ObjectiveThe prediction of the fuel moisture content is the major part of forest fire research and the moisture content of surface fine fuel, semi-humus and humus determines the persistence of vertical fire and the probability of underground fire. The change of moisture content is mainly affected by weather conditions and topographical features, while there are few studies on the dynamic changes of moisture content of semi-humus and humus in China. Therefore, analyzing the dynamic changes of moisture content of these three layers of fuel has a guiding role in establishing China ’s forest fire forecasting system.
MethodThe research conducted daily monitoring of surface fine fuel, semi-humus and humus moisture content under the typical coniferous and broadleaved mixed forest in Maoer Mountain of northeastern China, the meteorological data of forest were also monitored synchronously. This study also statistically analyzed the correlations between meteorological elements and the moisture content of three layers of fuel, and the meteorological element regression method was used to establish a moisture content prediction model for three layers of fuel.
ResultDuring the whole monitor period, the moisture content of surface fine fuel fluctuated the most, with the minimum value of 10.99% and the maximum value was 253.30%. The semi-humus was the second, and the minimum value was 19.21% and the maximum value was 238.07%. The moisture content of humus was the most stable, and the minimum value was 48.45% and the maximum value was 193.83%. The change of moisture content of surface fuel was the most sensitive to the response of meteorological factors, mostly related to the meteorological factors on the current day or the previous day, followed by semi-humus, and the moisture content of humus was only related to air temperature. Three fuel types of regression prediction model for meteorological elements of moisture content were established. The average absolute error and the average relative error of the moisture content prediction model of surface fine fuel, semi-humus and humus were 22.2%, 23.5%, 17.1% and 7.1%, 14.8%, 23.4%, respectively. The prediction model of moisture content of fine fuel and semi-humus both can basically achieve the accuracy of forest fire prediction, while the accuracy of prediction model of moisture content of humus was bad.
ConclusionThe three layers of fuel may have a forest fire during the fire prevention period. Attention should be paid to the prediction of underground fuels, including the moisture content of semi-humus and humus, in future forest fire prevention work.
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