Developing an improved wildland fire emii i tissions inventory · 2015. 9. 10. · Developing an...
Transcript of Developing an improved wildland fire emii i tissions inventory · 2015. 9. 10. · Developing an...
Developing an improved wildland fire i i i temissions inventory
Sim Larkin, Tara Strand, Robert Solomon, USFS AirFire Team, PNW Research StationStacy Drury Sean Raffuse Neil Wheeler Ken Craig Lyle Chinkin Sonoma Tech IncStacy Drury, Sean Raffuse, Neil Wheeler, Ken Craig, Lyle Chinkin, Sonoma Tech, Inc.Nancy Martinez, Harvard; Pete Lahm, USFS WO
EPA Emissions Inventory ConferenceSeptember 29 2010September 29, 2010San Antonio, TX
Towards an improved EITowards an improved EI
Increasing Demand for Fire EIIncreasing Demand for Fire EIMany groups, not just EPA:
–land agencies, states, first nationsFaster EI development:Faster EI development:
–yearly reportingregional/national
l t t lMultiple purposes:–GHG/Carbon reporting
annual total
p g–Short lived climate forcers
Air quality–Air quality localized
daily/hourly
Current Fire EIsCurrent Fire EIs
• Many Fire EIs done for many purposesMany Fire EIs done for many purposes• Methodologies differ
Diff t Fi D t t– Different Fire Detects– Different Fuel Loadings
ff C– Different Consumption Models– Different Emissions Factors– databases and models change over time
• Resulting differences can be large– reasons not generally clear
Current Fire EIsCurrent Fire EIs
• Resulting differences can be largeResulting differences can be large– reasons not generally clear
250
300
ions
(Tg/
yr) Wiedenmyer and Neff
2008 WF NEIU.S. EPA UNFCCCRanderson et al.
150
200
re C
O2
Em
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100
150
48 W
ildla
nd F
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2003 2004 2005 2006
Low
er 4
Cross-cutting model intercomparison projectintercomparison project
Evaluations at many different output levels
Anyone can add data/model output
Standard test cases
Standard comparison metrics
All documented on web: http://semip.org
Test CasesTest CasesSEMIP utilizes a series of test cases to analyze to serve as a starting point for SEMIP'sanalysis These case have been selected to try to represent a wide array of conditions fire types
National Cases1 Fires Everywhere 2 National
analysis. These case have been selected to try to represent a wide array of conditions, fire types,regions, vegetation, weather, and use cases. It is expected that additional test cases will be added overtime.
1. Fires Everywhere 2. National Emissions Inventory 2008
Regional Wildfire Case3 C lif i Wildfi (200 & 2008)3. California Wildfires (2007 & 2008)
Individual Wildfire Cases4. Bugaboo Complex 2007 5. Tripod4. Bugaboo Complex 2007 5. Tripod
Complex 2006
Prescribed Fire Cases6 N th t R i l R S 76. Northwest Regional Rx Season 7.
Southeast Regional Rx Season 8. Naches, WA Rx Fire
Emissions Bulk FormulaEmissions Bulk FormulaAREA FUEL
LOADBURNING
EFFICIENCYEMISSIONS
FACTORx x xEMISSIONS = AREA LOAD EFFICIENCY FACTORx x x
CONSUMPTION
?1 ? ? ?certa
inty
? ? ? ?
Unc
A priori, might expect uncertainties to rank as:Area < Consumption < EF
Preliminary resultsPreliminary resultsfrom literature searchCourtesy N. Martinez
Not
S
peci
fied
Preliminary resultsfrom literature searchCourtesy N. Martinez
dN
ot
Spe
cifie
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Fuels and Consumption
Example: Tripod Fire 2006
175,000+ acres(71,000 hectares)( , )
Multiple fuel typesMultiple fuel types
ICS Report location in f b dcorner of area burned
How does point variability p yscale to a large fire area?
