Carbon and Water Vapour Exchange in a Temperate Freshwater Marsh

Carbon and water vapour exchange in a temperate freshwater marsh

Stephanie Crombie

Masters of Science

Department of Natural Resources Sciences

McGill University Montreal, Quebec July 2012

A thesis submitted to McGill University, in partial fulfillment of the requirement of the degree of Masters of Science

The ability of wetlands to sequester carbon has given them a considerable amount of attention, especially in light of global climate change. To date, many wetland studies have focused on peatlands, however very few studies have been conducted on marshes.

This study used the eddy covariance (EC) technique to measure net ecosystem carbon

exchange (NEE) and energy exchange at a temperate freshwater cattail marsh near

Ottawa, Canada. The objectives of the study were to use a four year dataset to determine

the environmental controls on the variability of carbon and water vapour exchange. The

annual cumulative NEE was on average -246 ± 31 gCm-2yr-1 ranging from -216 to -260 gCm-2yr-1. The variability in accumulation between years was a result of the timing of

spring and fall transitions in the carbon uptake and the length of the growing seasons,

each of which were determined by prevailing weather conditions. Evaluation of the

interannual variability indicated that the marsh may be sensitive to carbon (C) losses

through enhanced respiration under warmer autumn periods. Maximum daily average

values of evapotranspiration (ET) reached 10.75, 9.07, 11.70 and 8.36 mm day-1 in 2005,

2006, 2007 and 2008, respectively. Bowen ratio values varied seasonally with values

well below unity during the growing season (May to October) illustrating the dominance

of latent heat. Evaluation of the evaporative fraction and Priestley-Taylor α indicated the

seasonal importance of ET and mid-season high values of the decoupling coefficient (Ω)

indicated that the marsh ET is radiatively driven owing its smooth aerodynamic surface

and abundance of water. Overall, the marsh ecosystem was a large annual sink for CO2 as compared to other wetland ecosystems and ET rates were highly dependent on radiative input.

Résumé

La capacité des milieux humides à séquestrer du carbone a beaucoup attiré

l’attention, notamment dans le contexte des changements climatiques. À ce jour, bien

que plusieurs études aient été menées sur les tourbières, très peu portent sur les marais.

Cette étude a utilisé la technique de covariance des turbulences afin de mesurer l’échange

écosystémique net (EEN) de CO2 et l’échange d’énergie d’un marécage de quenouilles

de l’est de l’Ontario, Canada. Les objectifs de cette étude étaient d’utiliser un ensemble

de données de quatre ans afin de déterminer les contrôles environnementaux sur la

variabilité des échanges de carbone et de vapeur d’eau. Le EEN annuel cumulé était en

moyenne de -246 ± 26,8 gCm-2a-1 allant de -216 à -260 gCm-2a-1. La variabilité de

l’accumulation entre les années était le résultat de la synchronisation du printemps et de

l’automne au niveau de l’absorption du carbone et de la longueur des saisons de croissance, chacune ayant été déterminée par les conditions météorologiques qui prévalaient. L’étude de la variabilité interannuelle a indiqué que le marécage pourrait

être sensible aux pertes de C causées par une augmentation de la respiration au cours de

périodes plus chaudes d’automne. Les valeurs moyennes quotidiennes maximales

d’évapotranspiration (ET) ont atteint 10,75, 9,07, 11,70 et 8,36 mm jour-1 en 2005, 2006,

2007 et 2008 respectivement. Les valeurs du rapport de Bowen variaient selon la saison,

avec des valeurs bien en dessous de l’unité pendant la saison de croissance (mai à octobre), illustrant la dominance de la chaleur latente. Une évaluation de la fraction d’évaporation et du facteur α de Priestley-Taylor indiquaient l’importance saisonnière de l’ET et les valeurs élevées de mi-saison du facteur de découplage (Ω) indiquaient que

l’ET du marais est dominé par les radiations en raison de sa surface aérodynamique lisse

iii

et de l’abondance d’eau. Dans l’ensemble, l’écosystème du marais était un grand puits de

carbone annuel par rapport aux écosystèmes de tourbière et les taux d’ET étaient fortement dépendants de l’apport radiatif.

Acknowledgements

First and foremost I would like to start by dedicating the completion of this thesis

to my late grandfather, James Mardell, whom unfortunately passed away in July 2011

and therefore did not get to see the end result. Dada, you are greatly missed, this one’s

for you!!

I would like give infinite thanks to my supervisor, Dr. Ian Strachan. Thank you

for giving me the chance to prove myself in a field where I previously had no expertise.

Thank you for your continued support throughout the past two years in providing your expert advice and guidance but also for catching my silly little mistakes and for enduring all my csticky notes. I experienced many frustrating times and had many obstacles to overcome, so thank you for giving me confidence in my abilities and continuously encouraging me to “tell a story”. You surely don’t hear this often enough, but you truly are a fantastic teacher and I could not have asked for a better supervisor.

Thank you also to Dr. Nigel Roulet for sharing your knowledge of wetlands and providing comments and advice in committee meetings.

I would also like to thank everyone at the AER lab. Thank you to MCB not only for collecting the data but also for processing all the data and answering my questions.

To Eric Christensen and Cheryl Rogers who showed me the ropes in the AER lab and at

McGill. To Luc Pelletier who surely faced some of the same challenges in the beginning

of this process as I did and to Kelly Nugent, my good friend and partner in crime.

I would like to acknowledge my parents for their continued encouragement and

support, especially to my mom who I know is my #1 fan. I wouldn’t be the person I am

today if it weren’t for you guys, I love you. To my brother, we have had our ups and

downs, but regardless we are family. To my best friend Amanda Daly, you keep me

grounded and are the first person I go to for advice and to Tara Despault, my other

partner in crime.

Contribution of Authors

“As an alternative to the traditional thesis format, the thesis can consist of a collection of

papers of which the student is an author or co-author. These papers must have a

cohesive, unitary character making them a report of a single program of research.”

This thesis consists of one manuscript.

Carbon and Water Vapour Exchange in a Temperate Freshwater Marsh Stephanie Crombie, Ian B. Strachan & Marie-Claude-Bonneville Dept. of Natural Resource Sciences, McGill University, Montreal, Québec

This manuscript is the original work of Stephanie Crombie with the following exceptions:

Marie-Claude Bonneville oversaw the collection of the data, performed post-processing and cleaning procedures on raw CO2 and energy flux data. Marie-Claude Bonneville ran

the gap-filling procedures for the CO2 fluxes. All data analysis and gap-filling of the

energy flux data were performed by Stephanie Crombie. Dr. Ian Strachan provided

analytical insight, expert advice and financial support and contributed to the editing

process of the manuscript.

vii

Table of Contents

Abstract ...... ii

Résumé ...... iii

Acknowledgements ...... v

Contribution of Authors ...... vii

Table of Contents ...... viii

List of Figures ...... xi

List of Tables ...... xiii

Chapter 1: Introduction ...... 1

Chapter 2: Literature review ...... 5

2.1 Overview of wetlands ...... 5

2.2 Wetland ecosystem functions ...... 6

2.3 Freshwater marshes ...... 9 2.3.1 Vegetative characteristics ...... 9 2.3.2 Marsh remediation potential ...... 13

viii

2.4 Wetland microclimatology ...... 14 2.4.1 Radiative balance ...... 14 2.4.2 Energy exchanges in marsh ecosystems ...... 15

2.5 Carbon and water vapour exchange in freshwater marshes ...... 17 2.5.1 Carbon exchange ...... 18 2.5.2 Water vapour exchange ...... 23 2.5.3 Measuring carbon and water vapour exchange ...... 26

Preface to Chapter 3 ...... 31

Chapter 3: Environmental controls on carbon and water vapour exchange in a temperate freshwater marsh ...... 32

3.1 Introduction ...... 32

3.2 Methods ...... 34 3.2.1 Site description ...... 34 3.2.2 Instrumentation and flux measurements ...... 36 3.2.3 Data processing and gap-filling procedures ...... 38 3.2.4 Ecosystem diagnostics ...... 44 3.2.5 Determination of biophysical characteristics ...... 47

3.3 Results ...... 48 3.3.1 Climate...... 48 3.3.2 Canopy properties – biomass, density, height and LAI ...... 50 3.3.3 Diurnal and seasonal patterns of C exchange ...... 51 3.3.4 Annual patterns of C exchange ...... 52 3.3.5 Diurnal and seasonal patterns of energy fluxes ...... 55 3.3.6 Ecosystem diagnostics ...... 56 3.3.6 Further evidence of a radiatively driven system - PAR ...... 58

3.4 Discussion ...... 59 3.4.1 Controls on carbon and water vapour exchange ...... 59

3.4.2 Does the Mer Bleue marsh respond as expected? ...... 68 3.4.3 Comparison of annual cumulative NEE and ET to other studies ...... 72

3.5 Summary and Conclusions ...... 75

Chapter 4: Conclusion ...... 100

REFERENCES ...... 104

List of Figures

Figure 3.1 Wetland classes found in the Mer Bleue wetland complex courtesy of Touzi et al. (2007). The study site is highlighted by subset A...... 78

Figure 3.2 Eddy covariance tower and instrumentation set-up at the Mer Bleue marsh. 79

Figure 3.3 Temperature anomalies for the study period...... 80

Figure 3.4 Precipitation anomalies for the study period...... 80

Figure 3.5 Results from biomass sampling. Points represent daily average aboveground live biomass where bars are the standard deviations from the mean...... 81

Figure 3.6 Mean monthly diurnal pattern of NEE for 2007. Non growing season months (November – April) are combined...... 82

Figure 3.7 Annual cumulative NEE from November 1st to October 31st of each year. .. 83

Figure 3.8 Inter annual pattern for C exchange...... 84

Figure 3.9 Monthly cumulative sums of NEE, ER and GEP...... 85

Figure 3.10 Mean monthly diurnal pattern of QE for the 2005 growing season...... 86

Figure 3.11 Inter annual pattern of QE, QH and Q* for the study period...... 87

Figure 3.12 Results for the ecosystem diagnostics. a. Bowen ratio. b. Evaporative fraction. c. Priestley and Taylor coefficient (α). d. Decoupling coefficient (Ω)...... 89

Figure 3.13 Aerodynamic and surface resistance for the growing season in 2005...... 90

Figure 3.14 Seasonality in NEE-PAR for the growing season in 2005...... 91

Figure 15 Diurnal patterns of PAR, NEE and QE for a sunny and cloudy day in July/August. Sunny days are illustrated by closed dark circles and cloudy days open circles...... 93

Figure 3.16 Relationship between daytime average Bowen ratio and live biomass in 2005...... 94

xii

List of Tables

Table 3.1 Daily average C exchange (gCm-2day-1) for the growing and non-growing seasons...... 95

Table 3.2 Annual cumulative NEE, ER and GEP (gCm-2yr-1)...... 95

Table 3.3 Cumulative sums of NEE for the growing and non-growing seasons (gCm-2yr- 1)...... 96

Table 3.4 Comparative spring/fall turnover dates, peak uptake and CUP for each year. 96

Table 3.5 Seasonal variations in monthly cumulative NEE (gCm-2day-1)...... 97

Table 3.6 Model parameters for hyperbolic relationship between NEE and PAR (summer months)...... 98

Table 3.7 Coefficient of variation for NEE, ER and GEP...... 99

xiii

Chapter 1 Introduction

Concerns over climate change have led to a multitude of studies investigating the

processes controlling the mass and energy exchanges of different ecosystems including

forests, grasslands and agricultural ecosystems (Amiro et al., 2006; Baldocchi et al.,

1997; Bergeron et al., 2007; Suyker and Verma, 2008; Verma et al., 2005; Xu and

Baldocchi, 2004). Despite their small areal coverage, wetlands, specifically peatlands, are significant contributors to the world’s soil organic carbon pool. Recent estimates

propose that they store 202-535Gt C accounting for approximately 30% of the world’s

soil carbon pool (Mitra et al., 2005). Their carbon sequestration potential has given them

a considerable amount of attention in recent years which has led to a growing number of

studies in organic wetlands (peatlands; (Aurela et al., 2004; Bubier et al., 2003; Lafleur et

al., 2003; Lund et al., 2010; Lund et al., 2007; Pelletier et al., 2011; Sonnentag et al.,

2010)). Mineral wetlands on the other hand have received very little attention. As it happens, these two distinct wetland types are often compared to one another however they have markedly different soil properties, hydrological regimes and plant assemblages and therefore different interactions controlling carbon (C) related processes should be expected (NWWG, 1997).

Despite their ability to retain carbon, freshwater marshes can be significant contributors to the global carbon cycle through the emissions of greenhouse gases

(GHG), namely dioxide carbon (CO2) and methane (CH4) (Christensen et al., 2003;

Roulet, 2000; Wieder et al., 2007). In marshes, C is released from soil and plant

respiration and into streams through the decomposition of organic matter (Raich and

Schlesinger, 1992) while the anaerobic soil conditions that develop under saturated conditions produce CH4 which is then released to the atmosphere through ebullition and

plant-mediated transport (Tokida et al., 2007; Yavitt and Knapp, 1995). The net uptake

or release of CO2 and emissions of CH4 in marsh ecosystems largely depends on the

nature and origin of the marsh environment, its hydrological regime, geomorphologic

properties, and vegetation type, all of which are influenced by meteorological and

climatic conditions (Mitsch, 2007).

In response to global climate change, temperature and precipitation patterns are expected to shift causing more frequent and intense droughts, storms and floods (Erwin,

2009; IPCC, 2007). Mineral wetlands, whose hydrological regimes are quite sensitive, are particularly vulnerable to the changes associated with climate change, in particular, to changes in the severity of extreme events (Erwin, 2009). Hydrological alterations have

implications not only for wetland functioning but for their carbon and water cycles as

well (Admiral and Lafleur, 2007; Admiral et al., 2006; Guo et al., 2010; Wenying et al.,

2008; Zhou et al., 2009). Additionally, changes in climate are expected to impact wetlands in the northern latitudes in particular, where temperatures are predicted to increase disproportionately (IPCC, 2007).

While there have been a few studies on mineral marsh wetlands located in semi- humid, continental monsoon and Mediterranean climates, temperate freshwater marshes remain understudied (Bonneville et al., 2008; Lafleur, 2009). With few exceptions, measurements of net ecosystem CO2 exchange (NEE) and evapotranspiration (ET) in

mineral wetlands have been restricted to the months associated with the growing season

or span for no more than 1-2 years. As we are only just beginning to understand the

processes controlling carbon and water vapour exchange in marsh ecosystems, there is a

need for more long-term continuous studies which evaluate both warm and cold periods

in order to determine the large-scale radiative forcing associated with the net uptake or

release of GHG’s. To the best of our knowledge, no long-term studies on carbon and

water vapour exchange have been reported for North American temperate freshwater

marshes. Therefore, there is a need to study these wetland types to add to the growing

pool of literature on the impacts of climate change in natural ecosystems.

This study used the eddy covariance (EC) technique to measure net ecosystem

carbon exchange (NEE) and energy exchange at a temperate freshwater cattail marsh near

Ottawa, Canada for four years. The specific objectives of this research were:

1. to determine the diurnal, seasonal and inter-annual patterns of CO2 and water

vapour exchange; and

2. to determine the environmental drivers and variability in CO2 and water vapour

exchange.

Following this introduction, a literature review (Chapter 2) provides an overview

of wetlands, with an emphasis on freshwater marshes and an examination of wetland

functioning in the context of mass and energy exchanges in marsh wetlands, reporting on

preferred methods used for measuring carbon and water vapour exchange. A primary

results chapter provides the diurnal, seasonal and inter-annual patterns of carbon and

water vapour exchange from four years of nearly continuous EC field measurements

(Chapter 3). The environmental drivers and variability in net ecosystem CO2 exchange

and water vapour exchange are explained using ecosystem diagnostics such as the Bowen

ratio, evaporative fraction, PT-alpha and the decoupling coefficient (Chapter 3). The

main thesis results are then summarized in a concluding chapter (Chapter 4).

Chapter 2

Literature Review

2.1 Overview of wetlands

Wetlands cover 6-9% of the terrestrial surface and can be found on all continents

(except Antarctica) with the largest concentrations occurring in the northern latitudes

(Erwin, 2009; Mitsch, 2007; Zedler and Kercher, 2005). The internationally accepted

definition for wetlands describes them as “areas of marsh, fen, peatland or water, whether

natural or artificial, permanent or temporary, with water that is static or flowing, fresh,

brackish or salt, including areas of marine water the depth at which at low tide does not

exceed six meters” (Ramsar, 2011a). This definition incorporates a wide range of

habitats however it is often criticized as being rather vague and as often as not, countries

appropriate their own definitions based on the wetland attributes found within their

geographic boundaries (Finlayson and Valk, 1995; Scott and Jones, 1995). For instance,

in Canada, where the majority of wetlands are peat-forming and/or marshy environments,

wetlands are defined as “lands that are seasonally or permanently covered by shallow

water or land where the water table is at or close to the surface” (Environment Canada,

2002).

In addition to the complexity in defining wetlands, their vast global distribution

has led to the development of several classification schemes based on their hydrologic

and geomorphologic features, chemical and biological properties and plant assemblages.

Consequently, a large number of markedly different classification systems exist because

they have been designed to satisfy the needs of specific interest groups i.e. biologists,

agronomists etc. (Zoltai and Vitt, 1995). In order to facilitate wetland classification

systems, the National Wetlands Working Group (NWWG) generated the Canadian

Wetland Classification System (CWCS) which subdivides wetlands into two broad

categories based on their soil properties: organic and mineral wetlands. Following the

CWCS, organic wetlands, or peatlands, are identified as having accumulated more than

40 cm of peat while mineral wetlands have little or no peat accumulation (NWWG,

1997). Compared to other classification systems, this broad hierarchical organization is intended for multi-disciplinary use where the two categories can be further divided based on wetland class (origin and nature of the environment), form (hydrology and geomorphology) and type (vegetation) to please individual users. In Canada, the wetland class levels are the most widely used. The CWCS recognizes five wetland classes, which include bogs, fens, swamps, marshes and shallow open waters (Rubec, 1988). Bogs and fens are peat-accumulating wetlands with fluctuating water tables while swamps, marshes and shallow open waters are seasonally or permanently flooded environments with little or no peat accumulation i.e. mineral wetlands (Rubec, 1988).

2.2 Wetland ecosystem functions

Wetlands have ecological, socio-cultural and economic values, and while wetland

studies encompass a multitude of interdisciplinary domains, much of the literature

focuses on the importance of their ecosystem functions (De Groot et al., 2002).

Wetlands support biodiversity and maintain genetic diversity by acting as

transitional habitats between aquatic and terrestrial ecosystems (Brinson and Malvarez,

2002; Zedler and Kercher, 2005). In this manner they are often referred to as biodiversity

reservoirs because they are host to an array of flora and fauna species, many of which are

endangered (Ramsar, 2011b). Canadian wetlands host more than 600 species, one third

of which are listed by the Committee on the Status of Endangered Wildlife in Canada

(COSEWIC) as vulnerable or endangered (Environment Canada, 2011; Kennedy and

Mayer, 2002). They also provide important breeding, nesting and wintering grounds for

Canada’s migratory birds, including ducks, mallards and geese (Environment Canada,

2011).

Wetland plants possess natural mechanisms that help improve water quality by

intercepting, purifying and even removing harmful pollutants. In this respect they are

especially noted for trapping excess nutrients, predominantly nitrogen (N) and

phosphorus (P) loads stemming from agricultural practices (Zedler and Kercher, 2005).

These abilities make wetlands ideal candidates for remediation projects. For that reason,

constructed wetlands are emerging as attractive, low-cost alternative methods for water

quality improvement (Coleman et al., 2001; Kennedy and Mayer, 2002). Knight &

Kadlec (2009) report that North America alone hosts over 1000 constructed wetlands

used primarily for rural domestic wastewater treatment. In these systems, sediment

trapping lowers suspended solid concentrations, biochemical oxygen demand (BOD), and

trace metals and pathogens associated with sewage (Coleman et al., 2001).

Wetland soils, particularly those with organic constituents, have higher porosity

and high holding capacity, which enable them to temporarily store large volumes of water

(Hey and Philippi, 1995). This storage function moderates flood discharge and reduces

peak flood. Furthermore, wetland plant roots bind shorelines and act as physical barriers,

overall protecting coastal banks from storm surges and wave erosion (Ramsar, 2011b).

Wetlands are valuable contributors to the terrestrial carbon sink. High rates of

primary production in wetland ecosystems enable carbon sequestration (Lafleur, 2009).

Carbon sequestration rates vary with wetland type, and can fluctuate on daily, seasonal

and annual basis (Lafleur, 2009). Despite their small areal coverage, wetlands,

specifically peatlands, also play a crucial role in the global C budget. Recent estimates

propose that they store 202-535 Gt C accounting for approximately 30% of the world’s

soil carbon pool (Mitra et al., 2005).

