潜热通量缺失数据插补方法比较研究

Comparative study of interpolation methods for missing latent heat flux data

【目的】分析比较不同插补方法对生态系统潜热通量(F_(LE))缺失值的插补精度。【方法】利用涡度相关法于2019年对北京市松山国家级自然保护区典型天然落叶阔叶林生态系统F_(LE)与环境要素进行原位连续监测,通过3种插补方法(边缘分布抽样法、线性回归法、人工神经网络法)对F_(LE)缺失数据(0.5 h数据中随机剔除)进行插补,分析实测F_(LE)、插补F_(LE)与环境因子间的关系。【结果】3种插补结果均低估了实测F_(LE),其中人工神经网络插补值最接近实测值(决定系数R^(2=0.40)。实测F_(LE)与空气温度(T_(a))、饱和水汽压差(D_(VP)))间均呈指数关系。边缘分布抽样法插补F_(LE)与T_(a)、D_(VP)间的关系最接近实测F_(LE),然而3种插补方法都不同程度改变了F_(LE)对T_(a)和D_(VP)的敏感性。【结论】人工神经网络法的插补结果与实测值最接近,边缘分布抽样法的结果与环境因子间的关系最接近实测值与环境因子间的关系,因此未来研究应依据研究目的选取合适的插补方法。

[Objective]The aim of this study is to analyze and compare the interpolation accuracy of different interpolation methods for missing latent heat flux value(F_(LE))in ecosystem.[Method]Eddy-covariance(EC)method was used to continuously monitor F_(LE) and environmental factors of a typical natural deciduous broad-leaved forest ecosystem in Songshan National Nature Reserve,Beijing in 2019.Three interpolation methods,namely marginal distribution sampling method(MDS),linear regression method(REG),and artificial neural network method(ANN)were applied to interpolate the missing F_(LE) data(randomly removed from 0.5 h data),and the relationship between measured F_(LE),interpolated F_(LE) and environmental factors was analyzed.[Result]All the three interpolation results underestimated the measured F_(LE),among which ANN interpolation value was the closest to the measured one(R^(2)=0.40).The measured F_(LE) showed an exponential relationship with air temperature(T_(a))and saturated vapor pressure deficit(D_(VP)).MDS interpolated the relationship between F_(LE) and T_(a) and D_(VP),and was the closest to the measured F_(LE).All the three interpolation methods changed the sensitivity of F_(LE) to T_(a) and D_(VP) to varying degrees.[Conclusion]The interpolation results of ANN are the closest to the measured values.The relationship between the results of MDS and environmental factors is the closest to the relationship between measured F_(LE) and environmental factors.Therefore,appropriate interpolation methods should be selected in future research based on the research purpose.