USING OF BIOMARKERS FOR ANALYSIS OF FIRE PLUMES IN COMPLEX RESEARCH - - PowerPoint PPT Presentation

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USING OF BIOMARKERS FOR ANALYSIS OF FIRE PLUMES IN COMPLEX RESEARCH - - PowerPoint PPT Presentation

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USING OF BIOMARKERS FOR ANALYSIS OF FIRE PLUMES IN COMPLEX RESEARCH OF WILDFIRES IN CENTRAL SIBERIA Alexey Panov 1 , A. Prokushkin 1 , A. Bryukhanov 1 , M. Korets 1 , E. Ponomarev 1 , A. Myers-Pigg 2 , P. Loucharn 2,3 , N. Sidenko 1 , R. Amon 2 ,

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USING OF BIOMARKERS FOR ANALYSIS OF FIRE PLUMES IN COMPLEX RESEARCH OF WILDFIRES IN CENTRAL SIBERIA

1 V.N. Sukachev Institute of Forest SB RAS, Krasnoyarsk, Russia 2 Department of Oceanography, Texas A&M University, Texas, USA 3 Department of Marine Sciences, Texas A&M University, Texas, USA 4 Max Planck Institute for Chemistry, Mainz, Germany 5 Max Planck Institute for Biogeochemistry, Jena, Germany

alexey.v.panov@gmail.com

Alexey Panov1, A. Prokushkin1, A. Bryukhanov1, M. Korets1, E. Ponomarev1,

  • A. Myers-Pigg2, P. Loucharn2,3, N. Sidenko1, R. Amon2, M. Andreae4, M.

Heimann5

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Anthropogenic Perturbation of the Global Carbon Cycle: Northern hemisphere has slightly higher concentrations

Keeling et al., 2005, updated

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Anthropogenic Emissions

IPCC, Assessment Report 5, 2013

Total emissions in 2011: ≈10 PgC a-1 3

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The fate of anthropogenic emissions (2002 - 2011)

Global Carbon Project, 2015

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Gillett et al., 2014

Growth of amount of wildfires and areas burned: what is more crucial over the long-term?

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Siberian forests comprise ~ 10% of the global C stored in vegetation and soils, and contribute up to 10% of the global terrestrial net primary productivity

Boreal forests

The world's largest land biome, and makes up 29% of the world's forest cover with the largest areas located in Russia and Canada Peylin et al., 2013

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Top-down/bottom-up observation strategies

…uses observations of the atmospheric composition at remote locations and

  • nly insignificantly influenced by local

processes …is based on local in-situ observations

  • f fluxes or changes in ecosystems, to be

extrapolated and scaled up in order to make inferences at continental scale ‘bottom-up’ ‘top-down’ Tall Towers bridge the gap in scales between global integrative approaches and local process studies

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Since 2006, as part of a global cooperative effort the Zotino Tall Tower Facility (ZOTTO; www.zottoproject.org) - unique international research platform for large-scale climatic

  • bservations is operational in the middle of Siberia

ZOTTO is embedded in the NEESPI, an external project of the International Geosphere- Biosphere Program (IGBP)

Metal 300-m tall mast Underground measurement laboratory Amazing stars…

Part of global tall tower network

ZOTTO

Living and infrastructure facilities

The Zotino Tall Tower Facility (ZOTTO)

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ZOTTO site

… is located in a boreal zone, in the center of Siberian taiga, 20km west

  • f

the Yenisei River and ≈600km north

  • f

Krasnoyarsk, Siberia

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… covers mosaic of light, dark and mixed forests and wetlands – the most representative ecosystem types in Central Siberia

ZOTTO footprint area

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Output Estimates

  • 2. Atmospheric Composition

Data Analysis (I): Integral Signal

Study of wildfires: multilevel research platform

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  • 1. Remote Sensing Data

Analysis

  • 3. Atmospheric Composition

Data Analysis (II): Drone based

(field studies for 2016)

  • 4. Ground Validation
  • 5. Temporal and Spatial Analysis

TSA RS AC GV

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Wildfires in July-August 2012 (Case Study)

July 2012 MODIS Active Fire Detections from Aqua and Terra Satellites (FIRMS)

ZOTTO 2014 2013 2012 2011 2010 2009

July 2012 – Fires near ZOTTO Areas disturbed by wildfires in Siberia (2009-2014), ha CО ì up to 8 ppm!!!

