1. Fire Growth Modelling at Multiple Scales
Kerry Anderson
Canadian Forest Service

2. Fire Growth Modelling at Multiple Scales
Introduction
Weather and fire activity can be thought of occurring on different time and space scales.
Forecasting at each of these scales has its individual practical and physical limitations.

3. Scales SYNOPTIC Time MESO MICRO Length year Jet Stream month week
Longwaves
day
Fronts
SYNOPTIC
Cyclones
Thunderstorms
Time
hour
Cumulus
Clouds
MESO
min
MICRO
Turbulence
sec
1 mm
1 m
1 km
1000 km
Length

4. Scales SYNOPTIC Time MESO MICRO Length year Jet Stream month Campaign
Fires
week
Longwaves
Holdover
Fires
Class E
Fires
(> 200 ha)
day
Fronts
SYNOPTIC
Cyclones
Thunderstorms
Time
hour
Detected
Fires
(~0.1ha)
Cumulus
Clouds
MESO
Fire Whirls
min
Ignitions
MICRO
Turbulence
sec
1 mm
1 m
1 km
1000 km
Length

5. Fire Growth Modelling at Multiple Scales
Introduction
Fire growth modelling can fall into three scales depending on weather forecasting ability:
Short-range: 1-2 days
Medium-range: 3-7 days
Long-range: 8+ days

6. Fire Growth Modelling at Multiple Scales
Introduction
As the run time increases, the model becomes less deterministic and more probabilistic.
Short-range
Medium-range
Long-range
deterministic
weather-based
detailed
probabilistic
climate -based
generalized

7. Fire Growth Modelling at Multiple Scales
The Models
This presentation shows a fire growth modelling system designed to run consecutively over three time scales.
The models are designed so that they may be run in sequence, with the results of one model initializing the subsequent model run.

8. Fire Growth Modelling at Multiple Scales
The Models
The Probabilistic Fire Analysis System is a suite of models designed to capture the transition across multiple scales.

9. Short-range Fire Growth

10. Fire Growth Modelling at Multiple Scales
Short-range Fire Growth
USFS Farsite and the
Canadian Prometheus
are examples of operational, deterministic, short-range fire growth models.

11. Fire Growth Modelling at Multiple Scales
Short-range Fire Growth
The short-range model is a deterministic, sixteen -point fire growth model that uses hourly weather.

12. Fire Growth Modelling at Multiple Scales
Short-range Fire Growth
Hourly weather is obtained from numerical weather models.

13. Fire Growth Modelling at Multiple Scales
Short-range Fire Growth
Fire locations are determined using NOAA/AVHRR and MODIS hot spot data.

14. Fire Growth Modelling at Multiple Scales
Short-range Fire Growth
Short-range predictions are produced for the next 24 to 48 hours.

15. Fire Growth Modelling at Multiple Scales
Short-range Fire Growth
Short-range predictions are currently being presented through the CWFIS interactive web-page and GoogleEarth.
August 15, 2013

16. Medium-range Fire Growth

17. Fire Growth Modelling at Multiple Scales
Medium-range Fire Growth
The medium-range model uses the same deterministic fire growth but uses more general, daily weather information.
Day Two
Tmax: 20
Tmin
Rhnoon 35
POP: 0%
Day Three
Tmax: 22
Tmin 8
Rhnoon 25
POP: 0%
Day Four
Tmax: 21
Tmin
Rhnoon 30
POP: 0%
Day Five
Tmax: 18
Tmin
Rhnoon 60
POP: 30%

18. Fire Growth Modelling at Multiple Scales
Medium-range Fire Growth
Daily forecasts are combined with diurnal trend models to produce hourly weather profiles.

19. Fire Growth Modelling at Multiple Scales
Medium-range Fire Growth
Uncertainty in the forecast can be introduced as perturbations
(e.g. Temp + 1oC),
each producing a new weather series.

20. Fire Growth Modelling at Multiple Scales
Medium-range Fire Growth
Ensemble forecasts are produced by running the model through a range of weather conditions creating probable fire perimeters.
Temp + 1 oC
RH + 5%
WD + 10o
WS + 3 kmh

21. Fire Growth Modelling at Multiple Scales
Medium-range Fire Growth
Probable fire perimeters can be further enhanced by introducing the probability of precipitation.
rain
no rain

22. Fire Growth Modelling at Multiple Scales
Medium-range Fire Growth
11%
33%
55%
77%
100%
Current hotspot
Old hotspot
(now extinguished)
Case studies show an improvement in fire growth predictions when using an ensemble approach.

