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Author(s): Mika Korkiakoski, Tiia Määttä, Krista Peltoniemi, Timo Penttilä & Annalea Lohila 
Title: Excess soil moisture and fresh carbon input are prerequisites for methane 
production in podzolic soil 
Year: 2022 
Version: Published version 
Copyright:   The Author(s) 2022 
Rights: CC BY 4.0 
Rights url: http://creativecommons.org/licenses/by/4.0/ 
 
Please cite the original version: 
Korkiakoski M., Määttä T., Peltoniemi K., Penttilä T., Lohila A. (2022). Excess soil moisture and 
fresh carbon input are prerequisites for methane production in podzolic soil. Biogeosciences 19(7): 
2025-2041. https://doi.org/10.5194/bg-19-2025-2022. 
Biogeosciences, 19, 2025–2041, 2022
https://doi.org/10.5194/bg-19-2025-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
R
esearch
article
Excess soil moisture and fresh carbon input are prerequisites for
methane production in podzolic soil
Mika Korkiakoski1, Tiia Määttä2, Krista Peltoniemi3, Timo Penttilä3, and Annalea Lohila1,4
1Institute for Atmospheric and Earth System Research/Physics (INAR), Faculty of Science, P.O. Box 68,
00014 University of Helsinki, Helsinki, Finland
2Department of Geography, Faculty of Science, University of Zürich, Winterthurerstrasse 190, 8057 Zürich, Switzerland
3Natural Resources Institute Finland (Luke), Latokartanonkaari 9, 00790 Helsinki, Finland
4Finnish Meteorological Institute, P.O. Box 503, 00101 Helsinki, Finland
Correspondence: Mika Korkiakoski (mika.korkiakoski@helsinki.fi)
Received: 17 August 2021 – Discussion started: 2 September 2021
Revised: 21 February 2022 – Accepted: 9 March 2022 – Published: 13 April 2022
Abstract. Boreal upland forests are generally considered
methane (CH4) sinks due to the predominance of CH4 ox-
idizing bacteria over the methanogenic archaea. However,
boreal upland forests can temporarily act as CH4 sources
during wet seasons or years. From a landscape perspec-
tive and in annual terms, this source can be significant as
weather conditions may cause flooding, which can last a con-
siderable proportion of the active season and because of-
ten, the forest coverage within a typical boreal catchment
is much higher than that of wetlands. Processes and con-
ditions which change mineral soils from acting as a weak
sink to a strong source are not well understood. We mea-
sured soil CH4 fluxes from 20 different points from regu-
larly irrigated and control plots during two growing seasons.
We also estimated potential CH4 production and oxidation
rates in different soil layers and performed a laboratory ex-
periment, where soil microcosms were subjected to differ-
ent moisture levels and glucose addition simulating the fresh
labile carbon (C) source from root exudates. The aim was
to find the key controlling factors and conditions for boreal
upland soil CH4 production. Probably due to long dry pe-
riods in both summers, we did not find occasions of CH4
production following the excess irrigation, with one excep-
tion in July 2019 with emission of 18 200 µg CH4 m−2 h−1.
Otherwise, the soil was always a CH4 sink (median CH4 up-
take rate of 260–290 and 150–170 µg CH4 m−2 h−1, in con-
trol and irrigated plots, respectively). The median soil CH4
uptake rates at the irrigated plot were 88 % and 50 % lower
than at the control plot in 2018 and 2019, respectively. Poten-
tial CH4 production rates were highest in the organic layer
(0.2–0.6 nmol CH4 g−1 d−1), but some production was also
observed in the leaching layer, whereas in other soil layers,
the rates were negligible. Potential CH4 oxidation rates var-
ied mainly within 10–40 nmol CH4 g−1 d−1, except in deep
soil and the organic layer in 2019, where potential oxida-
tion rates were almost zero. The laboratory experiment re-
vealed that high soil moisture alone does not turn upland
forest soil into a CH4 source. However, a simple C source,
e.g., substrates coming from root exudates with high mois-
ture, switched the soil into a CH4 source. Our unique study
provides new insights into the processes and controlling fac-
tors on CH4 production and oxidation, and the resulting net
efflux that should be incorporated in process models describ-
ing global CH4 cycling.
1 Introduction
Methane (CH4) is a greenhouse gas with a significant im-
pact on the global climate. CH4 increases the global tem-
peratures by absorbing infrared radiation into its carbon–
hydrogen bonds, resulting in a higher amount of heat en-
ergy within the atmosphere (e.g., Chai et al., 2016; Dlugo-
kencky et al., 2011; Whalen, 2005). In soil, CH4 is predom-
inantly formed in biological anaerobic decomposition pro-
cesses (Le Mer and Roger, 2001; Wuebbles and Hayhoe,
2002). Archaea called methanogens are responsible for the
biological production of CH4 in anoxic conditions, whereas
Published by Copernicus Publications on behalf of the European Geosciences Union.
2026 M. Korkiakoski et al.: Excess soil moisture and fresh carbon input
methanotrophs conduct aerobic CH4 oxidation (Hanson and
Hanson, 1996; Orata et al., 2018; Thauer et al., 2008). The
dynamics behind soil CH4 sources and sinks depend on the
ratio between CH4 production and oxidation and its trans-
port from the soil to the atmosphere, all of which are af-
fected by an extensive network of numerous biotic and abi-
otic variables. The interannual fluctuations in global and re-
gional CH4 emissions are influenced by so-far largely un-
known variables, the investigation of which is thus essen-
tial for understanding the changing dynamics in the current
and future CH4 budgets (Bousquet et al., 2006; Crill and
Thornton, 2017; Dlugokencky et al., 2011; Fischer et al.,
2008; Kirschke et al., 2013). The boreal zone in the North-
ern Hemisphere regularly presents large CH4 emissions due
to the abundance of anoxic wetlands, but part is counterbal-
anced by high oxidation rates in boreal upland forests. The
CH4 emission estimates from the boreal zone lie between 25
and 100 Tg yr−1, which combined with subarctic tundra envi-
ronments account for approximately 3 %–10 % of the global
CH4 emissions (Olefeldt et al., 2013).
Boreal upland forests are broadly considered CH4 sinks
due to strongly oxic soils (Gulledge and Schimel, 2000;
Megonigal and Guenther, 2008; Oertel et al., 2016; Whalen
et al., 1991; Yavitt et al., 1990, 1995). In upland soils, high-
affinity methanotrophs can consume CH4 at atmospheric
concentrations (Knief et al., 2003; Kolb, 2009). Despite the
abundance of oxygen in the boreal upland forest soil, there
are some indications of smaller-scale CH4-producing areas,
such as wet depressions (Christiansen et al., 2012; Mego-
nigal and Guenther, 2008; Vainio et al., 2021). In addition,
some studies have found that upland forest soils may be-
come CH4 sources of varying significance after long peri-
ods of heavy precipitation (Lohila et al., 2016; Savage and
Moore, 1997). Methanogenic population can stay constant in
forest and other dry aerated soils and becomes active under
wet and anoxic conditions (Angel et al., 2012; Peter Mayer
and Conrad, 1990). With upland forests occupying a signifi-
cant portion of the boreal zone, a more thorough examination
of the complex dynamics behind the sink–source transitions
of the forests is needed, especially in the context of climate
change which may alter global and regional precipitation and
temperature patterns (e.g., Beier et al., 2012; Lehtonen et al.,
2014; Lohila et al., 2016). Lohila et al. (2016) also suggested
that wet conditions can potentially affect the CH4 exchange
patterns differently in forests and wetlands by increasing and
decreasing the CH4 emissions in those ecosystems, respec-
tively, amplifying the vital role of upland forests in the re-
gional CH4 balance in wet years. Furthermore, as precipi-
tation may increase during summer and autumn in northern
latitudes (Jylhä et al., 2009), this flooding-induced source of
CH4 may be activated more frequently in the future. This
source is accounted for in the models of global CH4 emis-
sions, but there are recent observation-based indications that
its magnitude may be severely underestimated. The global
CH4 uptake by mineral soils is only 5 % of global CH4 sink
(625 Tg CH4 yr−1; Saunois et al., 2020) and during wet years
the CH4 sink of mineral soils is significantly suppressed. It
has already been suggested that the emissions from wet min-
eral soils can be the primary driver for the interannual vari-
ability in global CH4 emissions (Spahni et al., 2011).
