20141212 dissertation decode

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A Dissertation

entitled

Response and Biophysical Regulation of Carbon Fluxes to Climate Variability and

Anomaly in Contrasting Ecosystems

by

Housen Chu

Submitted to the Graduate Faculty as partial fulfillment of the requirements for the

Doctor of Philosophy Degree in Biology (Ecology Track)

_________________________________________

Jiquan Chen, PhD, Committee Chair

_________________________________________

Johan F. Gottgens, PhD, Committee Co-Chair

_________________________________________

Richard Becker, PhD, Committee Member

_________________________________________

Ankur R. Desai, PhD, Committee Member

_________________________________________Ge Sun, PhD, Committee Member

_________________________________________Patricia R. Komuniecki, PhD, Dean

College of Graduate Studies

The University of Toledo

December 2014


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Copyright 2014, Housen Chu

This document is copyrighted material. Under copyright law, no parts of this documentmay be reproduced without the expressed permission of the author.


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An Abstract of

Response and Biophysical Regulation of Carbon Fluxes to Climate Variability andAnomaly in Contrasting Ecosystems

by

Housen Chu

Submitted to the Graduate Faculty as partial fulfillment of the requirements for theDoctor of Philosophy Degree in

Biology (Ecology Track)

The University of Toledo

December, 2014

Severe weather and climate anomalies have been observed increasingly in recent

decades in United States. Large uncertainties still exist about to what extent ecosystems

may respond to such drastic variability of external environmental forcing in terms of their

carbon sequestration rates. Challenges also remain in predicting and assessing the

potential impact of climate variability and anomaly under anticipated climate change.

This study targeted the three most prevalent ecosystems (i.e., a deciduous woodland, a

conventional cropland, and a coastal freshwater marsh) in northwestern Ohio, USA.

Using the eddy covariance method and supplementary measurements, I examined theeffects of recent climatic variability and anomalies (2011-2013) on ecosystem carbon

fluxes (i.e., net ecosystem CO2/CH4exchanges (FCO2/FCH4) and lateral hydrologic fluxes

of dissolved organic carbon (FDOC), particulate organic carbon (FPOC), and dissolve

inorganic carbon (FDIC)). Gross ecosystem production (GEP) and ecosystem respiration

(ER) were the two largest fluxes in the annual carbon budget at all three ecosystems. Yet,

these two fluxes compensated each other to a large extent and their balanceFCO2

depended largely on the interannual variability of these two large fluxes. Around 57-58%,

91-96%, and 77-78% of the interannual FCO2variability was attributed to functional

changes of ecosystems among years, suggesting that the changes of ecosystem structural,

physiological, or phenological characteristics played an important role in regulating

interannual variability of GEP, ER and FCO2. Freshwater marshes deserve more research

attention for their high FCH4(~50.81.0 g C m2yr1) and lateral hydrologic carbon


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inflows/outflows. Lateral hydrologic flows were an important vector in re-locating

carbon among ecosystems in the region. Considerable hydrologic carbon flowed both into

and out of the research marsh (108.35.4 and 86.210.5 g C m2yr1, respectively).

Despite marshes accounting for only ~4% of area in this agriculture-dominated

landscape, they are potentially efficient in turning over and releasing newly fixed carbon

(allochthonous and autochthonous) as CH4and should be carefully addressed in the

regional carbon budget. In sum, this study highlights that different carbon fluxes respond

unequally and even oppositely to climate variability and anomaly and thus, their balances

may vary largely among ecosystems and years.


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This dissertation is dedicated to my familyMing, Dylan, my parents and sisterswho

always stand by me and give me the most support. I would also like to acknowledge my

mentorHsiawho shows me the road less traveled by, and that has made all the

difference.


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Acknowledgements

This project was funded by the National Oceanic and Atmospheric Administration

(NA10OAR4170224) and the National Science Foundation (NSF1034791), USA. The

author was also supported by the Graduate Assistantship of Department of Environmental

Sciences at University of Toledo and Studying Abroad Scholarship of Bureau of

International Cultural and Educational Relations, Ministry of Education, Taiwan. I thank

J. Simpson (Winous Point Marsh Conservancy), T. Schetter, K. Menard, R. Maneval

(Metroparks of the Toledo Area), and W. Berger for supporting the research platform and

logistical assistance. I would like to acknowledge my advisorsDrs. Jiquan Chen and

Hans Gottgensfor their fully support and guidance through the research and PhD

program. I also acknowledge my doctoral committee membersDrs. Richard Becker,

Ankur R. Desai, and Ge Sunfor their valuable guidance, challenges, advice, and

assistance. I thank K. Czajkowski, S. Qian, and Z. Ouyang for valuable suggestions and

assistance for the quality of publication. I thank T. Fisher, J. Martin-Hayden, D.R.

Cahoon, K. Roderick-Lingema, S.A. Heckathorn, and T.B. Bridgeman, A. Richardson, A.

Noormets, K. Kirschbaum, R. John, B. Muller, J. Pritt, and H. Guo for helpful assistances

and advice. I gratefully acknowledge M. Deal, J. Xu, O. Babcock, C. Becher, C. Shao,

Y.-J. Su, J. Xie, J. Teeple, W. Shen, and M. Abraha for infrastructure construction,

instrument maintenance/calibration, and data collection/management. I also thank L.D.

Taylor for language editing.


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Table of Contents

Abstract .............................................................................................................................. iii

Acknowledgements ..............................................................................................................v

Table of Contents ............................................................................................................... vi

List of Tables .................................................................................................................. xii

List of Figures .................................................................................................................. xiv

List of Abbreviations ....................................................................................................... xvi

List of Symbols ................................................................................................................ xxi

1 Introduction ..........................................................................................................1

1.1 Introduction ........................................................................................................1

1.2 Objectives and Hypotheses ................................................................................6

References ..........................................................................................................9

2 Net ecosystem methane and carbon dioxide exchanges in a Lake Erie coastal marsh and

a nearby cropland ........................................................................................................14

Abstract ........................................................................................................14

2.1 Introduction ......................................................................................................15

2.2 Materials and Methods .....................................................................................18

2.2.1 Study Sites ........................................................................................18

2.2.2 Flux Measurements and Calculations ...............................................21

2.2.3 Gap Filling and Partitioning of FCO2 ....................................................................... 22


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2.2.4 Modeling and Gap Filling of FCH4 ...................................................23

2.2.5 Micrometeorology Measurements ....................................................25

2.2.6 Satellite-Based Vegetation Index ......................................................26

2.2.7 Statistical Analysis ............................................................................27

2.4 Results ........................................................................................................27

2.3.1 Micrometeorology and Hydrology ...................................................27

2.3.2 Satellite-Based Vegetation Characteristics .......................................30

2.3.3 Seasonal Variability in FCO2 .......................................................................................... 32

3.3.4 Seasonal Variability in FCH4 .......................................................................................... 352.3.5 Regulation of FCH4 ................................................................................................................ 36

2.3.6 Annual Atmospheric Carbon Budget ................................................44

2.4 Discussion ........................................................................................................45

2.4.1 Physical Regulation of FCH4at the Marsh .........................................45

2.4.2 Plant Modulation of FCH4at the Marsh .............................................47

2.4.3 Annual Atmospheric Carbon Budget ................................................49

2.5 Conclusions ......................................................................................................54

Acknowledgements ................................................................................................55

S2.1 CO2and CH4Flux Calculation and Uncertainty Analysis ............................56

S2.1.1 Flux Calculation..............................................................................56

S2.1.2 FCO2Partitioning .............................................................................56

S2.1.3 Uncertainty Analysis ......................................................................58

S2.1.4 Footprint Analysis ..........................................................................58

