A gap-filling model for eddy covariance latent heat flux: Estimating evapotranspiration of a subtropical seasonal evergreen broad-leaved forest as an example

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题名:	A gap-filling model for eddy covariance latent heat flux: Estimating evapotranspiration of a subtropical seasonal evergreen broad-leaved forest as an example
作者:	李明旭;Chen, Yi-Ying;Chu, Chia-Ren;Li, Ming-Hsu
贡献者:	地球科學學院水文與海洋科學研究所
关键词:	broadleaved evergreen forests;Earth sciences;Earth, ocean, space;eddy covariance;environmental factors;Estimating;Evapotranspiration;Exact sciences and technology;Flux;Gap-filling model;Gaps;heat transfer;Hydrology;Hydrology. Hydrogeology;Interpolation;K-nearest neighbors;Latent heat;leaf area index;Mathematical models;Multiple regressions;Nonlinearity;Principal component analysis;soil water deficit;wind speed
日期:	2012-10-25
上传时间:	2026-04-23 11:07:30 (UTC+8)
出版者:	Elsevier;Kidlington: Elsevier B.V
摘要:	摘要： ► We present a gap-filling model for eddy covariance latent heat flux. ► Flux gap was weighted with the principal components of the K-nearest neighbors. ► Clear sky (nighttime/cloudy) requires 30 (50) neighbors for interpolations. ► Evapotranspiration time series were consistently estimated with gap filled. 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. 出版者： Kidlington: Elsevier B.V 出版日期： 2012-10-25 出處： Journal of hydrology (Amsterdam), 2012-10, Vol.468-469, p.101-110 資源來源： ScienceDirect (Elsevier) Journals 版權： 2012 Elsevier B.V. 版權： 2015 INIST-CNRS 識別號： ISSN: 0022-1694 識別號： EISSN: 1879-2707 識別號： DOI: 10.1016/j.jhydrol.2012.08.026 識別號： CODEN: JHYDA7
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