Introduction
Peatlands cover only 3–5 % of the Earth's land area but store large
amounts of soil organic carbon (SOC). This carbon is primarily located in
the boreal and subarctic regions (75–80 %), while about 15 % is
located in tropical regions (Frolking et al., 2011; Page et al., 2011).
Current estimates of the northern peatland SOC vary from 270 to 450 Pg C
(Gorham, 1991; Turunen et al., 2002; Yu et al., 2010). Northern peat
accumulation occurred mainly during the Holocene, originating from plant
litter production that exceeds decomposition in water-logged soil
conditions, with low pH and low temperatures (Parish et al., 2008). The
future of the carbon stored in these peatlands under a warmer environment
and altered hydrological regimes is very uncertain. Logically, higher
CO2 concentrations and elevated temperatures will stimulate higher
carbon uptake because of longer growing seasons and higher photosynthetic
rates (Aurela et al., 2004; Adkinson et al., 2011). However, the
accumulation is also coupled with a high evaporative demand that will lower
the groundwater table, resulting in increased heterotrophic respiration
rates (i.e., carbon loss; Mertens et al., 2001; Sulman et al., 2009;
Adkinson et al., 2011). In addition to these potential climatic influences,
other natural and anthropogenic disturbances (permafrost thaw, drainage,
fires, etc.) further play a role in determining the future carbon balance of
these vulnerable ecosystems (Turetsky et al., 2002; Parish et al., 2008).
Drainage and fires have particularly important impacts on the carbon balance
of the tropical peatlands (Page et al., 2002; Hooijer et al., 2010).
A number of peat carbon models have been reported in the literature. For
example, Frolking et al. (2010) developed the Holocene Peat Model (HPM),
which includes feedbacks between plant communities, water table, peat
properties, and peat decomposition. This model was applied at Mer Bleue Bog
in southern Canada and validated with data from peat-core observations. HPM
is a long-term peat accumulation model that works at an annual time step but
cannot simulate seasonal variations of key water processes in peatlands.
Wania et al. (2009a, b) integrated peatlands and permafrost into the
Lund–Potsdam–Jena model (LPJ-WHy), where the upper 0.3 m of peatland soils
(the acrotelm) experience a fluctuating water table and the underlying
layer (the catotelm) is permanently inundated. A constant soil moisture
modifier (0.35) was used to reduce acrotelm decomposition. Spahni et al. (2013) adopted and improved LPJ-WHy by considering the effects of varying
water table depth on acrotelm decomposition rates, using a weighted average
of the aerobic and anaerobic respiration modifier, and implementation of a
dynamic nitrogen cycle. In the dynamic global vegetation model (DGVM)
CLIMBER2-LPJ, Kleinen et al. (2012) quantified the fraction of oxic
decomposition in the acrotelm by comparing the water table position and the
acrotelm height. Chaudhary et al. (2017a, b) included a dynamic
multilayer peat accumulation functionality in a customized Arctic version
of the Lund–Potsdam–Jena General Ecosystem Simulator (LPJ-GUESS). In their
approach, new layers of litter were added at the top of the soil every year,
and the remaining litter mass, after decomposition, was treated as a new
individual peat layer from the first day of the following year. The
decomposition rate of peat, modulated by temperature and moisture, declined
over time. In these four peatland models, the water table depth was
calculated from a bucket model. In the context of Earth system modeling, the
land surface processes are better represented by multilayer schemes, such
as multilayer plant canopy and root, multilayer snow, multilevel soil
carbon, and energy budgets (Best et al., 2011; Mcgrath et al., 2016; Zhu et
al., 2016). To model peatlands consistently in land surface models, a
multilayer soil hydrology scheme is needed. Meanwhile, a more
physically based multilayer scheme can provide more prognostic power in
predicting peatland water table dynamics.
In this study, we present the development of a multilayer peat hydrology
and carbon model in the Organising Carbon and
Hydrology In Dynamic Ecosystems (ORCHIDEE) land surface scheme, with a focus on the
water table dynamics and its effects on the energy budgets, and on carbon
decomposition occurring within the oxic and the water-saturated parts of the
peat profile. CH4 fluxes and DOC loss through runoff are important
components of the carbon balance of a peatland (Chu et al., 2014; Olefeldt
et al., 2012) but are not included in this study. This new peat model is
incorporated consistently into the land surface scheme in order to conserve
water, carbon, and energy at scales from local sites to grid-based
large-scale applications in an Earth system modeling context.
Model description
General structure of the model
The ORCHIDEE land surface model simulates biophysical processes of rainfall
interception, soil water transport, latent (LE) and sensible (H) heat
fluxes, heat diffusion in the soil, and photosynthesis on a 30 min time step
(Ducoudré et al., 1993). Carbon cycle processes (e.g., carbon
allocation, respiration, mortality, litter, and soil carbon dynamics) are
simulated on a daily time step (Krinner et al., 2005).
ORCHIDEE discretizes the vegetation into plant functional types (PFTs): eight
for trees, two for natural C3 and C4 grasses, two for C3 and C4 crops, and
one for bare-soil type. Across the PFTs, plants are described with the same
equations but different parameter values, except for leaf onset and
senescence that follow PFT-specific equations (Botta et al., 2000). In
grid-based simulations, PFTs are grouped into three soil tiles: one with
bare soil, one with all tree PFTs, and one with all short vegetation. The
water budget of each soil tile is calculated independently. The version of
ORCHIDEE implemented in this study uses the same (dominant) soil texture for
all the soil tiles of a grid cell to define the reference saturated
hydraulic conductivity (Ks-ref) and the saturated and residual
volumetric water contents (θs, θr). Dominant soil
textural classes are taken from the Zobler's soil texture map (Zobler, 1986)
at 1∘ resolution. The original five soil textures (fine,
medium-fine, medium, medium-coarse, coarse) in Zobler's map are reduced to
three (fine, medium, coarse) by grouping the medium-fine, medium, and
medium-coarse textures into a single class. Hydrological parameters of the three
dominant soil textures are taken from Carsel and Parrish (1988) (Table 1).
Schematic of the hydrology module in ORCHIDEE. Water balance
components: (a) a soil tile with either trees or grasses; (b) a peatland
soil tile. Black dashed lines indicate the position of nodes in the 11
soil layers of the model. Blue lines: vertical profiles of saturated
hydraulic conductivity for different soil textures. Green lines: diffusivity
for different soil textures. The vertical axis indicates soil depth, the
horizontal axis indicates values of saturated hydraulic conductivity (K, mm day-1)
and diffusivity (D, mm2 day-1). Note that the horizontal axis is on a base-10 logarithmic scale.
![]()
Each soil tile in ORCHIDEE has 11 vertical layers (up to 2.0 m) with
exponentially coarser vertical resolution (Fig. 1). The Fokker–Planck
equation is used to describe the vertical diffusion of water in the soil.
The Mualem (1976) and Van Genuchten (1980) model (Eqs. 1 and 2) is used to
define the hydraulic conductivity (K, m s-1) and diffusivity (D, m2 s-1) as a function of volumetric water content (θ,
m3 m-3):
Kθ=Ksθf1-1-θf1/mm2,Dθ=1-mKθαm1θ-θrθf-1mθf-1m-1-m,
where θ is the volumetric water content (m3 m-3), θs
is the saturated water content (m3 m-3), θr
is the residual water content (m3 m-3), θf is the
relative water content and is calculated as
θf=θ-θrθs-θr,
Ks is the saturated hydraulic conductivity (m s-1), α is the inverse of the air entry suction (m-1), and
m is a dimensionless parameter.
Van Genuchten parameters used for different soil texture classes
for non-peat soils (coarse, medium, fine) and for peat. θs
is the saturated water content (m3 m-3); θr is the
residual water content (m3 m-3); Ks-ref is the reference
saturated hydraulic conductivity (m s-1); α is the
inverse of the air entry suction (m-1); n is a dimensionless parameter.
In Eqs. (1) and (2), m=1-1/n.
