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www.biogeosciences.net/6/209/2009/
© Author(s) 2009. This work is distributed under
the Creative Commons Attribution 3.0 License.

Biogeosciences

Methane dynamics in different boreal lake types

S. Juutinen1,*, M. Rantakari 2, P. Kortelainen2, J. T. Huttunen3,†, T. Larmola1,** , J. Alm4, J. Silvola1, and
P. J. Martikainen3

1Department of Biology, University of Joensuu, Finland
2Finnish Environment Institute, Helsinki, Finland
3Department of Environmental Sciences, University of Kuopio, Finland
4Finnish Forest Research Institute, Joensuu Research Unit, Finland
* now at: Mount Holyoke College, Environmental Studies Program, USA
** now at: Department of Forest Ecology, University of Helsinki, Finland
†Passed away during the course of the project

Received: 30 July 2008 – Published in Biogeosciences Discuss.: 1 September 2008
Revised: 6 January 2009 – Accepted: 23 January 2009 – Published: 16 February 2009

Abstract. This study explores the variability in concen-
trations of dissolved CH4 and annual flux estimates in the
pelagic zone in a statistically defined sample of 207 lakes
in Finland. The lakes were situated in the boreal zone, in
an area where the mean annual air temperature ranges from
−2.8 to 5.9◦C. We examined how lake CH4 dynamics re-
lated to regional lake types assessed according to the EU wa-
ter framework directive. Ten lake types were defined on the
basis of water chemistry, color, and size. Lakes were sam-
pled for dissolved CH4 concentrations four times per year,
at four different depths at the deepest point of each lake.
We found that CH4 concentrations and fluxes to the atmo-
sphere tended to be high in nutrient rich calcareous lakes,
and that the shallow lakes had the greatest surface water con-
centrations. Methane concentration in the hypolimnion was
related to oxygen and nutrient concentrations, and to lake
depth or lake area. The surface water CH4 concentration
was related to the depth or area of lake. Methane concen-
tration close to the bottom can be viewed as proxy of lake
status in terms of frequency of anoxia and nutrient levels.
The mean pelagic CH4 release from randomly selected lakes
was 49 mmol m−2 a−1. The sum CH4 flux (storage and dif-
fusion) correlated with lake depth, area and nutrient content,
and CH4 release was greatest from the shallow nutrient rich
and humic lakes. Our results support earlier lake studies
regarding the regulating factors and also the magnitude of
global emission estimate. These results propose that in bo-

Correspondence to:S. Juutinen
(sjuutine@mtholyoke.edu)

real region small lakes have higher CH4 fluxes per unit area
than larger lakes, and that the small lakes have a dispropor-
tionate significance regarding to the CH4 release.

1 Introduction

With accumulating information, lakes have grown in signifi-
cance as regional and global sources of atmospheric methane
(CH4). Most recent annual lake CH4 emission estimates are
8–48 Tg, i.e. 6–16% of the global natural CH4 emissions
(Bastviken et al., 2004), and 24.2±10.5 Tg (Walter et al.,
2007). Saarnio et al. (2008) estimated that large lakes alone
contribute to 24% of all wetland CH4 emissions in Europe.
The current study contributes to the fact that small lakes may
have proportionally high significance in element fluxes in
the landscapes (see Cole et al., 2007). The smallest lakes
are shown to have high sedimentation rates and large CO2
and CH4 emissions per unit area in samples of arctic, bo-
real and temperate lakes (Michmerhuizen et al., 1996; Ko-
rtelainen et al., 2000; Bastviken et al., 2004; Kortelainen et
al., 2004 and 2006; Walter et al., 2007). Particularly small
lakes in the areas of thawing permafrost form significant spot
sources of atmospheric CH4 (Hamilton et al., 1994; Walter et
al., 2007). The new estimates of number and area of global
lakes emphasized the high number of small lakes in the bo-
real and arctic regions (Downing et al., 2006). These small
water bodies are susceptible to ongoing changes in climate
and land use, which may notably alter the lake environment
and their CH4 fluxes. For example, increasing or decreasing
lake areas as a consequence of shifts in water balance have

Published by Copernicus Publications on behalf of the European Geosciences Union.

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210 S. Juutinen et al.: Methane dynamics in different boreal lake types

20º

69º

31º

60º

Arctic Circle

Sweden

Norway

Russia

Baltic Sea 0 100 km

Fig. 1. Geographical distribution of the statistic sample of 177 lakes
from Finnish Lake Survey data base (open symbols), and the addi-
tional sample of 30 lakes with the highest total phosphorous con-
centration (filled triangles).

been documented recently for northern lakes (e.g. Smith et
al., 2005; Smol et al., 2007). In order to better understand
the drivers behind the variability in the observed emissions,
to reduce uncertainty in global estimates, and to estimate
the anthropogenic influence on lake-derived CH4 emissions,
comparison of CH4 dynamics and net emissions in different
types of lakes is required.

The production of CH4 in freshwater lake sediments is
a microbial process, mainly regulated by the presence of
anoxia, temperature, and the amount and quality of substrates
(Rudd and Hamilton, 1978; Strayer and Tiedje, 1978; Kelly
and Chynoweth, 1981; Liikanen et al., 2003). Methane con-
centration in the water column, in turn, is affected by many
biological and physical processes. A large proportion of CH4
produced in the sediment can be consumed at the sediment
surface or in the water column by methanotrophs, a process
that contributes to oxygen deficiency (e.g. Rudd and Hamil-
ton, 1978; Bastviken et al., 2002; Liikanen et al., 2002;
Kankaala et al., 2006). The retention of CH4 in the water

column, the rate of gas transport and liberation of CH4 from
the surface are determined by several factors: stratification
and seasonal overturns of the water mass driven by temper-
ature, wind forced mixing, diffusion along the concentration
gradient, boundary layer dynamics, bubble formation and
plant mediated transport (Dacey and Klug, 1979; Chanton
et al., 1989; MacIntyre et al., 1995; Michmerhuizen et al.,
1996; Bastviken et al., 2004; Bastviken et al., 2008). Gen-
erally, high micro- and macrophyte production rate, small
water volume, and high organic carbon content all promote
the formation of anoxic hypolimnion and are related to in-
creased concentrations and fluxes of CH4 (Michmerhuizen et
al., 1996; Riera et al., 1999; Huttunen et al., 2003; Bastviken
et al., 2004; Kankaala et al., 2007).

Lake typology might provide a tool to deal with the phys-
ical and biological features of lake ecosystems, and to find a
reasonable basis, for example, for estimation of CH4 fluxes.
The European Union water framework directive (Directive
2000/60/EC) requires the Member States to typify lakes in
order to recognize and improve the ecological status of lakes.
The aim is to meet the natural status of the each lake type.
Regional typologies are based on morphometry and wa-
ter chemistry. Besides the European Union, an ecosystem-
specific framework for nutrient criteria was recently pre-
sented in the North-America by Sorrano et al. (2008). This
kind of approach could link the studies of the greenhouse gas
methane to overall environmental monitoring of lakes.

We report the variability in dissolved CH4 concentrations
and storage change and diffusive CH4 fluxes as derived from
the concentrations in a statistically defined sample of 207 bo-
real lakes in Finland. The data are distributed according to
regional lake typology (Vuori et al., 2006) based on simple
water quality and morphometric measurements. We exam-
ine 1) a lake type as an indicator of the CH4 concentrations
and fluxes, 2) quantitative relationships among CH4 concen-
trations and fluxes and water chemistry, morphological, and
climatic variables, and 3) the relationship between the occur-
rence of anoxia, nutrient content and CH4 concentration. The
same water samples have been analyzed for CO2 and those
results were presented in Kortelainen et al. (2006).