Despite their ecosystem functions, increased globalization has subjected wetland

environments to many stressors. In agricultural regions, wetlands are drained for

expansion; in the northern territories of Quebec and China, vast areas are flooded for

hydroelectric projects; in coastal areas wetlands are buried to allow for urban

development and they are constantly degraded through deforestation practices (Zedler

and Kercher, 2005). As a consequence of these activities, Zedler and Kercher (2005)

estimate that more than half of the wetland area globally has been lost. In Canada, more

than 80% of the available wetland area has been lost, with 85% of the losses attributed to

drainage for agricultural purposes (Rubec, 2003).

Concerns over global wetland loss have led to the creation of several conservation

programs. The most noteworthy, the Convention on Wetlands (or Ramsar Convention, as

it is most commonly referred to), is an intergovernmental treaty signed in Iran in 1971,

which aims at conserving wetlands internationally. More specifically, the goal has been

to reduce the global wetland loss and to promote the sustainable use of wetlands and

wetland services. Since its implementation in 1975, the Ramsar Convention has listed

over 2000 wetlands in 160 nations, covering nearly 1.90 million km2 (Ramsar, 2011a).

Canada is currently host to 37 sites covering more than 130, 000 km2.

2.3 Freshwater marshes

Freshwater marshes are characterized as wetlands that are seasonally or

permanently flooded (NWWG, 1997). Under saturated conditions, freshwater marshes have developed two defining features for which they are known for: anaerobic soil conditions and large emergent plant species (Richardson, 2001; Van der Valk, 2012).

Under saturated conditions, marshes, as well as all other wetlands types, develop hydric soils. As water fills the pore spaces in the underlying soil material, oxygen diffusion between the atmosphere and plants roots is reduced producing anaerobic soil conditions (Batzer and Sharitz, 2006; Wegner, 2010). The resulting hydric soils can be comprised of mineral and/or organic properties. The main distinction between these two soil properties lies in their formation; organic soils are composed of accumulated dead plant matter i.e. peat. Mineral soils in comparison are textured soils originating from rock material and essentially are made up of sands, clays and silts (Richardson, 2001;

Van der Valk, 2012). While all soils contain some degree of organic accumulation, mineral soils, as are common in freshwater marshes, are identified as having acquired less than 20-35% organic matter (Mitsch, 2007). Mineral soils composed primarily of clays create an impermeable substrate in which the water accumulation is favored.

2.3.1 Vegetative characteristics

The anaerobic soil conditions associated with marsh wetlands supports the growth of emergent macrophyte species. Common species in marsh ecosystems include Typha

spp., Carex spp., Juncus spp. and Phragmites spp. regularly referred to as cattails, sedges,

rushes and reeds (Van der Valk, 2012). These species have established themselves in

oxygen-poor environments through morphological adaptations. Under anaerobic soil

conditions, the small quantity of oxygen available in the pore spaces (or what has been

dissolved in the water), is quickly utilized by soil microbes. Plant roots therefore lack

oxygen and are required to close their stomates leading to a reduction in photosynthetic

activity and water uptake (Cronk and Fennessy, 2001). As a result, ATP production is

reduced which, if oxygen deprivation continues, can lead to death (Cronk and Fennessy,

2001).

Wetland plant species have developed aerenchyma, porous tissue located in the

roots and shoots, to prevent this asphyxiation. These internal structural mechanisms

assist in diffusing oxygen from the atmosphere to the roots belowground (Cronk and

Fennessy, 2001; Van der Valk, 2012; Wegner, 2010). Although aerenchyma can develop

in 10% of the total root area of flood-intolerant species, in flood tolerant species, porous

tissue can occupy up to 50-60% of the total root area (Cronk and Fennessy, 2001).

Aerenchyma allow aeration of the root zone but also provide trace gas storage and

exchange. In Typha spp. the internal leaf concentration of CO2, can be 18 times more

elevated than atmospheric levels (Cronk and Fennessy, 2001). This storage provides a

valuable resource for plant species however aerenchyma also enable the release of plant-

produced gases such as respired CO2, as well as ethylene (C2H4) and methane (CH4) to the atmosphere (Cronk and Fennessy, 2001; Le Mer and Roger, 2001).

2.3.1.1 A species of interest: Typha

Typha are an invasive emergent macrophyte species, which form dense

monotypic stands that often reduce the opportunity for the establishment of other plant

species (McNaughton, 1966). Reproduction of the vegetative portion of the plant occurs

through an extensive rhizome system located belowground. In the spring, the Typha

break dormancy and utilize the energy stored in their rhizomes to initiate shoot growth.

At this time, Typha experience a certain degree of oxygen deficiency before the

development of new shoots which connects the belowground roots and rhizomes to the

atmosphere (Cronk and Fennessy, 2001). Stem growth generally occurs rapidly and these

wetland plants are not required to utilize all of their winter reserves. Instead, the

remaining stores can be utilized later in the summer to survive under anoxic conditions

(Cronk and Fennessy, 2001). Typha flower in mid-summer after which the plant allocates

the majority of its energy to the production of new rhizomes for use the subsequent

spring (Inoue and Tsuchiya, 2006; Sojda and Solberg, 1993). In the fall, the Typha

senesce, causing significant litter accumulation.

There are three species of Typha native to North America: T. angustifolia, T.

latifolia and T. domingensis (Grace and Harrison, 1986; Smith, 1986). Other species

have prevailed as well, such as in regions where ecological distributions overlap. For

example, a hybrid between T. angustifolia and T. latifolia exists, however, its range

(along with many other hybrid species) is limited (Grace and Harrison, 1986). All

species of Typha have relatively broad overlapping distributions. T. latifolia occupies the

broadest areas ranging from central Alaska down to southern Florida and Guatemala

(Smith, 1986). T. angustifolia’s range is much narrower occupying the temperate regions

of Canada and the U.S. while T. domingensis can be found mainly in the tropics (Smith,

1986). All species occupy fresh and/or brackish waters and tolerate varying flooding

depths from 1.5 m for T. domingensis, 1.2 m for T. angustifolia to 1.0 m for T. latifolia

(Smith, 1986). Typha species shift spatially with fluctuating water levels and constantly

adapt to their surroundings. A drainage regime and/or some degree of drying is required to expose the seed bank for regeneration. Oppositely, extreme flood events can kill

Typha species. Long-term experiments on the influence of water level fluctuations on wetland plant distribution along the shore of Lake Manitoba have shown that extended flood conditions can kill marsh species while drawdown promotes seedling establishment

(Christensen et al., 2009). Christensen et al., (2009) found that flooding of 1 m above mean water table depth for a period of two years resulted in emergent species death while drawdown of 0.5 m below mean water table for a period of 1-2 years promoted recruitment by reducing litter inputs.

Typha species located in the temperate regions of North America undergo extreme seasonal variations both in temperature and photoperiod. These species freeze in

the winter and are exposed to hot summers in which the growing season is restricted to a

short window between May and October. As a result of rapid growth during these short

time frames, the accumulation of organic matter below the water surface leads to the

formation of thick buoyant mats (Mallik and Wein, 1986). Trapped gases, namely CO2 and CH4, create the buoyancy for these floating mats (Van der Valk, 2012). Over time,

paludification occurs in Typha marshes however the transition from marsh to peatland

requires a shift in the hydrological balance of the ecosystem either in the form a natural

decrease in water levels or drainage resulting from human modifications (Mallik and

Wein, 1986).

2.3.2 Marsh remediation potential

In recent years, freshwater marshes have emerged as ideal candidates for

remediation projects. Marshes in particular are favored for a variety of reasons. Marshes

are highly productive systems that require large quantities of nutrients for growth

(Westlake, 1963). They therefore remove large quantities of nutrients from the

ecosystem, predominantly N and P, through soil adsorption. These two nutrients are

commonly associated with agricultural practices and provoke the growth of algae, which

then consume oxygen and decrease its availability in water bodies, resulting in eutrophic

conditions. Hypoxic zones are uninhabitable and aquatic species that cannot escape or

adapt to these conditions, ultimately die (Zedler and Kercher, 2005). Studies report that

macrophytes can store between 50-150 kgPha-1year-1 and 1000-2500 kgNha-1year-1 (Brix,

1994; Brix, 1997; Kadlec and Wallace, 2009).

Aerenchyma development assists in diffusing oxygen from the atmosphere down to the rhizosphere (Batzer and Sharitz, 2006; Mitsch, 2007; Van der Valk, 2012; Wegner,

2010). Macrophytes are then required to leak oxygen from their roots to oxygenate the rhizomes. This leakage produces oxidized conditions favorable for aerobes and nitrifying bacteria (Brix, 1994; Brix, 1997).

Finally, dense root systems anchor to the soil surface providing retention capabilities that slows water movement for trapping and filtration (Brix, 1994; Brix,

1997). A study on the remediation potential of three marsh species, Juncus effuses,

Scirpus validus and Typha latifolia reports that the presence of these plants in constructed

wetlands resulted in a 70% reduction in suspended solids and BOD and a 50-60%

reduction of N, P and ammonia (NH3) (Coleman et al., 2001). Additionally, the study

found that Typha species out-compete other marsh species in relation to growth and water quality enhancement due to their aggressively invasive nature (Coleman et al.,

2001).

2.4 Wetland microclimatology

2.4.1 Radiative balance

Ecosystem processes at the Earth’s surface are driven by solar energy, and

wetlands are no exception. The sun emits energy as shortwave radiation with

wavelengths from 0.15-3 μm on the electromagnetic spectrum (EM) (Oke, 1987). The majority of this solar radiation arrives at the surface as direct beam radiation that is neither absorbed nor diffused. The amount of shortwave radiation that is reflected from the surface is dependent on its albedo. This is especially evident in northern wetland ecosystems during the changing of seasons. Fresh snow is highly reflective with an albedo of 0.75-0.95 thus surface radiation absorption is much lower during the winter months due to the presence of snow (Lafleur, 2008; Matson et al., 2011; Oke, 1987). As the snow melts in the spring exposing the soil surface, albedo decreases to 0.05-0.40, then as wetland vegetation emerges, albedo slowly attains a stable mid-summer value of about

0.25 (Lafleur, 2008). The wetland surface, like all terrestrial surfaces, emits energy in the longwave portion of the EM spectrum with wavelengths from 3-100 μm while gases and particles in the atmosphere re-radiate a portion of this energy back to the wetland surface

(Lafleur, 2008; Oke, 1987). Rates of longwave emission depend on the nature of the

surface and on its emitting temperature (Oke, 1987).

The radiative balance at the wetland surface is the sum of the net difference

between the incoming and outgoing components of shortwave and longwave radiation

computed as

Q* K↓ K↑ + L↓ L↑ , (2.1)

where Q* is net radiation, K↓ and K↑ are the components of incoming and outgoing

shortwave radiation and L↓ and L↑ are the components of incoming and outgoing

longwave radiation, all measured in watts per square meter (Wm-2) (Oke, 1987).

2.4.2 Energy exchanges in marsh ecosystems

During the daytime period, there is a net accumulation of radiative flux energy.

This excess energy is either stored or released in turbulent exchanges of heat and water

vapour. The balance of the surface energy fluxes can be written as

Q* QH QE QS , (2.2)

where QH, QE are the sensible and latent heat fluxes and QS is the heat stored in the water,

vegetation and canopy air space, all measured in Wm-2 (Burba et al., 1999; Meyers and

Hollinger, 2004). In some ecosystems, QS can be negligible over the course of the day

because the energy conducted into the system during the day is lost to the atmosphere at

night (Matson et al., 2011). However, in wetland ecosystems, this flux can be

considerable depending on the wetland type. For example, in permanently or seasonally

flooded mineral wetlands, QS can be large because of the large heat capacity of water.

Burba et al., (1999) report that QS contributed to as much as 20-30% of the available

energy in a reed wetland in Nebraska. Similar values have been reported for a sedge

wetland in Northeast China (Sun and Song, 2008) while researchers at a cattail marsh in

California report QS values <10% (Goulden et al., 2007; Sun and Song, 2008).

In wetlands, the latent and sensible heat fluxes are the largest consumers of available energy. The Bowen ratio (Bowen, 1926) is therefore a useful tool to examine how much energy is partitioned into QH and QE. The Bowen ratio is expressed as

Q β H . (2.3) QE

When Bowen ratio values are below unity i.e. QE>QH, the majority of available energy is

consumed by the latent heat flux and is representative of a humid climate. Oppositely,

when Bowen ratio values are above unity, i.e. QH>QE, sensible heat consumes the

majority of available energy and is representative of a dry climate (Lafleur, 2008). In

wetlands the Bowen ratio also varies depending on wetland type and/or climatic

influences. Additionally, Bowen ratio values vary diurnally and seasonally and are

driven by incoming radiation and canopy growth characteristics such as plant emergence

and senescence, with distinct differences between the growing and non-growing seasons

(Admiral et al., 2006; Burba et al., 1999; Guo et al., 2010). For example, Guo et al.

(2010) found that β reached a minimum value of 0.4 from April to November and a maximum of 2.5 in December for a reed ecosystem in Northeast China.

In addition to the turbulent exchanges of heat and water vapour, solar energy also

drives the mass exchange of carbon dioxide and methane (Lafleur, 2009). During

photosynthesis, wetland plants take in CO2 through the small pores on the underside of

their leaves (Batzer and Sharitz, 2006). These pores, called stomates, are regulated by

guard cells that open during the day, close at night, and fluctuate during the day in

response to light, temperature, humidity and CO2 concentrations in the leaves (Batzer and

Sharitz, 2006). CO2 is produced in wetland soils and is released to the atmosphere

through soil and plant respiration (Raich and Schlesinger, 1992). As a result of organic

matter decomposition, C is also released into streams as dissolved organic and dissolved

inorganic C (DOC and DIC, respectively) (Clair et al., 2002). Under anaerobic soil

conditions, methanogenic microbes produce CH4. Methanotrophic bacteria in the soils

consume a portion of the CH4 produced, while the rest diffuses upwards and is released to

the atmosphere through ebullition and plant-mediated transport (Le Mer and Roger, 2001;

Tokida et al., 2007; Yavitt and Knapp, 1995).

2.5 Carbon and water vapour exchange in freshwater marshes

Most studies on wetland C exchange take place in peatlands (Aurela et al., 2004;

Bubier et al., 2003; Lafleur et al., 2003; Lund et al., 2010; Lund et al., 2007; Pelletier et al., 2011; Sonnentag et al., 2010) and while there have been a few studies from mineral marsh wetlands in Northeastern China (Guo et al., 2009; Guo et al., 2010; Guo and Sun,

2012; Song et al., 2011; Sun and Song, 2008; Zhou et al., 2009; Zhou et al., 2010) and one in California (Goulden et al., 2007; Rocha et al., 2008; Rocha and Goulden, 2008;

Rocha and Goulden, 2009; Rocha and Goulden, 2010) marshes are often compared with peatlands out of necessity. These comparisons are not appropriate as marshes and peatlands have markedly different soil properties, hydrological regimes and plant assemblages (NWWG, 1997). As outlined previously, the formation of the organic and mineral soils associated with these wetlands types are dissimilar and are the basis of the two broad categories in the wetland classification system. Additionally, bog peatlands,

can be ombrotrophic, receiving water via precipitation inputs only. Their raised surface

isolates them from sub-surface runoff and lateral inflows, limiting the accumulation of

water (NWWG, 1997). Marshes on the other hand are minerotrophic. In addition to

receiving atmospheric inputs, they are connected to groundwater sources and surface

runoff and/or littoral sources such as along the shores of lakes, the sides of roads and in

tidal areas (NWWG, 1997). In this manner, marshes experience more extreme water

level fluctuations on daily, seasonal and annual bases. They are also subject to large

open water areas and pond formation due to their impermeable substrates. Finally,

marshes are highly productive systems due to their large/high standing emergent

vegetation as compared to the mosses and shrubs species present in peatlands (NWWG,

1997). Based on the dissimilarities, different interactions controlling C related processes

should be expected.

2.5.1 Carbon exchange

2.5.1.1 Patterns in C exchange

In marshes, net ecosystem CO2 exchange (NEE) patterns depend on the interplay

between the photosynthetic uptake by plants (GEP), and release from soil and plant

respiration (ER) (Lafleur, 2009). NEE follows the micrometeorological sign convention

where negative values represent a net sink of CO2 in an ecosystem and positive values

represent a net source to the atmosphere. During the day, NEE follows both ER and GEP

however more negative NEE results when photosynthesis exceeds respiration (Lafleur,

2009). At night, in the absence of photosynthesis, NEE follows ER and is positive.

Studies have shown that seasonally NEE is small in the spring, and increases rapidly as temperatures and light levels increase and plant canopies develop (Rocha and Goulden,

2008; Zhou et al., 2009). NEE generally peaks midsummer, at the timing of peak canopy

growth and declines in the fall with canopy senescence (Rocha and Goulden, 2008; Song

et al., 2011; Zhou et al., 2009). Marsh ecosystems are typically CO2 sources at night and during the cold season due to the absence of vegetation and C sinks during the growing season resulting from the assimilation of CO2 through photosynthetic abilities (Rocha and

Goulden, 2008; Zhou et al., 2009). Studies have shown that the magnitude of the C sink during the growing season depends on the length of the growing season (Churkina et al.,

2005), meteorological conditions (Rocha and Goulden, 2008; Song et al., 2011; Zhou et al., 2009) and the CO2 efflux during the cold season (Zhou et al., 2009). For instance,

Zhou et al., (2009) showed that the total sum of the CO2 losses during the cold season

offset NEE gains by 83%. Non-growing season losses in marsh ecosystems are therefore

especially important in terms of the annual C balance.

2.5.1.2 Controls on C exchange

On daily and seasonal time scales, the main environmental controls on marsh C

exchange are light and temperature. These conditions influence the biophysical

properties of marsh vegetation (plant height, biomass and leaf area index (LAI)), and the growth and senescence of marsh vegetation. In this manner, canopy characteristics are

also strong determinants of wetland NEE and therefore influence the magnitude and trend

of C fluxes as well. The inter-annual variability on wetland NEE can be attributed to

climatic influences, such as the timing in spring snowmelt which ultimately determines

the start of the growing season, and wintertime emissions (Zhou et al., 2009). More

recently, studies have shown that drought can significantly influence the inter-annual

variability in wetland NEE (Dušek et al., 2012; Rocha and Goulden, 2010).

Photosynthesis is light-dependant and is a function of the amount of

photosynthetically active radiation (PAR) received (Bubier et al., 2003; Frolking et al.,

1998). In marshes, NEE increases with increasing PAR (Zhou et al., 2009) as is typical

of other ecosystems. This trend is apparent at both hourly and daily time scales when

NEE increases to a maximum value centered at solar noon and decreases there after

(Rocha and Goulden, 2008; Zhou et al., 2009). NEE responses to the effects of light are

especially evident on rainy or heavily overcast days during the growing season. For

instance, Song et al. (2011) found a reduction in GEP by 45-68% compared to adjacent

sunny days. These reductions resulted in near zero and even positive (net emission) daily

C fluxes (Song et al., 2011). On a seasonal basis, years with a larger number of sunny

days and higher PAR levels can lead to higher annual accumulated C sums (Dušek et al.,

2012).

Temperature influences both assimilation and respiration rates but in different

ways. In northern latitudes, spring and autumn warming amplifies carbon sequestration

by enhancing photosynthetic activity and increasing the length of the growing season

(Churkina et al., 2005; Zhou et al., 2001). During the growing season, warmer

temperatures can also enhance plant growth which further enhances assimilation (Zhou et

al., 2001). However, due to the light-dependency of GEP, these instances only occur if

light levels are unaffected. Ecosystem respiration on the other hand is directly coupled

with temperature only (Raich and Schlesinger, 1992). Warmer soils enhance autotrophic

and heterotrophic respiration and therefore, in wetlands, CO2 emissions increase with increasing temperatures (Alm et al., 1999; Raich and Schlesinger, 1992). Respiration responses to 10°C changes in temperature can be demonstrated using Q10 models. For a

reed ecosystem, Zhou et al. (2009) found that lower Q10 values during the growing season

were associated with higher air temperatures illustrating the temperature-dependency of

ER. This relationship suggests that increased temperature changes associated with climate change will lead to CO2 enhancement. For instance, at a Californian cattail

marsh (Rocha and Goulden, 2008), the Mediterranean climate subjects the marsh to year-

long warm temperatures. As a result, this particular marsh has annual C sums ranging

from sequestration in some years to a carbon release in others resulting from high

respiration rates despite high cattail productivity (Rocha and Goulden, 2008). In northern

latitudes, as temperatures increase and the growing season potentially expands to earlier

spring and/or later autumn, it remains to be determined whether respiration or production

will be dominant and in which direction net C accumulation will move.