S2.2 Modeling Processes of the Daily and Half-hourly FCH4 ............................................... 59


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S2.2.1 Daily FCH4 ................................................................................................................................ 59

S2.2.2 Half-hourly FCH4 ................................................................................................................. 60

S2.3 Ground-Based NDVI Measurements and Upscaling Processes ....................60

S2.3.1 Methodology of the Ground-Based Reflectance Measurement ......60

S2.3.2 Comparison of the Ground-Based and MODIS NDVI...................62

References ........................................................................................................74

3 Climatic variability, hydrologic anomaly, and methane emission can turn productive

freshwater marshes into net carbon sources ....................................................................85

Abstract ........................................................................................................853.1 Introduction ......................................................................................................86

3.2 Methods ........................................................................................................88

3.2.1 Site Information ................................................................................88

3.2.2 Micrometeorological Measurements ................................................90

3.2.3 Net Ecosystem CO2and CH4Exchanges..........................................90

3.2.4 Lateral Hydrologic Carbon Fluxes....................................................93

3.2.5 Hydrologic Carbon Concentration ....................................................94

3.2.6 Calculation of Hydrologic Carbon Fluxes ........................................95

3.2.7 Sediment Core and Radioactive Dating ............................................95

3.2.8 Statistical Analysis ............................................................................96

3.3 Results ........................................................................................................97

3.3.1 Microclimate Condition ....................................................................97

3.3.2 Water Budget ....................................................................................98

3.3.3 Net Ecosystem CO2Exchange ........................................................100


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3.3.4 Net Ecosystem CH4Exchange ........................................................103

3.3.5 Hydrologic Carbon Concentrations and Fluxes ..............................104

3.3.6 Sediment and Organic Carbon Deposition Rate .............................109

3.3.7 Carbon Budget ................................................................................111

3.4 Discussion ......................................................................................................114

3.4.1 Carbon Budget at Freshwater Marshes ...........................................114

3.4.2 Uncertainties and Challenges in Closing Marsh Carbon Budgets ..117

3.4.3 Implications of Hydrologic Carbon Fluxes .....................................123

3.4.4 Responses to Climate Variability and Anomaly .............................124Acknowledgements ..............................................................................................128

S3.1 FCO2Partitioning and Flux Uncertainty Analysis ........................................129

S3.1.1 FCO2Partitioning and GEP/ER Model Parameterization .............129

S3.1.2 Gap-filling of FCH4 ......................................................................................................... 130

S3.1.3 Flux Uncertainty Analysis ............................................................131

S3.1.4 Energy Balance Closure Analysis ................................................131

S3.2 Hydrologic Carbon Flux Calculation and Uncertainty Analysis .................133

S3.2.1 Partition of Qinand Qout .............................................................................................. 133

S3.2.2 Validation of Qinand Qout ......................................................................................... 134

S3.2.3 Uncertainty of Qnet, Qin, and Qout .................................................135

S3.2.4 Sampling and Uncertainty Analyses of POC, DOC, and DIC ....136

S3.2.5 Uncertainty in Calculating Hydrologic Carbon Fluxes ................137

S3.3 Sediment and Organic Carbon Deposition Rate Analysis ...........................138

References ......................................................................................................152


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4 Response and biophysical regulation of ecosystem carbon dioxide fluxes to interannual

climate variability and anomaly in contrasting ecosystems ..........................................163

Abstract ......................................................................................................163

4.1 Introduction ....................................................................................................164

4.2 Materials and Methods ...................................................................................167

4.2.1 Experiment Design..........................................................................167

4.2.2 Sites and Date Description ..............................................................169

4.2.3 Model Description ..........................................................................172

4.2.4 Model Parameterization and Model Error Assessment ..................1774.2.5 Phenological Indices .......................................................................178

4.3 Results ......................................................................................................180

4.3.1 Micrometeorology...........................................................................180

4.3.2 Model Diagnostics and Error Statistics...........................................182

4.3.3 Functional Parameters and Phenological Indices........................................ 185

4.3.4 Effects of Drivers and Parameters on Interannual FCO2Variability192

4.3.5 Effects of Drivers and Parameters on Local FCO2Variability............ 196

4.4 Discussion ......................................................................................................201

4.4.1 Direct Climatic and Indirect Parameter Effects ..............................201

4.4.2 Impact of the Climatic Variability and Anomaly ..........................204

4.4.3 Implication and Limitation of the Two Modeling Approaches ......208

4.5 Conclusions ....................................................................................................211

Acknowledgements ..............................................................................................213

References ......................................................................................................220


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5. Summary ..................................................................................................................229

5.1 Lessons Learned.............................................................................................229

5.2 Recommendation for Future Research...........................................................232

References ........................................................................................................................236


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List of Tables

S2-1 List of micrometeorological sensors and calibration standards .............................63

S2-2 Footprint contribution of the measured fluxes at the marsh site ............................64

S2-3 Summary of micrometeorology .............................................................................65

S2-4 Summary of net ecosystem CO2and CH4exchanges ............................................66

S2-5 Summary of the regression models for the daily net ecosystem CH4exchange ....67

S2-6 Reported annual net ecosystem CH4exchange in freshwater marshes. ................68

S2-7 Reported annual net ecosystem CO2and CH4exchanges in wetlands. ................70

3-1 Summary of the annual carbon fluxes from 2011 to 2013. .................................101

S3-1 List of micrometeorological sensors at the marsh tower. ...................................140

S3-2 Summary of gaps in FCO2

, FCH4

, and ET ..............................................................141

S3-3 Summary of the annual energy budget from 2011 to 2013. ...............................142

S3-4 Model coefficients for gross ecosystem production and ecosystem respiration..143

S3-5 Summary of the annual discharge-weighted carbon concentrations ....................144

S3-6 Reported annual carbon budget in freshwater wetlands and small lakes ............145

4-1 Summary of the site location and vegetation types .............................................171

4-2 Summary of the posterior distributions of model parameters .............................187

S4-1 Micrometeorological sensors at the three sites ....................................................214

S4-2 Initial values, priors and acceptable ranges of model parameters of Model 1 .....215

S4-3 Initial values, priors and acceptable ranges of model parameters of Model 2 .....216


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S4-4 Summary of the phenological indices ..................................................................217

S4-5 Model error statistics for the two modeling approaches ......................................219


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List of Figures

1.1 Map and photos of the study area in northwestern Ohio, USA ...............................5

1.2 Conceptual diagram of the major ecosystem carbon fluxes ....................................7

2.1 Map of the study marsh and cropland in northwestern Ohio, USA .......................20

2.2 Time series of the daily micrometeorological variables ........................................29

2.3 Sixteen day normalized difference vegetation index .............................................31

2.4 Time series of half-hourly and daily fluxes at the marsh and cropland sites .........33

2.5 Regression models of the daily net ecosystem CH4exchange ..............................38

2.6 Multiple linear regression against half-hourly net ecosystem CH4exchange .......41

2.7 Summertime net ecosystem CH4exchange and meteorological variables ............42

2.8 Wintertime net ecosystem CH4exchange and meteorological variables ...............43

S2.1 Model information of the multiple linear regression against half-hourly FCH4......72

S2.2 Model parameters of the multiple linear regression against half-hourly FCH4.......73

3.1 Daily water fluxes and storage changes during the ice-free season .......................99

3.2 Time series of the daily carbon fluxes from 2011 to 2013 ..................................102

3.3 Time series of the dissolved organic carbon and particulate organic carbon ......106

3.4 Dissolved inorganic carbon concentration and daily DIC fluxes in 2013 ...........108

3.5 Profile of the organic carbon content and 137Cs activity for the sediment core ...110

3.6 Three-year average carbon budget and annual carbon budget at the marsh ........113

S3.1 Map and photos of the Winous Point North Marsh .............................................148