2 Materials and methods

2.1 Study lakes and lake typology

Dissolved methane concentrations were examined from 207
Finnish lakes (Fig. 1). Data consisted of a random sam-
ple, including 177 lakes, and 30 additional lakes with the
highest total phosphorus content from the Finnish Lake Sur-
vey database (see Mannio et al., 2000; Rantakari and Korte-
lainen, 2005 and Kortelainen et al., 2006 for details). The
30 lakes were included in order to balance the distribution of
oligotrophic and eutrophic lakes in our CH4 study. In all, the
Finnish Lake Survey database contains 874 lakes larger than

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S. Juutinen et al.: Methane dynamics in different boreal lake types 211

Table 1. Lake type definitions.

Lake Type Abreviation Definition

Nutrient rich and calcareous NRC Alkalinity>0.4 Winter turbidity>5 FTU
Clear, large CL Color<30 Pt mg L−1 Area≥40 km2

Clear, small and middle size CSm&M Color<30 Pt mg L−1 Area<40 km2

Clear, shallow SSh Color<30 Pt mg L−1 Mean depth<3 m
Humic, large HL Color 30–90 Pt mg L−1 Area≥40 km2

Humic, middle size HM Color 30–90 Pt mg L−1 Area 5-40 km2

Humic, small HSm Color 30–90 Pt mg L−1 Area≤5 km2

Humic, shallow HSh Color 30–90 Pt mg L−1 Mean depth<3 m
Very humic VH Color>90 Pt mg L−1 Mean depth≥3 m
Very humic, shallow VHSh Color>90 Pt mg L−1 Mean depth<3 m

four hectares (0.04 km2), but our sample was restricted to
lakes smaller than 100 km2. The data includes one eutrophic
lake having a larger area.

The study lakes were situated in an area reaching from
the margin of hemi/south boreal zone over the north boreal
vegetation zone in Finland. Within this region the annual
mean temperature ranges from−2.8 to 5.9◦C, annual pre-
cipitation varies from 449 to 879 mm (Finnish Meteorolog-
ical Institute, 1999 and 2000), and the ice-covered period
lasts about 5 months in the South and about 7 months in the
North (Hyvärinen and Korhonen, 2003). Lakes in Finland
are mostly of glacial origin, and set in non-calcareous granite
bedrock or till. Shallow and small humic lakes are the most
numerous. The catchments are largely forested, and peat-
lands are common. Generally, nitrogen and phosphorus con-
centrations are greatest in southern and western Finland and
lowest in northern Finland (Mannio et al., 2000; Rantakari et
al., 2004).

Lakes were typified according to the Finnish lake typology
required for the ecological lake status classification governed
by the EU water framework directive (Directive 2000/60/EC;
Vuori et al., 2006). At first, the naturally nutrient rich and/or
calcareous lakes were distinguished on the basis of alkalinity
and winter turbidity (Table 1). The rest of the lakes were first
divided into three groups according to their humic content
using water color as a criterion, and then grouped accord-
ing to the surface area and mean depth. The lake sample
did not include any lakes above the northern tree line. Fur-
thermore, the lakes with very short residence times were not
identified. Some lakes were already typified by Finnish Re-
gional Environmental Centres on the basis of long term ob-
servations (HERTTA register). For those lakes that had no
pre-registered type, the type was derived on the basis of mor-
phological and chemical data from our study. The surface
water chemistry in autumn was used in typification. Winter
turbidity was needed to determine the nutrient rich and cal-
careous type.

2.2 Sampling and gas and water chemistry analyzes

Each lake was sampled four times during either the year
1998 or the year 1999 in order to capture CH4 concentra-
tions during potential winter and summer stratification and
after spring and autumn overturn periods. Timing of sam-
pling was thus as follows: 1) before thaw in March–April,
2) after thaw in May–June, 3) during late summer in the end
of August–early September, and 4) in October. Water sam-
ples were drawn from 1) 1 meter below the surface, 2) in the
middle of the water column, 3) 1 meter above the sediment
surface, and 4) 0.2 m above the sediment surface – all at the
deepest point of each lake. In very shallow lakes the amount
of samples was smaller. Water samples of 30 ml for CH4 con-
centration determination were drawn from the silicone tube
of the Ruttner water sampler using a hypodermic needle and
60 ml polypropylene syringes equipped with three-way stop-
cocks. In addition, water temperature was recorded and wa-
ter samples for chemical analyses were collected.

Water samples were transported in coolers to the labo-
ratories of the universities of Kuopio and Joensuu, where
analyses of dissolved CH4 concentrations were conducted
the day after sampling. According to the headspace equi-
libration technique (McAuliffe, 1971), 30 ml ultra pure N2
gas was added to each syringe and shook vigorously for 3
minutes. The headspace gas CH4 concentration was quan-
tified with a gas chromatograph (Hewlett Packard Series II
and Shimadzu GC-14-A) equipped with an FI-detector. The
CH4concentration dissolved in water was calculated from the
headspace gas concentration according to Henry’s law using
the values after Lide and Fredrikse (1995).

Oxygen, alkalinity, turbidity, pH, water color, total nitro-
gen (Ntot), total phosphorus (Ptot), and total organic carbon
(TOC) were analysed from unfiltered samples in the labora-
tories of the Regional Environment Centres (National Board
of Waters, 1981). Oxygen was determined by adding H3PO4
to the sample in the field and titration of the acidified sam-
ple in the laboratory with the Winkler method. Alkalinity

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212 S. Juutinen et al.: Methane dynamics in different boreal lake types

was measured by Gran titration. Conductivity was measured
conductometrically with temperature compensating cell. The
values of pH were obtained electrometrically at 25◦C with
a pH meter. Water color (milligrams platinium per liter)
was measured by optical comparison with standard platinum
cobalt chloride disks. Total nitrogen was determined by ox-
idation with K2S2O8. Total phosphorus was measured with
spectrophotometer. Total organic carbon was determined by
oxidizing the sample by combustion and measuring C using
IR-spectrophotometry.

2.3 Morphometric and catchment characteristics

Data on area, mean depth, total volume and volume of water
layers for the lakes in the sample were either derived from
the register or measured directly in this study. If lake basin
volume was not available in the register, it was estimated us-
ing regressions based on representative lake data (n=1831)
available in the Finnish Environment Institute. The catch-
ment boundaries were interpreted using topographic maps,
and were digitized. We used a Landsat TM grid and digi-
tal elevation model with ArcView geo-referencing software
to obtain catchment and lake areas, catchment to lake ratios,
and proportions of agricultural land, peatlands, and forests on
upland soil, and areas of water and human settlements. The
peatland category included both pristine and forestry drained
areas.

2.4 Calculation of CH4 fluxes and CH4 storage in water

Annual flux estimate is the sum of the spring and fall storage
change fluxes and the diffusive efflux over the open water
period. Ebullition was not measured and it is not part of the
estimate. Fluxes were calculated for the whole lake and then
divided by the lake area to get estimate per unit area. The
diffusion rate between the water and the atmosphere was es-
timated on the basis of surface water CH4 concentration.

The diffusive fluxF (mol m−2 d−1) between the water sur-
face and the atmosphere was calculated as:

F = k × (Cw − Ceq) (1)

wherek is the gas transfer coefficient (m d−1) and Cw the
measured CH4 concentration (mol m−3) in the surface wa-
ter (at the depth of 1 m) andCeq the methane concentra-
tion in water that is in equilibrium with the atmosphere at
in situ temperature. The CH4 concentration in the lake water
in equilibrium with the atmosphere was calculated assum-
ing the atmospheric CH4 concentration of 1.72µL L−1 for
the year 1994 and taking into account the annual increase of
0.01% (Houghton et al., 1996). Gas transfer coefficientk was
estimated according to Cole and Caraco (1998). They deter-
mined experimentallyk for tracer gas SF6 in small sheltered
lake and normalized it to Schmidt number 600 (CO2 at tem-
perature of 20◦C). An empirical relationship between wind

speed andk600 value based on several tracer studies (Cole
and Caraco, 1998), was used to determinek600 (cm h−1):

k600 = 2.07+ 0.215× U1.7
10 (2)

whereU10 denotes the wind speed at 10 m height. We ap-
plied a value of 3 m s−1, which is the average wind speed at
10 m height in the inland stations of Finnish Meteorological
Institute during the open water period.

