Wetlands are one of the most significant natural
sources of methane (CH
Methane (CH
Freshwater wetlands emit CH
The three transport mechanisms and the CH
As CH
HIMMELI does not bring any new processes as such into the CH
Sensitivity analyses on the complete peatland models have been presented,
mostly concentrating on the sensitivity to model parameters (e.g.,
Berrittella and Huissteden, 2009, 2011; Tang et al., 2010; Wania et al.,
2010; Zhu et al., 2014), but we are not aware of any studies which would
have analyzed the sensitivity of the CH
In the present work, we (a) define key factors for CH
The rate of CH
Roots of sedges, particularly those of
Gas ebullition occurs, in principle, when the concentration of a dissolved
gas reaches saturation, but in practice CH
Properties of the peat column also affect the diffusion of CH
Methanotrophic bacteria occur in all soils, not only wetlands, and
methanotrophy in upland soils is the largest biogenic sink of atmospheric
CH
Model parameters and their values. The reference is given in the cases where the value is directly from one study; otherwise, the parameter value is discussed in Sect. 3.2.
HIMMELI as a simplified schematic picture. The microbial and
transport processes are simulated in a vertically layered one-dimensional
peat column in which roots of aerenchymatous gas-transporting plants are
distributed according to the exponential root distribution function. The
input anoxic respiration is distributed along the root distribution. Input
water table depth (WTD) determines the thickness of the possible extra layer
that is introduced in the event the WTD does not match any of the fixed background
layer borders. This ensures that all the simulated layers are either
completely water filled or air filled. The
The model (Fig. 1) simulates microbial and transport processes that take
place in a one-dimensional peat column, keeping track on the concentration
profiles of CH
The model is driven with
peat temperature, leaf area index of aerenchymatous gas-transporting vegetation, LAI
(m water table depth, WTD (m); and anaerobic carbon decomposition rate, i.e., the rate of anoxic respiration
for the area of the peatland,
The reaction–diffusion equations governing the concentrations of the three
compounds (CH
The model has been developed principally using a daily time step for input
and output, as our main target has been to use it with models that provide
daily input. However, we also tested running HIMMELI on a shorter time step
(Sect. 3.3.2). The internal time step is determined by the turnover time of
CH
The model basically describes a one-dimensional, vertically layered peat column. Peat depth and layer thicknesses are not fixed but different setups can be used. The only limitation for the layer structure is that if the peat thickness exceeds 2 m, there has to be a layer border exactly at the 2 m depth, because of how the roots are treated in the model. The layering below 2 m must start from that depth.
In the model, WTD is a strict divider of the peat into water-filled and air-filled parts. This has been implemented by adding an extra layer in the pre-described layer composition (Fig. 1). Its thickness is adjusted so that the water surface is always exactly at the interface between the two layers. This approach enables using the exact given WTD as input. Only in the case that the boundary of the extra layer would be closer than 1 cm to a boundary of the background layering, the WTD is rounded to this nearest permanent layer boundary. Strict division of the peat to air-filled and water-filled parts is a simplification since anoxic sites can occur above the WTD (Estop-Aragonés et al., 2012). However, as in site-level and larger-scale simulations, even an observation-based WTD is an approximate value over peatland areas, and we consider the strict division to anoxic and oxic parts a robust approach.
In HIMMELI, the water level can also be above the peat surface, and in this case an extra water layer is located above the peat surface. In nature, wind mixing can affect the concentrations of different compounds in free water but this is not considered in the model. This simplification is justified, as there often is vegetation that decreases the wind mixing via affecting wind speed.
