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Atmospheric Environment 59 (2012) 328e337
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Review
Comparison of biogenic methane emissions from unmanaged estuaries, lakes,
oceans, rivers and wetlands
M.J. Ortiz-Llorente*, M. Alvarez-Cobelas
CSIC-Museo Nacional de Ciencias Naturales, Serrano 115 dpdo., E-28006 Madrid, Spain
h i g h l i g h t s
< We report maximal and yearly CH4 emissions in different latitudes, climates and ecosystem types.
< We outline how CH4 emissions vary among different wetland- and marine ecosystem types.
< Seasonality of emissions in different aquatic environments has been described.
< The influence of spatial variability on emissions has been taking into account.
< We assess the influence of abiotic factors in aquatic environments and how different types of plants affect emissions.
a r t i c l e i n f o
a b s t r a c t
Article history:
Received 1 June 2011
Received in revised form
8 May 2012
Accepted 11 May 2012
A literature review of quantitative data was carried out to conduct a cross-system study on methane
emissions relating peak emissions (PE) and annual emissions (AE) in five types of non-managed
ecosystems: estuaries, lakes, oceans, streams and wetlands. PE spanned eight orders of magnitude
(0.015 mg CH4 m2 h1e300 mg CH4 m2 h1) while AE spanned seven (0.078e19044 g CH4 m2 yr1).
PE and AE were strongly related worldwide (r2 ¼ 0.93). There was no relationship between AE and
latitude, with highly variable PE across latitudes and climates. The coefficient of variation (CV) was
greatest for emissions in oceans and estuaries, while the highest emission rate was recorded in wetlands
and lakes. Efflux from coastal areas and estuaries was higher than that from upwelling areas and deep
seas. Concerning wetland types, marshes showed the highest PE with the highest wetland emissions
occurring in sites dominated by big helophytes. Non-stratifying- and eutrophic lakes displayed more
emissions than other lake types, but there was no environmental variable that might predict methane
emissions from lakes on a worldwide basis. Generally, most ecosystem types followed a seasonal pattern
of emissions, with a maximum in summer, except in estuaries which did not show any distinct pattern.
Regarding the importance of hot spots within most ecosystems, more spatial variability of CH4 emissions
was observed in lakes than in wetlands and oceans; however, no relationship between emissions and
spatial variability was found. A positive relationship, albeit weak, was found between methane flux and
either temperature or irradiance in wetlands; a narrow range of both negative and positive values of the
water table promoted CH4 emissions. Previously, little was known about the factors controlling efflux
from river and marine environments. Our study suggests that local conditions are important in
controlling CH4 emissions, because the variability explained by the more commonly studied abiotic
factors is low worldwide. This precludes the use of these variables to develop models to predict emissions at regional scales or wider, despite the many attempts made in the past. This makes local
assessments of emissions essential, particularly in warm, temperate and tropical areas of the world.
Future research aiming to shed light on CH4 fluxes from estuaries, lakes, oceans, rivers and wetlands
must: 1) produce more detailed data on controlling factors; 2) increase efforts to fully characterize spatial
and temporal heterogeneity; 3) combine bottom-up (measurements) and top-down (modelling)
approaches.
Ó 2012 Elsevier Ltd. All rights reserved.
Keywords:
Seasonal emissions
Global emissions
Environmental factors
Scale of observation
Spatial heterogeneity
1. Introduction
* Corresponding author.
E-mail address: [email protected] (M.J. Ortiz-Llorente).
1352-2310/$ e see front matter Ó 2012 Elsevier Ltd. All rights reserved.
http://dx.doi.org/10.1016/j.atmosenv.2012.05.031
Methane (CH4) is a greenhouse gas, whose concentration
increased in the atmosphere from 1950 (IPCC, 2007) until the
M.J. Ortiz-Llorente, M. Alvarez-Cobelas / Atmospheric Environment 59 (2012) 328e337
1990s, when emissions decreased; however, there is considerable
inter-annual variation (Bousquet et al., 2006). The global warming
potential of CH4 is 25 times greater than that of CO2 on a 100-year
time horizon (IPCC, 2007). Indeed, despite its lower concentration
(6 ppb CH4 y1 from 1990 to 2005) compared to CO2 (1.7 ppm y1
from 1990 to 2005), CH4 contributed w20% of the global warming
effect (IPCC, 2007). The major sources of CH4 are submerged soils
(natural wetlands, mangroves and rice fields), oceans and freshwater, CH4 hydrates, fossil fuels, enteric fermentation, animal
wastes, domestic sewage, landfills and biomass burning (Dalal and
Allen, 2008).
Wetlands are the main type of aquatic ecosystem emitting
biogenic CH4 (Matthews and Fung, 1987). They cover between 4%
and 9% of the global surface area (Matthews and Fung, 1987), but
store nearly 37% of the global terrestrial carbon (Bolin and Sukumar,
2000). The role of wetlands in terrestrial carbon cycling is particularly complex, due to the fact that methane production and
oxidation are a part of carbon cycling (Roehm, 2005), contributing
nearly 40% of global atmospheric methane annually (Fung et al.,
1991). Wetlands are the largest single source of atmospheric
methane, even when considering all anthropogenic emissions
(Christensen, 2011).
Studies on emissions from water-related ecosystems other than
wetlands are less frequent. Streams are very poorly studied and
ocean areas have been studied in more detail during the last two
decades, but many unsolved questions still remain, concerning CH4
origin, production, oxidation and advection, and controlling factors
(Reeburgh, 2003, 2007). Studies on lake emissions have recently
been undertaken (Bastviken et al., 2011), but they are still more
geographically-restricted than those of wetlands.