Ks-ref
n
α
θs
θr
(m s-1)
(m-1)
(m3 m-3)
(m3 m-3)
Coarse
1.23 × 10-5
1.89
7.5
0.41
0.065
Medium
2.89 × 10-6
1.56
3.6
0.43
0.078
Fine
7.22 × 10-7
1.31
1.9
0.41
0.095
Peat
2.45 × 10-5
1.38
5.07
0.90
0.15
Following d'Orgeval (2006) and d'Orgeval et al. (2008), Ks exponentially
decreases with soil depth (z) below zlim= 30 cm (Fd (z)),
while a root-fracturing factor increases Ks where roots are denser
(Froot (z)):
Ks(z)=Ks-ref×Fd(z)×Froot(z),
with Fdz=minmaxexp-fz-zlim,0.1,1 and
Frootz=∏jϵcmax1,KsmaxKs-ref1-αjz2fj,
where Ks-ref is the reference top-soil saturated hydraulic
conductivity determined by soil texture (m s-1),
Ksmax is the value of the coarser (sandy)
texture and equals 8.25 × 10-5 m s-1, αj is a root profile decay factor for PFT
j with a coverage fraction fj, and c is the soil tile to which PFT j was
assigned.
Modifications in ORCHIDEE-PEAT
To simulate peat, we (1) modified the parameters of plants growing on peat,
(2) added a new peat soil tile with specific peat soil hydraulic properties,
and (3) changed the decomposition of peat carbon as being controlled by
saturated conditions, through the modeled water table (WT).
Modified peat plant parameters
As a response to the unique stress conditions in peatlands (i.e., oxygen
deficit, nutrient limitation), peatland vegetation has shallow and extensive
root systems (Boutin and Keddy, 1993; Iversen et al., 2015). Previous
peatland models have incorporated more than one PFT to represent peatland
plants and dynamically simulate fractional vegetation cover. For example,
Wania et al. (2009b) separated flood-tolerant C3 graminoids and Sphagnum moss in
LPJ-WHy to represent peatland-specific vegetation, with peatland extent
defined from an organic soil map and the fractional cover of PFTs determined
by bioclimatic conditions including temperature, water table depth,
inundation stress, etc. Stocker et al. (2014) applied a version of this
model but removed the upper temperature limitation of the peatland-specific
PFTs and further included three additional PFTs: flood-tolerant C4
grasses, tropical evergreen, and tropical raingreen tree PFTs, with peatland
extent diagnosed by the TOPMODEL scheme. At present, however, ORCHIDEE-PEAT
lacks representation of dynamic moss and shrub covers, and we do not know
the fractional coverage of different vegetation types at each site in
grid-based simulations. Previous studies have shown that there are
considerable overlaps for the plant trait ranges among different plant
functional types, while variations in plant traits within a PFT can be
larger than the differences in means of different PFTs (Verheijen et al.,
2013; Wright et al., 2005; Laughlin et al., 2010). Therefore, for
simplicity, we applied only the PFT of C3 grass with a shallower rooting
depth to represent the average of vegetation growing in northern peatlands.
Only one key photosynthetic parameter (Vcmax) of this PFT has been
tuned to match with observations at each site. This simplification may cause
discrepancies between model output and observations. Druel et al. (2017)
added non-vascular plants (bryophytes and lichens), boreal grasses, and
shrubs into ORC-HL-VEGv1.0. Their work is parallel to our model and
will be incorporated into the model in the future. It will then be possible
to verify how many plant functional types are needed by the model to
reliably simulate the peatlands at site level and larger scale. The maximum
rate of carboxylation (Vcmax) typically varies across peat sites
(Rennermalm et al., 2005; Bubier et al., 2011) and further varies with leaf
nitrogen, phosphorus content, and specific leaf area (Wright et al., 2004;
Walker et al., 2014). For instance, Vcmax for Sphagnum at the Old Black Spruce
site (53.985∘ N, 105.12∘ W) in Canada was 5, 14, and
6 µmol m-2 s-1 during spring, summer, and autumn, respectively,
while Vcmax for Pleurozium was 7, 5, and 7 µmol m-2 s-1 during the
three seasons (Williams and Flanagan, 1998). Bui (2013) conducted a
fertilization experiment at the Mer Bleue Bog (Canada; 45.41∘ N,
75.52∘ W) on the dominant ericaceous shrub and reported that
Vcmax values ranged between 6 and 179 µmol m-2 s-1, with
significantly higher Vcmax values after addition of nitrogen (6.4 g N m-2 yr-1) at 20 times the growing season ambient wet N
deposition rate with or without phosphorus (P) and potassium (K). In this
study (Sect. 4.1), we calibrated Vcmax at each site so that modeled
peak gross primary production (GPP) matched peak values derived from direct
EC measurements, and then regressed this adjusted Vcmax value with
environmental and climate variables. We note that this adjustment of
Vcmax may over- or undercompensate for biases in other model
parameters that impact maximum GPP, such as leaf area index (LAI), specific
leaf area (SLA), canopy light absorption parameters, water, and temperature
stresses (Fig. S1 in the Supplement).
Peat-specific soils hydraulics
Peatlands generally occur in flat areas that are poorly drained and/or
receive runoff and subsurface water from the surrounding landscape
(Graniero and Price, 1999). The low permeability catotelm peat layer is
permanently saturated. In ORCHIDEE-PEAT, the new soil tile added in a grid
cell to represent peatland as a landscape element was assumed to receive
surface runoff from the other three soil tiles (bare soil, trees, grasses)
and has a drainage flux reduced to zero (Largeron et al., 2017). Further,
considering that the water table of a peatland can rise above the ground
surface, an above-surface water reservoir with a maximum height of 10 cm was
added (Fig. 1b). In the model, the partitioning between water infiltration
and surface runoff is computed through a time-splitting procedure, with the
maximum infiltration rates described as an exponential probability density
distribution (d'Orgeval, 2006). The infiltration-excess water of peatland
first fills the above-surface water reservoir, then leaves the grid cell as
runoff. Water in this above-surface reservoir re-infiltrates into the peat
soil on the next time step (Largeron et al., 2017). We verified that the
measured standing water remained below 10 cm above the soil surface at 16
out of 20 northern peat sites where water table depth was recorded in this
study (Table S1 in the Supplement). The four exceptions were Winous Point North Marsh
(US-WPT), Himmelmoor (DE-Hmm), an Alaska fen (US-Fen), and an Alaskan bog
(US-Bog), where observed water tables reached up to 77, 39, 46, and
34 cm above the soil surface, respectively.
Peat soils cannot be described with any of the mineral soil textures used
for other tiles (Table 1) because the low bulk density and high porosity
increase the downward water percolation (Rezanezhad et al., 2016). Observed
peat-saturated hydraulic conductivity (K) and diffusivity (D) strongly vary
in space, depth, and time. This is partly related to the degree of
decomposition and compression of organic matter (Gnatowski et al., 2010).
Morris et al. (2015) reported near-surface saturated hydraulic
conductivities (K) of 2.69 × 10-2 to 7.16 × 10-6 m s-1 in bogs.
Gnatowski et al. (2010) measured values of 5 × 10-6 m s-1 in a moss-covered peat, which was 2 orders of
magnitude larger than for a woody peat (5.56 × 10-8 m s-1). Peat hydraulic parameters values used in this study
were applied after Largeron et al. (2017), based on Letts et al. (2000) and
Dawson (2006) (Table 1). The peat-saturated hydraulic conductivity value of
2.45 × 10-5 m s-1 is comparable to the harmonic mean value (6 × 10-5 m s-1)
of Morris et al. (2015). The values of the other Van
Genuchten parameters for peat (Table 1) are similar to those employed in
other peatland models (Wania et al., 2009a; Wu et al., 2016).
The peatland water table depth (cm) is diagnosed by summing water
heights in the 11 soil layers, calculated from the relative water
content (Largeron et al., 2017):
WT=Htot-∑i=111θfi×dzi-Hab,withθfi=θi-θrθs-θr,
where θfi is the relative volumetric water content of the
ith soil layer, θs is the saturated water content (m3 m-3),
θr is the residual water content (m3 m-3),
dzi is the distance between node i-1 and node i (Fig. 1; m), Htot is
the total soil column height being fixed to 2.0 m, and Hab is the
height of the water reservoir above soil surface (m). Thus, when the water
table is above the surface, the modeled WT takes negative values.
