Extraction of Wheat Irrigation Operation Curve using Simulation-Optimization Approach

Document Type : Research Paper

Authors

1 school of civil, Water and the environment, Campus of Engineering, Shahid abbaspoor, University of Shahid Beheshti, Tehran, Iran.

2 Civil, Water and Environmental Engineering Faculty, Shahid Beheshti University

3 Department of water resources management, school of civil, Water and the environment, Campus of Engineering, Shahid abbaspoor, University of Shahid Beheshti, Tehran, Iran.

Abstract

As agriculture consumes the most parts of water resources, management and control in this sector plays a significant role in water resources management. In this study, simulation-optimization approach was applied using soil and water assessment tool (SWAT) in combination with non-dominated sorting differential evolution (NSDE) algorithm to find the best operation curve for wheat irrigation in Mahabad basin. The wheat production in 2011 to 2013 was considered for SWAT calibration and validation. According to the hedging rule, a two-objective function was used to increase the crop yield and reduce the irrigation volume. The optimum results showed by reducing the annual irrigation rate from 200 mm to about 100 mm, the wheat production will be 2.114 ton/ha which is equal to the current irrigation pattern yield. This approach could maximize the economic cost by introducing the best irrigation pattern and consequently reduce the groundwater recharge and surface run-off 34% and 13%, respectively.

