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Evaluating landscape flammability through simulation modeling Marc Parisien 1, Victor Kafka 2, Bernie Todd 1, Kelvin Hirsch 1, and Suzanne Lavoie 1 1 Canadian.

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Presentation on theme: "Evaluating landscape flammability through simulation modeling Marc Parisien 1, Victor Kafka 2, Bernie Todd 1, Kelvin Hirsch 1, and Suzanne Lavoie 1 1 Canadian."— Presentation transcript:

1 Evaluating landscape flammability through simulation modeling Marc Parisien 1, Victor Kafka 2, Bernie Todd 1, Kelvin Hirsch 1, and Suzanne Lavoie 1 1 Canadian Forest Service 2 Parks Canada Agency

2 Presentation to the NCE Meeting in Toronto, Feb 11/12, 2003 Natural Resources Canada Ressources naturelles Canada Canadian Forest Service Service canadien des forets

3 Presentation to the NCE Meeting in Toronto, Feb 11/12, 2003 Natural Resources Canada Ressources naturelles Canada Canadian Forest Service Service canadien des forets

4 Presentation to the NCE Meeting in Toronto, Feb 11/12, 2003 Natural Resources Canada Ressources naturelles Canada Canadian Forest Service Service canadien des forets Increasing knowledge of factors affecting landscape flammability However, predicting landscape flammability has been largely unattained This is even more challenging in the North American boreal forest Most area burned is caused by few large and intense fires Large spatial variations in the fire regime Introduction

5 Presentation to the NCE Meeting in Toronto, Feb 11/12, 2003 Natural Resources Canada Ressources naturelles Canada Canadian Forest Service Service canadien des forets What we need To quantatively evaluate landscape flammability (i.e., burn probability) Introduction What we know It is possible to predict individual fire behavior using the factors that affect physical fire spread (weather, fuels, topography) Larger-scale aspects of the fire regime are best predicted probabilistically (i.e., ignitions, fire weather) What we have Fire growth models simulating physical fire spread Historical wildfire information

6 Presentation to the NCE Meeting in Toronto, Feb 11/12, 2003 Natural Resources Canada Ressources naturelles Canada Canadian Forest Service Service canadien des forets Objective 1. 1.To evaluate burn probabilities (BP) across a landscape using a modelling approach that combines   the physical components of fire spread   other probabilistic components of the fire regime BURN-P3 (Probability, Prediction, and Planning) actual landscape current burning conditions historical variability Maps the probability of burning of the actual landscape under current burning conditions submitted to historical variability

7 Presentation to the NCE Meeting in Toronto, Feb 11/12, 2003 Natural Resources Canada Ressources naturelles Canada Canadian Forest Service Service canadien des forets Study Area: Central Saskatchewan Area: 15 M hectares

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9 Presentation to the NCE Meeting in Toronto, Feb 11/12, 2003 Natural Resources Canada Ressources naturelles Canada Canadian Forest Service Service canadien des forets Components of the Model PHYSICAL FIRE GROWTH Determined from historical fire databases The information is drawn from statistical distributions Simulates the growth of escaped fires (  200 ha) Mechanistic FIRE WEATHER NUMBER OF ESCAPED FIRES NUMBER OF FIRE SPREAD DAYS IGNITION LOCATIONS Probabilistic

10 Presentation to the NCE Meeting in Toronto, Feb 11/12, 2003 Natural Resources Canada Ressources naturelles Canada Canadian Forest Service Service canadien des forets Raster-based model Based on Canadian Forest Fire Danger Rating System theory Rate of spread equations Fire behavior Inputs Hourly fire weather Forest fuels Topography Produces maps of the fire perimeter and fire behavior WILDFIRE Fire Growth Model Fire growth modeling

11 Presentation to the NCE Meeting in Toronto, Feb 11/12, 2003 Natural Resources Canada Ressources naturelles Canada Canadian Forest Service Service canadien des forets Components of the Model NUMBER OF ITERATIONS FIRE WEATHER NUMBER OF ESCAPED FIRES NUMBER OF FIRE SPREAD DAYS IGNITION LOCATIONS PHYSICAL FIRE GROWTH SIMULATIONS STORED Probability of burning = Number of times each cell burned  Number of iteration

12 Presentation to the NCE Meeting in Toronto, Feb 11/12, 2003 Natural Resources Canada Ressources naturelles Canada Canadian Forest Service Service canadien des forets

13 Presentation to the NCE Meeting in Toronto, Feb 11/12, 2003 Natural Resources Canada Ressources naturelles Canada Canadian Forest Service Service canadien des forets

14 Presentation to the NCE Meeting in Toronto, Feb 11/12, 2003 Natural Resources Canada Ressources naturelles Canada Canadian Forest Service Service canadien des forets

15 Presentation to the NCE Meeting in Toronto, Feb 11/12, 2003 Natural Resources Canada Ressources naturelles Canada Canadian Forest Service Service canadien des forets

16 Presentation to the NCE Meeting in Toronto, Feb 11/12, 2003 Natural Resources Canada Ressources naturelles Canada Canadian Forest Service Service canadien des forets

