Production Function Analysis of Human Factors Influencing Wildfire Risk in the WUI

1. This presentation will probably involve audience discussion, which will create action items. Use PowerPoint to keep track of these action items during your presentation • In Slide Show, click on the right mouse button • Select “Meeting Minder” • Select the “Action Items” tab • Type in action items as they come up • Click OK to dismiss this box • This will automatically create an Action Item slide at the end of your presentation with your points entered. Production Function Analysis of Human Factors Influencing Wildfire Risk in the WUI Evan Mercer and Jeff Prestemon Southern Research Station, RTP, NC

2. Objectives • Describe principal categories of wildfire production functions. • Discuss advantages, limitations, and complementarities of alternative functional forms for wildfire production functions. • Outline alternative methods for using wildfire production functions to model wildfire risk. • Illustrate application of wildfire production function analysis in WUI: Florida case study

3. Wildfire Production Functions • Quantitative models of how wildfire responds to: • ecological variables (weather, climate, ecosystem type • Management variables (fuels management, pre-suppression, suppression) • Socioeconomic variables (housing density, income, employment) • Allow empirical analysis at multiple spatial and temporal scales. • Useful for • Risk analysis • economic modeling • evaluating trade-offs among alternative wildfire policies and strategies

4. Categories of Wildfire Production Functions • Fire Event Models (e.g. ignitions) • Individual Fire Extent (area) • Aggregate Fire Extent (area) • Fire Effects (Intensity, severity, damage)

5. Event Models • Count or Point Process Models • Number of occurrences of an event within discrete units of time and/or space • Useful for modeling fire ignitions • Relate observed counts of ignitions to covariates based on Poisson or negative binomial • Useful when ignition is part of economic optimization model (e.g. separate costs associated with responding to ignitiion, extent, and intensity) • Example: Prestemon and Butry analysis of Arson

6. Individual Fire Extent Models • Relate hypothesized covariates to the area or change in area for a single wildfire . • Relate the area burned or not burned within a temporal unit (e.g., a day) to variables expected to affect the rate of spread. • Best suited for identifying effectiveness of suppression or fuel conditions on fire spread. • May be complicated by multiple production processes. • Examples: Farsight, Behave

7. Aggregate Extent Models • Generalizes the individual extent model over space and time • Relates aggregate extent of all fires in a spatial unit to weather, ecological, management, and socio-economic covariates. • Collective risk model when expressed relative to size of the spatial unit. • Examples, Prestemon et al. (2002), Westerling et al (2002)

8. Fire Effects Models • Production output is a fire characteristic (e.g. intensity, severity, damage, fuel consumption, ecological benefit) that is related to covariates. • Most useful in combination with other wildfire production (e.g. wildfire area) • Example, Mercer et al. model wildfire intensity-acres per unit of forest area

9. Applying Fire Production Functions to Wildfire Risk Analysis • Estimate three wildfire production functions • Fire Event (ignitions) • Aggregate Fire Extent (wildfire area) • Fire Effect (wildfire intensity) • Cross-Sectional Time Series Panels • Cross sections: all counties in Florida • Time series: 1995-2001 • Data Sources: Florida Division of Forestry, Census, BEBR, FIA, NOAA, FDLE

10. Fire Event Model • Conditional fixed-effects Poisson • Dependent variable: • number of ignitions per county per year • Independent variables: • 12 years previous wildfire extent (wildfire/total forest acres) • Weather: El Niño and North Atlantic Oscillations • 3 years prescribed burn (acres p.b./total forest) • WUI variables: population, poverty rate, unemployment rate, housing density, police

11. Aggregate Fire Extent • General Least Squares, Fixed Effects, Heteroskedastic Log-log Model • Dependent Variable: • Log of total wildfire acres per county per year • Independent Variables • Logged 12 years previous wildfire extent (wildfire/total forest acres) • Weather: El Niño and North Atlantic Oscillations • Logged 3 years prescribed burn (acres p.b./total forest) • WUI variables: population, poverty rate, unemployment rate, housing density, police

12. Fire Intensity Model • General Least Squares, Fixed Effects, Heteroskedastic Log-log Model • Dependent Variable: • Log of “intensity-acres”: intensity calculated from flame-length data for every fire—aggregated across all acres of each fire, and all fires within each county each year. • Independent Variables • Logged 12 years previous wildfire extent (wildfire/total forest acres) • Weather: El Niño and North Atlantic Oscillations • Logged 3 years prescribed burn (acres p.b./total forest) • WUI variables: population, poverty rate, unemployment rate, housing density, police

13. Results

14. Results

15. Conclusions • Production Function Analysis: • Methods for empirically inter-relating socio-economic variables with wildfire outputs • Models give slightly different results • Choice depends on questions to be asked • Human factors that influence wildfire • Prescribed Burning (negative) • Population Density (negative) • Unemployment (negative) • Population (positive) • Poverty (depends on dependent variable) • Police (depends on dependent variable)