When piston velocity is known for one gas and tempera-
ture, it can be applied to another gas and temperature by the
ratio of the Schmidt numbers. To calculatek for CH4 we
used

kCH4 = k600 × (ScCH4/600)−0.5, (3)

where Schmidt numbers for CH4 (ScCH4) evaluated for par-
ticular temperature and water density were calculated from
empirical third-order polynomial fit with water temperature
as an independent variable (Jähne et al., 1987). For the expo-
nent we used value−0.5 according to Hamilton et al. (1994)
and MacIntyre et al. (1995).

To calculate the diffusive flux over the whole ice-free pe-
riod, the before ice-out concentration was extrapolated over
0.5 months after the ice-out. Similarly, the after ice-out con-
centration was assumed to last for 1.5 months, the summer
time concentration for 3 months, and the autumn concentra-
tion over 2 months of the ice-free period of 7 months. These
same periods were used when estimating the CO2 emissions
from our lakes (Kortelainen et al., 2006). In the current study,
time spans were proportionally the same for lakes having
shorter ice-free period.

The CH4 storage was calculated for the water column (m2)

at sampling points and for whole lakes by multiplying con-
centration values by the volume of each layer assuming hor-
izontal mixing of CH4. Storage change fluxes were calcu-
lated from the differences in CH4 storage between winter and
spring, and between late summer and autumn. This flux com-
pared with the estimate of potential flux. Potential flux is the
CH4 storage exceeding the equilibrium concentration in the
water column during late winter and late summer, which is
is assumed to be released to the atmosphere during circu-
lation (Michmerhuizen et al., 1996). If storage was larger
during spring and autumn than during late winter and late
summer, the larger storage was used to calculate storage flux,
since the timing of the sampling might have been too early.
Methane storage in the water column at sampling point (m2)

was calculated by extrapolating the measured dissolved CH4
concentration over depth ranges 0–0.5 m above the sediment,
0.5–2 m above sediment, 2 m above sediment to 2 m below
the lake surface, and 2–0 m below the lake surface. The
weighted estimate was produced by calculating storage in the
whole volume of lake, integrating storage in the above depth
ranges and dividing it by lake area.

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S. Juutinen et al.: Methane dynamics in different boreal lake types 213

2.5 Data analyses

Statistical distributions of CH4 concentrations and fluxes are
presented for the different lake types. We used multiple lin-
ear regression analysis (SPSS 15.0 for Windows) to quan-
tify relationships between environmental variables and CH4
concentration during different phases of the annual lake cy-
cle. Correlations among methane, climatic, chemical and
morphological variables were inspected using regression and
principal component analyses. Those showed that many of
the chemical variables determined for the water samples typ-
ically correlate strongly with each other. A limited set of
variables were kept in further analyses. Effect of area, max-
imum depth, mean depth, area:maximum depth, area:mean
depth, oxygen saturation, Ptot, Ntot, TOC, Ptot:TOC, mean
annual temperature, and water temperature on the CH4 con-
centrations was examined. Each variable was used as an in-
dependent variable alone. Thirdly, selected variable combi-
nations were used to build regression models. Patterns within
humic categories were examined, and lake types of different
size categories but with same humic content (Table 1) were
pooled into one group in order to increase the number of ob-
servations in the group and to facilitate the statistical anal-
yses. In the tables we give results only for the whole data.
Relationships between central values of some environmental
variables and CH4 flux components for the lake types were
explored visually (Fig. 6) and by regression analysis. For the
concentrations, near-bottom and surface water samples were
analysed independently during both the late summer (sum-
mer stratification) and the late winter (winter stratification).
Loge andarc sin

√
x were used for unevenly distributed data.

We also examined the relationship between the CH4 con-
centration in near-bottom water in winter or summer and the
lake status in terms of phosphorus and oxygen. For this pur-
pose the lakes were divided in groups according to their to-
tal phosphorus concentration and occurrence of anoxia in the
near-bottom water. Three groups were identified according
to total phosphorus: Ptot<30µg L−1, 30≤Ptot≤50µg L−1,
Ptot>50µg L−1. Four groups were identified according to
anoxia: Lakes never facing anoxia, and the lakes in which
the near bottom water was anoxic either in winter or sum-
mer, or more often. The water was considered anoxic if O2
saturation was below 5%. Differences in mean CH4 concen-
trations between the categories were tested using the Mann-
Whitney U-test. Differences in catchment land cover be-
tween these groups were similarly tested. We also show cen-
tral CH4 emissions estimates for the lakes of statistic sample
(177 lakes) in size classes 0.04–<0.1, 0.1–<0.5, 0.5–<1, 1–
<10,>10 km2.

3 Results

3.1 Distribution of lake types

The most numerous lake types were very humic shallow
(VHSh), humic shallow (HSh) and nutrient rich and calcare-
ous (NRC) (Table 2). The surface area of lakes ranged from
0.04 to 119.8 km2. The median lake area was 0.28 km2 and
only 25% of the lakes had an area over 1.6 km2. Most of
those small lakes were typified into the three shallow types
(Table 2). Most NRC lakes were also shallow and had a small
area. Defined by color (Pt mg L−1), proportions of lakes
with clear, humic or very humic water were 22%, 40%, or
38%, respectively. The lakes in type NRC included many
highly humic lakes. Nutrient-rich and calcareous lakes, and
very humic lakes were more common in the southern part
of the study region, while the distributions of humic large,
clear shallow, and humic small lakes were more northern.
Catchments of NRC lakes had the greatest proportional cover
of agricultural land, while proportional peatland cover was
largest in the catchments of larger humic lakes and very hu-
mic lakes. Very humic lakes and NRC lakes had small water
area in catchments and large catchments relative to the lake
size. Those lake types had the greatest total nutrient con-
centrations; in humic lakes the nutrients are largely bound in
organic matter (Table 2).

3.2 Methane concentrations

Average surface water CH4 concentration was 1.0µmol L−1,
and the bottom water concentration averaged 20.6µmol L−1,
yet it was less than 2.3µmol L−1 in 75% of the lakes. Very
high CH4 concentrations were rare (three samples had con-
centration over 1000µmol L−1) (Table 3). Methane concen-
trations were generally greatest in the water layer closest to
the sediment during late winter (md 7.9µmol L−1), and dur-
ing late summer (md 0.3µmol L−1). The surface water con-
centrations were most often the largest during the late sum-
mer, the median value being 0.2µmol L−1. The vertical con-
centration gradient was the largest during the late winter (Ta-
ble 3, Fig. 2). Median concentration in the bottom was 113
times and 1.6 times the surface concentration under the ice
cover and during the late summer, respectively. The relation-
ship between bottom and surface water CH4 concentrations
was weak. It was significant among all the lakes (r2=0.14),
and among the clear small and middle size and shallow lakes,
humic shallow and very humic lakes during the stratification
periods.