Climatic conditions can enhance or constrain plant canopy growth (Rocha and

Goulden, 2008; Song et al., 2011; Zhou et al., 2009). Spring temperatures are

particularly important for germination and shoot growth in many emergent species and

can either impede or accelerate the start of the C uptake period. Bonnewell et al. (1982)

found that Typha seed germination increases with increasing temperature. However,

while cold temperatures are required to initiate the process, exposure to prolonged cold

temperatures at the start of the germination period decreases the percentage of Typha that

can germinate thereby reducing the CO2 uptake potential (Ekstam and Forseby, 1999).

The seasonality in C exchange in marsh ecosystems is strongly related to the growth and

senescence of vegetation with NEE becoming more negative as plant canopies develop

(Rocha and Goulden, 2008; Zhou et al., 2009). Differences in annual NEE between

marsh sites depends on the biophysical properties of the vegetation present (Humphreys

et al., 2006). For example, in marshes LAImax is reported as 1-4 for Carex spp. (Aerts et al., 1992; Song et al., 2011), 3 for Phragmites spp. (Burba et al., 1999; Karunaratne et al.,

2003; Zhou et al., 2009) and 3-6 for Typha spp. (Rocha and Goulden, 2008). Studies on

emergent macrophyte species report that biomass production ranges between 857-1160 gm-2, 1110-1118 gm-2, and 428-2464 gm-2 for Carex, Phragmites and Typha species,

respectively (Pratt, 1981). In this manner, Typha-dominated marshes are expected to

yield the highest annual C rates as compared to the other mineral wetland species.

The effects of changes in water table on marsh NEE depend on the timing and

severity of the event (Lafleur, 2009). Most floods coincide with the timing in spring

snow melt, however, flood events associated with extreme precipitation events during the

growing seasons can have considerable effects on wetland NEE. At a sedge-grass

ecosystem, a single precipitation event during the growing season generated a 16-day

flood. Flooding halted assimilation and damaged aboveground plant canopy parts (Guo

et al., 2009). Reduced canopy growth can result in lower annual uptake rate as compared

to years when no mid-season flooding events occur (Guo et al., 2009). During flood

events, waterlogged conditions also suppress soil CO2 efflux since water creates a barrier

against gas diffusion (Dušek et al., 2012; Guo et al., 2009).

There is general agreement that droughts impact marsh NEE through alterations

in ER and GEP. For example, at an experimental site in California, Rocha and Goulden

(2010) simulated long-term drought effects. Under drought conditions they found that

daytime carbon uptake was suppressed, leading to a weak diel cycle. Drought also

inhibited LAI development resulting in daytime losses throughout the entirety of the

growing season (Rocha and Goulden, 2010). Subsequently, in the two years following

drought, they found that LAI development was delayed consequently deferring the onset

of the carbon uptake period and reducing LAImax leading to lower peak uptake. In their

experiment, Rocha and Goulden (2010) show that impact of drought on marsh NEE can

last for several years after the flood event.

2.5.2 Water vapour exchange

2.5.2.1 Patterns in water vapour exchange

In wetlands, water loss is a function of evaporation from the surface, and

transpiration from plants (Batzer and Sharitz, 2006). These processes occur

simultaneously and are termed evapotranspiration (ET) or water vapour exchange. The

pathways for ET include evaporation from the soil surface and open water and

transpiration from the canopy and sub-canopy (Goulden et al., 2007; Lafleur, 2008).

When wetland plants close their stomates throughout the day in response to high

temperatures, humidity and CO2 concentrations in the leaves, the air inside the leaves

becomes saturated, enabling water vapour release when opened (Batzer and Sharitz,

2006). Wetland plants therefore simultaneously fix carbon while losing water because carbon dioxide and water vapour share the same diffusion pathway. In this manner, the patterns for water vapour exchange are in accordance with those for C exchange. During

the day, ET increases to a maximum at the timing of peak daily NEE and decreases

afterwards (Goulden et al., 2007). At night, transpiration ceases due to stomatal closure.

Seasonally, ET is small in the spring and increases with increasing light levels and with

plant canopy development (Sun and Song, 2008; Zhou et al., 2010). ET rates also peak

mid-summer at the timing of peak canopy growth and decline in the fall with senescence

(Sun and Song, 2008; Zhou et al., 2010). Studies report large wetland to wetland

differences in ET rates owing to the presence of standing water, differences in wetland

vegetation and climatic influences (Lafleur, 2008). Notwithstanding, reports of high ET

rates in marsh ecosystems are not uncommon as compared to other wetland types

(Lafleur, 2008).

2.5.2.2 Controls on water vapour exchange

The main environmental control on wetland evapotranspiration is radiative energy

(Guo and Sun, 2012; Sun and Song, 2008; Zhou et al., 2010). In marshes, daily and

seasonal ET patterns closely follow variations in net radiation (Sun and Song, 2008; Zhou

et al., 2010) which is dominated by incoming solar radiation. Additionally, studies have

shown that inter annual variations in wetland ET can be attributed to reduced Q*

resulting from cloudy conditions and anomalous precipitation events (Zhou et al., 2010).

Zhou et al. (2010) show that ET and Q* have a positive linear relationship during the

summer months that becomes scattered with the inclusion of overcast periods and

rainfall.

The dimensionless decoupling coefficient (Ω) can be used to determine the

relative importance of solar energy in an ecosystem. Ω is useful as it describes the degree

of coupling between the vegetation and the free air stream above the canopy

(McNaughton and Jarvis, 1983). For a perfectly coupled system, Ω approaches 0

signifying that surface water vapour fluxes are driven by atmospheric demand along a

humidity gradient between the surface and the lower atmosphere. In a decoupled system

Ω approaches 1 denoting that ET is controlled by solar radiation input rather than the

vapour gradient (McNaughton and Jarvis, 1983). At two sites in China, Ω was shown to increase with the onset of the growing season, and range between 0.5-0.8 for a reed

marsh (Zhou et al., 2010) and stayed above 0.9 for the majority of the growing season in

a sedge wetland (Sun and Song, 2008). These findings reinforce solar radiation as a

major driver of wetland ET.

The seasonal patterns in marsh ET are also related to the growth and senescence of marsh vegetation. Marsh ET is low in the spring when LAI is small and increases as

LAI increases in response to warmer temperatures and canopy growth (Sun and Song,

2008; Zhou et al., 2010). ET decreases as LAI diminishes (Zhou et al., 2010). LAI is a surrogate for the number of stomates and therefore under non-stressed conditions, greater

LAI means greater canopy transpiration. One measure of this is stomatal conductance

(gs); wetland plants have higher gs than other natural ecosystems such as forests and

grasslands, but lower than that in crops (Korner, 1979). Therefore, depending on the

proportion of live vegetation to open water present in a marsh, the overall ET rates can be

much greater that the evaporation rates from water alone. However, vegetation can also

reduce the wetland ET by shading the underlying vegetation. At a cattail marsh, Goulden

et al. (2007) show that evaporation was less than expected because a large expanse of

standing litter, approximately 1-2 m, prevented direct solar radiation subsequently

restricting any ET from beneath the primary canopy. In their case, the litter layer covered

the standing water and therefore evaporation from the open water was also reduced

(Goulden et al., 2007).

Reports from the limited marsh studies to date suggest that ET rates are

unaffected by fluctuations in standing water levels (Goulden et al., 2007; Sun and Song,

2008). For example, at the California cattail marsh, standing water disappears by mid-

summer, however, ET continues (Goulden et al., 2007). The emergent plant species in

marsh ecosystems have deep roots that move soil moisture through the plant system to the atmosphere (Goulden et al., 2007). As previously noted, drought can significantly influence the inter-annual variability in wetland NEE however there are no reports on

whether or not drought influences wetland ET. In their drought experiment, Rocha and

Goulden (2010) report that in the years following the drought event, LAI was greatly

affected. Based on the dependence of stomatal conductance on leaf area it can be

assumed that drought would have significant effects on wetland ET as well.

2.5.3 Measuring carbon and water vapour exchange

2.5.3.1 Eddy Covariance

At present, the eddy covariance (EC) technique is the preferred method for direct

measurement of trace gas exchange as it provides ecosystem scale fluxes of a quantity of

interest (Baldocchi, 2003). Flux footprints associated with the technique enable

measurements over a few hundred feet to several kilometers over time scales which can

span from days to years (Baldocchi, 2003).

The general concept of EC is as follows: within the horizontal wind movement

are rotating atmospheric air parcels called eddies (Burba and Anderson, 2010). Eddies

are set into motion through free or forced convection. Free convection results from

density differentiations in the surrounding air. For instance, when eddies are warmer

than the surrounding air, the lower density differentiation makes them rise. Oppositely,

higher density differentiations of colder eddies promotes sinking (Oke, 1987). With

forced convection, parcels are set into motion by the surface as air flows over objects.

This form of convection is therefore greatly dependant on the roughness of the surface

and the horizontal speed of the air flow (Oke, 1987).

Eddies have three-dimensional components and move horizontally and vertically

in such a manner that they are carried downstream (Burba and Anderson, 2010). While

they vary in size, they are typically smaller at the surface, and increase in size as they

move up within the atmosphere (Lafleur, 2008). Eddies are responsible for transporting

the mass exchanges of carbon and energy from one location to another at any given time

via convection. The EC technique measures the covariance of the concentration of a

trace gas of interest and the vertical wind speed of the eddy that is transporting it. Carbon

and water vapour fluxes can then be determined from the instantaneous departure of the

average air density, wind speed and mixing ratio expressed as

F ρw c , (2.4)

where ρa is the average air density, w' is the instantaneous departure from the mean

vertical wind speed and c' is the instantaneous departure from the mean mixing ratio

(Baldocchi, 2003). Measurements are made at a high speed (typically 10 Hz) to capture

all turbulent exchange components and are averaged over 30-minutes to exclude longer time scale events (e.g. weather systems).

The EC technique is not without its limitations. It assumes that advection from outside the ecosystem of interest and storage within the vertical profile from the surface are both non-existent. The method therefore works best when instruments are placed on flat terrain surrounded by a homogeneous landscape which negates the advection term

(Baldocchi, 2003). Under these conditions, a flux can be derived by directly measuring the turbulent flux (equation 2.4) and calculating the storage flux (Aubinet, 2008).

Furthermore, ideal measurements are recordable only during unstable conditions

(Baldocchi, 2003). Systematic errors associated with the technique become apparent

when weather conditions are less than ideal, a notable example being convectively stable

atmospheres which frequently occur overnight and into the early morning hours (Aubinet,

2008). During stable conditions, turbulence is absent or intermittent and EC

instrumentation is unable to capture the true flux above the canopy (Aubinet, 2008;

Baldocchi, 2003). Errors associated with nighttime fluxes lead to overestimations in

daily fluxes because the source flux term is underestimated and the advective term plays

a greater role at night than during the day (Aubinet et al., 2012). Aubinet et al. (2012)

report that errors associated with systematic deviations can range from 30-200 gCyr-1 for a variety of ecosystems including forests, grasslands and crops.

Another systematic error associated with the EC method which has received noticeable attention is the apparent lack of energy balance closure (Wilson et al., 2002).

Lack of closure is said to occur when the sum of the available energy exceeds the sum of the measured turbulent fluxes (QE and QH); this is thought to result from sampling errors,

instrumentation bias, neglected energy sinks, frequency loses and/or advection (Wilson et al., 2002) and is typically reported as an underestimation of 10-30%.

2.5.3.2 Model estimation of ET

In addition to the EC method, wetland ET can also be estimated through

numerous empirical formulas. The more common are the Penman-Monteith (PM) and

Priestley-Taylor (PT) equations (Monteith, 1965; Penman, 1948; Priestley and Taylor,

1972). The PM equation is a combination equation which incorporates the radiative

energy and aerodynamics required for vaporization (Drexler et al., 2004; Monteith and

Unsworth, 2008; Shuttleworth, 1993). The PM model is a one dimensional

representation of the surface and assumes that all water vapour moves through the plant

canopy system (i.e. there is no open water or soil contributions). In the PM model, water must first diffuse out of the leaf against a surface resistance (rs) before diffusing into the

atmosphere against an aerodynamic resistance (ra). QH diffuses upwards against ra.

Following Monteith and Unsworth (2008), wetland evapotranspiration can be calculated as

∆Q* QG ρc e e/r ET r , (2.5) ∆γ1 r

-1 -1 where ρa is the density of air, cp is the specific heat capacity of air (Jkg °K ), es and ea are the saturation and actual vapour pressures respectively (kPa), ra and rs are the surface

and aerodynamic resistances, respectively (sm-1), Δ is the change in saturation vapour

pressure with temperature (kPa°K-1), γ is the psychrometric constant (0.067 kPa°K-1 at

20°C) and other terms are as previously defined. The PM equation follows the “big-leaf” assumption in that the surface can be viewed as a uniform expanse of vegetation or a single evaporating layer where a single surface resistance and a single aerodynamic resistance are representative of the entire surface (Shuttleworth, 1993). Additionally, under this assumption it is also expected that QE and QH operate at the same height and at

the same temperature (Monteith and Unsworth, 2008; Shuttleworth, 1993). Despite

limitations on its applicability in marsh ecosystems, by using the direct QE measurements

from EC, the PM model can be inverted and the component variables in the model

derived to indicate the surface controls on ET.

The PM model is complex and requires many inputs for its use. In wet

environments, ET can be calculated from a simple approximation that uses temperature

and radiation inputs alone. In theory, as air passes over a saturated surface, the vapour

pressure deficit gradually decreases until the actual evaporation from the surface reaches

an equilibrium endpoint (QEeq). Priestley and Taylor (1972) found that many environments exceeded this equilibrium rate and suggested an empirically derived coefficient (α) be used to augment the equilibrium rate. Wetland ET can therefore be estimated as

∆ ET α Q* , (2.6) ∆γ

This approximation is appropriate in environments where the effects of solar radiation outweigh the effects of turbulence. Priestley and Taylor (1972) found α to vary between

1.08 and 1.34 with an overall average of 1.26 for well-watered surfaces with minimum

advection (Mao et al., 2002). There is much debate as to whether or not α should equal

1.26 because of its empirical derivation. For instance, McNaughton and Spriggs (1986)

argue that the “size” of α depends on the dry air entrainment from above and increases in

the boundary layer depth. The Priestley and Taylor coefficient is therefore most effective

when derived locally.

Preface to Chapter 3

A survey of literature reveals a growing number of studies on the dynamics of carbon

exchange in peatlands (Aurela et al., 2004; Bubier et al., 2003; Lafleur et al., 2003; Lund et al., 2010; Lund et al., 2007; Pelletier et al., 2011; Sonnentag et al., 2010). Temperate freshwater marshes on the other hand remain understudied (Bonneville et al., 2008;

Lafleur, 2009). In the following chapter, four years of nearly continuous eddy covariance measurements of carbon and water vapour exchange are presented. To gain a better understanding on net ecosystem CO2 and water vapour exchange in a temperate

freshwater marsh, the main environmental drivers and variability are investigated.

Chapter 3

Environmental controls on carbon and water vapour exchange in a temperate freshwater marsh 3.1 Introduction

Wetlands can be found on all continents (except Antarctica) with the largest

concentrations occurring in the northern latitudes (Erwin, 2009; Mitsch, 2007; Zedler and

Kercher, 2005). Wetlands provide a number of ecosystem services, however they are

increasingly noted for supporting biodiversity, providing water quality control, aiding in

flood management and sequestering carbon (Mitsch, 2007; Zedler and Kercher, 2005).

Their carbon sequestration potential has given them a considerable amount of attention in

recent years, especially in light of global climate change. The literature provides a

growing number of studies on the dynamics of carbon exchange in peatlands (Aurela et

al., 2004; Bubier et al., 2003; Lafleur et al., 2003; Lund et al., 2010; Lund et al., 2007;

Pelletier et al., 2011; Sonnentag et al., 2010), however tropical wetlands and temperate

freshwater marshes remain understudied (Lafleur, 2009).

Like other plants, wetland vegetation simultaneously fixes carbon while losing water because carbon dioxide and water vapour share the same diffusion pathway. In this manner, water loss is a concern in some regions as well. In wetlands, water loss is a function of evaporation from the surface, and transpiration from plants (Batzer and

Sharitz, 2006). These simultaneous processes are collectively termed evapotranspiration

(ET), or more simply, water vapour exchange. As can be expected, concerns over climate change have also led to an increasing number of studies on understanding the factors controlling ET. In wetlands, ET is the largest consumer of both water and energy

thus it has implications not only for wetland functioning but for their carbon and water

cycles (Admiral and Lafleur, 2007; Admiral et al., 2006; Guo et al., 2010; Wenying et al.,

2008; Zhou et al., 2009).

There are many methods available for measuring surface-atmosphere exchanges

of carbon and water vapour. At present, the eddy covariance (EC) technique is the

preferred method as it provides a direct method for measuring ecosystem scale fluxes of

carbon and energy (Baldocchi, 2003) while averaging through both time and space scales.

EC measures the instantaneous covariance between a scalar property of interest and the

vertical velocity of the eddy transporting it (Baldocchi, 2003). In addition to the eddy

covariance method, wetland evapotranspiration can also be determined through empirical formulas such as the Penman-Monteith and Priestley-Taylor equations (Monteith, 1965;

Penman, 1948; Priestley and Taylor, 1972). The parameters in these formations enable the evaluation of the driving forces behind ET, specifically canopy characteristics

(McNaughton and Jarvis, 1983). For instance, the Penman-Monteith equation, which accounts for aerodynamic, canopy and surface conductances, can be used to identify ET interactions with canopy and atmospheric properties including meteorological variables such as radiation dynamics (Brümmer et al., 2011; Drexler et al., 2004; Guo et al., 2010).

While useful, these parameters have often been ignored in wetland ecosystem studies.

This chapter reports four years of nearly continuous eddy covariance measurements of carbon and water vapour exchange for a temperate freshwater marsh in the Mer Bleue wetland complex (Figure 3.1). In order to determine the environmental controls on carbon and water vapour exchange our objectives were: 1) to determine the diurnal, seasonal and inter-annual patterns of carbon and water vapour exchange with a

focus on how weather (temperature and precipitation) influences these fluxes; and, 2) to

use ecosystem diagnostics such as the Bowen ratio (β), evaporative fraction (EF), the

Priestley and Taylor coefficient (α) and the decoupling coefficient (Ω) to explain the

environmental drivers and variability in CO2 and water vapour exchange. We

hypothesize that the marsh will be an annual sink for CO2 in each of the four years of

study and, due to the presence of open water and large annual biomass production, we

hypothesize that the latent heat flux will dominate the energy balance during the growing

season.

3.2 Methods

3.2.1 Site description

Located 10 km east of Ottawa, Ontario (45.4°N, 75.5°W), the Mer Bleue wetland

complex encompasses an area of roughly 28 km2 and contains four wetland classes

specified by the Canadian Wetland Classification System: a treed bog, a shrub bog, a fen

and a marsh (Bonneville et al., 2008; NWWG, 1997; Roulet et al., 2007; Touzi et al.,

2009).

In 1983, the City of Ottawa appointed Mer Bleue as an area for scientific interest

due to its natural and rare features (Ramsar, 2007; Taylor et al., 1995). For instance, the

majority of the complex consists of a raised peat dome that is typically found further

north in the Boreal biome. This undisturbed, natural ecosystem supports 22 mammal

species and numerous rare fish, reptile and insect species including the spotfin shiner

(Cyprinella spiloptera) and the spotted turtle (Clemmys guttata) (Ramsar, 2007). The

area is also host to numerous flora species that are nationally and provincially significant.

The National Capitol Commission (NCC) owns, protects and manages Mer Bleue. As a

Crown corporation, the NCC provides recreational and educational opportunities such as a 1 km long boardwalk, picnic areas and various hiking, snowshoeing and cross-country skiing trails. The Mer Bleue wetland complex became a conservation area in 1995 when it was placed on the List of Wetlands of International Importance and became a Ramsar site (Ramsar, 2011a). The Ramsar convention, an intergovernmental treaty signed in

1971, aims at conserving wetlands internationally by promoting the sustainable use of wetlands and wetland services. Since its implementation in 1975, the Convention has listed over 1960 wetlands in 160 nations covering nearly 1.90 million km2 (Ramsar,

2011a). In 1998, the Peatland Carbon Study (PCARS) was initiated through the

collaborative efforts of McGill University and Trent University to measure and model

carbon cycling in a northern peatland. The study was then integrated into the Canadian

Carbon Program (CCP) formerly known as FluxNet Canada, a network of

micrometeorological flux tower sites with a goal to reduce the uncertainty in estimating

the carbon budget of Canada and North America.

The freshwater marsh is located in the southern portion of the wetland complex

(Figure 3.1; (Touzi et al., 2009)) and is accessed via Anderson road on the western edge

of the marsh. Approximately 12% of the marsh is open water (Bonneville et al., 2008),

while the remaining area is dominated primarily by narrow-leaved cattails, (Typha

angustifolia) (Linnaeus, 1753). Other species in the area include Purple Loosestrife

(Lythrum salicaris), a highly competitive and invasive herbaceous perennial species, as

well as Yellow Flag (Iris pseudacorus), a similar but less invasive species. These two

species can be seen along the edges of the pond. Lythrum species have the ability to

crowd and out-compete native species such as Typha over time (Wilson et al., 2004),

however this has not yet occurred at the Mer Bleue marsh.

