Canadian Forest Service Publications
Evaluation of Gridded Precipitation Data and Interpolation Methods for Forest Fire Danger Rating in Alberta, Canada. 2019. Cai, X.; Wang, X.; Jain, P.; Flannigan, M.D. Journal of Geophysical Research: Atmospheres 124(1): 3-17.
Available from: Great Lakes Forestry Centre
Catalog ID: 39492
CFS Availability: PDF (download)
Available from the Journal's Web site. †
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Plain Language Summary
The Canadian Forest Fire Weather Index (FWI) System is the fundamental measurement of fire danger in Canada, and many other parts of the world. Spatial interpolation of daily precipitation, which is one of the inputs for the FWI System, has found to be a key challenge in obtaining accurate fire danger in areas without sufficient weather stations. In this study, we compared the gridded Canadian Precipitation Analysis (CaPA) System with the conventional and geostatistical interpolation methods to achieve the best fire danger rating in Alberta, Canada. Our results showed that regression kriging was the best-performing method when the CaPA System estimate was used as a covariate. The CaPA System on its own was a middle-performing method, except within the 120 km Doppler radar covered areas. Ordinary kriging, regression kriging with elevation, and thin-plate smoothed spline were similarly good performing methods. The FWI System fuel moisture codes responded differently to precipitation estimation methods due to differences in their drying timelags. The fine fuel moisture code with short timelag was best estimated by the CaPA System because of its enhanced skill in estimating small precipitation events. The duff moisture code and drought code with longer timelags were best estimated by regression kriging with CaPA because it better predicted significant precipitation events. The sensitivity of interpolation methods to changes in weather station density was also examined. We found that the performance of interpolation methods decreased with the decreasing weather station density, and the CaPA System became the best methods when station density dropped to 0.5 /10 000 km2.
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