Surface to atmosphere carbon exchange
in peatlands with permanent open water pools
Luc Pelletier
Dept. of Natural Resource Sciences, Macdonald Campus of McGill University, St-Annede-Bellevue.
November 2014
A thesis submitted to McGill University in partial fulfillment
of the requirement of the degree of Doctor of Philosophy
© Luc Pelletier, 2014
Abstract
Peatland ecosystems are long-term net carbon (C) sinks but the open water pools that are
characteristic of these temperate to subarctic wetlands have been found to be C sources to
the atmosphere. Their contribution to the ecosystem level C budget has often been
ignored even if they cover a significant area of the peatland surface. There is therefore a
question as to whether peatlands containing pools are smaller net sinks of atmospheric C
compared to peatlands without pools; the total C loss measured from pools is the same
order of magnitude, but with the opposite sign, as the published average net ecosystem
carbon balance for pool-free northern peatlands. In this study, I evaluated the annual C
release from peatland open water pools, its impact on the net ecosystem carbon dioxide
exchange (NEE-CO2) and assessed the NEE-CO2 variability and controls between
peatlands with different pool cover.
The annual C release from pools was estimated at 103 g C m-2 yr-1, of which 15% was
released during the spring ice melt. Although this estimate is within the range of other
published studies, my results suggest that previous assessments of C release
underestimate the annual loss by not including the spring melt release and by using
sporadic measurement techniques. The ecosystem scale measurements performed using
the eddy covariance technique (EC) showed that the 30-min maximum CO2 uptake rates
decreased by close to 25% for an increase in pool fraction within the EC tower source
area from 0-10% to 20-30%. I found that pools lower the overall NEE-CO2 components
(maximum photosynthetic uptake and ecosystem respiration rates) by reducing the
ecosystem’s aboveground vegetation biomass. The lower total vegetation biomass at the
ecosystem scale reduced net CO2 uptake during the day but also reduced ecosystem
respiration (ER) at night, as ER from the pools is mainly heterotrophic. The net effect
was that the site remained a sink for CO2 during the measurement period despite the
inclusion of pools. When comparing sites with different pool cover fraction and climate, I
observed significant variability in NEE-CO2 and its components between sites, and found
that biophysical controls on CO2 exchange previously identified for vegetated surface
peatlands (leaf area index, plant functional type) apply to peatlands containing openwater pools.
ii
Overall, my results demonstrate that peatland open water pools can play a significant role
in the ecosystem level surface to atmosphere C exchange. Measurements of interannual
NEE-CO2 variability from different peatlands with pools will expand the validity of these
findings. Future studies should also include an evaluation of the annual CH4 exchange,
CO2 loss during the winter along with dissolved organic carbon export from peatland
with pools in order to obtain a complete net ecosystem carbon balance.
iii
Résumé
Les tourbières représentent des puits à long terme pour le carbone (C). Cependant, les
mares observées à la surface de plusieurs de ces milieux humides des régions tempérées à
subarctiques sont des sources de C vers l’atmosphère. La contribution de ces plans d’eau
au budget du C de ces écosystèmes tourbeux est souvent ignorée malgré le fait que les
mares peuvent couvrir des superficies importantes sur les tourbières. La question se pose
à savoir si la capacité des tourbières à mares à séquestrer le C atmosphérique est
diminuée comparativement aux tourbières sans mare, les flux de C en provenance des
mares étant de la même amplitude, mais dans la direction opposée, à la balance nette du
C à des tourbières sans mare. La présente étude évalue les émissions annuelles de C en
provenance des mares de tourbière, l’impact de ces flux sur l’échange écosystémique net
de dioxyde de carbone (ÉÉN-CO2), ainsi que la variabilité et les contrôles associés de
l’ÉÉN-CO2 entre des tourbières caractérisées par des couvertures de mares distinctes.
La perte annuelle de C des mares a été évaluée à 103 g C m-2, dont 15% provient du
relâchement durant la fonte des glaces. Malgré que cet estimé se situe à l’intérieur des
limites des valeurs précédemment publiées, ces résultats suggèrent que les évaluations
précédentes sous-estiment la perte annuelle de C en ne tenant pas compte du relâchement
durant la fonte des glaces ainsi que par l’utilisation de techniques de mesure sporadiques.
Les mesures effectuées à l’échelle de l’écosystème à l’aide de la méthode de covariance
des turbulences révèlent que les taux de fixation maximaux de CO2 diminuent de 25%
pour une augmentation de la couverture des mares de 0-10% à 20-30% à l’intérieur de la
zone source. Les résultats démontrent que les mares abaissent les composantes de l’ÉÉNCO2 (taux de photosynthèse maximal et de respiration de l’écosystème) en réduisant la
biomasse de la végétation à l’échelle de l’écosystème. La biomasse végétale totale plus
basse réduit la capacité de fixation du CO2 durant le jour ainsi que son relâchement
durant la nuit puisque la respiration en provenance des mares est majoritairement
hétérotrophique. La comparaison entre les sites présentant différent climat et couverture
de mares suggère que les contrôles biophysiques identifiés pour expliquer la variabilité de
iv
l’ÉÉN-CO2 entre les tourbières sans mare (indice de surface foliaire, type fonctionnel
végétal) s’applique pour expliquer une partie de la variabilité entre les sites.
Dans l’ensemble, ces résultats démontrent que les mares de tourbières jouent un rôle
important dans les échanges de C entre la surface des tourbières et l’atmosphère. Des
mesures interannuelles d’ÉÉN-CO2 sur différentes tourbières à mares permettront de
d’accroitre la validité de ces observations. Afin d’établir la balance nette du C à l’échelle
de ce type d’écosystème, les études futures devraient aussi incorporer les pertes annuelles
de CH4, de CO2 durant la saison froide, ainsi que les pertes de C sous forme de carbone
organique dissout.
v
Table of contents
Abstract .............................................................................................................................. ii
Résumé .............................................................................................................................. iv
Table of contents .............................................................................................................. vi
List of figures .................................................................................................................... ix
List of tables...................................................................................................................... xi
List of acronyms .............................................................................................................. xii
Acknowledgements ........................................................................................................ xiv
Contribution of co-authors............................................................................................ xvi
1. Introduction ................................................................................................................... 1
1.1 Context of research ............................................................................................................. 1
1.2 Thesis structure ................................................................................................................... 3
2. Literature review .......................................................................................................... 5
2.1 Northern Peatlands ............................................................................................................. 5
2.1.1 Peatland definition, types and extent ............................................................................. 5
2.1.2 Peatland microforms and pools...................................................................................... 5
2.1.3 Carbon Storage and accumulation ................................................................................. 7
2.2 NEE-CO2 in peatlands ........................................................................................................ 8
2.2.1 Gross ecosystem photosynthesis and ecosystem respiration ......................................... 8
2.2.2 Controls on NEE-CO2 in peatlands ............................................................................... 8
2.2.3 Temporal and spatial variability in NEE-CO2 ............................................................. 10
2.3 CH4 fluxes from peatlands................................................................................................ 12
2.3.1 CH4 production, oxidation ........................................................................................... 12
2.3.2 CH4 release pathways .................................................................................................. 13
2.3.3 Controls on peatland CH4 fluxes.................................................................................. 14
2.3.3 Spatial and temporal variability in CH4 fluxes from peatlands ................................... 15
2.4 CO2 and CH4 exchange measurement technique ........................................................... 17
2.5 Conclusion of the literature review ................................................................................. 18
vi
3. Carbon release from boreal peatland open water pools: implication for the
contemporary C exchange .............................................................................................. 20
3.1 Abstract .............................................................................................................................. 21
3.2 Introduction ....................................................................................................................... 22
3.3 Methods .............................................................................................................................. 24
3.3.1 Study Area, Peatland and Pool Descriptions, and Climate .......................................... 24
3.3.2 Dissolved CO2 and CH4 measurements ....................................................................... 25
3.3.3 Flux calculation ........................................................................................................... 27
3.3.4 DOC measurements ..................................................................................................... 28
3.3.5 Eddy covariance measurements ................................................................................... 28
3.3.6 Supporting measurements ............................................................................................ 30
3.4 Results ................................................................................................................................ 31
3.4.1 Headspace dissolved CO2 and CH4.............................................................................. 31
3.4.2 NDIR sensor dissolved CO2 ........................................................................................ 34
3.4.3 Dissolved organic carbon ............................................................................................ 37
3.4.4 CO2 and CH4 evasion ................................................................................................... 39
3.4.5 CO2 and CH4 release upon ice melt ............................................................................. 43
3.4.5.1 Discrete measurements ........................................................................................................43
3.4.5.2 Eddy covariance tower measurements ................................................................................43
3.4.6 Annual release estimation from pool #1 ...................................................................... 43
3.5 Discussion........................................................................................................................... 45
3.5.1 Pools dissolved gas concentrations controls and origin of CO2................................... 45
3.5.2 CO2 and CH4 evasion from pools ................................................................................ 50
3.5.3 Effects on contemporary C budget estimates............................................................... 51
3.6 Conclusion.......................................................................................................................... 52
4. Are peatlands with pools a net sink for CO2? .......................................................... 54
4.1 Abstract .............................................................................................................................. 55
4.2 Introduction ....................................................................................................................... 56
4.3 Study Site and Methods .................................................................................................... 57
4.4 Results ................................................................................................................................ 60
4.5 Discussion........................................................................................................................... 61
4.6 Conclusion.......................................................................................................................... 66
vii
5. Multiscale spatial variability in surface-atmosphere CO2 exchange in a peatland
with open water pools ..................................................................................................... 67
5.1 Abstract .............................................................................................................................. 68
5.2 Introduction ....................................................................................................................... 69
5.3 Methods .............................................................................................................................. 71
5.3.1 Peatland location, description, and climate.................................................................. 71
5.3.2 Vegetated surface CO2 exchange ................................................................................. 73
5.3.3 Eddy covariance measurements ................................................................................... 75
5.3.3.1 Environmental measurements .............................................................................................75
5.3.3.2 EC data handling .................................................................................................................75
5.3.4 Source area analysis ..................................................................................................... 76
5.4 Results ................................................................................................................................ 77
5.4.1 Plant community CO2 exchange .................................................................................. 77
5.4.2 Eddy covariance tower fluxes: NEE-CO2 .................................................................... 81
5.5 Discussion........................................................................................................................... 85
5.6 Conclusion.......................................................................................................................... 89
6. Variability in summer net CO2 exchange from three peatlands along a temperate
to boreal maritime climate transect .............................................................................. 91
6.1 Abstract .............................................................................................................................. 92
6.2 Introduction ....................................................................................................................... 93
6.3 Methods .............................................................................................................................. 94
6.3.1 Peatland locations, description, and climates .............................................................. 94
6.3.2 Eddy covariance and ancillary measurements ............................................................. 95
6.3.3 NEE-CO2 data analysis ................................................................................................ 97
6.4. Results ............................................................................................................................... 98
6.4.1 Environmental conditions ............................................................................................ 98
6.4.2 NEE-CO2 ................................................................................................................... 100
6.5 Discussion......................................................................................................................... 102
6.6 Conclusion........................................................................................................................ 105
7. Summary, contribution and directions for future research.................................. 107
References ...................................................................................................................... 111
viii
List of figures
Figure 3.1: Location of the study area with pools and eddy covariance tower ................ 29
Figure 3.2: Dissolved CO2 and CH4 in the five pools in the studied peatland ................. 33
Figure 3.3: Daily average dissolved CO2, sediment and water temperature from pool #1,
along with daily average wind speed, air temperature and total daily precipitation during
the 2012 open water season .............................................................................................. 35
Figure 3.4: Diurnal pattern in dissolved CO2 in pool #1, measured by the NDIR sensors at
the surface and 1m depth in August, September, October and November 2011 .............. 37
Figure 3.5: Average DOC concentration from the five pools in 2012; relationship
between headspace measurements dissolved CO2 and SUVA254; and relationship between
dissolved CO2 and dissolved organic carbon concentration ............................................. 38
Figure 3.6: Daily average CO2 evasion from pool #1 calculated with the NDIR sensor
CO2 measurements; manual headspace measurements, and headspace measurement
interpolation; and pool #3 manual headspace measurements and headspace measurement
interpolation ...................................................................................................................... 41
Figure 3.7: Daily average CH4 evasion from pool #1 manual headspace measurements
and headspace measurement interpolation; and pool #3 manual headspace measurements
and headspace measurement interpolation........................................................................ 42
Figure 3.8: NEE-CO2 eddy covariance measurements from April 9 to May 9, 2012 ...... 44
Figure 3.9: Relationship between sediment temperature and CO2 flux at the surface of
pool#1 and Arrhenius relationship for the same data set .................................................. 49
Figure 4.1: Peatland microform classification and EC tower location; Monthly wind
direction frequency for NEE-CO2 in % ............................................................................ 60
Figure 4.2: Mean monthly diurnal pattern in NEE-CO2 during summer 2012 on the
studied peatland ................................................................................................................ 62
Figure 4.3: Mean monthly NEEmax (PAR > 1000 μmol m-2 s-1), ER and daily average
NEE-CO2 between May and October 2012 at the studied peatland ................................. 63
ix
Figure 4.4: Relationship between Amax and R10 for temperate, boreal and subarctic
peatlands ........................................................................................................................... 64
Figure 5.1: Spatial distribution of plant communities and pools at the surface of the
studied peatland ................................................................................................................ 72
Figure 5.2: Relationship between CO2 exchange and PAR over the four plant
communities in May, July and August 2012 in the studied peatland. .............................. 80
Figure 5.3: Daytime 30-min EC NEE-CO2, clear chamber CO2 exchange from plant
communities and thin boundary layer CO2 fluxes from the open water pools in the studied
peatland between May and October 2012. ........................................................................ 81
Figure 5.4: Relationship between NEE-CO2 and PAR between 15 May and 10 October
2012................................................................................................................................... 82
Figure 5.5: Relationships between cover fraction of pools and selected plant communities
(Ericaceous hummocks and P. mariana hummocks) within the modeled 30-min source
areas of the EC tower between May and October 2012.................................................... 87
Figure 6.1: Air temperature, total daily PAR, water table position and precipitation at the
three sites between June and September 2012 .................................................................. 99
Figure 6.2: Peat temperature profiles at the three sites between June and September 2012.
......................................................................................................................................... 100
Figure 6.3: Diurnal patterns in NEE-CO2 at MB, BC and HSP in June, July, August and
September 2012. ............................................................................................................. 101
Figure 6.4: June to September 2012 monthly NEP (g CO2 m-2) at MB, BC and HSP. .. 102
Figure 6.5: Relationship between LAI and the June to August NEE-CO2 for the sites in
this study and those from Lund et al. [2010], and Humphreys et al. [2014]. ................. 104
x
List of tables
Table 3.1: Pool physical and chemical characteristics, and mean, maximum and minimum
dissolved CO2 and CH4 during the open water seasons as obtained from the headspace
measurement technique. .................................................................................................... 31
Table 3.2: Best exponential relationship parameters between pool #1 temperature at 1m
and dissolved CO2 from NDIR sensor. ............................................................................. 36
Table 3.3: Mean, minimum and maximum fluxes from headspace measurements,
interpolation and NDIR sensor ......................................................................................... 40
Table 4.1: Mean June to September daily average NEE-CO2 for the Petite Rivière
peatland and other boreal and subarctic peatlands. ........................................................... 65
Table 5.1: Plant communities, vegetation description, aboveground biomass and average
water table position below peat surface between May 15 and October 10, 2012. ............ 74
Table 5.2: LUE parameters for equation (1) from the relationship between plant
communities CO2 exchange and PAR during the 3 chamber measurement campaigns. .. 79
Table 5.3: LUE parameters for equation (2) derived from the NEE, PAR, and Tair as a
function of pools or plant communities’ fraction within the source area. ........................ 83
Table 5.4: LUE parameters for equation (2) derived from the NEE, PAR, and Tair as a
function of pools fraction within the source area for individual months. ......................... 84
Table 6.1: Sites characteristics and description of the eddy covariance systems ............. 97
Table 6.2: Monthly LUE parameters for equation (1) derived from the NEE, PAR, and
Tair as a function of pools or plant communities’ fraction within the source area. ......... 101
xi
List of acronyms
α
Initial quantum yield
Amax
Maximum photosynthetic uptake
BC
Baie-Comeau peatland site
C
Carbon
CH4
Methane
CO2
Carbon dioxide
Ca
Concentration of gas in the air
Cw
Concentration of gas in the water
DOC
Dissolved organic carbon
DOY
Day of year
EC
Eddy covariance
ER
Ecosystem respiration
FSAM
Flux source area model
GHG
Greenhouse gas
HSP
Havre-Saint-Pierre peatland site
Kx
Gas-specific exchange coefficient
K600
Gas exchange coefficient normalized for CO2 at 20°C
LAI
Leaf area index
LORCA
Long-term apparent rate of carbon accumulation
LUE
Light use efficiency
MB
Mer Bleue peatland site
NDIR
Nondispersive infrared
NECB
Net ecosystem carbon balance
NEE-CO2
Net ecosystem carbon dioxide exchange
NEEcap
Net ecosystem carbon dioxide exchange for PAR = 1800 μmol m−2 s−1
NEP
Net ecosystem productivity
PAR
Photosynthetically active radiation
PFT
Plant functional type
PSNmax
Maximum rates of photosynthesis for PAR > 1000 μmol m−2 s−1
PTFE
Polytetrafluoroethylene
xii
R
Dark respiration for plant community measurements
R10
ER calculated for an air temperature of 10°C
Sc
Schmid number
SUVA254
Specific ultraviolet absorbance at 254 nm wavelength
T
Water temperature
Tair
Air temperature
TOC
Total organic carbon
U10
Wind speed at 10 m height
U*
Friction velocity
WTP
Water table position
xiii
Acknowledgements
The work presented in this thesis was supported by the Natural Sciences and Engineering
Research Council of Canada (NSERC) through an NSERC-CRD grant to Dr. Michelle
Garneau and a NSERC Discovery Grant awarded to Dr. Ian B. Strachan. I was also
supported by a NSERC Postgraduate Scholarships-Doctoral, the McConnell Foundation,
the McGill Graduate Excellence program and GREAT travel grants from McGill
Department of Natural Resource Sciences.
This work would not have been possible without the help or presence of several people I
have worked with or met during these 4-years. Most importantly, I would like to thank
Dr. Ian B. Strachan for being more than a supervisor over this long journey. Ian’s
availability to discuss, to provide ideas and answer my multiple concerns with data,
analysis and equipment was simply exceptional; this did not change when there were
more important priorities. I am truly grateful for this amazing support.
I would also like to thank Dr. Nigel T. Roulet for scientific and personal advices, for his
support, and also for giving me the opportunity to meet many great scientists. Thank you
to Dr. Tim R. Moore for impromptu discussions and for constantly helping me improve
my work since I first started at McGill. Thanks to Dr. Patrick Crill for his comments and
for the challenging questions. I would also like to thank Dr. Michelle Garneau for giving
me the opportunity of working on this project and for financial support. I would like to
acknowledge Dr. Alain Tremblay at Hydro-Quebec for field support, Dr. Jean-François
Hélie at GEOTOP for allowing me to use his lab pump and vacuum line, and Dr. Onil
Bergeron for solving my EC data processing problems with Matlab.
Thanks to Manuel Helbig, Karoline Wichnewski and Dr. Oliver Sonnentag at the
Département de Géographie de l’Université de Montréal for helping with the source area
analysis. I am also really thankful for the field and lab assistance of Eric Christensen (the
most over qualified field assistant), Stephanie Crombie, Kelly Nugent, Annie Lamalice,
xiv
Valérie Lefrancois, Camille Lefrançois, Julien Minville, Caitlin Watt, Mathis Messager,
Rui Chen and Frank Ferber.
Thanks to Mike Dalva for help with the DOC analysis and for all the great morning
discussions. “It’s gonna work!”. Although we did not collaborate much over the course of
this research, I owe an enormous thanks to Jean-Louis Fréchette for sharing his
experience over the years. Working with you on all these different field campaigns,
across the globe, has though me so much and I don’t think I would be where I am today
without having worked with you. I also owe a huge thanks to Hans Asnong for his
friendship. Merci pour tout Hans! Thanks to Julie Talbot for numerous discussions over
coffee or lunch! Also, special thanks to Silvie Harder, Tanja Zivkovic, Avni Malhotra,
Lorna Harris, Meng Wang in Geography.
Most importantly, I would like to thank my wife Sophie, my daughter Alexia and my son
Liam for their patience and comprehension. The countless trips to the North Shore
(odometer > 35 000km) and the incessant technical problems I ran into during the first 2
years never affected the support they had for me, even when I got close to throwing the
towel.
xv
Contribution of co-authors
The thesis research results (Chapters 3 to 6) are written in a series of manuscripts for
publication in peer-review journals. These chapters are co-authored by my supervisor Dr.
Ian B. Strachan who contributed to the research planning, provided scientific advice
through the data analysis and writing processes, helped in editing the manuscripts, and
contributed financially to the completion of the research. The roles of the other coauthors are detailed below.
Manuscript #1 (Chapter 3) “Carbon release from boreal peatland open water pools:
Implication for the contemporary C exchange” by Luc Pelletier, Ian B. Strachan,
Michelle Garneau and Nigel T. Roulet (Journal of Geophysical Research-Biogeosciences,
2014). Michelle Garneau contributed financial support for the research, provided lab
resources for gas chromatography and contributed to the editing of the manuscript. Nigel
T. Roulet provided scientific advice and helped in editing the manuscript.
Manuscript #2 (Chapter 4) “Are peatlands with pools a net sink for CO2” by Luc
Pelletier, Ian B. Strachan, Nigel T. Roulet and Michelle Garneau (Re-Submitted to
Geophysical Research Letters, July 2014). Nigel T. Roulet provided scientific advice and
helped in the editing of the manuscript. Michelle Garneau contributed financial support
for the research, helped in setting the experiment in the early phase of the project and
contributed to the editing of the manuscript.
Manuscript #3 (Chapter 5) “Multiscale spatial variability in surface-atmosphere CO2
exchange in a peatland with open water pools” by Luc Pelletier, Ian B. Strachan, Nigel T.
Roulet and Michelle Garneau (to be submitted to Biogeochemistry). Nigel T. Roulet
provided scientific advice and contributed to the editing of the manuscript. Michelle
Garneau contributed financially, helped in setting the experiment in the early phase of the
project and contributed to the editing of the manuscript.
Manuscript #4 (Chapter 6) “Variability in summer net ecosystem CO2 exchange from
three peatlands along a temperate to boreal maritime climate transect” by Luc Pelletier,
xvi
Ian B. Strachan, Nigel T. Roulet, Michelle Garneau and Elyn Humphreys (to be
submitted). Nigel Roulet provided scientific advice and helped in the editing of the
manuscript. Michelle Garneau contributed financially, helped in setting the experiment in
the early phase of the project and contributed to the editing of the manuscript. Elyn
Humphreys provided data from the Mer Bleue site and provided scientific advice.
xvii
1. Introduction
1.1 Context of research
Peatlands are important sinks for carbon (C), storing the equivalent of 60% of the C
found in the atmosphere as carbon dioxide (CO2) [Yu et al., 2010]. This C storage is the
result of the imbalance between productivity and decomposition; more CO2 is absorbed
by the surface vegetation than is simultaneously released through plant root respiration
and organic matter decomposition. Additional C is also lost through methane (CH4)
emissions and dissolved organic carbon (DOC) export. While peatlands are generally a
net sink for CO2 and a net source of CH4, the actual rates and the direction of gas
exchange can vary significantly within a single peatland.
The microforms found on peatlands (hummocks, lawns, hollows, pools) are characterized
by different water table positions, vegetation assemblages and biomass, which in turn
control the fluxes of CO2 and CH4 between the ecosystem and the atmosphere. Vegetated
surfaces and ephemeral pools (those that dry up during the growing season) generally
represent a net C sink. In contrast, studies examining the CO2 and CH4 exchange at the
surface of permanent open water pools have shown that these water bodies release C to
the atmosphere at a rate that is similar to, but in the opposite direction as, the published
long-term rates of carbon accumulation.
Peatlands containing open-water pools are found on every continent, with the exception
of Antarctica [Glaser, 1999], and these water bodies are common features of boreal and
subarctic peatlands. Data on the distribution of peatlands with pools or the coverage that
pools represent within a peatland are scarce. In the Hudson Bay lowlands, peatlands with
open-water pools are estimated to represent approximately 16% of the total peatland
surface and the pools themselves accounted for more than 30% of the surface coverage
on some of the sites [Roulet et al., 1994]. In fens of northeastern Quebec, Canada, the
1
surface covered by open water pools has been found to be greater than 50% at some sites
[White, 2011].
Although pools are important considering the direction of the C flux and the surface area
that they cover, these water bodies have received limited interest. Overwhelmingly,
peatland studies have focused on the terrestrial portions and have examined several
aspects of the CO2 and CH4 fluxes: variability in space and time, and associated controls
[Bubier et al., 1995a, 1998]; the impact of the short and long term water table drawdown
[Strack and Waddington, 2007; Talbot, 2010]; and the response of increased nitrogen,
and phosphorus deposition [Juutinen et al., 2010], to name a few. The literature on
peatland open-water pool CO2 and CH4 fluxes comprises only eight studies [Hamilton et
al., 1994; Waddington and Roulet, 1996, 2000; Pelletier et al., 2007; Repo et al., 2007;
McEnroe et al., 2009; Cliche Trudeau et al., 2013, 2014]. A common aspect of these
studies is that they all use periodic (non-continuous) measurements to evaluate the C
release and further, that the period of measurement covers only the growing season.
