Research Of Microclimatic Characteristics Of Forest In Beijing

Along with the continuous development of cities and gradual deterioration of the environment, we're aware of the importance of the forest to our environment and sustainable development。The value of urban greenbelt and forest in suburban area, is mainly reflected in the ecosystem services. Forest microclimate is one important components of the ecosystem services function, and also one of the ecological functions of forest. Forest microclimate and meteorology research is universal currently, but it is rare that researching on the influence of the suburban forest ecosystem to the microclimate, especially the forest ecosystem of the capital.To reflect objectively the microclimate effect of the suburban forest ecosystem in Beijing, my dissertation samples from three different polt and approaches the research by automatic continuous observation and artificial intermittent observation. We samples in the standard plot of the capital ecological station, and the forest department of the Miaofeng forest mountain, at the foot of Miaofeng mountain——Beijing Forestry University, and on an open green space of the campus as the campus test field.(1)Forest shelters the solar radiation. While the diurnal variation of the total radiation intensity out of the forest shows inverted U-curve, the diurnal variation of the total radiation intensity curve in the forest is affected by the locations and size of the gap. The difference of different weather of canopy shelters:sunny>rainy>cloudy.(2)The monthly average temperature in the forest is lower than the bare ground and urban green-belt yearly. The time of the diurnal extremum air temperature is postponed in the forest. The cooling function to the air temperature in the spring and summer of forest is more obvious than in the autumn and winter. But, as to the urban green-belt, it is on the contrary.(3)The monthly average relative humidity in the forest is lower than the bare ground and urban green-belt. The time of the diurnal maximum relative humidity is approximately the same in and out of the forest. The humidification function of forest is outstanding, and it is better in autumn and winter than in spring and summer in the forest compared to urban green-belt.(4)The numbers of raindrops in unit time per unit area are growing as the increasing rain intensity out of the forest, while this trend relationship in the forest shows inverted U-curve. In the same circumstances of rain intensity, the average diameter of the raindrops is less in the forest than out of it. The characteristics of the diameter-velocity distribution are in accordance with the Gunn-Kinzer curve out of the forest, while it is more dispersion in the forest. The canopy has certain convergence and stability function to the kinetic energy of the rainfall. And the kinetic energy of the rainfall is not relevant to the rain intensity in the forest, while it is significant correlated out of the forest.(5) Forest can reduce the wind velocity obviously. The wind velocity in the forest is lower than the bare ground and urban green-belt yearly, while the difference of different wind velocity in seasons:spring>winter>autumn>summer. The diurnal maximum of wind velocities are all at noon in all of the three plot. The diurnal wind velocity changes are irregular in the forest during September to the next February.(6)The negative ion concentration of air in the forest is higher than the bare ground and urban green-belt during observation months. It is more fluctuant in the growing season of plants, and less fluctuant in the seeding and withering phase of plants. The diurnal trend line of negative ion concentration of air is more gentle in the campus than in and out of the forest. Forest influences the negative ion concentration of air in this order:growth phase> withering phase>seeding phase。(7) Forest can retain the stability of soil temperature, influences in this order:seeding phase>growth phase> withering phase.(8)Conduct two control:in the forest and in the campus; in the forest and out of it. Establish a B.P. neural network model to predict the microclimate in the forest. Using this model to simulate and predict the temperature,humidity and wind velocity, the differences of accuracy are in this order: temperature>humidity>wind velocity.