Changing WTD essentially means addition or removal of water to/from the peat
column. At the same time, the masses of CH
An essential role is played by the vertical distribution of plant roots
since that determines how the input anoxic respiration and the
gas-transporting root mass is distributed vertically. The formulation has
been adopted from Wania et al. (2010):
The input anaerobic respiration (
This choice of distributing the anoxic respiration with root mass (as
opposed to distributing it, e.g., evenly across the peat column) was motivated
by the fact that recently fixed carbon, such as root exudates, seems to be
the main source of CH
CH
All the O
The rate of CH
The reaction rates of oxidation and aerobic respiration depend on
temperature following the form of the Arrhenius equation (Eq. 10):
The ebullition model takes into account concentrations of CH
If the sum of the partial pressures pp (Pa) of the dissolved CH
In reality, bubble movement in porous media is a highly complex problem that
depends on the fine-scale structure of the media. After a bubble has been
formed, there are several processes that take place before the bubble reaches
the surface and contributes to the CH
Simulation of diffusion in the porous water-filled or air-filled peat takes
into account the reduction in the diffusivity compared with pure water or
air (see, e.g., Iiyama and Hasegawa, 2005). The diffusion coefficients used in
this study are listed in Appendix A. The effective diffusivities in the
porous peat (
Formulation of plant transport rate
Table 1 lists the parameter values used in this study, as well as the literature references of cases where the value was taken directly from one study. Here, we go through the parameter values that were based on several papers or some calculation. The parameterization of HIMMELI has been analyzed in more detail in a separate study by Susiluoto et al. (2017).
The CH
The model of aerobic respiration has three parameters:
The fraction of anaerobic respiration becoming CH
Peat porosity
SLA values for graminoids or sedges varied widely in literature. Raivonen
et al. (2015) found that the SLA of sedges in one peatland site was 7 m
We analyzed HIMMELI's sensitivity to the driving input variables, length of
time step, and the description of the peat column, i.e., peat column depth
and layer thickness. The model sensitivity to input variables and time step
length was analyzed using steady-state tests and transition tests (see
Sect. 3.3.1 and 3.3.2). The effect of the peat column setup was analyzed by
running HIMMELI with data from the Siikaneva peatland site with
different peat column descriptions (Sect. 3.4.1). In addition, we compared
the modeled CH
Summary of the steady-state sensitivity tests in which response of HIMMELI to different input combinations was analyzed.
Summary of the transition tests on model sensitivity to input data and the input combinations used in the tests.
The steady-state tests were conducted to study how sensitive the model is to the input data and to understand how the sensitivity depends on the modeled processes. We tested the model by running it into equilibrium with several different input value combinations, starting from empty concentration profiles of all the compounds. Specifically, we tested the sensitivity of the model to peat temperature, WTD, LAI (and corresponding root mass), and rate of anoxic respiration, by varying these one by one. Temperature was always constant throughout the soil profile in these experiments, unlike in the simulations of the peatland sites. We also conducted three transition tests to study the model response to changing WTD, temperature, and anoxic respiration rate. In those, the model was first equilibrated with one set of driver values and after that the WTD, peat temperature, or anoxic respiration was alternated. The different input combinations, details of the tests and their names are summarized in Tables 2 and 3.
The tests are labeled so that the first letter (
In these mechanistic sensitivity tests, the anoxic respiration rate (mol m
In order to find out whether eliminating the diurnal temperature variation
with the daily time step affects the modeled fluxes, we compared a model run
done on a 30 min time step to a run done on the daily time step. We chose an
arbitrary summer day, 1 July 2006, and took the soil and air temperature data
measured at Siikaneva at 30 min intervals. All other input values were
constant over the day in both runs. To avoid possible complications
originating from the fact that the first and last temperatures of the chosen
day differed by 3
Daily variation of air and soil temperatures in the time step test. Observed temperatures are directly from measurement data, but in order to smooth the difference between the last and first temperatures of the day, we modified the afternoon temperatures as shown in the plot.
