Document Type : Research Paper
Authors
1 Department of Irrigation & Reclamation Engineering, Faculty of Agriculture, University of Tehran, Karaj
2 Water Engineering Group, Faculty of Agriculture Sciences, University of Guilan, Iran
Abstract
Keywords
Main Subjects
Addressing escalating challenges of climate change and water scarcity in Iran, this study aimed to estimate crop water requirements and predict irrigation timing and volume for efficient agricultural water management. Specifically, it focused on developing a cell-based water balance model to evaluate water consumption and irrigation schedules for wheat fields in Qom and Neyshabur during the 2022-2023 agricultural year.
A remote sensing-based soil water balance model was employed to simulate crop irrigation water requirements. This model tracked daily changes in soil water storage, considering effective rainfall and irrigation as inputs, and deep percolation and actual evapotranspiration (ETa) as outputs. Daily farm-scale evapotranspiration maps were generated using the GeeSEBAL algorithm, applied to Landsat 8 & 9 satellite imagery and the ERA5 meteorological dataset. Crop growth curves, root depth, and plant height were derived from Sentinel-2 NDVI vegetation index data. Soil water holding capacity was extracted from the HiHydroSoil dataset. Model validation utilized field observational data from Qom and Neyshabur for the 2022-2023 agricultural year, including soil texture, irrigation timing and method, crop type, and planting/harvest dates.
Wheat in Neyshabur was planted on November 1st, with harvest between June 6th and 10th, 2024. In Qom (Plot 14), planting occurred on December 8th, 2023, and harvest between June 1st and 5th, 2024, as determined by NDVI imagery.
Reference evapotranspiration (ETr) during the growing season was 538 mm in Neyshabur and 415 mm in Qom. Precipitation was approximately 150 mm at both sites. Actual evapotranspiration (ETa) in Neyshabur wheat fields ranged from 520 to 730 mm, while in Qom, it varied from 377 to 502 mm.
Farmers applied a constant volume of irrigation water across all plots. However, the net irrigation requirement calculated by the water balance model ranged from 3582 to 4524 m³/ha. Due to rainfall contributing to water requirements, ETa exceeded both farmer-applied and model-calculated irrigation amounts in all plots. Neyshabur's Plots 5 to 8 showed the highest irrigation water requirement and ETa, with Plot 1 having the lowest. The largest discrepancy between model-calculated and farmer-applied irrigation was in Neyshabur's Plot 4, while in Qom, this discrepancy was more significant at 1265 m³/ha.
Neyshabur farmers consistently performed 9 irrigation events, whereas the model predicted only 6 to 7 events were needed. In Qom (Plot 14), both farmer and model applied 7 irrigation events. Neyshabur's Plots 5, 6, 7, 8, and 10, along with Qom's Plot 14, showed closer alignment with the model when considering one additional irrigation. Other blocks received two extra irrigation events beyond actual crop need. Although the model effectively predicted irrigation timing, its predicted irrigation depth differed from farmer practices. In Qom, despite similar irrigation event numbers, timing and volume varied from the model. The constant 60-mm irrigation depth applied by farmers often exceeded actual crop requirements. Instances of over-irrigation, particularly immediately after rainfall, were observed at both sites.
Conclusion:
This study effectively assessed water consumption and optimized wheat irrigation schedules in Qom and Neyshabur during the 2022-2023 agricultural year. Analysis revealed significant regional differences in irrigation patterns and water requirements. Although precipitation was similar, Neyshabur's ETr was approximately 100 mm higher than Qom's, emphasizing the need for region-specific irrigation management.
A critical finding was the identification of unnecessary and excessive irrigation events at both sites. While the model accurately scheduled irrigation timing, Neyshabur farmers applied 2 to 3 more irrigation events than required, indicating clear over-irrigation.
These findings highlight the necessity of transitioning from traditional irrigation practices to approaches based on precise data and advanced modeling. Given Iran's limited water resources in arid and semi-arid regions, optimizing irrigation timing and volume according to actual crop needs can yield substantial water savings. This study demonstrates the potential of such models as powerful tools for achieving more precise and monitored irrigation water management in the future.
This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.
Conceptualization, Majid Vazifeh Doost and Javad Bazrafshan; methodology, software Majid Vazifehdoost; formal analysis, methodology, Majid Vazifeh Doost and Mahnaz Ahnadi Namin; data curation, Majid Vazifeh Doost and Mahnaz Ahnadi Namin; writing—original draft preparation, Majid Vazifeh Doost and Mahnaz Ahnadi Namin, and Javad Bazrafshan; writing—review and editing, Majid Vazifeh Doost and Javad Bazrafshan; visualization, Majid Vazifeh Doost and Mahnaz Ahnadi Namin. All authors have read and agreed to the published version of the manuscript.
There is no use of any type of artificial intelligence-based technology in this paper.
Data available on request from the authors.
The authors would like to thank Iran Meteorological Organization for supporting the data needed in the present study.
