-0898-0, 2017.Joabsson, A. and Christensen, T. R.: Methane emissions from wetlands and
their relationship with vascular plants: an Arctic example, Glob. Change
Biol., 7, 919–932, 10.1046/j.1354-1013.2001.00044.x, 2001.Kang, W. X., Zhao, Z. H., Tian, D. L., He, J. N., and Deng, X. W.: CO2
exchanges between mangrove- and shoal wetland ecosystems and atmosphere in
Guangzhou, Chin. J. Appl. Ecol., 19, 2605–2610, 2008.
Keddy, P. A.: Wetland ecology: principles and conservation, Cambridge
University Press, Cambridge, UK, 2010.Keller, J. and Bridgham, S.: Pathways of Anaerobic Carbon Cycling Across an
Ombrotrophic–Minerotrophic Peatland Gradient, Limnol. Oceanogr., 52,
96–107, 10.4319/lo.2007.52.1.0096, 2007.King, J., Reeburgh, W., Thieler, K., Kling, G., Loya, W., Johnson, L., and
Nadelhoffer, K.: Pulse-labeling studies of carbon cycling in Arctic tundra
ecosystems: The contribution of photosynthates to methane emission, Global
Biogeochem. Cy., 16, 1062, 10.1029/2001GB001456, 2002.Kingsford, R. T., Basset, A., and Jackson, L.: Wetlands: conservation's poor
cousins, Aquat. Conserv., 26,
892–916, 10.1002/aqc.2709, 2016.
Kirschke, S., Bousquet, P., Ciais, P., Saunois, M., Canadell, J. G.,
Dlugokencky, E. J., Bergamaschi, P., Bergmann, D., Blake, D. R., and
Bruhwiler, L.: Three decades of global methane sources and sinks, Nat.
Geosci., 6, 813–823, 2013.Koh, H. S., Ochs, C., and Yu, K.: Hydrologic gradient and vegetation
controls on CH4 and CO2 fluxes in a spring-fed forested wetland,
Hydrobiologia, 630, 271–286, 10.1007/s10750-009-9821-x, 2009.Kramer, K., Leinonen, I., Bartelink, H., Berbigier, P., Borghetti, M.,
Bernhofer, C., Cienciala, E., Dolman, A., Froer, O., and Gracia, C.:
Evaluation of six process-based forest growth models using eddy-covariance
measurements of CO2 and H2O fluxes at six forest sites in Europe,
Glob. Change Biol., 8, 213–230, 2002.Kwon, M. J., Beulig, F., Ilie, I., Wildner, M., Küsel, K., Merbold, L.,
Mahecha, M. D., Zimov, N., Zimov, S. A., Heimann, M., Schuur, E. A. G.,
Kostka, J. E., Kolle, O., Hilke, I., and Göckede, M.: Plants,
microorganisms, and soil temperatures contribute to a decrease in methane
fluxes on a drained Arctic floodplain, Glob. Change Biol., 23, 2396–2412, 10.1111/gcb.13558, 2017.
Lehner, B. and Döll, P.: Development and validation of a global
database of lakes, reservoirs and wetlands, J. Hydrol., 296, 1–22,
2004.Li, T., Huang, Y., Zhang, W., and Song, C.: CH4MODwetland: A
biogeophysical model for simulating methane emissions from natural wetlands,
Ecol. Model., 221, 666–680, 2010.Li, T., Zhang, W., Zhang, Q., Lu, Y., Wang, G., Niu, Z., Raivonen, M., and Vesala, T.: Impacts of climate and reclamation on temporal variations in CH4 emissions from different wetlands in China: from 1950 to 2010, Biogeosciences, 12, 6853–6868, 10.5194/bg-12-6853-2015, 2015.Li, T., Xie, B., Wang, G., Zhang, W., Zhang, Q., Vesala, T., and Raivonen,
M.: Field-scale simulation of methane emissions from coastal wetlands in
China using an improved version of CH4MODwetland, Sci. Total Environ.,
559, 256–267, 10.1016/j.scitotenv.2016.03.186, 2016.Li, T., Zhang, Q., Cheng, Z., Wang, G., Yu, L., and Zhang, W.: Performance
of CH4MODwetland for the case study of different regions of natural
Chinese wetland, J. Environ. Sci., 57, 356–369, 2017.Li, T., Li, H., Zhang, Q., Ma, Z., Yu, L., Lu, Y., Niu, Z., Sun, W., and
Liu, J.: Prediction of CH4 emissions from potential natural wetlands on
the Tibetan Plateau during the 21st century, Sci. Total Environ., 657,
498–508, 10.1016/j.scitotenv.2018.11.275, 2019a.Li, T., Lu, Y., Yu, L., Sun, W., Zhang, Q., Zhang, W., Wang, G., Qin, Z., Yu, L., Li, H., and Zhang, R.: valuation of two process-based models used to estimate global CH4 emissions from natural wetlands, Zenodo, 10.5281/zenodo.3537621, 2019b.