Methane emissions from wetlands appear to be strongly influenced by environmental variables, such as soil temperature and
inundation, at a local scale, and are likely to be influenced by
climate change (Bartlett and Harris, 1993). Many studies have
related methane flux changes with the capability of plants to
transport gas depending on plant activity and environmental
factors. Temperature, water table and irradiance all affect plant
cycles, thereby affecting plant-mediated CH4 emissions and even
CH4 emissions from vegetated soils (Kankaala et al., 2005; Käki
et al., 2001; Kutzbach et al., 2004; Mikkelä et al., 1995). In addition, these abiotic factors affect emissions through control of
microbial activity; therefore, they have been widely studied and
used to estimate global emissions (Roehm, 2005; Walter et al.,
1996). In lakes, regional studies suggest that shallow and small
lakes release more methane per unit area than deep and large lakes
(Bastviken et al., 2004; Juutinen et al., 2009); since there is
a covariance between shallowness and eutrophication (Wetzel,
2001), it is not surprising that CH4 efflux from eutrophic lakes is
also higher (Juutinen et al., 2009).
Many global and regional reviews on CH4 emissions from
wetland environments have been carried out (Aselmann and
Crutzen, 1989; Bartlett and Harris, 1993; Bridgham et al., 2006;
Matthews and Fung, 1987; Roehm, 2005; Segers, 1998). Such
studies have analysed yearly CH4 emissions in wetlands, taking into
account environmental variables and locations. Reeburgh (2003)
attempted to summarize our knowledge of the global methane
budget, also taking into account ocean environments (Reeburgh,
2007) while Saarnio et al. (2009) carried out a study estimating
annual emissions from wetlands and watercourses in Europe.
However, it appears that there are no studies which compare
emissions among wetlands, lakes, rivers, oceans and estuaries.
More specifically, reviews on either maximal or yearly CH4 emissions have not been carried out that include all these ecosystems.
Seasonality and spatial variability of emissions have scarcely been
dealt with either (Alford et al., 1997; Purvaja and Ramesh., 2001).
329
Due to the importance of CH4 in terms of global warming, and
since there are no recent studies describing how this gas behaves in
water-related ecosystems at a global scale, this study compiles all
the information available about biogenic methane emissions
worldwide.
Furthermore, this study emphasizes the fact that the distribution
of existing studies is not uniform. Here the global area for an
ecosystem type was compared with the number of studies carried
out therein and no significant correlation (p > 0.05) was found. For
example, there were only 51 studies found on oceans, with a global
area of 363 106 km2 (Bange et al., 1994) whereas there were 317
studies on wetlands, which have a much lower surface area
(Aselmann and Crutzen, 1989). This could be because biogenic
methane emissions in oceans are lower than those in wetlands;
however the mechanisms regulating these emissions in the ocean
are largely unknown due to the scarce number of studies carried out.
We conducted a cross-systems review compiling quantitative
estimates of CH4 emissions and their responses to controlling
factors across estuaries, lakes, oceans, rivers and wetlands. This
cross-system approach has enabled us to summarize research
findings across different studies and quantitatively synthesize the
ecological information about the process, as well as to explore
general patterns among these ecosystems. More specifically, our
aims are: (1) to report peak and yearly CH4 emissions at different
latitudes, climates and ecosystem types; (2) to outline how CH4
emissions vary among different wetland and marine ecosystems;
(3) to describe seasonality of emissions in these environments; (4)
to consider the influence of spatial variability on emissions; and (5)
to assess the influence of abiotic factors in these environments and
how different types of vegetation affect emissions.
2. Methods
2.1. Data collection
This study was carried out by reviewing literature quantifying
biogenic CH4 emissions from estuaries, lakes, oceans, rivers and
wetlands. It was restricted to measurements made in unmanaged
ecosystems, without any external modification, thus disregarding
man-made fertilization (e.g. rice fields). Potential emission rates
and CH4 oxidation rates were excluded from this analysis. Two
main types of units appeared in the reviewed articles:
weight area1 time1 and weight volume1 time1. Only studies
using the first unit were chosen, which enabled comparisons of
most emission data. The dataset was collected from texts, tables
and figures of articles published from 1985 to December 2010,
gathering information on the measurement site, dominant vegetation, maximal and annual CH4 emission, spatial heterogeneity
and seasonality of emissions as well as the factors controlling
emissions (Appendices 1e4). Despite the fact that CH4 emission can
result from different processes (diffusion, ebullition, diffusion
through aquatic plants), our compilation did not discriminate
among them because most published studies did not attempt to
make such a distinction; therefore, our reported data must be
considered as “total emissions” from a given site, irrespective of the
underlying process. Ebullition is negligible in most marine environments (McGinnis et al., 2006) and storage can also be overlooked in shallow lakes, wetlands and rivers due to the lack of
thermal stratification.
It was deemed necessary to divide the study in two groups to
tackle different time scales that might be important in CH4 emission studies. The first group comprised those conducted over an
annual cycle (AE) with at least monthly periodicity of sampling, and
was used to study seasonal emissions in different ecosystems.
However, most studies on CH4 emission were restricted to shorter
330
M.J. Ortiz-Llorente, M. Alvarez-Cobelas / Atmospheric Environment 59 (2012) 328e337
periods. Therefore, we also compiled data collected over shorter
time periods, focussing on emissions occurring in the most
favourable month of the year (peak emissions, PE), namely, when
environmental factors controlling emission were less limiting.
When only monthly data over an entire year were supplied, AE was
estimated by the numerical integration of these data (Appendix 3).