The authors avoided data fabrication, falsification, and plagiarism, and any form of misconduct.
The authors declare no conflict of interest.
Abedinpour, M., & Razaghi, M. H. (2020). Simulation of soil and wheat yield with AquaCrop model in Nayshabur plain field conditions. Journal of Water Research in Agriculture, 34(1). http://dx.doi.org/10.22092/jwra.2024.364259.1020/ .(inPersian).
Allen, R., Pereira, L., Raes, D., & Smith, M. (1998). Crop evapotranspiration - Guidelines for computing crop water requirements - FAO Irrigation and drainage paper 56. Rome: FAO - Food and Agriculture Organization of the United Nations. Retrieved from https://www.researchgate.net/publication/235704197_Crop_evapotranspiration-Guidelines_for_computing_crop_water_requirements-FAO_Irrigation_and_drainage_paper_56
Allen, R. G., Trezza, R., Tasumi, M., 2006. Analytical integrated functions for daily solar radiation on slopes. Agric. For. Meteorol. 139, 55–73. https://doi.org/10. 1016/j.agrformet. 2006. 05. 012.
Allen, R. G., Tasumi, M., Trezza, R., 2007. Satellite-based energy balance for mapping evapotranspiration with internalized calibration (METRIC)—model. J. Irrig. Drain. Eng. 133, 380–394. https://doi.org/10. 1061/(ASCE)0733-9437(2007)133:4(380).
Amiri, E., Bahrami, A., Khorsand, A., & Haghjoo, M. (2015). Evaluation of AquaCrop model in predicting grain yield and biomass of wheat under water stress. Water and Soil Science, 25(4/2), 217-229. (inPersian).
Arnold, J., Williams, J., Nicks, A., & Sammons, N. (1991, July 1). SWRBB: A basin scale simulation model for soil and water resources management. 20(1), 82-83. https://doi.org/10.1016/0378-3774(91)90038-K
Bastiaanssen, W. G. M., Menenti, M., Feddes, R. A., Holtslag, A. A. M., 1998a. A remote sensing surface energy balance algorithm for land (SEBAL): 1. Formulation. J. Hydrol. 212–213, 198–212. https://doi.org/10. 1016/S0022-1694(98)00253-4.
Bastiaanssen, W. G. M., Pelgrum, H., Wang, J., Ma, Y., Moreno, J. F., Roerink, G. J., van der Wal, T., 1998b. A remote sensing surface energy balance algorithm for land (SEBAL): 2. Validation. J. Hydrol. 212–213, 213–229. https://doi.org/10. 1016/S0022-1694 (98)00254-6.
Bastiaanssen, W. G. M., 2000. SEBAL-based sensible and latent heat fluxes in the irrigated Gediz Basin. Turkey. J. Hydrol. 229, 87–100 https://doi. org/https://doi.org/10.1016/S0022-1694(99)00202-4.
Bastiaanssen, W.G.M., E.J.M. Noordman, H. Pelgrum, G. Davids, B.P. Thoreson, and R.G. Allen (2005). "SEBAL model with remotely sensed data to improve water-resources management under actual field conditions". Journal of irrigation and drainage engineering, 131, 85-93.
Dehghani Sanij, H., Asadi Eskouei, E., & Taghizadeh Ghasab, A. (February & March 2022). An analysis of agricultural water consumption based on actual evapotranspiration. Iran Irrigation and Drainage, 15(6), Serial No. 90, 1251-1262. Doi: 20.1001.1.20087942.1400.15.6.2.2. (inPersian).
Goodarzi, M., & Abedi Kohpayeh, J. (2020). Effects of climate change on water requirement and irrigation requirement in irrigated areas, case study: Najafabad plain. Journal of Water Extension and Productivity. (inPersian).
Haji Hosseini, A., & Yazdani, A. (2011). Water management and productivity in agriculture. Coordination Management of Agricultural Extension, Yazd Jihad-e-Agriculture Organization, First Edition, Yazd. (inPersian).
Heidari, H. A., Bagheri, A., & Ketabchi, H. (2023). Serious games in water resources management. Third National Conference on Water Resources Management Strategies and Environmental Challenges. Sari. 1-8. (inPersian).
Hosseini, S. T., Khoshroo, M., & Ziatabar, M. (2015). Investigation of climate change effects and evaluation of planting date change on soybean yield. Journal of Water Research in Agriculture, 29(4). (inPersian).
Jaafar, H. H., Ahmad, F. A., 2019. Time series trends of Landsat-based ET using automated calibration in METRIC and SEBAL: The Bekaa Valley, Lebanon. Remote Sens. Environ. https://doi.org/10.1016/j. rse. 2018. 12. 033.
Khosravipour, B., & Loimyi, K. (Winter 2024). The role of modern irrigation in optimal water management during drought. Geography and Human Relations, 7(3), 303-318. (inPersian).
