An approach to operational forest fire growth predictions for Canada

This paper presents an operational approach to predicting fire growth for wildland fires in Canada. The approach addresses data assimilation to provide predictions in a timely and efficient manner. Fuels and elevation grids, forecast weather, and active fire locations are entered into a fire-growth model; then predicted fire perimeters are mapped and presented on the web. The Moderate Resolution Imaging Spectroradiometer (MODIS) and the National Oceanic and Atmospheric Administration Advanced Very High Resolution Radiometer (NOAA/AVHRR) satellite-based detection systems are used to detect current wildland fires (referred to as hotspots). For selected regions, fire-growth simulation environments are assembled. Fuel type data from several fire management agencies are available in grid format at a resolution of 100 m or less; in areas where such data are not available, a national fuels map based on Satellite Pour l'Observation de la Terre Vegetation sensor (SPOTVGT) land cover and forest inventory is used. Similarly, terrain data are available from a variety of sources. Current hotspots are used as ignition points while past hotspots are used to delineate area burned. Surface wind, temperature, and dew-point values (forecast by Environment Canada) are used to determine the fire weather conditions at the fire location. A case study of two large fires in Canada consisting of 54 fire simulation days is used to test these hypotheses.