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

Contents lists available at SciVerse ScienceDirect

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,⇑
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 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...

References: Central University. Kindly supports of tower construction by Director Jeen-Lian Hwong of the LHC center of the Taiwan Forestry Re-
Alavi, N., Warland, J.S., Berg, A.A., 2006
Apipattanavis, S., Podesta, G., Rajagopalan, B., Katz, R.W., 2007. A semiparametric
multivariate and multisite weather generator
Aubinet, M., Chermanneb, B., Vandenhaute, M., Longdoz, B., Yernaux, M., Laitat, E.,
2001
Baldocchi, D., 2008. ‘Breathing’ of the terrestrial biosphere: lessons learned from a
global network of carbon dioxide flux measurement systems
X., Malhi, Y., Meyers, T., Munger, W., Oechel, W., Paw, U.K.T., Pilegaard, K.,
Schmid, H.P., Valentini, R., Verma, S., Vesala, T., Wilson, K., Wofsy, S., 2001.
Meteorol. Soc. 82, 2415–2434.
Berbigier, P., Bonnefond, J.M., Mellmann, P., 2001. CO2 and water vapour fluxes for
2 years above Euroflux forest site
Chen, Y.Y., Li, M.H., 2013. Determining adequate averaging periods and reference
coordinates for eddy covariance measurements of surface heat and water vapor
Dakhlaoui, H., Bargaoui, Z., Bárdossy, A., 2012. Toward a more efficient calibration
schema for HBV rainfall–runoff model
Dirmeyer, P.A., 1994. Vegetation as a feedback mechanism in midlatitude drought. J.
Clim. 7, 1463–1483.
Rebmann, C., Suyker, A., Tenhunen, J., Tu, K., Verma, S., Vesala, T., Wilson, K.,
Wofsy, S., 2001
Foken, T., Wichura, B., 1996. Tools for quality assessment of surface-based flux
measurements
Gockede, M., Rebmann, C., Foken, T., 2004. A combination of quality assessment
tools for eddy covariance measurements with footprint modelling for the
Guo, X., Zhang, H., Kang, L., Du, J., Li, W., Zhu, Y., 2007. Quality control and flux gap
filling strategy for Bowen ratio method: revisiting the Priestley–Taylor
Gurmessa, T., Bárdossy, A., 2009. A principal component regression approach to
simulate the bed-evolution of reservoirs
Hsieh, C., Katul, G.G., Chi, T., 2000. An approximate analytical model for footprint
estimation of scalar fluxes in thermally stratified atmospheric flows
Hui, D., Wan, S., Su, B., Katul, G., Monson, R., Luo, Y., 2004. Gap-filling missing data in
eddy covariance measurements using multiple imputation (MI) for annual
Jarvis, A.J., Stauch, V.J., Schulz, K., Young, P.C., 2004. The seasonal temperature
dependency of photosynthesis and respiration in two deciduous forests
Jolliffe, I.T., 2002. Principal Component Analysis, second ed. Springer-Verlag, New
York.
Juang, J.Y., Katul, G.G., Porporato, A., Stoy, P.C., Siqueira, M., Detto, M., Kim, H.-S.,
Oren, R., 2007
Thorgeirsson, H., Valentini, R., Verma, S., Vesala, T., Wilson, K., Wofsy, S.,
2002
Li, M.H., Tien, W., Tung, C.P., 2009. Assessing the impact of climate change on the
land hydrology in Taiwan
Heinmann, M., Hui, D.F., Jarvis, A.J., Kattge, J., Noormets, A., Stauch, V.J., 2007.
Monteith, J., Unsworth, M., 2008. Principles of Environmental Physics, third ed.
Continue Reading

Please join StudyMode to read the full document

You May Also Find These Documents Helpful

  • Gap Model Essay
  • Essay about Latent Heat Fusion
  • Essay about Latent Heat of Fusion
  • The Latent Heat of Vaporization of Water Essay
  • Lab6 latent heat Essay
  • Essay about Filling The Gaps
  • Eddy Essay
  • Gaps model of Service Quality Essay

Become a StudyMode Member

Sign Up - It's Free