Li, Y. J., Cheng, Z. L., Wang, D. Q., Hu, H., and Wang, C.: Methane emission
in the process of wetland and vegetation succession in salt marsh of Yangtze
River estuary, Acta Sci. Circumst., 34, 2035–2402, 2014.Long, K. D., Flanagan, L. B., and Cai, T.: Diurnal and seasonal variation in
methane emissions in a northern Canadian peatland measured by eddy
covariance, Glob. Change Biol., 16, 2420–2435, 10.1111/j.1365-2486.2009.02083.x, 2010.
Loveland, T., Reed, B., Brown, J., Ohlen, D., Zhu, Z., Yang, L., and
Merchant, J.: Development of a global land cover characteristics database
and IGBP DISCover from 1 km AVHRR data, Int. J. Remote Sens., 21, 1303–1330,
2000.Marthews, T. R., Dadson, S. J., Lehner, B., Abele, S., and Gedney, N.: High-resolution global topographic index values for use in large-scale hydrological modelling, Hydrol. Earth Syst. Sci., 19, 91–104, 10.5194/hess-19-91-2015, 2015.
Mastepanov, M., Sigsgaard, C., Dlugokencky, E. J., Houweling, S., Ström,
L., Tamstorf, M. P., and Christensen, T. R.: Large tundra methane burst
during onset of freezing, Nature, 456, 628–630, 2008.McEwing, K. R., Fisher, J. P., and Zona, D.: Environmental and vegetation
controls on the spatial variability of CH4 emission from wet-sedge and
tussock tundra ecosystems in the Arctic, Plant Soil, 388, 37–52, 10.1007/s11104-014-2377-1, 2015.Meirink, J. F., Bergamaschi, P., and Krol, M. C.: Four-dimensional variational data assimilation for inverse modelling of atmospheric methane emissions: method and comparison with synthesis inversion, Atmos. Chem. Phys., 8, 6341–6353, 10.5194/acp-8-6341-2008, 2008.Melack, J. M., Hess, L. L., Gastil, M., Forsberg, B. R., Hamilton, S. K.,
Lima, I. B. T., and Novo, E. M. L. M.: Regionalization of methane emissions
in the Amazon Basin with microwave remote sensing, Glob. Change Biol., 10,
530–544, 10.1111/j.1365-2486.2004.00763.x, 2004.
Melillo, J. M., McGuire, A. D., Kicklighter, D. W., Moore, B., Vorosmarty,
C. J., and Schloss, A. L.: Global climate change and terrestrial net primary
production, Nature, 363, 234–240, 1993.