We ran the model with a 7-year input data series from the Siikaneva fen
and tested how sensitive the results are to peat depth and peat layer
thicknesses. We used the same input anoxic respiration, WTD, and LAI for all
the model runs. The only factor that changed slightly between the different
setups was the soil temperature since the interpolated temperature profile
always followed the layering. In these simulations, anoxic respiration was
not constant but simulated (see Appendix B). The model spin-up was conducted by
running the model through the entire 7-year time series of input data
until the peat CH
We tested four peat depths (1, 2, 3, and 5 m) using 0.2 m layer thickness in every case. In addition, we tested two evenly spaced layerings, 0.1 and 0.2 m, as well as one logarithmic layer structure, in a 2 m deep peat column. The logarithmic structure was based on the one used in the land surface model JSBACH (Ekici et al., 2014) and the layer thicknesses from top to bottom were 0.06, 0.13, 0.26, 0.52, and 1.03 m.
In order to demonstrate that HIMMELI outputs realistic fluxes when run with
realistic input (which is not so evident if looking only at the
mechanistic sensitivity tests), we compared the modeled and measured
CH
The eddy covariance flux measurement site is located in Siikaneva in
Ruovesi, southern Finland (61
The measurement setup for CH
The flux data were post-processed using EddyUH software (Mammarella et al., 2016). The fluxes were calculated using block-averaging and sector-wise planar fitting. High-frequency losses were corrected by empirically determined transfer functions (Mammarella et al., 2009). For 2008 to 2011, the dilution effect by water vapor was corrected with the Webb–Leuning–Pearman method (Webb et al., 1980), whereas for 2005 to 2007 this correction was not needed due to the usage of a drier in the sampling line.
The Lompolojänkkä measurement site is an open, nutrient-rich sedge
fen located in the aapa mire region of northwestern Finland
(67
The eddy covariance system used for measuring the vertical CO
Evolution of the concentration profiles
of
Half-hour flux values were calculated using standard eddy covariance methods. The original 10 Hz data were block averaged, and a double rotation of the coordinate system was performed (McMillen, 1988). The time lag between the anemometer and gas analyzer signals, resulting from the transport through the inlet tube, was taken into account in the online calculations. An air density correction related to the sensible heat flux is not necessary for the present system (Rannik et al., 1997), but the corresponding correction related to the latent heat flux was made (Webb et al., 1980). Corrections for the systematic high-frequency flux loss due to the imperfect properties and setup of the sensors (insufficient response time, sensor separation, damping of the signal in the tubing, and averaging over the measurement paths) were carried out offline using transfer functions with empirically determined time constants (Aubinet et al., 2000). We used here a gap-filled time series, in which measurement gaps were filled with running means.
We forced the model with daily averages of WTD, peat temperature profile,
LAI, and anoxic respiration rate, and compared the results with daily medians
of CH
In Siikaneva, peat temperature has been monitored at five depths (
Results of the sensitivity testing. The rightmost column tells how
much the CH
Via the tests, we wanted to verify that the model dynamics are robust, and
to find out how sensitive the output CH
According to the model, the steady-state dissolved CH
Contribution of different transport routes in the total CH
Contribution of different transport routes to the total CH
Anoxic respiration rate and the corresponding potential methane production
rate (PMP) (tests starting with R_) governed the outputted
CH
Dependence of the total output CH
Output CH
In the tests in which the input respiration was constant and we analyzed the
sensitivity of CH
Relationship between the relative CH
Although temperature did not have a significant effect in steady state,
temperature change in the temperature transition tests had a clear effect on
the CH
Response of CH
One interesting result was that the CH
Dependence of the total CH
The main conclusion that can be deduced from the results reviewed above is
that O
Effect of abrupt changes in WTD on the total output CH
The impact of temperature on the output fluxes in the steady-state tests was
also transmitted via O
The tests thus revealed that O
As mentioned above, effects of the input factors on CH
Dependence of total and plant-transported fluxes of CH
Direct comparison of our results and sensitivity studies done on other