The relationship between peak and annual emissions in the same
sites was also studied. Moreover, the distribution of annual emission data has been studied and divided into two series, each analysed separately. Most comparisons and analyses among ecosystem
types would be carried out for PE due to the fact that there were
more degrees of freedom in the PE dataset.
Whenever available, data of environmental factors were
collected. Thus the relationship was studied between water table,
air temperature, soil temperature, PAR in wetlands and lakes.
However, in the case of oceans, rivers and estuaries, data were
insufficient to search for relationships between CH4 emissions and
environmental factors.
2.2. Data analysis
We studied PE and AE vs latitudes and climate types, the latter
following Köppen’s Climate Classification by FAO-SDRNAgrometeorology Group (1997). Five ecosystems were studied:
estuaries, lakes, oceans, rivers and wetlands. Hopkinson and Smith
(2005) define estuaries as “.those regions at the interface of the
terrestrial and oceanic realm where seawater is measurably diluted
by freshwater runoff from land.” The Ramsar Convection (2000)
defines wetlands as “the site where water is at or near the soil
surface for a significant part of the growing season.” The MerriamWebster Dictionary (2011) defines river as “any natural stream of
water that flows in a channel with defined banks.” And lakes are
defined, as “an inland body of standing water” while the definition
of ocean is: “the whole body of salt water that covers nearly three
fourths of the surface of the earth”.
There are some reports of emissions by impoundments, which
refer to lakes, while emissions from the littoral fringe of lakes are
included within wetlands. Ocean environments have also been
studied specifically, splitting them into estuaries, coastal shelves,
deep seas and upwelling areas. Likewise, wetlands have been
further divided into other categories: peatlands, marshes, forested
wetlands and coastal wetlands (estuaries have been considered as
different from coastal wetlands when freshwater inputs were
suspected to occur), following the outline by Mitsch and Gosselink
(2001), who define peatland as “a generic term of any wetland that
accumulates partially decayed plant matter”, and marsh as “a
frequently or continually inundated wetland characterized by
emergent herbaceous vegetation adapted to saturated soil conditions.” Forested or riparian wetlands are defined as “occurring along
rivers and streams, which are occasionally flooded by those bodies
of water but are otherwise dry for varying portions of the growing
season.” Moreover, we classified wetlands as either ice-free or
winter-frozen when the authors mentioned this in their articles.
We analysed the relationship between PE and predominant vegetation, classifying them within the main physiognomic groups
(hydrophytes, bryophytes, small helophytes, big helophytes and
trees).
Thermal stratification and trophic status are the main drivers of
lake ecological functioning (Wetzel, 2001). We compare PE in
stratifying vs non-stratifying lakes and oligotrophic vs eutrophic
lakes. Data on thermal regimes were obtained from the studies
reporting lake emissions, whereas their trophic status was ascertained using yearly-averaged total phosphorus in the water column
of each lake as an index, those lakes having more than 30 mg l1
were considered as eutrophic (OECD, 1982).
Due to the lack of both Gaussian distribution and homogeneity
of variances for most variables, non-parametric tests were undertaken for comparisons (Siegel and Castellan, 1988). While a ManneWhitney test was employed to compare medians of CH4
emission between two categories, a KruskaleWallis test was used
to assess differences in CH4 emission among the categorized
aquatic ecosystems when these exceeded two. Latitudinal, climatic,
ecosystem types, wetland types and their main vegetation, marine
ecosystems and lake trophic status (as total phosphorus in the
mixed layer) and stratification types were considered to make
comparisons.
The Kavvas and Delleur (1975) method to visualize periodicities
in a time series was used with the annual dataset to study seasonality in different aquatic ecosystems and wetlands types.
Seasonal dynamics in oceans could not be ascertained because of
the lack of data. Seasonal data reported from the Southern Hemisphere were rearranged to enable comparisons with data taken in
the Northern Hemisphere. An average over a constant time unit
(year) was calculated and formed a new series as the new difference between the original series and the mean. For any CH4
emission time series, a scaled difference, MEt, was calculated to
represent the observed rate, Xt, on time t
MEt ¼ 100*ððXt XÞ=XÞ
(1)
where X was the yearly mean calculated for that time series. This
procedure was repeated for each time series in all aquatic ecosystems of the annual database (Appendix 3).
The range (as difference between the minimum and maximum
flux measured) statistic was used to study the spatial variability of
emissions, an approach suggested by the lack of both Gaussian
distributions and homogeneities of variances in these data. We
chose studies reporting data of CH4 emission, measured at the same
time but in different areas within the same study area (Appendix 4).
We only used ranges of CH4 emissions in wetlands, lakes, estuaries
and the ocean because of the lack of stream data. Moreover, since
we suspected that the larger study areas corresponded to the
higher variability in methane emission, we used the distance
between the most distant sites in the studied areas as a surrogate of
the spatial scale.
To study the relationship between peak CH4 emissions and
abiotic factors (air and water temperature, water table and irradiance) we employed the Spearman correlation. For lakes, average
and maximal depth and total phosphorus were also related with
AE. All statistical analyses were performed using STATISTICA 6.1
(Statsoft Inc., 1997).
3. Results
3.1. Emissions and their ranges
Most continental studies were carried out in Canada, USA,
Finland and China. Marine environments were more evenly covered
throughout the world but only during PE (Appendix 1). Methane
emissions were more commonly measured in the Northern Hemisphere, probably due to the abundance of environments related to
water and the funding available to create the infrastructure needed
to measure methane emissions. A total of 551 estimations were
compiled for PE, of which 18 were estuaries, 143 were lakes and
impoundments, 51 were oceans, 22 were rivers and 317 were
wetlands. As concerning different wetlands, 179 were peatlands,
101 were marshes, 42 were forested wetlands and 8 coastal
wetlands (Appendix 1, Table 1). Concerning AE, records were taken
from 6 estuaries, 55 lakes, 12 rivers and 126 wetlands, but no annual
data were available for any oceanic sites (Appendix 3, Table 1).
