Integrated Belowground Greenhouse Gas Flux Measurements and Modeling, Howland Forest ME.

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Information about Integrated Belowground Greenhouse Gas Flux Measurements and Modeling,...

Published on December 22, 2016

Author: NIFA_IBCE

Source: slideshare.net

1. Integrated Belowground Greenhouse Gas Flux Measurements and Modeling, Howland Forest ME. Kathleen Savage, Debjani Sihi, Eric A. Davidson, David Hollinger, Andrew Richardson, Julie Shoemaker

2. Howland Howland Forest, ME • Owned by New England Wilderness Trust • (NEWT) • 558 acre parcel • Temperate boreal transitional forest • Mature- hemlock, spruce and cedar • Continuous NEE and soil respiration measurements since 1996, one of the longest records • Recently added net CH4 exchange

3. Howland Forest EMS tower NEE and CH4 measurements

4. Automated GHG chambers 2004-2016 wetland transitional upland

5. GHG sampling system

6. Partnership in Education Program (PEP) students Erica Valdez Spatial Heterogeneity of Greenhouse Gases at Howland Forest (presented at AGU 2015) Liomari Diaz Measuring the Spatial Distribution of Soil Carbon at the Howland Forest (attended SSSA 2016)

7. Introduction to Greenhouse Gases • Soils are a significant source of carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O) - the most important greenhouse gases (GHG) • Carbon dioxide: Soils release CO2 produced by both autotrophic (root) and heterotrophic (microbial) respiration processes (aerobic). • Methane: Wetlands are a significant natural source of CH4 (anaerobic), and dry upland soils (aerobic) a natural CH4 sink. • Nitrous Oxide: Soils are the dominant natural source of N2O, and have been shown to be a small sink under N-limited conditions. The production and consumption of N2O is also highly dependent on spatial and temporal variation in soil moisture. • Variation in soil moisture can be very dynamic, and it is one of the dominant factors controlling soil aeration, and hence the balance between aerobic and anaerobic processes.

8. Objective: To improve understanding of and modeling capacity for interactions of belowground temperature, moisture, and substrate supply that control the net soil emissions of the three most important GHGs: CO2 , CH4 , and N2O. • Most biogeochemical models generally simulate CO2, CH4, and N2O emission separately mainly by tweaking model parameters to fit data for one of these gases as it remains challenging to explain mechanistically and to simulate numerically these dynamics. • GHG fluxes are often linked through heterotrophic dependence on fixed C sources for energy, and the same soil profile may alternate between being a net source or sink for CH4 and N2O depending on the concentration of O2 at microbial microsites. • O2 plays a contrasting role a potential substrate or an inhibitor for these GHG production and consumption. • Emissions of multiple GHGs can be simultaneously simulated using a parsimonious modular framework for estimating the consumption of soil C and O2 and for diffusion of gases through the soil profile-basic structure of the DAMM model

9. (Davidson et al., 2002) Howland Forest, ME is a mosaic of well drained upland, wetland and small transitional upland/wetland soils which makes for a unique and challenging environment to measure the effects of soil moisture and hence O2 on the net exchange of these important greenhouse gases.

10. Dual Arrhenius and Michaelis-Menten (DAMM) Arrhenius function

11. An example of the change in headspace [N2O] over time from closure to end of flux calculation period. Flux calculation period is from time 60 to 300. (Flux is -1.2 µg N m-2 hr-1 , 95% confidence ±0.03 µg N m-2 hr-1, R2 = 0.93). Time in seconds 0 50 100 150 200 250 300 [N2 O]ppb 321.0 321.1 321.2 321.3 321.4 321.5

12. CO2 LiCor IRGA CH4, N2O & H2O Aerodyne Quantum Cascade Laser pump Automated Chamber System • Deployed in wetland, transitional and upland soils • Newly developing laser technology- high frequency, highly accurate CO2, CH4, N2O and H2O gas concentration • 2 hour sampling frequency • 15 minutes per chamber • Gas sampling frequency 1 Hz • Soil temperature, soil moisture, %O2

13. • Initially developed and tested these modules on 2011 data collected at a wetland/transitional site at Howland forest. • GHG models were parameterized using fluxes, temperature and moisture.

