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Research on the Relationships Between Rainfall and Meteorological Yield in Irrigation District

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  • Jinping Zhang
  • Yong Zhao
  • Zhihong Ding

Abstract

Rainfall and grain yield are two closely related random variables to be worthy of studying. The meteorological yield explains the influences of weather changes on grain yield. Based on the data series from 1980 to 2006 in Jinghuiqu irrigation district of Shaanxi Province in China, the meteorological yield is achieved from grain yield. Then, the empirical mode decomposition method is applied to analyze fluctuating periods and local features of rainfall and meteorological yield. Meanwhile, the copula method is introduced into describe the joint probability distribution of rainfall and meteorological yield. The studied results show that rainfall and meteorological yield exist vary fluctuation periods with multi-time scales, including 2 to 4 years of short period level, 4 to 6 (or 7) years of middle period level and 19 (or 10 to 11) years of long period level. Using the frank copula method, the bivariate distribution and return period of rainfall and meteorological yield was successfully developed to reveal the encounter risk of their different magnitudes. Finally, similarly with rainfall and meteorological yield, the complex changes and fluctuation periods are also proven to be existed in their joint probability. Copyright Springer Science+Business Media Dordrecht 2014

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  • Jinping Zhang & Yong Zhao & Zhihong Ding, 2014. "Research on the Relationships Between Rainfall and Meteorological Yield in Irrigation District," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(6), pages 1689-1702, April.
    • Handle: RePEc:spr:waterr:v:28:y:2014:i:6:p:1689-1702
    DOI: 10.1007/s11269-014-0577-3
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    References listed on IDEAS

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    1. Sheng Yue & ChunYuan Wang, 2004. "The Mann-Kendall Test Modified by Effective Sample Size to Detect Trend in Serially Correlated Hydrological Series," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 18(3), pages 201-218, June.
    2. Seydou Traore & Aytac Guven, 2012. "Regional-Specific Numerical Models of Evapotranspiration Using Gene-Expression Programming Interface in Sahel," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 26(15), pages 4367-4380, December.
    3. Ali Rahimikhoob & Mahmood Behbahani & Javad Fakheri, 2012. "An Evaluation of Four Reference Evapotranspiration Models in a Subtropical Climate," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 26(10), pages 2867-2881, August.
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    Cited by:

    1. Jinping Zhang & Hongbin Li & Xixi Shi & Yang Hong, 2019. "Wavelet-Nonlinear Cointegration Prediction of Irrigation Water in the Irrigation District," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(8), pages 2941-2954, June.
    2. Wei Pei & Qiang Fu & Dong Liu & Tianxiao Li & Kun Cheng & Song Cui, 2019. "A Novel Method for Agricultural Drought Risk Assessment," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(6), pages 2033-2047, April.
    3. Ji, Zhonglin & Pan, Yaozhong & Li, Nan, 2021. "Integrating the temperature vegetation dryness index and meteorology parameters to dynamically predict crop yield with fixed date intervals using an integral regression model," Ecological Modelling, Elsevier, vol. 455(C).

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