M.J. Ortiz-Llorente, M. Alvarez-Cobelas / Atmospheric Environment 59 (2012) 328e337
Table 1
Statistics of peak and annual emissions in different ecosystem types worldwide.
Data for calculations are reported in Appendix 1 and 3 (Supplementary material). No
data are available to estimate annual emissions from any oceanic site. CV: coefficient
of variation; n: number of data; SD: standard deviation.
Most favourable CH4 emission (mg CH4 m2 h1)
Estuary
Lake
Ocean
River
Wetland
3.29
11.04
259
0.116
45.970
0.002
18
18.137
35.913
198
5.500
300.000
0.009
143
1.45
9.14
630
0.001
64.87
1.49E-05
51
10.511
19.704
187
3.125
83.333
0.008
22
13.648
26.614
195
5.000
288.000
0.001
317
Annual CH4 emission (g CH4 m2 yr1)
Estuary
Lake
River
Wetland
539.327
602.657
112
370.395
1620.337
1.700
6
64.296
345.378
537
1.560
2576.900
0.117
55
465.266
668.648
144
162.430
2266.370
62.050
12
470.072
2300.428
489
18.200
19,044.000
0.068
126
There were no significant differences in CH4 emissions between
latitude or climate zone classes (Fig. 1). The highest emissions
appeared in lakes between 0e20 and 60e80 , whereas highest
emissions for wetland varied between 40 and 60 .
Annual Emissions differed among ecosystem types (Table 1).
Efflux from lakes and wetlands was far more variable (CVs: 537 and
489%, respectively) than that from estuaries and rivers. All
3
a
Peak emission
(log mg m -2 h-1)
1
0
-1
-2
-3
-4
b
2
1
0
-1
-2
Latitude
[70-80)
[60-70)
[50-60)
[30-40)
Latitude
3
Methane emission
(log mg m -2 h-1)
[40-50)
[0-10)
(60-70]
(50-60]
(40-50]
(30-40]
(20-30]
-3
(10-20]
-5
(0-10]
(log kg m -2 yr-1)
Annual emission
2
[10-20)
Average
SD
CV
Median
Max
Min
n
environments presented contrasting maximal values. The highest
values occurred in wetlands, followed by those in lakes, rivers and
estuaries. Mean values showed differences in different ecosystem
types with maxima in estuaries and wetlands and minima in lakes
and rivers (Table 1).
The scenario was different concerning PE (Table 1). It varied
greatly in oceans and estuaries (CV: 630 and 259%, respectively),
as compared with those from rivers, wetlands and lakes
(CV: 187e198%). Ranges were wider in lakes and wetlands. Average
values were roughly similar in inland waters, with those of the sea
being much lower (Table 1).
Oceanic ecosystems differed in their biogenic PE, as judged by
a KruskaleWallis test (P < 0.05; Fig. 2a). Coastal shelves (range:
0.0001e64.9 mg CH4 m2 h1) appeared to be the most variable,
whereas estuaries (mean STE 3.29 11.04 mg CH4 m2 h1)
reached the highest PE averages.
Regarding wetland types (Fig. 2b), marshes presented the
highest
range
of
methane
release
(range:
0.007e280 mg CH4 m2 h1) and the highest mean emission
(mean SD 20.026 41.98 mg CH4 m2 h1). Wetlands, where big
helophytes predominate (Fig. 2c), showed the highest range of
emissions (range: 0.003e288 mg CH4 m2 h1) and also the highest
mean emission (mean SD 27.75 45.67 mg CH4 m2 h1).
CH4 emission in lakes depended upon the lake type. Eutrophic
lakes (Fig. 2d) showed more PE variability than oligotrophic ones
(range: 0.009e300 mg CH4 m2 h1) (ManneWhitney test,
P < 0.05). Mean values were higher in eutrophic lakes
(mean SD 25.53 50.64 mg CH4 m2 h1) than in oligotrophic
ones (mean SD 6.19 14.13 mg CH4 m2 h1), as judged by
a ManneWhitney test (P < 0.05). Variability in CH4 fluxes also
[20-30)
Average
SD
CV
Median
Max
Min
n
331
c
2
1
0
-1
-2
-3
CC E
M
O
S
ST
T WT
Climate
Fig. 1. Methane emissions worldwide in different latitudes and climate types according to Köppen’s climate classification (a) Annual emissions vs latitudes, (b) Peak emission vs
latitudes and (c) Peak emissions and climate. 0e10 , n ¼ 13 and 102, respectively for (a) and (b), 10e20 , n ¼ 6 and 33, respectively; 20e30 , n ¼ 5 and 20, respectively; 30e40 ,
n ¼ 38 and 47, respectively; 40e50 , n ¼ 66 and 105, respectively; 50e60 , n ¼ 50 and 87, respectively; 60e70 , n ¼ 69 and 119, respectively; 70e80 , n ¼ 6 Cold continental (CC)
sites are 115, equatorial sites (E) 114, Mediterranean sites (M)-7, oceanic sites (O)-15, subarctic (S)-77, steppe sites (ST)-37, tropical sites (T)-71, and warm temperate sites (WT)-84.
Boxes include 25e75% of overall data, open squares are median values and whiskers comprise the whole range of data.