Melling, L., Hatanoa, R., and Gohc, K. J.: Methane fluxes from three
ecosystems in tropical peatland of Sarawak, Malaysia, Soil Biol. Biochem.,
37, 1445–1453, 2005.Melton, J. R., Wania, R., Hodson, E. L., Poulter, B., Ringeval, B., Spahni, R., Bohn, T., Avis, C. A., Beerling, D. J., Chen, G., Eliseev, A. V., Denisov, S. N., Hopcroft, P. O., Lettenmaier, D. P., Riley, W. J., Singarayer, J. S., Subin, Z. M., Tian, H., Zürcher, S., Brovkin, V., van Bodegom, P. M., Kleinen, T., Yu, Z. C., and Kaplan, J. O.: Present state of global wetland extent and wetland methane modelling: conclusions from a model inter-comparison project (WETCHIMP), Biogeosciences, 10, 753–788, 10.5194/bg-10-753-2013, 2013.Meng, L., Hess, P. G. M., Mahowald, N. M., Yavitt, J. B., Riley, W. J., Subin, Z. M., Lawrence, D. M., Swenson, S. C., Jauhiainen, J., and Fuka, D. R.: Sensitivity of wetland methane emissions to model assumptions: application and model testing against site observations, Biogeosciences, 9, 2793–2819, 10.5194/bg-9-2793-2012, 2012.Moore, T., Roulet, N., and Knowles, R.: Spatial and temporal variations of
methane flux from subarctic/northern Boreal fens, Global Biogeochem. Cy.,
4, 29–46, 10.1029/GB004i001p00029, 1990.Moore, T., Heyes, A., and Roulet, N.: Methane emissions from wetlands,
southern Hudson Bay Lowland, J. Geophys. Res., 99, 1455–1467, 10.1029/93JD02457, 1994.Moore, T., Young, A., Bubier, J., Humphreys, E., Lafleur, P., and Roulet,
N.: A multi-year record of methane flux at the Mer Bleue Bog, Southern
Canada, Ecosystems, 14, 646–657, 10.1007/s10021-011-9435-9, 2011.Morse, J. L., Ardón, M., and Bernhardt, E. S.: Greenhouse gas fluxes in
southeastern U.S. coastal plain wetlands under contrasting land uses, Ecol.
Appl., 22, 264–280, 10.1890/11-0527.1, 2012.
Myhre, G., Shindell, D., Bréon, F. M., Collins, W., Fuglestvedt, J.,
Huang, J., Koch, D., Lamarque, J. F., Lee, D., Mendoza, B., Nakajima, T.,
Robock, A., Stephens, G., Takemura, T., and Zhang, H.: Anthropogenic and
Natural Radiative Forcing, in: Climate Change 2013: The Physical Science
Basis. Contribution of Working Group I to the Fifth Assessment Report of the
Inter-governmental Panel on Climate Change, edited by: Stocker, T. F., Qin,
D., Plattner, G. K., Tignor, M., Allen, S. K., Boschung, J., Nauels, A.,
Xia, Y., Bex, V., and Midgley, P. M., Cambridge University Press, Cambridge,
UK and New York, NY, USA, 2013.Nakano, T., Kuniyoshi, S., and Fukuda, M.: Temporal variation in methane
emission from tundra wetlands in a permafrost area, northeastern Siberia,
Atmos. Environ., 34, 1205–1213, 10.1016/S1352-2310(99)00373-8, 2000.Nisbet, E., Manning, M., Dlugokencky, E., Fisher, R., Lowry, D., Michel, S.,
Lund Myhre, C., Platt, S., Allen, G., Bousquet, P., Brownlow, R., Cain, M.,
France, J., Hermansen, O., Hossaini, R., Jones, A., Levin, I., Manning, A.,
Myhre, G., and White, J.: Very strong atmospheric methane growth in the four
years 2014–2017: Implications for the Paris Agreement, Global Biogeochem. Cy., 33, 318–342, 10.1029/2018GB006009, 2019.Olefeldt, D., Roulet, N. T., Bergeron, O., Crill, P., Bäckstrand, K.,
and Christensen, T. R.: Net carbon accumulation of a high-latitude
permafrost palsa mire similar to permafrost-free peatlands, Geophys. Res.
