A Gap Filling Model For Eddy Covariance Latent Heat Flux Estimating

Journal of Hydrology 468–469 (2012) 101–110

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A gap-filling model for eddy covariance latent heat flux: Estimating evapotranspiration of a subtropical seasonal evergreen broad-leaved forest as an example
Yi-Ying Chen a, Chia-Ren Chu b, Ming-Hsu Li a,⇑ a b

Graduate Institute of Hydrological and Oceanic Sciences, National Central University, Taiwan
Department of Civil Engineering, National Central University, Taiwan

a r t i c l e

i n f o

Article history:
Received 8 February 2012
Received in revised form 13 June 2012
Accepted 12 August 2012
Available online 25 August 2012
This manuscript was handled by A.
Bardossy, Editor-in-Chief, with the assistance of K.P. Sudheer, Associate Editor
Keywords:
Gap-filling model
Principal component analysis
K-nearest neighbors
Multiple regressions
Evapotranspiration

s u m m a r y
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

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