332
M.J. Ortiz-Llorente, M. Alvarez-Cobelas / Atmospheric Environment 59 (2012) 328e337
3
a
1
0
-1
-2
CS
DS
E
3
0
-1
-2
-3
-4
-1
-2
(log mg m h )
0
M
FW
CW
d
2
-1
Methane emission
1
P
3
c
2
(log mg m -2 h -1)
Methane emission
1
U
-2
-3
b
2
Methane emission
(log mg m -2 h -1)
2
(log mg m -2 h -1)
Methane emission
3
1
0
-1
-2
-3
B
BH
H
SH
T
O
E
NS
S
Fig. 2. (a) Peak emissions in different marine ecosystems in coastal shelf (CS, n ¼ 26), deep sea (DS, n ¼ 20), estuary (E, n ¼ 18) and upwelling (U, n ¼ 5) ecosystems. Outliers in
estuaries (n ¼ 2) and coastal shelves (n ¼ 1) have not been represented; see Appendix 1 (Supplementary Material) for their values. (b) Peak emissions in different wetland types:
coastal wetlands (CW, n ¼ 9), forested wetlands (FW, n ¼ 43), marshes (M, n ¼ 102), and peatlands (P, n ¼ 179). (c) Peak emission in wetlands with different plant dominance.
Bryophyte-(B, n ¼ 75), big helophyte-(BH, n ¼ 60), hydrophyte-(H, n ¼ 14), small helophyte-(SH, n ¼ 128) and tree-dominated (T, n ¼ 53) wetlands are shown. (d) Peak emission in
lakes as related with eutrophication and thermal regime (separate by a dashed line). Oligotrophic (O, n ¼ 98), eutrophic (E, n ¼ 44), non-stratifying lakes (NS, n ¼ 132) and
stratifying lakes (S, n ¼ 10). Plots as in Fig. 1.
depended on lake stratification (Fig. 2d); the range was smallest in
stratifying lakes (range: 0.34e4.93 mg CH4 m2 h1). As a result,
mean values were much higher in non-stratifying lakes
(mean SD 15.83 37.87 mg CH4 m2 h1) than in stratifying ones
(mean SD 1.24 1.64 mg CH4 m2 h1).
3.2. Seasonality and spatial heterogeneity of emissions
Lakes and wetlands usually showed a peak in summer (Fig. 3a),
with a maximum in August and February in the Northern and
Southern hemispheres, respectively. Meanwhile, rivers presented
three maxima of CH4 emission, the first at the beginning of spring and
W
R
L
E
b
200
300
250
200
150
100
50
0
-50
-100
-150
Frozen
Non Frozen
150
100
(mg m -2 h-1)
CH4 emission
a
the highest at the end of summer, with a short peak at the beginning
of the autumn season. Estuaries did not show any distinct pattern of
seasonality (Fig. 3a). Regarding wetland types (Fig. 3b), seasonal
patterns of CH4 fluxes in frozen and ice-free wetlands were similar as
both presented a peak in August, higher in winter-frozen wetlands.
The range of spatial variability of CH4 emission in
lakes (0.546e233.6 mg CH4 m2 h1) was higher than those of
wetlands (0.02e216.63 mg CH4 m2 h1) and oceans
(0.00003e0.0109 mg CH4 m2 h1), as judged by a KruskaleWallis
test (P < 0.05; Fig. 4). However, no statistically significant relationship was found when we studied how varied methane emissions
depended on the distances between the measured sites.
50
0
-50
-100
J F M A M J
J A S O N D
J F M A M J J A S O N D
Fig. 3. Seasonality of methane emission in (a) ecosystem types and (b) in either ice free- or winter-frozen wetlands, as shown by the Kavvas and Delleur (1975) approach (see text).
Estuary (E), lake (L), river (R), and wetland (W). No seasonal data are available for ocean sites. All time series for each ecosystem-year have been scaled by expressing emission as the
percent difference from the annual mean. All ecosystems are either located in the Northern Hemisphere or their time course of emissions has been Fig. 2 changed accordingly to
enable seasonal comparisons. The horizontal line represents the mean.
M.J. Ortiz-Llorente, M. Alvarez-Cobelas / Atmospheric Environment 59 (2012) 328e337
3,0
4. Discussion
(log mg m -2 h -1)
Spatial range of CH 4
2,5
4.1. Emissions and their ranges
2,0
1,5
1,0
0,5
0,0
-0,5
-1,0
-1,5
-2,0
333
L
O
W
Fig. 4. Spatial variability of peak emission in lakes (L, n ¼ 9), wetlands (W, n ¼ 20) and
oceans (O, n ¼ 19). No data are available for streams. The few data available for
estuaries are included within the ocean type. Plots as in Fig. 1.
3.3. Factors controlling emissions
Annual Emission
g CH4 m-2 yr-1
A positive relationship (r2 ¼ 0.93, P < 0.05) was found between
PE and AE (Fig. 5). Therefore, the higher the PE, the higher the
annual emission is. We studied the distribution of annual emission
data and divided them in two intervals, above and below
50 g m2 yr1, where more of the points are. A positive relationship
between PE and AE was found in both intervals (AE < 50 g m2 yr1
r2 ¼ 0.92 and AE > 50 g m2 yr1 r2 ¼ 0.89; p < 0.05). Statistical
significant relationships (p < 0.05) appearing between two
functions.
