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International Journal of Wildland Fire International Journal of Wildland Fire Society
Journal of the International Association of Wildland Fire
RESEARCH ARTICLE

Fire-growth modelling using meteorological data with random and systematic perturbations

Kerry Anderson A D , Gerhard Reuter B and Mike D. Flannigan C
+ Author Affiliations
- Author Affiliations

A Canadian Forest Service, Northern Forestry Centre, 5320 122 Street, Edmonton, AB T6H 3S5, Canada.

B University of Alberta, Earth and Atmospheric Sciences, 1-26 Earth Sciences Building, University of Alberta, Edmonton, AB T6G 2E3, Canada.

C Canadian Forest Service, Great Lakes Forestry Centre, 1219 Queen Street East, Sault Ste Marie, ON P6A 2E5, Canada.

D Corresponding author. Email: kanderso@nrcan.gc.ca

International Journal of Wildland Fire 16(2) 174-182 https://doi.org/10.1071/WF06069
Published: 30 April 2007

Abstract

The focus of this investigation is to quantify the effects of perturbations in the meteorological data used in a fire-growth model. Observed variations of temperature, humidity, wind speed, and wind direction are applied as perturbations to hourly values within a simulated weather forecast to produce several forecasts. In turn, these are used by a deterministic eight-point fire-growth model to produce an ensemble of possible final fire perimeters. Two studies were conducted to assess the value of applying perturbations. In the first study, fire growth using detailed, one-minute data was compared to growth based on the more commonly used hourly data. Results showed that the detailed weather produced fire growth larger and wider than the hourly based data. By applying perturbations, variations in the flank and back-fire spread were captured by the random-perturbation model while the forward spread fell within the 20 to 30% probability prediction. A sensitivity analysis based on the observed variations showed that wind speed accounted for a 44% difference in area burned, while temperature accounted for only a 16% difference. In the second study, case studies were conducted on four observed forest fires in Wood Buffalo National Park. Results showed that daily fire-growth predictions using simulated weather forecasts over-predicted fire growth using actual hourly weather observations by 27%. Systematic-perturbation models best compensated for this with most fire growth falling within the predicted range of the models (52 out of 63 days).

Additional keywords: sensitivity analysis, Wood Buffalo National Park.


Acknowledgements

The authors acknowledge Peter Englefield for his GIS assistance, Mike Wotton for the detailed, minute weather datasets, the staff of Wood Buffalo National Park for the fire weather and fuels data for the park, and Tanya Letcher for her description of the historical fires.


References


Ball GL , Guertin DP (1992) Improved fire growth modelling. International Journal of Wildland Fire  2, 47–54.
Crossref | GoogleScholarGoogle Scholar | Englefield P, Lee BS, Fraser RH, Landry R, Hall RJ, Lynham TJ, Cihlar J, Li Z, Jin J, Ahern FJ (2004) Applying geographic information systems and remote sensing to forest fire monitoring, mapping and modelling in Canada. In ‘Proceedings of the 22nd tall timbers fire ecology conference: fire in temperate, boreal and montane ecosystems’. (Eds RT Engstrom, KEM Galley, WJ de Groot) pp. 240–245. (Tall Timbers Research Station: Tallahassee, FL)

Filmon G (2004) ‘Firestorm 2003 provincial review.’ (Province of British Columbia: Victoria, BC)

Forestry Canada Fire Danger Group (1992) Development and structure of the Canadian Forest Fire Behavior Prediction System. Forestry Canada Information Report ST-X-3, Science and Sustainable Development Directorate. (Ottawa, ON)

Finney MA (1998) FARSITE: Fire area simulator – model development and evaluation. USDA Forest Service, Rocky Mountain Research Station Research Paper RMRS-RP-4. (Ogden, UT)

Kalnay E (2003) ‘Atmospheric modeling, data assimilation and predictability.’ (Cambridge University Press: New York)

Knight I , Coleman J (1993) A fire perimeter expansion algorithm based on Huygens’ wavelet propagation. International Journal of Wildland Fire  3, 73–84.
Crossref | GoogleScholarGoogle Scholar | Kourtz P, Nozaki S, O'Regan WG (1977) Forest fires in the computer – a model to predict the perimeter location of a forest fire. Forest Fire Research Institute Information Report FF-X-65, Canadian Forestry Service. (Petawawa, ON)

Lawson BL, Armitage OB, Hoskins WD (1996) Diurnal variations in the fine fuel moisture code: tables and computer source code. Forest Resource Development Agreement Report 245, Canadian Forest Service. (Victoria, BC)

Linn R, Reisner J, Colman J , Winterkamp J (2002) Studying wildfire behavior using FIRETEC. International Journal of Wildland Fire  11, 233–246.
Crossref | GoogleScholarGoogle Scholar | Quayle B, Lannom K, Finco M, Norton J, Warnick R (2003) Monitoring wildland fire activity on a national-scale with MODIS imagery. In ‘2nd international wildland fire ecology and fire management congress and 5th symposium on fire and forest meteorology’. (CD-ROM) (American Meteorological Service: Boston)

Richards GD (1994) The properties of elliptical wildfire growth for time dependent fuel and meteorological conditions. Combustion Science Technology  95, 357–383.
Stanski HR, Wilson LJ, Burrows WR (1989) Survey of common verification methods in meteorology, 2nd edn. Report Number (MSRB) 89-5, Atmospheric Environment Service, Downsview, ON.

Stocks BJ, Mason JA, Todd JB, Bosch EM , Wotton BM (2003) Large forest fires in Canada, 1959–1997. Journal of Geophysical Research  108, FFR5.1–FFR5.12.
Van Wagner CE (1977) A method of computing fine fuel moisture content throughout the diurnal cycle. Ontario Information Report PS-X-69, Canadian Forestry Service. (Chalk River)

Van Wagner CE (1987) Development and structure of the Canadian Forest Fire Weather Index System. Forest Technology Report 35, Canadian Forestry Service. (Ottawa, ON)