Lett., 39, L03501, 10.1029/2011GL050355, 2012.Olson, D., Griffis, T., Noormets, A., Kolka, R., and Chen, J.: Interannual,
seasonal, and retrospective analysis of the methane and carbon dioxide
budgets of a temperate peatland, J. Geophys. Res.-Biogeo., 118, 226–238, 10.1002/jgrg.20031, 2013.Page, S., Rieley, J., Shotyk, W., and Weiss, D.: Interdependence of peat and
vegetation in a tropical peat swamp forest, Philos. T. Roy. Soc. Lond. B, 354, 1885–1897, 10.1098/rstb.1999.0529, 1999.Parmentier, F. J. W., van Huissteden, J., van der Molen, M. K.,
Schaepman-Strub, G., Karsanaev, S. A., Maximov, T. C., and Dolman, A. J.:
Spatial and temporal dynamics in eddy covariance observations of methane
fluxes at a tundra site in northeastern Siberia, J. Geophys. Res.-Biogeo., 116, G03016, 10.1029/2010jg001637, 2011.Poffenbarger, H. J., Needelman, B. A., and Megonigal, J. P.: Salinity
influence on methane emissions from tidal marshes, Wetlands, 31, 831–842, 10.1007/s13157-011-0197-0, 2011.
Potter, C. S.: An ecosystem simulation model for methane production and emission from wetlands, Global Biogeochem. Cy., 11, 495–506, 1997.Poulter, B., Bousquet, P., Canadell, J. G., Ciais, P., Peregon, A., Saunois,
M., Arora, V. K., Beerling, D. J., Brovkin, V., and Jones, C. D.: Global
wetland contribution to 2000–2012 atmospheric methane growth rate dynamics,
Environ. Res. Lett., 12, 094013, 10.1088/1748-9326/aa8391, 2017.Riley, W. J., Subin, Z. M., Lawrence, D. M., Swenson, S. C., Torn, M. S., Meng, L., Mahowald, N. M., and Hess, P.: Barriers to predicting changes in global terrestrial methane fluxes: analyses using CLM4Me, a methane biogeochemistry model integrated in CESM, Biogeosciences, 8, 1925–1953, 10.5194/bg-8-1925-2011, 2011.Rykiel, E. J.: Testing ecological models: the meaning of validation, Ecol.
Model., 90, 229–244, 10.1016/0304-3800(95)00152-2, 1996.Sachs, T., Giebels, M., Boike, J., and Kutzbach, L.: Environmental controls
on CH4 emission from polygonal tundra on the microsite scale in the
Lena river delta, Siberia, Glob. Change Biol., 16, 3096–3110, 2010.Saunois, M., Bousquet, P., Poulter, B., Peregon, A., Ciais, P., Canadell, J. G., Dlugokencky, E. J., Etiope, G., Bastviken, D., Houweling, S., Janssens-Maenhout, G., Tubiello, F. N., Castaldi, S., Jackson, R. B., Alexe, M., Arora, V. K., Beerling, D. J., Bergamaschi, P., Blake, D. R., Brailsford, G., Brovkin, V., Bruhwiler, L., Crevoisier, C., Crill, P., Covey, K., Curry, C., Frankenberg, C., Gedney, N., Höglund-Isaksson, L., Ishizawa, M., Ito, A., Joos, F., Kim, H.-S., Kleinen, T., Krummel, P., Lamarque, J.-F., Langenfelds, R., Locatelli, R., Machida, T., Maksyutov, S., McDonald, K. C., Marshall, J., Melton, J. R., Morino, I., Naik, V., O'Doherty, S., Parmentier, F.-J. W., Patra, P. K., Peng, C., Peng, S., Peters, G. P., Pison, I., Prigent, C., Prinn, R., Ramonet, M., Riley, W. J., Saito, M., Santini, M., Schroeder, R., Simpson, I. J., Spahni, R., Steele, P., Takizawa, A., Thornton, B. F., Tian, H., Tohjima, Y., Viovy, N., Voulgarakis, A., van Weele, M., van der Werf, G. R., Weiss, R., Wiedinmyer, C., Wilton, D. J., Wiltshire, A., Worthy, D., Wunch, D., Xu, X., Yoshida, Y., Zhang, B., Zhang, Z., and Zhu, Q.: The global methane budget 2000–2012, Earth Syst. Sci. Data, 8, 697–751, 10.5194/essd-8-697-2016, 2016.Schimel, J., Nadelhoffer, K., Shaver, G., Giblin, A., and Rastetter, E.: Methane
