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Automated Measurements of CO2, CH4, and N2O Fluxes
from Tree Stems and Adjacent Soils
Josep Barba, Rafael Poyatos and Rodrigo Vargas
University of Delaware
15 papers reporting CH4 and/or N2O stem emissions under field conditions in upland forests
What we clearly know from these studies?
• Stems can emit CH4 and exchange N2O with the atmosphere
• High spatial variability within trees and between trees,
species and ecosystems
• High temporal variability at seasonal scales
Temporal variability
Drivers
Magnitudes &
uncertainties
But what is still unclear?
Temporal patterns
Drivers
Magnitudes &
Uncertainties
Study aims
Use AUTOMATIC measurements of CO2, CH4 and N2O to…
- quantify magnitudes of emissions
- understand seasonal and diurnal patterns of emissions
- describe drivers
- bring some light on the origin
75cm
150cmUpperStem
LowerStem
Soil
CO2, CH4 and N2O
Every hour for 100 days(April – July 2017)
7200 measurements
• Sap flow
• Stem temperature
• Soil temperature
• Soil water content
• Meteorological variables
Carya cordiformis Upland forested area, St Jones Reserve, DE
Experimental design
Experimental design
Li-8150Multiplexer
Li-8100A
Li-8100IRGA
PicarroG2508
ExponentialLinear
Exp: 0.28 nmols m-2 s-1
Lin: 0.16 nmols m-2 s-1
LI-CORVersion SoilFluxPro™Soil Gas Flux Software
Flux calculation
CO2
CH4 & N2O
• DIURNAL patterns and drivers Wavelet coherence analysis
(hourly data)
• SEASONAL patterns and drivers Mixed-effects models
(daily averaged data)
(interactions and temporal autocorrelation)
Statistical analysis
Results – SEASONAL TRENDS
Seasonal course of sap flow per unit sapwood area (SF) and CO2, CH4 and N2O fluxes associated withUpperStem, LowerStem and Soil chambers. Points are hourly means taken from Day of the Year 102 to 202.
Sap Flow
CO2
CH4
N2O
Results – SEASONAL TRENDS
UpperStem: 0.28 ± 0.02
LowerStem: 0.46 ± 0.03
Soil: -0.66 ± 0.06(nmol CH4 m-2 s-1)
UpperStem: -0.014 ± 0.006
LowerStem: -0.017 ± 0.008
Soil: -0.046 ± 0.011(nmol N2O m-2 s-1)
CH4
N2O
MODEL Variables MODEL Variables MODEL Variables
UpperStem Temperature UpperStem Temperature UpperStem TemperatureadjR2 = 0.93 SWC adjR2 = 0.40 SF adjR2 = 0.10 SWCp < 0.001 SF p < 0.001 p = 0.032 SF
Temp*SFSWC*SF LowerStem Temperature LowerStem
adjR2 = 0.33 SWC p = n.s.LowerStem Temperature p < 0.001
adjR2 = 0.92 SWC Soil Temperaturep < 0.001 SF Soil Temperature adjR2 = 0.22
Temp*SWC adjR2 = 0.92 SWC p = 0.001Temp*SF p < 0.001 Temp*SWC
Soil TemperatureadjR2 = 0.99 SWCp < 0.001 SF
Temp*SWC
CO2 CH4 N2O
Results – SEASONAL PATTERNS – mixed-effects model
(daily data)
MODEL Variables MODEL Variables MODEL Variables
UpperStem Temperature UpperStem Temperature UpperStem TemperatureadjR2 = 0.93 SWC adjR2 = 0.40 SF adjR2 = 0.10 SWCp < 0.001 SF p < 0.001 p = 0.032 SF
Temp*SFSWC*SF LowerStem Temperature LowerStem
adjR2 = 0.33 SWC p = n.s.LowerStem Temperature p < 0.001
adjR2 = 0.92 SWC Soil Temperaturep < 0.001 SF Soil Temperature adjR2 = 0.22
Temp*SWC adjR2 = 0.92 SWC p = 0.001Temp*SF p < 0.001 Temp*SWC
Soil TemperatureadjR2 = 0.99 SWCp < 0.001 SF
Temp*SWC
CO2 CH4 N2O
R2 = 0.26 R2 = 0.08
Soil Temperature
adjR2 = 0.92 SWC p < 0.001 Temp*SWC
adjR2 = 0.92
CH4
Temperature SWC
Results – SEASONAL PATTERNS – mixed-effects model
(daily data)
Results – DIURNAL PATTERNS – Wavelet coherence analysis
(hourly data)
Temperature Sap Flow
CO2
CH4
N2O
23%
10%
22%
25%
14%
24%
Wavelet coherence analyses output and the percentage of days with significant correlations between CO2,CH4 and N2O of the LowerStem with Temperature (left panels) and SF (right panels) using hourly data.Yellow color indicates significant temporal correlations (p<0.05).
Temporal & spatial
variability
Drivers
UPSCALING
PROCESSES
ORIGIN
Magnitudes &
uncertainties
Conclusions
Thanks