Decomposition of peat carbon controlled by water saturation
In the standard version of ORCHIDEE, plant litter carbon is added to two
litter pools: the metabolic and the structural pool. Decomposed litter
carbon from these two pools is then distributed into three soil carbon
pools: the active, slow, and passive pools, similar to the CENTURY model
(Parton et al., 1988). Both temperature and moisture functions are used to
control soil carbon decomposition rates (Text S1 in the Supplement). In ORCHIDEE-PEAT, these
standard processes are kept the same as in Krinner et al. (2005) for
non-peatland vegetation (Fig. S2, black dashed box). For the peatland
vegetation, we added a peat carbon module, in which the three soil carbon
pools (active, slow, and passive) are replaced by two pools forming distinct
layers, following Kleinen et al. (2012) (Fig. S2, red dashed box).
Specifically, carbon from decomposed litter pools is added to the acrotelm
carbon pool where it is decomposed aerobically above the simulated water
table and anaerobically below it. The permanently saturated deep catotelm
carbon pool receives a prescribed fraction of the acrotelm carbon, and is
decomposed only anaerobically at a very slow rate. While the acrotelm depth
is fixed to 30 cm in some peat decomposition models (Yurova et al., 2007;
Wania et al., 2009a; Spahni et al., 2013), we used the average of simulated
minimum summer water table position (WTmin) over the observational
period to demarcate the boundary between the acrotelm and the catotelm at
each site to take into account local site conditions. We conducted a
“preparation run (S0)”, in which the model was run at each site using the
same protocol (Sect. 3.3) but with the peat carbon module deactivated.
WTmin was diagnosed from the output of S0 before feeding into the peat
carbon module in S1 and S2 (Sect. 3.3). Soil carbon exerts no feedback
effects on the soil temperature and hydraulic in the structure of our model;
thus, S0 and S1 produce the same simulated water table. WTmin values were estimated based on current climate due to the lack of
knowledge of initiation histories of these sites. For the long-term carbon
accumulation estimations, the Holocene climate may be a better proxy since
northern peatlands show peak initiation in the early Holocene (Yu et al.,
2010). By comparing the height of the acrotelm (Fig. S2, Eq. 9) with the WT
depth, we derived the fraction of the acrotelm where carbon decomposes under
oxic (β) vs. anoxic conditions (1β). Acrotelm height
(HA, Eq. 10) was calculated from acrotelm carbon stock (CA in Eqs. 5–7),
acrotelm carbon fraction (Cf,A) and acrotelm bulk density (ρA). Decomposition of peat carbon is controlled by temperature
(fT) and parameterized as an exponential function: fT=Q10 exp((T-Tref)/10 ∘C) with Q10= 2.0 and
Tref= 30 ∘C (Text S1). Soil carbon fluxes are given by
FAC=kpfTCA,RA,o=βkAfTCA,RA,a=(1-β)vkAfTCA,RC=kCfTCC,β=β=1,WTmin-WT≤0β=HA-(WTmin-WT)HA,0<WTmin-WT<HAβ=0,WTmin-WT≥HA,HA=CAρA⋅Cf,A,
where FAC is the carbon flux from acrotelm to catotelm; RA,o is
aerobically decomposed acrotelm carbon; RA,a is anaerobically
decomposed acrotelm carbon; RC is decomposed carbon in catotelm;
CA is carbon stored in the acrotelm; CC is carbon stored in the
catotelm; and β is the fraction of acrotelm under oxic conditions. A
10 100-year spinup was conducted to initialize peat depth at each site
(Sect. 3.3). Following the study of Kleinen et al. (2012), the catotelm
formation rate kp= 1.91 × 10-2 yr-1, the acrotelm decomposition rate
kA= 0.067 yr-1, the catotelm decomposition rate kC= 3.35 × 10-5 yr-1,
the ratio of anaerobic to aerobic CO2 production
μ= 0.35, carbon fraction in the acrotelm peat Cf,A= 0.50,
the acrotelm density ρA= 35.0 kg m-3, carbon fraction in
the catotelm peat Cf,C= 0.52, and the catotelm density ρC= 91.0 kg m-3.
The distribution of 30 peatland sites used in this study. Triangles
are bogs; circles are fens; squares are tundra and marsh. Colors of the
markers indicate peatland fractions in the 0.5∘ grid cell. Mean
air temperatures is the annual mean from 1999 to 2015, based on the 6-hourly
CRU-NCEP 0.5∘ global database.
![]()
Site characteristics of the 30 peatlands (sites are sorted by
latitude from south to north). The first column denotes if the site is used
in the second set of simulation (S2, with water table prescribed in the
model equal to observed values): y – YES; n – NO. Lat: latitude; Long: longitude;
MAT: long-term mean annual air temperature; MAP: long-term mean annual
precipitation; peatland fraction (%): fraction of peatland in the
0.5∘ grid cell which is read from the map of Yu et al. (2010); for
cells where there is no peatland, mean fraction (22 %) is used. Note
that, at US-Bog and US-Fen, the precipitation is the growing season (from 16 May to
31 August) mean value, thus clarified as “GS” in the table. Details of S2
and peatland fraction are provided in Sect. 3.3.
S2
Code
Lat
Long
Climatic
Type
MAP
MAT
Elevation
Peatland
Period
Dominant
LAI
Aboveground
Citation
zone
(mm)
(∘C)
(m a.s.l.)
fraction
vegetation
(m2 m-2)
biomass
type
(kg m-2)
n
US-WPT
41.5
-83.0
temperate
marsh
840
9.2
175
mean
2011–2013
grasses
area average: 2.3;
area average: 1.94;
Chu et al. (2014,
emergent ;
emergent vegetation
2015)
vegetation: 3.3
area: 3.04;
open water: 1.0
open water area: 0.44
n
CA-Mer
45.4
-75.5
temperate
bog
944
6
70
mean
1999–2012
shrubs, mosses
1.5
moss: 0.144 ± 0.03;
Lafleur et al. (2005)
vascular: 0.356 ± 0.1
y
US-Los
46.1
-90.0
temperate
fen
666
3.8
470
mean
2000–2010
trees, shrubs,
4.24
1.336
Sulman et al. (2009)
grasses
n
LA-GUE
47.3
2.3
temperate
fen
880
11
145
mean
2011–2013
grasses
D'Angelo et al. (2016);
Laggoun-Défarge et al. (2016)
y
DE-Sfn
47.8
11.3
temperate
bog
1127
8.6
590
3.01 %
2012–2014
trees, shrubs,
Hommeltenberg et al. (2014)
grasses, mosses
y
CZ-Wet
49.0
14.8
temperate
fen
614
7.4
426.5
mean
2007–2013
grasses
2.45
0.57
Dušek et al. (2009)
n
DE-Spw
51.9
14.0
temperate
fen
559
9.5
61
11.01 %
2010–2014
trees
3.6
Petrescu et al. (2015)
y
IE-Kil
52.0
-9.9
temperate
blanket
2467
10.5
150
28.97 %
2002–2012
shrubs, grasses,
from 0.4 to 0.6 in
Sottocornola et al. (2009);
bog
mosses
different years
McVeigh et al. (2014)
y
DE-Bou
52.7
7.2
temperate
bog
799
10
19
63.98 %
2011–2014
grasses,
0.7
grass dominated:
Hurkuck et al. (2016)
mosses
0.577 ± 0.029;
heather and moss
dominated:
0.517.0 ± 0.026;
mixed:
0.303 ± 0.015
n
PL-Wet
52.5
16.2
temperate
fen
526
8.5
54
4.01 %
2006–2013
shrubs, grasses,
Chojnicki et al. (2007);
mosses
Barabach (2012);
Milecka et al. (2017)
n
PL-Kpt
53.6
22.9
temperate
fen
600
7.1
109
mean
2013–2015
grasses, reeds,
sedges: 4.3;
Fortuniak et al. (2017)
and ferns
reeds and ferns: 4.8
n
DE-Hmm
53.7
9.9
temperate
bog
838
9
12
15.99 %
2012–2014
90 % bare peat,
Vanselow-Algan et al. (2015)
10 % vegetation
cover: trees, grasses
n
DE-Zrk
53.9
12.9
temperate
fen
584
8.7
< 0.5
23.16 %
2013–2014
grasses
Franz et al. (2016)
n
CA-Wp3
54.5
-113.3
boreal
fen
504
2.1
670
29.77 %
2004–2006
grasses, mosses
1.1
0.157
Adkinson et al. (2011)
n
CA-Wp1
55.0
-112.5
boreal
fen
504
2.1
540
0.20 %
2003–2009
trees, shrubs,
2.6
1.08
Flanagan and Syed (2011)
mosses
n
CA-Wp2
55.5
-112.3
boreal
fen
504
2.1
730
8.07 %
2004–2006
shrubs, grasses,
1.5
0.231
Adkinson et al. (2011)
mosses
y
SE-Faj
56.3
13.6
temperate
bog
700
6.2
140
mean
2005–2009
shrubs, grasses,
dwarf shrub: 0.153;
Lund et al. (2007, 2012)