The study area is subject to a cool-temperate climate with a 30-year (1971-2000)

mean annual average temperature and precipitation of 6°C and 944 mm, respectively

(Environment Canada, 2012). Temperatures reach an average daily maximum of 20.9°C

in July and a daily minimum of -10.8°C in January. Precipitation is proportionately

spread over twelve months with the maximum occurring in July, as rain, and the

minimum occurring in February, as snow (Environment Canada, 2012).

3.2.2 Instrumentation and flux measurements

The eddy covariance technique was used to measure the surface-atmosphere

exchanges of carbon, water and energy. Flux measurements were calculated at half

hourly intervals from May 8th 2005 to December 31st 2008. Instruments were mounted

4.6 m above the ground and 2.2 m above the fully grown vegetation (Figure 3.2) on a flux

tower located 365 m east of the main road. The site was accessible by canoe except

during the winter months when it was accessible by foot over the frozen water surface.

The EC system consisted of a three-dimensional sonic anemometer (CSAT-3,

Campbell Scientific, Edmonton, Canada) to measure the vertical wind speed, an open- path infrared gas analyzer (IRGA: LI-7500, LI-COR, Lincoln, NE) to measure the concentrations of CO2 and H2O and a fire-wire thermocouple to measure air temperature.

Fluctuations were sampled at 10 Hz and averaged over 30-min periods. The EC system was powered by four deep-cycle batteries, which were trickle charged by three 60 W solar panels. During the winter months, measurement frequency was lowered to 5 Hz

and the system was programmed to shut off automatically during low battery voltage periods. High frequency data was recorded continuously using a fast-response data logger (CR5000, Campbell Scientific, Edmonton, Canada) and stored on 2 GB removable flash cards. The data was retrieved every 4-6 weeks and fluxes were computed using an in-house Matlab script (v.7.0, Mathworks, Natick, MA).

In addition to the EC system, supporting meteorological measurements were also made. Radiation measurements included all radiation balance components (CNR1, Kipp and Zonen, Delft, Netherlands) and incoming and reflected photosynthetically active radiation (PAR) (LI-190Sa, LI-COR, Lincoln, NE). Air temperature and relative humidity (HMP3, Vaisala, Helsinki, Finland) and wind speed and direction (Model

05103, RM Young, Traverse City, MI) were measured. Precipitation was monitored using a tipping bucket rain gauge (TE525M, Texas Electronics, Dallas, TX) however a more continuous dataset from the nearby Ottawa/Macdonald-Cartier International Airport

was used for the data analysis. A soil heat flux plate (HFT3, Campbell Scientific,

Edmonton, Canada) and averaging soil temperature probes (TCAV, Campbell Scientific,

Edmonton, Canada) were used to obtain temperature and heat flux in the cattail mats.

Additional vertical thermocouple profiles provided water and vegetation temperatures at

30 cm intervals. At each site visit, water levels were recorded manually.

Net ecosystem exchange (NEE) of CO2 is the net difference between ecosystem

respiration (ER), which accounts for both autotrophic and heterotrophic respiration, and

the photosynthetic uptake by plants, gross ecosystem production (GEP). In our study

NEE follows the micrometeorological sign convention where negative values represent a

net sink of CO2 in an ecosystem and positive values represent a net source to the

atmosphere. Half-hourly NEE was calculated as

NEE F F , (3.1)

where Fc is the turbulent CO2 flux measured by the eddy covariance system and Fs is the

change in CO2 storage in the column of air below the instruments. Fc was derived as the

30-minute average covariance between vertical wind speed and CO2 mixing ratio as

F ρw c , (3.2)

where ρa is the average air density, w is the vertical wind speed and c' is the CO2 mixing

ratio, the primes indicate the instantaneous departure from the mean and the overbar represents a time average (Baldocchi, 2003). The WPL correction was applied to account for density variations (Webb et al., 1980) and a three-axis coordinate system rotation was applied to rotate the vertical velocity to zero (Tanner and Thurtell, 1969).

Fs was calculated according to Morgenstern et al. (2004) as

∆c F h ρ , (3.3) ∆t where hm is the measurement height, ∆t is the change in time between previous and

subsequent half hours and other symbols are as described previously.

3.2.3 Data processing and gap-filling procedures

3.2.3.1 NEE, ER and GEP

Data processing followed international Fluxnet protocols to maintain the

intercomparability with other data sets. In detail, data was first screened for periods

when the diagnostic signal of the IRGA was obstructed due to precipitation and/or other

instrument malfunctions. The data was then screened to reject values indicating CO2 uptake at night and during the winter months – periods when this is not physiologically possible. The occurrence of such false negative NEE fluxes is commonly associated with the use of open-path (OP) IRGA’s. OP IRGA’s are advantageous in that they do not require a pump to take in air as do closed-path (CP) IRGA’s. They demand less power which is essential for studies in remote locations, however they have a tendency to self- heat and intercept radiation causing density variations that the WPL correction cannot account for (Amiro et al., 2010). Several solutions for eliminating anomalous winter fluxes have been proposed including the application of the heating correction suggested by Burba et al. (2008) and removing data when Tair<0. However, Amiro et al. (2010) conclude that excluding negative wintertime fluxes and gap-filling accordingly remains the most favorable option especially since there remains conflicting evidence of the reasons behind the false CO2 uptake in the literature (Giasson et al., 2006; Haslwanter et

al., 2009).

Data was then separated into day and night using a solar radiation (K↓) threshold

of ≥ 10 Wm-2 for daytime values. After partitioning, values that differed by more than

three standard deviations from the monthly mean were also rejected.

During the early morning hours and in the late evening, thermal stratification

occurs at the wetland-atmosphere boundary preventing fluxes from attaining the

instrumentation reference height therefore causing a flux underestimation (Baldocchi,

2003). A relationship was developed between NEEnight and the friction velocity (u*) to

determine a threshold below which there is not enough turbulent mixing (i.e. calm

conditions) to apply the EC technique. We used the u* threshold determined by

Bonneville et al., (2008) and all nighttime NEE values associated with turbulence

conditions below this threshold (u* = 0.1 ms-1) were rejected.

Missing data is a common feature associated with the eddy covariance technique

resulting from the data screening processes illustrated above and resulting from

instrumentation failure, maintenance and poor weather conditions. Gaps of fewer than

four 30-minute periods were filled using linear interpolation. Longer gaps were filled using empirical models. At night and during the winter months, NEE gaps were filled using a relationship between NEEnight and Tair as it represents the period when

photosynthesis is absent. This relationship was also used to model ER during the day and

during the summer months. GEP was then calculated as the residual between NEE and

ER. Daytime summer NEE gaps were filled using a relationship between GEP and PAR.

This relationship can be expressed by a hyperbolic function as

α PAR GP , GEP (3.4) α PAR GP where α is the initial slope of the curve, commonly referred to as the apparent quantum yield and GPmax is the maximum gross productivity (Bonneville et al., 2008). During the

growing seasons (May to October) data was grouped into weekly periods and multipliers

were used to gap-fill GEP. A large portion of data loss occurred during the growing

season of 2008 therefore an average of GEP from the three other years was used to obtain

the weekly multipliers and gaps were filled accordingly. NEE gaps were then filled as the

residual between GEP and ER.

3.2.3.2 Latent and sensible heat fluxes

Latent (QE) and sensible (QH) heat fluxes were gap-filled following Amiro et al.

(2006). QE data was first screened for periods when the diagnostic signal of the IRGA was obstructed. Both QE and QH data were then screened using the same u* threshold

found for NEE (i.e. u* = 0.1 ms-1) and all gaps less than four half hourly periods were

filled using linear interpolation.

For QE, data was then separated into day and night using the same solar radiation

threshold of ≥ 10 Wm-2 and all nighttime values were set to zero. For the growing season,

larger gaps were filled based on a regression between QE and Q* using a five-day moving

window and missing data were filled using the regression output equations. Much larger

gaps (larger than ten days) occurred during the 2007 growing season therefore we opted

to increase our window size to ten days when the five-day window was insufficient.

Despite the increase in window size, several large periods with missing data remained

and were left un-filled. A complete power failure in 2008 restricted data availability to a

period between May 1st and July 19th; gaps were filled accordingly. For the non-growing

season, QE gaps were filled using a five-day mean diurnal variation (MDV), which

replaces missing half-hourly periods with an average of the mean observations for that

time period.

Sensible heat fluxes were calculated based on temperature readings from both the

sonic anemometer (CSAT) and the fine-wire thermocouple. Gaps in thermocouple data

were first replaced with CSAT data when available. For the remaining QH gaps, we used

the same technique as for QE but using a regression between QH and Q*. Missing data

during both the growing and non-growing seasons were filled using the regression

equations.

3.2.3.3 Energy balance closure

The surface energy balance for the Mer Bleue marsh can be written as

Q* Q QE QH , (3.5)

where Q* is the net radiation, QS is the heat stored in the water, vegetation and canopy air

space and QE and QH are the latent and sensible heat fluxes respectively measured in

watts per meter squared (Wm-2). To evaluate the quality of our EC flux measurements

we calculated the energy balance closure (CR) which can be determined from the

regression fit between QE+QH and Q*-QS (Tanaka et al., 2003). Attempts were made to

measure the heat flux in the vegetative mats and heat storage in the water and in the

canopy air space throughout the study period. Heat storage in the air and water column

(Qsv and Qsw) were calculated according to Burba et al., (1999) as

∆T Q c h , (3.6) SW ∆t

and

∆T Q c h , (3.7) SV ∆t

-1 -1 where cw and cv are the heat capacities of water and air, taken as 4187 and 1005 Jkg °K respectively, hs is the height above or below the water surface (m) and ΔT/Δt is the mean

rate of change in temperature between 30-minute periods in either the water or the

vegetation.

In all years, the water level depth at the marsh fluctuated between 0.5 and 1 m

depending on snowmelt and precipitation inputs throughout seasons. While seemingly

large, the water level never dropped below 0.5 m. Half-hourly storage values were

typically positive during the day indicating an addition to the storage term and negative at

night denoting a release of energy from storage. These values ranged from 20-40 Wm-2 however, during periods of large 30-min temperature changes, values reached upwards of

200 Wm-2. These large half-hourly temperature fluctuations were prevalent in the

morning and late afternoons as well as at the end of the measurement period. These

findings are consistent with Burba et al. (1999) who report peak half-hourly storage

values of 170-200 Wm-2 and with Wilson et al. (2002) who report improved afternoon closure as opposed to the mornings when the storage term is large. Over the course of a day, we found that the daily average storage in the water ranged from -20 to 10 Wm-2 while storage in the vegetative air space was near-zero due to the relatively short vegetation height of the cattails. Again, these findings are consistent with Burba et al.

(1999) who report daily average values ranging from -30 to 20 Wm-2 for a 0.5 m body of

water. We agree with Burba et al. (1999) in that on a half-hourly basis the storage term

can account for 20-30% of Q* or more however, based on daily averages, storage at the marsh accounted for only 5-10% of Q*. For the linear relationship between QE+QH and

Q*-QS we calculated daytime average values of QS assuming 5 and 10% storage. With

5% storage, our CR was on average 0.62 ± 0.02. With 10% storage, the average value for our CR increased to 0.70. Wilson et al., (2002) report that values of latent and sensible heat measured by the eddy covariance technique are typically underestimated by 10-30% therefore our imbalance is in agreement with other studies and with the notion that there

is a general lack of closure with the eddy covariance technique. Lack of closure can result from sampling errors, instrumentation bias, neglected energy sinks, frequency losses and/or advection (Wilson et al., 2002). For our marsh site it is likely that the imbalance is largely due to missing energy sinks as was demonstrated by an improved

CR assuming storage was a given fraction of radiative energy.

3.2.4 Ecosystem diagnostics

Energy balance term combinations often provide insight into ecosystem functioning. For instance, the Bowen ratio (Bowen, 1926) is a useful tool used to examine how much available energy is partitioned into the respective turbulent exchange components. When β values are below unity i.e. QE>QH, the majority of available energy

is being consumed by the latent heat flux and indicates an abundance of water in the

system. Oppositely, when β values are above unity, i.e. QH>QE, sensible heat consumes

the majority of available energy (Oke, 1987). We calculated the Bowen ratio as

Q β H . (3.8) QE

The evaporative fraction (EF) on the other hand can be used as a determinant of the

proportion of incoming solar energy used for evaporative processes. It is especially noted

for its so-called daytime self-preservation in which, under clear sky conditions, the flux ratio remains constant (Gentine et al., 2007; Shuttleworth et al., 1989). It has been proposed that under these circumstances, the diurnal variability associated with available energy is excluded and instead the processes controlling flux partitioning at the surface

i.e. soil and plant properties, are isolated (Gentine et al., 2007). In this manner, daily ET

can be estimated from only a few estimates of midday Q* and QE (Crago and Brutsaert,

1996). While practical on daily time scales, for monthly and seasonal estimates, auxiliary

weather data is required for temporal interpolations (Crago and Brutsaert, 1996). This ratio is also useful for comparing ET differences between wetland types (Lafleur, 2008).

Radiation regimes amongst wetlands are affected by surface heterogeneity such as contrasting vegetation and varying canopy heights (Goodin et al., 1996). The EF facilitates comparisons by eliminating these factors. In our study, EF was derived as

Q EF E . (3.9) Q*

The parameters in the Penman, Penman-Monteith (PM) and Priestley-Taylor (PT)

equations enable the evaluation of the driving forces behind ET, specifically with regards

to canopy characteristics which can be difficult to measure in the field (Monteith, 1965;

Penman, 1948; Priestley and Taylor, 1972). For instance, Priestley and Taylor (1972)

suggest that a well-watered evaporating surface should come into equilibrium with its overlying airstream. This equilibrium latent heat flux, QEeq is given as

∆Q* Q . (3.10) E ∆γ

where Δ is the change in saturation vapour pressure with temperature (kPa°K-1), γ is the

psychrometric constant (0.067 kPa°K-1 at 20°C) and other terms are as previously

defined. Upon further examination of the equilibrium evaporation, Priestley and Taylor

(1972) discovered that on average, latent heat from the evaporative surface exceeded the

corresponding state of the overlying airstream (Monteith and Unsworth, 2008). In this

manner, they reasoned that solar radiation effects outweighed turbulent effects. More

specifically they found that under equilibrium conditions, the influence of aerodynamic

properties would tend toward a constant fraction of the radiative input (Brümmer et al.,

2011). In their model, Priestley and Taylor (1972) suggested excluding any aerodynamic

terms and instead proposed a dimensionless coefficient (α) which was derived as

Q α E Q , (3.11) E

Priestley and Taylor (1972) found that α should be equal to 1.26 for well-watered

surfaces with minimum advection. When calculated for any surface, this coefficient is

useful in that it can be used to indicate the seasonal importance of QE in relation to the radiatively driven rate (QEeq; Komatsu, 2005).

McNaughton and Jarvis (1983) also used the equilibrium evaporation concept and

introduced the decoupling coefficient, Ω which describes the degree of coupling between the vegetation and the free air stream above the canopy. Ω ranges between 0 and 1. For

a perfectly coupled system, Ω approaches 0 signifying that surface water vapour fluxes

are driven by atmospheric demand along a humidity gradient between the surface and the

lower atmosphere. In a decoupled system, Ω approaches 1 denoting that ET is controlled

by solar radiation input rather than the vapour gradient (McNaughton and Jarvis, 1983).

The decoupling coefficient, Ω was computed as

∆ γ 1 Ω , (3.12) ∆ g 1 γ g

-1 where gs and ga are the surface and aerodynamic conductances, respectively (ms ) and other terms are as previously defined. To calculate, Ω requires an estimate of bulk aerodynamic and surface conductance. The surface conductance differs from a physiological conductance (stomatal) as it incorporates all composite surface aspects (i.e.

plants, open water etc.). Therefore, by rearranging the PM equation we computed surface

conductance, following Monteith and Unsworth (2008) as

ρcVPD 1 ∆ Q g 1 1 , (3.13) γQE g γ QE

where VPD is the vapour pressure deficit (kPa), Qa is the available energy and other

terms are as previously defined. The vapour pressure deficit, kPa was computed as the

difference between the saturation vapour pressure and the actual vapour pressure, es and ea respectively using the following:

. , (3.14) es 0.6108.

RH e e , (3.15) a s 100

where T is the mean daily air temperature (°C) and RH is the mean relative humidity

(Yoder et al., 2005) in our case derived from measurements. The aerodynamic

conductance, ga was calculated following Monteith and Unsworth (2008) using

measurements of mean wind speed (ū; ms-1) as

u . (3.16) g 6.2u u

3.2.5 Determination of biophysical characteristics

With the exception of 2007, the cattails were harvested throughout each of the

growing seasons. Cattails were collected through a destructive sampling method in order

to determine aboveground plant biomass, height, density, and leaf area index (LAI).

Sampling was performed along four transects perpendicular to the main road that were 50

to 100 m apart. Based on accessibility, three to five sampling locations along each

transect were identified and each transect location was placed 4-5 m apart to account for

the spatial and temporal heterogeneity of cattail growth. At each sampling location, a

0.25 m2 quadrat was used to sample the cattails. On site, the number of shoots and the

average plant height was recorded. Additionally, the water depth above the sediment

surface was recorded. In order to avoid desiccation, laboratory analyses were performed

within one day of sampling. In the laboratory, total plant height was measured to include

the base of the stem to the tip of the longest leaf. After dividing each of the plants into its

main components (green leaves, inflorescence, stem and dead portions) and recording the

fresh weight of each, the plants were oven-dried at 65°C until constant weight. Plant

biomass was determined as the dry weight per area of ground (gDWm-2). Green leaves

were passed through an area meter (Area Measurement System, Delta-T Devices LTD.,

Cambridge, England) and LAI was determined as the area of green leaves per area of

ground (m2m-2). We are confident in the LAI determined for 2005 and 2006 as

calibration standards run on the instrument proved consistent, however we suspect after

the fact, that the area meter may have been malfunctioning in 2008 and therefore that

year’s results are suspect.

3.3 Results

3.3.1 Climate

In order to evaluate the effects of temperature and precipitation, monthly standard

deviations were calculated based on the Canadian Climate Normals from the nearby

Ottawa Macdonald-Cartier International Airport (45.19°N, 75.40°W) (Environment

Canada, 2012). When the measured monthly average temperature (precipitation) differed

from the normal by one standard deviation, that value was considered to be significantly

warmer or colder (wetter or drier) than normal (Figures 3.3 and 3.4).

The average temperature for the 2005-2008 study period was 6.1°C, which was

0.1°C warmer than the 30-year average. Temperatures were above normal in the

beginning of the study and were then more variable in the latter half. The monthly mean

air temperature during 2005 and 2006 ranged from -11.7 to 21.2°C and -7.8 to 21.6°C,

respectively and were the warmest years of the measurement period. The most notable

anomalies were recorded in June and September of 2005 and January, November and

December of 2006, all of which were significantly warmer than normal. The monthly

mean air temperature in 2007 ranged from -11.2 to 18.6°C. April 2007 was significantly

warmer than normal. Similarly, October of the same year was also significantly warmer

than normal, while July and August were significantly colder than normal. Monthly

mean air temperature in 2008 ranged from -7.4 to 17.9°C and was the coldest year of the

study period. While January was significantly warmer than normal, all months during the

growing season were significantly colder than normal.

Maximum monthly average air temperatures occurred in July of each year with

the exception of 2007, which due to significantly colder than normal temperatures

recorded in July and August, resulted in the monthly average maximum occurring in

June. January 2006 experienced the warmest above average temperatures over the study

period while August 2008 experienced the coldest.

The average precipitation for the 2005-2008 study period was 84 mm, 5 mm

above the 30-year average. Growing season precipitation ranged from a maximum of

144 mm in April 2005 to a minimum of 48 mm in May of the same year. The

distribution of rain varied considerably over the study period. 2005 received 63 mm

more precipitation than normal. April and June of that year were significantly wetter

than normal while March of the same year was significantly drier than normal. On

average, 2006 received the highest amount of annual precipitation accounting for 169

mm more than normal. Notable anomalies were recorded in January, May, September

and October with these months receiving an average of 47 mm more precipitation than

normal while March 2006 received 48 mm less than normal. July 2007 was subject to

one major rain event which produced 68 mm of rain accounting for 50% of the monthly

total and thus was significantly wetter than normal. April and December of the same year

were also significantly wetter than normal while February, June and September were

significantly drier than normal. On average, 2007 was recorded as being the driest year

over the study period receiving 43 mm less precipitation than normal. Finally, 2008

received 55 mm more precipitation than normal. The only notable anomaly was recorded

in March.

