A coupled surface resistance model to estimate crop evapotranspiration in arid region of northwest China

Authors

  • Sien Li,

    1. Center for Agricultural Water Research in China, China Agricultural University, Beijing, China
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  • Xingmei Hao,

    1. Center for Agricultural Water Research in China, China Agricultural University, Beijing, China
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  • Taisheng Du,

    1. Center for Agricultural Water Research in China, China Agricultural University, Beijing, China
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  • Ling Tong,

    1. Center for Agricultural Water Research in China, China Agricultural University, Beijing, China
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  • Jianhua Zhang,

    Corresponding author
    1. School of Life Sciences and State Key Laboratory of Agrobiotechnology, The Chinese University of Hong Kong, Hong Kong
    • Correspondence to: Shaozhong Kang, Center for Agricultural Water Research in China, China Agricultural University, Beijing, 100083, China.

      E-mail: kangsz@cau.edu.cn

      Correspondence to: Jianhua Zhang, School of Life Sciences and State Key Laboratory of Agrobiotechnology, The Chinese University of Hong Kong, Hong Kong.

      E-mail: jhzhang@cuhk.edu.hk

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  • Shaozhong Kang

    Corresponding author
    1. Center for Agricultural Water Research in China, China Agricultural University, Beijing, China
    • Correspondence to: Shaozhong Kang, Center for Agricultural Water Research in China, China Agricultural University, Beijing, 100083, China.

      E-mail: kangsz@cau.edu.cn

      Correspondence to: Jianhua Zhang, School of Life Sciences and State Key Laboratory of Agrobiotechnology, The Chinese University of Hong Kong, Hong Kong.

      E-mail: jhzhang@cuhk.edu.hk

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Abstract

The Penman–Monteith (PM) model has been widely used to estimate crop evapotranspiration (ET), but it performs poorly with sparse vegetation. By combining the Jarvis canopy resistance model and the soil resistance model, we have developed a coupled surface resistance model to address this issue. Maize field and vineyard ET, measured by the eddy covariance method during 2007 and 2008, were used to test the estimations produced by the PM model combined with our coupled surface resistance model and Jarvis model, respectively. Results indicate that PM model combined with the coupled surface resistance model produces higher determination coefficient and lower root mean square error when compared with the PM–Jarvis method, either for maize field or for the sparse vineyard, on half-hourly or daily time scales. Our study confirms that the coupled surface resistance model produces higher accuracy than the Jarvis model and provides a method to calculate resistance parameters for using the PM model to simulate the ET of sparse vegetation. Copyright © 2013 John Wiley & Sons, Ltd.

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