Modeling Wildfire Emissions Using Geographic Information

Systems (GIS) Technology and Satellite Data

STI-3009

Presented by Neil J. M. WheelerSonoma Technology, Inc.

Petaluma, California

at theFifth Annual Community Modeling and Analysis System (CMAS)

ConferenceOctober 16-18, 2006

Chapel Hill, North Carolina

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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@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 ApproachEmission estimates prepared using:• Fire activity data (location, acres burned)

– Satellite derived– Human reported

• Vegetation data (classification, fuel loading)– EPA’s 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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GIS Land Use/Vegetation Cover

Cross - Walk to (a) Fuel Loading and (b) Emission Factors

(a) (b)

Telephone and Mail Surveys

Development of Fire Histories: Locations, Land Types, Seasons,

and Areas Burned

(a) (b)

Overlay

Multiply: Burn Area x EF x Fuel Loading

Emissions

Satellite Fire Detections 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 ModelBasic EquationEmissions (lb) =

Burn area (acres) * Fuel loading (ton/acre) * Emission factor (lb/ton)

First Order Fire Effects Model (FOFEM):– Cross-walk developed with EPA’s 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 EstimationWildfire 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

NOx Emissions Densities 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

Emissions by Source Type 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