Fire Growth Modelling at Multiple Scales

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

4. year Jet Stream month Campaign Fires week Longwaves Holdover Fires Class E Fires (> 200 ha) Fronts day SYNOPTIC Cyclones Thunderstorms Time hour Detected Fires (~0.1ha) Cumulus Clouds Scales 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 Short-range Introduction As the run time increases, the model becomes less deterministic and more probabilistic. 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 Probabilistic Fire Analysis System is a suite of models designed to capture the transition across multiple scales. The Models

9. Short-rangeFire Growth

10. Fire Growth Modelling at Multiple Scales USFS Farsite and the Canadian Prometheus Short-range Fire Growth 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 predictions are produced for the next 24 to 48 hours. Short-range Fire Growth

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

16. Medium-rangeFire 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 10 Rhnoon 35 POP: 0% Day Three Tmax: 22 Tmin 8 Rhnoon 25 POP: 0% Day Four Tmax: 21 Tmin 19 Rhnoon 30 POP: 0% Day Five Tmax: 18 Tmin 14 Rhnoon 60 POP: 30%

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

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

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

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

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

27. Fire Growth Modelling at Multiple Scales Pspread(r > ro) = e-λ r 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 o

28. Fire Growth Modelling at Multiple Scales 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. Long-range Fire Growth Mean rates of spread in C2 fuel type for August

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

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

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. Historic Fire Perimeter 10% 30% 50% 0 50 100 150 200 250 km Fire Growth Modelling at Multiple Scales Long-range Fire Growth Comparisons with historical fires indicate the model produces realistic results …but is difficult to validate this way.

33. 10% 10% 30% 30% 50% 50% c) Long-range model probabiltiy a) 40 simulated fires b) 40 simulation probability 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. The study showed a close agreement between the long-range model and the deterministic model.

34. CombinedPredictions

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 Medium Long Combined Prediction The results of one model are used to initialize the subsequent model run. Short 2 days 7 days 21 days

37. 20 Fires2009-2010

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 76 000 ha) _____ 15 day area at risk (approx 180 000 ha) (50 % probability) _____30 day area at risk (approx 317 000 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