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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1
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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CH4fluxes from arctic coastal tundra: Influence from vegetation, wetness, and the
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Tan, Z.-H. et al., 2012. An observational study of the carbon-sink strength of East Asian
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ecosystem carbon dioxide exchange at a temperate ombrotrophic bog.
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Xiao, J. et al., 2010. A continuous measure of gross primary production for the
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Yi, C. et al., 2010. Climate control of terrestrial carbon exchange across biomes and
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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