基于深度神经网络的城市典型乔木日内蒸腾特征模拟研究

以城市典型乔木小叶榕全天24小时每10分钟的树干液流及同步气象观测数据为训练集,建立基于深度神经网络的城市典型乔木植被蒸腾估算模型,得到10分钟尺度的小叶榕蒸腾模拟结果,系统地探讨干湿季和昼夜影响小叶榕蒸腾的环境控制因子.基于深圳市91个气象观测站的常规气象观测数据,应用训练好的深度神经网络模型,估算得到站点尺度的深圳...

Simulation of Sub-Daily Transpiration Characteristics of Typical Arbor Trees in Cities Based on Deep Neural Network

Based on the sap flow system and synchronous meteorological observation data of the typical arbor tree in the city, a transpiration estimation model for urban arbor tree was built using deep neural network. The simulation results can systematically figure out the environmental controlling factors that affect the transpiration of Ficus microcarpa in the dry or wet seasons as well as day or night. Based on the routine meteorological observation data from 91 meteorological observation stations in Shenzhen, the trained deep neural network was used to estimate the station-scale hourly transpiration characteristics of typical arbor trees in Shenzhen. The results show that 1) compared with the measured data of the sap flow system, the deep neural network can accurately simulate the transpiration change of the Ficus microcarpa at 10-minute intervals with a R² of 0.91, MAPE of 21.77%, RMSE of 0.02 mm/h. 2) The main controlling factors of urban Ficus microcarpa during the wet and dry seasons are solar radiation and air temperature in the daytime, while at night is saturated water vapor pressure deficit. 3) Urban Ficus microcarpa still has transpiration at night, and average value can be 0.03 mm/h and 0.01 mm/h in dry season and wet season, respectively. 4) There are differences among vegetation transpiration in different areas of Shenzhen, with a maximum difference of 0.10 mm/h. In general, the transpiration during the dry season is higher than that during the wet season, and the vegetation transpiration at most sites are close to 0 at night. For some specific sites, the average transpiration at night can reach 0.07 mm/h in dry season, and can reach 0.10 mm/h in the wet season.