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Biomass burning signal

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Remote Sensing Data Analysis

Active fires and disturbed areas - Hot Spot Detection Technology from NOAA and Terra MODIS “Burn severity” - Normalized Burn Ratio index (dNBR) – preliminary estimation Calibration of “Burn severity” - a field based Composite Burn Index (CBI) – verified estimation Fire heat release intensity - fire radiative power (FRP) – key for feedback estimations

700 000 ha 25 - 50% 2100

  • 3200

MWt 500 – 720 (rel. un.)

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Lichen pine forest 12% of area Moss pine forest 14% of area Mixed forest 35% of area Dark coniferous 6% of area Peat Bog 13% of area

Ground Validation of the Remote Sensing and Atmospheric Signal: Network of Study Plots

Permanent study plots within the fire scars areas in dominant ecosystems – will further used for long-term research of biogeochemical processes during ecosystem restoration Major carbon pools – advance Field Map mapping technology

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Atmospheric Composition Data Analysis: Detection of Biomass Burning Signal

CО, ppm CН4, ppm CО2, ppm

01.07.12 01.08.12 01.09.12

GHG mixing ratios at 300 m a.g.l. (hourly averages)

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ΔCO2 = 30 ppm (8 %) ΔCH4 =2 ppm (200 %) ΔCO = 8 ppm (8000 %) Meas: EnviroSense 3000i Meas: EnviroSense 3000i Meas: APMA-370

1 2

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Atmospheric Composition Data Analysis: Integration with Backward Trajectory Modeling (HYSPLIT)

24-hrs back trajectories Wind rose/disturbed areas

CО, ppm CН4, ppm CО2, ppm

01.07.12 01.08.12 01.09.12

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Dark coniferous Pine forests

2 1

180o

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Atmospheric Composition Data Analysis: Integration with the Remote Sensing

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Pine forests Dark coniferous

30 % - Smoldering phase 70 % - Flame phase

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Atmospheric Composition Data Analysis: Identification of Biological Sources with Biomarkers

Levoglucosan and its isomers (mannosan and galactosan) as dehydro- monosaccharide derivatives are formed exclusively during incomplete fuel combustion containing cellulose/hemicellulose

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… confirms biomass burning events and fire intensity

Pine forests Dark coniferous

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Atmospheric Composition Data Analysis: Identification of Biological Sources with Biomarkers

Lignin phenols (vanillyl, syringyl and cinnamyl) - used to differentiate signals among tissue types and vascular plant groups

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Dark coniferous Pine forests

Up to 60 % - Gymnosperm nonwoody tissues and 30 %

  • Angiosperm woody tissues
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  • Remote estimates of atmospheric composition in fire plumes (trace gases,

aerosols) over the large area of Central Siberia;

  • Emission ratios, factors and other gas/aerosol related parameters (averaged

and site-specific) to be used in terms of C emission estimates;

  • Biomass burning emissions (total and site-specific);
  • Remote and field-based identification of biological sources of OM in wildfire

plumes – and conversely - remote detection of fire characteristics based on the biomarkers;

  • Feedback modeling of different research outputs in this platform: atmospheric

composition, ground estimates and the remote sensing data;

  • Temporal and spatial estimates of carbon changes in different Central Siberian

ecosystems after wildfires (long-term task);

  • Linking

ecosystem signal during restoration after wildfires and the atmospheric response (short-term/long-term).

Integration of the Remote Sensing, Atmospheric Composition in BL and Ground Validation: Output Estimates

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Thank you for your time!