old new
old new
Scaling from a point to westwideScaling from a point to westwide
e
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mpt
ion/
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☐
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onsu
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orm
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Aver
od oc od ve Ave
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ern
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Trip
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epor
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Trip
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rea
Av
WA
A
WA
/OID
/MT
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Based on 12 model pathwaysTripod Average also includes high-res fuels -> CONSUME (squares)
Changes in 2008 Due to PathChanges in 2008 Due to Path• Only large fires (from MTBS)• Fuel maps: FCCS1-1km FCCS2-30m LANDFIRE-30m fuels• Fuel maps: FCCS1-1km, FCCS2-30m, LANDFIRE-30m fuels• Consumption & Emissions: CONSUME3.0 & FOFEM5.7
Fire InformationFire Information • Emissions inventory y
depends explicitly on fire area, location, and timing
• Major regional differences in reporting systems, firein reporting systems, fire size, fire types, and fire detection
Quantity and TimingChoice of fire information system affects timing
Quantity and Timing
Bl ICS209 Wildfi R tBlue = ICS209 Wildfire ReportsRed = MODIS Fire DetectsYellow = HMS Fire Detects
2003-2006 Average Acres Burned
1.6 ICS209
0.8
1.0
1.2
1.4
on a
cres
)
MO DIS
SMARTFIRE
0.0
0.2
0.4
0.6
Are
a (m
illi
0.0
Janu
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Februa
ry
March
April
May
June Ju
ly
Augus
t
Septem
ber
Octobe
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Novem
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Decem
ber
Fire InformationFire Information
Green = EPA NEI fires missing from gNC DFR database
Red = NC DFR fires missing from
Courtesy G. Curcio
EPA database
The West• WRAP states 2002 inventory compared with various firecompared with various fire reporting systems (both ground and satellite-based).
• Most systems only reported a fraction of the WRAP EI acres.
• SMARTFIRE overall detected• SMARTFIRE overall detected only 52% of WRAP EI acres.
• But, SMARTFIRE does not include Ag burns (~1/3 of total).
Soja et al, 2009
Some Preliminary ConclusionsSome Preliminary ConclusionsRelative weight of uncertainties:
Area ≈ Consumption ≈ best known EFs
f i l ifor regional summaries.
But, for air quality uses, the uncertainty in area becomes even more important.
Caveat: in much of this, we don’t know what we don’t know. This is especially true for EFs.
Plume Rise
We matched 163 plumes.
• MISR dynamic range less• MISR dynamic range less than modeled
• Model underpredicting small fires, overpredicting l filarge fires
• Clear regime difference in MISR data between West and Southeast
21
• Poor performance throughout the West
Changes UnderwayChanges UnderwayFocus on:
• Plume height constraints• Additional emission calculation schemes• Better treatment of fuel moisture• Spatially explicit fuels (all fuels in perimeter)• More recent fuel map (FCCS2 30m national)• Incorporating more local data
Fire information e g from states• Fire information, e.g. from states• Local fuel surveys
NeededNeededMore local data:
f l•fuels•fire perimeters•management information•ancillary data
More types of information:More types of information:•cross comparison of various types of data may be able to constrain uncertainties
More explicit treatment of uncertainty
Aggregation databases (gathering information in one spot)
