No abstract

We examined the seasonal distribution of lightning- and human-caused wildfires ≥ 2ha in Canada for two time periods: 1959–2018 and 1981–2018. Furthermore, we investigated trends in seasonality, number of fires per year and number of days with fire starts per year for human- and lightning-caused fires. Nationally, lightning fires peaked from June to August, whereas human fires peaked during May. There was, however, notable variation in the seasonal distributions of human- and lightning-caused fires between ecozones. Likewise, trends in season start and end dates varied among ecozones and time series, with trends generally being stronger for human-caused fires. Trends in the number of fires from 1959 to 2018 suggested significant increases in the number of lightning-caused fires and days with lightning ignitions across almost all ecozones, while from 1981 to 2018 there was a significantly decreasing trend in the number of human-caused fires and days with human ignitions in almost all ecozones. The highest densities of human-caused fires occurred in the Montane Cordillera and Atlantic Maritime, while the highest density of lightning-caused fires occurred in the Boreal Shield West. The Montane Cordillera and Taiga Shield West showed significant increases in the number of lightning fires and days with lightning ignitions across both time series.

The Canadian Forest Fire Weather Index System is the primary measurement of wildfire danger in Canada. Interpolating daily precipitation, one of the inputs for the Fire Weather Index System is a key challenge in areas without sufficient weather stations. This work evaluates the performance of gridded precipitation from the Canadian Precipitation Analysis (CaPA) System and six interpolation methods to achieve the best fire danger rating in Alberta, Canada. Results show that the CaPA System has only average performance due to limited radar coverage (10%) in the forested region; however, using the CaPA System as a covariate with regression kriging generates significantly better precipitation estimates. Ordinary kriging, regression kriging with elevation as a covariate, and the thin‐plate smoothed spline are methods with similar performance. Fuel moisture codes of the Fire Weather Index System respond differently to precipitation amounts due to differences in their time constants for drying. Fine fuels with a short drying time (Fine Fuel Moisture Code) are best estimated by the CaPA System because of its enhanced skill in estimating small precipitation events. Compacted organic fuels with longer drying times (Duff Moisture Code and Drought Code) are best estimated by regression kriging with CaPA because it better predicts significant precipitation events. The dense fire weather station network in our study area (~3.0 stations/10,000 km2) allows us to perform a sensitivity analysis, and we find that a threshold of >0.5 stations/10,000 km2 is needed for regression kriging with CaPA to become appreciably better than the CaPA System.