peatland CH
Comparing the outputs of the model run using a 30 min time step with the
outputs from the run with a daily time step showed that eliminating the
diurnal temperature variation does not have any significant effect on the
model output. When using the shorter time step, diurnal variation in the
flux was evident and, for instance, a small (around 0.05 to 0.1
Daily CH
Time series of CH
Comparison of simulated and measured CH
The sensitivity tests with different soil layerings and peat thicknesses
conducted using the input data set from Siikaneva site showed that the
setup of the peat column does not have any significant effect on the
output. The mean total CH
This sensitivity test indicated that when simulating CH
The anoxic respiration inputs created for Siikaneva and
Lompolojänkkä (Appendix B) had a clear annual pattern and the rates
varied between 0.02 and 0.6
Figure 14 shows the daily observed CH
The simulated CO
Correlations between
Summer 2010 at Siikaneva was interesting since both model and measurements
showed the highest emission peaks then. The maximum emissions do not coincide
exactly on the same days, but they are temporally close. In HIMMELI, the
main reason was an exceptionally abrupt temperature rise in the peat water,
followed by decreasing gas solubilities and increased ebullition – as was
observed in the temperature transition tests. Summer 2010 was unusually hot
in Finland and so the heat can very well be the cause of the observed high
emissions also in nature. We do not know whether the effect really can be
transmitted via gas solubilities instead of, for instance, increased
respiration. Grant and Roulet (2002) compared simulated and measured
CH
Taking a closer look at Siikaneva only, the model was a slightly better
predictor for the measured CH
Anoxic respiration alone thus seems a good basis to estimate CH
The new model for simulating CH
Sensitivity tests conducted on HIMMELI revealed mechanisms controlling the
simulated CH
The Fortran codes of the HIMMELI model are available as a Supplement to this article. The data used in these analyses are available upon request.
The solubilities of gases are computed following Sander (2015). The
temperature (
LAI is not continuously monitored at the peatland sites Siikaneva and
Lompolojänkkä; therefore, we utilized the method introduced by
Wilson et al. (2007) to obtain LAI input data for the model runs. We
simulated the LAI with a lognormal function (Wilson et al., 2007) (Eq. B1):
Parameter values of the models used for producing input for the Siikaneva and Lompolojänkkä runs. The value marked with * is the only one specific for the Lompolojänkkä site. The parameter value marked with ** is fitted in this study, and the value *** is based on Szafranek-Nakonieczna and Stepniewska (2014); the others are from the original references of the photosynthesis and respiration models.
The input anoxic respiration was created from two components: simulated NPP
and temperature-dependent anoxic peat decomposition
The NPP of Siikaneva was calculated by running models of gross
photosynthesis (
The daily averages of net photosynthesis
For Lompolojänkkä, the GPP time series over the years 2006 to 2010 was
available (Aurela et al., 2009); thus, we derived the NPP of vascular
vegetation directly from the GPP data. Again, we assumed that autotrophic
respiration contributes 50 % to the GPP (Gifford, 1994) and the
contribution of
The anoxic peat respiration for both sites was computed for the peat layers
below WTD using the
SS and LB developed the model. MR participated in model development and designed and carried out the tests with contribution from LB, JS, TA, TM, JM, and TV. MT, XL, MH, SS, TK, and VB contributed to the model development. JR, OP, MA, and AL provided observational data from the Siikaneva and Lompolojänkkä sites. TL, SJ, and EST provided knowledge and advice about peatland methane processes for model development. MR prepared the manuscript with contributions from all co-authors.
The authors declare that they have no conflict of interest.
We thank the Academy of Finland Centre of Excellence (272041), Academy Professor projects (284701 and 282842), CARB-ARC (285630), ICOS Finland (281255), NCoE eSTICC (57001), EU-H2020 CRESCENDO (641816), and MONIMET (LIFE12 ENV/FI/000409), and the Maj and Tor Nessling Foundation (projects 2008336, 2009067, and 2010212) for support. The Academy of Finland is also acknowledged by Eeva-Stiina Tuittila (project 287039) and Tuula Larmola (121535, 286731, and 293365). Thomas Kleinen acknowledges funding by the German Federal Ministry of Education and Research (BMBF) in projects CarboPerm and PalMod. Edited by: Jason Williams Reviewed by: three anonymous referees