Still fewer studies have examined the role of open-water pools on the ecosystem C
exchange of peatlands. Such evaluations have been made by spatially extrapolating and
temporally interpolating the sporadic chamber measurements made on vegetated and pool
surfaces [Waddington and Roulet, 2000; Pelletier et al., 2007; Cliche Trudeau et al.,
2013, 2014]. While the chamber approach provides information on the instantaneous CO2
and CH4 fluxes and processes, the errors that result from the interpolation can be large
[Bubier et al., 1999]. Net ecosystem CO2 exchange (NEE-CO2) measurements using eddy
covariance technique have been made on peatlands with ephemeral pools [Aurela et al.,
2001, 2002, 2004] but not over peatlands with deeper and permanent open water bodies.
The magnitude of the published annual release of C from open water pools suggests that
the NEE-CO2 for peatlands with pools could be significantly lower than currently
reported for northern peatlands without pools and therefore raises the question if the
generalized C uptake figures for peatlands without pools apply to peatlands with pools.
2
In this thesis, I used a combination of continuous and discrete measurement techniques to
evaluate the C exchange above pools, vegetated surface plant communities and at the
ecosystem level to understand the impact of open-water pools on the C exchange between
the peatland surface and the atmosphere. My specific research objectives were to:
(1) Evaluate the importance of the annual C loss from pools on the peatland net
ecosystem carbon balance;
(2) Assess the CO2 sink potential of a peatland with pools;
(3) Identify the pool’s CO2 flux signal from the ecosystem level measurements;
(4) Assess if biophysical controls on NEE-CO2 previously identified for vegetated
surface peatlands apply to peatlands with open-water pools.
1.2 Thesis structure
This thesis comprises seven chapters including this introductory Chapter. Chapter 2
presents a review of the literature on peatland characteristics and their global importance,
and peatland CO2 and CH4 dynamics and controls. The research results are organized into
four Chapters (3-6), which have been or will be submitted to peer-reviewed journals. In
Chapter 3, the first manuscript presents the annual C release from peatland open-water
pools (Objective 1). This includes quantifying the C loss during the spring melt period,
the spatial and temporal variability in fluxes, and the identification of the environmental
controls on the fluxes. The second manuscript, Chapter 4, presents NEE-CO2 of a
peatland with pools during the growing season and evaluates if the site can represent a
net sink for CO2 (Objective 2). The NEE-CO2 components (gross photosynthesis and
respiration) are also compared with published values from other sites without pools to see
how these water bodies influence peatland NEE-CO2. The third manuscript, Chapter 5,
examines the variability in CO2 exchange at the local scale (plant communities and pools)
and evaluates if this spatial variability can be identified from the ecosystem scale NEE3
CO2 measurements (Objective 3). The final results are presented in the fourth manuscript,
Chapter 6, in which NEE-CO2 variability between three peatlands (two with pools, one
without pools) is presented and where I evaluate if the biophysical controls previously
identified for peatlands without pools, apply to peatlands with open-water pools
(Objective 4). The thesis concludes in Chapter 7, where I summarize my results and
propose directions for future research.
4
2. Literature review
2.1 Northern Peatlands
2.1.1 Peatland definition, types and extent
Peatlands are organic wetlands, characterized by close to surface water table and biomass
productivity that exceeds decomposition, leading to large accumulation of partially
decomposed organic matter commonly known as peat. Based on the Canadian System of
Soil Classification, these deposits must be greater than 40 cm in depth to be considered
peatlands [Soil Classification Working Group, 1998]. These organic wetlands can be
divided in two different categories, bogs and fens. Bogs are peatlands that are
oligotrophic and ombrotrophic, receiving limited amounts of nutrients, exclusively from
atmospheric precipitation. Fens, in contrast, are minerotrophic and their nutrient input
comes from both the surrounding mineral substrate water and from atmospheric
precipitation. Fens can be oligotrophic or eutrophic depending on if they are considered
poor, moderately-poor, or rich in nutrients. The pH in bogs is generally higher than in
fens; 3.5 to 5 in bogs compared to 5 to 7 in fens. Around the globe, peatlands cover more
than 4 × 106 km2 of the Earth’s land surface and close of 90% of these are found in boreal
and subarctic regions [Yu et al., 2010]. The largest concentrations of these ecosystems are
found in the Western Siberia lowlands in Russia, and in the Hudson Bay lowlands in
Canada.
2.1.2 Peatland microforms and pools
The surfaces of peatlands are characterized by plant communities and microtopographical
features which differ slightly between peatland types. In fens, this microtopography is
generally represented by alternating strings, dominated by cyperaceae and brown moss,
and flarks, where water pools and vascular plants such as Menyanthaceae dominates the
vegetation. In the bogs, the surface is generally composed of hummocks, lawns, hollows
and pools. These ombrotrophic peatland microforms differ in terms of their relative water
table position, plant communities, vegetation biomass, nutrient level, and pH. The water
table is generally 20 to 60 cm below the surface for hummocks and gets closer to the
surface as one moves towards lawns, hollows and pools, where it can be more than 200
5
cm above the peat deposit. The aboveground vegetation biomass, which is composed of a
combination of Sphagnum moss and vascular plants, generally decreases from hummocks
to hollows [Moore et al., 2002] and is almost non-existent in pools. Hummock vegetation
is comprised of a Sphagnum layer whose species are adapted to lower water table (e.g.
Sphagnum fuscum, Sphagnum angustifolium), and dwarf ericaceous shrubs such as
Chamaedaphne calyculata and Rhododendron groenlandicum. On the lawns, the
Sphagnum spp. are adapted to shallower water table (e.g. Sphagnum fallax, Sphagnum
rubellum) and ericaceous plants such as Andromeda glaucophylla and Kalmia polifolia
along with some graminoids (e.g. Carex and Eriophorum spp.) can also be found. The
vegetation in hollows is generally limited to graminoids (e.g. Carex and Eriophorum
spp.) and Sphagnum spp. that are adapted to water table near or at the surface (e.g.
Sphagnum cuspidatum). While vegetation biomass is quite low in open water pools, some
Nuphar lutea are commonly observed.
The development of peatland microtopography has been subject to a lot of interest over
the years [Foster and Fritz, 1987; Foster and Wright, 1990; Belyea and Clymo, 2001;
Morris et al., 2013]. The difference in elevation between hummocks and hollows has
been explained by the positive feedback between the thickness of the oxic peat layer and
the rate of peat formation [Belyea and Clymo, 2001]. The microtopography has also been
associated with Sphagnum spp. decomposition rates, water table position and plant
species interaction [Vitt, 1990; Malmer et al., 1994; Belyea, 1996]. Pool formation has
been linked to changes in hydrological conditions, such as higher than normal
precipitation that results in a rise of the water table. This rise in water table in turn results
in water ponding in the hollows where the water table is already close to the surface. This
standing water above the vegetated surface triggers a reduction in vegetation productivity
while the hummocks remain unaffected and are still productive [Belyea and Clymo,
1999]. If these conditions are maintained for a long enough period of time, the general
peatland water table will rise and the submerged hollows will become shallow pools that
will deepen over time [Belyea and Clymo, 1999]. The open water pool diameter is
generally on the order of tens of meters although larger pools can be several hundred
meters across, and their depth varies from less than a meter to more than 2 meters [Foster
6
and Fritz, 1987; Foster and Wright, 1990; Karofeld and Tõnisson, 2012]. Most pools
documented in the literature seem to be secondary features of the peatland as their bottom
is underlain by peat that accumulated prior to the pool formation. Mineral bottom
peatland pools have also been observed in the Hudson Bay lowlands by [Hamilton et al.,
1994] but are not as common. In the Hammarmossen raised bog in Sweden, Foster and
Wright [1990] also observed gyttja sediments above the peat accumulated prior to pool
formation; the gyttja layer reached close to 1 m in some pools. Conversely, Thibault et al.
[submitted] observed no gyttja sediment at the bottom of their pool in an ombrotrophic
peatland, located in eastern Canada. Pools with gyttja suggest a significant benthic
productivity as opposed to mineral bottom pools.
2.1.3 Carbon Storage and accumulation
The total amount of C stored through peat accumulation in peatlands has been estimated
at between 270 and 547 Gt C [Gorham, 1991; Turunen et al., 2002; Yu et al., 2010]. The
long-term apparent carbon accumulation rates (LORCA) in peatlands since the last
glaciation vary between 16 and 80 g C m-2 yr-1 [Turunen et al., 2002; Gorham et al.,
2003; Garneau et al., 2014]. These rates vary significantly between peatland type (bog
greater than fen), and location, with accumulation rates generally decreasing with
increasing latitude [Turunen et al., 2002; Gorham et al., 2003; Beilman et al., 2009;
Garneau et al., 2014]. Boreal and subarctic peatlands play an important role in the global
greenhouse gas (GHG) and C dynamic. Peatland initiation has contributed significantly to
the increase in atmospheric CH4 concentration during the Holocene [MacDonald et al.,
2006]. Despite the large CH4 global warming potential, the impact of peatlands on
climate over the last 8000 to 11000 years has been estimated to be a net cooling effect
with a net radiative forcing of about -0.2 to -0.5 W m-2 [Frolking and Roulet, 2007],
mainly because of the large amount of atmospheric CO2 that has been accumulated as
peat.
7
2.2 NEE-CO2 in peatlands
2.2.1 Gross ecosystem photosynthesis and ecosystem respiration
Peatland’s NEE-CO2 results from the difference between CO2 uptake by the surface
vegetation (gross ecosystem photosynthesis - GEP) and CO2 release to the atmosphere
(ecosystem respiration - ER). ER comprises two components: the autotrophic respiration
(growth and maintenance) and the heterotrophic respiration (peat decomposition).
Partitioning studies from vegetated surface measurements have shown that autotrophic
respiration represents between 17 and 50% of the total ER [Silvola et al., 1996b; Moore
et al., 2002; Crow and Wieder, 2005]. In this thesis, the meteorological sign convention
is used where positive NEE-CO2 values represent a net loss of CO2 from the peatland to
the atmosphere, and negative values a net uptake by the peatland.
2.2.2 Controls on NEE-CO2 in peatlands
The photosynthetically active radiation (PAR) is the dominant control on the GEP as it
represents the energy source for the CO2 exchange to occur between vegetation and the
atmosphere. The relationship between CO2 exchange and PAR can be fit with a
rectangular hyperbola as
GEP = Amax * α * PAR / (Amax + (α * PAR))
(1)
where: α is the apparent quantum yield and Amax is the maximum gross productivity.
The surface vegetation CO2 uptake in peatlands increases almost linearly with light
intensity before reaching photosaturation at PAR levels between 1000 and 1500 μmol m-2
s-1 [Frolking et al., 1998]. However, photosaturation levels also vary as a function of the
vegetation type [Small, 1972; Limbach et al., 1982]. The published mid-growing season
peatland Amax ranges from 4 to 26 μmol m-2 s-1 and varies mainly as a function of leaf
area index (LAI) and plant functional types (PFT) [Frolking et al., 1998; Humphreys et
al., 2006, 2014].
Temperature represents an important control on both photosynthesis and respiration. The
lower temperature during the spring and fall explains partly why NEE-CO2 values are
8
less during these periods [Frolking et al., 1998]. In the spring, the CO2 uptake in
peatlands begins rapidly when snow starts to melt and the moss layer is exposed [Bubier
et al., 1998]. Bubier et al. [2002] used automated chamber to measure CO2 exchange
during spring snow melt and observed CO2 uptake when the ground temperature was
above 0°C. Different studies have also shown that CO2 uptake rates decrease when
temperature approaches or exceeds 25oC [Limbach et al., 1982; Shurpali et al., 1995;
Suyker et al., 1997].
While having an indirect effect on autotrophic respiration through photosynthesis,
temperature also controls heterotrophic respiration by regulating the rates of peat
decomposition. Peat temperature has been shown to predict 84 and 80% of the CO2
emissions in a fen and a bog located in Minnesota [Updegraff et al., 2001]. Similarly,
temperature was found to explain 58-62% of the variability ER in a temperate bog in
Ontario [Lafleur et al., 2005]. The published rates of change in peatland CO2 emissions
with an increase in temperature of 10°C (Q10) range between 2.0 and 4.1, and vary as a
function of water table and peatland trophic gradient [Silvola et al., 1996a; Bubier et al.,
1998]. This relationship has also been documented through laboratory experiments where
found Moore and Dalva [1993] an average of 2.4 times larger emissions for an increase
in temperature from 10° to 23°C. Microbial activity below 0°C has also been observed,
which could contribute to peatland winter CO2 fluxes [Zimov et al., 1993] along with
previously stored CO2 diffusion [Roehm and Roulet, 2003]. The positive relationship
between flux and temperature was also observed in peatland pools where CO2 fluxes
increase with increasing temperature [Repo et al., 2007].
Water table position and moisture content also exert a control on photosynthesis and
respiration. Bubier et al. [2003a; 2003b] measured NEE during wet and dry summers in a
bog and a poor fen and observed an increase in overall uptake from evergreen and
deciduous shrubs, and an earlier senescence of Cyperaceae during the dry year. The
opposite was observed by Pelletier et al. [2011] where CO2 uptake rates were greater on
hummocks during a wetter than normal summer where water table was closer to surface.
The surface volumetric water content (VWC) has also been shown to be positively
9
correlated with GEP; Sphagnum productivity decreased significantly with VWC below
28%, corresponding to a water table position below 55cm [Strack et al., 2009]. Strong
relationships between the water table position and ER have been observed in both field
and laboratory studies with lower water table position increasing decomposition by
promoting aerobic conditions [Silvola et al., 1996a; Bubier et al., 1998, 2003a, 2003b]. In
a peat column experiment, Moore and Dalva [1993] measured an average increase in
CO2 emissions of 4.3 times after lowering the water table from the peat surface to 40 cm
below the surface for different peat types (bog, fen and swamp). Oppositely, Lafleur et al.
[2005] observed a weak correlation between multiyear ER, and water table position
suggesting that the relatively dry state of the bog explained the lack of relationship; the
change in water table position has an impact mainly on the upper portion of the peat
profile (the first 30 cm from the surface).
The physical aspects of the surface vegetation influence peatland GEP and ER. The leaf
area index (LAI) has been shown to explain the difference in daily summertime NEECO2 between ten wetland sites ranging from temperate to arctic regions [Lund et al.,
2010]. Analogously, positive relationships were observed between plant community-scale
vascular leaf biomass, and between maximum photosynthesis and respiration [Bubier et
al., 2003b]. Still, the relationship between LAI and NEE-CO2 presented by Lund et al.
[2010] shows a lot of scatter for LAI < 2 and highlights the importance of plant
functional types (PFT), which have different photosynthetic capacity [Humphreys et al.,
2014]. Comparing biomass and NEE-CO2 from four mires, Laine et al. [2011] showed
that a site with smaller aboveground green biomass with different species composition
can yield higher NEE-CO2 than another site that would have larger aboveground green
biomass.
2.2.3 Temporal and spatial variability in NEE-CO2
The NEE-CO2 varies significantly in time (day, month, season, years) and space (within
and between peatlands). These variations can be attributed to the environmental controls
presented above. At the peatland scale, variations in NEE-CO2 are measured across the
different microforms and plant communities [Laine et al., 1996; Waddington and Roulet,
10
1996, 2000; Bubier et al., 2003b; Leppälä et al., 2008; Pelletier et al., 2011]. On the
vegetated surface, microforms such as hummocks have greater GEP and ER compared
with hollows and drier microforms are generally more effective CO2 sinks even if
respiration rates are greater [Laine et al., 1996; Waddington and Roulet, 2000]. Peatland
pools are on the other hand are net sources of CO2 to the atmosphere with an annual
release ranging from 23 to 419 g CO2-C m-2 yr-1 and instantaneous fluxes that range from
a small uptake of 0.14 g CO2-C m-2 d-1 to a release of 16.6 g CO2-C m-2 d-1 [Waddington
and Roulet, 1996, 2000; McEnroe et al., 2009; Cliche Trudeau et al., 2014]. There is
limited data available on temporal concentration and flux patterns in pools. Hamilton et
al. [1994] observed that pools were constantly supersaturated in CO2 and CH4 in regards
to the atmosphere and that that dissolved concentrations and fluxes decreased during the
day and increased at night. However, they found no direct correlations between those
changes and the environmental data (temperature, PAR, wind speed). When looking at
the growing season patterns, Hamilton et al. [1994] found that the fluxes were maximum
in the fall. The gas fluxes at the surface of water bodies are a function of the wind speed
and gas concentration in the water [Wanninkhof, 1992]. In lakes, the concentration of
CO2 is controlled by the C loading from the surrounding terrestrial surface,
photooxidation of DOC and respiration [Algesten et al., 2005]. The origin of the emitted
CO2 at the surface of peatland pools has received limited attention. Hamilton et al. [1994]
suggested that the CO2 fluxes were supported by the degradation of underlying peat and
that the peak in concentrations measured in the fall could be the result of death and
decomposition of algal mats. In lakes, the dissolved CO2 concentrations have been
strongly correlated with the DOC explaining the variability in dissolved CO2 between
lakes [Hope et al., 1996; Jonsson et al., 2003; Roehm et al., 2009].
Peatland annual net ecosystem productivity (NEP) from multi-year data sets vary
between 4 and 216 g C m-2 yr-1 [Aurela et al., 2004, 2009; Roulet et al., 2007; Nilsson et
al., 2008; Sagerfors et al., 2008] and several studies have also shown NEP to vary
significantly between years for the same site [Arneth et al., 2002; Lafleur et al., 2003;
Aurela et al., 2004, 2009; Roulet et al., 2007; Nilsson et al., 2008; Sagerfors et al., 2008].
In a subarctic fen in Finland, Aurela et al. [2004] measured NEE-CO2 during six
11
consecutive years and found NEP to range between 4 and 53 g C m-2 yr-1 with an average
of 22 g C m-2 yr-1. They found the timing of snow melt to have the most important impact
on annual NEP by controlling the length of the growing season. At the Mer Bleue bog
(Ottawa, Canada), Roulet et al. [2007] found annual NEP to vary between 2 and 112 g C
m-2 yr-1 with a 6-year mean of 40.2 ±40.5 g C m-2 yr-1. The lowest NEP value presented
in their study corresponds to the year where water table dropped significantly during the
summer due to lower precipitation. Peatlands in which NEE-CO2 measurements have
been performed are characterized by relatively homogeneous surface vegetation, and
typically, an absence of pools. The Kaamanen site in northern Finland [Aurela et al.,
2001, 2002, 2004] could be considered an exception as it has ephemeral pools, although
these pools have been shown to be net sinks for CO2 [Heikkinen et al., 2002]. To date, no
NEE-CO2 measurements have been made over a peatland with permanent open water
pools.
2.3 CH4 fluxes from peatlands
Northern peatlands are an important source of CH4 to the atmosphere, estimated at 20 and
50 Tg CH4 yr-1 [Mikaloff Fletcher et al., 2004a, 2004b]. As with NEE-CO2, fluxes show
variability over time and space, and within and between peatlands. In a meta-analysis of
methane fluxes from 71 wetlands, Turetsky et al. [2014] present a range of CH4 fluxes for
peatlands of -20 to 5722 mg CH4 m-2 d-1. This large range is linked to environmental
controls that affect the rates of CH4 production, oxidation and transport, which ultimately
lead to the fluxes measured at the surface of peatlands.
2.3.1 CH4 production, oxidation
Methanogens produce CH4 under anoxic conditions beneath and just above the water
table in the peat profile. CH4 production occurs only in anaerobic conditions [Fetzer et
al., 1993]. On peatland vegetated surfaces, the production zone corresponds generally
with the boundary between the acrotelm and catotelm. The production of CH4 depends
mainly on microbial activity, which is function of the substrate availability and quality,
the presence of sulphate reducers, peat temperature and the type of vegetation [Whiting
and Chanton, 1992; Moore and Roulet, 1993; Westermann, 1993; Bubier, 1995;
12
Bellisario et al., 1999; Dise and Verry, 2001]. Methanogens produce CH4 through the
fermentation of simple organic compounds such as acetate or through oxidation of H2
with the production of CO2. The substrate supply is therefore the primary control on
methane production if anoxic conditions are met [Whalen, 2005]. Quality of the substrate
is also important. Fresh, more labile, carbon is easier to decompose than the more
degraded peat from deeper zones in the peat profile. Deeper peat layers are more
recalcitrant and therefore represent a substrate limitation [Yavitt et al., 1987; McKenzie et
al., 1998; Whalen, 2005]. The presence of sulphate reducers in the peat profile has an
important impact on methenogenegis because sulfate-reducing bacteria are more efficient
competitors than methanogens for labile carbon [Lovley and Klug, 1983]. Field
manipulation studies have shown reduced methane production and emissions by
increasing sulphate deposition using ammonium sulphate [Dise and Verry, 2001].
Methanotrophic bacteria in the oxic layer of the peatland perform CH4 oxidation. In
contrast to CH4 production, only a limited number of studies have looked at the CH4
oxidation in peatlands. Still, oxidation represents an important element of the CH4
exchange as rhizospheric oxidation can transform 20 to 90% of the CH4 produced into
CO2 [Ström et al., 2005]. The level of oxidation is strongly dependent on the emission
pathway used by CH4 to reach the atmosphere.
2.3.2 CH4 release pathways
There are three emission pathways for the CH4 produced in a peatland to reach the
atmosphere: diffusion, ebullition and plant mediated transport. The diffusion flux is the
result of the CH4 concentration gradient that forms between the production zone and the
atmosphere. Since the CH4 concentrations in the production zone are greater than the
atmospheric concentration, CH4 is released to the atmosphere. CH4 ebullition events
represent a sporadic release of CH4-containing bubbles, from both the vegetated surface
and pools [Repo et al., 2007; Goodrich et al., 2011]. CH4 bubbles are formed because the
partial pressure of the gas exceeds the hydrostatic pressure in the peat [Chanton and
Whiting, 1995]. Low atmospheric pressure or a drop in hydrostatic pressure can trigger
ebullition fluxes. Although the fluxes are difficult to measure, ebullition may represent
the most important pathway for CH4 emissions in peatlands as such events can release
13
large volumes of gas in a short time period (minutes to hours). Glaser et al. [2004]
estimated a single ebullition event to have released 35 g CH4 m-2 by interpreting the
topographic oscillations of the peat surface and measuring changes in the peat pore-water
pressure and peat volume. Tokida et al. [2007b] estimated the contribution of ebullition
to the total CH4 emission from their peatland to be 50-64%. However, because of the
sporadic nature of these fluxes, ebullition measurements are difficult to obtain and their
contribution to the peatland CH4 budget is difficult to establish. Vascular plants such as
sedges have intra cellular air-spaces called aerenchyma that allow gaseous exchange
between the shoots and the roots. This air-space therefore provides a conduit for CH4
from the production zone to the atmosphere, bypassing the oxic peat layer [Whiting and
Chanton, 1992, 1993; Chanton et al., 1995; Waddington et al., 1996; Bellisario et al.,
1999; Pelletier et al., 2007].
2.3.3 Controls on peatland CH4 fluxes
Several field and laboratory studies have shown temperature to have a strong control on
CH4 fluxes [e.g. Turetsky et al., 2014], influencing both CH4 production and oxidation.
Higher peat temperature is associated with greater CH4 production [Moore and Dalva,
1993; Westermann, 1993] with Q10 values ranging from 1.5 to 28 with an average of 4.1
[Segers, 1998]. Simultaneously, oxidation also increases with higher temperatures with
an average Q10 of 1.9 [Segers, 1998]. Published optimal temperatures for CH4 production
are variable and depend on sediment type and location. Dunfield et al. [1993] found
optimal peat temperature for CH4 production from subarctic peatlands samples to be
around 25oC while [Williams and Crawford, 1984] measured optimal productivity at
12°C from temperate peat samples from Minnesota. The temperature control on CH4
production has been observed through ebullition fluxes in a lake that a peatland
discharged into. Wik et al. [2014] observed a strong relationship between total summer
CH4 ebulitive flux and total incoming short wave radiation.
The water table position also exerts a strong control on both vegetated surface CH4
production and oxidation by controlling the thickness of the anoxic and oxic peat layers
14
where CH4 is produced and consumed. A larger oxic layer tends to reduce the CH4 flux at
the surface of peatlands as shown by the negative relationship between mean summer
water table position and CH4 fluxes reported in many studies [Roulet et al., 1992; Moore
and Roulet, 1993; Moore et al., 1994, 2011; Bubier, 1995; Bubier et al., 1995a, 1995b;
Liblik et al., 1997; Nykanen et al., 1998; Pelletier et al., 2007].