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Abbaspour, C. K. (2008). SWAT Calibrating and Uncertainty Programs. A User Manual. Eawag Zurich, Switzerland.
Ahmadzadeh, H., Morid, S., Delavar, M., & Srinivasan, R. (2016). Using the SWAT model to assess the impacts of changing irrigation from surface to pressurized systems on water productivity and water saving in the Zarrineh Rud catchment. Agricultural water management, 175, 15-28.
Allen, M. R., & Ingram, W. J. (2002). Constraints on future changes in climate and the hydrologic cycle. Nature, 419(6) 903, 224
Arnold, J. G., Srinivasan, R., Muttiah, R. S., & Williams, J. R. (1998). Large area hydrologic modeling and assessment part I: model development. JAWRA Journal of the American Water Resources Association, 34(1), 73-89.
Bhuvaneswari, K., Geethalakshmi, V., Lakshmanan, A., Srinivasan, R., & Sekhar, N. U. (2013). The impact of El Nino/Southern oscillation on hydrology and rice productivity in the Cauvery Basin, India: application of the soil and water assessment tool. Weather and Climate Extremes, 2, 39-47.
Devia, G. K., Ganasri, B., & Dwarakish, G. (2015). A review on hydrological models. Aquatic Procedia, 4, 1001-1007.
Du, F.-h., Tao, L., Chen, X.-m., & Yao, H.-x. (2019). Runoff Simulation Using SWAT Model in the Middle Reaches of the Dagu River Basin. In Sustainable Development of Water Resources and Hydraulic Engineering in China (pp. 115-126): Springer.
Fadil, A., Rhinane, H., Kaoukaya, A., Kharchaf, Y., & Bachir, O. A. (2011). Hydrologic modeling of the Bouregreg watershed (Morocco) using GIS and SWAT model. Journal of Geographic Information System, 3(04), 279.
Faramarzi, M., Yang, H., Schulin, R., & Abbaspour, K. C. (2010). Modeling wheat yield and crop water productivity in Iran: Implications of agricultural water management for wheat production. Agricultural water management, 97(11), 1861-1875.
Fereidoon, M., & Koch, M. (2018). SWAT-MODSIM-PSO optimization of multi-crop planning in the Karkheh River Basin, Iran, under the impacts of climate change. Science of the Total Environment,630, 502-516.
Garg, K. K., Bharati, L., Gaur, A., George, B., Acharya, S., Jella, K., & Narasimhan, B. (2012). Spatial mapping of agricultural water productivity using the SWAT model in Upper Bhima Catchment, India. Irrigation and Drainage, 61(1), 60-79.
Gassman, P. W., Reyes, M. R., Green, C. H., & Arnold, J. G. (2007). The soil and water assessment tool: historical development, applications, and future research directions. Transactions of the ASABE, 50(4), 1211-1250.
Golmohammadi, G., Prasher, S., Madani, A., & Rudra, R. (2014). Evaluating three hydrological distributed watershed models: MIKE-SHE, APEX, SWAT. Hydrology, 1(1), 20-39.
Grusson, Y., Sun, X., Gascoin, S., Sauvage, S., Raghavan, S., Anctil, F., & Sáchez-Pérez, J.-M. (2015). Assessing the capability of the SWAT model to simulate snow, snow melt and streamflow dynamics over an alpine watershed. Journal of Hydrology, 531, 574-588.
Immerzeel, W., Gaur, A., & Zwart, S. J. (2008). Integrating remote sensing and a process-based hydrological model to evaluate water use and productivity in a south Indian catchment. Agricultural water management, 95(1), 11-24.
Kijne, J. W., Barker, R., & Molden, D. J. (2003). Water productivity in agriculture: limits and opportunities for improvement (Vol. 1): Cabi.
Lin, B., Chen, X., Yao, H., Chen, Y., Liu, M., Gao, L., & James, A. (2015). Analyses of landuse change impacts on catchment runoff using different time indicators based on SWAT model. Ecological Indicators, 58, 55-63.
Madani, K., AghaKouchak, A., & Mirchi, A. (2016). Iran’s socio-economic drought: challenges of a water-bankrupt nation. Iranian Studies, 49(6), 997-1016.
Maurer, E. P. (2010). The utility of daily large-scale climate data in the assessment of climate change impacts on daily streamflow in California.
Neitsch, S. L., Arnold, J. G., Kiniry, J. R., & Williams, J. R. (2011). Soil and water assessment tool theoretical documentation version 2009. Retrieved from
Rafiee, V., & Shourian, M. (2016). Optimum multicrop-pattern planning by coupling SWAT and the harmony search algorithm. Journal of Irrigation and Drainage Engineering, 142(12), 04016063.
Reddy, M. J., & Kumar, D. N. (2006). Optimal reservoir operation using multi-objective evolutionary algorithm. Water Resources Management, 20(6),861-878.
Reder, A., Rianna, G., Vezzoli, R., & Mercogliano, P. (2016). Assessment of possible impacts of climate change on the hydrological regimes of different regions in China. Advances in Climate Change Research, 7(3), 169-184.
Rijsberman, F. R. (2006). Water scarcity: fact or fiction? Agricultural water management, 80(1-3), 5-22.
Stom, R., & Price, K. (1995). DE-a Simple and Efficient Adaptive Scheme for Global Optimization Over Continuous Space. International Computer Science Institute, Technical report TR-95-012, 1-12.
Thavhana, M., Savage, M., & Moeletsi, M. (2018). SWAT model uncertainty analysis, calibration and validation for runoff simulation in the Luvuvhu River catchment, South Africa. Physics and Chemistry of the Earth, Parts A/B/C.
Vaghefi, S. A., Mousavi, S., Abbaspour, K., Srinivasan, R., & Arnold, J. (2015). Integration of hydrologic and water allocation models in basin-scale water resources management considering crop pattern and climate change: Karkheh River Basin in Iran. Regional environmental change, 15(3), 475-484.
Vigiak, O., Malagó, A., Bouraoui, F., Vanmaercke, M., Obreja, F., Poesen, J., . . . Grošelj, S. (2017). Modelling sediment fluxes in the Danube River Basin with SWAT. Science of the Total Environment, 599, 992-1012.
Yazdi, J., & Moridi, A. (2018). Multi-Objective Differential Evolution for Design of Cascade Hydropower Reservoir Systems. Water Resources Management, 32(14), 4779-4791.
Yazdi, J., Yoo, D., & Kim, J. (2017). Comparative study of multi-objective evolutionary algorithms for hydraulic rehabilitation of urban drainage networks. Urban Water Journal, 14(5), 483-492.
Yesuf, H. M., Assen, M., Alamirew, T., & Melesse, A. M. (2015). Modeling of sediment yield in Maybar gauged watershed using SWAT, northeast Ethiopia. Catena, 127, 191-205.