3.3.2 Canopy properties – biomass, density, height and LAI

At the Mer Bleue marsh, the cattails typically broke dormancy and initiated shoot

growth in May, experienced rapid growth until canopy completion mid-summer and had

fully senesced by October as described by Bonneville et al. (2008). Peak biomass

occurred on DOY 230 and DOY 205 in 2005 and 2006, respectively. While sampling

likely missed peak biomass in 2008 we can assume that it occurred sometime after the

final sampling date in August (Figure 3.5). The peak biomass dates reported here are

based on our sampling dates however it is possible that peak biomass may have been

reached in between sampling dates or missed entirely as was the case in 2008. We did

however manage to capture the general pattern for cattail growth. Based on the transect

sampling, peak biomass was 1634 ± 67 gm-2 in 2005 and 1445 ± 158 gm-2 in 2006. 2008

may have extrapolated to a value slightly larger than 2006 but less than 2005.

Density at the time of peak biomass (2005, 2006) and the final sample date in

2008 was 46.3 ± 5, 50.5 ± 6 and 58.5 ± 7 plants m-2, respectively. Cattail height was

similar in all years. At peak biomass, cattail height was 240 ± 5 cm in 2005, 233 ± 6 cm

in 2006. LAImax was also similar in all years. At the time of peak biomass, LAImax was

3.85 ± 0.52 in 2005 and 3.78 ± 0.47 in 2006. As previously noted, calibration standards run on the area meter proved inconsistent for the 2008 season and the LAI numbers are not reported.

3.3.3 Diurnal and seasonal patterns of C exchange

There were clear diurnal and seasonal variations for carbon exchange at the Mer

Bleue marsh (Figure 3.6). During the growing season (May to October) the diurnal pattern of NEE exhibited a parabolic curve with maximum uptake centered on solar noon and nighttime release.

There were large seasonal variations in carbon uptake. During the growing season, uptake followed the vegetative growth cycle of the cattails. Uptake increased at the end of May when the Typha broke dormancy and initiated shoot growth from their rhizomes. Early in the growing season net ecosystem uptake was small because of the relative size of the cattails. Uptake increased considerably until it reached maximum values mid-summer when the canopy was fully developed. Rates decreased starting in

September when the Typha began to senesce until October when the cattails had completely died off.

During this period the marsh acted as a sink from June to September (Figure 3.6).

Mean daily fluxes ranged from -2.2 to -2.6 gCm-2day-1 with an average of -2.4 ± 0.17

gCm-2day-1 for all years (Table 3.1). The seasonal pattern of carbon exchange also shows

the effect of day length on diurnal turnover. For instance, in July, the marsh was a net

sink between 5:30 and 19:30 while in October, carbon uptake occurred between 8:30 and

17:30.

Outside the growing season (November to April), no diurnal pattern was visible,

rather the marsh acted as a small carbon source. Mean daily fluxes for the non-growing

season ranged from 0.42 to 0.58 gCm-2day-1 with an average of 0.48 ± 0.07 gCm-2day-1 for all years (Table 3.1).

3.3.4 Annual patterns of C exchange

The Mer Bleue marsh was an annual sink for carbon in all four years as was expected (Figure 3.7). In our study annual cumulative NEE was calculated from

November 1st to October 31st of each year. This specific year was chosen because we felt

it best represented the Typha growth cycle and it has been used by other researchers at

the Mer Bleue bog (ex: Roulet et al., 2007). Since measurements began on May 8th 2005 we modeled the beginning of the 2004-2005 season (i.e. from November 1st 2004 to May

7th 2005) by taking an average of the other years for this period. We found that annual

cumulative NEE ranged from a minimum of -216 gCm-2day-1 in 2004-2005 to a

maximum of -284 gCm-2day-1 in 2007-2008 (Figure 3.7; Table 3.2) with an average of

246 ± 31 gCm-2day-1 for the study period. Our annual cumulative NEE sums were similar in all years, however there were noticeable variations in the amount of carbon accumulated during the non-growing season, the timing of the spring and fall transitions and the length of the carbon uptake period (CUP).

We found that 20-30% of the cumulative NEE during the growing season was lost through ecosystem respiration during the non-growing season (Table 3.3). Daily winter

emissions may be low in comparison to growing season sequestration rates however, as

has been demonstrated in other studies, their cumulative values are important for annual

sums (Aurela et al., 2002; Aurela et al., 2004; Oechel et al., 1997). The low average

winter emissions during the 2007-2008 season are likely due to a deeper snow cover

coupled with colder temperatures compared to the other years. Snowfall during the 2007-

2008 period was 433 cm compared to 203 and 150 during the 2005-2006 and 2006-2007 winter periods, respectively. Sparser snow cover and warmer temperatures during the

2006-2007 winter therefore resulted in the highest winter emissions.

Many methods exist for establishing C turnover dates, the carbon uptake period

(CUP) and the length of the growing season (Churkina et al., 2005; Gu et al., 2003). For the purpose of this study, transition dates and the CUP were derived from the data. The spring and fall turnover dates were determined as the day after which the marsh had been a sink or source for five consecutive days. The CUP was then identified as the number of days between these start/end days.

The first day of C uptake occurred by mid-June in each year (Table 3.4). The

earliest spring turnover date occurred in 2006 on DOY 161, while the latest turnover

occurred nine days later in 2005, on DOY 170. The marsh transitioned back to a source

of carbon in October of each year with the exception of 2006 when turnover occurred

earlier on DOY 265. 2008 was subject to the latest turnover date, which occurred on

DOY 283 while the remaining years had similar fall turnover dates.

On average, the length of the carbon uptake period was 109 ± 5 days. In 2008, the CUP was a week longer than the average while in 2006 the CUP ended four days earlier than average. Not surprisingly there was a good correlation between annual cumulative NEE and the CUP. A linear regression between these two parameters showed that annually, the marsh gained 5.84 gCm-2 for each additional CUP day with a

coefficient of determination equal to 0.81.

The inter-annual pattern for C exchange shows that the marsh is a strong sink during the growing season and a small source during the non-growing season (Figure

3.8). In all years, peak uptake coincided with the completion of canopy development and with maximum air temperatures. Peak uptake occurred in August of every year with the exception of 2006 when peak uptake occurred in July.

We further examined the inter-annual variability in NEE at the Mer Bleue marsh by separating our annual cumulative values into monthly cumulative sums (Table 3.5).

As is evident by the results illustrated in Figure 3.9, the majority of the variability appears to occur during the growing season. To validate these findings and to pinpoint the origin of the variability we calculated the coefficient of variation (CV) for each month

(Table 3.5). During the growing season, the largest deviations in NEE from the monthly

mean occurred in June and October when CV was 0.91 and 0.79 for both months,

respectively. During the non-growing season, the largest deviation occurred in March.

3.3.5 Diurnal and seasonal patterns of energy fluxes

The diurnal pattern for QE shows the same pattern as was found for carbon

exchange (Figure 3.10). At night, the marsh experienced small positive values which

-2 were typically less than 10 Wm . During the day, QE increased with increasing Q* until

reaching maximum values midday. Maximum midday values therefore once again

coincided with the timing in peak solar radiation.

At the Mer Bleue marsh both QH and QE (Figure 3.11a) were closely related to the

seasonal cycle of Q* (Figure 3.11b). There were also clear seasonal variations in both QH and QE with each flux dominating in different seasons. QH was highest before cattail growth and after senescence, reaching maximum values in late May just before the emergence of the cattails. During this period daytime average values of QH varied

-2 between 0 and 200 Wm . Cattail growth caused QH to decrease until September with

-2 daytime average values varying between -30 and 30 Wm . QH increased again in

September when the marsh vegetation showed visible signs of senescence and became

the dominant flux in October once the cattails had died off. QE was lowest during the

-2 non-growing season with values rarely exceeding 50 Wm . QE increased in May as the

cattails broke dormancy and initiated shoot growth. Once the cattails grew past last year’s dead matter, QE increased rapidly. QE remained high throughout July and August and

began to decrease in September as the marsh senesced. During the growing season,

-2 daytime average QE ranged from 20-310 Wm with peak daytime values reaching 307,

259, 331 and 239 Wm-2 in 2005, 2006, 2007 and 2008, respectively. The equivalent ET

rate in mmday-1 following Sun and Song (2007) can be calculated as

QE ET , (3.17) λρ

6 -1 where λ is the latent heat of vaporization (2.454 x 10 Jkg at 20°C) and ρw is the density

of water (1000 kgm-3). ET was determined to be 10.8, 9.1, 11.7 and 8.4 mmday-1 for all years. Cumulative ET sums for the growing seasons were 774 mm and 688 mm in 2005

and 2006 respectively. We cannot provide realistic cumulative ET sums for the growing

seasons in 2007 and 2008 due to large gaps in these data sets; however, we are confident

in those produced for the first two years of the study. Additionally, because the major

power failure in 2008 restricted the availability of data to a window between May 1st and

July 19th, maximum ET potential had also not been reached.

Peak QE occurred in August of each year, with the exception of 2006 when peak

QE occurred in July. As previously noted, in all years, the water level depth at the marsh

fluctuated between 0.5 and 1 m depending on snowmelt and precipitation inputs

throughout seasons. Both QE and QH appeared to be unaffected by these fluctuations.

3.3.6 Ecosystem diagnostics

The ecosystem diagnostics described in section 3.2.4 were calculated for daytime

averages during the growing season only. The results from 2008 are not reported here

because there wasn’t enough data for comparison with the other years. Instead those

results have been included in the figures to show that even in the absence of data, the

trends that form in the beginning of the 2008 growing season compliment those observed

in the other years.

3.3.6.1 Bowen ratio (β)

The results for the Bowen ratio compliment the seasonal variations of the energy

fluxes previously reported. Prior to leaf out, β > 1 illustrating QH dominance with

maximum values reaching 2.2, 2.3 and 2.9 in 2005, 2006 and 2007, respectively (Figure

3.12a). As the vegetation began to grow, β fell below unity until peak biomass at which

point β stabilized near zero indicating QE dominance. |β| fell as low as 0.2 in 2005, 0.16 in 2006 and 0.09 in 2007. In all years, the lowest β occurred around the timing of peak

biomass. As the canopy senesced at the end of the season, β rose above unity again with

QH becoming the dominant energy release pathway. At this time, β rose to a maximum of

2.4 in each of the years. The average Bowen ratio for the growing seasons over the study was 0.60 ± 0.16 varying from 0.42 in 2005, to 0.63 in 2006 and 0.74 in 2007.

3.3.6.2 Evaporative fraction (EF)

The seasonal variation for EF (Figure 3.12b) was also closely related to the

growth and senescence of the cattails and the seasonal cycle of Q*. Prior to leaf out, EF

was near zero, then as cattail growth peaked mid-summer, almost 100% of the available

energy was being utilized for evaporative processes. Maximum daytime averages

approached unity mid-summer in all years with the exception of 2006 when maximum

daytime average values rarely surpassed 0.77.

3.3.6.3 Priestley and Taylor coefficient (α)

α increases steadily throughout the growing season as the cattails, covering 88%

of the marsh, grew and transpiration increased (Figure 3.12c). Monthly average α values

follow the seasonal progression of the cattails with values varying from 0.43 ± 0.01 in

May, 0.60 ± 0.07 in June, 0.83 ± 0.08 in July to maximum values of 1.05 ± 0.11 in

August. While the trend for values in September and October decreased, monthly

averages remained high at 0.87 ± 0.20 and 1.11 ± 0.14 for both months respectively

resulting from a large degree of scattering at the end of the season. In all years,

maximum α coincided with the timing of peak biomass.

3.3.6.4 Decoupling coefficient (Ω)

We found large seasonal variations in Ω, which follow the seasonal cycle of

cattail growth (Figure 3.12d). At the beginning and end of the seasons, Ω was very low

ranging from 0 to 0.5 while mid-season, at peak vegetation growth, Ω reached 0.8-0.9

indicating a highly decoupled ecosystem. The average mid-season peak was 0.84 ± 0.04

for the study period and, as was seen with α, there is also a large degree of scattering at

the end of each season.

Surface resistance (rs; the inverse of surface conductance) showed much greater

fluctuations than did the aerodynamic resistance (ra; Figure 3.14). At the beginning and

end of the seasons, rs was very high owing to the predominance of dead vegetation when

-1 values reached as high as 1215 sm . For the growing seasons, average rs was 247 ± 23

-1 sm . The aerodynamic resistance, ra in comparison showed no significant seasonal

-1 changes (Figure 3.14). Average ra for the growing seasons was 41 ± 2 sm with values

rarely exceeding 150 sm-1 during calm conditions.

3.3.6 Further evidence of a radiatively driven system - PAR

Photosynthesis is light-dependant and is a function of the amount of PAR

received (Bubier et al., 2003; Frolking et al., 1998). To see how the marsh responded to

changes in PAR we generated light response curves (LRC). The relationship between

NEE and PAR was described using the same hyperbolic function and model parameters

described in equation 3.4 (i.e. α and GPmax). In addition to these parameters, we also determined the dark respiration value, R which can be obtained from the y-axis intercept of the hyperbolic function (Bubier et al., 2003; Frolking et al., 1998). We used non gap- filled values of NEE and PAR for each of the months during the period of green vegetation (June to September) to generate LRC and the associated parameter estimates

(Table 3.6).

There was a great deal of seasonality in the NEE-PAR relationship for the growing season (Figure 3.14). PAR values increased to a maximum of 2000 μmolm-2s-1 in July and August corresponding to the period of rapid plant growth and then decreased to 1500 μmolm-2s-1 in September and 1000 μmolm-2s-1 in October. During the growing

seasons in 2005 and 2006 the general trend for the parameter estimates (Table 3.6) also

showed increasing values until reaching a peak mid-summer. In these years, both GPmax and apparent quantum yield reached maximum values in August. Similar to PAR, values then decreased at the end of the season. Not surprisingly, variations in LRC parameters followed the vegetative growth cycle of the cattails. We see the same trend for the parameter estimates for the growing seasons in 2007 and 2008, however, larger gaps during these periods led to difficulty in producing realistic parameter estimates.

3.4 Discussion

3.4.1 Controls on carbon and water vapour exchange

3.4.1.1 Diurnal variability

Since wetland plants simultaneously fix carbon while losing water through their

stomates it was not surprising that the diurnal patterns for NEE and QE were similar.

Both NEE (Figure 3.6) and QE (Figure 3.10) increased with increasing Q* until reaching

maximum values centered at solar noon and were therefore strongly controlled by

radiative energy (i.e. sunlight). The responses of NEE and QE to light were especially

evident when we compared sunny and cloudy days. The diurnal courses for

representative cloudy and sunny days are presented in Figure 3.15; data were carefully

chosen from two days in July and early August to ensure that the ecosystem was near

peak productivity. The diurnal pattern for the sunny day illustrates a smooth diurnal

pattern in C uptake with maximum uptake coinciding with peak light levels around noon.

In comparison, on a day with cloud-cover punctuated with periodic sunny breaks, the

diurnal course of NEE mimics the PAR pattern where carbon uptake is markedly reduced

during overcast periods. The same patterns were apparent for QE (Figure 3.15).

The high decoupled values (large Ω) (Figure 12d) during the period of active

green vegetation when both transpiration and evaporation are viable pathways for water

transfer, also signifies that the marsh is mainly controlled by radiative energy. Values of

Ω below 0.5 in May and early June and then again in September suggest numerically that

the Mer Bleue marsh is being controlled by VPD rather than solar energy. However, the

absence of transpiration contributing to ET is the direct cause of the increase in Ω. This

coefficient therefore nicely illustrates the development cycle of the cattails while its value

corresponding to peak growth stage provides the evidence for the radiative dominated ET.

3.4.1.2 Growing season

The growing season cycles of NEE and QE were also similar. During the growing

season the main environmental controls on marsh NEE and QE were temperature and

light. Temperature and light in turn influenced the biophysical properties of the marsh

vegetation i.e. plant height, biomass and LAI, and the growth and senescence of marsh

vegetation. In this manner, the canopy characteristics of the cattails greatly affected the

magnitude and trend of C fluxes and energy exchanges as well. NEE and QE were small

in the spring and increased rapidly as temperatures and light levels increased and the

cattail canopy developed. These trends were apparent in the LRC and associated

parameter estimates (Figure 3.14; Table 3.6) when the seasonal variations followed the

vegetative growth cycle of the cattail and both GPmax and apparent quantum yield reached

maximum values in August. In all years, peak C uptake and QE coincided with the

completion of canopy development; both NEE and QE then declined in the fall when the

cattails senesced.

The seasonal importance of NEE and QE in relation to marsh canopy

characteristics were especially evident in the evaluation of the ecosystem diagnostics.

Due to the presence of standing water at the Mer Bleue marsh, the average Bowen ratios

for the growing seasons were consistently below unity as would be expected (Figure

3.12a). The dominance of QH at the beginning and end of the season were a result of

canopy heating. In the spring, prior to the emergence of cattails, the marsh is largely

composed of dry, dead mats that absorb solar radiation and heat up. In the absence of

transpiration, QE fluxes originate from the open water only, however, this is only 12% of

the surface. This QH dominance repeats after canopy senescence in the fall. During the

months where transpiration is prevalent, we found that daytime averaged β values often

were negative. This is an example of what is termed the oasis effect. The Mer Bleue

marsh, with its abundant water, is surrounded by land with lower moisture content. The

agricultural fields, roads, hill slopes and treed areas (Figure 3.1) that surround the marsh

have higher temperatures and the sensible heat from the surrounding land is advected

towards the marsh where a temperature inversion allows heat movement towards the

marsh surface providing extra energy for ET.

In wetlands, reports of β below unity during the growing are ubiquitous (Lafleur,

2008). For example, at the Mer Bleue bog, daytime average β ranges from 0.5 to 0.72 in

July and August for open and treed portions of the peatland respectively (Strilesky and

Humphreys, 2011). At the Panjin wetland complex in northeastern China, Guo and Song

(2012) found an average β = 0.63 for Phragmites spp. while Souch et al. (1996) found that daytime average β was 0.38 for Typha and Carex spp. at the Indiana Dues National

Lakeshore, a marsh located along lake Michigan. While there is a wide range of β values among wetland types, the differences between sites are largely due to climatic influences

and surface cover (Eugster et al., 2000; Lafleur, 2008).

To emphasize the seasonal dependence of β in relation to the marsh’s canopy

features, we plotted the relationship between daytime average Bowen ratio values and

biomass estimates for 2005 (Figure 3.16). When the cattails developed their leaves

around DOY 180, β = 0.24 and fell to a minimum of 0.08 at the timing of peak biomass.

This trend was apparent in all years and the lowest β always occurred around the timing

of peak biomass.

The evaporative fraction is directly related to the Bowen ratio in that EF = 1/(β+1)

(Crago and Brutsaert, 1996). EF increased steadily throughout the growing season to a

maximum at the time of peak biomass (Figure 3.12b) and was closely related to the

seasonal cycle of Q* (Figure 3.11). Prior to leaf out, EF was near zero. Again, due to the

absence of transpiration, QE fluxes originated from small portions of open water only.

When cattail growth peaked mid-summer, almost 100% of the available energy was being utilized for evaporative processes.

α and Ω also increased steadily throughout the growing season to maxima at the timing of peak biomass (Figures 3.12c-d). The decoupling coefficient is useful in that it

takes into account bulk surface and aerodynamic resistances; examination of these two resistances gives us greater insight into the processes actually controlling QE at the

surface. The surface resistance, rs showed much greater fluctuations than the

aerodynamic resistance, ra (Figure 3.13). At the beginnings and ends of the season, rs was very high owing to the predominance of dead vegetation. The removal of the transpiration pathway and the small proportion of open water lead to a large bulk surface resistance. In comparison, ra showed no significant seasonal changes; the marsh does not

significantly change its aerodynamic properties despite the increasing biomass and

vegetative height changes. The surface resistance therefore explained the seasonal variations in α and Ω. At the beginnings and ends of the season, the predominance of dead vegetation resulted in a high rs which then decreased to a minimum during the

period of maximum greenness (Figure 3.13).

The overall indication from the decoupling coefficient is that ET at the Mer Bleue

marsh is radiatively driven owing to its smooth aerodynamic profile (low aerodynamic

resistance) and abundance of water. These findings are in agreement with other wetland

studies (Guo and Sun, 2012; Sun and Song, 2008; Zhou et al., 2010). Our average mid-

season Ω was 0.84 ± 0.04 for the study period; contrast this with a typical mid-season

average of 0.22-0.28 for a deciduous forest (Wilson et al., 2000). As has been illustrated,

the decoupling coefficient depends on the aerodynamic characteristics of the surface.

Forests are tall, aerodynamically rough surfaces and are therefore highly coupled to the

humidity gradient between the surface and the lower atmosphere (Pereira, 2004). Forests

respond more to changes in humidity and less to changing light levels than do shorter,

aerodynamically smooth surfaces like the marsh.