The vegetation type, biomass and productivity can have a strong impact on the vegetated
surface CH4 fluxes. As mentioned previously, aerenchyma can act as a conduit between
the rhizosphere and the atmosphere. Therefore, a positive relationship between the
biomass of aerenchymous peatland plants and CH4 flux has been observed in different
peatlands [Whiting and Chanton, 1992; Bellisario et al., 1999]. Simultaneously, the
import of oxygen to the rhizosphere increases CH4 oxidation and decreases potential
emissions [Whiting and Chanton, 1992; King et al., 1998; Bellisario et al., 1999]. The
surface vegetation can also provide labile C through root decay and exudation, which can
act as a substrate for CH4 production, explaining the relationship between NEP and CH4
fluxes for sites dominated by vascular plants and where the water table is close to the
surface [Whiting and Chanton, 1992, 1993; Chanton et al., 1995; Waddington et al.,
1996; Bellisario et al., 1999].
2.3.3 Spatial and temporal variability in CH4 fluxes from peatlands
CH4 fluxes vary significantly within and between peatlands because of the variability in
microtopography and environmental conditions. In bogs or poor fens, larger fluxes of
CH4 are observed from hollows compared to hummocks mainly because of the difference
in the water table position [Roulet et al., 1992; Moore and Roulet, 1993; Moore et al.,
1994; Bubier, 1995; Bubier et al., 1995a, 1995b; Updegraff et al., 1995; Liblik et al.,
1997; Nykanen et al., 1998]. An inverse relationship was also observed in fens with
fluxes decreasing as the water table moved closer to the surface. In their study, sedge
biomass was a stronger predictor of summer average CH4 flux [Bellisario et al., 1999].
Although being a significant source of CH4, peatland pool CH4 fluxes have been
measured only in few studies [Moore and Knowles, 1990; Hamilton et al., 1994; Moore
15
et al., 1994; Waddington and Roulet, 1996, 2000; Pelletier et al., 2007; Repo et al., 2007;
McEnroe et al., 2009]. Fluxes from pools are variable and generally within the same
range as observed on the vegetated surface. CH4 release through ebullition in pools is
also highly variable with values from 0 mg CH4 m-2 d-1 in a Hudson’s Bay lowland
peatland to 160 mg CH4 m-2 d-1 in a beaver pond underlain by peat [Hamilton et al.,
1994; Dove et al., 1999].
The temporal variability in CH4 fluxes has been examined over different time scales
(diurnally, seasonally, yearly). Diurnal variations in CH4 fluxes have been observed in
field and laboratory studies; however, peak emission has been reported both during the
day and at night. Different hypotheses have been used to explain these patterns such as
inhibition of oxidation at night because of lower temperature and the relationship
between stomatal conductance and methane emission [Mikkela et al., 1995; Koch et al.,
2007]. Diurnal patterns measured with static chambers could potentially be the result of
periods of low turbulence conditions enhancing gas emission and high turbulence
reducing them [Lai et al., 2012]. At the ecosystem scale, the diurnal cycle has been
linked to thaw-freeze cycles during the snow-thaw period [Friborg et al., 1997; Gažovič
et al., 2010] and during the growing season linked to dissolved CH4 released from the
transpiration stream and plant mediated transport via convective flow.
The CH4 release varies significantly between seasons. Jackowicz‐ Korczyński et al.
[2010] reported that summer represented 65% of the annual flux, while fall and autumn
contributed to 25% and winter emissions to 10% of the total annual CH4 release. Others
have measured significant CH4 release during spring thaw corresponding to methane
accumulated under the frozen surface during the winter [Hargreaves et al., 2001; Tokida
et al., 2007a; Mastepanov et al., 2008] or during the fall, explained by a bifrontal freezing
from the surface and the bottom of the peat layer that could occur with the presence of a
permafrost bed [Mastepanov et al., 2008]. The winter CH4 fluxes are generally smaller
with a range of average daily values between -0.36 and 23 mg CH4 m-2 d-1 [Dise, 1992;
Alm et al., 1997; Panikov and Dedysh, 2000; Pelletier et al., 2007]. Although the daily
fluxes are small, over a long winter they contribute between 8 and 17% of the annual
16
release [Alm et al., 1997; Pelletier et al., 2007; Jackowicz-Korczynski et al., 2010]. Dise
[1992] suggested that the CH4 released to the atmosphere during the cold season was
produced and stored in peat during the previous summer.
2.4 CO2 and CH4 exchange measurement technique
Two techniques have been widely used for measuring NEE-CO2 and CH4 exchange
between the atmosphere and peatland surface; micrometeorological (eddy covariance)
and chamber methods. Eddy covariance measures fluxes by sampling the turbulent
motion of air and determines the flux as the covariance between instantaneous deviations
in vertical wind speed and the gas-mixing ratio [Baldocchi, 2003]. This method provides
direct flux measurements with minimal disturbance to the ecosystem using instruments
installed on a tower. It also allows continuous high frequency (typically 10Hz) flux
measurements over a large upwind surface (≈104-105 m2). The eddy covariance
measurements are made using open- or closed-path gas analyzers. Open path analyzers
require less power than closed path systems which more readily allows measurements in
remote environments where grid power is not available and generators are impractical.
However, obstruction of the optical path during precipitation events leads to data
rejection [Baldocchi, 2003]. They also have shown biologically implausible ecosystem
CO2 uptake during winter in cold environments [Burba et al., 2008]. Although eddy
covariance can provide continuous measurements, in practice data loss is common due to
equipment malfunction, or in the absence of turbulent conditions. Losses of data require
gap-filling strategies, which are time consuming and complex to develop for
heterogeneous sites.
Chamber measurements have been used widely on peatlands to measure CO2 and CH4
exchange [Moore, 1989b; Bubier et al., 1995a, 2003a; Pelletier et al., 2007; Cliche
Trudeau et al., 2014]. Compared to eddy covariance, this method has the advantage of
measuring variability in fluxes across the different features of the ecosystem because it
covers surfaces generally at a scale of 1 m2. Chambers requires less post-processing
following flux measurements. CO2 exchange measurements are generally made with a
clear polycarbonate chamber connected to a portable infrared gas analyzer. Opaque
17
chambers are generally used for CH4 measurements and fluxes are computed by
calculating the change in gas concentration with time from samples analyzed on a gas
chromatograph. Chamber measurements also have drawbacks. Recent studies have
shown that chamber measurements can be biased when measurements are performed
under low turbulence conditions or over too short time periods [Davidson et al., 2002;
Schneider et al., 2009; Lai et al., 2012]. Chamber measurements do not integrate fluxes
over the entire ecosystem and high precision mapping or remote sensing may be required
to extrapolate fluxes leading to errors. Unless auto-chambers are used, this method is
time consuming and does not provide continuous measurements. Interpolation through
time leads to additional errors [Bubier et al., 1999]. Chamber measurements have been
used on both vegetated surfaces and pools in peatlands. However, the floating chamber
technique tends to over-estimate fluxes between 2 to 10 times on small water bodies
when compared to the thin boundary layer method [Lambert and Fréchette, 2005;
Vachon et al., 2010]. The chamber itself produces turbulence that disturbs the air-water
interface and consequently the gas exchange. The thin boundary layer method is another
technique used to estimate fluxes from water bodies and does not disturb the air-water
interface. This method, if used with automated measurements of gas concentrations
allows high frequency continuous flux estimation.
2.5 Conclusion of the literature review
Peatlands play an important role in the global C dynamic, storing important amounts of C
while representing a significant source of CH4 to the atmosphere and a sink for CO2.
Although studies have documented the surface to atmosphere C fluxes, and their spatialtemporal variability and controls, a limited number of studies have examined the
contribution and the role of open-water pools on the ecosystem C exchange of peatlands.
The current knowledge cannot explain how these permanent water bodies that are
common features in many peatlands affect the ecosystem surface to atmosphere C
exchange. Aside from extrapolating sporadic chamber measurements made on vegetated
surface and pools [Waddington and Roulet, 1996, 2000; Pelletier et al., 2007; Cliche
Trudeau et al., 2013, 2014], no continuous ecosystem level NEE-CO2 measurements
have been made over permanent open water pool peatlands. Considering pools ubiquity
18
in northern peatlands and the magnitude of the C loss from these water bodies, a better
understanding of the C exchange at the surface of peatland with pools is needed. In the
following chapters, I will address these important gaps in knowledge in order to improve
our understanding of northern peatland C exchange.
19
3. Carbon release from boreal peatland open water pools:
implication for the contemporary C exchange
Bridging statement to Chapter 3
The literature review presented in Chapter 2 showed that our current knowledge of
carbon fluxes from peatland pools is incomplete. To date no research exists that has
accounted for the release of carbon following the spring melt using continuous
measurements. The following chapter investigates the spatial and temporal variability in
the CO2 and CH4 concentrations in pools, and the corresponding surface fluxes over 16
months in an ombrotrophic peatland. This study addresses the first objective of my thesis
to evaluate the importance of the annual C loss from pools on the peatland net ecosystem
carbon balance.
20
3.1 Abstract
While peatland ecosystems overall are long-term net carbon (C) sinks, the open-water
pools that are characteristic of boreal peatlands have been found to be C sources to the
atmosphere. However, the contribution of these pools to the ecosystem level C budget is
often ignored even if they cover a significant area of the peatland surface. Here we
examine the annual CO2 and CH4 ecosystem-atmosphere exchange, including the release
following ice melt, from pools in a boreal maritime peatland, in order to estimate the
annual loss of C from these water bodies. Over a 16 month period, dissolved CO2 and
CH4 were measured periodically in five pools while continuous measurements of CO2
were made in one pool using a nondispersive infrared (NDIR) sensor. Fluxes were
calculated using the thin boundary layer model and the eddy covariance technique (spring
release only). We calculated an annual C release from pools of 103.3 g C m -2 yr-1 of
which 15% was released during the spring ice melt. This release is the same order of
magnitude, but with the opposite sign, as the average net ecosystem carbon balance for
pool-free northern peatlands (-22 to -70 g C m-2 yr-1). We discuss the origin of the
released C, as the magnitude of the release could have a significant impact on the
contemporary C exchange of boreal peatlands.
21
3.2 Introduction
Peatlands are ecosystems characterized by a water table that is close to the surface and
biomass productivity that exceeds decomposition, leading to large accumulation of
partially decomposed organic matter define as peat. The long-term apparent carbon (C)
accumulation rates (LORCA) in peatlands since the end of the last glaciation vary
between 3.4 and 70.6 g C m-2 yr-1 [Turunen et al., 2002; Gorham et al., 2003; Yu et al.,
2009]. Peatlands absorb carbon dioxide (CO2) through photosynthesis via the surface
vegetation and release CO2 through autotrophic (growth and maintenance) and
heterotrophic respiration (i.e. peat decomposition). Simultaneously, methane (CH4) is
produced by anaerobic peat decomposition through acetate fermentation and/or CO2
reduction, and is released to the atmosphere through a combination of diffusion, plant
mediated transport and ebullition. Although peatland ecosystems are generally a net sink
for CO2 and a net source of CH4, the actual rates and direction of gas exchange can vary
significantly among the different microforms (hummock, lawns, hollows, pools) as a
function of the environmental conditions within a single peatland [e.g., Waddington and
Roulet, 1996; Pelletier et al., 2007, 2011]. Studies looking at CO2 and CH4 exchange at
the surface of open water pools have shown that these water bodies release C to the
atmosphere [e.g., Hamilton et al., 1994; Repo et al., 2007]. In contrast, Macrea et al.
[2004] found peatland open water pools in the subarctic regions to be net sinks for C and
estimated LORCA between 7 and 26 g C yr-1. Macrea et al. [2004] attributed the C
accumulation at the bottom to peat erosion resulting from wave action on the pool border.
This situation has been highlighted in lake or reservoir studies where the systems are C
sinks in sediments but also act as sources to the atmosphere [e.g., Prairie, 2008; Tranvik
et al., 2009].
Peatlands’ vegetated surfaces CO2 and CH4 fluxes have received more attention than their
aquatic components. Measured fluxes from vegetated surfaces have been used to examine
seasonal and interannual variability at the ecosystem level using eddy covariance (EC)
towers, and at the local scale using manual and automatic chambers with a sporadic or
continuous measurement approach [e.g., Lafleur et al., 2003; Forbrich et al., 2011;
Pelletier et al., 2011; Lai et al., 2012]. On the other hand, pool fluxes have been
22
measured using static chambers [e.g., McEnroe et al., 2009] or the thin boundary layer
method [e.g., Hamilton et al., 1994]. However, recent studies have shown that floating
chamber measurements tend to over-estimate fluxes between 2 to 10 times especially on
small water bodies [e.g., Lambert and Fréchette, 2005; Vachon et al., 2010]. The larger
fluxes obtained from chamber measurements result from turbulence produced by the
chamber that disturbs the air-water interface and therefore the gas exchange. In contrast,
the thin boundary layer does not create disturbances at the air-water interface, as the flux
is calculated using the gas concentration gradient between water and air, and the gas
transfer coefficient. When combined with automated measurements of gas concentrations
and other required environmental variables, this method allows high frequency
continuous flux estimation. However, the gas transfer coefficient for wind speed below 3
m s-1 remains difficult to establish [Cole and Caraco, 1998].
Most open water season average CO2 and CH4 releases from peatland open water pools
are based on weekly measurements and therefore do not capture daily variation.
Automated high frequency dissolved CO2 measurements in peatland streams and beaver
ponds have shown important diurnal and seasonal patterns in CO2 concentration
[Dinsmore and Billett, 2008; Dinsmore et al., 2009]. Furthermore, a number of studies
have shown a strong correlation between DOC concentration and dissolved CO2 in lakes;
variations in DOC explaining the variability in dissolved CO2 between lakes [e.g., Hope
et al., 1996; Jonsson et al., 2003; Roehm et al., 2009]. However, this type analysis has
not been done for peatland open water pools.
GHG fluxes from peatland pools measured sporadically during the ice-free period range
from 0.14 to 16.6 g CO2-C m-2 d-1 and -0.001 to 1.87 g CH4-C m-2 d-1 [Hamilton et al.,
1994; Waddington and Roulet, 2000; Pelletier et al., 2007; Repo et al., 2007; McEnroe et
al., 2009; Cliche Trudeau et al., 2013]. Published total C release from pools during the
ice-free season are limited and do not cover the entire open-water season (generally late
spring to early fall) with values ranging from 24-419 g C m-2 [Waddington and Roulet,
2000; Repo et al., 2007]. For example, the estimate from Repo et al. [2007] is based on
100 to 120 days while Waddington and Roulet [2000] covered a period from May to
September. The open water season in boreal Canada can last more than 200 days (this
23
study) and the resulting annual release of GHG’s from peatland pools could therefore be
significantly greater than has been reported to date. Furthermore, none of these studies
accounts for the CO2 and CH4 that is released during the spring ice melt. It is
hypothesized that the ice melt period plays an important role in the annual CO2 and CH4
emissions due to the build-up of gases linked to microbial activity in the sediments and
pool water during winter. In a Finnish lake, Huttunen et al. [2001] estimated potential
springtime emissions following ice melt to be between 31 and 51 g CO2-C m-2 and 2.3 to
17 g CH4-C m-2 for CH4. The contribution of gas released during the ice melt period in
lakes has been estimated to represent 30% of the annual release for CO2 and between 40
and 60% for CH4 [Michmerhuizen et al., 1996; Karlsson et al., 2010].
In the present study, we use several techniques (headspace [CO2 – CH4], submerged nondispersive infrared [NDIR] sensors and eddy covariance) to evaluate the annual dissolved
CO2 and CH4 dynamics and to estimate the annual release of CO2 and CH4 from boreal
peatland pools. The aims of this study are: 1) to document the spatial and temporal
variability in pool dissolved CO2 and CH4; 2) to estimate the release of GHG (CO2 and
CH4) to the atmosphere during the spring ice melt; 3) to establish an estimate of the
annual release from these pools. We hypothesize that the net C exchange from peatland
pools is on the same order of magnitude, but with the opposite sign, as the net exchanges
reported in the literature for vegetated surface of peatlands. Furthermore, we hypothesize
that the annual accumulated C exchange from pools is sufficiently large that if they are
not accounted for, the net ecosystem exchanges for peatlands containing pools could be
seriously in error.
3.3 Methods
3.3.1 Study Area, Peatland and Pool Descriptions, and Climate
The studied peatland is located near Baie Comeau on the North Shore region of Quebec,
Canada. The peatland (49°08’N, 68°17’W; altitude: 19 m) sits in the center of the
Manicouagan peninsula on deltaic sands left by the Outarde and Manicougan Rivers. The
peatland covers ~600 ha and is classified as a raised bog [Glaser and Janssens, 1986],
24
dominated by Sphagnum fuscum, ericaceaous shrubs (mainly Chamaedaphne calyculata,
Rhododendron groenlandicum) and dwarf Picea mariana. Basal dates indicate that peat
started to accumulate 4100 years BP [Magnan and Garneau, 2014]. The open-water
pools cover approximately 5 % of the peatland surface and range in size from 2 to 9000
m2. The pool depths in the studied sector vary between 0.4 and 2 m which is similar to
the range of pool depth observed in other regions of the world (Sweden: Foster and Fritz
[1987], Foster and Wright [1990]; Hudson Bay lowlands, Canada: Hamilton et al.
[1994]; West Siberian Lowlands, Russia: Repo et al. [2007]; Estonia: Karofeld and
Tõnisson [2012]). The five pools selected are hydrologically independent from each other
and are fed from precipitation; there are no streams connected to the pools. The studied
sector is located more than 500 m from the closest peatland margin, and approximately 5
m above the surrounding non-peatland area. Peat cores (pool #1 and 5) showed no
sediment accumulation at the bottom [Thibault et al., submitted]. The 30-year climate
normal (1971-2000) for mean daily temperature during the coldest and warmest months
is -14.6°C in January and 15.6°C in July. The area receives on average 1014.4 mm in
precipitation per year of which 361.5 mm is snow [Environment Canada, data available at
http://climate.weatheroffice.gc.ca /Welcome_e.html].
3.3.2 Dissolved CO2 and CH4 measurements
Five pools were chosen for dissolved gas measurements as representative of the range of
pool sizes and depths found in the peatland (Table 3.1). Manual measurements of
dissolved CO2 and CH4 in the five pools were made using the headspace technique [e.g.,
Kling et al., 1991; Dinsmore et al., 2009]. A sample of pool water (160 mL) collected in
a Wheaton glass bottle was equilibrated with an ambient air sample (60 mL) by shaking
under water for 1 min. A 20 mL sample of the headspace was then extracted and
transferred to an evacuated Labco exetainer (Labco, High Wycombe, UK) for storage.
Laboratory testing showed that the exetainers maintained an average vacuum level of
94.3 % (SD±1.3) after 18 Days [Wittebol, 2009]. Analysis was done within two weeks
after sampling on a Shimadzu GC-14B gas chromatograph equipped with a flame
ionization detector (FID) for CH4 and a thermal conductivity detector (TCD) for CO2.
Samples were hand-injected (5-mL) in a 1-mL injection. Column and detector
25
temperature were 100°C and 315°C respectively. Ultra-high purity N2 was used as the
carrier gas at a flow rate of 25 mL min-1. Pool water dissolved gas concentrations were
calculated using Henry’s law for the headspace and ambient concentrations.
Measurements were made between June 2011 and October 2012 each time the site was
visited during the open water and frozen seasons. These discrete measurements were
performed to account for spatial and low-resolution temporal (1 week approximately
every 50-days) variability in dissolved CO2 and CH4 concentration during the study
period. The ice cover reached the bottom of the shallowest pools and technical problems
such as ice forming in the peristaltic pump used for sampling resulted in only two to three
pools sampled per site visit during the ice-covered season.
Automated continuous measurements of dissolved CO2 were made in Pool #1 using
NDIR CO2 sensors (CARBOCAP transmitter series Model GMT220, Vaisala, Finland)
enclosed in a water tight, gas permeable polytetrafluoroethylene (PTFE) membrane [see
Johnson et al., 2010] and connected to a datalogger (CR1000, Campbell Scientific,
Edmonton, Canada). During summer 2011, two sensors were installed in pool #1; one at
the surface and the other at 1m depth. The sensor at the surface was removed before ice
formation to prevent damage to it. The sensor was not reinstalled at the surface in 2012 as
it was used to replace the sensor at 1m that stopped functioning over the cold season.
Automated measurements were made from August to mid-December 2011 and from May
to August 2012. The measurements from the NDIR sensor were corrected for temperature
and pressure [Tang et al., 2003]. The reference temperature and pressure for the Vaisala
sensor were 22.5°C ±1% and 100.7kPa ±1%, respectively. Water and sediment (5cm
depth) temperature were measured every 30-minutes in pool#1 and pool#3 (2012 only)
using thermocouples connected to a datalogger (CR1000, Campbell Scientific,
Edmonton, Canada). In 2011, the water temperatures in pool#1 were measured at the
surface, 100cm and 200cm. In 2012, measurements were made every 25cm from the
bottom of pools #1 and #3.
26
3.3.3 Flux calculation
The diffusive fluxes of CO2 and CH4 from the pools were calculated using the thin
boundary layer technique:
𝐹𝑙𝑢𝑥 = 𝐾𝑥 (𝐶𝑤 − 𝐶𝑎 )
where Cw is the concentration of gas in the water, 𝐶𝑎 is the concentration of gas in the air
Kx is a gas–specific exchange coefficient found from an expression of the gas-specific
Schmid number (Sc), and;
𝐾𝑥 = 𝐾600 (𝑆𝑐 ⁄600)−𝑏
where b = 0.66 for wind speed ≤ 3 m/s or b = 0.5 for wind speed > 3 m/s.
The Schmid number was found from Wanninkhof [1992] as a function of water
temperature (T; °C) using:
𝑆𝑐(𝐶𝑂2 ) = 1911.1 − 118.11𝑇 + 3.4527𝑇 2 − 0.04132𝑇 3
and
𝑆𝑐(𝐶𝐻4 ) = 1897.8 − 114.28𝑇 + 3.2902𝑇 2 − 0.039061𝑇 3
K600 is the gas exchange coefficient (cm h-1) normalized for CO2 at 20°C in fresh water
with a Schmid number of 600 and was approximated following Cole and Caraco [1998]
as a function of wind speed at 10 m height (U10) as:
𝐾600 = 2.07 + (0.215 ∙ 𝑈101.7 ).
Wind speeds at 10m height are not typically available and therefore these were
approximated from measurements of wind speed on the meteorological tower at a height
27
of 2m above surface. The U10 was estimated using a logarithmic wind profile with a
roughness length of 0.001 m, and assuming a neutral boundary layer [Oke, 1987].
Fluxes were calculated from the continuous dissolved CO2 data from the NDIR sensor in
(pool #1) and from the sporadic headspace measurements using continuous wind speed
from the meteorological tower (see section 2.6) and water temperature measurements for
other pools. Water temperature measurements for the headspace flux calculation were
made at the time of sampling using a portable meter (model 3500, Kestrel, Birmingham,
MI). Continuous open-water season fluxes in pool #1 (2011 and 2012) and pool #3
(2012) were also estimated by linearly interpolating the instantaneous dissolved CO2 and
CH4 from headspace measurements. Flux estimates were not made for pools #2, 4 and 5
as no continuous temperature measurements were made in these pools.
3.3.4 DOC measurements
DOC measurements were performed once per field campaign in each of the five pools
between May and October 2012. Water samples were filtered through 0.45μm glass fiber
filters, acidified and kept in a dark refrigerator at 5°C. DOC analysis was performed on a
Shimadzu TOC-V total organic carbon analyzer calibrated using standards of 10, 50 and
100 mg L-1, prepared from a 1000 mg L-1 stock solution made from potassium bipthalate.
The composition of the DOC was evaluated by looking at UV-absorption at 250, 254 and
365 nm using a spectrophotometer (Spectronic Genesys 10, Thermo Scientific, Waltham,
MA) with distilled water as a blank. Disposable 10mm path length cells were used for the
analysis.
3.3.5 Eddy covariance measurements
Ecosystem level NEE-CO2 was measured using the eddy covariance (EC) technique in
the spring of 2012 in order to measure the release of CO2 from pools during ice melt. The
tower was located in the same sector as the pool dissolved CO2 and CH4 measurements
were performed and its source area included a varying percentage of pool surfaces
depending on the wind direction (Figure 3.1). The EC system consisted of a fast response
three-dimensional sonic anemometer (CSAT-3, Campbell Scientific, Edmonton, Canada),
28
a fine-wire thermocouple (FW05, Campbell Scientific, Edmonton, Canada), and an openpath CO2/H2O analyzer (LI-7500, LI-COR, Lincoln, NE). The instruments were
mounted on a tripod 2.5 m above the surface of the peatland. The variables used to
calculate the flux using the eddy covariance method were recorded and stored on a 2 GB
compact flash card using a data logger (CR3000, Campbell Scientific, Edmonton,
Canada) at 5 Hz. The 30-min CO2 fluxes were computed from the 5 Hz data on a
personal computer using an in-house script developed in Matlab (v.9.0, Mathworks,
Natick, MA). CO2 fluxes were derived from the covariance between vertical wind speed
and CO2 mixing ratio, corrected for the effect of temperature and water vapor fluctuation
on air density [Webb et al., 1980]. A two-dimensional coordinate rotation was applied.
No additional frequency response or energy budget corrections were applied to these
fluxes. Details on the quality control procedure used for NEE-CO2 are presented in
Bergeron and Strachan [2011]. Because of the sporadic nature of the spring release, no
gap filling was done for this measurement period. The flux source area during ice melt
was estimated using the Flux Source Area Model (FSAM) [Schmid, 1994].
