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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 Forest Service 2 Parks Canada Agency. Introduction. Increasing knowledge of factors affecting landscape flammability

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slide1

Evaluating landscape flammability through simulation modeling

Marc Parisien1, Victor Kafka2, Bernie Todd1, Kelvin Hirsch1, and

Suzanne Lavoie1

1Canadian Forest Service

2Parks Canada Agency

slide4

Introduction

  • 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
slide5

Introduction

What we need

  • To quantatively evaluate landscape flammability (i.e., burn probability)

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
slide6

Objective

  • 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)

Maps the probability of burning of the actual landscape under current burning conditions submitted to historical variability

slide9

Components of the Model

IGNITION LOCATIONS

Determined from historical fire databases

The information is drawn from statistical distributions

NUMBER OF ESCAPED FIRES

Probabilistic

NUMBER OF FIRE SPREAD DAYS

FIRE WEATHER

Simulates the growth of escaped fires (200 ha)

Mechanistic

PHYSICAL FIRE GROWTH

slide10

Fire growth modeling

WILDFIRE Fire Growth Model

  • 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
slide11

Components of the Model

NUMBER OF ITERATIONS

IGNITION LOCATIONS

Probability of burning =

Number of times each cell burned

Number of iteration

NUMBER OF ESCAPED FIRES

NUMBER OF FIRE SPREAD DAYS

FIRE WEATHER

PHYSICAL FIRE GROWTH

SIMULATIONS STORED

slide24

Burn probability (%)

Burn probability map

for the 2003 fire season

slide25

What’s next?

  • Integrating Prometheus in BURN-P3
  • More realistic fire growth modeling
  • Increased functionality
  • Greater user base
  • 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
slide27

Forest B

Forest A

25 km

Burn probability (%)

slide28

Spring

Summer

Number of fires

50

150

250

350

Julian Day

Number of fires from 1981 to 2002

Study area

slide32

Reduction in BP outside the perimeter

Large burns disrupt the paths of incoming fires

The chance that fires ignite nearby is decreased

slide33

500 iterations

73,000 ha

Objective #2: Heterogeneous (actual) landscape

Peripheral reduction in BP due to:

  • Different forest fuels
  • The amount and configuration of landscape features
  • The direction of predominant winds
slide34

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 weather
  • Number of fire spread days

Old burn (non fuel)

100,000 ha

Boreal spruce fuel type

(most flammable)

slide35

100,000 ha

1000 iterations

slide36

Inside buffer

Average BP = 1.9%

Outside buffer

Average BP = 2.6%

5-km buffer

1000 iterations

slide37

1-km buffer

Average BP = 1.5%

2 to 5-km buffer

Average BP = 2.0%

1-km buffer

2 to 5-km buffer

  • Percent difference in BP between:
  • The two buffers: 25%
  • The 1-km buffer and outside the buffer: 42%

1000 iterations

slide40

Kootenay Park

Lodge

2003 fire

2001 fire

slide43

Reduction in Landscape Flammability

Fires 1945 to 2002

Present FBP fuels (LANDSAT)

Non fuel

slide44

12 years of historical

daily fire weather data

(40 wx stations)

Retrieve days with high/extreme fire weather (ISI  8.6)

Store by season (2) and weather region (8)

TOTAL =

16 weather files

SELECTION OF FIRE WEATHER RECORDS (DAYS)

Determine ISI for HFI  4000 kW/m for C-2, C-3, and C-4

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