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S3.2 Time series of the daily micrometeorological variables ......................................149

S3.3 Observed and simulated surface water flow at the inlets and outlet. ...................150

S3.4 Regression models of the daily CH4flux against soil temperature .....................151

4.1 Time series of the daily micrometeorological variables ......................................181

4.2 Comparison between observed and modeled net ecosystem CO2exchanges......184

4.3 Reference respiration and maximum ecosystem CO2uptake from Model 1-2 ...186

4.4 Summary of the phenological indices ..................................................................191

4.5 Interannual variation partition and cross-year simulation of annual FCO2...........193

4.6 Effects of environmental drivers and model parameters on FCO2........................1954.7 Interannual variation partition and cross-year simulation of spring ER ..............197

4.8 Interannual variation partition and modeled FCO2in mid-summer ......................198

4.9 Interannual variation partition and modeled FCO2in late-summer .......................200

4.10 Year-to-year changes of GEP and ER and direct/indirect effect .........................205


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List of Abbreviations

137Cs ...........................Cesium-137210Pb ...........................Lead-210

a1.ER............................Empirical Parameter for Ecosystem Respiration Phenology Modela2.ER............................Empirical Parameter for Ecosystem Respiration Phenology Modela1.GEP...........................Empirical Parameter for Gross Ecosystem Production Phenology

Modela2.GEP...........................Empirical Parameter for Gross Ecosystem Production PhenologyModel

AAP............................Annual Assimilation PotentialAmax ...........................Maximum CO2Uptake Rate at Light SaturationAmax ..........................First Derivatives of Maximum CO2Uptake Rate at Light

Saturation With Respect to Day of YearANOVA .....................Analysis of VarianceAp ..............................Peak Maximum CO2Uptake Rate at Light SaturationAPRR...........................Peak Recovery Rate of Maximum CO2Uptake Rate at Light

Saturation during the Spring Recovery PeriodA

PSR...........................Peak Senescence Rate of Maximum CO

2Uptake Rate at Light

Saturation during the Fall Senescence PeriodRPRR ..........................Peak Recovery Rate of Base Respiration during the Spring

Recovery PeriodRPSR...........................Peak Senescence Rate of Base Respiration during the Fall

Senescence Period

ARP ............................Annual Respiration Potential

b1.ER............................Empirical Parameter for Ecosystem Respiration Phenology Modelb2.ER............................Empirical Parameter for Ecosystem Respiration Phenology Modelb1.GEP..........................Empirical Parameter for Gross Ecosystem Production Phenology

Modelb2.GEP..........................Empirical Parameter for Gross Ecosystem Production Phenology

Model

c1.ER............................Empirical Parameter for Ecosystem Respiration Phenology Modelc2.ER............................Empirical Parameter for Ecosystem Respiration Phenology Model


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c1.GEP...........................Empirical Parameter for Gross Ecosystem Production PhenologyModel

c2.GEP...........................Empirical Parameter for Gross Ecosystem Production PhenologyModel

CB ..............................Chamber MethodCH4.............................MethaneCI................................Confidence IntervalsCL ..............................Ohio Curtice Cropland SiteCO2.............................Carbon DioxideCSI .............................Campbell Sci., Inc., Logan, UT, USACXi..............................Instantaneous Concentration at Sampling Time i of Carbon X

DIC .............................Dissolved Inorganic CarbonDOY ...........................Day of YearDOY2d........................Timestamps Starting From 1 January, 2011 and Incrementing

Every 2 Days.DOC ...........................Dissolved Organic Carbon

E0................................Temperature SensitivityEBR1..........................Annual Energy Balance RatioEBR2..........................Slope Coefficient of the Linear Regression Model in Fitting the

Daily Turbulent Fluxes and Available EnergyEC ..............................Eddy CovarianceEM..............................Emergent VegetationER ..............................Ecosystem RespirationET ...............................Evapotranspiration

FCH4............................Net Ecosystem Methane ExchangeFCO2............................Net Ecosystem Carbon Dioxide ExchangeFCO2.fill........................Gap-Filled Net Ecosystem Carbon Dioxide ExchangeFCO2.model.....................Modeled Net Ecosystem Carbon Dioxide ExchangeFCO2.obs........................Observed Non-Gap-filled Net Ecosystem Carbon Dioxide

ExchangeFDIC.............................Lateral Hydrologic Dissolved Inorganic Carbon FluxFDOC............................Lateral Hydrologic Dissolved Organic Carbon FluxFL ...............................Floating-Leaved VegetationFPOC............................Lateral Hydrologic Particulate Organic Carbon FluxFX...............................Annual Flux of Target Carbon X

G .................................Ground Heat FluxGEP ............................Gross Ecosystem ProductionGS ..............................Growing Season

H .................................Sensible Heat FluxHCO3

........................Bicarbonate


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HF ..............................Hydrologic Flux

K .................................Unit Conversion Factor in Method 5Km..............................Quantum Flux at Which Half-Saturation of the Light Response

Curve Occurs

kVPD............................Sensitivities for Air Humidity Limitation EffectkVWC...........................Sensitivities for Soil Moisture Limitation Effect

LE ..............................Latent Heat FluxLI-COR ......................LI-COR, Cor., Lincoln, NE, USALOAA ........................Length of Active Assimilation PeriodLOAR .........................Length of Active Respiration PeriodLOPA .........................Length of Peak Assimilation PeriodLOPR .........................Length of Peak Respiration Period

MAE ...........................Mean Absolute Error

MCMC .......................Markov Chain Monte CarloMDS ...........................Marginal Distribution Sampling MethodML..............................Winous Point North Marsh SiteMODIS ......................Moderate Resolution Imaging Spectroradiometer

N .................................Sample/Simulation Numbern.a. ..............................Data Not AvailableNEE ............................Net Ecosystem ExchangeNDVI..........................Normalized Difference Vegetation IndexNGS............................Nongrowing SeasonNOAA ........................National Oceanic and Atmospheric Administration, USANSF ............................National Science Foundation, USA

OMEGA .....................Omega Engineering, Inc., Stamford, CT, USAOO ..............................Oak Openings Woodland Site

PAR ...........................Photosynthetically Active RadiationPmax............................Maximum CO2Uptake Rate at Light SaturationPOC ............................Particulate Organic CarbonPP ..............................Precipitation

Qi................................Instantaneous Discharge at Sampling Time iQin...............................Lateral Water Flow at the InletsQin.interpl.......................Linear Interpolation of Lateral Water Flow at the InletsQnet.............................Net Lateral Water Flow Normalized for Marsh AreaQout.............................Lateral Water Flow at the OutletQT...............................Annual Mean Discharge

R2................................Coefficient of DeterminationRFCH4..........................Base FCH4at the Period-Averaged Condition


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Rg................................Global RadiationRMSE .........................Root Mean Square ErrorRn................................Net RadiationRp................................Peak Base Respiration at the Reference TemperatureRref..............................Base Respiration at the Reference Temperature

Rref.............................First Derivatives of Base Respiration at the Reference TemperatureWith Respect to Day of YearRSSI ...........................Relative Signal Strength Indicator

SD ..............................Standard DeviationSTa..............................Sensitivities of FCH4to Change in Air TemperatureSTg..............................Sensitivities of FCH4to Change in Soil TemperatureSu*...............................Sensitivities of FCH4to Change in Fiction VelocitySVPD............................Sensitivities of FCH4to Change in Vapor Pressure DeficitSWT.............................Sensitivities of FCH4to Change in Water Table

t1.ER.............................Empirical Parameter for Ecosystem Respiration Phenology Modelt2.ER.............................Empirical Parameter for Ecosystem Respiration Phenology Modelt1.GEP...........................Empirical Parameter for Gross Ecosystem Production Phenology