Soil temperature and moisture manipulations in CH4 flux
studies from upland soils have been very few, but some ex-
isting manipulation studies exist that focus on carbon diox-
ide (CO2) fluxes (Allison and Treseder, 2008; Billings et al.,
2000; Niinistö et al., 2004; Wu et al., 2011). Recommen-
dations have been made to focus on precipitation manipula-
tions carried out either by wetting or drying and establishing
those experiments in mostly underrepresented forest ecosys-
tems (Wu et al., 2011). Methanotrophs are known to be more
sensitive to soil drying than methanogens (Ebrahimi and Or,
2018; Megonigal and Guenther, 2008). Since the processes
and conditions that change mineral soils from CH4 sink to a
source are not sufficiently well understood, direct laboratory
measurements of CH4 formation in different soil layers under
controlled temperature and moisture conditions are needed to
explain the processes in mineral soil in greater detail.
In this study, changes in forest floor CH4 fluxes were as-
sessed with an irrigation experiment during the growing pe-
riod in a boreal upland forest in Kenttärova in northern Fin-
land over 2 years. Kenttärova was chosen as the study site due
to significant soil CH4 emissions detected after a long period
of abundant precipitation in 2011 by Lohila et al. (2016). In
addition, CH4 production and oxidation potentials were de-
termined in different soil layers at flux measurement points.
Finally, a laboratory microcosm experiment was used to in-
vestigate the conditions (temperature, moisture) needed to
initialize CH4 production from the upland soil. The aims of
this study were: (1) to find if the irrigation has any impact
on the soil CH4 flux and oxidation and production poten-
tials; (2) to find which soil layers are most significant for
CH4 production and oxidation; and (3) to find the optimal
conditions and key controlling factors for upland soil CH4
production and oxidation. We hypothesized that: (1) wet con-
ditions prevailing for one or two summers could be seen in
the response of microbial populations so that at the irrigated
plot, the potential CH4 oxidation would be smaller and at
least short production episodes could be detected in the latter
part of the summer either after both summers or at least after
the second wet summer; (2) highest CH4 oxidation potential
are found in the surface soils while the maximum produc-
tion potentials are found in the deeper layers; and (3) both
wet conditions and fresh organic carbon are needed to cre-
ate conditions suitable for CH4 production in podzolic forest
soil.
Biogeosciences, 19, 2025–2041, 2022 https://doi.org/10.5194/bg-19-2025-2022
M. Korkiakoski et al.: Excess soil moisture and fresh carbon input 2027
2 Materials and methods
2.1 Study site
The study was carried out at the Kenttärova forest
(67◦59.237′ N, 24◦14.579′ E) in the Kittilä municipality in
Finland at the transition zone of the northern-boreal and sub-
arctic zones (Fig. 1). The site is located on a hilltop plateau
with an approximate elevation of 347 m above sea level and
60 m above the surrounding plains (Aurela et al., 2015). The
study site has climatic and vegetational characteristics typi-
cal for a northern-boreal environment. The long-term (1981–
2010) annual temperature and precipitation within the area
are −1.0 ◦C and 521 mm, respectively, with long-term aver-
ages in January and July being −14 and 14 ◦C (Pirinen et
al., 2012). The maximum snow depth (average peak: 73 cm)
is typically observed in late March; the median end date
of snowmelt is 14 May and snow cover start date 24 Octo-
ber, respectively (Lohila et al., 2015). The soil type is pod-
zol with glacial till as soil parent material (Aurela et al.,
2015). Typical of the region and soil type, the site repre-
sentsHylocomium-Myrtillus type (HMT; Cajander, 1926; Yl-
läsjärvi and Kuuluvainen, 2009), Picea abies being the dom-
inant tree species mixed with a variety of some deciduous
trees such as Betula pubescens, Populus tremula, and Salix
caprea. The forest floor vegetation at Kenttärova consists pri-
marily of forest shrubs, such as Vaccinium myrtillus, Em-
petrum nigrum, and Vaccinium vitis-idaea, and a continu-
ous and vigorous feather moss cover of Pleurozium schre-
beri, Hylocomium splendens, and Dicranum polysetum with
sporadic occurrences of lichens (Aurela et al., 2015). The
dominant height of the uneven-aged (1–250 years) tree stand
reached approximately 15 m while the heights of individual
spruce trees varied greatly. Some of the birches at Kenttärova
were logged for firewood in the 1960s, but since then the
forest has grown without human disturbances (Aurela et al.,
2015).
2.2 Experimental setup
For examining causal relationships between CH4 flux and
soil moisture and temperature, the field study included two
plots: irrigation (Si) and control (Sc) without irrigation treat-
ment (Fig. 1). The surface areas of Sc and Si were approx-
imately 280 and 120 m2, respectively. Both plots included
10 measurement points. Measurement points were assigned
somewhat randomly in both plots, with the aim to represent
as similar vegetational, topographical, and sun aspect char-
acteristics as possible. Both the Sc and Si and measurement
points were connected with wooden boardwalks to minimize
soil and vegetation disturbance from trampling.
Soil moisture was manipulated by irrigating part of the
experimental area with two water sprinklers. The irrigation
periods were 28 May to 7 September 2018 and 6 June to
29 August 2019. The sprinklers were set in the plot so that
the irrigated water would evenly reach each measurement
point. The irrigated area in practice reached approximately
118 m2 with 3–5.5 m width and 10–21 m length, depending
on the wind conditions. Tap water was transported to the ex-
perimental site with a 1000 L IBC water tank. For ensuring
a relatively even distribution of irrigated water in the plot,
the spatial distribution of irrigation was checked with rain
gauges (unit: mm) and plastic buckets. The amount of water
in each bucket was later proportioned to the rain gauges (in
mm) based on their dimensions. The precipitated water was
measured after each irrigation from the end of May to mid-
June 2018, after which the precipitated water was measured
only when the weather was notably windy and/or natural
rainfall occurred during the irrigation. It was estimated that
1000 L irrigation resulted on average to 11 mm and 2000 L
to 21 mm of precipitation. The amount of rainfall added with
irrigation was 11 mm on 2 d per week during 28 May to
1 June 2018, after which the amount was increased to 11 mm
on 3 d per week during 7–18 June 2018 and eventually to
21 mm on 5 d per week during 20 June to 7 September 2018.
In 2019, the plot was irrigated with 11 mm three times per
week throughout the summer.