mosses
Sphagnum: 0.192;
graminoid: 0.077
n
FI-Sii
61.8
24.2
boreal
fen
713
3.3
162
mean
2005–2014
shrubs, grasses,
0.55
Aurela et al. (2007);
mosses
(maximum value,
Riutta et al. (2007)
occurs in June–July)
n
DK-NuF
64.1
-51.4
arctic
fen
750
-1.4
40
mean
2008–2014
grasses, mosses
0.7
Westergaard-Nielsen
et al. (2013)
y
SE-Deg
64.2
19.6
boreal
fen
523
1.2
270
mean
2001–2005
shrubs, grasses,
0.47
moss: 0.065;
Sagerfors et al. (2008);
mosses
vascular: 0.049
Nilsson et al. (2008);
Peichl et al. (2014)
n
US-Bog
64.7
-148.3
boreal,
bog
146 (GS)
-2.2
100
28.01 %
2011–2015
trees, mosses
Euskirchen et al. (2014)
thermokarst
n
US-Fen
64.7
-148.3
boreal
fen
146 (GS)
-2.2
100
28.01 %
2011–2015
grasses,
Euskirchen et al. (2014)
forbs
y
FI-Lom
68.0
24.2
boreal
fen
521
-1
269
5.08 %
2007–2009
shrubs, grasses,
1.3
Aurela et al. (2009)
mosses
n
SE-Sto
68.4
19.1
boreal,
bog
322
-0.14
360
mean
2014–2015
shrubs, grasses,
Malmer et al. (2005);
permafrost
mosses
Olefeldt et al. (2012)
n
US-Ics
68.6
-149.3
arctic,
fen
318
-7.4
920
mean
2007–2011
shrubs,
Euskirchen et al. (2012,
permafrost
grasses
2016)
n
RU-Che
68.6
161.3
arctic,
tundra
200–215
-12.5
4
64.09 %
2002–2005
shrubs,
0.3–0.4
Corradi et al. (2005);
permafrost
grasses
Merbold et al. (2009)
n
NO-And
69.1
16.0
boreal
bog
1060
3.6
17
mean
2008–2014
shrubs, grasses,
Lund et al. (2015)
mosses
n
US-Bes
71.3
-156.6
arctic,
tundra
173
-12
4
mean
2005–2008
grasses,
Zona et al. (2009)
permafrost
mosses
n
DK-ZaF
74.5
-20.6
arctic,
fen
211
-9
35
mean
2008–2011
grasses,
0.65
0.471
Stiegler et al. (2016)
permafrost
mosses
n
NO-Adv
78.2
15.9
arctic,
fen
190
-6.7
17
mean
2011–2014
shrubs, grasses,
0.41 ± 0.12
0.85 ± 0.28
Pirk et al. (2017)
permafrost
mosses
* For most of the sites, NEE was partitioned into GPP and ecosystem
respiration following the nighttime partitioning method of Reichstein
et al. (2005), except that NO-And used a light response curve approach following
Lund et al. (2015); CA-Wp1 used the FLUXNET Canada Research Network (FCRN)
standard NEE partitioning procedure following Barr et al. (2004); and DE-Spw
used the online gap filling and flux partitioning tool
(http://www.bgc-jena.mpg.de/~MDIwork/eddyproc/) which uses
the method proposed by Lloyd and Taylor (1994). Note that the we grouped
sedges, grasses, and herbaceous plants into one class (grasses) in the
table.
Validation of ORCHIDEE-PEAT at Northern Hemisphere peatland eddy-covariance
sites
Sites description
To evaluate the performance of ORCHIDEE-PEAT in simulating CO2, water,
and energy fluxes on daily to annual timescales, we compiled data from 30
northern peatland sites where eddy-covariance data and physical variables
(water table, snow depth, soil temperature) were collected (Fig. 2, Table 2).
These sites are spread between the temperate and the arctic climate
zones, and include nine bogs and 18 fens. A marsh and two wet tundra sites
(note that these two wet tundra sites are neither a fen nor a bog; hereafter,
they are referred to as “tundra”) with a ∼ 30–50 cm thick
organic layer are also included in this study. Among them, six sites are
underlain by permafrost and one site is in a thermokarst area. The peatland
fractional cover in the 0.5∘ grid cell containing each site is
from the Yu et al. (2010) map (Fig. 2, Table 2). A short description of all
sites can be found in the Supplement.
Meteorological forcing data
We ran the model for 30 different 0.5∘ grid cells corresponding to
each peatland site (US-Fen and US-Bog are in the same grid cell but their
local meteorological data were different). Peatland fraction in each grid
cell was prescribed from Yu et al. (2010), adapted by Largeron et al. (2017)
to be matched with a high-resolution land cover map. For the 16 out of 30
cells without peatland (Fig. 2, Table 2) in the large-scale map from Yu et al. (2010), a mean peatland fraction of 22 % was assigned.
Time series of half-hourly air temperature, wind speed, wind direction,
longwave incoming radiation, shortwave incoming radiation, specific
humidity, atmospheric pressure, and precipitation were used to drive
ORCHIDEE-PEAT. All variables were from measurements made at each flux tower
where CO2 and energy (latent heat: LE; sensible heat: H) fluxes,
water table position, soil temperature, and snow depth were recorded on a
half-hourly time step. The linearly interpolated 6-hourly CRU-NCEP
0.5∘ global climate forcing dataset was used to fill the gaps in
the driving variables. A linear correction was applied to meteorological
forcing variables (except precipitation) in the CRU-NCEP dataset to match
observations before gap filling. For precipitation, no correction was
applied. At CA-Wp2 and CA-Wp3, meteorological forcing data were measured
only during the growing season, so CRU-NCEP data were linearly corrected
using relationships derived from the available data. For some sites, several
meteorological variables were not measured, such as longwave incoming
radiation at NO-And, atmospheric pressure, shortwave incoming radiation,
and longwave incoming radiation at CZ-Wet. In these cases, uncorrected
CRU-NCEP data were used.
Model setup
ORCHIDEE-PEAT was first spun up for 10 100 years, forced by the
pre-industrial atmospheric CO2 concentration of 285 ppm, with repeated
site-specific observational meteorological fields, and present-day
vegetation fractions for each site. In reality, the climate changed through
the Holocene, but since the initiation and climate history of each site are
unknown, we assumed a constant present-day climate condition and peatland
area. Thus, this model is only suitable for simulating water, energy and
CO2 fluxes from peat on timescales ranging from days to decades. To
accelerate the spinup, ORCHIDEE-PEAT was first run for 100 years to reach
the equilibrium for hydrology and soil thermal conditions, fast carbon pools,
and soil carbon input from dead plants. Then, a submodel simulating only
soil carbon dynamics (with fixed daily litter input from the previous
simulation) was run for 10 000 years to accumulate soil carbon. Peatlands
can reach equilibrium only when the addition of carbon equals carbon lost,
which is attained on timescales of 104 years (Clymo, 1984; Wania et
al., 2009b). The catotelm carbon pool in this study was still not fully
equilibrated even after 10 100 years due to the low carbon decomposition
rate in this reservoir (3.35 × 10-5 yr-1, Kleinen et al., 2012). The modeled peat carbon pool
thus depends on the time length of spinup, which was fixed at 10 100 years,
while in the real world, peat age at some sites can be younger. For example,
the sample from the second last 10 cm peat segment at CA-Wp1 has an
uncalibrated radiocarbon date of ∼ 2200 years (Flanagan and
Syed, 2011). Since we focus on carbon and water fluxes on daily to annual
scales in this study, rather than on the simulation of peat carbon stocks,
we conducted a sensitivity analysis of modeled heterotrophic respiration to
the length of the spinup, which shows only a slight increase of catotelm
respiration with increasing simulation time (Fig. S3). After the spinup,
transient simulations were conducted for each site, forced by repeated
site-specific climates and rising atmospheric CO2 concentration during
the period 1901–2015. Finally, the model outputs corresponding to the
respective measurement periods (all during 1999–2015) were compared to
observed time series for each site.
