Modeling Wildfire Emissions Using Geographic Information Systems (GIS) Technology and Satellite Data STI-3009 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

2 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;

3 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

4 Overview of Approach Emission 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)

5 Overview of Approach

6 Fire Histories: Satellite-Derived Data vs. Human Reports

7 Land Use and Vegetation Cover

8 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 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

9 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

10 Example Results: Central U.S.

11 Example Results: New Mexico NO x Emissions Densities for the New Mexico Modeling Domain July 1, 2005 Source TypeNMHC (tons)NOx (tons) Area Sources1, Non-road Mobile Sources7251,024 On-road Mobile Sources8711,558 Point Sources5172,397 Wildfires4, Total8,5545,542 Emissions by Source Type for the New Mexico Modeling Domain July 1, 2005

12 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

13 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.

14 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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16 Topography is too complex for 12-km modeling grid