2.3 CH4 flux measurements and calculation
Chamber measurements started on 30 May 2017 on eight
measurement points, of which four were located on Si and
Sc, respectively. The flux measurements in 2017 were made
to check possible differences between the experimental plots
before starting the irrigation experiment in 2018. Six addi-
tional points were added to both Si and Sc, and the measure-
ments from these points started on 29 May 2018. The mea-
surements were made mainly between June and September
every 2 weeks in 2017 and weekly in 2018 and 2019. The
measurements ended on 19 September 2019.
CH4 fluxes were measured with 5 min closure time
on the forest floor by the closed-chamber system
with an opaque rectangular chamber (60× 60× 20 cm,
length×width× height). The chamber included a fan to mix
the air inside the chamber and a vent tube to prevent pressure
differences between the chamber headspace and the atmo-
sphere. Also, chamber headspace temperature was recorded
with HOBO Pendant Temperature Data Logger (Onset
Computer Corporation, MA, USA). The bottom of the
chamber edges had a foam layer to prevent leakage between
the collar and the chamber. All the measurement points had
metal collars (58× 58× 30 cm, length×width× height)
installed about 2 cm deep into the soil. CH4 and water
vapor (H2O) mixing ratios were measured with G2301 and
G1301-m (both from Picarro Inc., CA, USA) before and af-
ter 28 June 2018, respectively. The gas analyzer was located
inside a cabin about 20 m away from the measurement point.
The gas sample from inside the chamber was transported to
the analyzer by 20 m long tubing (inner diameter 3.1 mm,
Bevaline IV) with a 1 L min−1 flow rate where the mixing
https://doi.org/10.5194/bg-19-2025-2022 Biogeosciences, 19, 2025–2041, 2022
2028 M. Korkiakoski et al.: Excess soil moisture and fresh carbon input
Figure 1. Experimental setup and location of the study site. Aerial image by Bastian Steinhoff-Knopp (Leibniz University Hannover, Septem-
ber 2018).
ratio was sampled every 3–4 s. The sampled gas was not
returned to the chamber, which causes underpressure inside
the chamber and underestimating the flux estimation.
Because the chambers had a vent tube, we corrected the
leakage with an assumption that the underpressure consisted
of ambient air.
CH4 fluxes were calculated as:
F =
(
dC(t)
dt
)
t=0
MPV
RTA
, (1)
where
(
dC(t)
dt
)
t=0 is the concentration change over time from
an exponential model (e.g., Korkiakoski et al., 2017) at the
beginning of the closure, M is the molecular mass of CH4 or
N2O (16.04 and 44.01 g mol−1, respectively), P is air pres-
sure, R is the universal gas constant (8.314 J mol−1 K−1), T
is the mean chamber headspace temperature during the clo-
sure, and V is the air volume of the chamber and the collar,
and A is the base area of the chamber or collar. The snow
depth and the height of mosses and other vegetation in the
chamber headspace volume were taken into account, ignor-
ing the pore space in the soil and snow. The height of the veg-
etation was measured once a summer. The vegetation height
was assumed to remain constant for that year.
When calculating the CH4 balances, measured CH4 fluxes
were assumed to be daily mean fluxes. The gaps in the data
were filled by linear interpolation. To avoid a biased 2019
balance estimate for point I1, the CH4 emission peak ob-
served on 27 June 2019 was ignored when calculating the
balance.
Biogeosciences, 19, 2025–2041, 2022 https://doi.org/10.5194/bg-19-2025-2022
M. Korkiakoski et al.: Excess soil moisture and fresh carbon input 2029
The micrometeorological sign convention is used through-
out the paper: a positive flux indicates a flux from the ecosys-
tem to the atmosphere (net emission), and a negative flux in-
dicates a flux from the atmosphere into the ecosystem (net
uptake).
2.4 CH4 production and oxidation potential
measurements
Samples for the potential CH4 production and oxidation were
taken on 23 August 2018 and 26 August 2019. Six com-
posite samples were collected from both Si and Sc next to
the chamber collars. Composite soil samples were combined
from three to five core samples taken by soil auger separat-
ing four soil horizons: the organic layer without vegetation
(O) and the three mineral soil layers below (zone of eluvia-
tion, i.e., leaching layer, E; zone of illuviation, i.e., enrich-
ment layer, I; and C-horizon representing the bottom layer,
C). Samples were kept at 4 ◦C during the shipment into the
lab and before analyses. The mean depths of the soil layers
were 5.7, 10.5, 18.9, and 32.3 cm, while the mean thicknesses
were 5.7, 4.8, 8.4, and 13.4 cm for the O, E, I, and C layers,
respectively. The layer depths and thicknesses were deter-
mined from six spots inside the experimental area.
Soil moisture and organic matter contents of the sam-
ples were determined with a TGA analyzer (LECO TGA-
701, Leco Corp., MI, USA) with the standard method
(ISO11465), which measures weight loss as a function of
temperature in a controlled environment. Soil pH was deter-
mined from methane oxidation bottles after measurement by
increasing the ratio of 1 : 3 of deionized H2O and measuring
them after 24 h. Average soil pH, soil moisture, and organic
matter contents for the 2018 and 2019 samples are presented
in Table S1 in the Supplement. Total nutrients and C and N
contents were determined from soil samples taken in 2018
with standard methods (ISO11466, 10694, and 13878). Sam-
ples for the total nutrients were digested by the closed wet
HNO3-HCl digestion method in a microwave (CEM MDS
2000), and the extract was analyzed by iCAP 6500 DUO
ICP-emission spectrometer (Thermo Fisher Scientific, MA,
USA). Total C and N were measured from sieved and air-
dried samples on a CN analyzer (Leco-TruMac, Leco Corp.,
MI, USA). Total nutrient, C, and N contents for the year 2018
samples are shown in Table S2 in the Supplement.
Fresh sieved soil (with 2 mm mesh size) was placed
into 120 mL sterile incubation bottles with a standardized
volume-based measuring scoop (20 mL), and 10 ppm of CH4
were added as a substrate into the bottles for determining
potential CH4 oxidation rates. Oxidation was measured by
gas chromatograph (GC) for 24 h. Two volumes of deion-
ized H2O were added into production potential bottles and
incubated two times with pure N2 gas to remove oxygen and
create anoxic conditions. Production bottles were measured
by GC first twice, and then once, a week for 42 d to detect
productions. Potential rates were calculated from the linear
part of the curve showing the decrease or increase in CH4
concentrations in time. The final potential rates are presented
as nmol CH4 g−1 (dry mass of soil) d−1.
2.5 Microcosm experiment
A microcosm experiment was designed to determine the con-
ditions (temperature and moisture) that are needed to initial-
ize the CH4 production from the soil. For the experiment,
soil profile samples were taken from the pit next to the Si.
Artificial soil profiles were constructed into the plastic jars
(volume of 1.6 L), including the vegetation and organic layer
and two mineral soils layers (leaching and enrichment lay-
ers). Half of the jar volume was left empty for headspace
measurements. Jars were placed into two different growth
chambers (Binder KBW, Tuttlingen, Germany) with two dif-
ferent temperatures at 15 and 25 ◦C. Both temperature con-
ditions had three replicate jars, including controls without
moisture increase (C) and two different levels of moisture
increase, lower (M1) and higher (M2) moisture. The experi-
ment also included separate triplicate jars with glucose added
into controls (Cglu) and moisture increase (M1glu, M2glu)
treatments. Glucose was added at the beginning of the mea-
surements to simulate the effect of fresh, simple C source
for microbes such as exist in root exudates. We added 14 mL
of 1 M glucose solution into each jar so that they had two
times more C that is approximated to be bound into micro-
bial biomass in forest soils to see the possible effect. Two
moisture conditions for the jars were adjusted to be different
enough to detect changes between the treatments. Average fi-
nal moisture conditions were adjusted so that in the jars, the
lower moisture content (M1) was about 50 % and the higher
content (M2) 80 % and the control jars (C) represented the
average moisture content in the soil, which was about 30 %–
35 % (Table S1).