Two sets of simulations were conducted. In the first one (S1), soil water
content and WT position were modeled by ORCHIDEE-PEAT, and the
WT was used in the carbon module to define the fraction of oxic and anoxic
decomposition in the acrotelm. S1 was performed for all the 30 sites. In the
second set (S2) of simulations, we prescribed water table in the model to
equal the observed values (WTobs). That is, soil moisture at layers
below the measured water table was prescribed as saturated (θ(z > WTobs) =θs), while soil moisture above
WTobs was simulated. WTobs was further used in the carbon module
in S2. S2 was performed only for a subset of eight sites where at least
2 years of water table measurements were available and where there were
sufficient observations to gap fill the WTobs time series (Table 2).
For these sites, the gaps of WTobs were filled with the mean value of
the same period from other years of measurement (Table S2). The simulation
S2 was designed to check if the model performance will improve (or
deteriorate) when prescribing WT exactly to its observed value, since WT is
known to be a critical variable impacting peat water, CO2, and CH4
fluxes (Dušek et al., 2009; Parmentier et al., 2011; Strack et al.,
2006). Fixing the simulated water table to WTobs in S2 violated the
water mass conservation of the model but allowed us to evaluate the carbon
module independently from the hydrological module biases.
Measures for evaluating model performance
Following Jung et al. (2011) and Tramontana et al. (2016), we used
site-specific daily means, annual means, seasonal variations, and daily
anomalies to evaluate the model performance. For each site, seasonal
variations are calculated by removing the annual mean value from the mean
seasonal cycle (averaged value for each month across all available years).
Anomalies are calculated as the deviation of a daily flux value from the
corresponding mean seasonal cycle.
A series of measures was used to assess the model performance (Kobayashi
and Salam, 2000; Jung et al., 2011; Tramontana et al., 2016).
The root mean square deviation (RMSD) reports the model accuracy by
measuring the differences between simulation and observation.
RMSD=1n∑i=1n(xi-yi)2,
where xi is simulated variable, yi is measured variable, and n is the
number of observations.
Two signals (SDSD and LCS) are discriminated from the mean squared deviation
(Kobayashi and Salam, 2000). The squared difference (SDSD) between the
standard deviation of the simulation (SDs) and the measurement
(SDm) shows if the model can reproduce the magnitude of fluctuation
among the n measurements.
SDSD=(SDs-SDm)2;withSDs=1n∑i=1n(xi-x¯)2,SDm=1n∑i=1n(yi-y¯)2,
where x¯ is simulated mean value; y¯ is measured mean value.
The lack of correlation weighted by the standard deviations (LCS) is a
measure to examine if the model reproduces the observed phase of
variability.
LCS=2SDsSDm1-r;withr=1n∑i=1nxi-x¯yi-y¯/(SDsSDm),
where r is Pearson's correlation coefficient.
The Nash–Sutcliffe modeling efficiency (MEF) is used to indicate the
predictive accuracy of the model. MEF varies between negative infinity
(-inf) and 1: an efficiency of 1 indicates a perfect fit between
simulations and observations; an efficiency of 0 indicates the simulations
are as accurate as the mean value of observations; a negative MEF indicates
that the mean value of observations has greater predictive power than the model.
The modeling efficiency is defined as
MEF=1-∑i=1n(xi-yi)2∑i=1n(yi-y¯)2.
Results
Site-specific <inline-formula><mml:math id="M293" display="inline"><mml:mrow><mml:msub><mml:mi>V</mml:mi><mml:mi mathvariant="normal">cmax</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula> reduces errors in carbon flux
simulations
Out of the 30 sites, 22 sites provided observed daily GPP (based on measured
NEE). The values of optimized Vcmax at each site were listed in Table 3.
The optimized Vcmax varied from 19 to 89 µmol m-2 s-1
(Table 3), with a mean value of 40 µmol m-2 s-1. The
calibration of Vcmax may compensate for biases in other model
parameters. A brief comparison between simulated and reported
(measured/estimated) LAI and aboveground biomass showed that there are no
systematic errors (Fig. S1).
Taylor diagrams were used to evaluate model results at these 22 sites (Fig. 3). The model had the best performance for GPP, with the correlation
coefficient between simulated and observed GPP varying between 0.66 and 0.93,
and all data points fell within the 0.9 root mean square difference circle.
Simulated water table depth had a larger spread in correlation (0.16–0.82)
and root mean square difference (0.4–4.0). We found no significant patterns
of model–data misfits among different peatland types (fen, bog, and others) or
climate zones (temperate, boreal, and arctic; Fig. 3).
Optimized Vcmax (µmol m-2 s-1) at each site.
Site
Vcmax
Site
Vcmax
US-WPT
80
FI-Sii
19
CA-Mer
25
DK-NuF
31
US-Los
65
SE-Deg
23
DE-Sfn
45
US-Bog
42
CZ-Wet
54
US-Fen
56
DE-Spw
89
FI-Lom
28
IE-Kil
28
RU-Che
35
DE-Bou
34
NO-And
21
DE-Zrk
33
DK-ZaF
37
CA-Wp1
38
NO-Adv
28
SE-faj
21
PL-Kpt
52
Taylor diagrams of (a) GPP
(g C m-2 day-1); (b) ER (g C m-2 day-1);
(c) NEE (g C m-2 day-1); (d) LE
(W m-2); (e) H (W m-2); and (f) water table
depth (cm). All statistics were calculated using daily averaged data.
All points were normalized by dividing the standard deviation of model
results by the standard deviation of the corresponding measurement; thus, the
reference point is 1.0. Light green markers – temperate sites; dark
green markers – boreal sites; blue markers – arctic sites.
![]()
For the 22 sites where NEE and ER measurements were available, the errors in
the three carbon fluxes (GPP, ER, and NEE) were significantly reduced by
optimizing Vcmax at each site (Table 4, Figs. 4, S4). With
site-specific Vcmax values (site-by-site model performances are shown
in Figs. S5 to S10), the overall (all the daily
data from all the 22 sites) performance of the model was high for GPP
(r2= 0.76, MEF = 0.76) and ER (r2= 0.78, MEF = 0.75),
and lower for NEE (r2= 0.38, MEF = 0.26; Fig. 4, Table 4).
Seasonal variations in carbon fluxes were well captured by the model
(r2= 0.61 to 0.86). The spatial across-site gradients of annual
mean GPP and ER were generally good, with r2 of 0.93 and 0.89, and
lower for NEE (r2= 0.27). Compared to simulations with a fixed
Vcmax (the mean of the optimized values of 40 µmol m-2 s-1), there were large improvements in capturing spatial gradients of
carbon fluxes with a site-specific Vcmax (e.g., r2 increased from
0.20 to 0.93, from 0.27 to 0.89, and from 0.16 to 0.27 for GPP, ER, and NEE,
respectively, while the RMSD was reduced by 63, 48, and 9 %). This
result indicates that model–data disagreement can be largely reduced by
using site-specific Vcmax instead of a fixed (mean) value. In future
regional simulations, spatial variations in Vcmax should be taken into
account. There was, however, no significant improvement in LE, H, and WT by
using site-specific Vcmax values (Table 4). The model performance was
poor for predicting daily anomalies of all fluxes, with r2 < 0.20.
For both temporal and spatial variation, the MEF values of the WT were
negative, and r2 smaller than 0.10, indicating that the model had a low
predictive capability for the WT. Possible reasons for this could be
(1) peat disturbance was not parameterized; i.e., the removal of beaver dams
resulted in a decline of water level at US-Los; water levels at US-WPT,
CZ-Wet, and RU-Che were manipulated. (2) The model diagnosed all peatland
sites as fens by routing runoff from non-peatland areas into the peatland
soil tile, whereas in reality, bogs receive water and nutrients only through
precipitation. In other words, we included an extra water source for bogs
other than rainfall. However, the model did not perform better for fens
(Fig. 3f), possibly because the amount of water that was routed into the fen
was in error. (3) WT depends on water input from surrounding non-peatland
areas: the greater the peatland fraction in the grid cell, the smaller the
runoff input from other soils to the peatland, hence resulting in a deeper
water table in the peatland (Fig. S11). The peatland area fraction derived
from the map of Yu et al. (2010) cannot represent the local area providing water
for fens. (4) For global applications, the effects of micro-relief were not
represented in the model, although they have been shown to be an important
regulator of the local hydrology cycle (Gong et al., 2012; Shi et al.,
2015).