and carbon dioxide emissions were monitored in control, greenhouse, and
nitrogen and phosphorus fertilized plots of three different plant
communities Arctic LTER experimental plots, Toolik Field Station, 1992,
Environmental Data Initiative, 10.6073/pasta/3e2ae7928b00f7546338086d0dc3bd55, 1994.Schimel, J., Nadelhoffer, K., Shaver, G., Giblin, A., Rastetter, E.: Methane
and carbon dioxide emissions were monitored in control, greenhouse, and
nitrogen and phosphorus fertilized plots of three different plant
communities, Toolik Field Station, North Slope Alaska, Arctic LTER 1993,
Environmental Data Initiative, 10.6073/pasta/64c4ad25b7efb6f98acc22301dd1802a, 1995.Sebacher, D., Harriss, R., Bartlett, K., Sebacher, S., and Grice, S.:
Atmospheric methane sources: Alaskan tundra bogs, an alpine fen, and a
subarctic boreal marsh, Tellus B, 38B, 1–10, 10.1111/j.1600-0889.1986.tb00083.x, 1986.
Sellers, P. J., Hall, F. G., Kelly, R. D., Black, A., Baldocchi, D., Berry,
J., Ryan, M., Ranson, K. J., Crill, P. M., and Lettenmaier, D. P.: BOREAS in
1997: Experiment overview, scientific results, and future directions, J.
Geophys. Res.-Atmos., 102, 28731–28769, 1997.
Shannon, R. D., White, J. R., Lawson, J. E., and Gilmour, B. S.: Methane
efflux from emergent vegetation in peatlands, J. Ecol., 84, 239–246, 1996.Shindell, D., Kuylenstierna, J. C. I., Vignati, E., van Dingenen, R., Amann,
M., Klimont, Z., Anenberg, S. C., Muller, N., Janssens-Maenhout, G., Raes,
F., Schwartz, J., Faluvegi, G., Pozzoli, L., Kupiainen, K.,
Höglund-Isaksson, L., Emberson, L., Streets, D., Ramanathan, V., Hicks,
K., Oanh, N. T. K., Milly, G., Williams, M., Demkine, V., and Fowler, D.:
Simultaneously Mitigating Near-Term Climate Change and Improving Human
Health and Food Security, Science, 335, 183–189, 10.1126/science.1210026, 2012.Song, C., Zhang, J., Wang, Y., Wang, Y., and Zhao, Z.: Emission of CO2, CH4
and N2O from freshwater marsh in northeast of China, J. Environ. Manag., 88,
428–436, 10.1016/j.jenvman.2007.03.030, 2008.
Spiers, A. G.: Review of international/continental wetland resources, in:
Global review of wetland resources and priorities for wetland inventory,
edited by: Finlayson, C. M., and Spiers, A. G., Supervising Scientist Report
144, Supervising Scientist, Canberra, Australia, 63–104, 1999.Stanley, K. M., Heppell, C. M., Belyea, L. R., Baird, A. J., and Field, R.
H.: The Importance of CH4 Ebullition in Floodplain Fens, J. Geophys.
Res.-Biogeo., 124, 1750–1763, 10.1029/2018jg004902, 2019.Suyker, A. E., Verma, S. B., Clement, R. J., and Billesbach, D. P.: Methane
flux in a boreal fen: Season-long measurement by eddy correlation, J.