Lake type characteristics were reflected in CH4 concentra-
tions, but within each lake type the variation in CH4 concen-
trations was considerable. Statistical relationships between
CH4 concentrations and environmental variables were weak
though significant in the large data (Table 4). General pattern
was that surface water CH4 concentration was more related
to the morphological variables while the bottom water CH4

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214 S. Juutinen et al.: Methane dynamics in different boreal lake types

a) b) Spring, surface

0.0

0.3

0.6

0.9a) Winter, surface

C
H

4 
(µ

m
ol

 L
-1

)
0.0

0.3

0.6

0.9

e) Winter, bottom
C

H
4 

(µ
m

ol
 L

-1
)

0

10

20

30

40
f) Spring, bottom

0.0

0.3

0.6

0.9

1.2

c) Summer, surface

0.0

0.3

0.6

0.9 d) Autumn, surface

0.0

0.3

0.6

0.9

g) Summer, bottom

0.0

0.3

0.6

0.9

1.2 h) Autumn, bottom

0.0

0.3

0.6

0.9

1.2

Lake Type

NRC CL
CSmMCSh HL HM

HSmHSh VH
VHShC

H
4 

st
or

ag
e 

(m
m

ol
 m

-2
)

0.0

0.3

0.6

0.9

1.2

Lake Type

NRC CL
CSmMCSh HL HM

HSmHSh VH
VHSh

0.0

0.3

0.6

0.9

1.2

Lake Type

NRC CL
CSmMCSh HL HM

HSmHSh VH
VHSh

0.0

0.3

0.6

0.9

1.2

Lake Type

NRC CL
CSmMCSh HL HM

HSmHSh VH
VHSh

0.0

0.3

0.6

0.9

1.2i) Winter j) Spring k) Summer l) Autumn

 
Fig.2 

Fig. 2. CH4 concentrations and CH4 storages (whole lake integrated, mmol m−2) in the different lake types (see Table 1). Bars show medians
of surface(a–d)and bottom water CH4 concentrations(e–h), and the CH4 storages(i–l). Upper and lower quartiles are marked with dashed
lines (these mark the two lakes in the type HL). Note the different scales and that some upper quartiles are outside of the scale.

Table 2. Medians for alkalinity, turbidity, Ptot, Ntot, color and TOC of the surface water at fall, lake area (A), maximum depth (D) and
proportional cover of agricultural land (Agr.), forests (For.), peat and water (Wat) in the catchments in the whole data and the different lake
types. Mean annual temperature (T) was measured in the nearest weather stations (Finnish Meteorological Institute 1999 and 2000). Three
lakes could not be typified due to missing water chemistry data, and land cover distribution was analyzed only for 187 lakes. Type definitions
from the Table 1.

Lakes N Alk. Turb. Ptot Ntot Color TOC A D T pH Agr. For. Peat Wat.

(mmol l−) (FTU) (µg l−1) (µg l−1) (Pt mg l−1) (mg l−1) (km2) (m) (◦C) (%) (%) (%) (%)

Stat. 177 0.1 1.3 14 460 70 9 0.24 6.2 3.2 6.5 2.6 67 12 9
Eutr. 30 0.2 6.5 60 970 140 11 0.94 4.0 3.6 6.6 11.6 61 17 5
All 207 0.1 1.5 16 505 80 9 0.28 6.0 3.3 6.6 3.6 67 12 8

Types
NRC 27 0.4 5.3 57 840 75 9 0.52 5.1 4.1 6.8 20.4 60 2 7
CL 1 0.2 1.1 15 280 15 5 44.26 26.5 3.1 7.2 4.2 72 6 17
CSm&M 17 0.1 0.6 6 270 15 4 1.35 13.4 3.2 6.7 2.3 68 6 19
CSh 21 0.1 0.7 6 300 20 5 0.14 6.2 1.5 6.8 0.0 69 5 13
HL 2 0.2 1.3 16 300 60 9 52.75 17.8 0.1 7.1 0.9 60 29 10
HM 4 0.2 0.9 11 510 35 8 20.17 15.6 3.6 7.0 3.8 58 18 17
HSm 19 0.1 1.0 11 360 50 8 1.03 14.0 2.2 6.5 3.0 71 9 11
HSh 45 0.1 1.3 14 485 65 9 0.19 4.0 2.8 6.5 4.2 70 11 10
VH 11 0.1 1.6 31 635 170 16 1.40 12.5 3.3 6.3 10.0 64 19 5
VHSh 57 0.1 2.0 24 635 160 17 0.10 3.8 3.4 6.3 1.5 63 22 5

concentration was related to oxygen and nutrient concentra-
tions (Table 4, Fig. 3). Methane concentration in bottom wa-
ter correlated with morphology only during summer, but not
under the ice over.

Surface water CH4 concentration was primarily related to
lake depth. Both in winter and summer medians of CH4
concentration were greatest in the shallow lake types. The
medians varied from 0.12 in HSh to 0.26µmol L−1 in NRC
during winter and from 0.18 for VHSh to 0.31µmol L−1for

HSh during summer (Fig. 2a–d). Surface water CH4 concen-
tration correlated negatively with the lake depth, oxygen sat-
uration, and positively with concentrations of Ptot and TOC
during winter (Table 4). There was a weak positive correla-
tion with mean annual temperature. During summer, surface
water CH4 concentration had negative correlation with lake
area, depth, and area to depth ratio, and weakly negative cor-
relation with oxygen saturation.

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S. Juutinen et al.: Methane dynamics in different boreal lake types 215

Table 3. Statistical distributions of CH4 concentrations (µmol L−1) in 1 m below surface (surface) and 0.2 m above the sediment (bottom).

Depth Sampling Mean Lower Median Upper Max N

quartile quartile

Bottom Winter 53.62 0.13 7.94 47.31 3013.76 192
Bottom Spring 3.23 0.05 0.10 0.20 227.39 196
Bottom Summer 21.49 0.12 0.27 3.18 1331.15 183
Bottom Autumn 4.25 0.05 0.10 0.23 393.33 190
Surface Winter 3.39 0.03 0.07 0.55 60.19 201
Surface Spring 0.16 0.05 0.10 0.19 2.50 203
Surface Summer 0.25 0.08 0.17 0.31 1.69 200
Surface Autumn 0.24 0.04 0.08 0.18 5.12 201

Table 4. Relationships between CH4 concentrations and environmental variables that correlated significantly with CH4 at p level <0.05.
Sign indicating positive or negative correlations and the regression coefficient are given before and after the variable, respectively. Effect
of area, maximum depth, mean depth, area:maximum depth, area:mean depth, oxygen saturation, Ptot, Ntot, TOC, Ptot:TOC, mean annual
temperature, and water temperature on the CH4 concentrations was examined. Effect of each factor was tested independently in regression
analysis.

Winter, Surface Summer, surface Winter, bottom Summer, bottom

−Max depth, 0.24 −Area, 0.11 −O2%, 0.51 −O2%, 0.38
−Mean depth, 0.23 −Max depth, 0.10 +Ptot, 0.29 +Ptot, 0.16
−O2%, 0.22 −Mean depth, 0.08 +Ptot:TOC, 0.17 +Ptot:TOC, 0.07
+Ptot, 0.17 −Area:mean depth, 0.06 +TOC, 0.05 −Area, 0.06
+TOC, 0.09 −Area:max depth, 0.06 +Water T, 0.02 −Area:mean depth, 0.06
+Ptot:TOC, 0.07 −O2%, 0.02 −Area:max depth, 0.06
+Mean T, 0.02 +TOC, 0.05

+Water T, 0.03

Bottom water CH4 concentration was the highest in win-
ter in the large humic lakes (HL, HM), CSh and NRC lakes,
for those the medians were from 28.43 to 81.12µmol L−1

(Fig. 2e–h). These were followed by the smaller humic
and very humic lakes, and the types HSh>VH>VHSh had
medians from 10.38 to 18.5µmol L−1. During summer
the deeper very humic (VH) lakes had the greatest bottom
concentrations (md 1.08µmol L−1). Large CH4 concentra-
tions were common also in NRC lakes and in the other
humic lakes (HSh>HM>VHSh). Bottom water CH4 con-
centration in winter and summer alike correlated negatively
with oxygen saturation and positively with Ptot concentra-
tion and Ptot:TOC ratio (Table 4). It correlated weakly with
TOC concentration and water temperature. During summer,
CH4 concentration had also negative correlation with lake
area and area-to-depth ratio. The most extreme cases (CH4
>1000µmol L−1) were not associated with anoxic water,
and possibly sediment interstitial water was mixed with the
lower water layers during sampling in those cases.