Modelt2.GEP...........................Empirical Parameter for Gross Ecosystem Production Phenology

ModelTa...............................Air TemperaturetD.ER............................Up-Turn Day of Base Respiration during Fall Senescence PeriodtD.GEP...........................Up-Turn Day of Maximum CO2Uptake Rate at Light Saturation

during Fall Senescence PeriodTg...............................Soil TemperaturetPRR.ER

.........................Peak Recovery Day of Base Respiration during Spring RecoveryPeriod

tPRR.GEP........................Peak Recovery Day of Maximum CO2Uptake Rate at LightSaturation during Spring Recovery Period

tPSR.ER..........................Peak Senescence Day of Base Respiration during Fall SenescencePeriod

tPSR.GEP........................Peak Senescence Day of Maximum CO2Uptake Rate at LightSaturation during Fall Senescence Period

tR.ER.............................Recession Day of Base Respiration during Fall Senescence PeriodtR.GEP...........................Recession Day of Maximum CO2Uptake Rate at Light Saturation

during Fall Senescence PeriodTref .............................Reference TemperaturetS.ER.............................Saturation Day of Base Respiration during Spring Recovery PeriodtS.GEP...........................Saturation Day of Maximum CO2Uptake Rate at Light Saturation

during Spring Recovery PeriodtU.ER............................Up-Turn Day of Base Respiration during Spring Recovery PeriodtU.GEP...........................Up-Turn Day of Maximum CO2Uptake Rate at Light Saturation

during Spring Recovery PeriodTw ..............................Water Temperature


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u*................................Friction Velocity

VPD............................Vapor Pressure DeficitVPD*..........................Normalized Vapor Pressure Deficit

VPD0..........................Vapor Pressure Deficit Threshold for Air Humidity LimitationEffectVWC .........................Volumetric Soil Water ContentVWC* .......................Normalized Volumetric Soil Water ContentVWC0........................Volumetric Soil Water Content threshold for Soil Moisture

Limitation Effect

WPMC .......................Winous Point Marsh ConservancyWPNM .......................Winous Point North MarshWPSC .........................Winous Point Shooting ClubWT .............................Ground/Surface Water Table

Y0.ER...........................Empirical Parameter for Ecosystem Respiration Phenology ModelY0.GEP..........................Empirical Parameter for Gross Ecosystem Production Phenology

Model


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List of Symbols

a.................................Light Use Efficiency

0................................Intercept Coefficient of Linear Regression1................................Slope Coefficient of Linear Regression

ER ............................Parameter/Driver Effects on Ecosystem Respiration

FCO2..........................Parameter/Driver Effects on Net Ecosystem CO2ExchangeGEP..........................Parameter/Driver Effects on Gross Ecosystem ProductionWT ...........................Surface Water Level ChangeSair............................Heat Storage Changes of the AirSsoil...........................Heat Storage Changes of the SoilSwater.........................Heat Storage Changes of the Water

Qnet............................Uncertainties of Net Lateral Water FlowWT............................Uncertainties of Surface Water Level ChangePP..............................Uncertainties of PrecipitationET..............................Uncertainties of Evapotranspiration

FCO2...........................Model Error of Net Ecosystem Carbon Dioxide Exchange


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Chapter 1

Introduction

1.1. Introduction

Carbon cycling in terrestrial and aquatic ecosystems comprises important biogeochemical

processes, such as gross ecosystem production (GEP) and ecosystem respiration (ER) that

sustain ecosystem services essential to human welfare (Chapin III et al., 2011). These

biogeochemical processes have been studied extensively across a range of spatial and

temporal scales, driven by the urgent need to understand the roles terrestrial and aquatic

ecosystems play in the global carbon cycle under the potential impacts of global climate

change (Braswell et al., 1997; Melillo et al., 2014; Yi et al., 2010). Most recently, severe

weather and climate anomalies (e.g., heat/cold waves, drought, high precipitation) have

been observed increasingly in the continental North America (Karl et al., 2012; Wuebbles

et al., 2014). These rare but extremeevents were shown to impose disproportional

influences on ecosystem carbon cycling (Ciais et al., 2008; Wu et al., 2012; Xiao et al.,

2010). The interannual climatic variability and anomaly (either ongoing or prospective)

calls for research attention to better understand and evaluate their potential influence

(Heimann and Reichstein, 2008; Richardson et al., 2007; Schimel, 1995). Yet, challenges

remain to predict the responses of ecosystem carbon fluxes to prospective climatic


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variability and anomaly because the controls of carbon processes are often complex,

multi-scaled, hierarchal, and nonlinear (Baldocchi, 2014).

Recent advances in theory and instrumentation have facilitated the extensive

application of tower-based eddy covariance measurements (Baldocchi et al., 2001;

Dabberdt et al., 1993). These advances benefit the spatially integrative measurement of

ecosystem-scale mass (e.g., CO2, H2O, CH4) and energy (e.g., latent and sensible heat)

fluxes (Baldocchi et al., 1988), greatly enhancing our understanding of the

biogeochemical processes driving these fluxes (e.g., Jung et al., 2010; Tan et al., 2012; Yi

et al., 2010). The quasi-continuous measurement of these fluxes and ancillary physicalvariables (e.g., incident radiation, temperature) also allow researchers to examine these

fluxes and construct suitable models from a half-hourly to a decadal scales (e.g.,

Richardson et al., 2007; Teklemariam et al., 2010; Wu et al., 2012). Recent studies also

propose a research framework that adopts multiple flux towers to measure mass/energy

fluxes simultaneously across a gradient of ecosystem types or management intensity

across landscape (e.g., Anderson-Teixeira et al., 2011; Desai, 2010; Desai et al., 2010;

Miao et al., 2009; Zenone et al., 2011). The cluster-wise design facilitates the comparison

of similarity/coherence/difference of mass/energy fluxes among sites (Desai, 2010) and

most importantly, allows researchers to better interpret mechanisms that regulate the

carbon processes at both the ecosystem and landscape scales (Chen, 2011).

The importance of lateral hydrologic fluxes (e.g., runoff) among ecosystems is

increasingly addressed in recent carbon cycling studies (e.g., Algesten et al., 2004; Cole

et al., 2007). The terrestrial-aquatic continuum concept reveals the significance of

hydrologic processes in transporting and relocating a significant amount of carbon among


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ecosystems (Aufdenkampe et al., 2011; Cole et al., 2007; Jenerette and Lal, 2005;

Johnson et al., 2008; Richey et al., 2002; Tranvik et al., 2009). Carbon sequestered by

terrestrial ecosystems (e.g., forests, croplands) may be leached out, carried along aquatic

pathways, and buried in low areas of landscapes (e.g., wetlands, lakes) (Bedard-Haughn

et al., 2006; Bridgham et al., 2006; Buffam et al., 2011; McCarty and Ritchie, 2002).

There is also growing evidence showing that inland aquatic ecosystems (e.g., rivers,

wetlands, lakes) are more than just neutral pipes that merely convey terrestrial carbon to

the ocean (Aufdenkampe et al., 2011; Cole et al., 2007; Tranvik et al., 2009). The

imported carbon may be transformed within the aquatic ecosystems, released via CO2/CH4outgassing, or deposited in the sediment (Algesten et al., 2004; Cole et al., 2007;

Einola et al., 2011; Kling et al., 1991; Tranvik et al., 2009).

