Research On Modelling Of Surface Dead Fuel Moisture Content Of Main Forest Types In Xishan Mountain Of Beijing

Forest fire with a sudden, it is essential in the health of the forest, but a serious threat to the lives and property of citizens. Forest fire as an ecological factor, it’s distinct from other ecological factors. Forest fires have unpredictability, caused great harm, and dangerous. Among them, the combustible material is one of the important forest burning, especially greater impact on fuel moisture content on forest fires occurred. Through the establishment of fuel moisture content model, the researchers predict dynamically for different types of forest fire and fire behavior lay the technical foundation.Taking the dead fuel moisture content of four kinds of tree species as the research object, based on the daily observation of fuel moisture during the 2013.11.1～2014.5.31. We researched the fuel moisture content of Quercus variabilis, Q.aliena, Platycladus orientals and Pinus tabulaeformis during fire prevention period and related affecting factors, with the non-linear regression prediction models and the varying prediction model by differential equations for forest fuel moisture content established, according to the classification standard of dead leaves,1-,10-, and 100-h. And based on non-linear regression equation to study the dynamic variation of moisture content. The results are as followed:(1)The changing trends of dead fuel moisture content was similar between inside and outside forests, so the observational data can be used to instead of data within the forests. There were obvious differences in fuel moisture content of different tree species and among different fuel types. Quercus variabilis had higher dead fuel moisture content than that of other species. The moisture content and change range of dead leaves and 1-h was generally higher than 10-,100-h.(2)The non-linear regression equation selected each three meteorological factors in instant factors and pre-factors, according to the relevance of the most significant principles. The pre-factors of dead leaves and 1-,10,100-h were the average temperature、average relative humidity and average wind speed of former 2h,20h and 100h. Fuel moisture content was negatively correlated with temperature and wind speed, and was positively correlated with relative humidity. The established non-linear regression models could explain more than 30% of variance for fuel moisture content of the different species, and the predictive model performance well by the mean MRE and the mean MRE test. Change trend between the measured and predicted values were basically the same. So all the models can be used to forecast fuel moisture content.(3)The varying prediction model for forest fuel moisture content by differential equation selected instant fuel moisture, instant temperature, instant relative humidity and instant wind as affector, and fuel moisture content of the next moment can be predicted through the above factors.The models could explain more than 35% of variance for fuel moisture content of the different species, and the predictive model performance well by the mean MRE and the mean MRE test, change trend between the measured and predicted values are basically the same so the models can be used to forecast fuel moisture content of the next moment.(4)Fuel moisture content showed a trend of firstly increasing and then declining and lastly increasing during a day. Fuel moisture content peaks at 7:00-8:00p.m, and reached the lowest value at two o’clock p.m. During the period of fire prevention, fuel moisture content is relatively high from December to February, while it is relatively low from March to May and in November.