Canadian Forest Service Publications

An analysis of controls on fire activity in boreal Canada: comparing models built with different temporal resolutions. 2014. Parisien, M.-A.; Parks, S.A.; Krawchuk, M.A.; Little, J.M.; Flannigan, M.D.; Gowman, L.M.; Moritz, M.A. Ecological Applications 24(6):1341-1356.

Year: 2014

Issued by: Northern Forestry Centre

Catalog ID: 35319

Language: English

Availability: Order paper copy (free), PDF (request by e-mail)

Available from the Journal's Web site.
DOI: 10.1890/13-1477.1

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Fire regimes of the Canadian boreal forest are driven by certain environmental factors that are highly variable from year to year (e.g., temperature, precipitation) and others that are relatively stable (e.g., land cover, topography). Studies examining the relative influence of these environmental drivers on fire activity suggest that models making explicit use of inter-annual variability appear to better capture years of climate extremes, whereas those using a temporal average of all available years highlight the importance of land-cover variables. It has been suggested that fire models built at different temporal resolutions may provide a complementary understanding of controls on fire regimes, but this claim has not been tested explicitly with parallel data and modelling approaches. We addressed this issue by building two models of area burned for the period 1980-2010 using 14 explanatory variables to describe ignitions, vegetation, climate, and topography. We built one model at an annual resolution, with climate and some land-cover variables being updated annually, and the other model using 31-year fire "climatology" based on averaged variables. Despite substantial differences in the variables' contributions to the two models, their predictions were broadly similar, which suggests coherence between the spatial patterns of annually varying climate extremes and long-term climate normals. Where the models' predictions diverged, discrepancies between the annual and averaged models could be attributed to specific explanatory variables. For instance, annually updating land cover allowed us to identify a possible negative feedback between flammable biomass and fire activity. These results show that building models at more than one temporal resolution affords a deeper understanding of controls on fire activity in boreal Canada than can be achieved by examining a single model. However, in terms of spatial predictions, the additional effort required to build annual models of fire activity may not always be warranted in this study area. From a management and policy standpoint, this key finding should boost confidence in models that incorporate climatic normals, thereby providing a stronger foundation on which to make decisions on adaptation and mitigation strategies for future fire activity.

Plain Language Summary

In this project, Canadian and US researchers compared two approaches to computer modelling of fire risk for the entire Canadian boreal forest, using data from 1980 to 2010 (31 years). The two types of models commonly used tend to emphasize changes either over time or over space. Models that look at changes in climate and weather from season to season and year to year usually involve large areas and emphasize changes over time rather than differences among areas, whereas models that examine more non-climatic factors such as forest type tend to use climate variables such as temperature and moisture averaged over long periods. Two models were created, one capturing annual variations in climate and the other using time periods extending over decades, and their predictions were compared with wildfire data from the Canadian Forest Service database. The study found that predicted wildfire patterns from the two models were similar and closely reflected historical fire activity. Using both models provided a deeper understanding of factors involved in wildfire risk, but if resources are limited, using only the model with average climate variables is suitable for modelling wildfire risk in Canada. This finding boosts scientific confidence in models using averaged climate variables, which can provide data for fire prevention and mitigation.