Wetlands, the largest natural sources of CH4, were shown to have profound

effects in driving the atmospheric CH4concentration in recent decades (Bridgham et al.,

2006). Considering both the direct and indirect contributions of CH4to radiative forcing,

the warming effect of releasing 1 g CH4into the atmosphere is 25 times that of releasing

an equivalent mass of CO2on a 100 year time horizon (Forster et al., 2007). The interplay

of the net ecosystem CO2(FCO2) and CH4(FCH4) exchanges in terms of the wetland

greenhouse gas budget and global warming effects is still under debate (e.g., Hendriks et

al., 2007; Mitsch and Gosselink, 2007; Mitsch et al., 2012; Song et al., 2009). While

inundation of wetlands reduces the aerobic decomposition (i.e., CO2production) and

enhances the sediment deposition rate, such inundation also enhances the anaerobic

decomposition and thus CH4generation (Mitsch and Gosselink, 2007). Recent studies

suggest that greenhouse effects, mitigated by the uptake of CO2by wetland vegetation,


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could be partly or entirely offset by CH4emission (e.g., Frolking et al., 2006; Hendriks et

al., 2007; Olson et al., 2013; Song et al., 2009). Also, at regional to continental scales,

CH4emission from aquatic ecosystems may compensate a large portion of carbon uptake

by terrestrial ecosystems (e.g., forests, croplands) (Sturtevant and Oechel, 2013; Tian et

al., 2014; Tian et al., 2012). Hence, a better quantification of greenhouse gas budgets and

full examination of their controlling factors are crucial to understand and evaluate

ecosystem and regional carbon budgets.

This study targeted northwestern Ohio, USA (Figure 1.1), an area that was once

occupied by the Great Black Swamp (~4000 km

2

, glacially fed wetland, comprisingswamps and marshes) and Oak Openings (~476 km2, glacier-retreated sand barren,

comprising savannas, woodlands and wet prairies) (Brewer and Vankat, 2004; Mitsch

and Gosselink, 2007). The land use in this region has been significantly altered by

drainage, agriculture, urbanization, and fire suppression following Euro-American

settlement (1817-1850) (Brewer and Vankat, 2004). Most areas of the Great Black

Swamp were extensively drained starting in the 1850s and were largely converted into

cropland during 1850-1890. Also, ~45% and ~25% of the Oak Openings region has been

converted to urban/suburban and agriculture land use (Schetter and Root, 2009).

Cropland accounts for ~70% of the current land cover in the region and the majority of

cropland is planted with soybean, corn, and wheat. Only ~7% and ~4% of forests and

wetlands remain in the region. Most remaining forests are preserved or managed as

recreational parks. Most remaining wetlands are managed for waterfowl conservation and

are connected hydrologically with nearby croplands and/or forests (Mitsch and

Gosselink, 2007).


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Figure 1.1.Map and photos of the study area in northwestern Ohio, USA, including (a)

cropland (CL) site near Curtice, Oregon, (b) marsh (ML) site at the Winous

Point, Port Clinton, (d) Oak Openings (OO) site near Swanton, and (c) landuse map of the study area. Light brown, green, and light blue areas indicate

respectively the croplands, forests, and wetlands in Figure 1.1c while red,

pink, and blue areas show the urban, suburban, and open water (lakes and

rivers) areas. Triangles in the map indicate the tower site locations.


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1.2. Objectives and Hypotheses

This study targets the three most prevalent ecosystem typescropland, woodland, and

freshwater marshin the region. The goal is to examine the response and biophysical

regulation of carbon fluxes to climate variability and anomaly at these contrasting

ecosystems (Figure 1.2).The overarching question isto what extent different ecosystems

diverge/converge in their responses of carbon fluxes to similar climate variability and

anomaly and how environmental and/or biological factors lead to the varied responses.

Herein, I structure the following chapters (Chapters 2-4) in correspondence with three

specific sets of research questions and hypotheses I attempt to answer.First, net CO2uptake and CH4emission were measured at a freshwater marsh and

a nearby cropland (Chapter 2). I aimed to address the following questions: (1) What are

the contributions of FCH4and FCO2to the atmospheric carbon budget at a freshwater

marsh in comparison with a nearby cropland?(2) At the ecosystem and regional scales,

will the carbon released via FCH4be compensated by the carbon uptake via FCO2? (3)

What are the physical and biological regulators of FCH4and how do these controls vary

from half-hourly to yearly scales? I hypothesized that on an annual basis the marsh is a

net carbon sink in terms of the atmospheric carbon budget (i.e., net CO2uptake > CH4

emission). Also, I hypothesized that the carbon released via CH4emissionwill be

compensated by the ecosystem CO2uptake in the region.

Second, intensive and comprehensive field measurements on the FCO2, FCH4, and

lateral hydrologic carbon fluxes were conducted at a freshwater marsh (Chapter 3). In

addition, the long-term sedimentation rate of the marsh was updated. I aimed at

addressing the following questions: (1) What are the relative contributions of lateral


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hydrologic fluxes (e.g., dissolved and particulate organic carbon. dissolved inorganic

carbon) in terms of the annual carbon budget (i.e., FCO2, FCH4, hydrologic carbon fluxes)

in the marsh? (2) Is the carbon budget compatible with the long-term carbon

sedimentation rate at the marsh? (3) What is the seasonal and interannual variability of

the hydrologic carbon fluxes? (4) To what extent do these carbon fluxes and their budgets

respond to interannual climate variability? I hypothesized that on an annual basis the

amount of carbon imported from the lateral hydrological flows is larger than the amount

of carbon exported and both the autochthonous (via GEP) and allochthonous (via

hydrologic imports) carbon contribute to the carbon accumulation in the sediment.Third, FCO2was measured simultaneously at a freshwater marsh, a cropland, and a

woodland in the region. I attempted to answer the following questions in Chapter Four:

(1) To what extent do ecosystem carbon fluxes (GEP, ER, and FCO2) respond to recent

climate variability and anomalies? And, do the functional parameters and/or phenology of

GEP and ER also vary among years? (2) Do different ecosystems respond similarly (in

magnitude and direction) to recent climate variability and anomalies in terms of their net

CO2uptakes? Specifically, how similar do GEP and ER respond (in magnitude and

direction) at each ecosystem? (3) To what extent can the response of GEP, ER, and FCO2

be explained by the direct and indirect effects at different ecosystems, respectively?

Specifically, do the direct and indirect effects function synergistically (++) or

antagonistically (+) to the climate variability and anomalies? I hypothesized that GEP

and ER respond similarly to recent climate variability and the direct and indirect effects

function antagonistically.


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Figure 1.2.Conceptual diagram of the major carbon fluxes at the (a) woodland, (b)

cropland, and (c) marsh ecosystems, including major pools (rectangles) andfluxes (arrows). Potential major carbon pools and fluxes are labeled in colors

and will be quantified in the study. GEP and ER signify gross ecosystem

production and ecosystem respiration. FCH4, FDOC, FPOC, and FDICdenote the

net ecosystem CH4exchange and lateral hydrologic fluxes of dissolved

organic carbon, particulate organic carbon, and dissolved inorganic carbon,respectively. The dashed boxes indicate the targeted fluxes in each of the

subsequent chapters.


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Chapter 2

Net ecosystem methane and carbon dioxide exchanges

in a Lake Erie coastal marsh and a nearby cropland

Chu, H., J. Chen, J. F. Gottgens, Z. Ouyang, R. John, K. Czajkowski, and R. Becker.