Stepwise regression models with O2 saturation, Ptot, mean
depth and area as independent variables explained up to 34%

of variation in surface water CH4 concentration and up to
59% of variation in bottom water CH4 concentration when
all lakes were included (Table 5). All of these parameters
did not get significant parameter values in the humic class
specific models, which likely indicate shorter environmen-
tal gradients within a humic class than in the complete data
set. For example, oxygen saturation seemed to be insignifi-
cant factor in NRC lakes, because the majority of those lakes
suffered oxygen deficiency during the late winter.

3.3 Dissolved CH4 and lake status

Methane concentration of bottom close water could be
viewed as an indicator of lake status in terms of oxygen
and nutrients. In the late winter, the CH4 concentrations
were significantly greater in the lakes suffering anoxia and
having the highest Ptot levels (Md 151.1µmol L−1) than in
the lakes in which bottom water stayed oxic and Ptot lev-
els were low (Md 0.1µmol L−1) (Fig. 4a, Table 6). The
difference in CH4 concentrations between these two cate-
gories was smaller although statistically significant also dur-
ing the late summer (Fig. 4b). Within any category of anoxia,

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216 S. Juutinen et al.: Methane dynamics in different boreal lake types

Table 5. Linear regression models for the CH4 concentrations. The predicting variables were entered to the stepwise analysis in the order
O2 saturation, Ptot, maximum depth and area. All models are significant at levelp>0.01, and all parameter values are significant at level
p>0.05 (ns=non significant).

Parameters

Set r2
adj. Dfres Constant O2sat. Ptot Max Depth Area

Winter, surface 0.34 191 1.431 −0.882 0.178 −0.426 0.168
Winter, bottom 0.59 186 2.048 −0.872 0.490 ns. −0.308
Summer, surface 0.16 188 0.322 ns. ns. −0.050 −0.043
Summer, bottom 0.46 176 2.702 −2.138 0.230 −0.465 ns.

Surface, winter

C
H

4 
(µ

m
ol

 L
-1

)

0.01

0.1

1

10

100

O2 saturation (%)

0 20 40 60 80 100

Surface, summer

C
H

4 
(µ

m
ol

 L
-1

)

0.01

0.1

1

Bottom, winter

C
H

4 
(µ

m
ol

 L
-1

)

0.01
0.1

1
10

100
1000

Bottom, summer

Max Depth (m)

0 20 40

C
H

4 
(µ

m
ol

 L
-1

)

0.01
0.1

1
10

100
1000

Lake Area (km2)

0 20 40 120

NRC

CL
CSm&M
CSh

HSm
HM
HL
HSh

VH
VHSh

Fig. 3. Surface and bottom water CH4 concentrations before the
potential over-turn periods during late winter and late summer in
relation to maximum depth (left), lake area (middle), and oxygen
saturation (right).

the CH4 concentrations increased with increasing Ptot level.
The lakes where the near-bottom water stayed oxic over the
whole annual cycle had significantly smaller CH4 concentra-
tions than the lakes where near-bottom water turned anoxic
at least once during the annual cycle (Table 6). Among the
lakes where near-bottom water was anoxic at least once or
more during a year, the lakes having the lowest Ptot level
had also significantly smaller CH4 concentrations than the
lakes with higher Ptot levels (p<0.01) during winter. The
difference between these lake categories was not significant
during the summer (p=0.985).

Table 6. Bottom water CH4 concentrations in the lakes classified
according to mean total phosphorus concentration and occurrence
of anoxia over a year in bottom water. Results of different com-
parisons among anoxia and Ptot categories in winter and summer,
are separated by a row. The categories with no letter common are
significantly different.

CH4 (µmol L−1)

Category Mean Md SD N

Winter No anoxia, low P 6.263a 0.101 15.483 67
Winter Anoxia, high P 185.045b 151.106 117.62 14

Winter No anoxia 8.828a 0.325 17.703 95
Winter Anoxia, Ptot<30 43.203b 29.079 56.621 57
Winter Anoxia, Ptot>30 89.242c 57.05 100.249 47

Summer No anoxia, low P 2.037a 0.132 8.347 66
Summer Anoxia, high P 58.558b 6.36 80.369 11
Summer No anoxia 3.428a 0.159 13.009 93

Summer Anoxia, Ptot<30 17.649b 1.25 42.409 55
Summer Anoxia, Ptot>30 31.283b 0.779 69.6 42

Water was considered anoxic when O2 saturation was below 5%.
No anoaxia, Low P: Ptot below 30µg L−1, and bottom water never
anoxic, Anoxia, high P: Ptot over 50µg L−1, and bottom water was
anoxic at two or more sampling times. Three cases with CH4 con-
centration over 1000µg L−1 were not included in the analysis.

The catchments of lakes having prevailing anoxia and the
highest Ptot levels had significantly larger proportions of set-
tlement and agricultural land, and significantly smaller pro-
portions of peatland and water than the catchments of the
oxic and low Ptot lakes (p<0.05). The large proportions of
agricultural land and settlements in the catchments were par-
ticularly related to high Ptot levels.

3.4 Methane storages

Methane storage integrates the water volume and the concen-
tration of CH4 in different water layers (Fig. 2i–l)., Methane
storages were larger during the assumed stratification peri-
ods than during the following mixing or post mixing times,

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S. Juutinen et al.: Methane dynamics in different boreal lake types 217

0

100

200

Never
Winter

Summer
2+ times

<30 µg/L
30–50 µg/L

>50 µg/L

C
H

4 
(µ

m
ol

 L
-1

)

Ptot

0

100

200

Never
Winter

Summer
2+ times

<30 µg/L
30–50 µg/L

>50 µg/L

C
H

4 
(µ

m
ol

 L
-1

)

An
ox

ia

Ptot

A B

Fig. 4. Mean CH4concentrations of the bottom close water in the
winter (a) and the summer(b) in the lakes categorized by frequency
of anoxia and total phosphorus (Ptot). The two extreme categories
(circled) and the category groups never anoxic (open symbols), and
at least sometimes anoxic low Ptot lakes (grey symbols), and at least
sometimes anoxic high Ptot lakes (black symbols) were compared.
For the comparisons see the Table 5.

spring and autumn, in 58% and 75% of the lakes, respec-
tively. In those lakes median percentage of the storage that
was lost during the assumed turn over was 88% in spring
and 63% in autumn. The CH4 storage reached its seasonal
maximum during late winter in 40% of the lakes, and during
late summer in 36% of the lakes. In some lakes the stor-
age peaked during spring (11%) or in autumn (13%). These
might be lakes where the sampling possibly took place before
the turn over.

Accumulation of substantial CH4 storage during summer
was common among these lakes. Consequently, median of
CH4 storage was larger during the late summer than during
other times. Most lakes in the types CSh, VH, and HSh had
summer–autumn maximum in their CH4 storage. Most lakes
in the types HM, VHSh and NRC had peak storage during
late winter–spring period. Overall, NRC lakes had larger
CH4 storages than the other lake types both during winter
and summer (Fig. 2i–l).