α gives further evidence of a radiative driven system where ET equals or exceeds

the equilibrium rate during the actively growing part of the summer. Priestley and Taylor

(1972) suggested that α should be equal to 1.26 for well-watered surfaces with minimum

advection. They empirically found that in such systems, on average, QE exceeded QEeq by a factor of 1.26. McNaughton and Spriggs (1986) argue that the “size” of α depends on the entrainment of dry air from above and increases in the depth of the boundary layer.

At the Mer Bleue marsh we expect that α may exceed 1 but will not necessarily be equal to 1.26. The stomatal control, although small, is still apparent and will serve to reduce α.

However, the marsh is small and surrounded by drier surfaces, therefore advection may move drier air into the local boundary layer thereby enhancing ET. The average peak growth value of 1.05 certainly indicates a system dominated by available water as expected.

3.4.1.3 Inter-annual variability

Annual cumulative NEE was strongly determined by prevailing weather

conditions, which ultimately affected wintertime emissions, the timing of the spring and

fall transitions in carbon uptake and the length of the carbon uptake period (CUP) (Figure

3.7).

During the non-growing season, small persistent wintertime fluxes at the Mer

Bleue marsh may originate from respiration in sediments that then diffuse through the

unfrozen water in the channels and escape through cracks in the snow and in the ice. An

additional source may be small respiration from the vegetative mats themselves with CO2 diffusing through the snowpack. The cumulative emissions during the non-growing season accounted for 20-30% of the annual cumulative carbon uptake. Air temperatures were the primary driver for the differences in C losses during the cold season. Warmer air temperatures over the 2006-2007 winter likely contributed to a thinner ice layer over the channels, promoting more cracks and thus more atmospheric conduits enabling CO2 release. For instance, daily average C release during the cold season of 2006-2007 was the highest recorded at 0.58 gCm-2day-1, compared to the other years when daily average

NEE was 0.45 and 0.42 gCm-2day-1 for 2005-2006 and 2007-2008, respectively. Colder

temperatures, in comparison, would create a thicker ice layer over the channels, limiting

CO2 efflux.

Non-growing season losses in marsh ecosystems have been found to be especially important in terms of the annual C balance. For example, Zhou et al., (2009) showed that

soil microbial activity during the cold season offset NEE gains by as much as 83%. Our

findings, which suggest that colder temperatures result in thicker ice and deeper snow

cover for CO2 to diffuse through, contradict evidence from forest ecosystem studies. In a

comparison of annual C budgets for three Canadian black spruce forests, Bergeron et al.

(2007) found that an Eastern site, located in Chibougamou, Quebec, had annual NEP

(where NEP = -NEE) that was lower compared to corresponding sites in Manitoba and

Saskatchewan. They attributed the differences in annual NEP to snow depth. At the

Eastern site, the deeper snow pack acted as an insulator, led to warmer temperatures beneath the snow, preventing soil freezing, and overall lead to greater emissions

(Bergeron et al., 2007). Snow has therefore been found to decouple the soil from the overlying air stream by protecting soils from extreme air temperatures, which creates favorable conditions for microbial activity (Campbell et al., 2005; Goodrich, 1982;

Larsen et al., 2007). The data for snow accumulation at the marsh was obtained from the nearby airport therefore the snow distribution across the marsh ecosystem in general is unknown as it was not measured in situ. Nevertheless, in our marsh ecosystem there is no evidence to suggest that deeper snow depths lead to increased microbial activity because the marsh is very different structurally. It is composed of floating mats which are covered in snow and unfrozen water beneath an ice layer. There is a thick surface ice layer in the channel each winter and the water and the bottom sediments always remain unfrozen beneath thus allowing CO2 to diffuse through the water column and escape to

the air through cracks where the mats make contact along the channel edges.

We found that the first day of C uptake occurred by mid-June in each year (Table

3.4). The marsh then transitioned back to a source of carbon in October of each year with

the exception of 2006 when turnover occurred in late September. We also found that

2006 was subject to the earliest spring and fall turnover dates which lead to the shortest

carbon uptake period in the four year study. Additionally, we found that peak NEE and

QE occurred in August of every year with the exception again being 2006 when peak

uptake occurred in July. The differences in the timing of the spring and fall transitions in

carbon uptake were related to the timing of leaf emergence and plant senescence. March

2006 was warmer and significantly drier than normal while April was warmer and drier

than normal (Figures 3.3 and 3.4). Further examination of the precipitation data revealed

that March and April in that year received only 4 cm of snow combined; therefore,

below-average snowfall and warmer temperatures during 2006 created favorable

conditions for shoot initiation and carbon uptake. Monthly cumulative NEE sums

support these findings. For instance, June of 2006 was -56.6 gCm-2 compared to -1.8

gCm-2 in 2005, -32.9 gCm-2 in 2007 and -13.4 gCm-2 in 2008 (Table 3.5). In contrast,

September and October of 2006 were colder and significantly wetter than normal, with 70

and 46 mm more precipitation in these months, respectively (Figures 3.3 and 3.4).

Compared to the spring conditions, colder and significantly wetter conditions curtailed

carbon uptake promoting early senescence. Again, monthly cumulative NEE sums

provided additional evidence. In September of 2006, monthly cumulative NEE was -20.4

gCm-2 in contrast with -62.4, -68.3 and -62.3 gCm-2 for 2005, 2007 and 2008,

respectively. The biomass sampling provided corroborating and independent evidence of the anomalous behavior in 2006 where the cattails experienced accelerated spring growth as compared to the other years, peaked earlier and began to senescence earlier than in other years (Figure 3.5).

The anomalous weather conditions and early biomass peak in 2006 were also apparent in EF, α and Ω. Due to warm spring temperatures, in 2006 all three diagnostic

variables leveled off before the other years (Figure 3.12). In autumn, the overcast

conditions produced light limitations and induced stomatal closure, which impaired the utilization of available energy for ET. As a result, daytime averages for EF and α in 2006 were lower than the other years. The effects of reduced stomatal conductance were

-2 evident in QE as well. Maximum daytime values were > 300 Wm in all years with the

-2 exception of 2006 when maximum daytime QE = 259 Wm .

The trend for Ω in 2006 illustrates higher values in the beginning of the season leading to lower values towards the end of the season (Figure 3.12d). Because water

vapour must first diffuse out of the leaf against rs before diffusing into the atmosphere

against ra this trend implies that rs was lower at the beginning of the season as compared

to the other years because of early greening. Similarly, at the end of the season, rs was higher compared to the other years because the cattails senesced earlier.

3.4.2 Does the Mer Bleue marsh respond as expected?

3.4.2.1 Diurnal and seasonal patterns

The diurnal courses for NEE and QE exhibited at the Mer Bleue marsh are similar to those reported in other wetland ecosystems including the neighboring peatland

(Admiral et al., 2006; Lafleur et al., 2003), a freshwater marsh in California (Goulden et al., 2007; Rocha and Goulden, 2008), and a reed marsh in northeastern China (Zhou et al., 2009; Zhou et al., 2010). At night however, when transpiration ceased due to stomatal closure, evaporation from the open pond continued resulting in small positive nighttime values which were generally less than 10 Wm-2. These values are much lower

than those reported by Burba et al. (1999) who found that non-gap filled nighttime QE ranged from -30 to 30 Wm-2 for a prairie reed wetland. Our gap-filling procedures set all

missing nighttime QE to zero. Since the majority of the gaps in QE occurred at night, the

average nighttime values are undoubtedly biased.

Large seasonal variations in carbon uptake have been reported elsewhere as well

with the duration of CUP varying between sites. While we found that the marsh was a

net C sink from June to September, Zhou et al. (2009) report carbon uptake from May

through October for a reed ecosystem while Song et al. (2011) report net uptake from

June to August for a sedge marsh, both located in northeastern China. At temperate peatland sites, Lund et al. (2007) and Roulet et al. (2007) have found that net uptake commences in April just after the snowmelt and persists until November. The duration of the uptake period between marshes and peatlands differs greatly in this respect but this is not surprising; mosses and other shrub species can begin photosynthesis immediately after snowmelt, while marsh species require shoot initiation before photosynthesis can occur.

The average length of the carbon uptake period for the four-year study was 109 ±

5 days. We found that the marsh gained 5.8 gCm-2 for each additional CUP day. In the

Euroflux and Ameriflux networks, Churkina et al., (2005) found linear correlations between annual NEE and the CUP for a variety of sites, including deciduous and evergreen forests and grasslands. Of these, deciduous broadleaved forests gained 5.8 gCm-2 while grasslands and crops yielded the highest gain of carbon per CUP-day accounting for 7.9 gCm-2. For Boreal forests, studies have shown that warmer springs

promote greening resulting in higher C assimilation rates (Black et al., 2000; Piao et al.,

2008; Tanja et al., 2003). As a result of an earlier beginning to the growing season in

2006, the marsh absorbed more carbon earlier in the season than the other years.

Therefore, as climate change initiates earlier starts to the growing season, marsh

ecosystems, such as Mer Bleue, have the potential to store large quantities of carbon.

3.4.2.2 Inter-annual variability

Inter annual variability at the Mer Bleue marsh was strongly determined by

prevailing weather conditions. In wetlands, temperature has been shown to influence

both assimilation and respiration rates but in different ways. Since the eddy covariance

technique directly measures NEE, which depends on the interplay between ER and GEP,

we have to infer what is controlling these processes through the evaluation of NEE. Zhou

et al. (2001) showed that during the growing season, warmer temperatures enhanced plant

growth and assimilation. However, warmer temperatures also enhance autotrophic and

heterotrophic respiration resulting in increased CO2 emissions (Alm et al., 1999; Raich

and Schlesinger, 1992). In our study, temperatures were above normal in the beginning

of the study (Figure 3.3) and this translated into high GEP and ER as compared to the

other years (Figure 3.9). Similarly, all months during the growing season of 2008 were

significantly colder than normal and while GEP was only slightly smaller than the other

years, there were noticeable reductions in ER. July and August of 2007 were also

significantly colder than normal, which again resulted in minimal changes in GEP, but

noticeable reductions in ER. These trends suggest that despite colder temperatures in the

latter half of the study, light levels were consistent and GEP was less affected. The net

result is that the temperature-dependency of ER is the larger controlling factor at the

marsh.

The coefficient of variation around the mean is an easy way to see where relative

differences are larger. We calculated CV for monthly cumulative NEE, ER and GEP

(Table 3.7) and for annual cumulative NEE, ER and GEP (Table 3.2). In the absence of

photosynthesis during the cold season, NEE is driven by ER. CV is large in the winter

because the marsh is subject to freeze-thaw cycles, which can enable respiratory losses

from the wetland surface. The largest monthly CV in ER occurred in March when CV =

0.62; warmer temperatures in March 2007 resulted in larger ER compared to the other years (21.9 gCm-2day-1 compared to 10.2 and 6.7 gCm-2day-1 in 2006 and 2008,

respectively (Table 3.5)). During the growing season, the largest deviation in NEE

occurred in June when CV = -0.91. This signifies the importance of the starting

conditions in the spring for leaf emergence which ultimately depends on the previous

winter conditions. The increased carbon sequestration in June 2006 suggests that in the

spring, NEE is driven by photosynthesis rather than respiration. Years with favorable

spring conditions will have higher GEP than years with a cooler spring leading to an

overall larger CV for June. Larger CVs are also noted in the fall when CV = -0.42 and

0.79, in September and October, respectively, indicating the importance of

meteorological conditions for the timing of canopy senescence. While there were no

significant variations in ER or GEP in the fall it can be assumed that as the cattails

desiccate, respiration dominates. During the growing season, ER was relatively constant

with CV between 0.29 and 0.31 during the summer months, while GEP varied from 0.16

in July to 0.06 in August. Essentially, in the absence of drastically unfavorable summer

conditions, once the cattails are fully developed there is very little limitation on GEP

hence the low CVs for the months of July and August. Annually, NEE and GEP had CVs

of 0.13 while ER had a CV of 0.24. These values are not large but they suggest that light

levels at the marsh were fairly consistent between years and that inter annual variability

in NEE at the marsh was more driven by changes in ER and in turn, temperature.

Studies have shown that spring warming amplifies carbon sequestration by

enhancing photosynthetic activity and increasing the length of the growing season

(Churkina et al., 2005; Zhou et al., 2001). In response to autumn warming, Piao et al.

(2008) show that warmer temperatures terminate CUP while colder temperatures

lengthen CUP. Additionally, studies have shown that GEP and ER increase in response

to autumn warming; however increases in ER are larger (Piao et al., 2008). Spring

warming in 2006 led to increased carbon uptake in June however colder temperatures

promoted senescence, shortening the carbon uptake period. Meteorological conditions in

the fall of 2006 were colder and significantly wetter than normal, therefore the marsh

experienced reductions in both ER and GEP. As climate change initiates earlier starts to

the growing season and extends summers, marsh ecosystems, such as Mer Bleue, have

the potential to store large quantities of carbon. However, under the assumption that light

levels will remain unaffected, many ecosystems may lose C in response to autumn

warming which may diminish an ecosystem’s overall ability to sequester carbon.

3.4.3 Comparison of annual cumulative NEE and ET to other studies

The Mer Bleue marsh was an annual sink for carbon in each of the four years of

study with an average of 246.4 ± 31 gCm-2day-1. Studies on carbon exchange in other wetland ecosystems have also shown annual net uptake, however, the majority of these

studies report annual C sums for peatlands. For example, six years of data from the Mer

Bleue peatland yielded cumulative net uptake ranging from -2 to -112 gCm-2, with an

average of -40 ± 40 gm-2yr-1 (Roulet et al., 2007). Aurela et al. (2004) report annual C

sums ranging from -4 to -53 gCm-2, with an average of -22 gCm-2, for six study years, at

a fen in northern Finland. These two examples demonstrate how peatland sites have very

low annual uptake in comparison to marshes. To the best of our knowledge, no long- term studies on carbon exchange have been conducted in North American temperate freshwater marshes. In fact, there are few freshwater marsh studies to which we can compare our annual sums. Zhou et al. (2009) report an annual sum equivalent to -65 gCm-2 for a the reed marsh in the Panjin wetland complex in northeast China while Song

et al. (2011) report annual sums of -143 and -100 gCm-2 for two years of continuous data

at a sedge marsh located in the Sanjiang plain of northeastern China. Rocha and Goulden

(2008) report annual sums ranging from a release of 515 to an uptake of 251 gCm-2 for five study years at a California marsh. Evidence from the literature suggests that our

Typha marsh behaves more like an agricultural ecosystem. For example, Anthoni et al.

(2005) report two years of eddy covariance measurements for winter wheat in Thuringia,

Germany (51.06°N, 10.54°E) where NEE ranged from -185 to -245 gCm-2. In a

wheat/maize crop rotation south of Beijing, China, Lei and Yang (2010) found seasonal

sums of -303 to -395 gCm-2 and -201 to -244 gCm-2 for wheat and maize, respectively

leading to annual sums equivalent to -533 to -585 gCm-2 while Verma et al. (2005) found

annual NEE ranging from -381 to -517 for a continuous maize field in Nebraska. Our annual C sums are in the lower spectrum of the values presented for agricultural ecosystems, however these sites serve as better comparisons for annual NEE because they are well-watered, highly productive systems that experience the same rapid canopy development over a short 1-2 month period as is seen at the Mer Bleue marsh.

The cumulative NEE values in our study are much higher than those presented for other wetlands mainly due to the higher plant productivity of cattails compared to shrubs and mosses in peatlands and sedges and reeds in other freshwater wetland ecosystems.

Aboveground biomass in wetland ecosystems can vary between 100 gm-2 for bogs and

3500 gm-2 for marshes (Cronk and Fennessy, 2001). At the Mer Bleue peatland, the

average biomass for moss and shrub species is 587 gm-2 (Admiral and Lafleur, 2007).

Studies on emergent macrophyte species report that biomass production ranges from 428-

2464 gm-2 for Typha species with ranges from 564-1647 gm-2 specifically for Typha

angustifolia (Pratt, 1981). The results from our biomass sampling, 1634 ± 67 gm-2 in

2005 and 1445 ± 158 gm-2 in 2006, fall well within this range indicating that the biomass

at the Mer Bleue marsh is typical for that of freshwater marshes found elsewhere. Pratt

(1981) reports biomass production in other emergent macrophyte species as 450-852 gm-2 for Scirpus (grass) species, 1110-1118 gm-2 for Phragmites (reed) species and 857-1160 gm-2 for Carex (sedge) species. These values re-iterate the notion that cattail productivity

is much higher than that of other marsh species.

The ET rates at the Mer Bleue marsh, which reached 10.8, 9.1, 11.7 and 8.4 mm

day-1 in 2005, 2006, 2007 and 2008, respectively, are also much higher than reports from other wetlands. At the California marsh, midsummer ET rates varied between 3-4

-1 mmday (Goulden et al., 2007) while for sedge-dominated ecosystems, ETmax ranged between 3-5 mmday-1 (Guo and Sun, 2012; Sun and Song, 2008). ET was much higher at

Mer Bleue than the Mediterranean-climate marsh in California because our marsh

maintains open-water channels throughout the growing season and the abundant water

allows significant continuous transpiration. At the Mer Bleue peatland, maximum ET

rates were 4-5 mmday-1 (Lafleur et al., 2005) whereas for a range of northern peatlands

-1 ETmax has been found to vary between 5-8 mmday (Humphreys et al., 2006).

The average cumulative ET for two growing seasons (2005 and 2006) was 728

mm. This value is again much higher than reports for peatlands and is two times higher

than the Mer Bleue bog where Lafleur et al., (2005) found cumulative ET to range from

301 to 372 mm with an average of 351 mm. For the marsh sites in northeastern China

Zhou et al., (2010) found cumulative ET varied from 432 to 374 mm for the reed

ecosystem while Guo et al. (2010) report 299 mm for the sedge marsh. The seasonally dry California marsh was 490 mm (Goulden and Rocha, 2008). Our Typha marsh again behaved more like well-watered agricultural ecosystems. For a maize/soybean crop rotation Suyker and Verma (2008) report annual ET as 561 and 454 mm for maize and soybean, respectively.

Studies have shown that at the leaf level LAI and stomatal conductance (gs) greatly affect canopy transpiration (Lafleur, 2008). While we did not report measures for gs our LAImax values at the time of peak biomass were 3.85 ± 0.52 in 2005 and 3.78 ±

0.47 in 2006. LAImax at the Mer Bleue peatland has been reported as 1.3 (Admiral et al.,

2006). In other marshes, LAImax is reported as 2 for Carex spp. (Song et al., 2011), 3 for

Phragmites spp. (Zhou et al., 2009) and 3-6 for Typha spp. (Rocha and Goulden, 2008).

These comparisons demonstrate that our LAImax values are indeed higher than sedge and

reed marshes but fall well within the range of values expected for a cattail marsh.

3.5 Summary and Conclusions

In this study we examined the diurnal, seasonal and inter-annual dynamics of C

exchange and energy fluxes in a temperate freshwater marsh using four years of nearly

continuous field data from EC measurements. We used well-known ecosystem

diagnostic variables: the Bowen ratio, evaporative fraction, Priestley and Taylor α and the

decoupling coefficient to help explain the environmental drivers and variability in CO2 and water vapour exchange.

The diurnal and seasonal patterns for NEE and QE were similar to those reported

in other wetland ecosystems (Admiral et al., 2006; Goulden et al., 2007; Guo and Sun,

2012; Lafleur et al., 2003; Rocha and Goulden, 2008; Zhou et al., 2009; Zhou et al.,

2010). During the growing season, C uptake and QE followed the vegetative growth

cycle of the cattails and both peak NEE and QE coincided with peak biomass. The main

environmental controls on the seasonality of marsh NEE and QE were temperature and

light which influenced the biophysical properties of the marsh vegetation.

Annual cumulative NEE was on average -246 ± 31 gCm-2yr-1 and ranged from -

216 to -260 gCm-2yr-1. The variability in accumulation between years was a result of the

timing of spring and fall transitions in the carbon uptake and the length of the growing

seasons, each of which were strongly determined by prevailing weather conditions and

cattail growth. NEE depends on the interplay between the photosynthetic uptake by

plants and release from soil and plant respiration. In this study, we found that while NEE

was driven by GEP in the spring, inter-annual differences were more driven by ER.

Maximum daytime average ET values reached 307, 259, 331 and 200 Wm-2 in

2005, 2006, 2007 and 2008 respectively. Bowen ratio values varied seasonally with values well below unity during the growing season illustrating the dominance of latent heat. Evaluation of the evaporative fraction and Priestley-Taylor α indicated the seasonal

importance of ET and mid-season high values of the decoupling coefficient indicated that

the marsh ET is radiatively driven owing to the smooth aerodynamic surface and

abundance of water.