(2014) Net ecosystem methane and carbon dioxide exchanges in a Lake Erie coastal

marsh and a nearby cropland.Journal of Geophysical Research: Biogeosciences, 119(5):722-740. DOI: 10.1002/2013JG002520

Abstract

Net ecosystem carbon dioxide (FCO2) and methane (FCH4) exchanges were measured by

using the eddy covariance method to quantify the atmospheric carbon budget at a Typha-

andNymphaea-dominated freshwater marsh (March 2011 to March 2013) and a soybeancropland (May 2011 to May 2012) in northwestern Ohio, USA. Two year average annual

FCH4(49.7 gC-CH4m2yr1) from the marsh was high and compatible with its net annual

CO2uptake (FCO2: 21.0 gC-CO2m2yr1). In contrast, FCH4was small (2.3 g C-CH4m

2

yr1) and accounted for a minor portion of the atmospheric carbon budget (FCO2: 151.8 g

C-CO2m2yr1) at the cropland. At the seasonal scale, soil temperature associated with

methane (CH4) production provided the dominant regulator of FCH4at the marsh

(R2=0.86). At the diurnal scale, plant-modulated gas flow was the major pathway for CH4

outgassing in the growing season at the marsh. Diffusion and ebullition became the major

pathways in the nongrowing season and were regulated by friction velocity. Our findings


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highlight the importance of freshwater marshes for their efficiency in turning over and

releasing newly fixed carbon as CH4. Despite marshes accounting for only ~4% of area in

the agriculture-dominated landscape, their high FCH4should be carefully addressed in the

regional carbon budget.

2.1. Introduction

Wetlands, the largest natural sources of methane (CH4), were shown to have profound

effects in driving the atmospheric CH4concentration in recent decades (Bridgham et al.,

2006). It has been documented that climatic variations have resulted in large interannualvariations of CH4emissions from wetlands since the 1980s (Bousquet et al., 2006;

Bridgham et al., 2012). Considering both the direct and indirect contributions of CH4to

radiative forcing, the warming effect of releasing 1 g CH4into the atmosphere is 25 times

that of releasing an equivalent mass of carbon dioxide (CO2) on a 100 year time horizon

(Forster et al., 2007). The interplay of the net ecosystem CO2(FCO2) and CH4(FCH4)

exchanges in terms of the wetland greenhouse gas budget and global warming effects is

still under debate (e.g., Hendriks et al., 2007; Mitsch and Gosselink, 2007; Mitsch et al.,

2012; Song et al., 2009). While inundation of wetlands reduces the aerobic

decomposition (i.e., CO2production) and enhances the sediment deposition rate, such

inundation also enhances the anaerobic decomposition and thus CH4generation (Mitsch

and Gosselink, 2007). Recent studies suggest that greenhouse effects, mitigated by the

uptake of CO2by wetland vegetation, could be partly or entirely offset by CH4emission

(e.g., Frolking et al., 2006; Hendriks et al., 2007; Olson et al., 2013; Song et al., 2009).

Frolking et al. (2006) documented that the net warming effects of CH4may persist for


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hundreds to thousands of years before being compensated by the CO2uptake of wetlands.

Hence, a better comprehension of the wetland greenhouse gas budget and its regulation is

urgently needed in order to better understand the resilience of wetland ecosystems and

formulate adaptive management plans under global climate change.

Recent advances in theory and instrumentation have facilitated the extensive

application of tower-based eddy covariance measurements (Baldocchi et al., 2001;

Dabberdt et al., 1993). These advances benefit the spatially integrative measurement of

ecosystem-scale mass and energy fluxes (Baldocchi et al., 1988), greatly enhancing our

understanding of the biogeochemical processes driving these fluxes (e.g., Jung et al.,2010; Tan et al., 2012; Yi et al., 2010). The quasi-continuous measurement of these

fluxes and ancillary physical variables (e.g., incident radiation, temperature) also allow

researchers to examine these fluxes and construct suitable models from a half-hourly to a

decadal scale (e.g., Richardson et al., 2007; Teklemariam et al., 2010; Wu et al., 2012). In

addition, progress in integrating the eddy covariance measurements with satellite-based

vegetation indices (e.g., normalized difference vegetation index, NDVI) provides

researchers with a more comprehensive approach for examining the interaction between

mass/energy fluxes and vegetation characteristics (Lieth, 1974; Xiao et al., 2009).

While many sizeable efforts have been devoted to FCO2research, less research has

attempted to quantify FCH4using the eddy covariance method (see earlier works in

Edwards et al., 1994; Hargreaves and Fowler, 1998; Verma et al., 1992). Only recently

have a few papers examined the annual and interannual variability of FCH4in addition to

FCO2(Hatala et al., 2012; Herbst et al., 2011a; Kroon et al., 2010; Olson et al., 2013).

These pioneer studies suggested that CH4contributes a significant portion to the wetland


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greenhouse gas budget. In addition, the wetland FCH4is sensitive to the interannual

variations of hydrometeorological conditions (Olson et al., 2013; Tagesson et al., 2012)

and land management (Hatala et al., 2012; Herbst et al., 2013). Hence, a wide range of

FCH4(101

102g C-CH4m2yr1) has been reported among sites and years.

In this study, we targeted a temperate freshwater marsh and a conventional

cropland in northwestern Ohio, USA, in an area that was once occupied by the Great

Black Swamp (~4000 km2) (Mitsch and Gosselink, 2007). The Great Black Swamp was

extensively drained starting in the 1850s and was largely converted into cropland during

18501890. Currently, croplands and forests account for ~70% and ~7% of the landcover in the region, respectively. Only ~4% of wetlands (~150 km2, mostly marshes)

remain in the region and most of them are managed for waterfowl conservation (Mitsch

and Gosselink, 2007). For this purpose of waterfowl conservation, water levels in these

wetlands are often managed, including inputs from nearby agricultural drainages.

Gottgens and Liptak (1998) highlighted that these wetlands receive a considerable

amount of nutrients and organic carbon from the nearby croplands through agricultural

runoff. It is not clear how the current management may influence the dynamics of FCH4

and FCO2in these wetlands and to what extent these wetlands may contribute to the

regional carbon budget. As these wetlands are located within an agriculture-dominated

landscape and connected hydrologically with nearby croplands, we argued that their

importance needs to be examined in the context of this landscape. In this study, we aimed

to address the following questions: (1) What are the contributions of FCH4and FCO2to the

atmospheric carbon budget at the freshwater marsh in comparison with the nearby

cropland? (2) At the ecosystem and regional scales, will the carbon released via FCH4be


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compensated by the carbon uptake via FCO2? (3) What are the physical and biological

regulators of FCH4at the marsh and the cropland sites and how do these controls vary

from half-hourly to yearly scales?

2.2. Materials and Methods

2.2.1. Study Sites

The targeted freshwater marsh is located in the Winous Point Marsh Conservancy along

the shore of Lake Erie (N412751.28, W825945.02; Figure 2.1). A conventional

cropland located in Curtice, Ohio (N413742.31, W832043.18) is included in order

to provide a background FCH4and FCO2from the agriculture-dominated (~70%) region,

where soybean (Glycine max) and corn (Zea mays) are the major crops. The two sites are

~30 km apart and have similar climate conditions with a regional mean temperature of

~9.2 C and annual precipitation of ~840 mm in the last 30 years (Noormets et al., 2008).