14. 0 35 70 0 35 70 RhData(mgCm-2hr-1) Rh Model (mg C m-2 hr-1) 70 72 74 76 78 80 0 4 8 12 16 265 275 285 295 305 SoilMoisture(cm3cm-3) SoilTemperature(°C) DOY 2011 0 35 70 265 275 285 295 305 Rh(mgCm-2hr-1) Data Model R2=0.65 p<0.0001 Carbon Dioxide- Heterotrophic Respiration (Rh)

15. R2=0.54 p<0.0001 CO2 CH4 Soluble C DiffusionAir Soil Increasing Soil Moisture CH4 CH4 oxidation CH4 production 0 0.005 0.01 0.015 0.02 0.025 265 275 285 295 305 CH4Flux(mgCm-2hr-1) Model Data 0 0.01 0.02 0 0.01 0.02 CH4Model(mgCm-2hr-1) CH4 Data (mg C m-2 hr-1) 70 72 74 76 78 80 0 4 8 12 16 265 275 285 295 305 SoilMoisture(cm3cm-3) SoilTemperature(°C) DOY 2011 Methane

16. NH4 + NO3 - N2O N2 Diffusion N2O Air Soil Increasing Soil Moisture N2O Nitrification Denitrification -4 -2 0 -4 -2 0 N2OModel(µgNm-2hr-1) N2O Data (µg Nm-2hr-1) R2=0.41 p<0.0001 -4 -3 -2 -1 0 1 265 275 285 295 305 N2OFlux(µgNm-2hr-1) Data Model 70 72 74 76 78 80 0 5 10 15 265 275 285 295 305 SoilMoisture(cm3cm-3) SoilTemperature(°C) DOY 2011 Nitrous Oxide

17. Sun Shade Photosynthesis LabC WC FC RC Slow SOMC LitC Resp. SOM Resp. LitC Conductance Resp. FC Resp. RC Resp. LabC Drivers: PAR Air Temp. Soil Temp. VPD CO2 Passive SOMC Microbial SOMC Forest Biomass, Assimilation, Allocation and Respiration (FöBAAR ) Rh = f(ST) ST: Soil Temperature SM: Soil Moisture Rh = f(ST, SM) (Keenan et al., 2012)

18. FöBAAR DAMM-FöBAAR Year 2010 SoilResp(µmolm2S-1) Black: Data Red: Model 00.050.100.15 Model performance for dry year

19. Conclusion • Highly sensitive laser systems confirm the uptake of atmospheric N2O at low rates by well-drained soils. • The DAMM model uses consistent soil physics and M-M kinetic structures to simulate these 3 GHG’s, demonstrating that simultaneous CH4 oxidation and N2O reduction is possible. • DAMM-FoBAAR model improves below ground respiration predictions at Howland forest under drought conditions. Next Steps: • Test DAMM modules against longer term, dynamic dataset-2014-2016 GHG fluxes from Howland Forest CH4 flux (mgCm-2 hr-1 ) -0.2 -0.1 0.0 0.1 0.2 wetlandCH4flux (mgCm-2hr-1) -8 -6 -4 -2 0 2 4 6 8 N2 Oflux (ugNm-2 hr-1 ) -2 -1 0 1 2 3 CO2 flux (mgCm-2 hr-1 ) 0 100 200 300 400 500 2014.50 2014.75 2015.00 2015.25 2015.50 2015.75 2016.00 2016.25 2016.50 2016.75 Soilmoisture (cm3 cm-3 ) 0.00 0.05 0.10 0.15 0.20 0.25 soiltemperature (oC) 3 6 9 12 15 18 21 Upland Transitional Wetland

20. (Photo: Carrie-Ellen Gabriel) Award number 2014-67003-22073

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