Light conditions in the growth chambers were adjusted to
mimic the natural light conditions at the end of August in
northern Finland (about 15 h light and 9 h dark). Every week,
the jars were switched from one growth chamber to another
to avoid the differences due to features in the chamber itself.
Moisture conditions were kept constant by weighing the jars
twice a week and adding the water to minimize the effect of
evaporation. CH4 fluxes were measured from the headspace
of the jars once a week with LI-7810 (LI-COR Biosciences,
NE, USA). The fluxes for the 5-week measurement period
were calculated from the exponential model the same way as
described in Sect. 2.3.
2.6 Soil temperature and moisture measurements
Multiple soil temperature (ST) and moisture (SM) sensors
were used to record said variables next to the CH4 flux
measurement points. ST was measured with 10 HOBO Pen-
dant data loggers (Onset Computer Corporation, MA, USA)
and SM with 9 EC5 Soil Moisture Smart Sensor (Onset
https://doi.org/10.5194/bg-19-2025-2022 Biogeosciences, 19, 2025–2041, 2022
2030 M. Korkiakoski et al.: Excess soil moisture and fresh carbon input
Computer Corporation, MA, USA) with HOBO U30 USB
Weather Station Data Logger (Onset Computer Corporation,
MA, USA). In addition, 7 Soil Scout online sensors (Soil
Scout Ltd, Helsinki, Finland) were used to measure both
ST and SM. The time intervals for ST logging were 20 and
30 min for Soil Scouts and HOBO sensors, respectively. All
the sensors were installed during 23 May to 6 June 2018 5 cm
below the soil surface in the mineral soil layer next to the col-
lar and covered carefully with soil. The measurements con-
tinued until the experiment ended, except the SM measure-
ments made with EC5 sensors, which broke down at the be-
ginning of June 2019. The locations of the installed sensors
are listed in Table S3 in the Supplement.
SM was also measured from two different locations about
10 m distance from the Sc and Si. In both locations, SM was
measured at 5 and 20 cm depths with ThetaProbe soil mois-
ture sensor (type ML2, Delta-T Devices Ltd, Cambridge,
UK). In addition, ST was measured next to one of the soil
moisture sensors at 5 cm depth (PT100, PT4T, Nokeval Oy,
Nokia, Finland).
2.7 Statistical methods
Fluxes between the different moisture levels and glucose ad-
dition in the microcosm experiment were compared by us-
ing the one-way analysis of variance (ANOVA) by using aov
command in R programming language (R Core Team, 2021,
v4.0.5). The same method was used for comparing the CH4
production and oxidation potentials between the plots and
years. In the microcosm experiment, the glucose addition
was compared only with the sample without added glucose
on the same moisture level and temperature. The effect of
three different moisture levels was compared separately for
added glucose and without added glucose groups by using
Tukey’s honestly significant difference (HSD) test by “mult-
comp” package in R (v1.4-14; Hothorn et al., 2008).
Linear mixed-effect model with Tukey’s HSD post hoc test
was used for testing the statistical significance of differences
in CH4 fluxes between the Si and Sc. The linear mixed-effect
model was carried out with the R programming language us-
ing “lme4” package (Bates et al., 2015). The chamber points
were treated as a random effect. The normality of the model
residuals was visually checked using the quantile–quantile
plot (Q–Q plot) method.
The linear mixed-effect model was also used for finding
the most significant variables affecting CH4 fluxes (FCH4 ).
The variables used in the modeling were: 5 cm soil tem-
perature (ST) and moisture (SM), CH4 oxidation potential
(OPCH4,x , where x is one of the O, E, I soil layers or the mean
of all layers), CH4 production potential (PPCH4,x , where x is
one of the O, E, I soil layers or the mean), and carbon and
nitrogen content (CCx or NCx , where x is one of the O, E,
I soil layers or the mean). The model runs were divided into
three parts: using mean values of all soil layers, using only
values of a specific soil layer, and combining values of mul-
tiple different soil layers. Even though SM and temperature
were only measured at 5 cm depth, they were included in all
the model runs. Measurement points were always treated as
a random effect (u). The best model was selected by using
stepwise selection. We started with a full model and reduced
the number of variables one by one using the Akaike infor-
mation criterion (AIC), which was conducted using the drop1
function in R. The initial model in all but the combination
model run was:
FCH4 = β0+β1ST+β2SM+β3PPCH4,x +β4OPCH4,x
+β5CCx +β6NCx +β7(u+ e),
where e is the model error, β0 is the model’s intercept, and
parameters from β1 to β7 are the regression coefficients of
the explaining variables. We used a 95 % confidence interval
(p < 0.05) to determine whether the results were statistically
significant.
Pearson correlation matrix including potential CH4 pro-
duction and oxidation rates and soil data (SM, organic matter,
pH, nutrient elements) were created using commands rcor
and corrplot in R. Significance level for correlation coeffi-
cients between variables was p = 0.01. In addition, simple
linear regressions at 95 % confidence level with Pearson’s
correlation coefficient and smoothed marginal histograms
were used for primary correlation analyses between CH4 flux
and SM and CH4 flux and ST using “ggpubr” (v0.4.0; Kas-
sambara, 2020) and “cowplot” (v1.1.1; Wilke, 2020) pack-
ages in R.
2.8 Meteorological conditions
The mean air temperatures in the May–September period
were 8.0, 11.0, and 8.9 ◦C for 2017–2019. Compared with
the long-term (1981–2010; Pirinen et al., 2012) mean tem-
perature of the same period (9.3 ◦C), 2017 was cooler and
2018 warmer than the average, respectively. In 2019, the
monthly temperatures during the measurement period were
close to long-term averages (Table S4 in the Supplement).
The year 2017 was the coolest year of the measurement pe-
riod, primarily attributed to a much cooler May and a slightly
cooler August than other years (Table S4). On the other hand,
2018 was the warmest year, primarily due to a much warmer
May and July. July 2018 was exceptionally warm (18.8 ◦C)
compared with other years (2017: 13.0 ◦C; 2019: 12.6 ◦C)
and long-term mean (13.9 ◦C).
The precipitation sums in the May to September period
were higher than the long-term average (296 mm) in 2017
(335 mm) and 2019 (357 mm), but about the same in 2018
(293 mm). However, there were notable differences when in-
specting monthly precipitation sums. In 2017, May, June,
and September were drier than in 2018 and 2019 (Table S4).
On the other hand, in July 2017, the amount of precipitation
(129 mm) was about 100 mm higher than in 2018 (28 mm)
and 2019 (33 mm). Therefore, 2017 was markedly wetter
compared with the long-term average in July (75 mm). On
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M. Korkiakoski et al.: Excess soil moisture and fresh carbon input 2031
the other hand, 2018 and 2019 were markedly drier than on
average. In 2019, excluding July, the monthly precipitation
sums were very similar and higher than the long-term mean.
The snow cover melted on 9 June 2017, 21 May 2018, and
26 May 2019. In 2017 and 2019, the first measurement day
was made when snow was still on the ground (Fig. 2).
The meteorological data reported in this section was ob-
served by an official weather station (Kittilä Kenttärova; ID:
101987) maintained by the Finnish Meteorological Institute
and it was located about 80 m northeast from the experimen-
tal site.