Geophys. Res.-Atmos., 101, 28637–28647, 10.1029/96JD02751, 1996.Svensson, B., and Rosswall, T.: In situ methane production
from acid peat in plant communities with different moisture regimes in a
subarctic mire, Oikos, 43, 341–350, 10.2307/3544151, 1984.Tathy, J., Cros, B., Delmas, R., Marenco, A., Servant, J., and Labat, M.:
CH4 emission from flooded forest in Central Africa, J. Geophys. Res.,
97, 6159–6168, 10.1029/90JD02555, 1992.Tian, H., Chen, G., Lu, C., Xu, X., Ren, W., Zhang, B., Banger, K., Tao, B.,
Pan, S., and Liu, M.: Global methane and nitrous oxide emissions from
terrestrial ecosystems due to multiple environmental changes, Ecosystem
Health and Sustainability, 1, 1–20, 10.1890/EHS14-0015.1,
2015.Tsuruta, A., Aalto, T., Backman, L., Hakkarainen, J., van der Laan-Luijkx, I. T., Krol, M. C., Spahni, R., Houweling, S., Laine, M., Dlugokencky, E., Gomez-Pelaez, A. J., van der Schoot, M., Langenfelds, R., Ellul, R., Arduini, J., Apadula, F., Gerbig, C., Feist, D. G., Kivi, R., Yoshida, Y., and Peters, W.: Global methane emission estimates for 2000–2012 from CarbonTracker Europe-CH4 v1.0, Geosci. Model Dev., 10, 1261–1289, 10.5194/gmd-10-1261-2017, 2017.
Twine, T. E., Kustas, W., Norman, J., Cook, D., Houser, P., Meyers, T.,
Prueger, J., Starks, P., and Wesely, M.: Correcting eddy-covariance flux
underestimates over a grassland, Agr. Forest Meteorol., 103,
279–300, 2000.
Wagner, D., Kobabe, S., Pfeiffer, E. M., and Hubberten, H. W.: Microbial
controls on methane fluxes from a polygonal tundra of the Lena Delta,
Siberia, Permafrost Periglac., 14, 173–185, 2003.
Walter, B. P. and Heimann, M.: A process-based, climate-sensitive model to
derive methane emissions from natural wetlands: Application to five wetland
sites, sensitivity to model parameters, and climate, Global Biogeochem. Cy., 14, 745–765, 2000.
Walter, B. P., Heimann, M., Shannon, R. D., and White, J. R.: A process‐based model to derive methane emissions from natural wetlands, Geophys. Res. Lett., 23, 3731–3734, 1996.
Wang, D., Lv, X., Ding, W., Cai, Z., Gao, J., and Yang, F.: Methan emission
from narshes in Zoige Plateau, Adv. Earth Sci., 17, 877–880, 2002.Wei, D. and Wang, X.: Uncertainty and dynamics of natural wetland CH4
release in China: Research status and priorities, Atmos. Environ., 154,
95–105, 10.1016/j.atmosenv.2017.01.038, 2017.
Werle, P. and Kormann, R.: Fast chemical sensor for eddy-correlation
measurements of methane emissions from rice paddy fields, Appl. Optics,
40, 846–858, 2001.
Whalen, S. C. and Reeburgh, W. S.: Interannual variations in tundra methane
emission: A 4-year time series at fixed sites, Global Biogeochem. Cy., 6,
139–159, 1992.
Wille, C., Kutzbach, L., Sachs, T., Wagner, D., and Pfeiffer, E. M.: Methane
emission from Siberian arctic polygonal tundra: eddy covariance measurements
and modeling, Glob. Change Biol., 14, 1395–1408, 2008.Xu, X., Yuan, F., Hanson, P. J., Wullschleger, S. D., Thornton, P. E., Riley, W. J., Song, X., Graham, D. E., Song, C., and Tian, H.: Reviews and syntheses: Four decades of modeling methane cycling in terrestrial ecosystems, Biogeosciences, 13, 3735–3755, 10.5194/bg-13-3735-2016, 2016.Ye, Y., Lu, C., and Lin, P.: CH4 dynamics in sediments of Bruguiera
sexangula mangrove at Hegang Estuary, Soil Environ. Sci., 9,
91–95, 2000 (in Chinese).Zhang, Q., Zhang, W., Li, T., Sun, W., Yu, Y., and Wang, G.: Projective
analysis of staple food crop productivity in adaptation to future climate
change in China, Int. J. Biometeorol., 61, 1445–1460, 10.1007/s00484-017-1322-4, 2017.