Observed (x axis) vs. simulated (y axis) fluxes (GPP, ER, NEE,
LE, H, and WT) at the 22 sites where GPP derived from EC measurements were
available. Fluxes were simulated using site-specific optimized Vcmax. The
colors of points indicate the amount of data in each bin; in panel (b), each
data point represents one peatland site. The red line shows the
observations equal to the simulations.
![]()
Model performance measures for GPP, ER, NEE, LE, H, and WT. The
left-hand column shows results with site-specific optimized Vcmax at
each site; the right-hand column shows results with the fixed Vcmax (40 µmol m-2 s-1) at all sites.
Site-specific optimized Vcmax
Mean Vcmax (constant value, 40 µmol m-2 s-1)
Flux
RMSD
SDSD
LCS
r2
MEF
RMSD
SDSD
LCS
r2
MEF
Overall (daily variability)
Overall (daily variability)
GPP
1.39
0.11
1.80
0.76
0.76
2.17
0.06
4.60
0.47
0.41
ER
0.83
0.09
0.52
0.78
0.75
1.09
0.14
1.04
0.57
0.56
NEE
1.30
0.02
1.56
0.38
0.26
1.48
0.00
2.01
0.29
0.03
LE
31.67
21.65
932.76
0.42
0.14
31.67
21.19
933.95
0.42
0.14
H
35.40
96.59
1151.28
0.24
-0.50
35.40
97.21
1150.59
0.24
-0.50
WT
25.93
10.26
661.80
0.01
-0.56
26.14
7.63
675.51
0.01
-0.59
Across-site variability
Across-site variability
GPP
0.41
0.03
0.10
0.93
0.89
1.11
0.42
0.80
0.20
0.19
ER
0.38
0.01
0.06
0.89
0.79
0.72
0.16
0.33
0.27
0.23
NEE
0.60
0.06
0.20
0.27
-0.01
0.66
0.17
0.13
0.16
-0.21
LE
9.85
1.13
65.49
0.71
0.50
9.80
1.04
65.21
0.71
0.50
H
14.31
2.67
155.85
0.01
-1.04
14.28
2.83
154.38
0.01
-1.03
WT
24.40
15.20
444.83
0.02
-0.82
25.10
4.65
478.84
0.03
-0.92
Mean seasonal variability
Mean seasonal variability
GPP
0.92
0.03
0.81
0.86
0.86
1.36
0.02
1.83
0.70
0.69
ER
0.51
0.05
0.22
0.86
0.86
0.65
0.05
0.37
0.77
0.77
NEE
0.80
0.00
0.64
0.61
0.54
0.95
0.01
0.88
0.50
0.35
LE
11.49
7.75
124.23
0.83
0.78
11.47
7.46
124.02
0.83
0.78
H
17.85
65.77
252.65
0.57
0.11
17.85
66.40
252.30
0.57
0.11
WT
9.87
8.32
88.88
0.06
-1.38
9.77
12.73
82.69
0.12
-1.33
Anomalies
Anomalies
GPP
1.03
0.03
1.02
0.18
0.01
1.10
0.02
1.19
0.13
-0.13
ER
0.61
0.08
0.29
0.19
0.17
0.64
0.07
0.34
0.16
0.10
NEE
0.96
0.12
0.81
0.07
-0.07
0.99
0.12
0.85
0.04
-0.14
LE
27.43
26.14
726.25
0.07
-0.94
27.46
26.19
727.76
0.07
-0.94
H
28.09
81.43
707.43
0.12
-1.12
28.10
82.12
707.49
0.12
-1.12
WT
13.25
0.40
174.69
0.10
-0.47
13.43
0.47
179.41
0.09
-0.51
To better understand the influence of the water table dynamics on ER and NEE
in the model, we compared the second set of simulations (S2, with observed
water table used in the carbon module to define the fraction of oxic and
anoxic decomposition in the acrotelm) with the first set (S1, water table
calculated by the model). ORCHIDEE-PEAT showed only a small improvement in
reproducing ER and NEE when WTobs was used (Tables 5 and 6). To
illustrate this effect, we took the Lompolojänkkä (FI-Lom) fen site
as an example, in which WT was most severely underestimated among the 22
sites where NEE and ER measurements were available (Fig. S8). While modeled
WT varied between 5 and 54 cm below the surface, WTobs was always
above the soil surface. Figure 5a showed that in comparison to S1, there was
no aerobic respiration and larger anaerobic respiration in the acrotelm in
S2. Due to the smaller acrotelm respiration (aerobic plus anaerobic) in S2,
carbon input from acrotelm to catotelm was larger and consequently, more
carbon accumulated in the catotelm in S2. Thus, the catotelm respiration in
S2 was higher than that in S1 (Fig. 5c), even though the catotelm
respiration rate was very small. Because the growth of the peatland
vegetation was not constrained by water in the model, the simulated GPP
values were similar between S1 and S2 (Fig. 5a). With similar GPP but
smaller soil respiration (sum of the acrotelm and the catotelm respiration),
S2 simulations thus resulted in more negative NEE values than S1 (higher net
CO2 uptake). Simulated leaf onset occurred earlier than observed at
the Lompolojänkkä site, causing the ecosystem to switch from carbon
source to carbon sink in May, while the start of the carbon uptake was
observed to occur later (Fig. 5b). Although the modeled NEE was similar in
amplitude to the observations, the day-to-day variations of this flux were
not captured (Fig. 6), causing an overestimation (more negative values) of
NEE in the warm period (May–September).
Monthly mean (averaged over 2007–2009) of (a) GPP and ecosystem
respiration (ER); (b) NEE; (c) catotelm respiration at the Lompolojänkkä
fen site (FI-Lom). S1: simulated WT was used in the carbon
module; S2: observed WT values (WTobs) were used; ob: measured NEE. The
graph inserted shows catotelm respiration. By convention, a source of CO2 to
the atmosphere is a positive number.
![]()
The influence of WT on respiration was parameterized as the separation of
oxic (β in Eq. 6) vs. anoxic (1β in Eq. 7) decomposition in
the acrotelm. Although absolute values of simulated WT in S1 and WTobs
in S2 were quite different (Fig. S8), the values of β were not very
different (Fig. S12). Therefore, the simulated WT was good enough to properly
replicate ER (Fig. S13). An additional simulation (S3) performed at FI-Lom
showed that if WT was more severely underestimated, i.e., WT in S3 was
consistently 20 cm deeper than in S1, the acrotelm was exposed to oxygen for
a longer time, resulting in larger ER and hence smaller carbon sequestration
in S3 (Figs. S12, S13).
Model performance measures of ER simulations for the site-by-site
comparison, comparison across sites, mean seasonal cycle, and anomalies,
using modeled (S1) and observed (S2) WT.
Modeled WT used (S1)
Observed WT used (S2)
Site
RMSD
SDSD
LCS
r2
MEF
RMSD
SDSD
LCS
r2
MEF
CZ-Wet
1.45
0.86
0.87
0.81
0.68
1.51
1.05
0.79
0.81
0.66
DE-Bou
0.78
0.03
0.50
0.69
0.64
0.77
0.03
0.50
0.69
0.65
DE-Sfn
0.96
0.10
0.79
0.61
0.59
0.97
0.09
0.82
0.60
0.58
FI-Lom
0.46
0.00
0.19
0.85
0.84
0.45
0.02
0.18
0.85
0.84
IE-Kil
0.44
0.01
0.01
0.09
0.51
0.42
0.01
0.01
0.13
0.48
SE-Deg
0.69
0.26
0.19
0.75
0.62
0.64
0.16
0.23
0.75
0.68
SE-Faj
0.58
0.07
0.08
0.87
0.60
0.59
0.08
0.07
0.88
0.59
US-Los
0.63
0.01
0.39
0.85
0.85
0.60
0.00
0.35
0.87
0.87
Overall
0.79
0.09
0.51
0.78
0.76
0.79
0.09
0.51
0.78
0.76
Across sites
0.31
0.01
0.06
0.82
0.76
0.32
0.01
0.06
0.82
0.74
Seasonal
0.45
0.06
0.15
0.91
0.89
0.44
0.07
0.13
0.92
0.89
Anomalies
0.62
0.07
0.31
0.21
0.19
0.63
0.08
0.31
0.20
0.17
Model performance measures of NEE simulations for the site-by-site
comparison, comparison across sites, mean seasonal cycle, and anomalies,
using modeled (S1) and observed (S2) WT.
