hourly biomass burning emissions inventory derived … · summary • modis vegetation...

HOURLY BIOMASS BURNING EMISSIONS INVENTORY DERIVED FROM GOES DATA

Xiaoyang Zhang, Shobha Kondragunta, and M. K. Rama Varma Raja

17th EI conference, June 2-5, 2008

Outline1. Fuel loading dataset derived from MODIS

(Moderate Resolution Imaging Spectroradiometer) data2. Burned area simulated from GOES

(Geostationary Operational Environmental Satellites) fire data 3. Biomass burning emissions across the

Continuous USA4. Summary

Modeling Emissions from Biomass Burning

GOES fire size

Fuel type AVHRR moisture condition

MODIS vegetation properties

Emissions

Fuel loading Fraction of fuel consumption

Emission FactorBurned area

Model of Biomass Burning Emissions

E---biomass burning emissions (kg)A---burned area (km2)M--biomass density/fuel loading (kg.km-2)C--fraction of combustionF--fraction of emissioni and j define the fire (pixel) locations l is the fuel typek is the time period

(1)∑∑∑∑= = = =

=K

k

L

l

J

j

I

iijklijklijkijkl FCMAE

1 1 1 1

GOES fire productMODIS vegetation propertiesAVHRR moisture conditionAVHRR moisture condition

MODIS Vegetation Property-based Fuel System (MVPFS)

Forest foliage

Forest branch

GrassShrub

Litter

Coarse woody detritus

Vegetation Cover Fraction (MOD44B)

LAI (MOD15A2)

Land Cover Type (MOD12Q1)

tons/ha

Comparison of Fuel Loadings from NFDRS, FCCS and MVPFS

McNally fire area (35º49’-36º18’N, 118º10’-118º37’W).

TM TM NFDRS

FCCS MVPFS

Fire in July 2002

Simulation of Burned Area From GOES Fire Size

Fire occurrences detected from GOES satellite in 2002

•Spatial resolution: 4km•Temporal resolution: 30min• Instantaneous fire sizes in subpixels detected from 3.9 µm and 10.7 µm infrared bands

Subpixel fire size in GOES Fire Product

0%

20%

40%

60%

80%

100%

2002 2003 2004Fire-size processed Fire-pixel saturated Fire-pixel cloudyHigh probability fire Medium probability fire Low probability fire

With fire size

Without fire size

Low probability fire

Fourier-Fitted Diurnal GOES Fire Sizes---Representative diurnal curves

00.05

0.10.15

0.20.25

0.30.35

0.4

0 5 10 15 20Solar local time (hour)

Fire

siz

e (k

m2)

ForestSavannaShrubgrasscrop

Fitting Diurnal Fire Size for Individual GOES Fire Pixels

0

0.2

0.4

0.6

0 5 10 15 20

Solar local time (hour)

Fire

siz

e (k

m2)

Representive diurnal curvecurveFir size detected fromGOES Simulated diurnal curve

Burned Areas Converted from GOES Fire Size

FA α=A -- the area burned within a specified time period (km2) F -- the subpixel fire size (km2)α -- a constant coefficient

Training and Testing Burned Area Detected from TM Imagery

483km2

311km2

Difference: (483-311)/311=55%TM imagery and Fire Perimeter obtained from National Park Service-USGS National Burn Severity Mapping Project

Comparison of Simulated GEOS Burned Areas with TM Burn Scars

0

200

400

600

800

1000

1200

1400

1600

0 200 400 600 800 1000 1200 1400 1600

TM-based burn scars (km2)

GO

ES b

urne

d ar

eas

(km

2) GOES_area=23.16+0.95TM_scarR2=0.96n=20

Comparison of Simulated GOES Burned Area with National Inventory

0200400600800

100012001400

0 200 400 600 800 1000 1200 1400Burned area from Natioanl Inventory (NEI) data (km2)