23. Fire Growth Modelling at Multiple Scales
Medium-range Fire Growth
Currently we are examining CMC ensemble products for use in medium-range fire growth predictions.

24. Long-range Fire Growth

25. Fire Growth Modelling at Multiple Scales
Long-range Fire Growth
The long-range fire growth model is probabilistic and is based upon climatology.
Such models are based on probabilities and thus output is represented as probable fire extents.

26. Fire Growth Modelling at Multiple Scales
Long-range Fire Growth
Long term fire growth from one location to another within a given time period is the probability that the fire will spread across the distance before a fire-stopping rain event occurs.
p(t) = p (t)  P (t)
spread
survival

27. Fire Growth Modelling at Multiple Scales
Long-range Fire Growth
Probability of spread is calculated by assuming the rate of spread follows an exponential distribution
where 1/λ = mean rate of spread
Pspread (r > ro ) = e-λ r o

28. Fire Growth Modelling at Multiple Scales
Long-range Fire Growth
Mean rates of spread can be calculated for each fuel type, each month, each direction.
These are similar to wind roses but for the rate of spread for each fuel type.
Mean rates of spread in C2 fuel type for August

29. Fire Growth Modelling at Multiple Scales
Long-range Fire Growth
Probability of survival can be modelled using the DMC.
If the DMC drops below an extinction value, the fire is assumed to go out.

30. Fire Growth Modelling at Multiple Scales
Long-range Fire Growth
Probability of survival is solved using Markov Chains and DMC.
As time progresses, the probability of survival drops to zero.

31. Fire Growth Modelling at Multiple Scales
Long-range Fire Growth
The model calculates the probabilities of spread and of survival and combines them spatially to produce a probable fire extents map.
40%
50%
50%
60%
Spread
Survival
Extents

32. Fire Growth Modelling at Multiple Scales
Long-range Fire Growth
Comparisons with historical fires indicate the model produces realistic results 50
100
150
200
250 km
50%
30%
10%
Historic
Fire
Perimeter
…but is difficult to validate this way.

33. Fire Growth Modelling at Multiple Scales
Long-range Fire Growth
The long-range model predictions were compared with distributions of fire perimeters predicted by repeated simulations using the hourly-based, deterministic fire-growth model.
a) 40 simulated fires
b) 40 simulation probability
c) Long-range model probabiltiy
10%
30%
50%
The study showed a close agreement between the long-range model and the deterministic model.

34. Combined Predictions

35. Fire Growth Modelling at Multiple Scales
Combined Prediction
The models are designed so that they may be run in sequence, with the results of one model initializing the subsequent model run.
Short-range: day 1 to day 2
Medium-range: day 3 to day 7
Long-range: day 8 to day 21

36. Fire Growth Modelling at Multiple Scales
Combined Prediction
The results of one model are used to initialize the subsequent model run.
Medium
Long
Short
2 days
7 days
21 days

37. 20 Fires

38. Fire Growth Modelling at Multiple Scales
Long-range Fire Growth
The long-range model PFAS was used in BC during the 2009 and 2010 fire seasons.
Terrain
Fuels
15 day
Fire Growth Projection (Prescribed Fire Analysis System)
_____ Current fire perimeter (approx ha)
_____ 15 day area at risk (approx ha) (50 % probability)
_____ 30 day area at risk (approx ha) (50 % probability)
30 day
Predictions were used to assess modified response decisions.

39. Fire Growth Modelling at Multiple Scales
Long-range Fire Growth
In 2009, PFAS predictions were used to assess modified response decisions made on 37 fires.
In 2010, this was done on xxx fires.
In all cases, PFAS was used to supplement the decision support decision made by fire suppression officers. In a few cases, the predictions triggered a reassessment of the suppression response plan.

40. The Good

49. The Bad

55. The Ugly

64. Fire Growth Modelling at Multiple Scales
Conclusions
As forest protection agencies re-evaluate the role of fire in the landscape and its ecological benefits, as they face the prospect of excessive fuel build up resulting from years of fire exclusion policies, and as they contend with fiscal constraints, these agencies must start looking at possible fire growth over extended periods.
The methods presented through these models may serve as a foundation for such evaluation.

65. Thank you