3 Results
3.1 Impact of irrigation on soil moisture and
temperature
The growing seasons of the study years (2018 and 2019)
were generally dry, based on the soil moisture data collected
in long-term pits near the experimental area (Fig. 3). While
in 2019, the whole growing season was dry, in 2018, the dri-
est month was July. On the other hand, August was relatively
wet in terms of precipitation (Table S4), but after a severe
drought, the high precipitation was not enough to increase
the soil moisture to the same range observed in 2017. There
were large differences in SM profiles (located outside the ex-
perimental area) between the years (Fig. 3). In 2017, 5 cm
soil moisture (SM5 cm) mainly remained between 25 vol %
and 35 vol %. Also, SM5 cm in 2019 was relatively stable,
varying within 15 vol %–20 vol %, but it was markedly lower
than in the other years, except in July 2018. SM5 cm in 2018
had much temporal variation. In May 2018, SM5 cm rose
to 50 vol % but fell quickly to 25 vol % after the snow had
melted. In July 2018, the SM5 cm fell quickly below 15 vol %
and kept decreasing down to 12 vol % until the beginning of
May 2019, after which it started recovering up to 25 vol %
until the measurement period ended in September 2019. In
terms of absolute values, 20 cm soil moisture (SM20 cm) did
not differ between years compared with SM5 cm. In June, Au-
gust, and September, the SM20 cm did not usually differ more
than 3 vol % between the years. In May, the rapid increases
and decreases in SM20 cm associated with snowmelt occurred
at different strengths and times. In July, SM20 cm in 2017 was
about 5 vol % higher than in the other years, but the first half
of August 2019 was drier than the other years.
At the experimental area, SM5 cm was on average 6.5 vol %
lower at the Sc than at the Si in June to September 2018.
SM5 cm in 2018 varied typically within 14 vol %–23 vol %
and 6 vol %–14 vol % at the Si and Sc, respectively. However,
one SM sensor measured about 10 vol % higher values than
the other sensors at the Sc (Fig. 4). Also, at the beginning of
August, SM5 cm increased at one of the measurement points
by 5 vol %. SM5 cm remained on that higher level until the
end of the measurement period in 2018. In 2019, SM5 cm at
the Si remained within 15 vol %–18 vol %, except in August
and September.
In 2018, irrigation was performed on weekdays, and dur-
ing irrigation SM5 cm rose by 10 vol %–15 vol % (Fig. 4).
However, the SM5 cm decreased fast and usually returned to
the pre-irrigation level before the next irrigation 24 h later
(Fig. 4). In 2019, the rise of 5 cm SM5 cm due to irrigation
was usually between 2 vol % and 5 vol %.
The 5 cm daily mean soil temperatures (ST5 cm) were on
average 0.7 ◦C higher at the Si compared with the Sc in June
to September 2018 (Fig. 5). Also, spatial variation was higher
at the Sc (Fig. 5). The biggest difference in daily mean ST5 cm
between the plots was observed around mid-July 2018 when
the ST5 cm at the Si was on average 2.0 ◦C higher than at the
Sc. However, in 2019, the difference in daily mean ST5 cm be-
tween the plots was small, and the Si was only about 0.2 ◦C
warmer on average than the Sc. Also, the maximum differ-
ence between the plots was about 0.6 ◦C, which occurred at
the end of July and August.
3.2 The effect of irrigation on CH4 uptake
Before the irrigation experiment started, all the mea-
sured CH4 fluxes were negative, indicating CH4 uptake,
and did not differ significantly between Sc and Si. In
2017, the fluxes were measured from four points at each
plot and median fluxes (Sc: −220 µg CH4 m−2 h−1; Si:
−230 µg CH4 m−2 h−1) and mean June to September CH4
balances were similar between the plots (Fig. 6). However,
there was notable spatial variation between the points as the
June to September CH4 balances varied between −950 and
−470 mg CH4 m−2).
In 2018 and 2019, when irrigation started, the fluxes mea-
sured at the Si and Sc differed significantly from each other
in terms of points where measurements had started already
in 2017 (I2, I3, I4, I9, C1, C4, C6, C8; 2018: p < 0.001;
2019: p = 0.01). The mean summer CH4 uptake rates of
these points in 2018 were 37 % larger and 15 % smaller than
in 2017 at Sc and Si, respectively (Fig. 6; Table S5 in the
Supplement). In 2019, the mean June to September balances
(Sc: −940± 120 mg CH4 m−2; Si: −660± 70 mg CH4 m−2;
Fig. 6; Table S5) remained at about the same level as in 2018
and the fluxes did not differ significantly from fluxes mea-
sured in 2018.
The median measured CH4 uptake rate (Fig. 6; Ta-
ble S5) across all the measurement points at the Si
(150 µg CH4 m−2 h−1) was 48 % lower than at the Sc
(290 µg CH4 m−2 h−1) in 2018 and 35 % lower in 2019
(Si: 170 µg CH4 m−2 h−1; Sc: 260 µg CH4 m−2 h−1). The
fluxes differed significantly between the plots in both years
(p < 0.001). All but one of the measured fluxes were
negative, indicating CH4 uptake. One large CH4 emis-
sion pulse (18 200 µg CH4 m−2 h−1) was observed in point
I1 on 27 June 2019. Similar differences were also ob-
served in mean 4-month (June to September) CH4 bal-
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2032 M. Korkiakoski et al.: Excess soil moisture and fresh carbon input
Figure 2. Daily mean air temperature (a), daily mean 5 cm soil temperature (b), daily precipitation sum (c), and daily snow depth (d)
measured at Kenttärova weather station in May to September 2017–2019.
Figure 3. The daily mean (a) 5 cm and (b) 20 cm soil moisture time series measured from two different locations outside the experimental
area from May to October in 2017 (solid gray line), 2018 (solid black line), and 2019 (dashed black line).
ances (Table S5). There was lots of variation in fluxes be-
tween the measurement points. CH4 balances varied from
−1280 to −480 mg CH4 m−2 at the Sc and from −740 to
−180 mg CH4 m−2 at the Si in 2018. Some of the measure-
ment points at the Si had higher CH4 uptake rates than some
points located at the Sc, but on average CH4 uptake rates
were noticeably larger at the Sc (−850± 80 mg CH4 m−2)
than at the Si (−450± 60 mg CH4 m−2). In 2019, CH4 up-
take rates increased in most of the points at the Si, averaging
at−570±60 mg CH4 m−2, but the balances remained mostly
the same at the Sc (mean: −830± 70 mg CH4 m−2).
3.3 CH4 production and oxidation potentials
Oxidation potential rates were quite similar in all soil layers,
except for the C layer where the rates were lower. Between
the years, however, the rates differed as those in 2019 were
generally higher and more variable than in 2018 (Fig. 7a, b).
The most notable increase was detected in the organic layer,
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M. Korkiakoski et al.: Excess soil moisture and fresh carbon input 2033
Figure 4. Hourly mean soil moisture time series measured at control (gray) and irrigation (black) plots in August 2018. Vertical blue lines
show the times when the irrigation plot was irrigated.