Some statistically significant, albeit weak, relationships
(P < 0.05) between PE from wetlands and lakes with environmental
factors were observed. Positive relationships existed between CH4
emissions and irradiance, air and sediment temperature, being the
methane effluxeirradiance correlation stronger (r2 ¼ 0.44,
p < 0.05) than the others tested (r2 ¼ 0.11 and 0.20 for air and
sediment temperature, respectively; P < 0.05). The highest
methane release occurred when water level was between 10 and
15 cm. However, no statistically significant relationship between
methane fluxes and water table was found (P > 0.05). In lakes, we
found no statistically significant relationship between either
average or maximal depth and methane emissions (P > 0.05), both
PE and AE. Total phosphorus, used as an index of organic matter
production in lakes, was neither related to CH4 efflux, be it as PE or
AE (P > 0.05).
450
400
350
300
250
200
150
100
50
0
0
We did not find any geographical pattern when we studied the
relationship between climate types or latitude and methane
emission (Fig. 1), higher emissions occurred in warm temperate
climates rather than in tropical or equatorial ones, but also in cold
continental sites. The most outstanding feature that emerged from
our analyses was the strong variability of methane emissions in
these non-managed environments across latitudes and climates.
The highest CH4 emission in different ecosystem types during PE
occurred in lakes, whereas the highest AE were recorded in
wetlands (Table 1). Areal rates of PE varied over eight orders of
magnitude and AE spanned over seven orders of magnitude
(Table 1). However, it is likely that the latter range would widen if
some oceanic oligotrophic sites were studied for a year. Ranges and
maximum rates reported here were much higher than those
described in some recent, albeit regional, reviews (Reeburgh, 2003;
Roehm, 2005; Bridgham et al., 2006). This discrepancy arose from
the fact that we undertook a worldwide analysis of the data,
thereby increasing environmental variability which usually resulted in a wider range of emissions.
Estuarine and coastal shelves were shown to depict more
emissions than upwelling areas and deep seas (Fig. 2a). A part of the
estuarine and coastal emission may be augmented by increases in
their methane content delivered by rivers (Scranton and McShane,
1991; Rehder et al., 1998; Semiletov, 1999). Both estuarine and
coastal areas are richer in organic matter of continental origin than
other marine ecosystems, thereby being better methane-producing
places, provided they have more anoxic sites to promote CH4
production (Ward et al., 1987; Naqvi et al., 2010; Zhang et al., 2008).
Marshes were the wetland type presenting the highest CH4 flux,
followed by peatlands (Fig. 2b). Their warm and waterlogged soil
provides ideal conditions for methanogenesis (Roehm, 2005).
These data suggest that marshes are a substantial source of atmospheric CH4. The deep standing water in marshes could not only
provide enough C for CH4 production by inundating more surface
area covered with plant litter, but could also create an anaerobic
environment favouring CH4 production (Ding and Cai, 2007).
Gorham (1991) estimated that peatlands contained approximately
one-third of the global terrestrial C pool; thus, peatland is an
important source of CH4 to the global C cycle, contributing an
estimated 25e30% of global CH4 released to the atmosphere every
year (Aselmann and Crutzen, 1989; Alvarez-Cobelas and Ortiz-
450
400
a
b
350
300
250
200
150
100
50
10 20 30 40 50 60 70 80 90
Peak CH4 Emission
mg m-2 h-1
0
0
10
20
30
40
50
60
70
80
90
Pe ak CH4 Em is s ion
m g m-2 h -1
Fig. 5. (a) Annual emission worldwide plotted against methane peak emission. Annual emission ¼ 4.78 * peak emission e 7.33. n ¼ 44. r2 43 ¼ 0.93, p < 0.05. (b) Peak emission
plotted against annual emission < 50 g CH4 m2 yr1 (close squares), Annual emission ¼ 2.97 * peak emission þ 0.10. n ¼ 31. r2 ¼ 0.92, p < 0.05 and against annual emission above
50 g CH4 m2 yr1 (open square), annual emission ¼ 4.98 * peak emission e 13.96. n ¼ 13. r2 ¼ 0.89, p < 0.05.
334
M.J. Ortiz-Llorente, M. Alvarez-Cobelas / Atmospheric Environment 59 (2012) 328e337
Llorente, unpublished data). However, wetland emissions were not
explicitly considered in global climate models and hence, when
included in predictions of future climate warming, such models will
likely change (IPCC, 2007).
Some studies relate the changing fluxes of CH4 with the capability of plants to transport the gas depending on plant activity
(Käki et al., 2001), photosynthesis (Sutton-Grier and Megonigal,
2011), decaying plant organic matter accumulating in the sediment (Kankaala et al., 2004) and plant biomass (Käki et al., 2001).
Deep rooting helophytes with aerenchymatic cell structure are
generally associated with high CH4 flux rates (Bubier, 1995), supporting our results which show that the highest CH4 emission
occurred in soils whose dominant vegetation either comprised big
helophytes or hydrophytes (Fig. 2c); these are the plant species
with the highest root-system development in wetlands (Cronk and
Fennessy, 2001).
Eutrophic lakes presented higher emissions than oligotrophic
ones (Fig. 2d) due to the fact that more organic matter was available
for methane production and increased anoxia. However, these
relationships are of a qualitative nature since no statistically
significant relationship was found between either lake depth or
total phosphorus (as a surrogate of eutrophication) and PE or AE.
Such relationships have been demonstrated at a regional scale
(Juutinen et al., 2009); therefore, it seems such relationships with
lake variables were of a very broad nature, losing significance as the
geographical scope of the study increased.
