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MODELLING CANOPY RESISTANCE FOR ESTIMATING LATENT HEAT FLUX AT A TEA FIELD IN SOUTH CHINA

Published online by Cambridge University Press:  06 June 2017

HAOFANG YAN*
Affiliation:
Research Center of Fluid Machinery Engineering and Technology, Jiangsu University, Zhengjiang, 212013, China Department of Water Management, Delft University of Technology, Delft, 2600GA, Netherlands
CHUAN ZHANG*
Affiliation:
Department of Water Management, Delft University of Technology, Delft, 2600GA, Netherlands Institute of Agricultural engineering, Jiangsu University, Zhengjiang, 212013, China
GUANGJIE PENG
Affiliation:
Research Center of Fluid Machinery Engineering and Technology, Jiangsu University, Zhengjiang, 212013, China
RANSFORD OPOKU DARKO
Affiliation:
Research Center of Fluid Machinery Engineering and Technology, Jiangsu University, Zhengjiang, 212013, China
BIN CAI
Affiliation:
Research Center of Fluid Machinery Engineering and Technology, Jiangsu University, Zhengjiang, 212013, China
*
Corresponding authors. Email: yanhaofang@yahoo.com, zhangchuan@ujs.edu.cn
Corresponding authors. Email: yanhaofang@yahoo.com, zhangchuan@ujs.edu.cn

Summary

Determination of canopy resistance (rc) is necessary for accurate estimating hourly latent heat flux (LET), using the Penman–Monteith (PM) model for tea crop. In this study, a non-linear relationship between rc and climatic resistance (r*) was obtained for tea plants based on micro-meteorological data and LET from the end of 2014 to the beginning of 2016 in southern China. The proposed rc model was integrated to the PM method and compared with measured LET using a Bowen ratio energy balance method. The root mean square error (RMSE) and the index of agreement (d) were calculated for assessing the accuracy of the proposed rc model. RMSE and d values for rc and LET were 167.4 s m−1 and 29.7 W m−2 and 0.93 and 0.99, respectively. As compared to data from a single season, the rc sub-model based on data from different seasons was more reliable for estimating LET of tea field when integrated to the PM model.

Type
Research Article
Copyright
Copyright © Cambridge University Press 2017 

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