3.5 Diffusive and storage fluxes to the atmosphere

This flux estimate is the sum of the spring and fall storage
change fluxes and the diffusive efflux over the open water pe-
riod. Ebullition was not measured and it is not part of the esti-
mate. Fluxes were calculated for the whole lake and then di-
vided by the lake area to get estimates per unit area. The sum
of CH4 fluxes (storage and diffusion) ranged from very low
release of 2 to 1142 mmol m−2 a−1. Median and mean CH4
fluxes for all of the lakes were 25 and 65 mmol m−2 a−1, re-
spectively (Fig. 5a), and for the 177 lakes in the statistic sam-
ple those were 21 and 49 mmol m−2 a−1. Flux estimates for
the sampling point only would be higher due to smaller pro-
portion of surface water and larger proportion of storage flux,
e.g. median for the set of 177 lakes was 45 mmol m−2 a−1.

The sample of 30 eutrophic lakes had the highest median
of CH4 emission (Fig. 5a). The emission was 3.5 times
the median for the CH4 flux for the statistic lake sample.

Lake  Group/type

All
Stat

Eutr
NRC CL

CSmMCSh HL HM
HSmHSh VH

VHSh
S

to
ra

ge
 fl

ux
 (%

)
0

10

20

30

40

50
All

Stat
Eutr

NRC CL
CSmMCSh HL HM

HSmHSh VH
VHSh

C
H

4 
flu

x 
(m

m
ol

 m
-2

 a
-1

)

0

20

40

60

80
A

B

 
Fig.5. 

Fig. 5. Medians of CH4 flux estimates (sum of diffusive and stor-
age fluxes, mmol m−2 a−1) (a) and the proportion of storage flux
(b). Dashed lines indicate the upper and lower quartiles. Values
are given for lake sets: All: 207 lakes, Stat: statistic lake sam-
ple (n=177), Eutr: Eutrophic lakes (n=30), NRC: nutrient rich and
calcareous, CL: clear large, CSm&M: clear small and middle size,
CSh: clear shallow, LH: humic large (n=2), HM: humic middle
size, HSm: humic small, HSh: humic shallow, VH: very humic,
and VHSh: very humic shallow.

The lakes in the shallow types had commonly higher CH4
fluxes (medians 19–48 mmol m−2 a−1) than the larger lakes
(2–8 mmol m−2 a−1). Lake types ordered according to me-
dian flux were HSh> NRC > VHSh > CSh> CSm&M >

VH > HSm> HM > CL > HL. The shallow types had high
fluxes, because they had high surface water concentrations
during summer leading to higher estimate of diffusive flux.

Diffusive flux dominated the CH4 release in most of the
lakes, and the storage component was less than 5% of it in
the half of the lakes (Fig. 5b). Storage fluxes could, however,
make up to 91% of the sum, and the largest CH4 fluxes re-
sulted from the large storage fluxes. Those occurred in large
or deep lakes with substantial water volume. Storage fluxes
of CH4 were usually larger in spring than in autumn. The
lake types CSm&M, VH, CSh, and VHSh had the greatest
storage fluxes and proportionally it was largest in lake types
HM and VH (Fig. 5b).

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218 S. Juutinen et al.: Methane dynamics in different boreal lake types

Table 7. Regression models for the different flux components and their sum (mmol m−2 a−1). Independent variables were given in the
order of maximum depth (m), lake area (km2), and Ntot(µg L−1) for the stepwise linear regression analysis. Ptot as an independent variable
produced very similar result than Ntot.

Component Model r2 Dres F p

Sum y=0.202−0.285×MaxD−0.206×Area+0.548×Ntot 0.34 196 35.7 <0.001
Spring Storage y=1.239−0.211×MaxDepth 0.03 198 6.0 0.015
Autumn Storage y=−2.912−0.211×Area+0.383×MaxDepth+0.426×Ntot 0.25 196 23.3 <0.001
Sum of Storages y=−1.615−0.168×Area+0.432×Ntot 0.11 197 12.9 0.015
Diffusion y=0.558−0.360×MaxDepth−0.179×Area+0.488×Ntot 0.37 196 40.6 <0.001

Ntot (µg L-1)

400 600 800Su
m

 C
H

4 
Fl

ux
 (m

m
ol

 m
-2

a-
1 )

0

20

40

60

Ntot (µg L-1)

400 600 800

S
to

ra
ge

 F
lu

x 
(m

m
ol

 m
-2

a-
1 )

0

1

2

3

Area (km2)

0 1 2 3 4 5 20 40 60S
um

 C
H

4 
Fl

ux
 (m

m
ol

 m
-2

a-
1 )

0

20

40

60

Area (km2)

0 1 2 3 4 5 20 40 60

S
to

ra
ge

 F
lu

x 
(m

m
ol

 m
-2

a-
1 )

0

1

2

3

Depth (m)

0 5 10 15 20 25 30Su
m

 C
H

4 
Fl

ux
 (m

m
ol

 m
-2

a-
1 )

0

20

40

60

Depth (m)

0 5 10 15 20 25 30

S
to

ra
ge

 F
lu

x 
(m

m
ol

 m
-2

a-
1 )

0

1

2

3

y=-7.159+0.058x, r2=0.38, p=0.026 y=-0.299+0.003x, r2=0.72, p=0.001

y=26.561-0.488x, r2=0.38, p=0.025
lny+1=3.455-0.518x, r2=0.74, p,0.001

y=1.259-0.020x, r2=0.54, p=0.006
lny+1=0.865-0.154x, r2=0.57, p,0.004

y=43.184-1.961x, r2=0.67, p=0.001 y=1.764-0.067x, r2=0.57, p=0.004

A

B

C

D

E

F

NRC

CL
CSm&M
CSh

HL
HM
HSm
HSh

VH
VHSh

Fig. 6. Relationships between type specific central values of envi-
ronmental variables and CH4 fluxes. Sum flux includes diffusive
and storage fluxes and is dominated by the diffusion component(a–
c). Storage CH4 flux is sum of spring and autumn storages(d–f).

Regression models with maximum depth, lake area and
Ntot (or Ptot) as independent variables explained 3–37% of
the variation in different flux components (Table 7). Dif-
fusion components and sum of diffusion and storage fluxes
were related to lake area and depth, and to nutrient status.
Storage component showed variability that was harder to
predict, and there may be size dependent differences in the
relationships. Maximum depth received positive coefficient
value for the autumn storage fluxes, while the coefficient was

negative in the diffusion component model. Relationships
between mean values of CH4 fluxes and environmental vari-
ables (see Table 2) for the lake types hide much of the details
and the variation, but those illustrated the major factors asso-
ciated with the variation in CH4 fluxes (Fig. 6). Large storage
fluxes were related to eutrophic lake environments (Fig. 6d),
high Ntot concentration and large agricultural land cover in
the catchment, but also to small area. The sum of diffusive
and storage fluxes was higher in the shallow and small lakes
than in the large lakes. Among the small lakes, however, the
more eutrophic and more humic lakes had higher fluxes than
the clear ones (Fig. 6a–c).

4 Discussion

We present data on CH4 concentrations and storage change
and diffusive fluxes derived from the concentrations. Our
data are unique in the number of lakes examined, and statis-
tic sample of the lakes and large geographical area provided
some extensive environmental gradients. Lakes within this
sample are typically small and humic. Globally, however,
small lakes dominate: one third of the total world lake sur-
face area consists of lakes that are smaller than 0.1 km2,
and the estimated global average of lake size is 0.012 km2

(Downing et al., 2006). All of the lakes in our study were
0.04 km2 or larger, and consequently median lake area in the
current study, 0.28 km2, is larger than the estimate of average
global lake area.

The CH4 concentration in lake water is affected by many
processes, and the large overall variability in CH4 concen-
trations in this study may result from the large data. These
facts may contribute to the rather weak statistical relation-
ships between CH4 and environmental variables in this study.
We applied a regional lake typology to group lakes that have
more similar physical and biological processes contributing
to the CH4 within those groups. Results indicate that that
the grouping factors, natural nutrient content, humic content,
area and depth, are associated with the variation in CH4 con-
centration and with the flux estimates derived from the con-
centrations.