Figure 5. Daily mean CH4 flux measured at the irrigated (black) and control (gray) plots in 2018 (a) and 2019 (b). The error bars show the
standard error of the mean. Blue (irrigated) and red (control) lines represent daily mean 5 cm soil temperature (ST) and shading shows the
minimum and maximum daily values measured by different sensors (irrigation: n= 7; control: n= 9).
where the oxidation potential rates were mainly non-existent
in 2018, but about 15 nmol CH4 g−1 d−1 in 2019 at both Si
and Sc. However, the change was significant only at the
Si (p = 0.03). Oxidation rates were significantly (p = 0.03)
higher in 2019 (median: 22 nmol CH4 g−1 d−1) than in 2018
(median: 15 nmol CH4 g−1 d−1) also in the I layer at the Sc,
but there was no significant difference in the same layer at the
Si. There were no statistically significant differences between
the years in any other soil layers at either plot. Comparing
the soil layers between the plots revealed that the oxidation
rates were significantly higher (p = 0.01) in the C layer at
the Sc than in Si in 2018. The rates were significantly higher
(p < 0.01) at the Sc in the I layer in 2019, but there were no
other significant differences between the plots in other soil
layers.
The highest CH4 production potential rates occurred in the
O layer and some production potential was observed in the
E layer, while in the lowest soil layers, the production rates
were negligible (Fig. 7c, d). At the Si, the production poten-
tial rates were significantly lower in 2019 than in 2018 in the
O (p = 0.04) and I (p = 0.001) layers. At the Sc, the pro-
duction rates differed significantly (p < 0.02) only in the C
layer, but the rates were negligible in both years. Compar-
ing the production rates between the plots revealed that the
rates were significantly (p < 0.04) higher in the O layer at
the Sc in 2019. A significant difference (p < 0.01) between
the plots was also found in the C layer in 2018.
Potential CH4 production rates in 2018 had strong posi-
tive Pearson correlation coefficients (ρ) with organic matter
content (ρ = 0.94), moisture content (ρ = 0.90), and total N
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2034 M. Korkiakoski et al.: Excess soil moisture and fresh carbon input
Figure 6. CH4 fluxes measured by long-term (C1, C4, C6, C8, I2, I3, I4, I9) (a) and all (b) chamber points in May to September in different
years. Positive flux values indicate net emission and negative values indicate net uptake. For comparison, the flux of the points located on
the irrigated plot in 2018 and 2019 have been calculated already for 2017, even though the irrigation setup was established only in 2018.
The boxes show the quartiles of the dataset and the horizontal line inside the boxes is the median flux. Whiskers show the range of the data,
except for the points that are determined to be outliers, which are shown with black diamonds.
Figure 7. CH4 oxidation (a, b) and production (c, d) potentials in different podzolic soil layers (organic layer, O, mean depth: 5.7 cm;
leaching layer, E, mean depth: 10.5 cm; enrichment layer, I, mean depth: 18.9 cm; bottom layer, C, mean depth: 32.3 cm) in 2018 (a, c) and
2019 (b, d) (n= 9). The boxes show the quartiles of the dataset and the vertical line inside the boxes is the median flux. Whiskers show the
range of the data, except for the data that are determined to be outliers, which are shown with black diamonds.
and C amounts (ρ = 0.95 and 0.94, respectively) determined
from the soil samples (Fig. S1a in the Supplement). Potential
CH4 production rates in 2019 had a similar stronger positive
correlation with organic matter (ρ = 0.96) and moisture con-
tent (ρ = 0.93; Fig. S1b in the Supplement).
3.4 Factors controlling field CH4 fluxes
Correlations between field CH4 flux and SM were nearly
negligible (ρ < 0.2) in both Si and Sc in both years (Fig. 8),
with the exception of Si in 2019 with a ρ value of −0.5
(p < 0.001). In both 2018 and 2019, correlation trends were
weakly negative between CH4 flux and SM, except for Sc in
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M. Korkiakoski et al.: Excess soil moisture and fresh carbon input 2035
2018 with a weak positive correlation (ρ = 0.17, p = 0.07).
In contrast, CH4 flux and ST had generally stronger correla-
tions in both Si and Sc, the latter having the highest ρ val-
ues in both years (2018: ρ =−0.57; 2019: ρ =−0.49), only
2018, however, being statistically significant (p < 0.001). Si
showed differing correlation trends between years, 2018 hav-
ing relatively weak positive (ρ = 0.4, p = 0.01) and 2019 al-
most negligible negative (ρ =−0.19, p = 0.16) correlations.
Several mixed-effect model runs were made to investi-
gate the environmental drivers behind CH4 fluxes. SM5 cm
and ST5 cm were among the significant variables explaining
CH4 fluxes in all the model runs. The rest of the signifi-
cant drivers varied depending on the soil layer. In the organic
layer, the most significant model, in addition to SM5 cm and
ST5 cm, included oxidation potential and nitrogen content.
The model explained 51 % of the variation in CH4 fluxes (Ta-
ble 1). The significant drivers were otherwise similar to the O
layer in the E layer, except nitrogen content was replaced by
carbon. However, the model had weaker explanative power
(r2fix = 0.44) than the O layer model (r2fix = 0.51). It should
be noted that carbon and nitrogen contents had strong cross-
correlation, and using either of them in the model would
have given almost the same result. The I layer model had the
weakest model explaining CH4 fluxes (r2fix = 0.42), and the
significant drivers included only SM5 cm and ST5 cm and the
carbon content in the I layer. Using the drivers’ mean values
over all soil layers also resulted in a relatively weak model
(r2fix = 0.44), and it included only oxidation potential and
SM5 cm and ST5 cm. Finally, a model combining drivers from
multiple depths was made, and it explained the CH4 flux the
best (r2fix = 0.65). In that model, CH4 flux was most influ-
enced by SM5 cm and ST5 cm, oxidation potential in the or-
ganic layer, production potentials in the organic and E layer,
and carbon content in the E layer (Table 1).
3.5 Microcosm experiment
Adding glucose to the sample and keeping the moisture level
similar did not cause significant changes in the potential CH4
uptake rate, but on average, CH4 uptake rate was lower or the
CH4 emission was higher with added glucose on the same
moisture level (Fig. 9). There was an exception to this case at
high 25 ◦C temperature and moderate moisture (M1), where
the added glucose samples had a higher CH4 uptake rate, but
as said above, these were not statistically significant differ-
ences.
Generally, increasing soil moisture with no added glucose
decreased the mean potential CH4 uptake rate, but even with
the high SM (M2) group, the soil did not turn into a CH4
source (Fig. 9). Also, the differences between the different
moisture groups were generally not statistically significant.
Significant differences were only observed between the M2
and the control group in the first 2 weeks of measurements.
The weekly mean CH4 uptake rates also decreased further in
time in all groups, except in the M2 group, where the changes
in time were negligible.
In samples with added glucose, increasing SM signifi-
cantly (p < 0.05) decreased potential CH4 uptake rate in
both M1 and M2 groups compared with the control group.
On the other hand, M1 and M2 groups did not differ sig-
nificantly, except in week two at 25 ◦C temperature. In that
case, relatively high CH4 emission was measured in the M2
group with added glucose, but emission dropped rapidly al-
ready in the third week, although it remained a small CH4
source (Fig. 8). At 15 ◦C temperature, there was no such CH4
emission peak in the M2 group.
4 Discussion
In this study, our initial aim was to mimic a wet growing
season in a boreal upland forest with podzol soil in north-
ern Finland by irrigating the area regularly and studying the
conditions needed to switch the forest floor to a CH4 source.