Overall, the marsh ecosystem was shown to be a large annual sink for CO2 as compared to other wetland ecosystems. As the global climate continues to change, temperatures are predicted to increase the most in the northern latitudes (IPCC, 2007); this will undoubtedly have implications on C sequestration rates. Marsh ecosystems have the potential to store large quantities of carbon. As climate change initiates earlier starts to the growing season, GEP may be enhanced leading to earlier C uptake and potentially greater C storage, however, autumn warming and the corresponding increased losses through respiration may diminish the marsh’s overall ability to sequester carbon.

Correspondingly, more effort must be placed on understanding the complex relationships between NEE, GEP, and ER under changing climates.

Figure 3.1 Wetland classes found in the Mer Bleue wetland complex courtesy of Touzi et al. (2007). The study site is highlighted by subset A.

Figure 3.2 Eddy covariance tower and instrumentation set-up at the Mer Bleue marsh (photo courtesy of I.B. Strachan).

Temperature Anamoly (°C) (°C) Anamoly Temperature -2 Warmer/Colder Significantly w armer Signif icantly colder -4 2005 2006 2007 2008 2009

Year Figure 3.3 Temperature anomalies for the study period.

-20

-40 Wetter/Drier

Precipitation Anamoly (mm) Anamoly Precipitation -60 Significantly w etter Signif icantly drier -80 2005 2006 2007 2008 2009

Year Figure 3.4 Precipitation anomalies for the study period.

1600

2005 1400 2006 2008 1200 ) -2 1000

800

600

400 Live Biomass (g m (g Biomass Live

200

120 140 160 180 200 220 240 260 280 300 DOY Figure 3.5 Results from biomass sampling. Points represent daily average aboveground live biomass where bars are the standard deviations from the mean.

0 )

-1 -5 day -2 -10

May -15 June NEE (g C m (g NEE July August -20 September October Non-growing -25 00:00:00 04:00:00 08:00:00 12:00:00 16:00:00 20:00:00 00:00:00 Time of Day

Figure 3.6 Mean monthly diurnal pattern of NEE for 2007. Non growing season months (November – April) are combined.

200

100 ) -1 yr -2 -2 0

-100

-200

2004-2005= -216.4gCm-2 -2 Accumulated NEE (g C m (g NEE Accumulated -300 2005-2006= -225.9 gCm 2006-2007= -260.0gCm-2 2007-2008= -283.5gCm-2 -400 November February May August Month Figure 3.7 Annual cumulative NEE from November 1st to October 31st of each year.

) 2 -1 day

-2 -2 0 m

-2

-4

-6

Daily Average NEE (g C (g NEE Average Daily -8

-10 2005 2006 2007 2008 2009 Year Figure 3.8 Inter annual pattern for C exchange.

300 ) -2 NEE GEP 200 ER

100

-100 Montlhy Cumulative NEE, ER, GEP (g C m (g GEP ER, NEE, Cumulative Montlhy -200 t r r r r il t r r r r il t r r r r il t r r r r y e ly y y h r y e ly y y h r y e ly y y h r y e ly a n s e e e e r r c a n s e e e e r r c a n s e e e e r r c a n s e e e e u u b b b b a a r p u u b b b b a a r p u u b b b b a a r p u u b b b b u J u J u J u J M J g o u u a A M J g o u u a A M J g o u u a A M J g o u m t m m n r u m t m m n r u m t m m n r u m t m m e c e e b M e c e e b M e c e e b M e c e e A t v a A t v a A t v a A t v O c J e O c J e O c J e O c p o e p o e p o e p o e e F e F e F e N D N D N D N D S S S S

Month Figure 3.9 Monthly cumulative sums of NEE, ER and GEP.

350 May 300 June July Aug 250 Sept )

-2 Oct 200

150

100 Latent Heat (W m (W Heat Latent 50

-50 00:00:00 04:00:00 08:00:00 12:00:00 16:00:00 20:00:00 00:00:00 Time of Day Figure 3.10 Mean monthly diurnal pattern of QE for the 2005 growing season.

500 )

-2 Q 400 E QH (W m H 300 & Q E

200

100 Daily Average Q Average Daily

0 2005 2006 2007 2008 2009

Year

500 )

-2 400

300

200

Daily Average Q* (W m (W Q* Average Daily 100

0 2005 2006 2007 2008 2009

Year

Figure 3.11 Inter annual pattern of QE, QH and Q* for the study period.

3.0 2005 a. 2.5 2006 2007

) 2.0 2008 

1.5

1.0

Bowen Ratio ( Ratio Bowen 0.5

0.0

-0.5 100 150 200 250 300

DOY

3 b. 2005 2006 2007 2 2008

Evaporative Fraction (EF) Fraction Evaporative 0

100 150 200 250 300

DOY

3.0 c. 2005 2.5 2006 2007

 2.0 2008

1.5

1.0 Priestly-Taylor

0.5

0.0 100 150 200 250 300

DOY

2.0 2005 d. 2006 1.5 2007 2008

1.0

0.5 Decoupling Coefficient

0.0 100 150 200 250 300 DOY

Figure 3.12 Results for the ecosystem diagnostics. a. Bowen ratio. b. Evaporative fraction. c. Priestley and Taylor coefficient (α). d. Decoupling coefficient (Ω).

1400

1200 rs ra 1000

) 800 -1

(sm 600 a & r s r 400

200

-200 100 120 140 160 180 200 220 240 260 280 300 320 DOY Figure 3.13 Aerodynamic and surface resistance for the growing season in 2005.

-30 June July August -20 September October ) -1 s -2 -10 mol m 

0 NEE ( NEE

0 500 1000 1500 2000 2500

PAR (mol m-2 s-1)

Figure 3.14 Seasonality in NEE-PAR for the growing season in 2005.

2000 a.

) 1500 -1 s -2 -2

1000 mol m 

PAR ( PAR 500

0 00:00:00 04:00:00 08:00:00 12:00:00 16:00:00 20:00:00 00:00:00

Time of Day

10 b. 5

) 0 -1 s -2 -2 -5 mol m

 -10

NEE ( NEE -15

-20

-25 00:00:00 04:00:00 08:00:00 12:00:00 16:00:00 20:00:00 00:00:00

Time of Day

600 c. 500

400 ) -2 300 (W m

E 200 Q

100

-100 00:00:00 04:00:00 08:00:00 12:00:00 16:00:00 20:00:00 00:00:00

Time of Day

Figure 3.15 Diurnal patterns of a. PAR, b. NEE and c. QE for a sunny and cloudy day in July/August. Sunny days are illustrated by closed dark circles and cloudy days open circles.

2.0 1400 1.8 Bowen Ratio Biomass 1.6 1200

1.4

1000 ) -2 1.2 800 1.0

0.8 600 Bowen Ratio Bowen 0.6 400 Live Biomas (g m (g Biomas Live 0.4 200 0.2 0 0.0

120 140 160 180 200 220 240 260 280 300 DOY Figure 3.16 Relationship between daytime average Bowen ratio and live biomass in 2005.

Table 3.1 Daily average C exchange (gCm-2day-1) for the growing and non-growing seasons.

Growing 2005 2006 2007 2008 Average (May - October Minimum 2.0 3.2 1.4 1.7 2.1 uptake Average -2.3 -2.2 -2.6 -2.4 -2.4

Maximum -9.3 -7.2 -8.6 -8.3 -8.4 uptake Non- 2004-2005 2005-2006 2006-2007 2007-2008 Average growing (November- April) Average N/A 0.45 0.58 0.42 0.48 Minimum 1.7 1.5 1.4 1.5 uptake N/A

Table 3.2 Annual cumulative NEE, ER and GEP (gCm-2yr-1).

Year NEE ER GEP 2004-2005 -216 (-311) 658 (558) 874 (869) 2005-2006 -226 523 749 2006-2007 -260 509 769 2007-2008 -284 358 642 xbar -246 512 758 stdev 31 122 95 CV -0.13 0.24 0.13 Note* values in brackets represent annual sums from May 8th to October 31st 2005 only

Table 3.3 Cumulative sums of NEE for the growing and non-growing seasons (gCm-2yr- 1).

2004-2005 2005-2006 2006-2007 2007-2008

Non-growing 89.3 82.0 104.7 76.3 season Growing -305.5 -307.9 -364.8 -359.8 Season Offset 29% 27% 29% 21%

Table 3.4 Comparative spring/fall turnover dates, peak uptake and CUP for each year.

2005 2006 2007 2008 Spring DOY 170 DOY 161 DOY 165 DOY 167 Turnover Peak DOY 204 DOY 202 DOY 221 DOY 225 Uptake Fall DOY 277 DOY 266 DOY 274 DOY 283 turnover Carbon 107 days 105 days 109 days 116 days Uptake Period

Table 3.5 Seasonal variations in monthly cumulative NEE (gCm-2day-1).

Month/Year 2005 2006 2007 2008 Average January N/A 9.3 17.1 6.2 10.9 February N/A 4.8 10.7 5.0 6.8 March N/A 10.2 21.9 6.7 13.0 April N/A 21.7 25.1 19.5 22.1 May 27.1 37.4 28.7 25.4 29.6 June -1.8 -56.6 -32.9 -13.4 -26.3 July -154.3 -147.6 -143.5 -133.0 -144.6 August -141.0 -141.9 -178.7 -177.6 -159.8 September -62.4 -20.4 -68.3 -62.3 -53.4 October 26.9 21.3 29.9 1.5 19.9 November 21.9 19.2 22.9 11.8 19.0 December 14.1 10.7 16.0 5.9 11.7

Table 3.6 Model parameters for hyperbolic relationship between NEE and PAR (summer months).

2 Month/Year α GPmax R R 2005 June -0.028 -6.91 2.74 0.31 July -0.047 -29.05 4.57 0.78 August -0.046 -34.27 4.66 0.87 September -0.028 -27.30 3.31 0.77 2006 June -0.023 -16.15 3.01 0.46 July -0.041 -28.69 4.37 0.74 August -0.030 -30.46 2.64 0.78 September -0.019 -9.84 1.65 0.44 2007 June -0.020 -8.97 1.90 0.27 July -0.035 -29.79 3.74 0.71 August -0.037 -41.88 3.55 0.90 September no data 2008 June no saturation July -0.026 -23.16 4.14 0.70 August no data September no data

Table 3.7 Coefficient of variation for NEE, ER and GEP.

NEE ER GEP Month ave StdDev CV ave StdDev CV ave StdDev CV January 10.9 5.6 0.52 10.9 5.6 0.52 February 6.8 3.4 0.49 6.8 3.4 0.49 March 13.0 8.0 0.62 13.0 8.0 0.62 April 22.1 2.8 0.13 22.1 2.9 0.13 May 29.6 5.3 0.18 53.3 11.2 0.21 24.1 8.6 0.36 June -26.3 24.0 -0.9186.4 25.5 0.29 112.7 29.5 0.26 July -144.6 8.9 -0.0697.4 30.1 0.31 242.0 38.4 0.16 August -159.8 21.2 -0.13 86.2 26.1 0.3 246.0 15.2 0.06 September -53.4 22.1 -0.42 61.6 19.8 0.32 115.0 33.0 0.29 October 16.2 12.8 0.79 38.2 15.7 0.41 18.4 7.5 0.41 November 19.0 5.0 0.27 19.0 5.0 0.27 December 11.7 4.4 0.38 11.7 4.4 0.38

Chapter 4 Conclusion

Concerns over the fate of C cycling in our changing climate have led to a large

number of studies investigating the processes controlling the mass and energy exchanges

in different ecosystems. Despite their small areal coverage, wetlands are significant

contributors to the world’s soil organic carbon pool and thus play a crucial role in the

global C budget. However, only recently have climate-carbon modelers included

wetlands or more specifically, peatlands, into regional and global climate models (Wieder

et al., 2007). Classification systems divide wetlands into two broad categories based on

their soil properties: organic i.e. peatlands and mineral wetlands (NWWG, 1997).

Peatlands therefore represent only a portion of the wetland types worldwide. In fact

organic and mineral wetlands are often compared to one another despite having markedly

different soil properties, hydrological regimes and plant assemblages and therefore

different interactions controlling C related processes should be expected (NWWG, 1997).

To date, most studies on wetland C exchange have taken place in peatlands and

there have been a limited number of studies from marsh wetlands in Northeastern China

(Zhou et al., 2009; Zhou et al., 2010) and California (Goulden et al., 2007; Rocha and

Goulden, 2008); temperate freshwater marshes remain understudied (Bonneville et al.,

2008; Lafleur, 2009). The main objectives of this study were to determine the diurnal,

seasonal and inter-annual patterns of carbon and water vapour exchange for a temperate

freshwater marsh with an attempt to explain the environmental drivers and variability in

that exchange.

100

Our study, using four years of nearly continuous field data from EC

measurements, revealed that a temperate freshwater cattail marsh in eastern Ontario was a large annual sink for CO2 as compared to other wetland ecosystems including the neighboring peatland and that ET rates were highly dependent on radiative input. The

highly productive nature of this particular mineral wetland was due to the cattail species

present and the fact that water was not limiting. The main environmental controls on the

seasonality of marsh net ecosystem CO2 exchange were temperature and light which in turn influenced the biophysical properties of the marsh vegetation. Annual cumulative

NEE was on average -246 ± 31 gCm-2 yr-1 and ranged from -216 to -260 g Cm-2yr-1. The variability in accumulation between years was a result of the timing of spring and fall transitions in the carbon uptake and the length of the growing seasons, each of which were strongly determined by prevailing weather conditions and cattail growth.

Evaluation of the interannual variability indicated that the marsh may be sensitive to C losses through enhanced respiration under warmer autumn periods. Annual cumulative

ET on the other hand was on average 728 mm due to the abundance of water, which maintained open-water channels allowing significant continuous transpiration. Evidence from the literature suggests that our Typha marsh behaves more like an agricultural ecosystem. While our annual C sums are in the lower spectrum of the values presented for agricultural ecosystems, these sites serve as better comparisons because they are well- watered, highly productive systems that experience the same rapid canopy development over a short 1-2 month period as is seen at the Mer Bleue marsh.

The net ecosystem CO2 exchange examined in this study only represents the

vertical exchanges of C. In order to accurately describe the C budget for the Mer Bleue

101

marsh we would also need to explore C loses from methane and the lateral movement of

C in its dissolved forms. While not reported here, the concentrations of dissolved organic

carbon (DOC) were measured at two locations along the channel. On most days, the

difference between outflow and inflow was minor therefore lateral C losses are assumed

-2 -1 to be negligible. The Mer Bleue marsh was estimated to emit 275 gCH4m year , an

-2 -1 equivalent of 206 gCm year (Bonneville 2006, unpublished thesis). Despite high CH4 emissions, the marsh was still found to be a net sink of C (Bonneville, 2006, unpublished thesis).

The current study contributes to the knowledge gap on the processes controlling the mass and energy exchanges in temperate mineral wetlands. Such improved knowledge is especially important for wetland inclusion in regional and global climate models because we have shown that peatland sites have very low annual uptake in comparison to marshes owning to the highly productive nature of marsh vegetation.

Additionally, this research is key for identifying the status and major threats to freshwater wetlands (Brinson and Malvarez, 2002), for the inclusion of wetlands and wetland types in global inventories (Scott and Jones, 1995) and to identify the response of marshes in a changing climate, especially since freshwater marshes have emerged as ideal candidates for remediation projects (Kadlec and Wallace, 2009).

In this study, water was always available and although the channel depth varied, there were no significant effects noted on C exchange or ET. Other studies have noted the effects that water level (drawdown, flooding and drought) has on ecosystem C (Dušek et al., 2012; Rocha and Goulden, 2010) however none have focused on ET. In light of climate change impacts on water availability, especially in currently water-stressed areas,

102

future work on marsh gas exchange should incorporate these effects. We did not measure

all pathways of ET and therefore it would be interesting to consider evaporation from

open sources and transpiration from leaf level measurements to gain a more complete

understanding of marsh ET. Additionally, as we have only considered evaporation from

the primary canopy, it would be interesting to determine the overall contribution of

Esubcanopy on annual ET. Finally, as climate change potentially lengthens growing

seasons, there is a need to evaluate the response of marsh ecosystems to spring and

autumn warming in terms of the net ability of the marsh to sequester carbon.

103

REFERENCES

Admiral, S.W. and Lafleur, P.M., 2007. Partitioning of latent heat flux at a northern peatland. Aquatic Botany, 86(2): 107-116.

Admiral, S.W., Lafleur, P.M. and Roulet, N.T., 2006. Controls on latent heat flux and energy partitioning at a peat bog in eastern Canada. Agricultural and Forest Meteorology, 140(1): 308-321.

Aerts, R., De Caluwe, H. and Konings, H., 1992. Seasonal allocation of biomass and nitrogen in four Carex species from mesotrophic and eutrophic fens as affected by nitrogen supply. Journal of Ecology, 80(4): 653-664.

Alm, J. et al., 1999. Carbon balance of a boreal bog during a year with an exceptionally dry summer. Ecology, 80(1): 161-174.

Amiro, B. et al., 2010. Ecosystem carbon dioxide fluxes after disturbance in forests of North America. Journal of Geophysical Research, 115 (G00K02): 13PP.

Amiro, B. et al., 2006. Carbon, energy and water fluxes at mature and disturbed forest sites, Saskatchewan, Canada. Agricultural and Forest Meteorology, 136(3): 237- 251.

Anthoni, P.M., Freibauer, A., Kolle, O. and Schulze, E.D., 2004. Winter wheat carbon exchange in Thuringia, Germany. Agricultural and Forest Meteorology, 121(1): 55-67.

Aubinet, M., 2008. Eddy covariance CO2 flux measurements in nocturnal conditions: an analysis of the problem. Ecological Applications, 18(6): 1368-1378.

Aubinet, M. et al., 2012. Nighttime Flux Correction. Eddy Covariance: 133-157.

Aurela, M., Laurila, T. and Tuovinen, J.P., 2002. Annual CO2 balance of a subarctic fen in northern Europe: importance of the wintertime efflux. Journal of Geophysical Research, 107(5): 12PP.

104

Aurela, M., Laurila, T. and Tuovinen, J.P., 2004. The timing of snow melt controls the annual CO2 balance in a subarctic fen. Geophysical Research Letters, 31 (L16119): 4PP.

Baldocchi, D.D., 2003. Assessing the eddy covariance technique for evaluating carbon dioxide exchange rates of ecosystems: past, present and future. Global Change Biology, 9(4): 479-492.

Baldocchi, D.D., Vogel, C.A. and Hall, B., 1997. Seasonal variation of energy and water vapor exchange rates above and below a boreal jack pine forest canopy. Journal of Geophysical Research, 102(D24): 28939-28951.

Batzer, D.P. and Sharitz, R.R., 2006. Ecology of freshwater and estuarine wetlands. Univ of California Pr.

Bergeron, O. et al., 2007. Comparison of carbon dioxide fluxes over three boreal black spruce forests in Canada. Global Change Biology, 13(1): 89-107.

Black, T.A. et al., 2000. Increased carbon sequestration by a boreal deciduous forest in years with a warm spring. Geophysical Research Letters, 27(9): 1271-1274.

Bonneville, M.C., 2006. Measurement and modeling of surface-atmsophere exchange of carbon dioxide and methane in a cattail marsh in eastern Ontario, McGill University Montreal.

Bonneville, M.C., Strachan, I.B., Humphreys, E.R. and Roulet, N.T., 2008. Net ecosystem CO2 exchange in a temperate cattail marsh in relation to biophysical properties. Agricultural and Forest Meteorology, 148(1): 69-81.

Bowen, I.S., 1926. The ratio of heat losses by conduction and by evaporation from any water surface. Physical Review, 27(6): 779-787.

Brinson, M.M. and Malvarez, A.I., 2002. Temperate freshwater wetlands: types, status, and threats. Environmental Conservation, 29(2): 115-133.

Brix, H., 1994. Functions of macrophytes in constructed wetlands. Water Science and Technology, 29(4): 71-78.

105

Brix, H., 1997. Do macrophytes play a role in constructed treatment wetlands? Water Science and Technology, 35(5): 11-18.

Brümmer, C. et al., 2011. How climate and vegetation type influence evapotranspiration and water use efficiency in Canadian forest, peatland and grassland ecosystems. Agricultural and Forest Meteorology, 153(150): 14-30.

Bubier, J.L., Bhatia, G., Moore, T.R., Roulet, N.T. and Lafleur, P.M., 2003. Spatial and temporal variability in growing-season net ecosystem carbon dioxide exchange at a large peatland in Ontario, Canada. Ecosystems, 6(4): 353-367.

Burba, G.G., McDermitt, D.K., Grelle, A., Anderson, D.J. and Xu, L., 2008. Addressing the influence of instrument surface heat exchange on the measurements of CO2 flux from open‐path gas analyzers. Global Change Biology, 14(8): 1854-1876.

Burba, G. and Anderson, D., 2010. A brief practical guide to eddy covariance flux measurements. Li-COR Inc., Lincoln.