Figure 3.1: Location of the study area with pools and eddy covariance tower. The ellipse
delineates the tower source area during the spring melt.
29
3.3.6 Supporting measurements
Environmental measurements were made every 5-seconds throughout the study period
(June 2011 to October 2012) and averaged over 30-min. The variables measured include
components of the net radiation (CNR4, Kipp and Zonen, Delft, Netherlands), incoming
and reflected photosynthetic active radiation (LI-190SA, LI-COR, Lincoln, NE), air
temperature and relative humidity (HMP-45C, Vaisala, Helsinki, Finland), wind speed
and direction (05103-10, RM-Young, Traverse City, MI) and precipitation (TE525M
tipping bucket gauge, Texas Electronics, Dallas, TX). Daily time-lapse photographs of
pool #1 were taken using a game camera (Game Spy I-65 S, Moultrie, Alabaster, Al) to
be able to determine ice cover extent and duration.
30
3.4 Results
3.4.1 Headspace dissolved CO2 and CH4
Between June 2011 and October 2012, 219 discrete surface concentration measurements
were made at the five selected pools in the peatland. The five pools sampled were
constantly supersaturated in CO2 and CH4 relative to the atmosphere and the measured
concentrations varied significantly among some of the pools during the open water
season (Table 3.1). The smallest (pool #2) pool had the highest average dissolved CO2,
while the average dissolved CO2 from the other pools were not statistically different from
each other (Table 3.1). Average dissolved CH4 was slightly more variable among the
pools and the highest open-water average dissolved CH4 value was observed in pool #3,
which was the shallowest of the five pools sampled (Table 3.1).
Table 3.1: Pool physical and chemical characteristics, and mean, maximum and minimum
dissolved CO2 and CH4 during the open water seasons as obtained from the headspace
measurement technique.
CH 4 -C (µg L-1)
CO 2 -C (mg L -1)
Pool #
1
2
3
4
5
Size (m2)
Depth (m)
Mean
Min
Max
Mean
Min
Max
DOC (mg L -1)
499
128
766
2563
1866
2.0
0.7
0.4
1.9
1.9
0.55 (±0.18) (b) a
0.99 (±0.50) (a)
0.67 (±0.31) (b)
0.52 (±0.14) (b)
0.50 (±0.13) (b)
0.28
0.42
0.26
0.27
0.25
0.78
2.69
1.66
0.73
0.79
19.9 (±14.5) (b)
15.2 (±16.9) (b)
81.0 (±75.3) (a)
14.7(±31.7) (bc)
6.3 (±3.4) (c)
3.25
0.30
0.26
1.53
1.68
57.2
58.6
327
183
15.8
17.6 (±2.3) (b)
17.5 (±3.3) (b)
21.4 (±2.8) (a)
16.7 (±2.9) (b)
17.8 (±4.0) (ab)
Standard deviation given in parentheses
a
mean CO 2 and CH 4 values are significantly different if they have no letters in common (CO 2 and CH 4 - Kruskal–Wallis test
followed by post hoc Steel–Dwass. DOC - One-Way ANOVA followed by Tukey-Kramer).
Dissolved CO2 and CH4 in individual pools varied temporally over the study period
(Figure 3.2). The CO2 and CH4 concentrations increased in all pools between June and
August in 2011: increasing rates ranging from 3 and 9 μg L-1 d-1 for CO2, and from 0.05
to 1.3 μg L-1 d-1 for CH4 (Figure 3.2). The largest increases were observed in pool #2 for
CO2 and in pool #3 for CH4 (Figure 3.2). This period was followed by a decrease in
dissolved CO2 in all five pools in early November while CH4 concentrations continued to
increase in three of the five pools (Figure 3.2). Although pool #3 saw its concentration in
31
CH4 decrease in early November compared to values measured in August, values were
still higher than the other four pools.
Permanent ice cover formed on November 22 in 2011 (DOY 326) and lasted until April
28, 2012 (DOY 119) (156-days) as observed from daily time-lapse photographs. Pool
sampling was limited during the frozen water season because of the technically
challenging conditions related to water sampling in sub-freezing conditions. Pool #4 and
5 were sampled in January and pool #1 and #4 were sampled in March 2012. Dissolved
CO2 and CH4 increased considerably after the ice established at the surface of the pools
(Figure 3.2). Maximum dissolved CO2 and CH4 concentrations under the ice were
measured in March 2012 as 8.1 mg L-1 for CO2 in pool #4 and 472 μg L-1 for CH4 in pool
#1.
The first 2012 open water measurements were made on DOY 138, approximately three
weeks after ice melt. Dissolved CO2 values generally increase from spring throughout the
open water season but the pattern was not as clear as in the previous open water season
and concentrations did not decrease in the fall as they did in 2011. However, the open
water season increasing pattern in dissolved CH4 was more universal in 2012 as values
increased in all pools from May to October with the average rates ranging between 0.15
and 1.94 μg L-1 d-1 (excluding pool #5 which was not sampled in October 2012).
32
Figure 3.2: Dissolved CO2 (top) and CH4 (bottom) in the five pools in the peatland. The
frozen pool surface period is shown as the shaded area in each graph. Symbols represent
the individual pool average headspace CO2 or CH4 for each field campaign.
33
3.4.2 NDIR sensor dissolved CO2
The dissolved CO2 30-min data at 1m in pool #1 varied significantly over the study
period with a range of CO2 concentration between 0.3 and 3.25 mg L-1 (including the
frozen water season). The August 2011 dissolved CO2 values varied around 0.7 mg L-1
before increasing rapidly to systematically vary around 1.0 mg L-1 in early September.
The dissolved CO2 decreased in mid-September before rising again to reach the
maximum open water value of 1.7 mg L-1 on DOY 267. Following this peak value, the
dissolved CO2 values decreased to 0.5 mg L-1 on DOY 279 after which it fluctuated
between 0.6 and 0.8 mg L-1 until DOY 327 when ice started to form at the surface of the
pool. From there, dissolved CO2 increased significantly at variable rates, with the largest
rate observed between DOY 336 and 344 at 0.36 mg L-1 d-1. Maximum CO2 concentration
reached 3.25 mg L-1 before the sensor failed on DOY 346. NDIR measurements resumed
on DOY 141 in 2012.
Open water season 2012 NDIR dissolved CO2 increased from May to mid-August with
values reaching 1.35 mg L-1 on DOY 228. However, the trend was not consistent as the
longer-term increasing trend was interrupted by periodic drops in CO2 concentration.
These drops correspond with declines in water temperature over the entire pool profile
consistent with a decrease in air temperature and potentially strong winds that allowed
pool mixing (Figure 3.3). NDIR dissolved CO2 values started to decrease in mid-August
to reach a minimum of 0.68 mg L-1 on DOY 259. The decrease was similar to what was
observed in 2011, but occurred approximately a month earlier. Dissolved CO2 remained
around 0.9 mg L-1 until DOY 284, 2012 (Figure 3.2). While the overall trend in NDIR
sensor dissolved CO2 was similar to that observed from the headspace measurements at
the surface of the pools, the NDIR CO2 values measured at 1m were greater in August
2012. The CO2 concentrations measured by the NDIR sensor at 1 m were strongly
positively correlated with water temperature. The strongest relationships were found
when the data were split into large periods corresponding to the open water season
patterns such as the long-term CO2 concentration decrease from August to November
2011 and a period of increase from May to August 2012. Strong and significant
relationships were found for both 30-min and daily averaged data (Table 3.2). The
34
parameters of the relationships are statistically different between the two periods and
from the overall relationship (ANCOVA, p<0.01).
Figure 3.3: Daily average dissolved CO2, sediment and water temperature from pool #1,
along with daily average wind speed, air temperature and total daily precipitation during
the 2012 open water season. Note that water temperature depths are relative to the bottom
of the pool.
35
Table 3.2: Best exponential relationship parameters between pool #1 temperature at 1m
and dissolved CO2 from NDIR sensor.
Equation
r2
p
n
LogCO2 = 0.013 (±0.001)*Temp. - 0.24 (±0.02)
LogCO2 = 0.036 (±0.003)*Temp. - 0.94 (±0.05)
LogCO2 = 0.002 (±0.001)*Temp. - 0.16 (±0.02)
0.53
0.72
0.01
<0.001
<0.001
<0.149
83
81
249
LogCO2 = 0.013 (±0.0001)*Temp. - 0.24 (±0.02) 0.48
LogCO2 = 0.034 (±0.0001)*Temp. - 0.89 (±0.01) 0.61
LogCO2 = 0.001 (±0.0001)*Temp. - 0.16 (±0.002) 0.01
<0.001
<0.001
<0.001
3984
3921
12015
DOY
Daily average dissolved CO2
2011
2012
All-data
242-325
141-221
-
30-min dissolved CO 2
2011
2012
All-data
242-325
141-222
-
There was a strong agreement between the discrete surface concentration measurements
and
NDIR
dissolved
CO2
in
2011
(Surface
NDIRCO2
=
1.00(±0.06)*headspaceCO2+0.04(±0.04), r2=0.99, p<0.05). Surface NDIR dissolved CO2
in pool #1 measured from August to October 2011 followed a similar pattern as the CO2
measured at 1m and also the discrete surface measurements made at the surface for the
same period (Figures 3.2 and 3.4). The NDIR CO2 concentrations from the two depths
(30-min data) were strongly correlated, with surface values being generally higher than
those measured at 1m (SurfaceCO2 = 0.78(±0.01)*1mCO2+0.17(±0.01), r2=0.66, p<0.05,
n=4254). For daily average CO2, the difference between the two depths was smaller
(SurfaceCO2
=
0.80(±0.05)*1mCO2+0.17(±0.04),
r2=0.76,
p<0.05,
n=88).
The
relationship with daily averages attenuates the differences observed in the diurnal
dissolved CO2 pattern (30-min data) between the two depths; the diurnal variation being
more pronounced at the surface than at 1m especially in August and September (Figure
3.4). We found a significant positive relationship between the cumulative daily
photosynthetic active radiation (PAR) and the amplitude of the diurnal pattern in
dissolved CO2 measured at the surface of pool #1 with the NDIR sensor (r2=0.41,
p<0.05).
36
Figure 3.4: Diurnal pattern in dissolved CO2 in pool #1, measured by the NDIR sensors at
the surface (solid symbols) and 1m depth (open symbols) in August, September, October
and November (1m only) 2011.
3.4.3 Dissolved organic carbon
There was no clear temporal pattern in DOC concentration in the pools during the 2012
open water season (Figure 3.5). Concentrations of DOC ranged between 8 and 26 mg L-1
over the study period. Average DOC concentration was higher in pool #3, although not
different from pool #5, and there was no significant difference between open-water pools
#1, 2, 4, 5 (Table 3.1). The relationships between DOC and CO2 concentration during the
2012 open water period were significant (p<0.05) only in pool #2 where dissolved CO2
decreased with increasing DOC concentration (r2=0.36, p=0.04), and in pool #5 where it
increased with increasing DOC concentration (r2=0.48, p=0.04). The relationships
between SUVA254 and CO2 concentration for individual pools show significant positive
relationships (r2 > 0.6, p<0.05) in all the pools except pool #5 (Figure 3.5).
37
Figure 3.5: Average DOC concentration from the five pools in 2012 (top); relationship
between headspace measurements dissolved CO2 and SUVA254 (middle); and relationship
between dissolved CO2 and dissolved organic carbon concentration (bottom).
38
3.4.4 CO2 and CH4 evasion
The pools were constantly supersaturated in CO2 and CH4 over the study period and were
therefore constant sources of CO2 and CH4 to the atmosphere. The fluxes calculated from
headspace measurements during the open water season ranged from 0.04 to 2.09 g CO2-C
m-2 d-1 and 0.0001 to 0.16 mg CH4-C m-2 d-1 (Table 3.3; Figure 3.6). As would be
expected given how the fluxes are calculated, the patterns among pools were similar to
the patterns observed for dissolved CO2 and CH4; pool #2 had the highest average CO2
flux while pool #3 had the highest average CH4 flux (Table 3.3). Interpolated fluxes from
pool 1 and 3 were highly variable for both CO2 and CH4 as a result of changes in
dissolved gasses concentration over time and wind speed variability (Table 3.3; Figure
3.6). The range of fluxes calculated from interpolated data was therefore greater than for
the individual flux calculated with headspace data measurements. However, the average
fluxes for the two methods were similar (Table 3.3). The interpolated fluxes from the two
pools were different and pool #3 had higher variability in evasion rates for both CO2 and
CH4. The CH4 release in both pools increased from May to October 2012 but the increase
was more pronounced in pool #3 (Figure 3.7).
The 30-min CO2-C flux calculated from the NDIR sensor in pool #1 ranged from 0.06 to
2.48 g m-2 d-1 with a slightly higher average than the interpolated data of 0.45 (±0. 3) g m2
d-1 (Table 3.3; Figure 3.6). Fluxes followed a similar pattern to the CO2 concentrations,
with fluxes increasing from early August 2011 to reach a maximum release in September
before decreasing until ice cover formed. The first fluxes calculated in May 2012 were
low and similar to the last fluxes in November 2011. Similar to 2011, the 2012 open
water season fluxes increased to reach their maximum in August before decreasing until
the end of the experiment. When comparing with the interpolated fluxes, a similar pattern
to the NDIR sensor for 2011 is seen as fluxes decreased in both case (Figure 3.6). The
patterns were slightly different in 2012; the interpolated data fluxes did not increase as
much as the NDIR fluxes (Figure 3.6). The maximum flux from the interpolated data was
approximately 1.9 g CO2 m-2 d-1 compared to 2.48 g CO2 m-2 d-1 for NDIR sensor.
39
Table 3.3: Mean, minimum and maximum fluxes from headspace measurements, interpolation and NDIR sensor.
CO 2 -C (g m -2 d-1)
Headspace
Pool #
1
2
3
4
5
Mean
0.36
0.84
0.47
0.35
0.34
(±0.1)
(±0.4)
(±0.2)
(±0.2)
(±0.2)
CH 4 -C (g m -2 d-1)
Interpolation
NDIR
Headspace
Min
Max
Mean
Min
Max
Mean
Min
Max
0.16
0.36
0.16
0.12
0.04
0.57
2.09
0.92
0.75
0.56
0.33 (±0.2)
0.40 (±0.2)
-
0.09
1.90
0.08
-
1.76
-
0.45 (±0.3)
-
0.06
-
2.48
-
Standard deviation given in parentheses
40
Mean
0.021
0.014
0.078
0.016
0.004
(±0.013)
(±0.012)
(±0.042)
(±0.030)
(±0.003)
Interpolation
Min
Max
0.005
0.000
0.002
0.002
0.003
0.051
0.051
0.157
0.120
0.016
Mean
Min
Max
0.020 (±0.013) 0.005
0.090 (±0.085) 0.008
-
0.110
0.641
-
Figure 3.6: Daily average CO2 evasion from pool #1 calculated with the NDIR sensor
CO2 measurements (top); manual headspace measurements (symbols), and headspace
measurement interpolation (line) (middle); and pool #3 manual headspace measurements
(symbols) and headspace measurement interpolation (line) (bottom). The period of frozen
pool surface is shown as shaded areas in each graph.
41
Figure 3.7: Daily average CH4 evasion from pool #1 manual headspace measurements
(symbols) and headspace measurement interpolation (line) (top graph); and pool #3
manual headspace measurements (symbols) and headspace measurement interpolation
(line) (bottom graph). The period of frozen pool surface is shown as the shaded area in
each graph.
42
3.4.5 CO2 and CH4 release upon ice melt
3.4.5.1 Discrete measurements
The release of CO2 and CH4 following ice melt was estimated using the headspace
measurements made in March 2012 in pool #1 and #4 assuming that: a) concentrations
measured manually in March represented the CO2 and CH4 concentrations just before ice
melt; b) CO2 and CH4 are well mixed across the pool profile; and 3) all excess CO2 and
CH4 (relative to the atmosphere) would get released upon ice melt. Based on these
assumptions, we estimated that pool #1 and #4 released, respectively, 15.1 and 15.9 g
CO2-C m-2, and 0.9 and 0.8 g CH4-C m-2 following ice melt at the end of April 2012.
3.4.5.2 Eddy covariance tower measurements
The NEE-CO2 during, and right after, ice melt shows large losses of CO2 (DOY 118 and
119 inclusively), with fluxes reaching 0.17 mg CO2-C m-2 s-1 (Figure 3.8). Wind
direction during that period was 325° (SD±7) and the tower source area included pool #4.
Fluxes were rejected for the first part of DOY 118 due to high Li-7500’s automatic gain
control (AGC) values linked to condensation or water droplets on the open path analyzer
lens. The average release for the DOY 118-119 period was 0.1 mg CO2-C m-2 s-1 which
corresponds to a release of 16.8 g CO2-C m-2.
3.4.6 Annual release estimation from pool #1
The annual period, for the purposes of our study, was defined as September 1, 2011 to
August 31, 2012. The annual CO2 release from pool #1 was calculated using the CO2 flux
estimated from the NDIR sensor data (section 3.4) and the spring melt release from
headspace measurement (section 3.5.1). Since the NDIR sensor was not functioning
before May 20, 2012, the CO2 flux for that three week period was estimated using a
conservative average value of 0.22 g CO2-C m-2 d-1 obtained by averaging fluxes for the
periods including November 2011 and late May 2012. The total CO2 release from pool #1
was estimated at 98.7 g CO2-C m-2 yr-1 of which, 79.2 g CO2-C m-2 came from the flux
calculated using the NDIR sensor data and 15.1 g CO2-C from the ice melt release. The
remaining 4.4 g CO2-C m-2 comes from the estimated missing flux data between May 1st
and 20, 2012. If the annual release estimation had been made using the flux from the
43
interpolation of headspace measurements, total CO2 loss from pool #1 would have been
77.3 g CO2-C m-2 yr-1. The annual CH4 release from pool #1 was estimated using the
headspace interpolation combined with estimated spring evasion. The total annual CH4
release from pool #1 was 4.6 g CH4-C m-2 of which 0.9 g CH4-C m-2 was from the spring
release. Combining CO2 and CH4 release from pool #1 resulted in an annual loss of 103.3
g C m-2.
Figure 3.8: NEE-CO2 eddy covariance measurements from April 9 to May 9, 2012. The
period of frozen pool surface is shown as the shaded area.
44
3.5 Discussion
The results from this study show that the temporal variability of CO2 and CH4
concentrations in pools and the spatial variability among pools are important.
The
continuous supersaturation of CO2 and CH4 in the pools with respect to the atmosphere
results in a net flux of gases towards the atmosphere. Previous studies have shown the
importance of CO2 and CH4 release from pools by focusing mainly on the ice-free season
primarily using sporadic measurement techniques such as the headspace or floating
chamber techniques [e.g., Hamilton et al., 1994; Pelletier et al., 2007; McEnroe et al.,
2009]. Thus far, annual C release from peatland pools had been estimated from a small
number of discrete measurements, therefore missing the temporal variability and not
accounting for spring C release. Furthermore, we examine the source of the released C
and magnitude of the pools fluxes relative to the components of peatlands.
3.5.1 Pools dissolved gas concentrations controls and origin of CO2
Pools sampled in this study were always supersaturated in CO2 and CH4 with respect to
the atmosphere, with dissolved concentration ranges of 0.3 to 8.1 mg CO2-C L-1 and 0.4
to 472 μg CH4-C L-1. These concentrations are within the range of values found in the
literature for similar water bodies of 0.3 to 16 mg CO2-C L-1 and 0.8 to 766 μg CH4-C L-1
[Hamilton et al., 1994; Riera et al., 1999; Repo et al., 2007]. Different processes drove
the dissolved gas concentrations across the site and over time. The shallowest pool (#3)
consistently had the highest dissolved CH4 of all five pools. This result may be explained
by the higher measured sediment temperatures; although only measured in pool #1 and
#3, sediment temperature was constantly higher in pool #3. In a laboratory peat column
experiment, Moore and Dalva [1993] reported 6.6 times higher CH4 production when
temperature increased from 10°C to 23°C. The average 2012 sediment temperature in
pool #3 was 16.6°C compared to 14.6°C in pool #1. The higher temperatures in pool #3
are a direct result of pool depth (pool #3 was sampled at 25cm and pool #1 at 200cm
deep). Temperature also seemed to play an important role in CO2 concentrations in pool
#1, with specific relationships between CO2 and temperature corresponding to the fall
water temperature cooling (2011) and summer warming (2012) periods (Table 3.2). The
two relationships suggest that another factor influences the concentrations, as the rates of
45
CO2 increase and decrease were different between the two periods. While higher
temperature favors microbial degradation of organic matter and therefore higher CO2
concentrations, the slower decrease in concentration in the fall could potentially be
explained by the pool mixing, which would bring CO2 from the bottom of the pool.
Pool #2 had the highest average CO2 values during the open water season. This pool was
surrounded by relatively dense shrub vegetation compared to the other four pools
sampled and the vegetation sheltered the pool surface from the wind, potentially reducing
CO2 evasion and favoring CO2 accumulation in water [Kwan and Taylor, 1994]. The
sheltering effect could also explain the high CO2 values measured in May, which could
represent CO2 that accumulated under the ice layer and that was not released immediately
following ice melt. Although no continuous temperature data is available for this pool,
the shelter created by the vegetation could have also inhibited mixing and promoting
warmer water. Pool size could also explain the higher CO2 measured in pool #2. In a
study examining the relationships between peatland pool morphology and the CO2 fluxes,
McEnroe et al. [2009] observed a negative relationship between pool surface area and the
average CO2 fluxes. A similar relationship can be observed here (Table 3.1 and 3.3).
Roehm et al. [2009] observed a negative relationship between lake area and average CO2
concentration suggesting that the C loading from the surrounding watershed was an
important driver for the dissolved CO2 dynamics in lakes. However, peatland open water
pools are different from lakes as they are hydrologically isolated from the larger
catchment they are in, and receive their water from rainfall and lose water through
evaporation. Although carbon loading has been shown to explain CO2 concentration
variation in boreal lakes, the overall variability in headspace CO2 over time and space
could not be explained by the DOC concentrations at our site. Within pools, our results
showed negative and positive significant relationships between dissolved CO2 and DOC
in pool #2 and #5, respectively. Note that no samples were collected in pool 5 during the
last field campaign and the relationship is therefore based on a shorter period of time and
smaller sample size. The relatively narrow range of DOC concentrations could explain
the overall weak relationships with CO2 concentrations (Figure 3.5). Concurrently, the
negative relationship observed in pool #2 seems to be driven by high CO2 concentrations
46
measured early in the open water season that could be the result of a sheltering effect (see
above). Because of the low pool water pH (<4.5), we can assume that CO2 represented
the dominant inorganic carbon species.
As for the dissolved CO2-DOC relationship, the observed link between SUVA254 and
CO2 concentration in the five pools does not provide clear evidence that the open water
DOC is the main source for CO2. Specific UV-absorbance (SUVA254) has been shown to
correlate positively with DOC aromaticity and with lower microbial biodegradability of
dissolved organic matter [Kalbitz et al., 2003; Weishaar et al., 2003]. Therefore, if DOC
represented the main source of dissolved CO2 in our pools, we would expect CO2
concentrations to decrease with increasing SUVA254. Here however, the significant
relationships observed in the four small pools are positive, with dissolved CO2 increasing
with increasing SUVA254. However, in a lab study Olefeldt et al. [2013] found UVmediated degradation of highly aromatic leachates to produce greater C loss than for
lower aromacity compounds. This finding suggests that more recalcitrant dissolved forms
of terrestrial organic matter reaching aquatic ecosystems could be rapidly degraded and
released to the atmosphere. This could explain the positive relationships observed in four
of the five pools in our study. Conversely, the significant positive relationship found
between cumulative daily photosynthetic active radiation (PAR) and the amplitude of the
diurnal pattern in dissolved CO2 at the surface in pool #1 in 2011 suggest that DOC still
plays a role in CO2 variations, at least at a diurnal scale. The increase in CO2
concentration during the daytime at the surface of pool #1 could be the product of DOC
photoxidation. This is also supported by the less pronounced diurnal variability at 1m,
which can be explained by the limited light penetration at depth. DOC photooxidation
represents an important factor in lake CO2 supersaturation and the reaction has been
linked to energy absorption, which decreases with water depth [Graneli et al., 1996;
Bertilsson and Tranvik, 2000].
The open water peatland pools studied here differ largely from lake ecosystems. Peatland
pools are not connected to each other or to a stream, and their basins are composed
almost uniquely of organic matter, providing an almost unlimited amount of substrate for
47
decomposition. With pools being located on the highest portion of the ombrotrophic
peatland, the size of individual pool catchments is relatively small and their water input
comes exclusively from precipitation. A large DOC input into the pools would be
required to sustain the CO2-C release measured in the present study. We estimated the
size of the individual pool watershed using a topographic survey of the site done by
Simard [1976], and estimated the May to October runoff to 149 mm based on
precipitation (368 mm) and evaporation (219 mm) measured by the EC tower
(unpublished data). Using a DOC concentration of 30 mg L-1, we found DOC import to
the pools could range between 8 and 38 g C m-2 of pool area for the period between midMay and mid-October 2012. It should be stressed that the pools are hydrologically
isolated for most of May to October, similarly to Quinton and Roulet [1998], and that
using runoff to evaluate the potential water flow through the pool catchments is probably
an over estimation. Nevertheless, we use this estimate in order to be conservative with
our calculations. Based on the CO2 fluxes calculated from the daily average flux (NDIR
in pool#1 and headspace measurement in other pools) we estimate that DOC could
represent between 16 and 30% of the CO2 loss from our pools during the same period.