17 Presentation to the NCE Meeting in Toronto, Feb 11/12, 2003 Natural Resources Canada Ressources naturelles Canada Canadian Forest Service Service canadien des forets

18 Presentation to the NCE Meeting in Toronto, Feb 11/12, 2003 Natural Resources Canada Ressources naturelles Canada Canadian Forest Service Service canadien des forets

19 Presentation to the NCE Meeting in Toronto, Feb 11/12, 2003 Natural Resources Canada Ressources naturelles Canada Canadian Forest Service Service canadien des forets

20 Presentation to the NCE Meeting in Toronto, Feb 11/12, 2003 Natural Resources Canada Ressources naturelles Canada Canadian Forest Service Service canadien des forets

21 Presentation to the NCE Meeting in Toronto, Feb 11/12, 2003 Natural Resources Canada Ressources naturelles Canada Canadian Forest Service Service canadien des forets

22 Presentation to the NCE Meeting in Toronto, Feb 11/12, 2003 Natural Resources Canada Ressources naturelles Canada Canadian Forest Service Service canadien des forets

23 Presentation to the NCE Meeting in Toronto, Feb 11/12, 2003 Natural Resources Canada Ressources naturelles Canada Canadian Forest Service Service canadien des forets

24 Presentation to the NCE Meeting in Toronto, Feb 11/12, 2003 Natural Resources Canada Ressources naturelles Canada Canadian Forest Service Service canadien des forets Burn probability (%) Burn probability map for the 2003 fire season

25 Presentation to the NCE Meeting in Toronto, Feb 11/12, 2003 Natural Resources Canada Ressources naturelles Canada Canadian Forest Service Service canadien des forets What’s next? 1.Integrating Prometheus in BURN-P3 More realistic fire growth modeling Increased functionality Greater user base 2.Creating a graphic user interface for BURN-P3 BURN-P3 will become one of the first fire management strategic planning tools Available Canada-wide, to any interested user Flexible, user-friendly tool (i.e., not a strict model) On-going process, open to suggestions from operations

26 Questions?

27 25 km Forest B Forest A Burn probability (%)

28 Presentation to the NCE Meeting in Toronto, Feb 11/12, 2003 Natural Resources Canada Ressources naturelles Canada Canadian Forest Service Service canadien des forets Number of fires from 1981 to 2002 Julian Day Number of fires SpringSummer Study area

29 Presentation to the NCE Meeting in Toronto, Feb 11/12, 2003 Natural Resources Canada Ressources naturelles Canada Canadian Forest Service Service canadien des forets 1 Number of fires from 1981 to 2002 Study area

30 Presentation to the NCE Meeting in Toronto, Feb 11/12, 2003 Natural Resources Canada Ressources naturelles Canada Canadian Forest Service Service canadien des forets Fire Size Distribution Historical firesSimulated fires

31 Presentation to the NCE Meeting in Toronto, Feb 11/12, 2003 Natural Resources Canada Ressources naturelles Canada Canadian Forest Service Service canadien des forets Reduction in BP

32 Reduction in BP outside the perimeter 1. Large burns disrupt the paths of incoming fires 2. The chance that fires ignite nearby is decreased

33 500 iterations Objective #2: Heterogeneous (actual) landscape Peripheral reduction in BP due to: 1. 1.Different forest fuels 2. 2.The amount and configuration of landscape features 3. 3.The direction of predominant winds 73,000 ha

34 Presentation to the NCE Meeting in Toronto, Feb 11/12, 2003 Natural Resources Canada Ressources naturelles Canada Canadian Forest Service Service canadien des forets Objective #2: homogeneous landscapes Reduction in BP according to: The shape of old burns The size of old burns 1000 ha 10,000 ha 100,000 ha All factors are held constant except: Fire weatherFire weather Number of fire spread daysNumber of fire spread days Old burn (non fuel) 100,000 ha Boreal spruce fuel type (most flammable)

35 100,000 ha 1000 iterations

36 Inside buffer Average BP = 1.9% Outside buffer Average BP = 2.6% 5-km buffer 1000 iterations

37 1-km buffer 2 to 5-km buffer 1-km buffer Average BP = 1.5% 2 to 5-km buffer Average BP = 2.0% Percent difference in BP between: The two buffers: 25% The two buffers: 25% The 1-km buffer and outside the buffer: 42% The 1-km buffer and outside the buffer: 42% 1000 iterations

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40 Kootenay Park Lodge 2003 fire 2001 fire

41 Questions?

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43 Presentation to the NCE Meeting in Toronto, Feb 11/12, 2003 Natural Resources Canada Ressources naturelles Canada Canadian Forest Service Service canadien des forets Reduction in Landscape Flammability Fires 1945 to 2002 Present FBP fuels (LANDSAT) Non fuel

44 Presentation to the NCE Meeting in Toronto, Feb 11/12, 2003 Natural Resources Canada Ressources naturelles Canada Canadian Forest Service Service canadien des forets SELECTION OF FIRE WEATHER RECORDS (DAYS) Determine ISI for HFI  4000 kW/m for C-2, C-3, and C-4 12 years of historical daily fire weather data (40 wx stations) Store by season (2) and weather region (8) TOTAL = 16 weather files Retrieve days with high/extreme fire weather (ISI  8.6)


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