Multivariate Analysis of Wind Characteristics for Optimal Irrigation Planning in Miandoab Plain, Urmia Lake

Increases in greenhouse gas emissions have encouraged the replacement of fossil fuels with renewable energy sources. This paper investigates the potential of wind energy as a renewable resource for producing agricultural water. For this subject, a multivariate joint function was developed to estimate the wind speed and duration in different return periods. The maximum likelihood estimator, Bayesian information criterion, and Akaike information criterion were used to determine probabilistic fit priorities. Furthermore, a multi-objective framework was examined to highlight the importance of incorporating wind energy consideration into risk-based irrigation planning. Non-dominated sorting theory and a water cycle algorithm were combined to find the optimal strategies for maximization of water productivity and minimization of energy consumption. Miandoab plain in the Urmia Lake basin was conducted as a case study to simulate the cropping pattern based on the proposed probabilistic analysis framework for the characterization and optimization of water allocation in agricultural lands. The field data and conceptual model were evaluated from October 2021 to September 2022. The results showed that the Frank joint function was the best option for multivariate analysis of wind variables with a maximum likelihood estimator of 11.2. Specifically, the application of wind energy to withdraw irrigation increases agricultural water productivity by about 0.38