This is consistent with the results presented by Hamilton et al. [1994] where limited
groundwater movement was observed and the source of the C fluxes at the surface of the
water was attributed to the decomposition of the peat at the bottom of the pools. Hamilton
et al. [1994] also found the lowest CO2 and CH4 concentration in a pool that had a
mineral bottom.
Further analysis of our data showed that fluxes calculated using the NDIR-CO2 data from
pool #1 (Figure 3.6) were also strongly correlated with the sediment temperature during
the entire study period (Figure 3.9a). An Arrhenius plot (Figure 3.9b) of CO2 fluxes at the
surface and sediment temperature of pool #1 suggests the fluxes could be tightly related
to CO2 production in the pool’s sediment. It also indicates that the pool is close to steadystate i.e. the flux is approximately the same as production, and is greater than the changes
in CO2 storage in the peat at the bottom and pool water. This is circumstantial evidence
that the origin of the emitted CO2 lies mainly in the peat at the bottom of the pool and not
the open water DOC.
48
Figure 3.9: Relationship between sediment temperature and CO2 flux at the surface of
pool#1 (top) and Arrhenius relationship for the same data set (bottom). The flux data was
binned into temperature classes of a 1°C interval and the mean and standard deviation of
the fluxes was computed for each bin.
49
3.5.2 CO2 and CH4 evasion from pools
Fluxes presented in this study range between 0.04 and 2.5 g C m-2 d-1 for CO2 (headspace
and NDIR) and 0.004 and 0.64 g C m-2 d-1 for CH4 which is well within the range of
values presented in literature of 0.14 to 16.6 g C m-2 d-1 for CO2 and -0.001 to 1.87 g C
m-2 d-1 for CH4 [Hamilton et al., 1994; Waddington and Roulet, 1996; Pelletier et al.,
2007; Repo et al., 2007; Cliche Trudeau et al., 2013]. The estimated spring melt evasion
of 15 g C m-2 for pool #1 is, although smaller, consistent with values of 28 to 35 g C m -2
published for a small 11m deep lake in Finland [Huttunen et al., 2004]. Our estimation
also matches the flux measured by the eddy covariance tower during the spring melt (17
g C m-2). However, pool surfaces represented approximately 58% of the eddy covariance
tower source area during the ice melt (Figure 3.1). Since the CO2 release from the
terrestrial portion was limited due to the cold peat surface, the release measured by the
eddy covariance tower is an underestimation of the actual contribution from the pool. At
the time of spring melt, peatland vegetated surface soil temperature was below 2°C,
limiting heterotrophic respiration, and the vegetation had not recovered from dormancy
[e.g., Larcher, 2003]. To our knowledge, no other study has reported the spring melt
release from peatland pools using eddy covariance. These results provide corroboration to
the estimates from the discrete measurement techniques and have the potential to greatly
improve annual estimates of release from aquatic ecosystems. Direct measurement of the
CO2 release during spring melt using the eddy covariance has been made in a Finnish
Lake with 30-min flux reaching maximums between 0.01 to 0.06 mg CO2-C m-2 s-1
depending on the year [Huotari et al., 2011], compared to a maximum 0.17 mg CO2-C m2 -1
s presented here.
Estimates of the annual C release from peatland pools including spring release are nonexistent in the literature. The most relevant example where annual C release has been
measured is Riera et al. [1999] who measured two bog lakes in Wisconsin, USA. In their
study, Riera et al. [1999] estimated an annual C evasion for the two bog lakes of 84 and
124 g C m-2, including the spring release. Our annual estimate of 105 g m-2 yr-1 from pool
#1 is well within this range. However, our annual release estimate remains conservative
as no dissolved CO2 and CH4 values were measured just before the spring melt. This is
50
also supported by the EC tower measurements during spring melt which included
terrestrial area, and therefore, underestimated the flux from the pool. Riera et al. [1999]
observed an increase in dissolved CO2 and CH4 up until ice melt. Furthermore, our results
do not include the release of CH4 through ebullition. In two lakes adjacent to a subarctic
peatland in Sweden, Wik et al. [2011] estimated the release of CH4 through ebullition as
0.07 ±0.3 mg m-2 d-1 during the frozen season and 0.02 ±0.07 mg m-2 d-1 during the
open water season. Applied to our site, this would represent a release of 13.8 g CH 4-C
through ebullition, increasing the annual C loss to 117.1 g. This annual loss is larger than
the range reported by Repo et al. [2007] of 22 and 66 g C (4 to 7 days of headspace and
chamber measurements in July and August extrapolated to 100 to 120 days) and falls
within that of Waddington and Roulet [2000] of 86 to 419 g C (weekly chamber
measurements from May to September).
3.5.3 Effects on contemporary C budget estimates
Peatland net ecosystem carbon balances (NECB) that include annual eddy covariance
NEE-CO2, CH4 loss and DOC export are limited [Roulet et al., 2007; Nilsson et al., 2008;
Billett et al., 2010; Koehler et al., 2011; Olefeldt et al., 2012]; Dinsmore et al. [2009]
being the only one including CO2 and CH4 release from water bodies (streams). The
published annual NECB values range between a net source of 14 g C m -2 to a net sink of
101 g C m-2 [Roulet et al., 2007; Nilsson et al., 2008; Billett et al., 2010; Koehler et al.,
2011]. However, the sites where these continuous measurements were made either have
no pools, non-permanent pools or pools that are located outside of the eddy covariance
tower source area that measured the NEE-CO2. A 6-year NEE-CO2 study has been
published for a heterogeneous surface fen in Finland where the source area consisted of
60% flarks (shallow pools), 10% moist lawns and 30% dry strings [Heikkinen et al.,
2002; Aurela et al., 2004]. Although the flarks are inundated most of the time, emergent
vegetation composed of sedges (Carex spp.) and mosses is present. Microform level
measurements have shown the flarks to be sink for CO2 [Heikkinen et al., 2002] in
contrast with the deep permanent pools. If a large part of the released C comes from the
mineralization of the sediments, as our results suggest, peatlands with permanent pools
could have a significantly different net ecosystem carbon balance considering the release
51
of 103.3 g C m-2 yr-1 measured in the present study. Using an average NECB value of
30.9 g C m-2 yr-1, calculated from the published NECB, and assuming 50% of the release
103.3 g C m-2 yr-1 comes from the sediments, we estimate that peatlands that have pool
covers greater than 37% could represent net sources of C to the atmosphere. In a Swedish
peatland where pools covered less than 6% of the site, Waddington and Roulet [2000]
found that pool release turned their site into a net C source, based on a open water season
chamber-based peatland carbon exchange evaluation. The impacts of these findings are
important as they imply that the current NECB of a significant portion of the boreal
peatlands is negative, as they would be losing C to the atmosphere. However, peatland
pool coverage data is currently lacking for most regions of the world. Peatland rich
regions such as the Hudson Bay lowlands and Western Siberia lowlands exhibit large
pool surface area that can represent more than 30% of the peatland surface in some area
[Roulet et al., 1994]. Certainly this is an area of research that should be explored further.
3.6 Conclusion
This study highlighted the magnitude and spatial and temporal variability in CO2 and
CH4 release from boreal peatland pools. Our measurements showed that pool GHG
concentration varied on a diurnal and seasonal basis and with pool sizes and depths. All
pools were constant sources of CO2 and CH4 to the atmosphere during the ice-free season
and the annual C release from pool #1 was 103.3 g m-2 of which spring melt release
represented 15%. Overall, the different methods used in this study showed good
agreement. The CO2 headspace measurements and derived fluxes for pool #1 displayed
similar trends as the concentration and fluxes obtained with the NDIR sensor, although
the later captured more variation over short time periods. Spring melt release estimated
from headspace measurements was also similar to the flux measured using the eddy
covariance tower, emphasizing the potential of eddy covariance flux measurements over
aquatic ecosystems.
Although pools are a common feature in boreal peatlands, ecosystem level carbon
exchange studies have yet to be made on this type of peatland. Our results suggest that
37% pool cover on a peatland could turn the ecosystem into a net C source. Therefore we
52
suggest that studies should evaluate the surface area of peatland pools in the Hudson Bay
and James Bay Lowlands in Canada, and in the Western Siberia Lowlands to get a true
estimate of peatland net carbon balance.
53
4. Are peatlands with pools a net sink for CO2?
Bridging statement to Chapter 4
In Chapter 3, I showed that pools represent a significant source of C to the atmosphere
mainly through CO2 loss and therefore must be included in ecosystem scale estimates of
C exchange. Despite the fact that northern peatlands often contain a significant area of
pools, previous studies either have not included their contribution or have used point
measurements that are extrapolated in space and time leading to large error bounds
around estimates. In the next Chapter 4, I evaluate continuous measurements of the
ecosystem-scale CO2 exchange of a peatland that includes permanent open water pools.
These results are used to address the second objective of my thesis to evaluate the sink
potential of a peatland with permanent pools.
54
4.1 Abstract
Peatland open-water pools, a common feature on temperate to subarctic peatlands, are
sources of carbon (C) to the atmosphere but their contribution to the ecosystem level net
carbon dioxide exchange (NEE-CO2) is poorly known; there is a question as to whether
peatlands with pools are smaller sinks of atmospheric C, or even C neutral, compared to
other peatlands. We present growing season NEE-CO2 measurements using the eddy
covariance technique in a peatland with pools. The maximum photosynthetic uptake and
ecosystem respiration rates at 10°C were in the lower range of the published data. The
lower total vegetation biomass, due to the presence of pools, reduced CO2 uptake during
day and the autotrophic ecosystem respiration. The low CO2 uptake combined with
reduced CO2 loss resulted in the site being a net sink for CO2 of a similar magnitude as
other northern peatlands despite the inclusion of pools.
55
4.2 Introduction
Peatland open water pools are autogenic features that are, as opposed to the vegetated
portions of these sites, net sources of carbon (C) to the atmosphere (23 to 419 g C m -2 yr1
) [Hamilton et al., 1994; Waddington and Roulet, 2000; Repo et al., 2007; McEnroe et
al., 2009; Pelletier et al., 2014]. This release of C is due to peat decomposition at their
bottom, limited emergent vegetation to uptake CO2, and microbial and photo-degradation
of dissolved organic carbon (DOC). The published rates of C release from water bodies
on peatlands are of the same magnitude, but with an opposite sign, as the published net
ecosystem carbon balance (NECB) for peatlands without pools (e.g. -14 to 101 g C m-2
yr-1) [Roulet et al., 2007; Nilsson et al., 2008; Billett et al., 2010; Koehler et al., 2011;
Olefeldt et al., 2012]. Peatlands with pools are ubiquitous and of varying age [e.g., Foster
and Wright, 1990; Beilman et al., 2009; van Bellen et al., 2011; Magnan and Garneau,
2014] so the long-term carbon accumulation in the vegetated areas of these peatlands has
to exceed the C loss from the pools. However, assuming peatlands with pools have a
similar uptake as those without pools could result in a significant overestimation of the C
uptake attributed to peatlands. Pools form from differential biomass accumulation and
decomposition and their development is influenced by climate, topography, and
geographical setting [e.g., Foster and Wright, 1990; Belyea and Lancaster, 2002; Belyea,
2007; Eppinga et al., 2009; Morris et al., 2013]. Pool depth appears to vary between 0.5
to > 2 m and width from 1 m to > 100 m [e.g., Foster and Wright, 1990; Karofeld and
Tõnisson, 2012]. Peatlands with pools are found from the temperate to subarctic regions
in both the northern and southern hemispheres [Glaser, 1999] but there are only a few
estimates of the surface area of peatlands covered by pools. In the Hudson Bay Lowlands,
pool coverage is > 40% in some areas [Roulet et al., 1994], > 50% in fens in northeastern
Quebec, Canada [White, 2011], and between 5 and 40% in some of the major peatland
types in Russia [Botch et al., 1995]. Recently there has been an effort to include
peatlands [e.g., Wania et al., 2009; Kleinen et al., 2012; Spahni et al., 2012; Wu et al.,
2012] in models that simulate climate – carbon connections, but the resolution of these
models is far too coarse to include pools. Therefore it is relevant and timely to determine
if the carbon exchange from peatlands with pools is different than that of peatlands
56
without pools to establish if the simple generalized model parameterization might be used
for peatlands with pools.
Measurements of net ecosystem CO2 exchange (NEE-CO2) using the covariance (EC)
method have been made in several peatlands in temperate, boreal and surbarctic regions,
covering multiple years of continuous measurements [e.g., Aurela et al., 2004; Roulet et
al., 2007; Sagerfors et al., 2008]. However, these peatlands have relatively homogeneous
surface vegetation [e.g., Lafleur et al., 2003; Aurela et al., 2009] and no pools, with the
possible exception of the measurements from Kaamanen in northern Finland where there
are ephemeral pools [Aurela et al., 2001, 2002, 2004]. To our knowledge, no NEE-CO2
measurements have been made over a peatland with deeper and permanent open water
pools. The magnitude of the published annual release of C from open water pools raises
the question as to whether the generalized uptake figures for peatlands without pools
apply to peatlands with permanent open-water pools. Considering the efforts to integrate
peatlands into global climate models, it is important that the C exchange from different
peatland types be documented in order to provide guidance on how to parameterize these
models [Frolking et al., 2009].
Based on the reported net loss of CO2 from pools [e.g., Waddington and Roulet, 2000;
Pelletier et al., 2014] and the NEE-CO2 uptake for vegetated peat surfaces [e.g., Lafleur
et al., 2003; Sagerfors et al., 2008] we hypothesize that peatlands with pools are either
NEE–CO2 neutral or a smaller sink for CO2 than peatlands without pools. Here we
present the results of one growing season (May to October) of NEE-CO2 measurements
in a boreal ombrotrophic peatland with pools and compare these results with those
reported in the literature for peatlands without pools.
4.3 Study Site and Methods
We measured the NEE-CO2 using the EC technique [Baldocchi, 2003] from May 15 to
October 10, 2012 on a peatland located on the Manicouagan peninsula (49°08’N,
68°17’W; altitude: 19 m) 8 km south of Baie Comeau, on the north shore of the St.
Lawrence River in Quebec, Canada. The peatland is a raised bog that covers
57
approximately 600 ha with a surface pattern that consists of hummocks, lawns and pools.
Sphagnum fuscum (Schimp.) H.Klinggr., Chamaedaphne calyculata (L.) Moench, dwarf
Picea mariana (Miller) BSP and Rododendron groenlandicum (Oeder) Kron & Judd
dominate the hummocks while the vegetation on the lawns is mainly composed of S.
rubellum Wils., Andromeda polifolia L., Vaccinium oxycoccos L. and sedges
(Eriophorum spp.) [Simard, 1976; Magnan and Garneau, 2014]. The pools are free of
vegetation except for some Nuphar lutea found in the shallow sections (<1 m depth). The
pools cover approximately 12% of the entire peatland surface but they cover 22% of the
surface within 100 m of the EC tower. The pools are not uniformly distributed around the
EC tower with 37% pool coverage found between 180° and 360° azimuth, compared to
9% between 0° and 180° (Figure 4.1a). The 30-year climate normal (1981-2010) mean
annual temperature is 1.7°C and mean annual precipitation is 1001 mm. The coldest and
warmest months are January and July with mean daily temperature of -14.3 and 15.6°C
respectively. On average, 34% of the annual precipitation falls as snow, with average
snowfall
of
343
mm
http://climate.weatheroffice.gc.ca].
[Environment
Canada,
data
available
at
In 2012, the pools were ice covered from mid-
November to the end of April and the vegetated area was frozen to a depth of ~0.1 m for
4 months of the year.
The EC system consisted of a fast response three-dimensional sonic anemometer (CSAT3, Campbell Scientific, Edmonton, Canada), a fine-wire thermocouple (FW05, Campbell
Scientific, Edmonton, Canada), and an enclosed CO2/H2O analyzer (LI-7200, LI-COR,
Lincoln, NE). The instruments were mounted on a tripod 2.5 m above the surface of the
peatland. The variables used to calculate the flux were recorded and stored on a 4 GB
industrial grade USB flash drive using an analyzer control unit (LI-7550, LI-COR,
Lincoln, NE) at 10 Hz. The 30-min CO2 fluxes were computed from the 10 Hz data using
the EddyPro processing software (V5.1.0, LI-COR, Lincoln, NE). The CO2 fluxes were
derived from the covariance between vertical wind speed and CO2 mixing ratio [Burba et
al., 2012]. A two-dimensional coordinate rotation was applied. The EC CO2 data were
cleaned for quality flags output by the EddyPro processing software [Mauder and Foken,
2004]. The CO2 data showing uptake at night were removed using a PAR threshold of <
58
20 μmol m−2 s-1 [Lafleur et al., 2003]. Following this step, the CO2 data were separated
into day and night, and data were discarded if greater than ±3 standard deviations of the
monthly means [Baldocchi et al., 1997]. The nighttime NEE–CO2 were plotted (not
shown) against friction velocity (u*) and a threshold of 0.1 m s-1 was used to identify
insufficient turbulent mixing to assess reliable fluxes [e.g., Lafleur et al., 2001]; data not
meeting the criteria were discarded (43%). No gap filling procedure was applied to the
data set for the analysis we present below.
Environmental measurements were made every five seconds throughout the study period
and averaged every 30-min. The variables measured included net radiation (CNR4, Kipp
and Zonen, Delft, Netherlands), photosynthetically active radiation (LI-190SA, LI-COR,
Lincoln, NE), air temperature and relative humidity (HMP-45C, Vaisala, Helsinki,
Finland), wind speed and direction (05103-10, RM-Young, Traverse City, MI) and
precipitation (TE525M tipping bucket gauge, Texas Electronics, Dallas, TX).
59
Figure 4.1: Peatland microform classification and EC tower location (a); Monthly wind
direction frequency for NEE-CO2 in % (b). Data from wind directions between 65° to 95°
were discarded due to the upwind presence of measurement infrastructure.
4.4 Results
The 2012 monthly mean air temperatures between May and October were above the 30year normal (1981-2010). The average monthly temperatures were higher by 1.0 to 2.4°C
with largest differences observed in August. These differences represent 0.9 to 2.2
standard deviation from the normal monthly average temperature. July precipitation was
approximately half the normal value while October was double. Despite the warmer and
drier conditions in July, the vegetation at the site showed no sign of desiccation.
The NEE-CO2 measurements made between May and October 2012 covered the peatland
surface between wind directions 180° to 240° (36%), 270° to 360° (25%), and 30° to 60°
60
(12%) (Figure 4.1b). The same wind directions dominated for nighttime ecosystem
respiration (ER = NEE-CO2 for PAR < 20 μmol m-2 s-1). The dominant wind directions
were also relatively constant between months with the exception of June where the
contribution from 30° to 60° was more important (22%). The monthly average diurnal
trends in NEE-CO2 showed CO2 uptake during the day and CO2 release at night (Figure
4.2). The ER and NEEmax (when PAR > 1000 μmol m-2 s-1) varied statistically (p < 0.05)
between months over the measurement period (Figure 4.3). The monthly average ER rate
increased from early (May) to mid-growing season (July-August), before decreasing until
October (Figure 4.3). The monthly average NEEmax increased from early to late growing
season, reaching a maximum uptake of -4.1 μmol m-2 s-1 in September (Figure 4.3).
Overall, the monthly mean daily NEE-CO2 flux showed uptake for all months with a
range of -1.02 (SE ± 0.04) to -2.76 (±0.06) g CO2 m-2 d-1 and was higher in the first half
of the growing season (May-July) (Figure 4.3). The mean daily uptake for the entire
study period was -1.84 g CO2 m-2 d-1.
4.5 Discussion
This study is the first to report EC NEE-CO2 measurements made over a boreal peatland
with permanent pools. We found that the monthly average diurnal trends in NEE-CO2
followed a pattern similar to other peatland ecosystems [Humphreys et al., 2006], cattail
marsh [Bonneville et al., 2008] or forested ecosystems [Loescher et al., 2003] , where
CO2 uptake is observed during the day and CO2 is released during the night (Figure 4.2).
The maximum photosynthetic uptake (Amax) calculated using a rectangular hyperbola
relationship between GEP and PAR [see Frolking et al., 1998], and the ER calculated for
an air temperature of 10°C (R10) [Lloyd and Taylor, 1994] were in the lower range of
values for northern peatlands (Figure 4.4). This means that, despite the presence of pools,
the studied peatland has lower ER than other peatlands. The R10 we found is similar to the
Stordalen palsa mire (R10 = 1.01 μmol m-2 s-1) [Olefeldt et al., 2012] that is experiencing
permafrost thaw and the Kaamanen subarctic mesotrophic fen (R10 = 1.32 μmol m-2 s-1)
[Lindroth et al., 2007], which has ephemeral pools in the spring and early summer
[Heikkinen et al., 2002]. The autotrophic component of ER, which is generally about the
same magnitude or larger than heterotrophic respiration in peatlands [Silvola et al.,
61
1996b; Moore et al., 2002] is eliminated for those portions of the peatland with pools.
The presence of pools on the peatland can explain both the lower ecosystem Amax and R10.
Because of the absence of significant CO2 fixing vegetation in the pools and their
constant release of CO2 to the atmosphere [Pelletier et al., 2014], pool surfaces reduce
the maximum photosynthetic uptake and respiration at the ecosystem level.
Figure 4.2: Mean monthly diurnal pattern in NEE-CO2 during summer 2012 on the
studied peatland. Each symbol represents the 30-min average NEE-CO2 for the
corresponding half-hour and month. Error bars represent standard errors. No symbols are
shown if a half-hour contained fewer than five measurements.
62
Figure 4.3: Mean monthly NEEmax (PAR > 1000 μmol m-2 s-1), ER and daily average
NEE-CO2 between May and October 2012 at the studied peatland. Standard errors are
given in parentheses. Daily average NEE-CO2 was calculated by averaging the mean
monthly diurnal pattern presented in Figure 4.2. The daily average NEE-CO2 for May
only includes data from May 15 and onward. Limited data availability prevented
calculation of daily average NEE-CO2 for October. NEEmax, and ER values are
significantly different if they have no letters in common. Statistical differences between
the monthly averages were determined by performing Kruskall-Wallis test followed by
post hoc Steel-Dwass.
63
Despite the lower maximum photosynthetic uptake and respiration rates, the measured
mean daily NEE-CO2 for June-September (~60 g C m-2) is within the range of published
mean daily NEE-CO2 measured in pool-free peatlands (-1.51 to -5.20 g CO2 m−2 d−1;
Table 4.1). This suggests that the lower photosynthetic rates measured at our site were
offset by lower loss through ER, making this peatland with pools a net sink for CO2 for
the 2012 growing season, in the same range as that of peatlands without pools. It is
unknown how the higher than normal temperatures observed during the measurement
period affected the ecosystem level NEE-CO2. Pelletier et al. [2014] observed a strong
positive correlation between pool water temperature and their C fluxes at the same site
suggesting that pool C release may have been greater than ‘normal’ during the 2012
growing season.
Figure 4.4: Relationship between Amax and R10 for temperate, boreal and subarctic
peatlands. Data from Mer bleue poor fen, Mer bleue bog, AB-PF, AB-RF, AB-WF, SKWF are from Humphreys et al. [2006]; SK-RF from Frolking et al. [1998], Humphreys et
al. [2006] and Sonnentag et al. [2010]; Kaamanen from Lindroth et al. [2007] and Aurela
et al. [1998]; Stordalen from Olefeldt et al. [2012] and S. Harder (personal
communication).
64
Table 4.1: Mean June to September daily average NEE-CO2 for the Petite Rivière
peatland and other boreal and subarctic peatlands. Data from the Kamanen,
Lompolojänkkä, Fäjemyr and Siikaneva were extracted from figures in the respective
papers.
g CO 2 m -2 d -1
# of growing
season
Source
Petite Riviere, Canada
-1.83
1
this study
Mer Beue bog, Canada
-1.54 to -2.84
4
Lafleur et al., 2003
Degerö Stormyr, Sweden -2.05 to -2.59
3
Sagerfors et al., 2008
Kaamanen, Finland
-1.51 to -3.86
3
Aurela et al., 2001; 2002; Lindroth et al., 2007
Lompolojänkkä, Finland
-4.11 to -5.20
3
Aurela et al., 2009
Fäjemyr, Sweden
-1.88
1
Lund et al., 2007
Siikaneva, Finland
-2.70
1
Lindroth et al., 2007
Peatland site
Our results refute our hypothesis: the study peatland, pools and all, is not C neutral nor a
smaller sink for CO2 than what has been observed in other peatlands. This more generally
suggests that the presence of pools on a northern peatland does not significantly reduce
the carbon sink potential. Olefeldt et al. [2012] showed that low productivity combined
with lower ER led to the NECB of a permafrost peatland having a similar net overall sink
to boreal peatlands. For the permafrost peatland, the combined effect of limited
vegetation biomass, low ER linked to the presence of permafrost, and extended winter
periods still resulted in an average NECB of 56 g C m-2 yr-1. In our studied peatland, the
pools play a similar role in reducing the vegetation biomass therefore reducing both
photosynthesis and autotrophic respiration. Winter CO2 loss from peatlands represents an
important part of the annual budget [Aurela et al., 2002]. Although we did not do winter
measurements, the cold season CO2 loss is likely to be low since the R10 value is low
(Figure 4.4) and cold temperatures persist for more than 5 months of the year. Even
without winter measurements we are confident that the studied peatland is a net sink for
CO2. Humphreys et al., [2014] found when comparing the NEE–CO2 from two boreal
peatlands and a temperate bog that a shorter growing season CO2 uptake was offset by
smaller winter losses, resulting in very similar annual NEE-CO2.
