Increasing wildfire growth modelling decision support using ensemble weather forecasts over the province of Alberta, Canada

• Author / Creator

  Moore, Brett G

• Across Alberta, wildfires ignite each fire season and a small number achieve a size greater than 100 hectares, which account for the vast majority of the area burned. These fires often require large suppression efforts that include wildfire growth simulation modelling in order to understand their trajectory and likely destination. To date deterministic wildfire growth simulation has been the industry standard. With advances in numerical weather prediction, it is now possible to perform probabilistic wildfire growth simulation modelling via the regional ensemble prediction system, which forecasts 3 days. When probabilistic wildfire growth simulation is employed, an average of 2% increase in overall skill. Additionally, over prediction (represented as bias) was reduced from seven to one. This approach performs superior to the deterministic methods in boreal regions. The limitations and implications are also discussed.

• Subjects / Keywords

  • Fire
  • Wildifre
  • Modelling
  • Ensemble

• Graduation date

  Fall 2015

• Type of Item

  Thesis

• Degree

  Master of Science

• DOI

  https://doi.org/10.7939/R3X921Q2W

• License

  This thesis is made available by the University of Alberta Libraries with permission of the copyright owner solely for non-commercial purposes. This thesis, or any portion thereof, may not otherwise be copied or reproduced without the written consent of the copyright owner, except to the extent permitted by Canadian copyright law.