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S. Juutinen et al.: Methane dynamics in different boreal lake types 219

The lake types are defined for assessing the ecological sta-
tus of lakes in relation to each type’s natural conditions (Di-
rective 2000/60/EC; Vuori et al., 2006). At the time being we
could not relate CH4 data with ecological status categories,
because the work by the environmental authorities is still
continuing. Status changes are indicated by changes e.g. in
abundance and primary production of aquatic macrophyte or
planktic species, and frequency of oxygen deficiency (Man-
nio et al., 2000; Dodson et al., 2005; Vuori et al., 2006).
All these may have impact on CH4 production and oxidation
conditions, and cause variability observed in CH4 concentra-
tions.

4.1 Methane concentrations

Average CH4 concentrations in the surface waters in the
current study, 0.2–1.8µmol L−1 (Table 3) were comparable
to average surface water concentration of CH4 in 13 olig-
otrophic Swedish lakes of 0.1–1.9µmol L−1 and in 11 Wis-
consin lakes of 0.3–2.3µmol L−1 (Bastviken et al., 2004).
The highest dissolved CH4 concentrations measured close to
the sediment in our study were similar with, for example,
those reported for the eutrophic Lakes 277 and Wintergreen
(Rudd and Hamilton, 1978; Strayer and Tiedje, 1978). Ac-
cording to our data, the key variables associated with varia-
tion in lake water CH4 concentration are oxygen saturation,
nutrient status implying productivity, lake area and depth,
which is in line with studies of Michmerhuizen et al. (1998);
Huttunen et al. (2003), and Bastviken et al. (2004).

Poor oxygen saturation and high nutrient status predict
high CH4 concentration in the bottom water (Fig. 3, Table 4).
These factors indicate a condition favoring higher CH4 pro-
duction rates and hindering CH4 oxidation (e.g. Rudd and
Hamilton, 1978; Huttunen et al., 2003). In addition, bot-
tom CH4 concentration was positively yet weakly related
with TOC and water temperature, but this can be also due to
the southern distribution of most productive and humic lakes,
and strong stratification and oxygen consumption rates in hu-
mic lakes (Bastviken et al., 2004, Kankaala et al., 2007). The
negative correlation between area-to-depth ratio (considered
small for a deep lake with small area) and CH4 concentration
suggest that wind driven water column mixing can keep bot-
tom water CH4 concentration low during open water season.
The summer bottom water CH4 examined against the lake
types pointed that CH4 accumulation is likely when there is
a risk of oxygen deficiency (Fig. 2): e.g. in deep lakes, nutri-
ent rich lakes and in very humic lakes having strong stratifi-
cation (Riera et al., 1999; Huttunen et al., 2002a; Bastviken
et al., 2004; Kankaala et al., 2007). In winter CH4 accumula-
tion is likely in shallow lakes due to small water volume and,
thereafter, small oxygen storage.

The CH4 concentrations in bottom-close water during
winter were significantly higher in lakes having frequently
anoxic bottom water and highest Ptot levels than in lakes hav-
ing lower Ptot levels or less frequent anoxia (Fig. 4, Table 5).

Because of this connection among nutrient level, oxygen sat-
uration and CH4, the amount of CH4 in the sediment close
water might serve as a classification measure that integrates
oxygen and nutrient status (cf. Huttunen et al. 2006). Anoxia
and high nutrient levels are typically correlated due to oxy-
gen consumption by decomposition and by nutrient release
from the sediment in anoxic conditions. However, factors af-
fecting the water column mixing and gas exchange, i.e depth
and area (Fee et al., 1996) have an impact on lake oxygen
status during the open water season too (Table 4).

Factors that are associated with the surface water CH4 con-
centration are significant since the surface water CH4 con-
centration is the driver of diffusive CH4 flux to the atmo-
sphere. The surface water CH4 concentrations were typically
the highest in shallow lakes both during the late winter and
the late summer (Table 4, Figs. 2 and 3). Significant rela-
tionships between CH4 concentration and oxygen saturation,
Ptot and TOC in winter indicate that during the ice cover the
nutrient rich and humic shallow lakes tend to have propor-
tionally large anoxic water volume, allowing CH4 accumu-
lation at the water layers where oxygen deficiency hinders
oxidation of CH4.

During the open water season, in turn, lake area and depth
and their ratio affect, first, how much of the CH4 reaches
the surface layer and, second, the loss of CH4 from lake sur-
face to the atmosphere. In the shallow lakes the epilimnion
and productive littoral sediments have a large contribution
to the total sediment area and gas efflux to the water col-
umn. In the epilimnion the sedimentary gases are mixed
to the surface layer, while the hypolimnion is isolated from
the surface layer by stratification (den Heyer and Kalf, 1998;
Bastviken et al., 2004; Murase et al., 2005; Bastviken et al.,
2008). The shallow lakes may also maintain the high surface
CH4 concentration due to lower gas exchange rates between
the surface and the atmosphere, because these lakes likely
have small area and are wind sheltered (cf. Bastviken et al.,
2004). Correlation between bottom water and surface wa-
ter concentrations was poor, which follows from the multiple
processes in the sediment throughout the water column and
water surface determining the CH4 concentration (Rudd and
Hamilton, 1978; Bastviken et al. 2004; Kankaala et al., 2006,
2007). Besides, the surface water CH4 likely originates from
different sediments than just below the sampling point due to
the lateral mixing of the water (Bastviken et al., 2008).

Identifying the formation of CH4 storage in a lake is im-
portant, because CH4 storage can turn to large CH4 release.
Eutrophic and humic lakes with substantial volume can be
sites of big storage fluxes, but storage formation can be
also common in smaller lakes due to their susceptibility to
anoxia. The current study also shows that accumulation of
CH4 storage during summer is comparable to CH4 accumu-
lation during the ice covered season in many lakes of this
region (Fig. 2). It has been shown that summer storage can
make significant part of the annual CH4 release particularly
in the humic lakes (Kankaala et al., 2007).

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220 S. Juutinen et al.: Methane dynamics in different boreal lake types

Lake area (km2)

0.001 0.01 0.1 1 10 100

C
H

4 e
ffl

ux
 (m

m
ol

 m
-2

a-
1 )

0

200

400

600

1200 This study (177 lakes)
Swedish lakes
Wisconsin lakes
Mendota
Crystal
Lake 227
Priest Pond
2 Eutrophic Finnish lakes
Area <0.1 km2, mean

Fig. 7. Sum of the diffusive and storage fluxes in relation to lake sur-
face area in the lakes of this study and some other studied lakes. Me-
dian CH4 values (black circles) are plotted against median lake size
in the size classes<0.1, 0.1–<0.5, 0.5–1, 1–<10, 10–<100, and
>100 km2 for the lakes of statistic sample of our study. Estimates
of CH4 release (diffusion and storage fluxes) from some other lakes
studied by Rudd and Hamilton (1978), Fallon et al. (1980), Mich-
merhuizen et al. (1998), Casper et al. (2000), Huttunen et al. (2003)
and Bastviken et al. (2004, Table 1) are plotted for a comparison.

4.2 Diffusive and storage fluxes to the atmosphere

Two flux components, CH4 diffusion from the surface during
the open water season and the release of accumulated CH4
storage during the over turn periods, were estimated on the
basis of the concentration data. These estimates provide only
a partial fraction of annual CH4 release from the lakes, be-
cause ebullition and plant-mediated fluxes are not included.
The uncertainty in our flux estimates follows from assump-
tions concerning the use of concentration data and boundary
layer models, and those of horizontal and vertical scaling up
over the whole lake. A constant wind speed value was used
to estimate diffusive flux, which thus might be overestimated
for the small and sheltered lakes while underestimated for the
larger lakes where the surface is more exposed to wind (see
Bastviken et al., 2004). The boundary layer diffusion model
often gives values lower than chamber measurements, an-
other method to estimate fluxes, yet not in a consistent man-
ner (Phelps et al., 1998; Duchemin et al., 1999; Kankaala et
al., 2006; Repo et al., 2007).