In addition, our study showed that shallow lakes appeared to
produce more emissions than deep lakes, in contrast with Kiene
(1991). The latter stated that sediment methanogenesis was
limited in shallow aquatic systems that were well mixed and
oxygenated during summer periods. This could be explained taking
into account that while CH4 produced in hypolimnetic sediments
could mostly be oxidized to CO2 by methanotrophic microbes in the
water table (Striegl and Michmerhuizen, 1998), much of the sediment in non-stratifying lakes was in contact with the upper mixed
water layer, in contrast to stratifying lakes. This situation enhanced
mineralization over burial, and CH4 produced in shallow sediments
largely escaped oxidation by methanotrophic bacteria and entered
the atmosphere (Bastviken et al., 2008).
4.2. Seasonality and spatial heterogeneity of emissions
Seasonal CH4 emissions in lakes and wetlands (Fig. 3a) showed
a step-function response to soil temperature, emissions being zero
or near zero during winter and much higher in summer with rapid
shifts between both seasons. While lakes presented a sharp
increase in summer and lower values throughout the year, CH4
release by wetlands remained steadier throughout the year
(Fig. 3a). The same results were reported by Dise et al. (1993), Kang
and Freeman (2002) and Xing et al. (2005). The three riverine peaks
shown here were also reported by Alford et al. (1997). The spring
peak was the result of labile organic material accumulating over
winter months when low temperatures reduced microbial activity.
The summer peak appeared to match the physiological activity of
plants when the release of organic material by root systems was
increased. Autumn maxima were attributed to the dramatic influx
of organic carbon-rich leaf litter to the system, which was readily
converted into organic substrates by CH4 producing bacteria. These
results are in agreement with those by Bergström et al. (2007), who
demonstrated that lake littoral plant cover and the distribution of
littoral plant species must be taken into account when estimating
regional CH4 budgets.
Emission differences between winter ice-free and frozenwetlands (Fig. 3b) could be explained by the fact that ice cover
did not enable bacterial activity to begin, and that methane bubbles
were trapped within the ice, so the increased emission in nonfrozen wetlands started earlier. When high temperatures
promoted ice-cover thaw, bacteria could resume their activity and
methane was released to the atmosphere.
The preliminary spatial study demonstrates that there is no
relationship between CH4 emission variability and the spatial scale.
It seems the larger the ecosystem, the smoother the variability of
methane emissions, with areas of high and low emissions cancelling each other out. Thus, successful scale-up is difficult. Hot spots
of methane emission, however, appear to be the rule in spatially
heterogeneous environments (Bastviken et al., 2010; Bianchi et al.,
1996; Kock et al., 2008; Linke et al., 2010; Moore et al., 1994).
A positive relationship between PE and AE was found (Fig. 5) in
lakes and wetlands (Alvarez-Cobelas and Ortiz-Llorente, unpublished data). This could enable optimization of the duration of
experiments and related costs, thus diminishing work effort and
saving money.
4.3. Factors controlling emissions
Relationships tested between environmental variables and CH4
emissions, however, have poor explanative power. We studied
these relationships in wetlands and lakes, first individually and
then together. Both attempts gave the same results. A statistically
significant, albeit weak, positive relationship emerged between
irradiance and CH4 emission, which has also been reported in other
studies (Mikkelä et al., 1995; Käki et al., 2001). Concerning other
abiotic factors, we found a complex relationship between the depth
of water table and CH4 emission because its maximum values were
recorded within a short range of negative and positive values.
The water table position determines the relative extent of oxic
and anoxic horizons within soils and, consequently, the ratio
between CH4 production and CH4 oxidation, the fundamental
microbial processes underlying the CH4 cycle (Christensen et al.,
2001). The effect of the water table position on CH4 emissions
can be compared with an on-off switch. When the depth of water
table falls well below the soil surface, microbial CH4 oxidation is
increased and CH4 emission is hence reduced, as occurs when water
table level is increased above the sediment. Methane emissions in
water-related ecosystems are partly controlled by water table,
a parameter closely related with climate. Rainfall decrease,
temperature increase and an elevated extraction of water from
groundwater led to decreased water table and increased methane
emissions from lakes and wetlands. Ringeval et al. (2010) stated
that over the period 1993e2000, the variability in wetland areas
strongly influenced the seasonal and inter-annual variability of CH4
emissions. On a seasonal timescale, the variable flooded area
mainly plays a role in controlling variable fluxes at boreal latitudes
(w30%). The difference with other studies (Shannon and White,
1994) is the strength of these relationships. While these authors
have found very strong correlations between water table and
methane emissions, worldwide data have shown a much lower
statistical relationship.
The factors controlling CH4 emission in lakes and wetlands are
difficult to determine on a worldwide basis. Locally, they have been
related to water depth, dissolved organic carbon, total phosphorus
and anoxic volume fraction (Bastviken et al., 2004; Juutinen et al.,
2009). However, no such relationships hold when studying them
on a worldwide basis. Not only do CH4 emissions from lake environments appear to be more spatially heterogeneous than initially
suspected (Bastviken et al., 2010) but also, temporal variations in
efflux occur ranging from diel (Bastviken et al., 2010) to interannual changes (Bartlett and Harris, 1993; Pulliam, 1993; Shannon
and White, 1994; Hughes et al., 1999). Thus, controlling factors are
likely to change depending upon the spatial scale involved. This
M.J. Ortiz-Llorente, M. Alvarez-Cobelas / Atmospheric Environment 59 (2012) 328e337
335
makes us suspect that the relationship between methane emissions
and either water table or sediment temperature are only valid at
small scales, for individual study areas. By contrast, when we study
an ecosystem type (lake or wetland) at the global scale these
relationships disappear, and a more complex relationship may
emerge among them.