Burba, G., Verma, S. and Kim, J., 1999. Surface energy fluxes of Phragmites australis in a prairie wetland. Agricultural and Forest Meteorology, 94(1): 31-51.

Campbell, J.L., Mitchell, M.J., Groffman, P.M., Christenson, L.M. and Hardy, J.P., 2005. Winter in northeastern North America: a critical period for ecological processes. Frontiers in Ecology and the Environment, 3(6): 314-322.

Christensen, J.R., Crumpton, W.G. and van der Valk, A.G., 2009. Estimating the breakdown and accumulation of emergent macrophyte litter: A mass-balance approach. Wetlands, 29(1): 204-214.

Christensen, T.R. et al., 2003. Factors controlling large scale variations in methane emissions from wetlands. Geophysical Research Letters, 30(7): 4PP.

Churkina, G., Schimel, D., Braswell, B.H. and Xiao, X., 2005. Spatial analysis of growing season length control over net ecosystem exchange. Global Change Biology, 11(10): 1777-1787.

Clair, T., Arp, P., Moore, T., Dalva, M. and Meng, F.R., 2002. Gaseous carbon dioxide and methane, as well as dissolved organic carbon losses from a small temperate wetland under a changing climate. Environmental Pollution, 116(S1): 143-S148.

106

Coleman, J. et al., 2001. Treatment of domestic wastewater by three plant species in constructed wetlands. Water, Air, & Soil Pollution, 128(3): 283-295.

Crago, R. and Brutsaert, W., 1996. Daytime evaporation and the self-preservation of the evaporative fraction and the Bowen ratio. Journal of Hydrology, 178(1): 241-255.

Cronk, J. and Fennessy, M., 2001. Adaptations to growth conditions in wetlands. Wetland plants: biology and ecology. Lewis, Boca Raton, FL.

De Groot, R.S., Wilson, M.A. and Boumans, R.M.J., 2002. A typology for the classification, description and valuation of ecosystem functions, goods and services. Ecological Economics, 41(3): 393-408.

Drexler, J.Z., Snyder, R.L., Spano, D. and Paw U, K.T., 2004. A review of models and micrometeorological methods used to estimate wetland evapotranspiration. Hydrological Processes, 18(11): 2071-2101.

Dušek, J., Čížková, H., Stellner, S., Czerný, R. and Květ, J., 2012. Fluctuating water table affects gross ecosystem production and gross radiation use efficiency in a sedge- grass marsh. Hydrobiologia, 692(1): 1-10.

Evironment Canada, 2002. Great Lakes Fact Sheets: Great Lakes Coastal Wetlands - Science and Conservation. Minister of the Environment.

Environment Canada, 2011. COSEWIC: Wildlife Species Assessment. Government of Canada, Ottawa.

Environment Canada, 2012. National Climate Data and Information Archive. Environment Canada.

Ekstam, B. and Forseby, A., 1999. Germination response of Phragmites australis and Typha latifolia to diurnal fluctuations in temperature. Seed Science Research, 9(2): 157-163.

Erwin, K.L., 2009. Wetlands and global climate change: the role of wetland restoration in a changing world. Wetlands Ecology and Management, 17(1): 71-84.

107

Eugster, W. et al., 2000. Land–atmosphere energy exchange in Arctic tundra and boreal forest: available data and feedbacks to climate. Global Change Biology, 6(S1): 84-115.

Finlayson, C. and Valk, A.G., 1995. Wetland classification and inventory: A summary. Plant Ecology, 118(1): 185-192.

Frolking, S. et al., 1998. Relationship between ecosystem productivity and photosynthetically active radiation for northern peatlands. Global Biogeochemical Cycles, 12(1): 115-126.

Gentine, P., Entekhabi, D., Chehbouni, A., Boulet, G. and Duchemin, B., 2007. Analysis of evaporative fraction diurnal behaviour. Agricultural and Forest Meteorology, 143(1): 13-29.

Giasson, M.A., Coursolle, C. and Margolis, H.A., 2006. Ecosystem-level CO2 fluxes from a boreal cutover in eastern Canada before and after scarification. Agricultural and Forest Meteorology, 140(1): 23-40.

Goodin, D.G., Peake, J.S. and Barmann, J.A., 1996. Analysis and modeling of the radiation budget and net radiation of a Sandhills wetland. Wetlands, 16(1): 66-74.

Goodrich, L.E, 1982. The influence of snow cover on the ground thermal regime. Canadian Geotechnical Journal, 19(4): 421 – 432.

Goulden, M.L., Litvak, M. and Miller, S.D., 2007. Factors that control Typha marsh evapotranspiration. Aquatic Botany, 86(2): 97-106.

Grace, J.B. and Harrison, J.S., 1986. The Biology of Canadian Weeds.: 73. Typha latifolia L., Typha angustifolia L. and Typha xglauca Godr. Canadian Journal of Plant Science, 66(2): 361-379.

Gu, L. et al., 2003. Phenology of vegetation photosynthesis. Phenology: An integrative environmental science: 439(7): 67-485.

Guo, H. et al., 2009. Tidal effects on net ecosystem exchange of carbon in an estuarine wetland. Agricultural and Forest Meteorology, 149(11): 1820-1828.

108

Guo, H., Zhao, B., Chen, J., Yan, Y. and Li, B., 2010. Seasonal changes of energy fluxes in an estuarine wetland of Shanghai, China. Chinese Geographical Science, 20(1): 23-29.

Guo, Y. and Sun, L., 2012. Surface energy fluxes and control of evapotranspiration from a Carex lasiocarpa mire in the Sanjiang Plain, Northeast China. International Journal of Biometeorology, 56(2): 221-232.

Haslwanter, A., Hammerle, A. and Wohlfahrt, G., 2009. Open-path vs. closed-path eddy covariance measurements of the net ecosystem carbon dioxide and water vapour exchange: A long-term perspective. Agricultural and Forest Meteorology, 149(2): 291-302.

Hey, D.L. and Philippi, N.S., 1995. Flood reduction through wetland restoration: the Upper Mississippi River Basin as a case history. Restoration Ecology, 3(1): 4-17.

Humphreys, E.R. et al., 2006. Summer carbon dioxide and water vapor fluxes across a range of northern peatlands. Journal of Geophysical Research, 111(G04011): 16PP.

Inoue, T.M. and Tsuchiya, T., 2006. Growth strategy of an emergent macrophyte, Typha orientalis Presl, in comparison with Typha latifolia L. and Typha angustifolia L. Limnology, 7(3): 171-174.

IPCC, 2007. Climate change 2007: the physical science basis: contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press.

Kadlec, R.H. and Wallace, S., 2009. Treatment wetlands. CRC.

Karunaratne, S., Asaeda, T. and Yutani, K., 2003. Growth performance of Phragmites australis in Japan: influence of geographic gradient. Environmental and Experimental Botany, 50(1): 51-66.

Kennedy, G. and Mayer, T., 2002. Natural and constructed wetlands in Canada: An overview. Water Quality Research Journal of Canada, 37(2): 295-325.

109

Komatsu, H., 2005. Forest categorization according to dry‐canopy evaporation rates in the growing season: comparison of the Priestley–Taylor coefficient values from various observation sites. Hydrological Processes, 19(19): 3873-3896.

Korner, C., 1979. Maximum leaf diffusive conductance in vascular plants. Photosynthetica, 13(1): 45-82.

Lafleur, P.M., 2008. Connecting atmosphere and wetland: energy and water vapour exchange. Geography Compass, 2(4): 1027-1057.

Lafleur, P.M., 2009. Connecting atmosphere and wetland: Trace gas exchange. Geography Compass, 3(2): 560-585.

Lafleur, P.M., Hember, R.A., Admiral, S.W. and Roulet, N.T., 2005. Annual and seasonal variability in evapotranspiration and water table at a shrub‐covered bog in southern Ontario, Canada. Hydrological Processes, 19(18): 3533-3550.

Lafleur, P.M., Roulet, N.T., Bubier, J.L., Frolking, S. and Moore, T.R., 2003. Interannual variability in the peatland-atmosphere carbon dioxide exchange at an ombrotrophic bog. Global Biogeochemical Cycles, 17(2): 14PP.

Larsen, K.S., Grogan, P., Jonasson, S. and Michelsen, A., 2007. Respiration and microbial dynamics in two subarctic ecosystems during winter and spring thaw: effects of increased snow depth. Arctic, Antarctic, and Alpine Research, 39(2): 268-276.

Le Mer, J. and Roger, P., 2001. Production, oxidation, emission and consumption of methane by soils: a review. European Journal of Soil Biology, 37(1): 25-50.

Lei, H. and Yang, D., 2010. Seasonal and interannual variations in carbon dioxide exchange over a cropland in the North China Plain. Global Change Biology, 16(11): 2944-2957.

Lund, M. et al., 2010. Variability in exchange of CO2 across 12 northern peatland and tundra sites. Global Change Biology, 16(9): 2436-2448.

Lund, M., Lindroth, A., Christensen, T.R. and Ström, L., 2007. Annual CO2 balance of a temperate bog. Tellus B, 59(5): 804-811.

110

Mallik, A. and Wein, R.W., 1986. Response of a Typha marsh community to draining, flooding, and seasonal burning. Canadian Journal of Botany, 64(9): 2136-2143.

Mao, L., Bergman, M. and Tai, C., 2002. Evapotranspiration measurements and estimation of three wetland environments in the upper St.Johns river basin, Florida. Journal of the American Water Resources Association, 38(5): 1271-1285.

Matson, P.A., Vitousek, P.M. and Chapin, M.C., 2011. Principles of terrestrial ecosystem ecology. Springer.

McNaughton, K. and Jarvis, P., 1983. Predicting effects of vegetation changes on transpiration and evaporation. Water Deficits and Plant Growth, 7: 1-47.

McNaughton, K. and Spriggs, T., 1986. A mixed-layer model for regional evaporation. Boundary-Layer Meteorology, 34(3): 243-262.

McNaughton, S., 1966. Ecotype function in the Typha community-type. Ecological Monographs, 36(4): 298-325.

Meyers, T.P. and Hollinger, S.E., 2004. An assessment of storage terms in the surface energy balance of maize and soybean. Agricultural and Forest Meteorology, 125(1): 105-115.

Mitra, S., Wassmann, R. and Vlek, P.L.G., 2005. An appraisal of global wetland area and its organic carbon stock. Current Science, 88(1): 25.

Mitsch, W.J., Gosselink, J.G., 2007. Wetlands, 4th ed. John Wiley & Sons, Inc., Hoboken, New Jersey.

Monteith, J., 1965. Evaporation and environment. Symposia of the Society for Experimental Biology, 19:205-234.

Monteith, J.L. and Unsworth, M.H., 2008. Principles of environmental physics. Academic Press.

Morgenstern, K., Black, T.A., Humphreys, E.R., Griffis, T.J., Drewitt, G.B., Cai, T., Nesic, Z., Spittlehouse, D.L., Livingston, N.J., 2004. Sensivity and uncertainity

111

of the carbon balance of a Pacific Northwest Douglas-fir forest during an El Nino/La Nina cycle. Agricultural and Forest Meterology, 123(1): 201-219.

National Wetlands Working Group (NWWG), 1997. The Canadian wetland classification system. Wetlands Research Centre, University of Waterloo, Waterloo, Ontario

Oechel, W.C., Vourlitis, G. and Hastings, S.J., 1997. Cold season CO2 emission from arctic soils. Global Biogeochemical Cycles, 11(2): 163-172.

Oke, T.R., 1987. Boundary layer climates. Psychology Press.

Pelletier, L., Garneau, M. and Moore, T., 2011. Variation in CO2 exchange over three summers at microform scale in a boreal bog, Eastmain region, Québec, Canada. Journal of Geophysical Research, 116(G03019): 11PP.

Penman, H.L., 1948. Natural evaporation from open water, bare soil and grass. Proceedings of the Royal Society of London Series A. Mathematical and Physical Sciences, 193(1032): 120-145.

Pereira, A.R., 2004. The Priestley–Taylor parameter and the decoupling factor for estimating reference evapotranspiration. Agricultural and Forest Meteorology, 125(3): 305-313.

Piao, S. et al., 2008. Net carbon dioxide losses of northern ecosystems in response to autumn warming. Nature, 451(7174): 49-52.

Pratt, D., 1981. Wetland biomass production, pp. 119-126.

Priestley, C. and Taylor, R., 1972. On the assessment of surface heat flux and evaporation using large-scale parameters. Monthly Weather Review, 100(2): 81-92.

Raich, J.W. and Schlesinger, W.H., 1992. The global carbon dioxide flux in soil respiration and its relationship to vegetation and climate. Tellus B, 44(2): 81-99.

Ramsar, 2007. Ramsar Sites Information Service.

Ramsar, 2011a. The Ramsar Convention Manual: a guide to the Convention on Wetlands (Ramsar, Iran, 1971), 5th ed., Ramsar Convention Secretariat.

112

Ramsar, 2011b. Wetland Ecosystem Services. Ramsae Convention Secretariat, Switzerland.

Richardson, J.L., 2001. Wetland soils: genesis, hydrology, landscapes, and classification. CRC Press.

Rocha, A., Potts, D. and Goulden, M., 2008. Standing litter as a driver of interannual CO2 exchange variability in a freshwater marsh. Journal of Geophysical Research, 113(G04020): 10PP.

Rocha, A.V. and Goulden, M.L., 2008. Large interannual CO2 and energy exchange variability in a freshwater marsh under consistent environmental conditions. Journal of Geophysical Research, 113(G04019): 12PP.

Rocha, A.V. and Goulden, M.L., 2009. Why is marsh productivity so high? New insights from eddy covariance and biomass measurements in a Typha marsh. Agricultural and Forest Meteorology, 149(1): 159-168.

Rocha, A.V. and Goulden, M.L., 2010. Drought legacies influence the long-term carbon balance of a freshwater marsh. Journal of Geophysical Research, 115(G00H02): 9PP.

Roulet, N.T., 2000. Peatlands, carbon storage, greenhouse gases, and the Kyoto Protocol: Prospects and significance for Canada. Wetlands, 20(4): 605-615.

Roulet, N.T. et al., 2007. Contemporary carbon balance and late Holocene carbon accumulation in a northern peatland. Global Change Biology, 13(2): 397-411.

Rubec, C., 1988. Wetlands of Canada. Polyscience Publications Inc.

Rubec, C., 2003. Wetland stewardship in Canada. Contributed papers from the conference on Canadian wetlands stewardship. Ottawa, ON: North American Wetland Conservation Council (Canada).

Scott, D. and Jones, T., 1995. Classification and inventory of wetlands: A global overview. Plant Ecology, 118(1): 3-16.

113

Shuttleworth, W., 1993. Evaporation. Handbook of Hydrology, DR Maidment, Ed. McGraw-Hill.

Shuttleworth, W., Gurney, R., Hsu, A. and Ormsby, J., 1989. FIFE: the variation in energy partition at surface flux sites. IAHS Publication, 186: 67-74.

Smith, S.G., 1986. The cattails (Typha): Interspecific ecological differences and problems of identification. Lake and Reservoir Management, 2(1): 357-362.

Sojda, R.S. and Solberg, K.L., 1993. Waterfowl Management Handbook. Fish and wildlife leaflet, 13(13): 13.

Song, C., Sun, L., Huang, Y., Wang, Y. and Wan, Z., 2011. Carbon exchange in a freshwater marsh in the Sanjiang Plain, northeastern China. Agricultural and Forest Meteorology, 115(8): 1131 – 1138.

Sonnentag, O., Van Der KAMP, G., Barr, A. and Chen, J., 2010. On the relationship between water table depth and water vapor and carbon dioxide fluxes in a minerotrophic fen. Global Change Biology, 16(6): 1762-1776.

Souch, C., Wolfe, C.P. and Grimmtind, C.S.B., 1996. Wetland evaporation and energy partitioning: Indiana dunes national lakeshore. Journal of Hydrology, 184(3): 189- 208.

Strilesky, S.L. and Humphreys, E.R., 2011. A comparison of the net ecosystem exchange of carbon dioxide and evapotranspiration for treed and open portions of a temperate peatland. Agricultural and Forest Meteorology, 153(1): 45 – 53.

Sun, L. and Song, C., 2008. Evapotranspiration from a freshwater marsh in the Sanjiang Plain, Northeast China. Journal of Hydrology, 352(1): 202-210.

Suyker, A.E. and Verma, S.B., 2008. Interannual water vapor and energy exchange in an irrigated maize-based agroecosystem. Agricultural and Forest Meteorology, 148(3): 417-427.

Tanaka, K., Tamagawa, I., Ishikawa, H., Ma, Y. and Hu, Z., 2003. Surface energy budget and closure of the eastern Tibetan Plateau during the GAME-Tibet IOP 1998. Journal of Hydrology, 283(1): 169-183.

114

Tanja, S. et al., 2003. Air temperature triggers the recovery of evergreen boreal forest photosynthesis in spring. Global Change Biology, 9(10): 1410-1426.

Tanner, C.B. and Thurtell, G.W., 1969. Anemoclinometer measurements of Reynolds stress and heat reansport in the atmospheric surface layer, DTIC Document.

Taylor, J., Paine, C. and FitzGibbon, J., 1995. From greenbelt to greenways: four Canadian case studies. Landscape and Urban Planning, 33(1-3): 47-64.

Tokida, T. et al., 2007. Episodic release of methane bubbles from peatland during spring thaw. Chemosphere, 70(2): 165-171.

Touzi, R., Deschamps, A. and Rother, G., 2009. Phase of target scattering for wetland characterization using polarimetric C-band SAR. Geoscience and Remote Sensing, IEEE Transactions on, 47(9): 3241-3261.

Van der Valk, A.G., 2012. The biology of freshwater wetlands. Oxford University Press, USA.

Verma, S.B. et al., 2005. Annual carbon dioxide exchange in irrigated and rainfed maize- based agroecosystems. Agricultural and Forest Meteorology, 131(1): 77-96.

Webb, E.K., Pearman, G.I. and Leuning, R., 1980. Correction of flux measurements for density effects due to heat and water vapour transfer. Quarterly Journal of the Royal Meteorological Society, 106(447): 85-100.

Wegner, L.H., 2010. Oxygen Transport in Waterlogged Plants. Waterlogging Signalling and Tolerance in Plants: 3-22.

Wenying, Y., Guangsheng, Z., Daocai, C., Qijin, H. and Li, Z., 2008. Evapotranspiration of Phragmites communis community in Panjin wetland and its control factors. Acta Ecologica Sinica, 28(9): 4594-4601.

Westlake, D., 1963. Comparisons of plant productivity. Biological Reviews, 38(3): 385- 425.

Wieder, R.K. et al., 2007. Peatlands and the carbon cycle: From local processes to global implications. EOS, 88(295): 25.

115

Wilson, K. et al., 2002. Energy balance closure at FLUXNET sites. Agricultural and Forest Meteorology, 113(1-4): 223-243.

Wilson, K.B. and Baldocchi, D.D., 2000. Seasonal and interannual variability of energy fluxes over a broadleaved temperate deciduous forest in North America. Agricultural and Forest Meteorology, 100(1): 1-18.

Wilson, L.M., Schwarzlaender, M., Blossey, B. and Randall, C.B., 2004. Biology and biological control of purple loosestrife. USDA Forest Service/UNL Faculty Publications: 112.

Xu, L. and Baldocchi, D.D., 2004. Seasonal variation in carbon dioxide exchange over a Mediterranean annual grassland in California. Agricultural and Forest Meteorology, 123(1): 79-96.

Yavitt, J. and Knapp, A., 1995. Methane emission to the atmosphere through emergent cattail (Typha latifolia L.) plants. Tellus B, 47(5): 521-534.

Yoder, R.E., Odhiambo, L.O. and Wright, W.C., 2005. Effects of vapor-pressure deficit and net-irradiance calculation methods on accuracy of standardized Penman- Monteith equation in a humid climate. Journal of Irrigation and Drainage Engineering, 131(3): 228-237.

Zedler, J.B. and Kercher, S., 2005. Wetland resources: status, trends, ecosystem services, and restorability. Annual Reviews in Environment and Resources, 30: 39-74.

Zhou, L. et al., 2001. Variations in northern vegetation activity inferred from satellite data of vegetation index during 1981 to 1999. Journal of Geophysical Research, 106(D17): 20069-20083.

Zhou, L., Zhou, G. and Jia, Q., 2009. Annual cycle of CO2 exchange over a reed (Phragmites australis) wetland in Northeast China. Aquatic Botany, 91(2): 91-98.

Zhou, L., Zhou, G., Liu, S. and Sui, X., 2010. Seasonal contribution and interannual variation of evapotranspiration over a reed marsh (Phragmites australis) in Northeast China from 3‐year eddy covariance data. Hydrological Processes, 24(8): 1039-1047. Zoltai, S.C. and Vitt., D.H., 1995. Canadian wetlands: Environmental gradients and classification. Plant Ecology, 118(1-2): 131 – 137.

116