Another part of our budget is based on the assumption that
the difference between the before and after turnover storage
terms is released to the atmosphere, but part of the differ-
ence can be due to CH4 oxidation. For example, in Lakes
227, Williams and Kev̈atön oxidation consumed a small pro-
portion of CH4 in the water body during the spring over-
turn. Methane consumption was larger in relatively shallow
L. Kevätön, and 60–80% of the pre-overturn CH4 storage
was oxidized in deeper Lake 227 and meromictic Mekko-
jarvi in autumn (Rudd and Hamilton, 1978; Michmerhuizen
et al., 1996; Liikanen et al., 2002; Kankaala et al., 2007).

Ebullition may dominate CH4 flux in many of the nutrient-
rich and shallow lakes in our study (Huttunen et al., 2003;
Bastviken et al., 2004). Thus, concentration data alone
give a biased indication of CH4 emissions and, moreover,

CH4 released in bubbles may cause some problems to pre-
dict variation in observed concentration data. In addition,
the vegetated and very shallow littoral areas fall outside of
this estimation. They might have much higher CH4 emis-
sions than predicted on the basis of CH4 concentration in the
pelagic zone (Smith and Lewis, 1992; Juutinen et al., 2003;
Bergstr̈om et al., 2007). Although water layers are assumed
to be horizontally mixed, sometimes horizontal concentra-
tion gradients have been identified. Those can be caused by
CH4 inputs from littoral sediments, adjacent peatlands, or in-
coming streams (Schmidt and Conrad, 1993; Larmola et al.,
2004; Murase et al., 2005; Repo et al., 2007; Bastviken et al.,
2008). Moreover, Some uncertainty related to volume esti-
mates and vertical and horizontal extrapolation based on four
concentration measures is evident.

Median flux value for the 177 statistically selected lakes
is 21 mmol m−2 a−1 (Fig. 5). The average value for these
lakes, 49 mmol m−2 a−1, is close to the average estimate of
41 mmol m−2 a−1 for 8 Swedish lakes including the same
flux components (Bastviken et al., 2004). The estimates are
generally similar within the same lake size range (Fig. 7).
In addition, CH4 flux estimates with the same flux compo-
nents for some Wisconsin lakes (Michmerhuizen et al., 1998;
Bastviken et al., 2005) are comparable and show similar rela-
tion to the lake size within the same lake size range. The sum
of diffusion and storage components for the smaller Wiscon-
sin lakes, in turn, were much higher than the flux estimates
of our study. The difference could be partly related to the
smaller size, implying the large sediment area-to-volume ra-
tio. In addition, the more continental climate and quick warm
up of those lakes leading to longer stratification period and
thus larger storage flux of CH4. Those lakes had also warmer
sediments than the Swedish – and presumably other more
northern – lakes (Bastviken et al., 2004). Eutrophic lakes,
for example a hypereutrophic Finnish lake, L. Kevätön, had
higher CH4 emissions (Fig. 7) (Huttunen et al., 2003).

The explanatory power of the predictive models for CH4
fluxes remained rather low, but our data indicates that pro-
ductivity, oxygen saturation, and factors associated to the
gas transport from sediments to the water column and to the
atmosphere largely determine the estimated sum of storage
and diffusion flux. The lake area or depth seems to integrate
much of these factors among the studied lakes (Figs. 5, 6, 7,
Table 7). Bastviken et al. (2004) concluded that CH4 fluxes
can be predicted on the basis of total phosphorus, DOC, and
methane concentrations, and that ebullition is dependent on
water depth. Michmerhuizen et al. (1998) connected small
area with the highest storage fluxes. In small and shallow
lakes, sediments, where methanogenesis take place, are in
touch with large water volume. In addition, probability of
substantial littoral contribution, i.e. macrophyte and micro-
phyte production and input of organic matter to the system, is
considerable. Small water volume when associated with high
organic matter content is susceptible to formation of anoxic
conditions favoring CH4 accumulation.

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S. Juutinen et al.: Methane dynamics in different boreal lake types 221

Overall, humic and nutrient-rich lakes, which had higher
CH4 fluxes than the clear and oligotrophic lakes, were com-
mon among the small lakes resulting in higher concentrations
and fluxes of CH4 fluxes in small lakes than in larger lakes.
Climate and topography of the study region may thus affect
our conclusion that lake area could be a simple scaling up
tool (Fig. 7). However, factors determining productivity and
susceptibility to anoxia should be included to the predictive
models of CH4 fluxes. The climatic effect on the CH4 fluxes
within this geographical gradient was negligible, but a longer
climatic gradient might include greater differences in pro-
ductivity and also in CH4. The weak regression coefficient
in the model for spring storage might be due to the influence
of the length of the ice cover period to the storage formation.

4.3 Global CH4 flux

Estimate of global CH4 flux based on these data is 3.7 Tg a−1.
It is based on CH4 flux values for different size classes and
the recent estimates of total lake area in each size class
(Downing et al., 2006). Methane values come from the statis-
tic sample of 177 lakes in our study, but for lakes having
size<0.1 km2, we calculated mean (135 mmol m−2 a−1) us-
ing data from 55 lakes in our study and data from some other
lakes (see Fig. 7, Rudd and Hamilton, 1978; Bastviken et al.,
2004).

This estimate is smaller than the recent estimates, which
range from 8 to 48 Tg a−1 and include ebullition (Bastviken
et al., 2006; Walter et al., 2007). Ebullition and fluxes
from vegetated littoral zone are worth of recognition, because
those may contribute more than the sum of storage and diffu-
sion fluxes to the lake wide fluxes (Smith and Lewis, 1992;
Huttunen et al., 2003; Juutinen et al., 2003; Bastviken et al.,
2004; Bersgtr̈om et al. 2007; Walter et al., 2007). Assuming
that each flux component has an equal contribution, and thus
multiplying the estimate by three, it (about 10 Tg a−1) would
overlap the lower end of the previous estimates. Our data is
representative for the boreal zone, i.e. for a large number of
lakes, but it is unclear whether global CH4 emission estimate
would be affected by southern lakes possibly having different
conditions and CH4 emissions.

5 Conclusions

The current study focused on small northern lakes, where
lake size tended to be a strong predictor of methane concen-
trations and fluxes. Lake size, i.e. depth or area, seems to
integrate the combination of factors driving CH4 concentra-
tion dynamics, but knowledge on lake nutrient and oxygen
status would improve the estimation. The other way round,
CH4 concentration in bottom close water could serve as a
measure of excess nutrients and occurrence of anoxia when
doing environmental monitoring of lakes. In the absence of
more accurate data, lake area from remote surveys could be

used as an approximation for the CH4 emissions in boreal
and arctic landscapes with similar glacial history. Small lakes
seem to have a disproportionate significance with respect to
CH4 release, even if large lakes dominate regional emission
estimates in absolute numbers. Our study support earlier lake
studies on CH4 dynamics regarding to regulating factors and
also the magnitude of global CH4 emission estimate.

Acknowledgements.This work is dedicated to the memory of our
dear colleague and co-author Jari Huttunen who suddenly passed
away during the final stages of the project. We also acknowledge
David Bastviken and an anonymous reviewer for valuable sug-
gestions to improve this manuscript. Riitta Niinioja from Finnish
Environment Institute (Joensuu) is thanked for the discussions and
help with Finnish lake typology procedure. This study was funded
by the Finnish Academy, and we acknowledge the personal grants
to S. Juutinen (no. 213012) and to T. Larmola (no. 121353).

Edited by: T. J. Battin

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