The marsh site has been owned by the Winous Point Shooting Club since 1856

and has been managed by wildlife biologists since 1946 (Gottgens et al., 1998). The

hydrology of the marsh is relatively isolated by the surrounding dikes and drainages and

only receives drainage from nearby croplands through three connecting ditches (Gottgens

and Liptak, 1998). Since 2001, the marsh has been managed to maintain year-round

inundation with the lowest water levels in September. A 3 m triangular tower was built at

the center of the 129 ha North Marsh in July 2010 (Figure 2.1). Within the 0250 m fetch

of the tower, the marsh comprises 42.9% of floating-leaved vegetation, 52.7% of

emergent vegetation, and 4.4% of dike and upland during the growing season. Floating-

leaved vegetation covers the majority of area near the tower and extends about 60150 m


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from the tower (Figure 2.1). Dominant emergent plants include narrow-leaved cattail

(Typha angustifolia), rose mallow (Hibiscus moscheutos), and bur reed(Sparganium

americanum).Common floating-leaved species are water lily (Nymphaea odorata) and

American lotus (Nelumbo lutea) with foliage usually covering the water surface from late

May to early October.NymphaeaandNelumbostart to shed leaves after early October

and the floating-leaved vegetation area turns to open water through the winter and early

spring. The aboveground biomass (SD) is 0.220.03 and 1.520.27 kg C m2in the

floating-leaved and emergent vegetation areas, respectively, while the belowground

biomass is 0.210.10 and 12.553.87 kg C m

2

, respectively. The vegetation biomasswas harvested at 14 randomly selected 0.50.5 m2plots, of which six and eight plots

were dominated with floating-leaved and emergent vegetation, respectively. The soil is

classified as hydric and the organic layer extends to a depth of 1530 cm. The soil is

clay-rich mineral beneath the organic layer.

A 3 m triangular tower was installed at the center of a 50 ha soybean cropland in

July 2010 and had at least 300 m of homogeneous fetch in all directions. The cropland

site is rain-fed and no irrigation is applied. As it is located in a part of the historic Great

Black Swamp, drainage tiles are deployed around 0.51.0 m beneath the ground surface

in order to draw down the water level. The soil is classified as silty clay and silty clay

loam. The cultivation practices include minimum tillage and both insect and weed

control. Soybeans were planted and harvested on 10 June and 23 October in 2011,

respectively. The aboveground and belowground soybean biomass (SD) were 0.420.01

and 0.050.01 kg C m2at the peak growing season in 2011, with a leaf area index of

3.60.4.


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Figure 2.1.Map of the study marsh (open circle) and cropland (open triangle) in

northwestern Ohio, USA. The background aerial photo was obtained through

the Ohio Geographically Referenced Information Program in the State of

Ohio Office of Information Technology. The target marsh (Winous Point

North Marsh) is highlighted by the dash-dotted polygon. The aerial photowas taken on 13 April in 2011 before the floating-leaved plants emerged and

covered the open water area (dark grey area). The light grey area in the marsh

indicates the emergent vegetation area. The star and dotted circle indicate thetower location and the 250 m fetch. The black square represents the

geolocation of the four 250250 m2pixels of the normalized difference

vegetation index (NDVI, MOD13Q1) obtained from the Moderate

Resolution Imaging Spectroradiometer (MODIS) instrument.


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2.2.2. Flux Measurements and Calculations

The eddy covariance method was applied to quantify FCO2and FCH4at both sites. The

system, including a sonic anemometer (CSAT3, Campbell Sci., Inc., Logan, UT, USA

(CSI)), an open path CO2/H2O infrared gas analyzer (LI7500, LI-COR, Cor., Lincoln,

NE, USA (LI-COR)), and an open path CH4gas analyzer (LI7700, LI-COR), was

mounted 2 m above the water (marsh)/soil (cropland) surface. The height was determined

to ensure that the eddy covariance system is mounted at least twice the height of the

nearby canopy (0.40.6 m and 0.81.0 m at the marsh and cropland, respectively) in the

peak growing season. The measurement periods were 12 March 2011 to 27 March 2013(2 years), and 10 May 2011 to 10 May 2012 (1 year) at the marsh and cropland sites,

respectively. The raw data were sampled with a 10 Hz frequency and recorded by the

CR5000 data logger. Both LI7500 and LI7700 were calibrated routinely in the laboratory

(see Table S2-1 for calibration standards in the supporting information).

FCO2and FCH4were calculated following the FLUXNET methodology (Aubinet et

al., 2000). All calculations were performed with EdiRe (University of Edinburgh,

v1.5.0.29, 2011) following the workflow described in Chu et al. (2014; 2013). The details

of the general flux calculation and uncertainty estimation were discussed in the

supporting information (Text S2.1). In addition, the relative signal strength indicator

(RSSI) was adopted to screen out the periods when the mirror of LI7700 was

contaminated by rainfall or dust (RSSI < 10%) (McDermitt et al., 2011). We set the

LI7700 to check the signal strength at 0800 h every day. A cleaning solution

(alcohol/water mixture) was applied to clean the mirror every 10 min between 0800 h and

0900 h until the signal strength recovered. The cleaning protocol was determined to


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ensure that the LI7700 resumes quality CH4measurements no later than 1 day after the

intense rainfalls. The LI7700-specific correction was also applied to correct the

spectroscopic effects (LI-COR, 2010). The footprint contribution for each half-hourly

flux was examined by using the model developed by Kormann and Meixner (2001). The

majority of footprint (> 80%) was located within the 0250 m fetch at both sites (details

in Text S2.1 and Table S2-2). At the marsh site, floating-leaved vegetation covered the

majority of the area near the tower and extended 80150 m from the tower in the

prevailing wind direction (225315). Thus, floating-leaved vegetation area contributed

to ~74% of the measured flux at the marsh. In this study, positive FCO2and FCH4indicatea net flux from the ecosystem to the atmosphere. A near-neutral atmospheric carbon

budget was defined when the reported FCO2and FCH4were not significantly different from

zero based on the 95% uncertainty intervals.

2.2.3. Gap Filling and Partitioning of FCO2

Overall, 63% and 54% of FCO2passed the quality control checks at the marsh and

cropland sites, respectively. Data gaps of FCO2were filled using the marginal distribution

sampling method (Reichstein et al., 2005). FCO2was further decomposed into gross

ecosystem production (GEP) and ecosystem respiration (ER) following Reichstein et al.

(2005). Both GEP and ER were presented with positive signs (FCO2=ERGEP). The

uncertainties of flux partitioning were obtained through uncertainty propagation via the

Monte Carlo simulations (N=1000) technique of Richardson and Hollinger (2005). More

details on the modeling and partitioning processes are discussed in the supporting

information (Text S2.1). The start and end of the growing season were identified as the


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first and last consecutive 3 days with detectable daily GEPs (>1 g C-CO2m2d1) (Barr

et al., 2009).

2.2.4. Modeling and Gap Filling of FCH4

Overall, 40% and 42% of FCH4passed the quality control checks at the marsh and

cropland sites, respectively. Our data coverage was compatible with reports from the few

available short-term studies (< 1 year) that also used LI7700 to measure FCH4(4546%)

(Dengel et al., 2011; Yu et al., 2013). We adopted the marginal distribution sampling

method in FCH4gap filling and modified the method slightly by including friction velocity(u*) in selecting the similar micrometeorological conditions. The marginal distribution

sampling method takes advantage of the autocorrelation and short-term dependency of

the flux data. Hence, the marginal distribution sampling method is capable of

incorporating the unmeasured factors (e.g., phenology, substrate quality) and filling the

flux gaps when a robust regression model is not available (e.g., FCH4at the cropland in

the study).

In addition, the linear regression model was adopted in order to explore the

regulation of FCH4at different temporal scales. First, we examined the daily-to-yearly

regulation by exploring the relationship between the daily FCH4and biophysical factors.

We selected soil temperature, u*, and groundwater level as the targeted physical factors.

The biological regulation was examined via exploring the relationship between the daily

FCH4and GEP. The significance test and stepwise model simplification were performed

following the method described in Chu et al. (2014). More details on the modeling

processes are discussed in the supporting information (Text S2.1).