Modeled WT used (S1)
Observed WT used (S2)
Site
RMSD
SDSD
LCS
r2
MEF
RMSD
SDSD
LCS
r2
MEF
CZ-Wet
2.97
3.61
4.38
0.46
0.37
2.86
3.22
4.27
0.50
0.41
DE-Bou
1.30
0.02
1.40
0.31
-0.21
1.31
0.03
1.41
0.31
-0.23
DE-Sfn
2.98
2.98
4.27
0.20
0.02
2.98
3.08
4.15
0.21
0.02
FI-Lom
1.05
0.01
0.94
0.46
0.21
1.08
0.02
0.95
0.49
0.16
IE-Kil
0.48
0.000
0.16
0.29
-0.37
0.49
0.002
0.16
0.32
-0.44
SE-Deg
0.64
0.03
0.33
0.51
0.09
0.57
0.01
0.29
0.51
0.26
SE-Faj
0.65
0.01
0.33
0.31
-0.36
0.65
0.02
0.33
0.32
-0.39
US-Los
3.15
0.05
8.78
0.47
-3.37
3.10
0.06
8.57
0.39
-3.23
Overall
1.95
0.20
3.52
0.02
-0.35
1.92
0.18
3.42
0.04
-0.31
Across sites
0.67
0.27
0.16
0.40
0.29
0.65
0.26
0.14
0.46
0.32
Seasonal
1.30
0.05
1.64
0.25
0.13
1.27
0.03
1.58
0.28
0.17
Anomalies
1.18
0.22
1.17
0.003
-0.34
1.17
0.21
1.17
0.001
-0.33
Relationship between optimized <inline-formula><mml:math id="M406" display="inline"><mml:mrow><mml:msub><mml:mi>V</mml:mi><mml:mi mathvariant="normal">cmax</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula> and meteorological
variables
Several univariate ANOVA models were used to explain the spatial gradient
of optimized Vcmax, explanatory variables including air temperature
(T), precipitation (P), net radiation (NET_RAD), water use
efficiency (WUE), water balance (WB), and latitude (LAT). All explanatory
variables were calculated as daily mean values during the growing season.
Water use efficiency (g C m-2 mm-1 H2O) was calculated as
the ratio of GPP and evapotranspiration (ET). Water balance (mm day-1)
was calculated as the difference between precipitation and ET.
There was no significant difference between optimized Vcmax among
peatland types (fen vs. bog, p = 0.16), climate zones (temperate vs. boreal
vs. arctic, p = 0.17), or dominant vegetation types (grasses and/or mosses
dominated vs. shrubs and/or trees dominated, p = 0.67; Fig. S14). However,
we found a significant positive relationship between Vcmax and the
growing season mean air temperature (Fig. S15, Table 6, Vcmax=2.78T+8.74, with r2= 0.19, p < 0.05) and a significant
negative relationship between Vcmax and the latitude (Fig. S15, Table 6, Vcmax= -0.92LAT + 93.56,
with r2= 0.23, p < 0.05).
To verify the applicability of the empirical relationship found across sites
between optimized Vcmax and the latitude (Fig. S15), we used the seven
sites where there were no GPP observations available (US-Bes, DE-Hmm,
US-Ics, PL-wet, SE-Sto, CA-Wp2, and CA-Wp3) as cross-validated sites. We
compared model performance in simulating NEE, with Vcmax being
calculated according to the empirical relationship, and with Vcmax
being fixed to its mean value of all 22 sites from Table 3 (40 µmol m-2 s-1). The model performance in reproducing spatial gradients
of NEE was improved when the Vcmax values derived from the empirical
relationship were used (Fig. S16b, with RMSD reduced by 11 %, r2
increased from 0.20 to 0.38, and MEF increased from -0.04 to 0.17). This
implies that, compared to a fixed Vcmax, the usage of Vcmax value
from the empirical relationship can better capture spatial gradients of NEE.
It is worth mentioning that the empirical relationship was built on climate
conditions from the last two decades (1999–2015) and thus may change in the
future when the climate changes.
The results of the ANOVA analysis – the variance of optimized
Vcmax in relation to chosen variables.
Variable
F-ratio
p-value
r2 (%)
T
4.67
0.04*
18.95
P
0.95
0.34
4.52
NET_RAD
0.22
0.64
1.11
WUE
0.39
0.54
1.91
WB
1.35
0.26
6.32
LAT
6.08
0.023*
23.30
* Indicates statistical significance at a significance level of 0.05.
Observed and simulated daily mean NEE at the FI-Lom fen site in (a) S1
(simulated WT was used in the carbon module) and (b) S2 (modeled water table
was assimilated to WTobs and was used in the carbon
module).
![]()
Soil temperature and a snow depth underestimation in the model
For most of the sites, soil temperature was underestimated in winter and
overestimated in summer by our model (Figs. 7 and 8; results from sites
DK-NuF and CA-Wp1 are shown as illustrative examples). One possible reason
for the underestimation of soil temperature in winter is the underestimation
of snow depth (Fig. 9), since snow insulates the soil-changing thermal
conditions in comparison to a snow-free surface. The underestimation of the
snow depth can be caused by the bias in snow processes of the model, such as
underestimation of snow mass, and/or overestimation of snow density and
subsequently overestimation of snow compaction, and/or overestimation of
sublimation. The insulation effect of the moss layer and the top organic
layer is not included in this study, which may explain why soil temperature
was overestimated in summer but underestimated in winter. ORCHIDEE-PEAT
calculates one energy budget for the vegetation and soil columns in one grid
cell. Key parameters used for solving the heat diffusion equations in the
soil, such as soil heat capacity and thermal conductivity, were prescribed
by the dominant soil texture in the grid cell (Gouttevin et al., 2012).
Nevertheless, similarly to the case of the hydrology module, the three
default (coarse, medium, fine) soil textures cannot represent thermal
properties of a peat soil (Paavilainen and Päivänen, 1995;
Abu-Hamdeh and Reeder, 2000).
Measured (a) and simulated (b) soil temperature, and their
differences (c) at the DK-NuF (64.13∘ N, 51.39∘ W) fen site. Soil
temperature was measured at 2, 10, 20, 50, and 70 cm below soil surface. To
compare simulated soil temperatures with the measurements, we linearly
interpolated simulated soil temperature in different layers to the depths of
the measurements.
![]()
Discussion
ORCHIDEE-PEAT groups various peatland vegetation into one plant functional
type (PFT). This PFT cannot represent the true range in vegetation
composition (shrubs, sedges, mosses, etc.) of peatlands. However, by
optimizing the value of Vcmax at each site, simulated GPP well
represented observations and yielded reasonable soil carbon input. The
Vcmax values estimated in this study ranged from 19 to 89 µmol m-2 s-1,
with a mean value of 40 µmol m-2 s-1.
These values were not fully comparable with values reported for a specific
vegetation type, as they are averages for all plants growing in the peatland
ecosystem. As stated in Sect. 2.2, observed Vcmax varies strongly among
different species and sites. Vcmax of mosses at the Old Black Spruce
site (Canada) ranged from 5 to 14 µmol m-2 s-1 (Williams
and Flanagan, 1998). In a nutrient addition experiment conducted by Bubier
et al. (2011), Vcmax for ericaceous shrubs in a temperate bog ranged
from 67 to 137 µmol m-2 s-1, with Vcmax for
Vaccinium myrtilloides, Ledum groenlandicum, and
Chamaedaphne calyculata valued at 84.6 ± 13.5, 78.1 ± 13.4, and 132.1 ± 31.2 µmol m-2 s-1
in the plots with no nutrient addition. The optimized model
Vcmax in our study was within the range of these observations.
