# 1 Spatially Explicit Burn Probability across A Landscape in Extreme Fire Weather Year Wenbin Cui, David L. Martell Faculty of Forestry, University of Toronto.

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1 Spatially Explicit Burn Probability across A Landscape in Extreme Fire Weather Year Wenbin Cui, David L. Martell Faculty of Forestry, University of Toronto University of Toronto

2 Outline Burn Probability (BP) Model Burn Probability (BP) Model BP Model Application BP Model Application Predict BP in an Extreme Fire Weather Year Discussions Discussions Possible Applications, Limitations & Future Research Possible Applications, Limitations & Future Research

3 Burn Probability of Next Fire Season

4 Burn Probability Calculation Forest Burn Probability of next fire season at location(i,j) Forest Burn Probability of next fire season at location(i,j) BP xy : Burn probability at location (x,y) BP xy : Burn probability at location (x,y) N: number of years(iterations) N: number of years(iterations) N xy: number of times of having been burned at N xy: number of times of having been burned at location(i,j) location(i,j)

5 Main Factors affecting BP Fuel Type Fire Occurrence Topography (elevation, slopes & slope aspects) Weather Level of Protection Fire Spread

6 Burn Probability Model Fuel Type Fire Occurrence Level of Protection Fire Spread SPATIALSPATIAL Daily Weather Burn Probability

7 Fuel Type Classification The Canadian Forest Fire Behavior Prediction (FBP) System is used

8 Burn Probability Model Fuel Type Fire Occurrence Level of Protection Fire Spread Weather Burn Probability Fire Occurrence

9 That total number of fires will occur each year follows a Poisson distribution with an average number equal to historical average number of fires in this landscape. That total number of fires will occur each year follows a Poisson distribution with an average number equal to historical average number of fires in this landscape. The conditions that cause past ignition pattern will continue in the next fire season The conditions that cause past ignition pattern will continue in the next fire season affected by the fuel at the location and the weather condition at the time of ignition. affected by the fuel at the location and the weather condition at the time of ignition. Ignition Patterns differ by cause Ignition Patterns differ by cause

10 Fire ignition patterns (density) People-caused and Lightning-caused density maps People-caused and Lightning-caused density maps

11 Burn Probability Model Fuel Type Fire Occurrence Level of Protection Fire Spread Weather Burn Probability Level of Protection

12 Level of Protection Percent of forest fires controlled at initial attack (IA) Percent of forest fires controlled at initial attack (IA) If a fire is controlled, it only “burns” one cell of the landscape. Otherwise it escapes IA and we used a fire growth model to “spread” it. If a fire is controlled, it only “burns” one cell of the landscape. Otherwise it escapes IA and we used a fire growth model to “spread” it. Escape Index: (EI) Escape Index: (EI) HFI is the Head Fire Intensity (kW/m) HFI is the Head Fire Intensity (kW/m) RT is response time (hours) RT is response time (hours)

13 Spatially Different Response Time to Forest Fires 10 hours 2 hours 3 hours 4 hours

14 Burn Probability Model Fuel Type Fire Occurrence Level of Protection Weather Burn Probability Fire Spread

15 Fire Spread Fire Spread The escaped fires are simulated by using Wildfire program. The escaped fires are simulated by using Wildfire program. Wildfire is a fire growth model that incorporates GIS data, FBP System calculations and weather data to estimate patterns of hourly fire perimeters. (Todd 1999) Wildfire is a fire growth model that incorporates GIS data, FBP System calculations and weather data to estimate patterns of hourly fire perimeters. (Todd 1999)

16 Burn Probability Model Fuel Type Fire Occurrence Level of Protection Fire Spread Weather Burn Probability Weather

17 Weather Daily historical weather data Daily historical weather data Each record includes: Each record includes: Temperature, Relative Humidity, Wind speed, wind direction, rain fall, FFMC, DMC, DC, BUI, ISI Temperature, Relative Humidity, Wind speed, wind direction, rain fall, FFMC, DMC, DC, BUI, ISI Data from more than one station can be used. Data from more than one station can be used.

18 Output of the Model BP MapsFire InformationBurn Fractions BP Model Other

19 Application of BP Model in an Extreme Fire Weather Year Study area Application of the Model

20 Location of Romeo Malette Forest (RMF)

21 FBP Fuel Types 2,028,224 ha

22 Fire History  From 1976 to 1999 there are 909 fires.  The average is 37.875/year.  The average area burned a year is 1136.15 ha.

23 Historical Ignition Patterns

24 Annual Burn Probability 0.0567%

25 Burn Probability in an Extreme Weather Year What is an EXTREME Fire Weather Year? What is an EXTREME Fire Weather Year? The year that has most ESCAPED fires The year that has most ESCAPED fires Burned more area Burned more area 1991 1991 Average Number -75 (real number) Average Number -75 (real number) Daily Weather Daily Weather LOP - 90.7% (Uniform response time) LOP - 90.7% (Uniform response time)

26 Burn Probability under Extreme Weather Conditions (1991) 0. 258%

27 Burn Fractions by Fuel Type

28 Applications of Burn Probability Model BP Model Fuel Ignition Response time (LOP) Assessment & Decision FireSmart Harvest People-caused ignition control Fuel Management FireSmart Roads

29 Used in other models Forest Management Models (FireSmart) Forest Management Models (FireSmart) Burn Probability by Stands Burn Probability by Stands Burn Fractions by species, stand Burn Fractions by species, stand WUI Fire Management WUI Fire Management Wildlife Habitat Suitability Assessment Wildlife Habitat Suitability Assessment

30 Limitations and Future Work Limitations and Future Work Spotting is not included in fire spread - Prometheus with spotting capability will be used in future Spotting is not included in fire spread - Prometheus with spotting capability will be used in future Use of Regional Climate Model Use of Regional Climate Model Produce more BP maps! Produce more BP maps!

31 Acknowledgement Kelvin Hirsch, Victor Kafka, Marc-André Parisien, Bernie Todd Canadian Forest Service, Northern Forestry Center Jennifer Johnson, Mike B. Wotton, Ana C. Espinoza, Mariam Sanchez G, Justin Podur and Jennifer Beverly Faculty of Forestry, University of Toronto Sustainable Forest Management Network Jim Caputo, Robert McAlpine Ontario Ministry of Natural Resources Tembec

32 Thanks ! Comments & Questions?

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