Prediction models and changing rules of dead fuel moisture content of 8 forest species in Beijing area

ObjectiveThe forest fuel moisture content has great impact on the occurrence and spread of forest fire, particularly important on the fire behavior, and its prediction forecast models have significant effects on predicting forest fire and forest fire behavior.

MethodThrough the continuous determination of dead fuel moisture content of 8 kinds of forest trees in Beijing area during the period of fire prevention, we researched the relationship between fuel moisture content and the current and previous meteorological data, and established the prediction models by selecting the meteorological factors, which has great influence on the fuel moisture content. On this basis, we quantitatively analyzed the diurnal variation and fire protection period variation of the fuel moisture content.

ResultThe fuel moisture content of different tree species was significantly different, from descending order was Quercus variabilis, Q. aliena, Ulmus pumila, Robinia pseudoacacia, Acer truncatum, Platycladus orientalis, Pinus tabuliformis, Larix principis-rupprechtii. And the fuel moisture content of different types was also significantly different. Generally the broadleaf tree’s fuel moisture content was higher than that of coniferous trees, and the fuel moisture content of dead leaves and 1 hr dead branches were higher than 10 hr and 100 hr. That of 1 hr of dead branches was mainly affected by the current meteorological factors, while that of 10 hr and 100 hr of dead branches were affected by the early meteorological factors. The test indexes of 32 linear prediction models showed that the fitting effect was perfect. The diurnal variation of the fuel moisture content was higher and more stable at night, lower and varied greatly during the day, which peaked at 06:00−08:00, then dropped sharply, and later reached the lowest value at about 12:00−14:00. During the fire protection period, the fuel moisture content showed a trend of rising first, then decreasing. It was low in November but increased slowly and reached a high level from December to January of the following year. From early March to the end of April, the fuel moisture content kept a very low level.

ConclusionHigh-precision prediction methods of different tree species and among different fuel types could offer theoretical support for forest fire prevention, which can be used in practice. In March and at noon time in Beijing, it^,s windy, drying and hot and the fuel moisture content is low, so the fire management should be reinforced in such a fire danger period.