Meanwhile, the values we inferred from sites to match peak GPP are
comparable to those used in other land surface models: the McGill wetland
model used a value of 17 µmol m-2 s-1 for evergreen shrubs
(St-Hilaire et al., 2010); the CLASS-CTEM model (Wu et al., 2016) used 60,
50, and 40 µmol m-2 s-1 for evergreen shrubs, deciduous
shrubs, and sedges, respectively; the values for mosses in these two models were
adapted from the study of Williams and Flanagan (1998). Here, we found that
optimized Vcmax has a significant positive relationship with
temperature and a significant negative relationship with latitude of chosen
peatland sites. A decrease of Vcmax with latitude in the Northern
Hemisphere, like the one inferred from optimized site values, has also been
documented by Walker et al. (2017), who assumed that Vcmax was
constrained by the rate of N uptake, with the rate of N uptake calculated as
a function of soil C, N, and mean annual air temperature. We speculate that
the dependence of optimized Vcmax on latitude found in Sect. 4.2 can be
attributed to two effects. First, there is an increase of the length of the
growing season as latitude decreases. Simultaneously, temperature
and incoming solar radiation increase. The longer growing season may enhance
vegetation productivity (Fang et al., 2003; Nemani et al., 2003; Piao et
al., 2007). Second, temperature influences the nutrient availability for
plants. The decomposition of plant litter and the release of nitrogen can be
enhanced by high temperature, although litter decomposition is also driven
by soil moisture, vegetation composition, litter quality, and their
interactions with temperature (Aerts, 2006; Cornelissen et al., 2007; Gogo
et al., 2016). Because nitrogen (N) is one key element in proteins that are
involved in the photosynthesis process, photosynthesis capacity is highly
correlated to N availability (Evans, 1989; Takashima et al., 2004; Walker et
al., 2014). Since the N cycle is not explicitly included in
ORCHIDEE-PEAT, the relationship between Vcmax and the latitude (and
temperature) possibly reflected the impact of N on photosynthesis rates.
Previous studies have shown that peatlands can have contrasting responses to
variations in water table depth. Concerning sites analyzed in our study,
Aurela et al. (2007) reported that at the nutrient-poor fen FI-Sii site,
drought increased respiration and thus diminished carbon uptake; Adkinson et al. (2011) reported that reduced water availability constrained
photosynthesis capacity at the rich fen CA-Wp3 and consequently suppressed
NEE, while the poor fen CA-Wp2 did not show a significant response to the
lower water table. At the moderately rich treed fen CA-Wp1 site, Flanagan
and Syed (2011) reported that both photosynthesis and respiration increased
in response to the warmer and drier conditions; Hurkuck et al. (2016) stated
that temperature and light played a more important role than water table
depth in controlling respiration and photosynthesis at the DE-Bou bog. Based
on the field observations, the timing, duration, and intensity of drought
have a major impact on the responses of peatland ecosystems.
Lund et al. (2012) demonstrated that at the raised bog SE-Faj, a relatively short but
severe drought that occurred in the middle of growing season of 2006
amplified respiration while a long-lasting drought that occurred at the
beginning of growing season of 2008 reduced GPP. Lafleur et al. (2005) and
Sulman et al. (2009) concluded from their studies at the CA-Mer bog and US-Los
fen that wetter peatlands would show a stronger relationship between
respiration and water table than drier peatlands because in a narrow range
of the upper soils, small increases in WT (shallower WT) can result in a
large increase in soil water content and therefore respiration decrease,
while below a critical level, soil water content shows only small increase
with increasing WT, and respiration changes are not so pronounced.
Sulman et al. (2010) found that wetter conditions decreased respiration at fens but
increased respiration at bogs, mainly due to different vegetation
composition at these two types of peatlands: the fen sites had more shrubs
and sedges while the bog sites had more mosses. In this study, we did not
distinguish between fens and bogs, and growth of peatland vegetation was not
constrained by water table depth in the model. Therefore, the sensitivity of
GPP to WT fluctuations in observations was not included in the model. As a
consequence, the model captured neither the reported decrease of
photosynthesis due to drought at CA-Wp3 (Adkinson et al., 2011) and SE-Faj
(Lund et al., 2012) nor the increase of photosynthesis as a result of lower
water table at CA-Wp1 (Flanagan and Syed, 2011). However, the model can
reproduce the pattern where, above a critical level (acrotelm depth), peat
respiration decreases with increasing WT (Figs. 5, S13), as reported at
CA-Mer and US-Los (Lafleur et al., 2005; Sulman et al., 2009).
ORCHIDEE-PEAT adequately captured the daily, seasonal, and across-site
annual variations in GPP (with r2= 0.75, 0.86, and 0.93,
respectively) and ER (with r2= 0.78, 0.86, and 0.89, respectively)
but did not perform as well in reproducing NEE variations (with r2= 0.38, 0.61, and 0.27, respectively). Note that in the two-layer soil
carbon scheme, the dependence of soil respiration on temperature was
parameterized as an exponential function of the soil layer-weighted average
temperature (Text S1); the vertical temperature gradient in the soil profile
was ignored by the model. However, field studies have shown that soil
temperature is one of the most important predictors of respiration, and
values of Q10 coefficient depend on the soil depth (Lafleur et al.,
2005; D'Angelo et al., 2016).
Measured (a) and simulated (b) soil temperature, and their difference
(c) at the CA-Wp1 (54.95∘ N, 112.47∘ W) fen site. The
measured soil temperature (a) is the mean of a hummock and a hollow. Soil
temperature was measured at 2, 10, 20, 50, and 100 cm below soil surface. To
compare simulated soil temperatures with the measurements, we linearly
interpolated simulated soil temperature in different layers to the depths of
the measurements.
![]()
Simulated vs. measured snow depth (m) at the (a) DK-NuF and
(b) CA-Wp1 fen sites.
![]()
Correct representation of peatland hydrology is a challenging problem in
large-scale land surface models (Wania et al., 2009a; Wu et al., 2016). The
simulated water table by ORCHIDEE-PEAT depends on water inflows from the
surrounding non-peatland areas, and a water-routing analysis on subgrid
scales can be included to improve the model performance for water table in
the future (Ringeval et al., 2012; Stocker et al., 2014). Other studies have
shown that microtopography exerts important influences on hydrological
dynamics of peatlands; however, to capture the influence of microtopography
on water table, high-resolution microtopographic feature and vegetation
information are needed (Gong et al., 2013; Shi et al., 2015).
The poor correspondence between simulated and observed energy fluxes was not
completely unexpected, since ORCHIDEE-PEAT only calculates one energy budget
for the whole grid cell and not for each soil tile/PFT present in the same
grid cell. A site-varied and/or time-varied correction of LE and H
measurements to force energy balance closure, and parameterizations of an
independent energy budget in peatlands would be helpful for better comparison
of simulated and observed energy fluxes in peatlands.
Conclusions
We developed ORCHIDEE-PEAT to simulate soil hydrology and carbon dynamics in
peatlands. The model was evaluated at 30 northern peatland sites (Europe,
USA, Canada, and Russia). The optimization of Vcmax reduced the errors in
the simulated carbon budget. The model, generally, reproduced the spatial
gradient and temporal variations in GPP, ER, and NEE well. Water table depth
was poorly simulated, possibly due to uncertainties in water input from
non-peatland areas in the grid cell, and to a lack of representation of
micro-relief, as well as the lack of consideration of peat disturbance. A
significant relationship between Vcmax and latitude was found. This may
be attributed to the influence of temperature on growing season length and
nutrient availability. For ER and NEE, the improvement brought by forcing
the carbon module to use observed WT values (WTobs), instead of
being calculated by the model, was small, indicating that the simulated WT was
reliable to predict ER and NEE properly.
Our study shows that in order to reproduce spatial gradients of NEE for
northern peatlands, an average Vcmax value is not sufficient. To
represent a spatial gradient of carbon fluxes in large-scale simulations of
northern peatlands, incorporating the peatland nitrogen cycle would be
helpful. Alternatively, an empirical relationship between Vcmax and the
latitude (temperature) may be used as a proxy of nitrogen availability.
Effects of water table variations on soil carbon decomposition are modeled
as the partitioning of the acrotelm layer into oxic and anoxic zones, but
effects of water table changes on GPP were not modeled in this study. Future
priorities for improving ORCHIDEE-PEAT include better representing the
influence of the water table on photosynthesis and depth-dependent influence of
soil temperature on soil respiration, as well as including an independent
subgrid energy budget for peatland areas.