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Permanent link (DOI): https://doi.org/10.7939/R3X921Q2W

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

Descriptions

Other title
Subject/Keyword
wildifre
fire
modelling
modeling
ensemble
Type of item
Thesis
Degree grantor
University of Alberta
Author or creator
Moore, Brett G
Supervisor and department
Flannigan, Mike (Renewable Resources)
Examining committee member and department
Gibos, Kelsy
Anderson, Kerry
Department
Department of Renewable Resources
Specialization
Forest Biology and Management
Date accepted
2015-07-15T08:15:37Z
Graduation date
2015-11
Degree
Master of Science
Degree level
Master's
Abstract
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.
Language
English
DOI
doi:10.7939/R3X921Q2W
Rights
Permission is hereby granted to the University of Alberta Libraries to reproduce single copies of this thesis and to lend or sell such copies for private, scholarly or scientific research purposes only. The author reserves all other publication and other rights in association with the copyright in the thesis and, except as herein before provided, neither the thesis nor any substantial portion thereof may be printed or otherwise reproduced in any material form whatsoever without the author's prior written permission.
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