Earlier, we discovered that the soil of the same site turned
into a CH4 source in August after long-lasting rains during
the growing season of 2011 (Lohila et al., 2016). Therefore,
we assumed that we could reach the conditions needed to
initiate the CH4 production in the podzolic soil by at least
tripling the long-term mean precipitation. However, the two
study summers of 2018 and 2019 turned out to be the driest
summers of the decade, with a long warm and dry period in
June to July 2018 and generally dry summer in 2019. Unfor-
tunately, due to the remote location of the experimental site
and a distance of several kilometers to the closest water tap,
we were not able to counter the effect of the droughts. As a
result, our control plot could be considered a drought experi-
ment, while the irrigated plot followed the moisture and CH4
flux patterns of a “normal” summer. Therefore, it is recom-
mended that with similar studies in the future, the experiment
be set in a location where the irrigation system is able to dis-
tribute higher amounts of water and with higher frequency
than what was practically possible in this study. In the Lohila
et al. (2016) study, we also speculated that the reason for the
CH4 emission occurring in August and not in spring after the
snowmelt could be that fresh carbon substrates consumed by
soil microbes are needed to make the soil anoxic, i.e., the wet
soil alone is not enough to initiate CH4 production. To con-
firm this hypothesis, we conducted a laboratory mesocosm
experiment in which the temperature and moisture responses
were studied, and glucose was added to some of the samples
to mimic the root exudation providing fresh carbon substrates
to the soil microbes.
We found that the field CH4 uptake at the control plot was
higher during the study years than a more typical summer of
2017. This comparison was possible since some of our study
plots had been established already a year before the exper-
iment. On the other hand, the irrigated plot showed similar
uptake rates during the previous summer, which was close to
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2036 M. Korkiakoski et al.: Excess soil moisture and fresh carbon input
Figure 8. Correlations (Pearson’s coefficient, ρ) between soil moisture and CH4 flux (a, c) and soil temperature and CH4 flux (b, d) with
smoothed frequency histograms in 2018 and 2019. The emission case of 27 June 2019 was removed from the data in the correlation analyses
for more clear presentation.
Figure 9. Weekly mean CH4 flux measured at 15 ◦C (a) and 25 ◦C (b) without (solid lines) and with (dashed lines) added glucose on different
moisture levels (black: control; green: low added moisture; blue: high added moisture). The error bars show the standard error of the mean
(n= 3). Positive flux values indicate net emission and negative values indicate net uptake.
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M. Korkiakoski et al.: Excess soil moisture and fresh carbon input 2037
Table 1. Linear mixed-effect models fitted against CH4 fluxes (FCH4 ) and experimental factors. The fixed effects in the model were: SM
– 5 cm soil moisture; ST – 5 cm soil temperature; OPCH4,x – CH4 oxidation potential at soil layer x (O, E, I soil layers or the mean of all
layers); PPCH4,x – CH4 production potential at soil layer x; CCx – carbon content at soil layer x; and NCx – nitrogen content at soil layer x.
The table shows the r2 of the fixed effects (r2fix) and the whole model (fixed effects+ random effects, r2mod), p value of the model (p), AIC
of the model and the degrees of freedom (df). The models in bold are the best-fitted models.
Mixed-effect model equations R2fix R
2
mod p AIC df
Mean of layers
Model 1 FCH4 ∼SM+ST+OPCH4,mean+PPCH4,mean+Nmean 0.38 0.78 < 0.001 1662.9 8
Model 2 FCH4 ∼SM+ST+OPCH4,mean+PPCH4,mean 0.41 0.76 < 0.001 1660.9 7
Model 3 FCH4∼SM+ST+OPCH4 ,mean 0.44 0.74 <0.001 1658.9 6
Organic layer
Model 1 FCH4 ∼SM+ST+OPCH4,O+PPCH4,O+NCO 0.50 0.77 < 0.001 1659.6 8
Model 2 FCH4∼SM+ST+OPCH4 ,O+NCO 0.51 0.76 <0.001 1658.1 7
E layer
Model 1 FCH4 ∼SM+ST+OPCH4,E+PPCH4,E+CCE 0.42 0.75 < 0.001 1660.3 8
Model 2 FCH4∼SM+ST+OPCH4 ,E+CCE 0.44 0.75 <0.001 1659.4 7
I layer
Model 1 FCH4 ∼SM+ST+OPCH4,I+PPCH4,I+CCI 0.37 0.78 < 0.001 1662.8 8
Model 2 FCH4 ∼SM+ST+OPCH4,I+CCI 0.40 0.76 < 0.001 1660.8 7
Model 3 FCH4∼SM+ST+CCI 0.42 0.74 <0.001 1659.3 6
Combination
Model 1 FCH4∼SM+ST+OPCH4 ,O+PPCH4 ,O+PPCH4 ,E+CCE 0.65 0.74 <0.001 1650.2 9
normal in terms of temperature and precipitation. The same
pattern was observed for the soil moisture: the soil was as
moist in the irrigated plot in 2018 and 2019 as it was with-
out irrigation in 2017. One single occasion when clear CH4
emission was detected took place in the irrigated plot at the
end of June 2019, but the emission was only observed in one
of the irrigated plots. The mean emission rate during that day
from the irrigated plots was 1670 µg CH4 m−2 h−1 (data not
shown, the point removed from Fig. 5b). Although encour-
aging, the observation unfortunately did not provide means
to systematically study the conditions needed to switch the
soil into a CH4 source, since the soil moisture or any other
variable at the same measurement point did not differ from
the other points.
The laboratory experiments for studying the possible dif-
ferences in the CH4 production and oxidation potentials indi-
cated no significant differences between the control and irri-
gated soils. Initially, we hypothesized that the wet conditions
prevailing for one or two summers could be seen in the re-
sponse of microbial populations so that at the irrigated plot
the oxidation would be smaller and the production higher
either after both summers or at least after the second wet
summer. Unfortunately, the dry summers turned the whole
setup around so that we ended up examining the effect of dry
growing seasons on the response of microbial populations.
Hence, our results suggest that the period of one or two dry
summers did not impact soil production or oxidation poten-
tials, although we found differences in the actual CH4 uptake
between the irrigated and control plots. Therefore, it seems
likely that the differences in observed field fluxes were due to
the impact of soil moisture on the gas diffusion rate: the drier
the soil, the higher the air-filled porosity and the quicker the
diffusion of oxygen and CH4 into the soils, and the higher
the CH4 uptake rates (Dörr et al., 1993; Van Den Pol-van
Dasselaar et al., 1998; Striegl, 1993).
We also hypothesized that the oxidation potentials would
be highest in the topsoil, which is closest to the main source
of the substrate for oxidation, namely atmospheric CH4
(Bradford et al., 2001), while the production potentials would
be higher deeper in the soil, where the oxygen is more likely
to be depleted periodically after wet conditions when diffu-
sion rates are suppressed. The oxidation potentials indeed
peaked in the topsoil, but interestingly, so did the produc-
tion potentials, showing clearly the highest rates in the or-
ganic/humus layers (Fig. 7).
Our findings are parallel with the previous ones from for-
est soils since the highest CH4 oxidation has been detected
both in the uppermost mineral soil below the organic layer
(Saari et al., 1998) and, on the other hand, in the organic
layer (Wang and Ineson, 2003). Thus, the distribution of CH4
consuming organisms in the upland soil horizon seems to
vary somewhat depending on the year and prevailing phys-
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2038 M. Korkiakoski et al.: Excess soil moisture and fresh carbon input
ical and chemical conditions. High potential CH4 produc-
tions in the surface layers in 2018 and 2019 (Fig. 7) are most
likely linked to higher soil organic matter and moisture con-
tent of soils, which is also supported by a strong positive cor-
relation with the soil organic matter and moisture content.