Sim

ulat

ed a

rea

Simulated Burnt Area from GOES Fire Size vs Burned Area from NEI in 2002

1:1

Burned Area Simulated from GOES Fire Size(2006)

GOES Pixel-based Burned Areas(2000-2006)

GOES Emission Evaluation: CMAQ-Based CO at 500 mb and Surface Layer

Provided by George Pouliot

GOES Emission Evaluation: Comparison of AIRS CO with CMAQ (Atmospheric Infrared Sounder)CO Generated with and without Fire Emissions

Annual PM2.5 Emissions

Biomass Burning Emissions (CH4, CO and NOX) in 2005

Annual Emissions for Different Species (106kg)

3257.32081.31684.73109.53226.7CO

309.6199.9160.8296.4307.8PM2.5

228.0145.7117.9217.65225.9TNMHC

168.9111.688.6163.2170.3NOX

129.682.767.0123.7128.3CH4

52.034.427.350.352.5SO2

32.620.816.831.132.3NH3

9.96.65.29.610.0N2O

20062005200420032002

San Diego Fire Emissions in October 2007

Florida Fire Emissions in May 2007

Diurnal Variations in Biomass Burning Emissions for Different Years

Diurnal Pattern in Emissions in Different Ecosystems

PM2.5

0

5000

10000

15000

20000

0 2 4 6 8 10 12 14 16 18 20 22 24Local solar time (hour)

PM2.

5 (M

g)

ForestSavannaShrubGrassCrop

aNOX

0

2000

4000

6000

8000

10000


NO

X (M

g)


b

CO

0

50000

100000

150000

200000

250000


CO

(Mg)

-


c

Seasonal Shift of Diurnal Patterns in Burning Emissions.

CO (Mg)

0

20000

40000

60000

80000

100000

120000


CO

(Mg)

-

JanuaryFebruaryMarchAprilMayJuneJulyAugustSeptemberOctoberNovemberDecember

Seasonal Variations in Monthly Emissions.

Monthly variation

0

10000

20000

30000

40000

50000

60000

70000

80000

0 2 4 6 8 10 12Month

Em

issio

n (M

g)

0

100000

200000

300000

400000

500000

600000

700000

CO

em

issio

n (M

g)

N2ONH3SO2CH4NOXTNMHCPM2.5CO

Variations in Biomass Burning Emissions (CO) for Different States

Month with Severe Biomass Burning Emissions from 2002-2006

Correlation of Times Series of PM2.5 Emissions with Precipitation

in California

0

5000

10000

15000

20000

25000

30000

January

April

July

October

January

April

July

October

January

April

July

October

January

April

July

October

January

April

July

October

PM2.

5 (M

g)

020406080100120140160180200

Mon

thly

rai

nfal

l (m

m)

PM2.5 (Mg)Monthly rainfall (mm)

2002 20052003 2004 2006

Controls of Drought on Biomass Burning Emissions

California (2002-2006)

0

5000

10000

15000

20000

25000

30000

0 50 100 150 200 250 300Monthly rainfall (mm)

PM2.

5 (M

g)

Y=12167.29X-0.9449

R= -0.71n=60

Florida (2002-2006)

0

2000

4000

6000

8000

10000

0 50 100 150 200 250 300Monthly precipitation (mm)

PM2.

5 (M

g)

Y=37171.17X-0.9094

R= -0.58n=60

Summary• MODIS Vegetation Property-based Fuel System

(MVPFS) provides realistic fuel loading data and can be updated easily.

• Burned area at high temporal and spatial resolution can be simulated from GOES active fire observations with a reliable quality.

• Hourly biomass burning emissions (PM2.5, CH4, CO, N2O, NH3, NOX, SO2, and TNMHC) inventory can be created in near real time from satellite-based fuel data and burned area.

• Biomass burning emissions inventory developed in this study reveals distinct variations in diurnal, seasonal, and interannual patterns and spatial shifts in states and ecosystems.

hourly biomass burning emissions inventory derived … · summary • modis vegetation...

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