森林生态系统涡度相关法碳通量长时间连续性缺失数据插补方法的比较  

Comparison of Gap-filling Methods for Long-term Continuous Missing Data in Carbon Flux Observation by Eddy Covariance Method of Forest Ecosystem

作　　者：周宇 黄辉[1,2,3] 张劲松[1,2,3] 孟平 孙守家[1,2,3] ZHOU Yu;HUANG Hui;ZHANG Jin-song;MENG Ping;SUN Shou-jia(Research Institute of Forestry,Chinese Academy of Forestry,Beijing 100091,China;Key Laboratory of Tree Breeding and Cultivation,National Forestry and Grassland Administration,Beijing 100091;Co-Innovation Center for Sustainable Forestry in Southern China,Nanjing Forestry University,Nanjing 210037)

机构地区：[1]中国林业科学研究院林业研究所,北京100091 [2]国家林业和草原局林木培育重点实验室,北京100091 [3]南京林业大学南方现代林业协同创新中心,南京210037

出　　处：《中国农业气象》2021年第4期330-343,共14页Chinese Journal of Agrometeorology

基　　金：中央级公益性科研院所基本科研业务费专项资金项目(CAFYBB2018ZA001;CAFYBB2017ZX002)。

摘　　要：针对森林通量观测站涡度相关法碳通量观测普遍存在的长时间连续性数据缺失情景,为探究不同数据插补方法的有效性,以华北低丘山地栓皮栎人工混交林生态系统为例,以经EddyPro处理和质量控制的2017年3月1日-11月30日0.5h尺度净生态系统碳交换(NEE)数据为基准数据集,随机生成含有连续1、3、7、15和31d数据缺失的5类数据缺失集,重复10次,使用固定窗口平均昼夜变化法(MDV)、可变窗口平均昼夜变化法(MDC)、查表法(LUT)、非线性回归法(NLR)、边际分布采样法(MDS)、人工神经网络法(ANN)对缺失数据集进行插补,并将插补数据与实际观测数据进行对比,通过分析统计参数来评估不同方法的插补精度和稳定性,以评估不同方法的适用范围。结果表明:日间,当连续缺失少于15d时,ANN方法插补数据与实测数据间的R^(2)(决定系数)相对较高,NLR方法的R^(2)较低;LUT方法插补数据与实测数据间的相对均方根误差(RRMSE)较低,NLR方法的RRMSE较高。当缺失达到连续15d时,除NLR方法的R^(2)显著较低(P<0.05)外,其它方法间R^(2)差异不显著;LUT方法的RRMSE显著(P<0.05)较低,其它方法间RRMSE差异不显著。当缺失达到连续31d时,除NLR方法R^(2)显著较低(P<0.05)外,各方法间R^(2)和RRMSE无显著差异;MDV方法的平均绝对误差(MAE)出现较多异常值,各方法间的MAE开始出现分化的趋势。随着缺失片段长度的增加,除MDV方法外,各方法的R^(2)呈下降趋势,连续1d缺失与连续31d缺失情景下插补所得NEE与实测NEE的R^(2)差异显著(P<0.05);MDV和MDS方法的RRMSE呈增大趋势,连续1d缺失与连续31d缺失情景下的RRMSE差异显著(P<0.05),其它方法的RRMSE差异相对不显著。夜间,在各缺失情景下,ANN方法的R^(2)较高,LUT方法的R^(2)较低,二者之间差异显著(P<0.05);LUT方法的RRMSE最高,与其它方法存在显著差异(P<0.05)。在连续缺失大于31d的情景下,各方法的RRMSE差异均不显�There are often 20%to 65%data-missing in annual carbon flux observed by the eddy covariance method in the mountainous forest ecosystem,and there may also be continuous data-missing for a long period,as long as half a month,or even a month.To obtain complete and reliable flux data,reasonable imputation methods need to be adopted to impute the missing data.To explore the validity and performance of different gap-filling methods,five types of data-missing sets were generated with consequent 1 day,3 days,7 days,15 days,31 days data missing randomly and repeated 10 times,using the half-hourly NEE(Net Ecosystem Exchange)data in March 1st-November 30th,2017 of a mixed Quercus variabilis plantation ecosystem in North China low-hills regions calculated by EddyPro as a benchmark dataset,then Mean Diurnal Variation with fixed window(MDV),Mean Diurnal Variation with variable window(MDC),Look-Up Table(LUT),Non-Linear Regression(NLR),Marginal Distribution Sampling(MDS),and Artificial Neural Network(ANN)were used to interpolate the artificial sets.By comparing the imputed data with the actual observed data,the interpolation accuracy,stability and scope of each method were evaluated through statistical parameters.The results indicated that the effect of interpolation at daytime was significantly better than that at night.During the daytime,when the consecutive missing was less than 15 days,the R^(2)(coefficient of determination)between the interpolated NEE and the observed NEE of ANN was relatively higher,and that of NLR was lower,the Relative Root Mean Square Error(RRMSE)between the interpolated NEE and the observed NEE of LUT was lower,and that of NLR was higher.When the deletion reached 15 consecutive days,except for the significantly lower R^(2) of NLR(P<0.05),the difference of R^(2) among other methods was not significant;the RRMSE of LUT was significantly lower(P<0.05),and the difference of RRMSE between other methods was not significant.When the deletion reached 31 consecutive days,except for the significantly lower R^(2)

关 键 词：涡度相关 数据插补 净生态系统碳交换 固定窗口平均昼夜变化法(MDV) 可变窗口平均昼夜变化法(MDC) 查表法(LUT) 非线性回归法(NLR) 边际分布采样法(MDS) 人工神经网络法(ANN) 

分 类 号：S718.5[农业科学—林学]