Kroes, J., & van Dam, J. (2003). Reference Manual SWAP version 3.0.3. WIMEK Wageningen Environmental Research. Wageningen: Alterra. https://edepot.wur.nl/35471
Laipelt L., Henrique Bloedow Kayser R., Santos Fleischmann A., Ruhoff A., Bastiaanssen W., A. Erickson T., Melton F., 2021. Long-term monitoring of evapotranspiration using the SEBAL algorithm and Google Earth Engine cloud computing. ISPRS Journal of Photogrammetry and Remote Sensing. 178 (2021) 81–96. https://doi.org/10.1016/j. isprsjprs.2021.05.018.
Mianabadi, A., Alizadeh, A., Sanaeinejad, S. H., Ghahraman, B., & Davari, K. (2016). Evaluation of the SEBAL algorithm for estimating actual evapotranspiration in the Neyshabur basin using the SWAT model. Water Research in Agriculture, 30(4). http://dx.doi.org/10.22092/jwra.2017.109014. (inPersian).
Ministry of Energy. (2014). Instructional guidelines for calculating water resource balance. Publication No. 418 - A. - Deputy for Water and Wastewater Affairs. (inPersian).
Ministry of Energy. (2023). Guide to methods for estimating evaporation in different climatic conditions. Deputy of Production, Technical, and Infrastructure, Ministry of Energy, 1-218. (inPersian).
Mushtaq, Nahid; Jafari, Reza; Soltani, Saeed and Ramezani, Nafiseh (2015). Application of energy balance model and Landsat TM satellite data in estimating evapotranspiration. Soil and Water Sciences (Agricultural Sciences and Arts and Natural Resources), Volume 19, 73. 206-217. (inPersian).
National Drought Center. (2024). Meteorological Bulletin. Tehran: Meteorological Organization. (inPersian).
Nikzad Salami, M., Borna, R., & Asadian, F. (December 2019). The impact of climate change on agricultural products with an emphasis on soybean oilseed using LARS-WG (Case study: Moghan Plain). Natural Geography, Islamic Azad University, 12(6), No. 45, 75-86. (inPersian).
Paredes, P., J. Rodrigues, G., T. Petry, M., O. Severo, P., Carlesso, R., & Santos Pereira, L. (2018, April 26). Evapotranspiration Partition and Crop Coefficients of Tifton 85 Bermudagrass as Affected by theFrequency of Cuttings. Application of the FAO56 Dual Kc Model. Water, 10(558). https://doi.Org/10.3390/w1005055
Sabouhi, M., & Zare, S. (2013). An overview of developments, challenges, and laws for the protection and utilization of water resources in Iran. Agricultural Economics, Special Issue. (inPersian).
Saiz Rodríguez, R., Aguirre López, G., Rodríguez, J., Watts Thorp, C., Saiz Rodríguez, J., Ochoa Granillo, A., & Saiz Hernández, J. (2017). Estimación de evapotranspiración con imágenes de PROBA-V de un cultivo de sandía en la costa de Hermosillo, Sonora, México. TERRA LATINOAMERICANA, 35(4).
Sheikh, Vahedbari and Moshekhian, Yousef (2009). Preparing soil moisture map using BEAC dynamic model. Fifth National Conference of Iranian Watershed Science and Engineering. (inPersian).
Su, Z. (2002). The Surface Energy Balance System (SEBS) for estimation of turbulent heat fluxes. Hydrology and Earth System Sciences, 6(1), 85–100. doi:https://doi.org/10.5194/hess-6-85-2002
Tavakoli, A., & Ghadami Firouzabadi, A. (2020). Introduction to methods of implementing deficit irrigation management in wheat cultivation. Agricultural Education and Extension Institute, Knowledge and Media Network Office. Agricultural Education Publishing, pp. 1-32. ISBN: 978-964-520-743-2. (inPersian).
Tavakoli, Alireza and Ghadami Firouzabadi, Ali (2019). Familiarity with methods of implementing drought management in wheat cultivation. Agricultural Education and Extension Institute, Knowledge and Media Network Office. Agricultural Education Publication, - Page 1-32. - ISBN:978-964-520-743-2. (inPersian).
Yaghoobzadeh Mostafa, Boroomanad-Naseb, S., Izadpanah, Z., & Seyed Kaboli, H. (2016). The effect of climate change on evapotranspiration trends during the growth period of irrigated and rainfed crops using AOGCM models. Journal of Water Research in Agriculture, 30(4B). (inPersian).
Yaghoobzadeh, M. (2016). Estimation of actual evapotranspiration using an agro-hydrological model and remote sensing techniques. Journal of Water Research in Agriculture, 30(4). http://dx.doi.org/10.22092/jwra.2017.109013. (inPersian).
weatherandclimate. (2025). https://weatherandclimate.com/iran/qom.
weatherandclimate. (2025). https://weatherandclimate.com/iran/razavi-khorasan/neyshabur.
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