Estimating irrigation water in irrigation networks using satellite images

Document Type : Research Paper

Authors

1 Department of Water Science and Engineering, Faculty of Agriculture, Bu-Ali Sina University, Hamedan, Iran.

2 Department of Water Engineering, Faculty of Agriculture, University of Zanjan, Zanjan, Iran.

10.22059/jwim.2023.364899.1103

Abstract

Water is one of the most important resources needed by human society and the first and most important factor for the production of agricultural products, more than 90% of this vital liquid is consumed in this sector. One of the most important factors that affect the performance of a water conveyance and distribution network is the water distribution and delivery program. In order to obtain turnouts’ discharges, the water requirement of the eastern Aghili area was estimated using the Global Land Data Assimilation System (GLDAS) and controlled using the results of the NETWAT model. For this purpose, three-hour evapotranspiration was estimated with GLDAS, and the six-hour discharges of turnouts were calculated according to the cultivated area of each turnout and irrigation efficiency. The hydraulics of the eastern Aghili canal were simulated using the above-mentioned data for six hours. The results showed the appropriate accuracy of GLDAS so that at a maximum of 12.7%, GLDAS underestimated the evapotranspiration values compared to NETWAT. The minimum values of efficiency and adequacy indicators of 0.95 and 0.94, respectively, were obtained, which are in the "good" performance class.

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References

  1. Amini, A., Moghadam, M. K., Kolahchi, A. A., Raheli-Namin, M., and Ahmed, K. O. (2023). Evaluation of GLDAS soil moisture product over Kermanshah province, Iran. H2 Open Journal, 6(3), 373-386.
  2. Barkhordari, S., and Shahdany, S. M. H. (2021). Developing a smart operating system for fairly distribution of irrigation water, based on social, economic, and environmental considerations. Agricultural Water Management, 250, 106833.
  3. Du, J., and Sun, R. (2012). Estimation of evapotranspiration for ungauged areas using MODIS measurements and GLDAS data. Procedia Environmental Sciences, 13, 1718-1727.
  4. Fang, H., Beaudoing, H. K., Teng, W. L., and Vollmer, B. E. (2009). Global Land data assimilation system (GLDAS) products, services and application from NASA hydrology data and information services center (HDISC). ASPRS 2009 Annual Conference,
  5. faraji, z., and kaviani, a. (2019). Assessment of water balance components of GLDAS-2 and GLDAS-2.1 in Qazvin province. Iranian Journal of Irrigation & Drainage, 13(2), 462-474. (In persian).
  6. Fatemeh, O., Hesam, G., and Shahverdi, K. (2020). Comparing Fuzzy SARSA Learning (FSL) and Ant Colony Optimization (ACO) Algorithms in Water Delivery Scheduling under Water Shortage Conditions. Irrigation and Drainage Engineering, 146(9), 04020028.
  7. Gairola, R., Prakash, S., and Pal, P. (2015). Improved rainfall estimation over the Indian monsoon region by synergistic use of Kalpana-1 and rain gauge data. Atmósfera, 28(1), 51-61.
  8. Khaeez, S., and Shahdany, S. M. H. (2021). Non-structural modification of agricultural water distribution systems in large scale irrigation districts. Computers and Electronics in Agriculture, 184, 106102.
  9. Kim, S., and Brubaker, K. L. (2014). Comparison of gauge and MPE precipitation data for the Chesapeake Bay Watershed Model. Journal of Hydrologic Engineering, 19(5), 1042-1047.
  10. Liu, Y. Y., McCabe, M. F., Evans, J., Van Dijk, A., de Jeu, R. A., and Su, H. (2009). Comparison of soil moisture in GLDAS model simulations and satellite observations over the Murray Darling Basin. Proceedings of the International Congress on Modelling and Simulation.
  11. Mishra, A. K., Gairola, R., Varma, A., and Agarwal, V. K. (2011). Improved rainfall estimation over the Indian region using satellite infrared technique. Advances in space research, 48(1), 49-55.
  12. Molden, D. J., and Gates, T. K. (1990). Performance measures for evaluation of irrigation-water-delivery systems. Journal of Irrigation and Drainage Engineering, 116(6), 804-823.
  13. Moshir Panahi, D., Sadeghi Tabas, S., Kalantari, Z., Ferreira, C. S. S., and Zahabiyoun, B. (2021). Spatio-temporal assessment of global gridded evapotranspiration datasets across Iran. Remote Sensing, 13(9), 1816.
  14. Nazari, B., Liaghat, A., Akbari, M. R., and Keshavarz, M. (2018). Irrigation water management in Iran: Implications for water use efficiency improvement. Agricultural Water Management, 208, 7-18.
  15. Savari, H., Monem, M., and Shahverdi, K. (2016). Comparing the Performance of FSL and Traditional Operation Methods for On-Request Water Delivery in the Aghili Network, Iran. Journal of Irrigation and Drainage Engineering, 142(11), 04016055.
  16. Shahverdi, K., Alamiyan-Harandi, F., and Maestre, J. (2022). Double Q-PI architecture for smart model-free control of canals. Computers and Electronics in Agriculture, 197, 106940.
  17. Shahverdi, K., and Maestre, J. (2022). Gray Wolf Optimization for Scheduling Irrigation Water. Journal of Irrigation and Drainage Engineering, 148(7), 04022020.
  18. Shahverdi, K., Maestre, J., Alamiyan-Harandi, F., and Tian, X. (2020). Generalizing Fuzzy SARSA Learning for Real-Time Operation of Irrigation Canals. Water, 12(9), 2407.
  19. Shahverdi, K., Monem, M. J., and Nili, M. (2016). Fuzzy SARSA learning of operational instructions to schedule water distribution and delivery. Irrigation and Drainage, 65(3), 276-284.
  20. Wagner, P. D., Fiener, P., Wilken, F., Kumar, S., and Schneider, K. (2012). Comparison and evaluation of spatial interpolation schemes for daily rainfall in data scarce regions. Journal of Hydrology, 464, 388-400.
  21. Zaitchik, B. F., Rodell, M., and Olivera, F. (2010). Evaluation of the Global Land Data Assimilation System using global river discharge data and a source‐to‐sink routing scheme. Water Resources Research, 46(6).
Water and Irrigation Management
Volume 14, Issue 1
March 2024
Pages 157-167

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