Zhang, Y., Li, C., Trettin, C. C., and Li, H.: An integrated model of soil,
hydrology, and vegetation for carbon dynamics in wetland ecosystems, Global Biogeochem. Cy., 16, 1061–1078, 2002.Zhu, Q., Liu, J., Peng, C., Chen, H., Fang, X., Jiang, H., Yang, G., Zhu, D., Wang, W., and Zhou, X.: Modelling methane emissions from natural wetlands by development and application of the TRIPLEX-GHG model, Geosci. Model Dev., 7, 981–999, 10.5194/gmd-7-981-2014, 2014.Zhu, Q., Peng, C. H., Chen, H., Fang, X. Q., Liu, J. X., Jiang, H., Yang, Y.
Z., and Yang, G.: Estimating global natural wetland methane emissions using
process modelling: spatio-temporal patterns and contributions to atmospheric
methane fluctuations, Global Ecol. Biogeogr., 24, 959–972, 10.1111/geb.12307, 2015.Zhu, X., Zhuang, Q., Gao, X., Sokolov, A., and Schlosser, C. A.: Pan-Arctic
land–atmospheric fluxes of methane and carbon dioxide in response to
climate change over the 21st century, Environ. Res. Lett., 8, 045003,
10.1088/1748-9326/8/4/045003, 2013.Zhuang, Q., Melillo, J. M., Kicklighter, D. W., Prinn, R. G., McGuire, A.
D., Steudler, P. A., Felzer, B. S., and Hu, S.: Methane fluxes between
terrestrial ecosystems and the atmosphere at northern high latitudes during
the past century: A retrospective analysis with a process-based
biogeochemistry model, Global Biogeochem. Cy., 18, GB3010, 10.1029/2004gb002239, 2004.Zhuang, Q., Melillo, J. M., Sarofim, M. C., Kicklighter, D. W., McGuire, A.
D., Felzer, B. S., Sokolov, A., Prinn, R. G., Steudler, P. A., and Hu, S.:
CO2 and CH4 exchanges between land ecosystems and the atmosphere in northern
high latitudes over the 21st century, Geophys. Res. Lett., 33, L17403,
10.1029/2006GL026972, 2006.Zhuang, Q., Melillo, J., McGuire, A., Kicklighter, D., Prinn, R., Steudler,
P., Felzer, B., and Hu, S.: Net emissions of CH4 and CO2 in
Alaska: Implications for the region's greenhouse gas budget, Ecol. Appl.,
17, 203–212, 2007.
Zhuang, Q., Chen, M., Xu, K., Tang, J., Saikawa, E., Lu, Y., Melillo, J. M.,
Prinn, R. G., and McGuire, A. D.: Response of global soil consumption of
atmospheric methane to changes in atmospheric climate and nitrogen
deposition, Global Biogeochem. Cy., 27, 650–663, 2013.Zona, D., Oechel, W., Kochendorfer, J., Paw U, K., Salyuk, A., Olivas, P.,
Oberbauer, S., and Lipson, D.: Methane fluxes during the initiation of a
large-scale water table manipulation experiment in the Alaskan Arctic
tundra, Global Biogeochem. Cy., 23, GB2013, 10.1029/2009GB003487, 2009.Zona, D., Gioli, B., Commane, R., Lindaas, J., Wofsy, S. C., Miller, C. E.,
Dinardo, S. J., Dengel, S., Sweeney, C., Karion, A., Chang, R. Y.-W.,
Henderson, J. M., Murphy, P. C., Goodrich, J. P., Moreaux, V., Liljedahl,
A., Watts, J. D., Kimball, J. S., Lipson, D. A., and Oechel, W. C.: Cold
season emissions dominate the Arctic tundra methane budget, P.
Natl. Acad. Sci. USA, 113, 40–45, 10.1073/pnas.1516017113, 2016.