BlueSky FrameworkFireInfo
BlueSky FrameworkRed = Current NEI path
Fuels
Total
SMARTFIREICS-209Rx SysManualOther FCCS Total
Consumption
TimeRate
OtherNFDRSHardyLANDFIRE*Ag*Other
CONSUME 3FOFEMFEPS Rate
Emissions
Pl
FEPSEPMClearSky (Ag)*Satellite*Other
Rx / WFFEPSEPMWRAP FEPS Plume
Rise
Dispersion /Trajectories
IdealizedManualOther
FEPSLiterature*EPMFOFEMOther Briggs
Multi-core**D k ** TrajectoriesDaysmoke**Other CalPuff
HYSPLITCMAQ
GOES-10 and -12:
GOES NOAA-15,-16, and -17: POES
Terra and Aqua: MODIS
Global Fire Detects
Ground ReportsGround Reports
A H
azar
d ng
Sys
tem Fire Pix
& Sub
ParamWF
BlueSky Improvements Underway
NEWSMARTFIRE
TRMM: TMI, PR; DMSP: SSM/I; Aqua: AMSR-E;
17: POES
Terra and Aqua: MODIS Fire Detects
GOES-11 and 12: GOES
Western Hemisphere Fire DetectsFIRE INFO
(SMARTFIRE)
stur
e2R
T)
NO
AAM
appi
n
xel Location -Pixel Fire eterization
F-AB
BA
SSM/I; Aqua: AMSR E; NOAA: AMSU-BRainfall
Suite of GEO Satellites carrying infrared sensorsRainfall
FUELS
Dea
d Fu
el M
ois
TMP
A-R
T (3
B42
data
set FU
ELS
FIRE R
FLAM
BE’ PA
T
NASA/NOAA Earth
Key
Terra/Aqua: MODISDirect Broadcast
CONSUMPTIONive
Fuel
M
oist
ure
East
fire
Algo
rithm
EMISSIO
NS
RADIATIVE PO
WER
THW
AY
FUEL MOISTURE
NASA/NOAA Earth science results
NEW COMPONENTS AND CONNECTIONS ARE IN RED
Terra: MISRStereo Heights
CONSUMPTION
EMISSIONS
L M E A
R
eigh
tin
tsas
et
g
Unknown: MISP (Future)Stereo Heights
Terra/Aqua: MODISFire Radiative Power
GOES-R: ABI (Future)Fi R di ti P
NPP: VIIRS (Future)Fire Radiative Power
DISPERSION/
Fire Radiative
Power
MIN
X dataset
PLUME RISE
Plum
e H
eC
onst
rai
MIN
IX d
at
Fire Radiative PowerDISPERSION/CHEMISTRY
e
CURRENT BLUESKY &SMARTFIRE (BSF-SF)
Tackling the Fire Information ProblemSMARTFIRE 2
Raw fire information sources
Any number of input databases
• ICS-209Source-specific association
Fires in similar conceptual dimension for each data source
ICS 209• NOAA HMS• MBTS (burn scar)• Helicopter perimetersdimension for each data source
Spatio-temporal association
p p• FETS• State Rx DBs• Annual reports
Query-able database of associated fire information
•Local forest databases, e.g. fuels
Preserves all input dataReconciliation and other task-specific processing
Preserves all input data
Specialized output streams (e.g., real-time daily activity, retrospective, geographic, agency specific)
Customizable MetricsSMARTFIRE 2
Query-able database of associated fire information
Reconciliation and other task-specific processing
Specific outputs• probability of existence• lat, lon point location
Specialized output streams (e.g., real-time daily activity, retrospective, geographic, agency specific)
lat, lon point location• spatial extent• final size• time resolved growthagency specific)• fuels• emissions• moreMultiple processing streams
Each processing stream based on a customizable set of “trust” metrics for each i t dbinput db
Thank youThank you
In particular: JFSP NASA DOI/USFSIn particular: JFSP, NASA, DOI/USFS
Also: NFP, USFS, EPA and others
More information:• SEMIP: http://semip.orgg• BlueSky & SMARTFIRE: http://blueskyframework.org
End of Presentation
Thank you.
Preliminary resultsPreliminary resultsfrom literature searchCourtesy N. Martinez
Not
S
peci
fied
Not
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peci
fied
ScalingScalingExample: Tripod Fire 2006
175,000+ acres(71,000 hectares)( , )
Multiple fuel typesMultiple fuel types
ICS Report location in f b dcorner of area burned
How does point variability p yscale to a large fire area?
SMARTFIRE FireEvent Development (1 of 2)
S. Raffuse, Sonoma Tech
SMARTFIRE FireEvent Development (2 of 2)
S. Raffuse, Sonoma Tech