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A novel irrigation canal scheduling model adaptable to the spatial-temporal variability of water conveyance loss

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  • Liao, Xiangcheng
  • Mahmoud, Ali
  • Hu, Tiesong
  • Wang, Jinglin

Abstract

The water conveyance loss of irrigation canals shows significant spatial-temporal variability both along the canal reach and over time. An accurate representation of these variances in irrigation canal scheduling is essential to reduce water conveyance loss. However, the existing optimal irrigation scheduling models (OISMs) still have deficiencies in dealing with spatial-temporal variability of water conveyance losses, which seriously limits the model performance in many irrigation areas with high spatial-temporal variability. Borrowing a concept from hydrology, we propose a Dynamic Calculation Method of canal water conveyance loss (DMWCL), in which antecedent water conveyance index (AWCI) is defined to represent the effects of antecedent soil water content upon infiltration losses, and the integral-flow balance method is adopted to describe the characteristics of canal discharge decreasing along the canal reach. Based on the DMWCL, we present a novel canal scheduling model with the potential to adapt to spatial-temporal variability (DMWCL-CS). The new model is applied to the Hetao Irrigation District, Inner Mongolia, a typical agricultural watershed in China. Our results across the case studies show that the canal water delivery schedule derived by the DMWCL-CS model reduces water conveyance loss and improves irrigation performance, ranging from 2.49 million m3 to 10.60 million m3 in 2014 and from 0.60 million m3 to 6.17 million m3 in 2016, respectively, compared to that of three additional models. Interestingly, we find that the water delivery schedule of the DMWCL-CS model accelerates the wetting process of the canal bed soil to reduce the water conveyance loss and increases the discharge to adapt to the characteristics that the discharge decreases along the canal reach. The DMWCL-CS model provides essential insights for irrigation managers to formulate a canal water delivery schedule that adapts to spatial-temporal variability. The proposed framework is generic and can be integrated into any OISM.

Suggested Citation

  • Liao, Xiangcheng & Mahmoud, Ali & Hu, Tiesong & Wang, Jinglin, 2022. "A novel irrigation canal scheduling model adaptable to the spatial-temporal variability of water conveyance loss," Agricultural Water Management, Elsevier, vol. 274(C).
    • Handle: RePEc:eee:agiwat:v:274:y:2022:i:c:s037837742200508x
    DOI: 10.1016/j.agwat.2022.107961
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    References listed on IDEAS

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    1. Todd E. Clark & Michael W. McCracken, 2009. "Improving Forecast Accuracy By Combining Recursive And Rolling Forecasts," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 50(2), pages 363-395, May.
    2. Alam, M. M. & Bhutta, M. N., 2004. "Comparative evaluation of canal seepage investigation techniques," Agricultural Water Management, Elsevier, vol. 66(1), pages 65-76, April.
    3. Sara Azargashb Lord & Seied Mehdy Hashemy Shahdany & Abbas Roozbahani, 2021. "Minimization of Operational and Seepage Losses in Agricultural Water Distribution Systems Using the Ant Colony Optimization," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(3), pages 827-846, February.
    4. Mohamed Bakry & Ahmed Awad, 1997. "Practical Estimation of Seepage Losses Along Earthen Canals in Egypt," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 11(3), pages 197-206, June.
    5. Benli, Bogachan & Kodal, Suleyman, 2003. "A non-linear model for farm optimization with adequate and limited water supplies: Application to the South-east Anatolian Project (GAP) Region," Agricultural Water Management, Elsevier, vol. 62(3), pages 187-203, October.
    6. Goncalves, J.M. & Pereira, L.S. & Fang, S.X. & Dong, B., 2007. "Modelling and multicriteria analysis of water saving scenarios for an irrigation district in the upper Yellow River Basin," Agricultural Water Management, Elsevier, vol. 94(1-3), pages 93-108, December.
    7. Aditi Bhadra & Arnab Bandyopadhyay & Rajendra Singh & Narendra Raghuwanshi, 2010. "An Alternative Rotational Delivery Schedule for Improved Performance of Reservoir-based Canal Irrigation System," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 24(13), pages 3679-3700, October.
    8. Suryavanshi, A. R. & Reddy, J. Mohan, 1986. "Optimal operation schedule of irrigation distribution systems," Agricultural Water Management, Elsevier, vol. 11(1), pages 23-30, March.
    9. Kinzli, Kristoph-Dietrich & Martinez, Matthew & Oad, Ramchand & Prior, Adam & Gensler, David, 2010. "Using an ADCP to determine canal seepage loss in an irrigation district," Agricultural Water Management, Elsevier, vol. 97(6), pages 801-810, June.
    10. Santhi, C. & Pundarikanthan, N. V., 2000. "A new planning model for canal scheduling of rotational irrigation," Agricultural Water Management, Elsevier, vol. 43(3), pages 327-343, April.
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    1. Qiuli Zheng & Chunfang Yue & Shengjiang Zhang & Chengbao Yao & Qin Zhang, 2024. "Optimal Allocation of Water Resources in Canal Systems Based on the Improved Grey Wolf Algorithm," Sustainability, MDPI, vol. 16(9), pages 1-16, April.
    2. Mai, Zijun & He, Yupu & Feng, Chen & Han, Congying & Shi, Yuanzhi & Qi, Wei, 2024. "Multi-objective modeling and optimization of water distribution for canal system considering irrigation coverage in artesian irrigation district," Agricultural Water Management, Elsevier, vol. 301(C).

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