Potential CH4 oxidation did not show a strong correlation
with these. Similar results obtained from upland soils and
especially from forest soils are hard to find. However, high
organic C content has simulated CH4 production under hy-
poxia in agricultural soil (Brzezin´ska et al., 2012), and water
content was observed as a major influencing factor regarding
CH4 production potential in subalpine upland soil (Praeg et
al., 2014). Thus, over 2 times higher moisture content and
about 10 times higher organic matter content in the organic
layer compared with mineral layers below most likely ex-
plain partly the higher CH4 production potentials observed
in this study.
The mesocosm experiment provided interesting insights
into the CH4 dynamics of the podzolic soil. First of all,
this experiment confirmed the result of field fluxes by show-
ing that the CH4 uptake decreased along with higher soil
moisture. Also, CH4 uptake was totally ceased in the high
soil moisture treatment (M2) due to suppression of diffusion
rates in waterlogged conditions. The higher temperature in-
creased the net uptake, most likely by increasing the oxida-
tion, but this was true only for the mesocosms with “field
conditions” (no water added). In other words, CH4 uptake
was higher in warmer soils but smaller in wetter soils (as
expected), so it seemed that increasing either the temper-
ature or the soil moisture, or both, affect the CH4 oxida-
tion straightforwardly but is not able to induce CH4 produc-
tion in the soil. However, only if the soils were made wet
enough and glucose was added, significant CH4 production
was initiated, which was further increased by higher temper-
atures. Thus, the results obtained here supported our hypoth-
esis that both excess moisture and easily decomposable car-
bon are needed to initiate CH4 production in podzolic soil.
Indeed, the root exudate analogues containing simple sugars
accelerated CH4 production in tropical peat soil (Girkin et
al., 2018). However, in a study conducted in Japanese up-
land soil, added glucose was rapidly decomposed within 7 d
of the incubation, and part of the glucose-derived C flow
ended up to methanogens even under unflooded conditions
(Watanabe et al., 2011). Even though it is largely known
that methanogens can survive and tolerate dry and oxic con-
ditions for some periods, they become active only when
the conditions turn favorable for CH4 production (i.e., wet
and anoxic). Since methanogenic archaea cannot use glu-
cose directly as C source, methanogens probably utilized ac-
etate or CO2 produced by the glucose-decomposing bacte-
ria. Thus, the obtained results from the microcosm experi-
ment may reflect the situation that in wet conditions, glu-
cose has increased the activity of microbial communities that
supply methanogenic substrates (hydrogen-producing bacte-
ria or acetyl-producing bacteria), promoting the activity of
methanogens production as was detected in a forested wet-
land (Koh et al., 2009). Simultaneously decreased activity
of CH4 oxidizers may have been followed by the compe-
tition of other aerobic microorganisms, which have metab-
olized glucose rapidly, creating more anaerobic conditions
favoring CH4 production. However, the comparison of the
obtained results with earlier findings is rather obscure since
similar experiments conducted in boreal forest soil do not
exist. Thus, our results are one of the first attempts to under-
stand the complex conditions which initiate CH4 production.
In addition, our study is unique since we are presenting both
CH4 fluxes and laboratory CH4 potentials from the soil taken
from the same field points.
5 Conclusions
Based on our field and laboratory experiments, the main con-
clusion is that CH4 production from boreal upland forest soil
cannot occur solely by prolonged wet conditions, but there
also has to be enough fresh carbon in the soil. Therefore, we
expect the possible CH4 production episodes to occur in late
summer and autumn rather than in spring, even though the
soil can be very wet after snowmelt. These findings can be
applied in CH4 process models to improve estimations of re-
gional and global upland forest CH4 balances.
We did not observe any changes in CH4 production and
oxidation potentials due to irrigation over two summers,
meaning microbial communities were not very sensitive to
environmental variables. This suggests that the measured
field fluxes are rather controlled by the physical soil condi-
tions by limiting gas diffusion rates and not by the changes
due to microbial function. One conclusion from our results is
that CH4 production and oxidation are controlled by differ-
ent driving variables and processes: the oxidation is boosted
when the conditions for higher gaseous diffusion are optimal
(dry soil), while the production is boosted only if anoxic con-
ditions are created (wet soil reducing diffusion+microbial
activity consuming oxygen) and there are fresh organic sub-
strates available for CH4 production. In our field experiment,
CH4 production episodes were not detected (with one ex-
ception), and the changes in net field CH4 flux were solely
caused by the changes in CH4 oxidation. The net CH4 flux
(here total oxidation) was primarily controlled by soil tem-
perature and soil moisture. Increasing soil temperature en-
hanced oxidation and gas diffusion, and increasing soil mois-
ture limited oxidation by making conditions for methan-
otrophs unfavorable and diminished diffusion. We also found
that upland forest soils have the potential to produce CH4, but
contrary to wetlands, the potential is highest near the soil sur-
face and decreases rapidly as a function of soil depth. This
could happen because the conditions for methanogenic ar-
chaea are more favorable in the topsoil layer due to the higher
amount of organic matter.
Biogeosciences, 19, 2025–2041, 2022 https://doi.org/10.5194/bg-19-2025-2022
M. Korkiakoski et al.: Excess soil moisture and fresh carbon input 2039
Our study confirms that soil moisture is a critical variable
in explaining the soil CH4 uptake rate and suggests that the
diffusion rate of both CH4 and oxygen into the soil is the pri-
mary constraint of oxidation. For the onset of CH4 produc-
tion in podzolic soil, not only high soil moisture but also the
addition of sugar, mimicking root exudates from trees, was
needed. Glucose impacts CH4 production mainly by boost-
ing the consumption of oxygen in the soil and providing sub-
strates for CH4 production. We also found that the highest
potential production and oxidation rates were found in the
same topsoil layers, suggesting that the surface soil plays the
main role in the soil–atmosphere exchange of CH4 in boreal
upland forest
Data availability. The flux, meteorological, and soil data are avail-
able at Zenodo (https://doi.org/10.5281/zenodo.5153347, Korki-
akoski et al., 2021).
Supplement. The supplement related to this article is available on-
line at: https://doi.org/10.5194/bg-19-2025-2022-supplement.
Author contributions. AL, TP, and KP designed the study. MK, AL,
TP, and TM constructed the experimental site. KP took the soil
samples and calculated methane production and oxidation poten-
tials and did the laboratory work for the microcosm experiment.
TM made the flux measurements and took part in the data analysis.
MK calculated the fluxes for the field and microcosm experiments
and did the statistical analysis. MK prepared the paper with contri-
butions from all co-authors.
Competing interests. The contact author has declared that neither
they nor their co-authors have any competing interests.
Disclaimer. Publisher’s note: Copernicus Publications remains
neutral with regard to jurisdictional claims in published maps and
institutional affiliations.
Acknowledgements. Valtteri Hyöky, Päivi Pietikäinen,
Stephanie Gerin, and Petri Salovaara are acknowledged for
assisting in the fieldwork. We also thank Bastian Steinhoff-Knopp
for letting us use his aerial photo of the experimental site, and the
Finnish Forest Administration (Metsähallitus) for their generous
cooperation during the fieldwork in 2018–2019.
Financial support. This research has been supported by the
Academy of Finland (grant no. 308511).
Open-access funding was provided by the Helsinki
University Library.
Review statement. This paper was edited by Lutz Merbold and re-
viewed by two anonymous referees.
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