The latitudinal variability is also striking (Fig. 1), thus preventing
meaningful prediction of the factors controlling emissions worldwide. It seems that temperature, organic matter and anoxic
conditions may be some of them, but there are not enough data to
support this hypothesis on the worldwide scale.
It is more difficult to study the control of biogenic CH4 efflux in
the sea. Most reported studies do not address it causally, may be
because the multivariate nature of controls and the expense of
studying them all preclude a mechanistic approach. The picture is
clearer in estuaries and closed basins, where CH4 and organic
matter inputs transported by incoming streams and anoxic conditions prevail at the sedimentewater interface (Scranton and
McShane, 1991; Zhang et al., 2008; Naqvi et al., 2010). Deep seas
and upwelling areas are quite different because there are long,
advective effects (Brüchert et al., 2009) and short-range effects,
such as oxidation of methane (Reeburgh, 2007), vertical transport
associated to settling fecal pellets (Karl and Tilbrook, 1994), mixing
of different water masses (Rehder and Suess, 2001) and oxygenic
methane production due to methylphosphorus derivatives arising
from decomposition of algal blooms in phosphate-stressed waters
(Damm et al., 2008; Karl et al., 2008). It is therefore not surprising
that methane fluxes from the ocean are so poorly understood and
that the ocean methane paradox (Reeburgh, 2007) needs further
investigation. Strong seasonality is also evident for marine environments (Zhang et al., 2008; Yang et al., 2010), but to obtain a good
resolution of seasonal changes of methane efflux from the sea is
very expensive. Observations of biogenic methane efflux from
ocean areas with frequent sampling for a year or more, can hardly
be accomplished by oceanographic vessels and would be better
undertaken using remote sensing (Frankenberg et al., 2005) and
surface drifters (Boutin et al., 2008).
valuable still (e.g. Roehm, 2005; Bridgham et al., 2006; Bastviken
et al., 2011). To complicate the situation further, the occurrence of
emission hot spots and strong temporal variability of methane
fluxes also contribute to poor estimates at the ecosystem scale
(Bubier et al., 2005). Meanwhile, other neglected variables should
be considered; for example, despite claims by Segers (1998),
methanotrophic biomass is seldom quantified in aquatic environments but it might explain a high fraction of CH4 flux variability.
However, it must be stated that likely controlling variables are not
frequently reported, and this is a drawback when carrying out
global reviews of the topic, like the one presented here.
Not only are factors controlling methane efflux still unresolved
on a global scale, but for many areas of the world our knowledge of
methane emissions from water-related ecosystems is still very
inaccurate, particularly in oceans and inland waters of semiarid,
arid and tropical regions.
Reeburgh (2007) has recently reviewed novel techniques for
studying methane metabolism in the ocean, which can also be used
in freshwaters. Notwithstanding, there is still a lot of intercomparison work on methane flux methodologies to be done. Particularly, the technique most widely used for studying CH4 efflux in the
sea is that of the watereair gradient, which relies upon accurate
determination of the gas piston velocity. However, this mass
balance method poses theoretical problems (Wanninkhof et al.,
2009) and also wide discrepancies in results depending upon the
exchange equation used to calculate that velocity (e.g. Zhang et al.,
2008).
Furthermore, studies must be carried out highlighting shortrange spatial heterogeneity including areas of the Earth that are
virtually unknown as yet. Hence, bottom-up (incubation chambers,
surface drifters, remote sensors, eddy covariance towers) and topdown (inverse modelling) measurements must be combined to
enhance the global estimation of methane emissions to the atmosphere. The significance of methane as a greenhouse gas (IPCC,
2007) makes these improvements essential if a better and more
accurate picture of global emission is to be found, which is
a precondition to reducing methane emissions worldwide.
4.4. Conclusions and recommendations
Acknowledgements
The global distribution of studies in methane emissions is not
homogeneous. Countries like the USA, Brazil, Canada, Finland and
China account for almost 70% of studies worldwide. India, Russia,
Sweden and Taiwan make up for a further 10%. A database
including the real area of a specific ecosystem type around the
world does not exist, except some examples as reported in Saarnio
et al. (2009) for Europe. Furthermore, the estimation of areas using
satellite techniques has numerous errors. It would be extremely
difficult to estimate the percentage corresponding to the global
coverage area of these ecosystems. Our study highlights how the
existing studies concentrate on specific zones.
Moreover, data on the factors controlling methane emissions are
commonly restricted to wetlands and lakes, making it difficult to
study how methane emissions vary worldwide in each ecosystem
type. This study suggests that local conditions are very important in
controlling CH4 emissions, because the variability explained by the
more commonly studied abiotic factors is rather low, as the review
of wetland CH4 emission by Segers (1998) has already pointed out
(see his Table 1b). Regarding lakes, better knowledge of factors
controlling emissions has been acquired when studying them on
a local or, at most, regional basis (Juutinen et al., 2009) while
knowledge of these factors in seas and rivers is more elusive. This
precludes the use of these variables to either model or predict
emissions at regional and on even wider scales, despite many
attempts in the past, making local assessments of emissions very
The research has been supported by the Spanish Ministry of
Environment, projects 81/2005 and 1/2008 of the Spanish Network
of National Parks. Clara Blanco, who was the librarian of the late
Centre for Environmental Sciences (CSIC), was very helpful in
compiling most references used in this review. Juan Carlos Rodríguez-Murillo is also acknowledged for his insightful reviews of an
earlier draft of this study and Interglobe Language Links for his
review of English language. Referees suggested improvements to an
earlier draft.
Appendix A. Supplementary material
Supplementary data associated with this article can be found, in
the online version, at http://dx.doi.org/10.1016/j.atmosenv.2012.05.
031.
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