65
4.6 Conclusion
The results from the present study suggest that peatlands with pools can be net sink for
CO2 at the ecosystem level during the growing season and potentially on an annual basis
though we did not test this directly. Although the pools at our site represented net sources
of CO2 to the atmosphere, the reduced ecosystem CO2 uptake capacity is compensated by
the limited CO2 loss through respiration. This study is the first to present spatially
integrated NEE-CO2 for a peatland with pools; we present data from a single growing
season and for one specific site, which is an example of peatland with pools. The
representativeness of our site and results will only be determined if our work stimulates
others to do the same sort of measurements in other peatlands with pools in similar and
different geographical settings. We also recognize the importance of long-term C
exchange studies as those have, in some cases, shown significant inter-annual variability
in NEE-CO2 [e.g., Roulet et al., 2007], and in others shown comparatively little [e.g.,
Nilsson et al., 2008]. One season of measurements can say nothing about annual
variability but the study period was warmer than the climate normal, which would
suggest if anything that the heterotrophic respiration might have been greater than the
longer term average suggesting that our conclusions may be robust. At this point, our
results suggest that generalized model parameterizations based on peatlands without
pools may work until higher resolution models are possible. The development of peatland
open-water pools is an active research area and studies have shown that the coverage by
pools and their configuration are a function of topographic and geologic setting as well as
developmental stage of the peatland [Foster and Glaser, 1986; Foster and Wright, 1990;
Eppinga et al., 2009; Morris et al., 2013]. Our results raise the question as to how the
variation in pool proportion between peatlands affects the C exchange. These results also
warrant further study to include methane and DOC losses to establish a complete NECB
for peatland with pools; these components should account for only 20 to 40% of the
NEE-CO2 uptake [Roulet et al., 2007; Nilsson et al., 2008].
66
5. Multiscale spatial variability in surface-atmosphere CO2
exchange in a peatland with open water pools
Bridging statement to Chapter 5
In Chapter 3, I demonstrated that the permanent open water pools found at the surface of
the studied peatland were a constant source of CO2 to the atmosphere. My results from
Chapter 4 showed that despite the presence of these pools at the surface of the site, the
peatland was still a net sink for CO2 during the growing season at the ecosystem scale,
with an average NEE-CO2 rate within the range of published values for peatland without
pools. This suggested that the vegetated portion of the site represented a sink for CO2
capable of counterbalancing the CO2 loss from the pools. In the next Chapter, I evaluate
if the spatial variability in CO2 exchange at the surface of the studied peatland
(heterogeneity in plant communities and pools) can be identified from the ecosystem
level measurements. Furthermore, I examine if the measured variability of CO2 exchange
between the plant communities could explain the CO2 sink capacity of the site despite the
presence of pools. This chapter addresses the third objective of my thesis to identify the
pool’s CO2 flux signal from the ecosystem level measurements.
67
5.1 Abstract
Peatland carbon dioxide (CO2) exchange varies spatially over a few meters due to the
heterogeneity in plant communities and the presence of pools. As opposed to the plant
communities comprising the peatland vegetated surface, the permanent open water pools
that are characteristic of peatlands in temperate to subarctic regions are net sources of
CO2 to the atmosphere. Measurements of net ecosystem CO2 exchange (NEE-CO2) using
the eddy covariance (EC) technique over peatlands without permanent pools show that
this heterogeneity in fluxes, observed between plant communities, is not apparent in the
measurements because the atmosphere mixes the variations in fluxes from the plant
communities that make up the EC tower source area. Still, different vegetation
communities and pools should result in distinctive EC fluxes if their fluxes are
significantly different, and if in space, they vary significantly in their proportion within
the EC tower source area. In the present study, we evaluate if the observed variability in
peatland surface CO2 exchange (due to the heterogeneity in plant communities and
pools), can be identified from the 30-minute net ecosystem CO2 exchange measurements
using the proportion of the different plant communities or pools within the eddy
covariance tower source area. Our results show that the variability in CO2 exchange at the
local scale across the peatland surface has a measureable impact on the ecosystem level
measurement, primarily when open water pools were present within the tower source
area. Our results also suggest that the large CO2 exchange rates measured above the P.
mariana plant communities, combined with their large fractional contribution to the
source area, counterbalanced the CO2 loss from the open water pools, explaining why the
ecosystem as a whole was a net CO2 sink during the measurement period.
68
5.2 Introduction
Northern peatlands represent an important carbon (C) reservoir, storing between 473 and
621 Gt C [Yu et al., 2010]. This accumulation of C is the result of the positive balance
between productivity through carbon dioxide (CO2) uptake, and C loss through CO2
respiration, methane emissions and dissolved organic carbon (DOC) export.
Measurements of CO2 exchange using static chambers on the vegetated surfaces show
that the gross ecosystem productivity (GEP) and ecosystem respiration (ER) can vary by
more than tenfold between hummocks and hollows [e.g., Pelletier et al., 2011]. The
variability in the instantaneous CO2 exchange between microforms is explained by the
different controls on the GEP and ER. The spatial variability in GEP is mainly a function
of the vegetation biomass and plant functional types [Bubier et al., 2003b], which vary
across the microtopography with the water table position and trophic status of the site
[Bubier et al., 1998]. The peatland ER combines an autotrophic and a heterotrophic
component, which respectively represent plant respiration and peat decomposition. The
autotrophic respiration spatial variability is linked to photosynthesis rates and therefore,
to the vegetation biomass and plant functional types [Crow and Wieder, 2005]. The
heterotrophic component variability across the microtopography is mainly influenced by
the water table position that determines the thickness of the aerobic layer [Silvola et al.,
1996a]. Although the instantaneous CO2 flux range can be large (<−15 to >15 μmol m-2 s1
), the diurnal course of CO2 exchange follows a similar pattern between vegetation
communities, where CO2 is absorbed during the day and released at night. Overall, the
different plant communities at the surface of peatlands are generally net sinks for CO2
[e.g., Bubier et al., 1999].
Compared to the plant communities comprising the peatland vegetated surface, the open
water pools that are characteristic of peatlands in temperate to subarctic regions are net
sources of CO2 to the atmosphere [Hamilton et al., 1994; Waddington and Roulet, 2000;
Pelletier et al., 2014]. Although poorly documented, the surface covered by these water
bodies has been estimated to be more than 40% in sectors of the Hudson Bay lowlands,
and up to 70% in fens in Northern Quebec [Roulet et al., 1994; White, 2011]. The
published CO2 release rates from these water bodies range from 0.4 to 16 μmol CO2 m-2
69
s-1 [Hamilton et al., 1994; Waddington and Roulet, 2000; Repo et al., 2007; McEnroe et
al., 2009; Cliche Trudeau et al., 2014; Pelletier et al., 2014]. Relationships between CO2
fluxes and the pool’s morphometry (size and depth) have been observed [McEnroe et al.,
2009] but the general controls on the spatial variability in pool fluxes within and between
peatlands have not been clearly identified. Diurnal patterns in the pool water dissolved
CO2 concentrations have been measured and associated with DOC photooxydation
[Pelletier et al., 2014] and photosynthesis/respiration of algal communities [Hamilton et
al., 1994]. However, because the peatland pools are constantly supersaturated in CO2, the
fluxes are always towards the atmosphere.
The heterogeneity in the magnitude and direction of fluxes among peatland features has
been thoroughly documented, although the number of studies is biased towards the
vegetated surfaces of the ecosystem. Measurements of net ecosystem CO2 exchange
(NEE-CO2) using the eddy covariance (EC) method [Baldocchi, 2003] have been
performed at a number of sites from temperate to subarctic regions, representing
peatlands without pools or with ephemeral pools that dry during the growing season [e.g.,
Aurela et al., 2002; Humphreys et al., 2006; Lund et al., 2010]. While the chamber based
measurements of CO2 exchange show large variability over a few meters within sites, this
heterogeneity in fluxes is not apparent in the EC measurements because the atmosphere
mixes the variations in fluxes from the plant communities that make up the EC tower
source area [Riutta et al., 2007; Laurila et al., 2012]. Still, different vegetation
communities should result in distinctive EC fluxes if: 1) the fluxes from these diverse
vegetation communities or pools are significantly different, and, 2) if in space, they vary
significantly in their proportion within the EC tower source area. Ecosystem scale NEECO2 measurements above a peatland with open-water pools have been limited to a single
study [Pelletier et al., in review], and showed that despite a constant loss of CO2 from
open water pools [Pelletier et al., 2014] the peatland remained a net sink for CO2. The
presence of pools reduced the vegetation biomass at the scale of the ecosystem, reducing
the net CO2 uptake during day but also the autotrophic ecosystem respiration at night.
Considering the magnitude and the direction of the CO2 flux from the open water pools,
the overall NEE-CO2 sink suggests that the vegetated plant communities represented an
70
important sink for CO2, capable of compensating for the constant CO2 loss from these
water bodies.
In the present study, we look at the spatial variability in plant community CO2 exchange
in the same boreal maritime peatland with pools as Pelletier et al. [2014] and Pelletier et
al. [in review] and compare these fluxes to those from previously documented peatlands.
Simultaneously, we evaluate if the observed variability in peatland surface fluxes (due to
the heterogeneity in plant communities and pools) can be identified from the 30-minute
EC measurements and using the proportion of the different plant communities or pools
within the EC tower source area. Since peatland pools can vary in size from less than 2 to
> 1000 m2 and are constant sources of CO2 to the atmosphere, their signal is more likely
to be observable in the ecosystem level measurements. We hypothesize that there will be
large variability in CO2 exchange between plant communities across the peatland surface,
and that this variability along with the change in proportion of plant communities and
pools within the EC tower source area will result in distinctive NEE-CO2 for different
source areas. We also hypothesize that the magnitude of CO2 exchange in plant
communities explains why the peatland is a net sink for CO2 despite the presence of
pools.
5.3 Methods
5.3.1 Peatland location, description, and climate
The measurement site is located in Pointe-aux-Outardes on the Manicouagan peninsula
(49°08”N, 68°17”W; altitude: 19 m) in the North Shore region of Quebec, Canada. The
peatland covers approximately 600 ha and is a maritime raised bog characterized by
hummocks, lawns and pools. The vegetation on the hummocks consists of Sphagnum
fuscum, Chamaedaphne calyculata, Rhododendron groenlandicum and dwarf Picea
mariana. On the lawns, the vegetation is mainly composed of Sphagnum rubellum,
Vaccinium oxycoccos, Kalmia polifolia and sedges, while in the pools, vegetation is
almost non-existent, except for Nuphar lutea in < 1m deep sections. The pools cover
approximately 12% of the peatland surface and their surface area ranges from 2 to 9000
71
m2. The spatial distribution of the different plant communities and pools in the
measurement area is presented in Figure 5.1. The regional 30-year climate normal (19712000) mean annual temperature and precipitation are 1.5°C and 1014 mm. The coldest
and warmest months are January and July with mean daily temperature of -14.6 and
15.6°C, respectively. On average, 34% of the annual precipitation falls as snow, with
average
snowfall
of
343cm
(Environment
Canada,
data
available
at
http://climate.weatheroffice.gc.ca /Welcome_e.html). During the 2012 growing season,
monthly average temperatures were higher than the 30-year normal (1981-2010) by 1.0 to
2.4°C, which corresponded to 0.9 - 2.2 standard deviations from the mean. Precipitation
was lower than normal in July, with 48 mm compared to 89 mm on average (1981-2010).
Figure 5.1: Spatial distribution of plant communities and pools at the surface of the
studied peatland. The location of the EC tower is indicated by the star.
72
5.3.2 Vegetated surface CO2 exchange
Chamber measurements of CO2 exchange on vegetated surface were performed during
one-week in each of late May, early July and late August 2012. Groups of three collars
were installed on each of four dominant plant communities after surveying the tower area
(Table 5.1). Boardwalks were installed on the peat surface next to each group of collars
in order to minimize disturbance when performing flux measurements. Soil temperature
(5, 10, 20, and 40 cm) was measured continuously and recorded every 30-min next to
each group of collars using temperature probes (HOBO TMC6-HD Air/Water/Soil Temp
Sensor, Onset, Bourne, MA) connected to a 4-channel datalogger (HOBO U12, Onset,
Bourne, MA). The water table depth water table was continuously recorded using a level
logger (Odyssey Capacitance Water Level Logger, Dataflow Systems Pty Limited,
Christchurch, New Zealand).
The local scale CO2 exchange measurements were made using a closed system consisting
of an infrared gas analyzer (IRGA) (EGM‐4, PP-Systems, Amesbury, Massachusetts,
USA), a clear Plexiglas static chamber, and a cooling system to keep the temperature
inside the chamber close to ambient [Pelletier et al., 2011]. For each sampling of a collar,
four measurements were made under different light conditions, simulated by covering the
chamber with shrouds in order to derive light use efficiency (LUE) curves.
Photosynthetic active radiation (PAR) in μmol m-2 s-1 was measured with a Quantum
sensor (PP Systems, Amesbury, Massachusetts). The LUE curves were derived for each
plant communities for each measurement campaign by:
CO2 exchange = Amax * α * PAR / (Amax + (α * PAR)) + R
(1)
The first term represents gross productivity as a rectangular hyperbolic relationship
[Frolking et al., 1998] where Amax is the maximum photosynthesis rate and α is initial
quantum yield. The second term, R, represents the dark respiration value. We also
calculated the maximum rates of photosynthesis (PSNmax) for the four vegetation
communities during each campaign by averaging the individual gross productivity
measurements (CO2 exchange – R) for measurements with PAR > 1000 μmol m−2 s−1.
73
Table 5.1: Plant communities, vegetation description, aboveground biomass (g m-2) and
average water table position below peat surface between May 15 and October 10, 2012.
Plant
communities
Vascular Plants
Bryophytes
Aboveground
biomass
Aboveground
green biomass
WT (cm)
Sphagnum spp.
lawn
Sphagnum rubellum,
Vaccinium oxycoccos,
Sphagnum magellanicum,
Chamaedaphne calyculata, Carex sp.
Sphagnum fuscum
144 (±20)
139 (±24)
5.5 (±1.2)
Sphagnum spp.
& ericaceous
hummock
Kalmia angustifolia, C. calyculata,
V. oxycoccos, Rubus chamaemorus,
Rhododendron groenlandicum
S. fuscum, Sphagnum
capillifolium
215 (±13)
168 (±32)
28.4 (±3.2)
Eriophorum spp.
tussock
Eriophorum sp., K. angustifolia, V.
oxycoccos, R. groenlandicum
-
868 (±171)
40 (±36)
17.6 (±4.0)
Sphagnum spp.
& Picea mariana
hummock
Picea mariana, K. angustifolia, V.
oxycoccos, C. calyculata
Sphagnum fuscum, S.
capillifolium
859 (±411)
489 (±212)
17.6 (±4.0)
Due to their proximity, the same level logger was used to measure water table position for the Eriophorum spp.
tussock and Picea mariana hummock microforms.
Values in parantheses are standard deviation from the mean
a
74
5.3.3 Eddy covariance measurements
Ecosystem level NEE-CO2 was measured using the eddy covariance (EC) technique from
May 15th to October 10th 2012. The EC system consisted of a LI-7200 enclosed CO2/H2O
analyzer (LI-COR Lincoln, NE), a fast response three-dimensional sonic anemometer
(CSAT-3, Campbell Scientific, Edmonton, Canada) and a fine-wire thermocouple
(FW05, Campbell Scientific, Edmonton, Canada). The instruments were mounted on a
tripod 2.5 m above the surface of the peatland. The variables used to calculate the flux
using the eddy covariance method were recorded and stored on a 4 GB industrial grade
USB flash drive using an analyzer control unit (LI-7550, LI-COR, Lincoln, NE) at 10 Hz.
The 30-min CO2 and CH4 fluxes were computed from the 10 Hz data on a personal
computer using EddyPro, eddy covariance processing software (V4.1.0, LI-COR,
Lincoln, NE). CO2 fluxes were derived from the covariance between vertical wind speed
and CO2 mixing ratio [Burba et al., 2012]. A two-dimensional coordinate rotation was
applied.
5.3.3.1 Environmental measurements
Environmental measurements were made every 5-seconds throughout the study period
(May to October 2012) and averaged every 30-min. The variables measured included net
radiation (CNR4, Kipp and Zonen, Delft, Netherlands), photosynthetic active radiation
(LI-190SA, LI-COR, Lincoln, NE), air temperature and relative humidity (HMP-45C,
Vaisala, Helsinki, Finland), wind speed and direction (05103-10, RM-Young, Traverse
City, MI) and precipitation (TE525M tipping bucket gauge, Texas Electronics, Dallas,
TX). Soil temperature (5cm, 10cm 20cm and 40cm) next to the tower was recorded every
30-min using temperature probes (HOBO TMC6-HD Air/Water/Soil Temp Sensor,
Onset, Bourne, MA) connected to a 4-channel datalogger (HOBO U12, Onset, Bourne,
MA). The water table depth was recorded using a level logger (Odyssey Capacitance
Water Level Logger, Dataflow Systems Pty Limited, Christchurch, New Zealand).
5.3.3.2 EC data handling
The EC NEE-CO2 data were first cleaned for quality flags output by the Eddypro
software (V4.1.0, LI-COR, Lincoln, NE). The CO2 data showing uptake at night were
75
removed using solar radiation threshold of <10W m−2 [Bergeron and Strachan, 2011].
Following this step, the CO2 data were separated into day and night and data were
discarded if greater than ±3 standard deviations of the monthly means [Baldocchi et al.,
1997]. The night time CO2 flux were plotted (not shown) against friction velocity (U*)
and a threshold of 0.1 m s-1 was identified under which flux data may represent
insufficient turbulent mixing and therefore, unreliable fluxes [e.g., Lafleur et al., 2001].
CO2 fluxes for U* below this threshold were discarded. This approach resulted in 43 % of
the fluxes being rejected. The percentage of data loss for CO2 is similar to other
published studies [e.g., Lafleur et al., 2001]. Since one purpose of this study is to see if
the CO2 signal from the different plant communities and pools can be identified from the
ecosystem level NEE-CO2, no gap filling procedure was applied to the data set.
5.3.4 Source area analysis
A Worldview-2 image of the site taken on June 17th 2010 was used to evaluate the plant
communities distribution at the surface of the studied peatland. A supervised
classification was performed on the geometrically rectified and orthorectified image
based on the field observations from several field campaigns between June 2010 and
October 2012. Six classes were extracted from the image (pools, lawns, ericaceous
hummock, Picea mariana hummock, Eriophorum spp. tussock, large P. mariana). The
Eriophorum spp. tussock and large P. mariana covered on average less than 4% and 1%,
respectively, of surface around the tower area. The accuracy of the classification was
81% (Caitlin Watt and Mathis Messager, personal communication January 2014). The
proportion of the different plant communities contributing to the 30-minute eddy
covariance tower NEE-CO2 was evaluated by defining the source area of the surface flux
using an adapted 2-D version of the Hsieh et al. [2000] analytical footprint model as
presented by Detto et al. [2006]. The computed 30-min source areas were overlaid on the
supervised classification to evaluate the fraction corresponding to the different plant
communities.
We evaluated the effect of the fraction of individual plant communities or pools on the
NEE-CO2 by looking at the change in GEP and ER parameters with increasing
76
contribution of these different surface units. The GEP and ER parameters (Amax and R10)
from the NEE-CO2 data were evaluated using a modified version of equation 1, used for
the chamber measurements, which includes the effect of temperature on ER:
.
(2)
The first term represents GEP (see section 2.2). The second term represents ER and is
based on an exponential temperature response model (Lloyd and Taylor, 1994) using the
nighttime NEE-CO2 (assumed to be equivalent to ecosystem respiration, ER), where R10
is the respiration rate at 10 °C and T is air temperature (Tair) in K. Equation (2) was
parameterized (α, Amax, and R10) with measured NEE-CO2, PAR, and Tair using non-linear
least-squares regression in the MATLAB (v7.5) computing environment (Matlab,
v7.10.0.499 (R2010a), MathWorks, Matick, MA, USA). We grouped the data
sequentially for each classification with increments of 10% cover (e. g. NEE-CO2 data
for lawns covering <10%, 10 to 20%, 20 to 30% of the source area). For each group, we
then applied equation (2) and derived the model parameters. This analysis was done for
pools and all the previously identified plant communities present in the classification with
the exception of the large P. mariana class since it covered less than 1% of the surface.
5.4 Results
5.4.1 Plant community CO2 exchange
The CO2 exchange rates measured with the static chamber showed variability between
the four plant communities (Figure 5.2, Figure 5.3, Table 5.2). The communities’ PSNmax
seemed to follow the aboveground vegetation biomass (Table 5.1), increasing from lawns
to the P. mariana hummocks (Table 5.2). The R rates also increased similarly, but the
variability between communities was less pronounced than for the PSNmax and the P.
mariana hummocks R rates were similar to those of the Eriophorum spp. tussocks during
all three periods. The PSNmax also varied temporally, increasing from May to July for all
plant communities. This was followed by a decrease between the July and August
measurement periods on the P. mariana hummocks and Eriophorum spp. tussocks, while
77
PSNmax on the lawns and the ericaceous hummocks remained relatively stable. The
largest temporal variability in CO2 exchange was observed on the P. mariana hummocks
and Eriophorum spp. tussocks. R followed a similar temporal pattern as the PSNmax but
the variability was less pronounced. Overall, the P. mariana hummocks had the largest
CO2 exchange rate for PAR = 1800 μmol m-2 s-1 (NEEcap) of the four plant communities
present in the tower source area. The P. mariana hummocks NEEcap was 2.3 to 4.3 times
higher than for the other vegetation communities during the three measurement periods
(Table 5.2). The range in CO2 exchange measured with the clear chamber (no shrouds)
was larger than the daytime (8am to 18pm EST) NEE-CO2 flux measured by the EC
tower because of the high P. mariana hummock’s CO2 exchange rates (Figure 5.3).
78
Table 5.2: LUE parameters for equation (1) from the relationship between plant communities CO2 exchange and PAR during the 3
chamber measurement campaigns. The 95% confidence intervals are presented in parentheses. Amax, R and PSNmax are in μmol m-2 s-1,
α in μmol CO2-1 μmol PAR.
Plant
communities
Period
n
α
A max
R
r2
NEE cap
PSN max
Lawn
May
July
August
48
88
68
-0.005 (-0.006, -0.003)
-0.015 (-0.018, -0.012)
-0.016 (-0.021, -0.010)
-5.2 (-6.5, -3.9)
-4.9 (-5.3, -4.6)
-5.8 (-6.6, -4.9)
1.3 (1.1, 1.5)
1.2 (1.0, 1.3)
1.6 (1.3, 1.9)
0.90
0.95
0.87
-1.9 (-2.1, -1.7)
-3.0 (-2.9, -3.2)
-3.2 (-2.9, -3.4)
-3.2 (-3.5, -2.9)
-4.0 (-4.4, -3.6)
-4.6 (-5.0, -4.1)
Ericaceous
hummocks
May
July
August
48
72
72
-0.016 (-0.021, -0.010)
-0.019 (-0.023, -0.016)
-0.012 (-0.017, -0.007)
-5.8 (-6.6, -4.9)
-6.1 (-6.5, -5.7)
-6.0 (-7.2, -4.7)
1.3 (0.9, 1.7)
1.9 (1.8, 2.1)
2.0 (1.8, 2.3)
0.87
0.96
0.80
-3.5 (-3.1, -3.9)
-3.2 (-3.1, -3.4)
-2.6 (-2.4, -2.9)
-2.4 (-2.9, -1.8)
-4.8 (-5.2, -4.5)
-4.5 (-5.1, -3.9)
Eriophorum
spp. tussock
May
July
August
48
64
72
-0.004 (-0.005, -0.002)
-0.027 (-0.034, -0.020)
-0.017 (-0.023, -0.010)
-27.1 (-90.3, 36.2)
-10.3 (-11.5, -9.1)
-6.9 (-8.1, -5.7)
2.9 (2.6, 3.2)
4.9 (4.4, 5.3)
3.3 (3.1, 3.5)
0.79
0.91
0.83
-2.2 (-0.3, -4.2)
-3.6 (-3.2, -4.1)
-2.3 (-2.1, -2.5)
-4.5 (-5.2, -3.9)
-8.6 (-9.8, -7.4)
-5.3 (-5.8, -4.9)
P. mariana
hummocks
May
July
August
48
72
64
-0.010 (-0.017, -0.004)
-0.041 (-0.059, -0.023)
-0.030 (-0.047, -0.013)
-25.8 (-53.8, 2.2)
-17.2 (-20.3, -13.8)
-13.2 (-16.6, -9.9)
2.5 (2.3, 2.7)
4.7 (4.0, 5.4)
2.9 (2.5, 3.3)
0.71
0.78
0.73
-8.1 (-7.6, -9.0)
-9.2 (-8.5, -9.8)
-7.7 (-7.4, -8.1)
-9.5 (-11.5, -7.6)
-13.7 (-15.8, -11.7)
-10.0 (-11.3, -8.7)
79
Figure 5.2: Relationship between CO2 exchange and PAR over the four plant
communities in May, July and August 2012 in the peatland.
