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Prediction model for irrigation return flow considering lag effect for arid areas

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  • Jie, Feilong
  • Fei, Liangjun
  • Li, Shan
  • Hao, Kun
  • Liu, Lihua
  • Zhu, Hongyan

Abstract

Irrigation return flow (RF) in arid areas has a significant lag, and current RF prediction models have an inadequate consideration of this. This study proposed the concepts of lag time contour (LTC) and area frequency curve (AFC), both of which can be used for describing the spatial distribution of RF lag time in an arid area. AFC can be determined from LTC or obtained from RF observations and use of the trial-and-error method. A new RF prediction model was developed for arid regions based on LTC and AFC, and the model couples deep percolation (DP) and lag time in order to achieve the prediction of RF. This model was applied in the eastern part of the Jingdian irrigation district in northwest China, and the results indicate that the model predictions fit well with the observations and that the model is reliable. Analysis of DP and RF in the study area between 2000 and 2020 proved that when lag time is not greater than half a year, the main factor that influences yearly-scale RF is DP, and when lag time is greater than one year, the factors that influence yearly-scale RF are DP and lag time. The main influences on monthly-scale RF are DP and lag time, but this is to a lesser extent than their influence on yearly-scale RF. LTC and AFC help understand the process of RF production in arid regions and the influence lag time has on RF. Additionally, the RF prediction model developed for this study is suitable for RF prediction at different time scales, and also for RF predictions in arid areas where there is insufficient hydrogeological data. This is of great significance for the efficient use of water resources in arid regions’ irrigation areas.

Suggested Citation

  • Jie, Feilong & Fei, Liangjun & Li, Shan & Hao, Kun & Liu, Lihua & Zhu, Hongyan, 2021. "Prediction model for irrigation return flow considering lag effect for arid areas," Agricultural Water Management, Elsevier, vol. 256(C).
    • Handle: RePEc:eee:agiwat:v:256:y:2021:i:c:s0378377421003954
    DOI: 10.1016/j.agwat.2021.107119
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    References listed on IDEAS

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    1. Poch-Massegú, R. & Jiménez-Martínez, J. & Wallis, K.J. & Ramírez de Cartagena, F. & Candela, L., 2014. "Irrigation return flow and nitrate leaching under different crops and irrigation methods in Western Mediterranean weather conditions," Agricultural Water Management, Elsevier, vol. 134(C), pages 1-13.
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    3. Mohan, S. & Vijayalakshmi, D.P., 2009. "Prediction of irrigation return flows through a hierarchical modeling approach," Agricultural Water Management, Elsevier, vol. 96(2), pages 233-246, February.
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    1. Zhongwei Liang & Tao Zou & Yupeng Zhang & Jinrui Xiao & Xiaochu Liu, 2022. "Sprinkler Drip Infiltration Quality Prediction for Moisture Space Distribution Using RSAE-NPSO," Agriculture, MDPI, vol. 12(5), pages 1-32, May.
    2. Feilong Jie & Liangjun Fei & Shan Li & Kun Hao & Lihua Liu & Youliang Peng, 2022. "Effects on Net Irrigation Water Requirement of Joint Distribution of Precipitation and Reference Evapotranspiration," Agriculture, MDPI, vol. 12(6), pages 1-16, June.

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