Modeling Wildfire Emissions Using Geographic Information Systems (GIS) Technology and Satellite Data

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Modeling Wildfire Emissions Using Geographic
Information Systems (GIS) Technology and
Satellite Data

• Presented by Neil J. M. Wheeler
• Sonoma Technology, Inc.
• Petaluma, California
• at the
• Fifth Annual Community Modeling and Analysis
  System (CMAS) Conference
• October 16-18, 2006
• Chapel Hill, North Carolina

STI-3009
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Acknowledgements

• Authors
• Dana C. Sullivan
• Stephen B. Reid
• Bryan M. Penfold
• Sean M. Raffuse
• Lyle R. Chinkin
• Sponsors
• CENRAP
• NASA
• USFS
• City of Albuquerque

Corresponding author Dana C. Sullivan, Sonoma
Technology, Inc., 1360 Redwood Way, Suite C,
Petaluma, CA 94954 e-mail dana_at_sonomatech.com
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Introduction

• Purpose Support emissions assessments used for
  evaluating episodic visibility and air quality
  impacts from biomass burning.
• Approach Develop and apply a GIS-based emissions
  modeling system.
• - The approach was first applied to prescribed
  and agricultural burns in the Midwestern U.S.
• - Currently, the approach is being refined and
  applied to wildfires in Arizona, New Mexico,
  and surrounding states

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Overview of Approach

• Emission estimates prepared using
• Fire activity data (location, acres burned)
• Satellite derived
• Human reported
• Vegetation data (classification, fuel loading)
• EPAs Biogenic Emissions Landcover Database
  (BELD)
• Fuel Characteristic Classification System (FCCS)
• Fuel moisture data
• Weather Information Management System (WIMS) data
• Emission factors (specific to vegetation type and
  fuel moisture content)
• First Order Fire Effect Model (FOFEM)

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Overview of Approach
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Fire Histories Satellite-Derived Data vs. Human
Reports
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Land Use and Vegetation Cover
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Emissions Model

• Basic Equation
• Emissions (lb)
• Burn area (acres) Fuel loading (ton/acre)
  Emission factor (lb/ton)
• First Order Fire Effects Model (FOFEM)
• Cross-walk developed with EPAs Biogenic
  Emissions Landcover Database (BELD)
• Default or customized fuel loadings may be used
• Fuel moisture values set using day-specific
  Weather Information Management System (WIMS) data
• Produces vegetation-specific emission factors in
  lbs/acre burned

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Wildfire Plume Rise Estimation

• Wildfire Modeling
• Large fire events modeled as numerous individual
  point sources
• A plume bottom, plume top, and layer 1 fraction
  were calculated for each fire point source
• Non-layer 1 emissions were vertically allocated
  at 25m, 75m, 100m, and every 100m up to the plume
  top

Modeling of the Cave Creek Wildfire in Arizona
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Example Results Central U.S.
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Example Results New Mexico
Emissions by Source Type for the New Mexico
Modeling Domain July 1, 2005
Source Type NMHC (tons) NOx (tons)
Area Sources 1,507 374
Non-road Mobile Sources 725 1,024
On-road Mobile Sources 871 1,558
Point Sources 517 2,397
Wildfires 4,934 189
Total 8,554 5,542
NOx Emissions Densities for the New Mexico
Modeling Domain July 1, 2005
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Final Thoughts

• Advantages of a GIS-based approach
• Facilitates effective use of detailed spatial
  data for input to the emissions model
• vegetation cover
• satellite-derived fire data
• human-reported fire data
• Facilitates visualization of inputs and outputs

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Final Thoughts

• Results depend on the quality and completeness of
  the fire histories and emission factors.
• Emission factors are the subject of continuing
  research.
• Fire histories require significant effort.
• Satellite-derived data are timely and consistent,
  but only cover fires larger than several hundred
  acres.
• Human reports suffer from human errors, but are
  the only available means to capture small fires.
  
• Reconciliation of these data sets is necessary to
  avoid double counting, but can be challenging.

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Status and Future Direction

• Currently a set of procedures not a single tool
• Increasing interest in the effects of fire
  emissions on ozone formation
• Will be incorporated into a national operational
  modeling system with NASA and USFS funding
• Operational systems may eventually provide input
  to national inventories

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Topography is too complex for 12-km modeling grid