Wildfires in Florida David T. ButryUS Forest ServiceMarcia L. GumpertzNorth Carolina State University Marc G. GentonTexas A&M University
Funding provided by USDA Forest Service
Study Site:SJRWMDFrom 1996-2001:7249 small wildfiresArson25%Accidents43%Lightning32%53 large wildfiresArson17%Accidents28%Lightning55%
Size Distribution of Fires in SJRWMD 1981-2001
Large WildfiresSmall Wildfires
Wildfire ModelsWildfire modeled as a function of:Fuel type, fire cause, time, location Climate and weather at time of ignitionPrescribed burning, time to respond to fireLandscape attributes, previous fire
Three models estimated:Area burned. Fires 1000 acres or lessArea burned. Fires >1000 acresProbability that a fire becomes larger than 1000 acres.
Fire Characteristics (XF): Start timeStart year CauseClimate/Weather (XC):Nio3 KBDI Spread IndexBuildup IndexHumidity Wind speed Wind direction Management (XM): Response time Limited action fires Prescribed fire Includes current and lagged values, as well as neighboring current and lagged values
Landscape Characteristics (XS): Population density Income Percent of population who have attended college Amount of road in sectionDistance to nearest fire department Landscape landcover/landuseLatitude & longitude Fire district Previous wildfire Includes current and lagged values, as well as neighboring current and lagged valuesGIS hole
Spatial and Temporal Scales of DataIndividual fireLocation: centroid of sectionTime of ignitionSpatial and Temporal Variables in Models Latitude and longitude (Albers projection) Year Time of day (morning, afternoon, evening, overnight)
Lagged VariablesPrevious wildfireSection: same or neighboringYear: earlier in same year; or in previous 12 yearsPrescribed burning prior to the fire For hazard reduction:Section: same or neighboringYear: earlier in same year; or in previous 3 yearsFor silvicultural or other purposesSection: same or neighboringAny time in this year or previous 3 years
Model SpecificationArea burned:Ln(Wildfire Size) = X + ,
X may include lagged variables
Probability of Large Fire:
ResultsSmall Wildfires, n=7249, R2=.19Factors related to greater area burnedResponse time up to 16 hoursLimited-action fires (let burn)Spread indexPalmetto-Gallberry, Grass, and Pine fuel typesArson ignitions (as opposed to lightning)Afternoon ignitions (as opposed to overnight)Area burned by wildfire in neighboring sections in the previous 1-12 years Previous non-hazard mitigating prescribed fire lagged up to 3 yearsFactors related to smaller area burnedLa NiaHumidityHazard-mitigating prescribed burning in the current yearPopulation density, up to about 70 persons per square kilometer (which is the median)Fire districts 10, 14, & 16Smaller fires in 1999, 2000, 2001% of water and wetlands in section% of grasslands and upland forests in neighboring sections
InteractionsSeveral with KBDI
Some Factors Related to Area Burned (Fires 1000 Acres)
Effects of KBDI
ResultsLarge Wildfires , n=53, R2=.73Factors Related to Greater Area BurnedSpread indexWildfire in previous 12 yearsPercent of wetland in the sectionPopulation densityIncome1998
Factors Related to Smaller Area BurnedFuel type grassArson and accidental ignitions, as opposed to lightningFire District 12
InteractionsIn GIS hole areas, percent of water in the section is positively related to large fire size
Some Factors Associated with Area of Fire, Fires > 1000 Acres
ResultsProbability that Area Burned will Exceed 1000 Acresn=7302, max rescaled R2=.32Positive RelationLa NiaIncomeBuild-upLimited-action fires (let burn)Wind speed1998Neighboring upland forest
Negative RelationEl NioUpland forestNeighboring Urban areas
InteractionsLog odds increases with KBDI in upland forest sections but not in other types of land cover.
Log odds decreases with hazard-reducing prescribed burning if response time is short, but not if response time is longer.
If the response time is long, the log odds decreases as hazard-reducing prescribed burning increases in neighboring sections.
SummaryLagged variables in space and time capture much of the spatial/temporal information, but not quite all of it.
Models dont fit or predict nearly as wellas Id like. Theres more to be done.
Some encouraging results regarding ability to detect effects of managementpractices in section and neighbors.
Continuing work on estimating magnitude of effect of prescribed burning -- using propensity scores.