A Gap Filling Model For Eddy Covariance Latent Heat Flux Estimating

Topics: Linear regression, Regression analysis, Principal component analysis Pages: 10 (7845 words) Published: April 17, 2015
Journal of Hydrology 468–469 (2012) 101–110

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Journal of Hydrology
journal homepage: www.elsevier.com/locate/jhydrol

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,⇑

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
Gap-filling model
Principal component analysis
K-nearest neighbors
Multiple regressions

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 under different atmospheric states. The developed LE gap-filling model (PCA with KNN) works with a RMSE of 2.4 W mÀ2 ($0.09 mm dayÀ1) at a weekly time scale by adding 40% artificial flux gaps into original dataset. Annual evapotranspiration at this study site were estimated at 736 mm (1803 MJ) and 728 mm (1785 MJ) for year 2008 and 2009, respectively. Ó 2012 Elsevier B.V. All rights reserved.

1. Introduction
The exchange of heat and water vapor between the atmosphere
and the biosphere plays an important role in regulating the thermal environment. For example, the energy partitioning among latent heat (LE), sensible heat (SH) and ground heat (G) has a strong influence on weather and climate (Pielke et al., 1998; Wilson et al., 2002), such as near surface convection (Juang et al., 2007), land–sea breeze (Seth and Giorgi, 1996), long range transport of pollutants (Zhang and Rao, 1999), and drought events (Dirmeyer, 1994). In order to understand the impact of temporal and spatial variability of these surface fluxes on above issues, a global flux network (FLUXNET) has been established to coordinate these surface fluxes measured by the eddy covariance (EC) approach from regional networks (Aubinet et al., 2001; Baldocchi et al., 2001; Baldocchi, 2008). The EC approach directly computed these surface fluxes by calculating covariance between vertical

⇑ Corresponding author. Address: Graduate Institute of Hydrological and Oceanic Sciences, National Central University, 300 Jhongda Road, Jhongli, Taoyuan 32001, Taiwan. Tel.: +886 3 4222964; fax: +886 3 4222874.

E-mail addresses: spancer@cc.ncu.edu.tw (Y.-Y. Chen), crchu@ncu.edu.tw (C.-R. Chu), mli@cc.ncu.edu.tw (M.-H. Li).
0022-1694/$ - see front matter Ó 2012 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.jhydrol.2012.08.026

wind velocity and temperature and water vapor mixing ratio from a certain averaging period to obtain SH flux and LE flux, respectively. The spatial...

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