TY - JOUR
T1 - A gap-filling model for eddy covariance latent heat flux
T2 - Estimating evapotranspiration of a subtropical seasonal evergreen broad-leaved forest as an example
AU - Chen, Yi Ying
AU - Chu, Chia Ren
AU - Li, Ming Hsu
N1 - Funding Information:
The authors would like to thank the National Science Council of the Republic of China, Taiwan , for supporting this research under the Contract No. NSC 96-2116-M-008-002-MY3 with the National Central University. Kindly supports of tower construction by Director Jeen-Lian Hwong of the LHC center of the Taiwan Forestry Research Institute are highly appreciated. Yi-Ying Chen also wishes to thank Dario Papale and Joon Kim for providing their helpful suggestions during 2011 AsiaFlux workshop. Valuable suggestions and constructive comments given by anonymous reviewers are greatly appreciated.
PY - 2012/10/25
Y1 - 2012/10/25
N2 - In this paper we present a semi-parametric multivariate gap-filling model for tower-based measurement of latent heat flux (LE). Two statistical techniques, the principal component analysis (PCA) and a nonlinear interpolation approach were integrated into this LE gap-filling model. The PCA was first used to resolve the multicollinearity relationships among various environmental variables, including radiation, soil moisture deficit, leaf area index, wind speed, etc. Two nonlinear interpolation methods, multiple regressions (MRS) and the K-nearest neighbors (KNNs) were examined with random selected flux gaps for both clear sky and nighttime/cloudy data to incorporate into this LE gap-filling model. Experimental results indicated that the KNN interpolation approach is able to provide consistent LE estimations while MRS presents over estimations during nighttime/cloudy. Rather than using empirical regression parameters, the KNN approach resolves the nonlinear relationship between the gap-filled LE flux and principal components with adaptive K values under different atmospheric states. The developed LE gap-filling model (PCA with KNN) works with a RMSE of 2.4Wm -2 (~0.09mmday -1) at a weekly time scale by adding 40% artificial flux gaps into original dataset. Annual evapotranspiration at this study site were estimated at 736mm (1803MJ) and 728mm (1785MJ) for year 2008 and 2009, respectively.
AB - In this paper we present a semi-parametric multivariate gap-filling model for tower-based measurement of latent heat flux (LE). Two statistical techniques, the principal component analysis (PCA) and a nonlinear interpolation approach were integrated into this LE gap-filling model. The PCA was first used to resolve the multicollinearity relationships among various environmental variables, including radiation, soil moisture deficit, leaf area index, wind speed, etc. Two nonlinear interpolation methods, multiple regressions (MRS) and the K-nearest neighbors (KNNs) were examined with random selected flux gaps for both clear sky and nighttime/cloudy data to incorporate into this LE gap-filling model. Experimental results indicated that the KNN interpolation approach is able to provide consistent LE estimations while MRS presents over estimations during nighttime/cloudy. Rather than using empirical regression parameters, the KNN approach resolves the nonlinear relationship between the gap-filled LE flux and principal components with adaptive K values under different atmospheric states. The developed LE gap-filling model (PCA with KNN) works with a RMSE of 2.4Wm -2 (~0.09mmday -1) at a weekly time scale by adding 40% artificial flux gaps into original dataset. Annual evapotranspiration at this study site were estimated at 736mm (1803MJ) and 728mm (1785MJ) for year 2008 and 2009, respectively.
KW - Evapotranspiration
KW - Gap-filling model
KW - K-nearest neighbors
KW - Multiple regressions
KW - Principal component analysis
UR - http://www.scopus.com/inward/record.url?scp=84867101216&partnerID=8YFLogxK
U2 - 10.1016/j.jhydrol.2012.08.026
DO - 10.1016/j.jhydrol.2012.08.026
M3 - 期刊論文
AN - SCOPUS:84867101216
SN - 0022-1694
VL - 468-469
SP - 101
EP - 110
JO - Journal of Hydrology
JF - Journal of Hydrology
ER -