Abstract
Wildfire is a large-scale complex system. Insight into the mechanism that drives wildfires can be revealed by the distribution of the wildfire over a large time scale, which is one of the important topics in wildfire research. In this study, the scaling properties of four meteorological factors (relative humidity, daily precipitation, daily average temperature, and maximum wind speed) that can affect wildfires (number of wildfires per day) were investigated by using the detrended fluctuation analysis method. The results showed that the time series for these meteorological factors and wildfires have similar power exponents and turning points for the power exponents curve. The five types of time series have a lasting and steady long-range power law correlation over a certain time scale range, where the corresponding exponents were 0.6484, 0.5724, 0.8647, 0.7344, and 0.6734, respectively. They also have a reversible long-range power law correlation beyond a certain time scale, where the corresponding exponents are 0.3862, 0.2218, 0.1372, 0.2621, and 0.2678. The multifractal detrended fluctuation analysis results showed that the wildfire time series were multifractal. The results of the research based on the detrended cross-correlation analysis and the multifractal detrended cross-correlation analysis showed that relative humidity and daily precipitation have a considerable impact on the wildfire time series, while the impacts of daily average temperature and the maximum wind speed are relatively small. This study showed that identifying the factors causing the inherent volatility in the wildfire time series can improve understanding of the dynamic mechanism controlling wildfires and the meteorological parameters. These results can also be used to quantify the correlation between wildfire and the meteorological factors investigated in this study.
Highlights
Forest fires seriously threaten the stability and balance of the forest ecosystem and the health of local people
The results showed that the time series for these meteorological factors and wildfires have similar power exponents and turning points for the power exponents curve
The DCCA can be used to calculate the cross-correlation between two nonstationary time series, and its basic steps are similar to the detrended fluctuation analysis (DFA) method
Summary
Forest fires seriously threaten the stability and balance of the forest ecosystem and the health of local people. This model was revised by Drossel et al [9] to overcome the disadvantage that a fire propagates on regular fronts that proceed with a finite velocity and burn down a finite number of trees [10] This characteristic demonstrated that wildfires automatically reach a steady state and that this is characterized by the power law relationship between the ‘frequency-size’ distributions for forest fires [9]. The long-range correlations between the wildfire time series (number of wildfires per day) and meteorological factors (relative humidity, daily precipitation, daily average temperature, and maximum wind speed) were studied using statistical analysis (detrended fluctuation analysis [DFA], detrended cross-correlation analysis [DCCA], multifractal detrended fluctuation analysis [MF-DFA], and multifractal detrended cross-correlation analysis [MF-DCCA]). The scale characteristics of wildfire and the main external driving factor time series (meteorological factors) were compared; the scale invariance features of the wildfire time series were determined; and the relationships and differences between wildfire time series and meteorological factors were investigated
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