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Second, we adopted a moving window multiple linear regression in examining the

regulation of FCH4from a half-hourly to weekly scale. We applied a non-overlapping

moving window with a fixed width of 8 days to the entire time series. A separate

regression was fitted for each 8 day period. The window size was determined to include a

sufficient number of data (N > 48) while not to introduce the seasonality of FCH4. Soil

temperature, u*, and groundwater level were chosen as the predictor variables. After

preliminary data exploration, we log transformed FCH4and fit it with a multiple linear

regression model (Wille et al., 2008):

g g * *CH 4 FCH4.1 Tg u* WT

T T u u WT WTln(F ) ln(R ) S ( ) S ( ) S ( )

10 0.1 0.1

(2.1)

where the overbar indicates the mean value in each period, RFCH4.1(nmol m2s1) is the

base FCH4at the period-averaged soil temperature (Tg(oC)), u*(m s

1), and groundwater

level (WT (m)), STg, Su*, and SWTrepresent the sensitivities of FCH4to every 10 C

change in soil temperature, every 0.1 m s1change in u*, and every 0.1 m change in

groundwater level, respectively. For the marsh site, we further examined the plant

modulation via the relationship of FCH4against air temperature and vapor pressure deficit

(VPD) in the growing season. Air temperature and VPD were used here because they

were documented as the main drivers of plant modulated gas flow (Brix et al., 1992;

Dacey, 1981; Grosse, 1996; Tornberg et al., 1994):

a aCH 4 FCH 4.2 Ta VPD

T T VPD VPDln(F ) ln(R ) S ( ) S ( )

10 0.1

(2.2)


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where RFCH4.2(nmol m2s1) is the base FCH4at the period-averaged air temperature (Ta

(oC)) and VPD (kPa) and STaand SVPDrepresent the sensitivities of FCH4to every 10 C

change in air temperature and every 0.1 kPa change in VPD, respectively. More details of

the modeling processes are discussed in the supporting information (Text S2.1).

2.2.5. Micrometeorology Measurements

Micrometeorological variables were measured at both tower sites (details of the sensor

types and mounting locations are listed in Table S2-1), including long-/short-wave

radiation, albedo, photosynthetically active radiation (PAR), air temperature, relativehumidity, VPD, precipitation, soil temperature (at 0.1 and 0.3 m depth), groundwater

level, volumetric soil water content (only at the cropland), and surface water temperature

(only at the marsh). Because surface water temperature was measured at fixed locations

(0.1 and 0.3 m) above the sediment, recorded surface water temperature may not have

reached 0 C when only the uppermost layer of surface water was frozen in the winter.

We adopted albedo as an indicator in distinguishing the periods with frozen ice/snow

cover (albedo > 0.2) from those with open water (albedo < 0.2) (Bonan, 2002). All of the

variables were sampled every second and recorded every 30 min by the data logger

(CR5000, CSI).

Regional long-term meteorological data (i.e., air temperature and precipitation)

were obtained through the National Climatic Data Center of the National Oceanic and

Atmospheric Administration, USA. Three weather stations, Bowling Green (N412259,

W833639, 18932013), Fremont (N411959, W830708, 19012013), and Toledo


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Express Airport (N413518, W834805, 19552013), were selected because they all

had more than 50 years of records and are located less than 30 km from our sites.

2.2.6. Satellite-Based Vegetation Index

We adopted NDVI as the land surface vegetation index in order to provide seasonal

vegetation dynamics (Morisette et al., 2008; Zhang et al., 2003). NDVI has been

documented to adequately quantify the ecosystem-level vegetation dynamics (e.g.,

canopy coverage, greenness, and biomass) in wetlands and croplands (Jialin, 2011;

Lunetta et al., 2010). The 16 day NDVI data (MOD13Q1) of the Moderate ResolutionImaging Spectroradiometer (MODIS) instrument were obtained from the Land Process

Distributed Active Archive Center, US Geological Survey, USA. The target spatial

coverage was the nearest four 250250 m2MODIS pixels around the marsh and cropland

flux towers (Figure 2.1). The spatial extent was determined in correspondence with the

major footprint of the flux measurement. The long-term NDVI trend was calculated from

2000 to 2012. Additionally, we conducted a series of in situ surface reflectance

measurements in order to examine the suitability of MODIS NDVI in such a confined

spatial extent (500500 m2). In general, our upscale 500500 m2NDVI from the ground

spectrometer measurements showed agreements with the MODIS NDVI, suggesting that

the MODIS NDVI adequately monitored the ecosystem-scale vegetation dynamics at

both sites. The details of the in situ surface reflectance measurements and the validation

processes are discussed in the supporting information (Text S2.1).


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2.2.7. Statistical Analysis

All of the statistical tests and model fittings were conducted with the R language (R

Development Core Team, 2013, version 3.0.0). The parameter estimation in the FCO2

partitioning was conducted using the nlreg package (Bellio and Brazzale, 2003). The

univariate and multiple linear regressions were conducted using the lm function. The

correlations among variables were examined using the cor function. Unless specified,

the significance level was set to 0.05 and the uncertainty () always referred to 95%

confidence intervals in the following sections.

2.3. Results

2.3.1. Micrometeorology and Hydrology

The years 2012 and 2011 were recorded as the second and third warmest (2.1 C and 1.9

C higher than the long-term average of 10.0 C) over the last 118 years in the region

(Figures 2.2a and 2.2b and Table S2-3). The 2011 winter (December 2011 to February

2012) was exceptionally warm (Figure 2.2a). In total, there were only 29 days that had

daily air temperature below 0 C, much fewer than 59 days in the 2012 winter. The warm

2011 winter was followed by warmer spring temperature on 1125 March 2012. Air

temperature increased drastically to ~20 C during this early spring period and was much

higher than the long-term average of ~4 C. Despite the similarity in atmospheric climate

conditions (e.g., air temperature, PAR), soil temperature showed slightly different

patterns between the two sites. In general, the marsh site had higher winter soil

temperature and lower summer soil temperature than the cropland site (Figures 2.2c and

2.2d).


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In addition, 2011 had an extremely high amount of annual precipitation (~372

mm higher than the long-term average of 897 mm) (Figures 2.2g and 2.2h and Table S2-

3). The marsh manager opened the water outflow gate several times throughout the

summer and fall in 2011 in order to maintain the water level at 0.20.6 m above the

ground surface (Figure 2.2i). The warm winter in 2011 had the majority of precipitation

as rainfall instead of snowfall. Groundwater was continuously recharged at the cropland

site. Hence, groundwater level was high around 0.20.8 m beneath the ground surface

(Figure 2.2j). The 2012 summer was dry compared to 2011 and the long-term average.

Groundwater level was continuously drawn down from late May to late July and fromearly August to October at the marsh and the outflow gate was kept closed throughout

most of the late summer and fall (Figure 2.2i).


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Figure 2.2. Time series of the daily micrometeorological variables at the marsh andcropland sites, including (a, b) air temperature (Ta, grey circles), (c, d) soiltemperature (Tg, black lines) and surface water temperature (Tw, grey lines),

(e, f) photosynthetically active radiation (PAR, black lines), (g, h)

precipitation (PP, grey bars), and (i, j) groundwater level (WT, black lines)

and volumetric soil water content (VWC, grey lines). Seven day movingaverage and long-term (18932013) average Taare shown as black and grey

solid lines in Figures 2.2a and 2.2b. Annual cumulative PP and long-term

(18932013) average cumulative PP are shown as solid and thick lines inFigure 2.2g and 2.2h. Dates with the outflow gate open at the marsh are

marked as closed squares in Figure 2.2i. The average sediment (soil) surfacenear the tower was taken as the reference level (0) of the WT measurement

and positive WT indicated the water level above the ground. The water levelsensor was removed from the marsh site during ice-covered winter; hence, no

continuous data were available in those periods. Manual WT measurements

in the winter are marked as open circles in Figure 2.2i.


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2.3.2. Satellite-Based Vegetation Characteristics

The NDVI showed spring green-up roughly 16 days later and 4 days earlier than the

multi-year average (20002012) in 2011 and

20141212 dissertation decode

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