80
Figure 5.3: Daytime 30-min EC NEE-CO2, clear chamber CO2 exchange from plant
communities and thin boundary layer CO2 fluxes from the open water pools in the studied
peatland between May and October 2012.
5.4.2 Eddy covariance tower fluxes: NEE-CO2
The EC tower NEE-CO2 showed increased in CO2 uptake by the peatland with increasing
PAR (Figure 5.4), similar to the LUE functions for other peatlands without pools (e.g.
Frolking et al., 1998). However, there was significant and fairly systematic variability in
the LUE curves as the pool fraction within the tower source area increased. The
parameters of three LUE curves fitted to the 30-min data (pool fraction of 0-10%, 1020% and 20-30%) in Figure 5.4 show a decrease in NEE-CO2 uptake with increasing
pool cover. The Amax calculated for the three pool fraction classes was significantly lower
for pool fractions between 20 and 30%, with a mean Amax value 25% less than that for
pool fractions of 0-10% (Figure 5.4, Table 5.3). The R10 calculated for the same pool
fraction intervals was also lowest for pool fractions of 20-30%, suggesting that the
81
ecosystem as a whole is losing less CO2 at night when pool fraction increases within the
source area. The α decreased between the <10% fraction class and the other two classes,
but the decrease was not statistically significant as opposed to the decrease in Amax and
R10. The variability in the LUE function parameters with increasing pool fraction within
the source area could also be observed when looking at the monthly data (Table 5.4).
Although not always statistically different, the Amax calculated for pool fraction class
>20% was always lower than that for the <10% class. There were, however, no clear
patterns in α and R10 with increasing pool cover for individual months, potentially due to
the low number of observations for different months and pool fraction classes.
Figure 5.4: Relationship between NEE-CO2 and PAR between 15 May and 10 October
2012. Rectangular hyperbolic curves (lines) fit to 30-minute data (circles) corresponding
with a pool surface fraction contribution in the source area of 0-10% (blue), 10 to 20%
(green/yellow) and 20 to 30% (red).
Although not as clear as for the pools, we also observed variability in LUE function
parameters with changes in the plant communities’ fractions within the source area
82
(Table 5.3). The Amax and R10 decreased when lawns represented more than 20% of the
source area (Table 5.3). Conversely, the same parameters increased significantly with
increasing P. mariana hummocks cover (0 - 10% to 10 - 20% classes) or with increasing
ericaceous hummock cover (10 – 20% to 20 - 30%). Nevertheless, further increasing the
P. mariana hummock cover (above 10%) or ericaceous hummock (above 20%) did not
result in a change in either the Amax or R10 (Table 5.3). The Eriophorum spp. tussock had
the smallest fractional coverage representing on average 2.9% of the source area. The
analysis performed by class of 2% Eriophorum spp. tussock covers showed increase in
Amax and R10 between <2 and 2-4% fraction classes (Table 5.3).
Table 5.3: LUE parameters for equation (2) derived from the NEE, PAR, and Tair as a
function of pools or plant communities’ fraction within the source area. Amax and R10 are
in μmol m-2 s-1, α in μmol CO2-1 μmol PAR. The 95% confidence intervals are presented
in parentheses. Parameters are statistically different when they have no letter in common
(not between plant communities).
Fraction
A max
α
R 10
r2
n
Pools
<10%
-7.4a (-7.7, -7.1)
10 to 20% -6.8a (-7.2, -6.4)
>20%
-5.6b (-6.1, -5.1))
-0.020a (-0.022, -0.018)
-0.016a (-0.018, -0.014)
-0.016a (-0.019, -0.012)
0.97a (0.88, 1.04)
1.04a (0.98, 1.10)
0.73b (0.61, 0.85)
0.73
0.77
0.65
1683
1016
524
Lawns
<10%
10 to 20%
>20%
-7.5a (-8.1, -7.0)
-7.5a (-7.7, -7.2)
-4.8b (-5.7, -3.8)
-0.013a (-0.015, -0.011)
-0.019b (-0.021, -0.018)
-0.009a (-0.012, -0.006)
0.90a (0.79, 1.00)
1.04a (0.98, 1.09)
0.17b (-0.05, 0.39)
0.74
0.77
0.44
641
2295
287
Ericaceous
hummocks
10 to 20%
20 to 30%
30 to 40%
>40%
-4.5a (-5.9, -3.2)
-7.3b (-7.6, -6.9)
-7.3b (-7.7, -6.9)
-6.3b (-7.5, -5.0)
-0.011a (-0.017, -0.006)
-0.016a (-0.018, -0.015)
-0.018a (-0.020, -0.016)
-0.033a (-0.054, -0.012)
0.24a (0.07, 0.55)
0.99b (0.94, 1.05)
0.92b (0.83, 1.01)
0.86ab (0.47,1.25)
0.31
0.76
0.72
0.15
156
1697
1230
140
Eriophorum
spp. Tussocks
<2%
2 to 4%
>4%
-6.8a (-7.2, -6.3)
-7.7b (-8.0, -7.4)
-6.4a (-7.0, -5.8)
-0.016a (-0.019, -0.014)
-0.017a (-0.018, -0.015)
-0.017a (-0.020, -0.013)
0.80a (0.70, 0.90)
1.00b (0.94, 1.06)
1.12b (1.02, 1.22)
0.66
0.77
0.77
935
1863
425
P. mariana
hummock
<10%
10 to 20%
20 to 30%
>30%
-6.2a (-6.6, -5.7)
-7.6b (-8.0, -7.2)
-7.4b (-7.8, -6.9)
-7.4b (-8.1, -6.7)
-0.016a (-0.018, -0.013) 0.85a (0.76, 0.94)
-0.019a (-0.021, -0.016) 1.02b (0.95, 1.08)
-0.019a (-0.021, -0.016) 0.94ab (0.83, 1.06)
-0.013a (-0.016, -0.011) 0.97ab (0.85, 1.10)
0.70
0.80
0.66
0.75
763
1008
1071
381
83
Table 5.4: LUE parameters for equation (2) derived from the NEE, PAR, and Tair as a
function of pools fraction within the source area for individual months. Amax and R10 are
in μmol m-2 s-1, α in μmol CO2-1 μmol PAR. The 95% confidence intervals are presented
in parentheses. Parameters are statistically different when they have no letter in common
(within months only).
Pool Fraction
A max
α
R 10
r2
n
Mai
<10%
10 to 20%
>20%
-5.3a (-6.0, -4.6)
-4.5ab (-5.3, -3.7)
-3.4b (-4.6, -2.1)
-0.009a (-0.012, -0.006)
-0.008a (-0.010, -0.006)
-0.011a (-0.025, -0.003)
0.64a (0.47, 0.81)
0.78a (0.67, 0.89)
0.88a (0.27, 1.49)
0.76
0.77
0.41
182
156
65
June
<10%
10 to 20%
>20%
-8.9a (-9.7, -8.0)
-5.6b (-6.3, -5.0)
-5.7b (-7.6, -3.9)
-0.028a (-0.035, -0.022)
-0.021a (-0.027, -0.015)
-0.010b (-0.015, -0.005)
1.47a (1.18, 1.75)
1.14a (0.96, 1.32)
0.47b (0.17, 0.78)
0.68
0.74
0.77
314
200
51
July
<10%
10 to 20%
>20%
-8.7a (-9.3, -8.2) -0.027a (-0.033, -0.022) 1.42a (1.27, 1.58)
-8.6ab (-9.6, -7.6) -0.016b (-0.019, -0.012) 1.15b (1.04, 1.26)
-7.3b (-8.1, -6.6) -0.020ab (-0.025, -0.016) 1.05b (0.87, 1.23)
0.79
0.83
0.75
409
204
220
August
<10%
10 to 20%
>20%
-8.1a (-8.7, -7.5)
-8.6a (-9.5, -7.7)
-6.6b (-7.4, -5.9)
-0.023a (-0.028, -0.018)
-0.021a (-0.025, -0.017)
-0.031a (-0.042, -0.021)
1.11a (0.96, 1.26)
1.23a (1.12, 1.33)
1.23a (1.40, 1.06)
0.78
0.84
0.86
366
217
101
September
<10%
10 to 20%
>20%
-8.0a (-8.7, -7.4)
-8.5a (-9.9, -7.1)
-7.6a (-5.4, -9.9)
-0.019a (-0.021, -0.014)
-0.015a (-0.019, -0.012)
-0.011a (-0.016, -0.007)
0.91a (0.77, 1.06)
1.12a (0.97, 1.27)
0.70a (0.35, 1.05)
0.84
0.84
0.77
326
195
75
October
<10%
10 to 20%
>20%
-6.7a (-8.2, -5.3)
-7.4a (-11.0, -3.8)
-4.6a (-7.2, -2.1)
-0.015a (-0.022, -0.008)
-0.013a (-0.020, -0.006)
-0.020a (-0.047, -0.007)
1.00a (0.67, 1.34)
0.79a (0.22, 1.36)
0.77a (0.19, 1.73)
0.83
0.76
0.88
86
44
12
Period
84
5.5 Discussion
Our study site exhibited the expected variability in surface microtopography, ranging
from hummocks to open water pools. Peatland microtopographic gradients are associated
with different plant communities, and aboveground biomass that influence the CO2
exchange rates [e.g., Bubier et al., 2003b; Laine et al., 2009; Maanavilja et al., 2011;
Pelletier et al., 2011]. As hypothesized, our measurements revealed variability in CO2
exchange between the four dominant plant communities, as indicated through Amax,
PSNmax, R and NEEcap. The spatial variability in these parameters corresponded with the
observed differences in aboveground biomass and plant type. At our site, the P. mariana
hummocks and Eriophorum spp. tussock plant communities had the highest average
aboveground biomass (Table 5.1). The P. mariana hummock PSNmax was 1.6 to 4.1 times
higher than the other communities over the same periods while the R was 0.8 to 2.2 times
higher, explaining the larger NEEcap measured over this plant community. The
Eriophorum spp. tussock also had larger PSNmax than the lawns and ericaceous
hummocks but the higher R rates resulted in lower NEEcap values compared to the P.
mariana hummocks. While aboveground biomass was similar between the P. mariana
hummocks and Eriophorum spp. tussocks, the green aboveground biomass on the
Eriophorum spp. tussocks was low compared to the P. mariana hummocks (Table 5.1).
The relatively high Eriophorum spp. tussock PSNmax despite its low aboveground green
biomass highlights the importance of PFTs in contributing to the variability in CO2
exchange.
The PSNmax values measured above the lawns, ericaceous shrub hummocks and
Eriophorum spp. tussocks were all within the range of values reported for similar plant
communities in other northern peatlands [Bubier et al., 2003b; Laine et al., 2009;
Pelletier et al., 2011]. The exception was P. mariana hummock where measured PSNmax
was in the higher end of the range of published values and was more similar to values
obtained from minerotrophic plant communities (such as Larix laricina, Betula nana or
Betula pumiIa var. glandulifera) [Bubier et al., 1998; Leppälä et al., 2008].
For
example, Strilesky and Humphreys [2012] found P. mariana to have an Amax of
approximately 6 μmol CO2 m-2 s-1. At our site, P. mariana aboveground biomass was
85
larger than that presented by Strilesky and Humphreys [2012] (702 vs 164 g m-2).
Furthermore, the water table position at their treed site varied between 18 and 44 cm
below the peat surface, compared to 8 and 25 at our site. If CO2 exchange of P. mariana
is limited in relatively dry peatland sites by their shallow rooting depth, our site
conditions and larger biomass would have produced larger measured CO2 exchange.
The range of CO2 exchange rates from components of the peatland ecosystem becomes
even larger when including the open water pool CO2 fluxes, as the pools were constant
sources of CO2 towards the atmosphere (Pelletier et al., 2014). Components ranged from
daytime clear (no shrouds) chamber uptake of -13.4 to a pool release of 1.9 μmol CO2 m-2
s-1 (Figure 5.3). This surface heterogeneity in CO2 exchange was observable from the
tower NEE-CO2 as it had an impact on the ecosystem scale NEE-CO2 flux components
(Amax and R10). The EC tower Amax and R10 varied significantly as functions of the fraction
of pools or different plant communities within the source area. An increase in pool
surface fraction within the tower source area resulted in a decrease of estimates of both
Amax and R10 (Table 5.3). Pools have limited amounts of CO2 fixing vegetation compared
to vegetated surfaces and therefore as pool fraction increases in a measurement source
area, the capacity for CO2 uptake at the ecosystem scale is reduced. ER in peatlands is
composed of both autotrophic and heterotrophic components and the reduced presence of
CO2 fixing vegetation with increasing pool surface in the source area would logically
result in a reduction of the autotrophic component. This in turn reduces ER, as shown by
the lower R10 estimates with greater pool coverage. The pools could also indirectly
explain the increased Amax and R10 observed for increasing P. mariana hummock cover
from the 0 - 10% to the 10 - 20% class, and increasing ericaceous hummock cover from
the 10 - 20% to the 20 - 30% class. The lower Amax and R10 observed for the lowest
coverage class of these two communities corresponds to the maximum pool fraction
cover within the source area (Figure 5.5). The increase in P. mariana hummocks and
ericaceous hummock fractions corresponds with a decrease in pool surface fraction.
Therefore, the increase in Amax and R10 for greater P. mariana hummock and ericaceous
hummock cover within a source area is likely to result from a reduced pool fraction.
86
Figure 5.5: Relationships between cover fraction of pools and selected plant communities
(Ericaceous hummocks and P. mariana hummocks) within the modeled 30-min source
areas of the EC tower between May and October 2012.
These results confirm our hypothesis that variability in plant community and pool CO2
exchange rates, along with their relative proportion within the EC tower source area can
result in distinctive and explainable EC tower NEE-CO2 for different source areas. There
are a limited number of studies examining ecosystem level gas exchange over patterned
or heterogeneous surface peatlands [Aurela et al., 1998, 2001, 2002, 2004; Maanavilja et
al., 2011]. In a patterned blanket bog near Glencar, Ireland, Laine et al. [2006] observed
no variability in NEE-CO2 with change in wind direction suggesting that their site could
be considered homogeneous with respect to CO2 flux despite the visual vegetation
surface patterning. In the present study, we observed significant variability in the EC
tower NEE-CO2 as the pool and plant communities’ fractional contribution changed
within the source areas measured by the tower. The plant communities present in most
peatland sites where NEE-CO2 measurements are performed are net sinks for CO2 and
although the fraction of the different communities inside the source area changes as the
source area shifts, the variability in fluxes is attenuated and therefore difficult to discern.
If deep permanent pools represent a large enough portion of the tower source area and are
87
constantly releasing CO2 to the atmosphere, they can lower the net CO2 exchange rate at
the ecosystem scale enough to be observed by the EC tower as shown here. Furthermore,
our results showed that when pool cover fraction is at its lowest, an increase in the cover
of either P. mariana or ericaceous hummocks does not further increase Amax or R10. A
change in plant community proportion within a source area in the absence of pools does
not seem have the same significant effect as increasing pool cover. The CO2 exchange
rates between the plant communities at the surface of the peatland vary in terms of
magnitude but not direction. Hence, it is the presence of pools with their different
magnitude and direction of flux that alters the measured ecosystem NEE perceptibly.
The lack of variability in NEE-CO2 with wind direction reported at other heterogeneous
sites could be a result of the gap-filling procedure applied. Gaps in EC tower datasets
occur because of system failure, equipment maintenance or data quality rejection criteria
[Baldocchi, 2003]. The gap-filling procedures generally use mean diurnal variation or
semi-empirical methods (look-up tables or non-linear regression) [see Falge et al., 2001
for more details]. If these methods are used on heterogeneous sites without taking into
account the variability in fluxes as a function of wind direction or the fractional
proportion of vegetation communities within the source area at time of flux measurement,
the gap filling procedure will attenuate the spatial variability in flux. In essence,
variability is being averaged out. No gap filling procedure was applied to our data set
because our goal was to look at the effect of the change in plant communities’ fraction
within the source area on the NEE-CO2. A site-specific gap filling procedure taking into
account plant community distribution, fluxes and controls would be necessary to obtain a
continuous NEE-CO2 dataset, and would represent a first step in evaluating NEP for a
heterogeneous peatland with pools. Heterogeneous site gap filling procedures have been
developed for CH4 fluxes on an oligotrophic mire in Finland [Forbrich et al., 2011],
however, we recognize that developing a NEE-CO2 gap filling procedure for a
heterogeneous ecosystem with pools could represent a challenging task considering that
the local scale fluxes vary temporally and spatially (both within and between plant
communities and pools). Furthermore, the source area of the tower may cover specific
88
upwind sectors for only short periods of time limiting the development of a good gap
filling data set.
The results presented by Pelletier et al. [in review] showed that the studied peatland was
a sink for CO2 despite the presence of pools, and that the mean NEE-CO2 was within the
range of values published for peatlands without pools. We hypothesized that some plant
communities on the vegetated portion of the site had large CO2 uptake capacity, and that
combined with a significant surface cover, could explain why the site was a sink for CO 2.
As demonstrated, the P. mariana hummocks present on the site had larger CO2 uptake
rates compared to the other plant communities and also compared to those documented in
other bogs. The daytime flux over the P. mariana plant community was also larger than
the maximum daytime NEE-CO2 measured by the EC tower (Figure 5.3). Furthermore,
based on the source area analysis, the average contribution of P. mariana hummocks
communities to the source area NEE-CO2 was 19% over the measurement period. This
large surface area fraction, along with higher uptake capacity of P. mariana could explain
why the site was a sink for CO2, despite the presence of pools.
5.6 Conclusion
The results presented here demonstrate that the variability in CO2 exchange at the local
scale across the peatland surface has a measureable impact on the ecosystem level
measurement. This was found to be primarily true when open water pools were present
within the tower source area. Our results also suggest that the large CO2 exchange rates
measured above the P. mariana plant communities, combined with their large fractional
contribution to the source area, counterbalanced the CO2 loss from the open water pools,
explaining why the ecosystem as a whole was a net CO2 sink during the measurement
period. Future studies are needed to evaluate if the patterns observed in our site can be
applied to other peatlands with pools. Overwhelmingly, studies at the peatland ecosystem
scale have not considered the contribution of pools; focus has instead been on the
vegetated surfaces which represent net sinks of carbon and can be gap filled using
standard techniques. The true complexity of northern peatland ecosystems must be
89
considered. New gap-filling techniques are required and will result in revised annual
estimates of carbon exchange for northern peatlands.
90
6. Variability in summer net CO2 exchange from three
peatlands along a temperate to boreal maritime climate
transect
Bridging statement to Chapter 6
Previous results in my thesis have shown that a peatland with pools can represent a sink
for CO2 despite the presence of open water pools that are sources of CO2 to the
atmosphere. However, it is unknown how the NEE-CO2 varies between peatlands with
pools and what are the controls on this potential variability. In Chapter 6, I evaluate the
variability and controls on the ecosystem scale NEE-CO2 between three peatlands (2 with
pools; 1 without) located along a transect from temperate to boreal maritime conditions.
This study addresses the fourth objective of my thesis to assess if biophysical controls on
NEE-CO2 previously identified for vegetated surface peatlands apply to peatlands with
open-water pools.
91
6.1 Abstract
Peatlands with pools are common in temperate to subarctic regions, however, the spatial
variability in net ecosystem CO2 exchange (NEE-CO2) has typically been documented for
relatively homogeneous peatland sites without permanent open water pools. On such
peatlands, leaf area index (LAI) and differences in plant functional types (PFTs) have
been identified as the main controls explaining between site variability in NEE-CO2. In
this study, we quantify the NEE-CO2 for three sites (2 with pools, 1 without), following a
temperate to boreal maritime climate transect, to see if the controls previously identified
for non-pool peatlands apply to peatlands with pools. Overall, we found that both LAI
and PFTs explained the variability in the CO2 exchange between peatlands. The peatland
without pools had the highest LAI and average NEE-CO2 and although LAI differed
between the peatlands with pools, their average NEE-CO2 was similar. The maximum
gross photosynthesis (Amax) and respiration rates at 10°C (R10) differed between the three
sites, decreasing from the temperate peatland to the boreal maritime peatlands and
corresponded to the decrease in LAI between sites. Differences in PFTs between the two
peatlands with pools explained the variability in Amax and R10; the site dominated by
lichen had lower photosynthesic capacity than the one dominated by Sphagnum mosses.
92
6.2 Introduction
Peatland net ecosystem CO2 exchange (NEE-CO2) has been shown to vary over time and
between sites. The temporal variability in NEE-CO2 has been associated mainly with the
prevailing hydrometeorological conditions [Lafleur et al., 2003; Aurela et al., 2004;
Olson et al., 2013; McVeigh et al., 2014]. Spatially, the variability in C fixation between
peatland types and regions has been linked to a site’s vegetation biomass, leaf area index
(LAI) and plant functional type (PFT) [Humphreys et al., 2006, 2014; Lund et al., 2010;
Laine et al., 2011]. A number of studies comparing NEE-CO2 data from multiple sites
covering a wide range of climate from temperate to subarctic regions [Humphreys et al.,
2006 [7 sites], 2014 [12 sites]; Lund et al., 2010 [3 sites]] have shown that LAI represents
an important control on NEE-CO2; higher site LAI corresponds with greater
photosynthetic rates and overall greater CO2 uptake. The different PFTs present on the
sites have also been identified as an important control element on NEE-CO2. Laine et al
[2011] found that the presence of different PFTs between sites explained similar NEECO2, despite significantly different aboveground vegetation biomass at their two sites.
NEE-CO2 exchange measurements over peatlands have been performed at sites
characterized by different plant assemblages or microforms such as hummocks and
hollows. Commonly, the sites studied thus far do not contain permanent open-water pools
although peatlands with pools are found on every continent, with the exception of
Antarctica [Glaser, 1999]. Peatland open-water pools represent net sources of CO2 to the
atmosphere because of the absence of CO2 fixing vegetation, the decomposition of the
underlying peat material at their bottom, and microbial degradation of the dissolved
organic matter [e.g., Pelletier et al., 2014]. These water bodies have been shown to
release on an annual basis more C per unit area than that sequestered by the vegetated
surfaces, and to significantly decrease the overall CO2 exchange rates of the peatland at
the ecosystem scale [Pelletier et al., 2014, in review]. A limited number of studies have
looked at the C release from permanent open water pools and even fewer have examined
their impact on peatland NEE-CO2. Published data on open-water pool coverage are
sparse and variable with studies indicating that pools comprise more than 40% of
peatland surface in the some sectors of the Hudson’s Bay Lowlands [Roulet et al., 1994]
93
and between 25 and 70% in fens of subarctic Quebec [Dissanska et al., 2009; White,
2011]. To date, studies that have estimated the CO2 exchange on peatlands with pools
have relied on temporal interpolation and spatial extrapolation of sporadic static chamber
measurements, made at points across the vegetated surface and open-water pools
[Waddington and Roulet, 2000; Cliche Trudeau et al., 2014]. Even if the chamber
measurements are made frequently (weekly basis), errors propagate leading to net
ecosystem productivity (NEP) estimates that are not always significantly different from
zero [Bubier et al., 1999].
Considering that peatlands with pools are a common feature in wetland-rich sectors from
temperate to subarctic regions, there is a need to document their NEE-CO2 using
ecosystem level measurement methods (eddy covariance [EC]), to understand how NEECO2 varies between sites and to see how it compares with the peatlands without pools
that have been documented. In the present study, we compare the June-September NEECO2 from three peatlands located in Eastern Canada; two have permanent open-water
pools and one does not. The aim of our study is to evaluate the differences in NEE-CO2
and its components (photosynthesis and respiration) between the three sites and to see if
the variability measured can be explained by the previously identified controls for nonpool peatlands, such as LAI and PFTs [Humphreys et al., 2006, 2014; Lund et al., 2010;
Laine et al., 2011].
6.3 Methods
6.3.1 Peatland locations, description, and climates
The three studied peatlands are ombrotrophic bogs (Table 6.1) located along an east-west
gradient from Eastern Ontario through South-eastern Quebec, Canada. The most western
site is the Mer Bleue peatland (MB), located near Ottawa, Ontario. This wetland complex
covers ~2800 ha and the study area where flux measurements were performed is located
in the domed, ombrotrophic portion of the site [Lafleur et al., 2003]. The measurement
area has a relatively homogeneous surface with vegetation dominated by ericaceous
shrubs (Chamaedaphne calyculata, Kalmia Angustifolia, Rhododendron groenlandicum),
94
and Sphagnum spp. (S. capillifolium S. fuscum, S. magellanicum). The Baie Comeau site
(BC) is located in the center of the Manicouagan peninsula on the north shore of the StLawrence River in Quebec approximately 700 km north east of MB. Hummocks, lawns
and open water pools dominate the peatland microtopographical features at the site. The
vegetation species are similar to MB but in lower density (see LAI in Table 6.1), with
ericaceous shrubs such as Chamaedaphne calyculata, Kalmia angustifolia, and
Rhododendron groenlandicum, and Sphagnum spp. (S. capillifolium S. fuscum, S.
magellanicum, S. rubellum). Also present are Picea mariana shrubs. Open-water pools
are present near the EC tower [see Pelletier et al., 2014] covering approximately 12% of
the source area. The Havre-Saint-Pierre site (HSP) is located approximately 400 km north
east of BC, on the north shore of the Gulf of St-Lawrence. The peatland is part of a 30km
long plateau bog peatland complex formed on the Romaine River delta. The vegetation
consists primarily of lichens (Cladonia stellaris, Cladonia rangiferina), Sphagnum spp.
(S. fuscum), and low density of the same dwarf shrub species found at the other sites. The
peatland is treeless other than sparse Picea mariana krummholz. Open-water pools cover
approximately 18% of the HSP site. The 30-year climate normal (1981-2010) for mean
annual temperature, mean daily temperature during the coldest and warmest months, and
mean annual precipitation for the three sites are presented in Table 6.1. Note that for
HSP, the closest weather station with a 30-year climate normal is located in Rivière-auTonnerre, 85 km west of Havre-Saint-Pierre.
6.3.2 Eddy covariance and ancillary measurements
The NEE-CO2 at all three sites was measured using the EC technique [Baldocchi, 2003;
system details provided in Table 6.1]. The 30-min CO2 fluxes were computed from the
covariance between vertical wind speed and CO2 mixing ratio [Burba et al., 2012] using
the high frequency data. More details on the flux calculation procedures and data
cleaning steps can be found in Humphreys et al. [2014] for MB and in Pelletier et al. [in
review] for BC and HSP. No gap filling procedure was applied to the BC and HSP data
due to the complexity of the source area surrounding the towers. Although MB gap filled
data were available, we decided to use the non-gap filled data in order to compare with
the two open-water pool sites.
95
Environmental measurements were made every 5-seconds throughout the study period
(May to October 2012) and averaged every 30-min. The variables measured included net
radiation (CNR1 or CNR4, Kipp and Zonen, Delft, Netherlands), photosynthetic active
radiation (PAR; LI-190SA, LI-COR, Lincoln, NE), air temperature and relative humidity
(HMP-45C, Vaisala, Helsinki, Finland) and precipitation (TE252M or TE525M tipping
bucket gauge, Texas Electronics, Dallas, TX). Soil temperature (5cm, 10cm 20cm and
40cm) next to the towers was recorded every 30-min using thermocouples (MB) or
temperature probes (BC and HSP - HOBO TMC6-HD Air/Water/Soil Temp Sensor,
Onset, Bourne, MA) connected to a 4-channel datalogger (HOBO U12, Onset, Bourne,
MA). The water table position was recorded using a pressure transducer placed in a well
at MB (OTT CS450; Campbell Scientific Inc., Logan, Utah, U.S.A.) and with level
loggers at BC and HSP (Odyssey Capacitance Water Level Logger, Dataflow Systems
Pty Limited, Christchurch, New Zealand). Evaluation of LAI was made at the three sites
using the LAI-2000 at the peak of the growing season.
96
Table 6.1: Sites characteristics and description of the eddy covariance systems
Site
MB
BC
HSP
Open raised bog
Open raised bog with pools
Plateau bog with pools
Latitude
45°25’N
49°08’N
50°17’N
Longitude
75°31’W
68°17’W
63°37’W
70
19
38
2.0(0.18)
1.07(0.34)
0.41(0.33)
6.4(0.8)
1.7(0.9)
1.6(1.2)
-10.3
-14.3
-13.4
21
15.6
14.6
Total precipitation (mm)
943
1001
1094
Snow precipitation (mm)
224
343
255
Li-7000 (LI-COR Inc.,
Lincoln, Nebraska, U.S.A)
Li-7200 (LI-COR Inc.,
Lincoln, Nebraska, U.S.A)
Li-7200 (LI-COR Inc.,
Lincoln, Nebraska, U.S.A)
R3-50 (Gill Instruments,
Lymington, U.K.)
CSAT-3 (Campbell
Scientific, Edmonton,
CSAT-3 (Campbell
Scientific, Edmonton,
Personnal computer
Li-7550 (LI-COR Inc.,
Lincoln, Nebraska, U.S.A)
Li-7550 (LI-COR Inc.,
Lincoln, Nebraska, U.S.A)
3
2.4
1.9
20 Hz
10 Hz
10 Hz
Peatland classification
Elevation (m)
2
-2
LAI (m m )
o
Mean annual temperature ( C)
J anuary
J uly
Gas analyzer
Sonic anemometer
Data storage
Instrument height (m)
Frequency of measurements
Thirty-year climate normals from nearest Environment Canada weather stations (Ottawa Macdonald-Cartier
International Airport, ON, Baie Comeau, QC, and Rivière au Tonnerre, QC).
6.3.3 NEE-CO2 data analysis
The NEE-CO2 in peatlands is equal to the difference between the gross ecosystem
productivity (GEP, photosynthesis by the surface vegetation), and the ecosystem
respiration (ER, composed of autotrophic [growth and maintenance] and heterotrophic
[peat decomposition] respiration). The NEE-CO2 data was analyzed by fitting it to the
following equation:
.
(1)
The first term represents GEP by a rectangular hyperbolic relationship [Frolking et al.,
1998] where α is the initial quantum yield and Amax is the maximum photosynthesis rate.
The second term represents ER with an exponential temperature response model [Lloyd
97
and Taylor, 1994] using the nighttime NEE-CO2 (assumed to be equivalent to ER), where
R10 is the respiration rate at 10 °C and T is air temperature (Tair) in K. The equation was
parameterized (α, Amax, and R10) using measured NEE, PAR, and Tair using non-linear
least-squares regression in a MATLAB (v8.3) computing environment (Matlab,
v8.3.0.532 (R2014a), The MathWorks, Matick, MA, USA).
6.4. Results
6.4.1 Environmental conditions
The patterns in air temperature at the three sites between June and September 2012 reflect
the differences also observed in the climate normal. MB experienced higher daily average
temperature during most of the study period, followed by BC and HSP (Figure 6.1). The
presence of the St-Lawrence River likely explains the limited day-to-day and overall
small 4 month variability in temperature at BC and HSP compared to MB. The difference
between sites is also similar in peat temperature as MB shows higher peat temperature,
while BC and HSP profiles are comparable (Figure 6.2). All three sites showed similar
temporal patterns of PAR with total receipt for the four months decreasing towards the
east (4778, 4411 and 4342 mol PAR m-2, for MB, BC and HSP, respectively). The
precipitation patterns were relatively similar at BC and HSP while MB showed a period
with low precipitation between DOY 180 to 220. As a result, the water table position
dropped more at MB than at the other two sites. Water table seemed more variable at
both MB and HSP compared to BC. At the end of the study period, all three sites had
received a comparable amount of precipitation (Figure 6.1).
98
Figure 6.1: Air temperature, total daily PAR, water table position (WTP) and
precipitation at the three sites between June and September 2012.
99
Figure 6.2: Peat temperature (oC) profiles at the three sites between June and September
2012 (MB – top; BC – middle; HSP – bottom).
6.4.2 NEE-CO2
The amplitude of the monthly average diurnal NEE-CO2 varied between the three sites
with the largest day-night difference observed at MB and lowest at HSP. The mid-day
NEE-CO2 uptake and nighttime respiration were close to twice as high at MB compared
to BC and HSP (Figure 6.3). The monthly average Amax calculated from equation 1 also
showed large differences between the sites, with significant differences in June, August
and September, decreasing along the transect (MB>BC>HSP) (Table 6.2). However, the
July Amax at BC and HSP were not statistically different. The monthly average R10 values
followed a similar pattern to Amax (MB>BC>HSP) in June and August, but were not
statistically different between the three sites in July, or between BC and HSP in
September. The largest within site variability in NEE-CO2 between months was observed
primarily at MB and HSP (Figure 6.3). At MB, Amax was lower in July compared to the
other months, while the highest R10 was observed in August. At HSP, both Amax and R10
increased between May and June before decreasing until September. The Amax and R10 at
100
BC were not different between the four months, except for September where R10 was
statistically lower than July and August.
Figure 6.3: Diurnal patterns in NEE-CO2 at MB, BC and HSP in June (filled circles), July
(open circles), August (filled triangles) and September (open triangles) 2012.
Table 6.2: Monthly LUE parameters for equation (1) derived from the NEE, PAR, and
Tair as a function of pools or plant communities’ fraction within the source area. Amax and
R10 are in μmol m-2 s-1, α in μmol CO2-1 μmol PAR. The 95% confidence intervals are
presented in parentheses. Parameters are statistically different when they have no letter in
common.
Month
Site
A max
α
R 10
n
r2
June
MB
BC
HSP
-16.1 (-16.8, -15.5)a
-7.9 (-8.5, -7.4)c
-4.5 (-4.7,-4.3)f
-0.028 (-0.030, -0.025)b
-0.019 (-0.022, -0.016)c
-0.016 (-0.018, -0,014)d
1.42 (1.34, 1.50)b
1.18 (1.05, 1.31)c d
0.91 (0.83, 0.98)e
818
724
1130
0.89
0.71
0.68
July
MB
BC
HSP
-14.4 (-15.0, -13.8)b
-8.4 (-8.7, -8.0)c
-7.9 (-8.2, -7.6)c
-0.032 (-0.036, -0.028)a b
-0.022 (-0.024, -0.019)c
-0.028 (-0.031, -0.025)a
1.41 (1.31, 1.51)bc
1.24 (1.17, 1.32)c
1.40 (1.31, 1.49)bc
808
992
1133
0.82
0.82
0.80
August
MB
BC
HSP
-18.1 (-19.0, -17.2)a
-8.6 (-9.0, -8.2)c
-7.3 (-7.5, -7.1)d
-0.035 (-0.039, -0.031)a
-0.022 (-0.024, -0,019)c
-0.021 (-0.023, -0.019)c
1.71 (1.59, 1.83)a
1.24 (1.17, 1.31)c
1.10 (1.05, 1.15)d
841
870
1184
0.83
0.84
0.85
September
MB
BC
HSP
-16.6 (-17.9, -15.3)a
-8.8 (-9.4, -8.1)c
-5.8 (-6.0, -5.5)e
-0.023 (-0.026, -0.020)bc
-0.016 (-0.017, -0.014)d
-0.016 (-0.017, -0.014)d
1.31 (1.18, 1.44)bc
1.04 (0.96, 1.13) de
0.93 (0.87, 0.98)e
707
758
1108
0.84
0.84
0.78
101
Monthly NEP (Figure 6.4) was calculated from the average daily NEE-CO2 flux (Figure
6.3) multiplied by the number of days in the month. The results suggest that all three sites
were sinks for CO2 between June and September 2012 (Figure 6.4). The monthly NEP at
MB and BC decreased from June to August before increasing slightly in September. At
HSP, the maximum NEP was observed in July, before decreasing until end of the
measurement period. Overall, MB, BC and HSP sequestered 367(±18), 228(±10) and
235(±7) g CO2 m-2, respectively over the June to September period.
Figure 6.4: June to September 2012 monthly NEP (g CO2 m-2) at MB, BC and HSP.
6.5 Discussion
The three peatlands showed significant variability in hydroclimatic conditions and NEECO2 over the measurement period. The air temperature was generally higher at MB
(Figure 6.1) and had an impact on the growing season length. The 2012 growing season
length was 197 days for MB [Humphreys et al., 2014], 161 for BC and 157 days for HSP,
based on seven consecutive days with average temperature above 5 °C. The 30-year
normal (1981-2010) annual precipitation for the three sites is similar (Table 6.1) and the
amount received during the measurement period was also similar (Figure 6.1). However,
the timing of the precipitation events differed between the sites and had an impact on the
variations in water table position between sites, with largest variation observed at MB,
followed by HSP and BC. HSP was close to the Gulf of St-Lawrence and had no trees on
the site. The more exposed conditions resulted in slightly higher measured wind speed
102
(normalized for 10 m height) at HSP (3.1 m s-1) compared to BC (2.5 m s-1) during the
period.
These different hydroclimatic conditions observed at the sites were also linked to
differences in vegetation species and abundance, and LAI. The LAI at MB was twice that
at BC and close to four times higher than at HSP (Table 6.1). Studies of multiple peatland
sites have found LAI to represent an important control on the CO2 exchange [Humphreys
et al., 2006, 2014; Lund et al., 2010]. Lund et al. [2010] found LAI to explain 69% of the
variability in mean June to August daily CO2-C exchange between peatland and tundra
sites, where CO2-C uptake increased with higher LAI. We calculated the June to August
NEE-CO2 for BC and HSP and combined them with the peatland data of both Lund et al.
[2010] and Humphreys et al. [2014] which included MB (Figure 6.5). The two pool sites
(BC and HSP) are the lower end of the relationship, having relatively low LAI and low
NEE-CO2. Although LAI was more than twice as high at BC than HSP, the June to
August NEE-CO2 for the two sites was not markedly different. As observed by
Humphreys et al. [2014], sites with LAI < 2 show a lot of scatter in NEE-CO2 and it
seems from the present results that this is also true for sites with pools. Hence, LAI was
not a good predictor of the difference in NEE-CO2 for our two peatlands with pools. Still,
the decrease in LAI from southwest to northeast corresponded with a decrease in both
Amax and R10 between the sites. This relationship has also been observed for other
peatlands [Humphreys et al., 2006] and forest ecosystems [Lindroth et al., 2008].
The different vegetation types present at the three sites could also explain some of the
variability in the CO2 exchange. Besides the presence of stunted P. mariana at the BC
site, the shrub species observed at MB and BC were similar, differing only in terms of
biomass, which in turn affected the LAI (Table 6.1). The variety of Sphagnum spp.,
found at BC (S. fuscum, S. capillifolium, S. magellanicum and S. rubellum) differed
slightly from that at MB (S. capillifolium and S. magellanicum) [Moore, 1989a] and
could generate differences in CO2 exchange between the two sites. However, vascular
plants have stronger photosynthetic capacity and should therefore explain most of the
difference [Leppälä et al., 2008]. The difference in ground vegetation at HSP was more
103
obvious as the site had a combination of lichens (Cladonia stellaris, Cladonia
rangiferina) and in a lower proportion, Sphagnum spp. Measurements of CO2 exchange
between Cladonia spp. and Sphagnum spp. have shown the latter to have greater CO2
uptake capacity [Heijmans et al., 2004]. This difference between moss and lichen
communities supports the idea that the different PFTs can explain some of the difference
in CO2 exchange between HSP and the two other sites.
Figure 6.5: Relationship between LAI and the June to August NEE-CO2 for the sites in
this study and those from Lund et al. [2010], and Humphreys et al. [2014].
The open water pool surfaces at BC and HSP reduced the ecosystem scale Amax as these
water bodies were constantly releasing CO2 to the atmosphere [see Pelletier et al., 2014
for BC; unpublished data for HSP]. Because of the absence of vegetation, and
simultaneously, of an autotrophic respiration component, the pools also reduced the
ecosystem scale R10 [Pelletier et al., in review]. The overall impact of the presence of
open water pools on Amax and R10 is similar to decreasing the peatland LAI. Based on an
analysis of the flux tower source area, the open water pool surface area was 12% and 9%
for BC and HSP respectively. The difference in pool fluxes between the sites also needs
to be considered. The fluxes from the HSP pools were lower than those measured at BC,
with an average (± SD) of 0.34 ± 0.28 μmol CO2 m-2 s-1 [this study] compared to 0.52 ±
104
0.34 μmol CO2 m-2 s-1 from the BC pools [Pelletier et al., 2014]. While a larger pool CO2
release should have a greater impact decreasing the ecosystem level Amax, it was not the
case at our sites because the largest average pool CO2 release was also measured on the
site that had the highest Amax. This makes the impact of pools on NEE-CO2 between sites
difficult to assess. How the relationship between NEE-CO2 and LAI applies to peatlands
with larger proportions of open water pool surface remains to be documented.
Overall, the similar daily average NEE-CO2 between the two pool sites resulted in a NEP
that is slightly larger at HSP when compared to BC for the same period. This is
interesting considering the differences in LAI, PFTs and pool fluxes between the two
sites. The fact that NEE-CO2 was similar but that Amax and R10 were different between the
two pool sites shows how GEP and ER are correlated and how they compensate each
other similar to the observations of Humphreys et al. [2006]. Still, a significant portion of
the fixed CO2 may be lost through CO2 respiration over the winter. Furthermore, this
NEP estimate does not include the loss of C through dissolved organic carbon (DOC)
export or methane (CH4) emissions over the measurement period. Over seven years of
continuous measurements at MB, Roulet et al. [2007] found that on an annual basis,
almost 60% of the CO2 taken up during the growing season is lost during the winter
through heterotrophic respiration, and that approximately 50% of the remaining portion is
lost through CH4 emissions and DOC export during the snow free season.
6.6 Conclusion
This study is the first to compare NEE-CO2 from multiple peatland sites that include
peatlands with permanent open water pools. Our results indicate that LAI explains the
variability in Amax and R10, even with the presence of pools. However, the difference in
NEE-CO2 between the three sites could not be explained solely by the LAI as the two
pool sites, despite their different LAI, had similar NEE-CO2. The open water pools
decrease a site’s overall average vegetation biomass per unit area and LAI, affecting both
the productivity and respiration at the ecosystem scale. The fractions of open water pools
at our sites were 12% (BC) and 9% (HSP). It remains to be shown if our results would
change in a peatland with a larger proportion of open water pool surface. As the fraction
105
of open water pool surface increases, both Amax and R10 should decrease at the ecosystem
scale. However, it is not quite such a simple relationship; the CO2 release from pools has
been shown to vary with pool size and depth, with larger fluxes measured over smaller
and shallower pools [McEnroe et al., 2009; Pelletier et al., 2014]. This therefore suggests
that much like vegetation amount (LAI) and type (PFTs) influence the ecosystem
exchange in terrestrial portions of the peatland, the abundance and morphology of pools
could have a significant effect on the NEE-CO2.
106
7. Summary, contribution and directions for future research
There exist a large range of ecological and biogeochemical conditions within and
between peatlands from temperate to subarctic regions. Although, several studies have
looked at the surface to atmosphere C exchange at the microform and ecosystem scale,
peatland open water pools have received limited attention. These water bodies are
important as they represent sources of C to the atmosphere and are present in all the
peatland rich regions of the world. Still, the C fluxes from these water bodies have been
poorly documented and their impact on the ecosystem level surface to atmosphere C
exchange is relatively unknown. In my thesis, I used a combination of continuous and
sporadic measurements to evaluate the CO2 and CH4 exchange at the local scale along
with eddy covariance measurements to evaluate the NEE-CO2 on peatlands with open
water pools. The main goal of this research was to evaluate how peatland open water
pools affect the surface to atmosphere NEE-CO2 and assess how these peatlands compare
to other sites with and without pools.
The first objective of my thesis was to assess the importance of the annual C loss from
pools including the evaluation the C loss during the spring melt period, the spatial and
temporal variability in fluxes, and the identification of the environmental controls on the
fluxes. My results confirmed the important annual C release from open water pools and
showed that the spring melt loss can represent 15% of the total yearly loss. This suggests
that the values of annual pool surface C release presented by others are underestimations
by not considering the spring melt period. We also found flux evaluation using discrete
measurement method to yield lower mean seasonal fluxes compared to the continuous
measurement method. This difference was explained by the timing of the discrete
measurements, which did not correspond with the maximum dissolved gas concentrations
in water. Spatial variability in fluxes between the sampled pools was mainly observed for
CH4. Significantly higher CH4 release rates were observed in the shallowest pool sampled
as the result of higher sediment temperature. The temperature control was also important
in explaining the temporal variability in both the CO2 and CH4 fluxes from the pools. My
results also demonstrate that DOC was not the main source of CO2 emitted at the surface
107
of the pools as poor correlation was observed between dissolved CO2 concentrations and
DOC. This was confirmed by estimating DOC input in the pools over the measurement
period, which could have contributed to a maximum of 16 to 30% of the CO2 emitted.
These results are important in showing that the pools are sources of C to the atmosphere,
and that origin of this C is potentially from peat decomposition at the bottom of the pool.
The magnitude of the annual C release from pools being similar, but with reverse sign, as
the published LORCA or contemporary NECB, these results suggest that the pools could
significantly decrease the C sink capacity of peatlands with open water pool.
My second objective was to assess the CO2 sink potential of a peatland with pools and to
see how the NEE-CO2 and its components (Amax and R10) compare with published values
from other sites. This study is the first to report eddy covariance NEE-CO2 measurements
made over a peatland with permanent pools. This has important implications considering:
1) the ubiquity of this type of peatland across temperate to subarctic regions; 2) that
several peatland models are starting to include open water pools in theirs simulations; 3)
global climate models are starting to include peatland ecosystems and consequently, there
is a need to document if NEE-CO2 from peatland with pools compares with values
measured from non-pool sites. My results showed that the mean daily NEE-CO2 for the
site was negative and that the peatland was therefore a net CO2 sink for the period of June
to September. Furthermore, the mean daily NEE-CO2 rate was within the range of
published values for other temperate to subarctic peatlands. However, I found the
ecosystem Amax and R10 to be in the lower range of published values and to be closer to
those measured for subarctic peatlands. This is explained by the absence of vegetation at
the surface of the open water pools, which lower the vegetation biomass at the ecosystem
level. This reduces the peatland Amax, which in turn lowers the R10 as the pools only have
a heterotrophic respiration component, as oppose to the vegetated surface that has both an
autotrophic and heterotrophic component. These findings are important and suggest that
the current model parameterizations may be adequate even if based on non-pool
peatlands. Simultaneously, the results raise the question as to how the variation in pool
proportion between peatlands with pools would affect the NEE-CO2.
108
The third objective of my thesis was to assess the variability in CO2 exchange at the local
scale (plant communities and pools) and evaluates if the spatial variability, principally the
signal from the open water pools, can be identified from the ecosystem scale NEE-CO2
measurements. My results from the local scale measurements show the daytime fluxes to
vary significantly from a CO2 uptake of -13.4 μmol m-2 s-1 to an open water pool release
of 1.9 μmol CO2 m-2 s-1. I found the P. mariana hummocks community to have
significantly higher CO2 uptake capacity than the other communities with rates closer to
those published for minerotrophic peatlands plant communities. This finding is important
as it suggests that the uptake capacity of the P. mariana combined with their significant
fractional cover, could explain why the ecosystem as a whole was a net CO2 sink during
the measurement period despite the presence of pools. My results also demonstrate that
the important variability in the local scale CO2 exchange had a significant impact on the
NEE-CO2 components, primarily with varying pool cover within the source area of the
tower. Lower NEE-CO2 uptake was measured with increasing pool fraction within the
tower source area for the corresponding 30-min measurement periods. This translated
into a decrease of Amax by 25% for an increase in pool fraction cover from 0-10% to 2030%. The decrease was similar when looking at R10. These results are important and
confirm my hypothesis that the spatial variability in CO2 exchange on a peatland with
pools, combined with the variation in proportion of communities and pools within the
source area, can result in distinctive NEE-CO2. My findings highlight the heterogeneity
and complexity of these northern peatland ecosystems in terms of CO2 exchange, and
stress the need for new gap-filling techniques that would account for the heterogeneity of
the peatland surface.
The fourth objective of my thesis was to evaluate the variability in summer NEE-CO2
between three peatlands (two with pools; one without) and assess if the biophysical
controls on NEE-CO2 previously identified for vegetated surface peatlands (LAI, PFTs)
apply for peatlands with open-water pools. The monthly Amax and R10 calculated from the
EC tower measurements differed between the three sites, decreasing from the temperate
site (MB) to the boreal maritime site (HSP) (MB>BC>HSP) over most of the studied
period. I found that the differences in LAI to explain the variability Amax and R10 between
109
the sites, but not the one for the NEE-CO2. The BC and HSP sites had similar NEE-CO2
despite that the BC LAI was double the one HSP. I also found that the differences in
PFTs between the two peatlands with pools could contribute to the variability in Amax; the
HSP site was dominated by lichens which have lower photosynthesic capacity compared
to the Sphagnum spp. dominated at BCC. This study is unique as it is the first to compare
NEE-CO2 between peatlands, including peatlands with pools.
Overall, my results emphasize the significance of open water pools in the ecosystem level
C exchange from peatlands. These water bodies represent an important source of C to the
atmosphere with the same magnitude as the LORCA or NECB. Furthermore, the CO2
release from these water bodies has measurable impact on the ecosystem level NEE-CO2,
lowering the both the maximum photosynthetic capacity and respiration rates. My
research presents data from only two pool peatlands and whether or not the observations
made here are representative of other sites or other years in terms of environmental
conditions remain to be verified.
Various research directions should be pursued to develop a better understanding of the C
dynamic in peatland open water pools and their role on the peatland ecosystem level C
exchange. Studies should look at C fluxes from pools with various morphometry, in
different peatland types and regions to identify the main controls on the C release.
Similarly, interannual NEE-CO2 measurements over peatland with pools should be
performed to evaluate their response to changing environmental conditions. These studies
would contribute into developing eddy covariance gap filling procedures for
heterogeneous sites. Finally, efforts should be made to obtain a complete evaluation of
the NECB of peatland with pools, which would include an annual evaluation of the NEECO2, CH4 loss and DOC export.
110
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