Hydrological uncertainties are considered as the major components of agricultural water management. Determining the drought effects as a meteorological phenomenon should be evaluated to investigate the groundwater exploitation strategies for irrigation. The main objective of this study was to show how copula functions are used in the bivariate analysis of drought and increase the water use efficiency in the Khanmirza plain, Chaharmahal and Bakhtiari province, Iran. Moreover, the water amounts estimated by probabilistic analysis in different return periods were allocated to the cropping pattern using particle swarm optimization algorithm. Therefore, the drought characteristics including severity and duration were extracted using normalized rainfall index. Then, the frequency distributions were fitted to the mentioned drought characteristics, and the best fitted marginal distribution was specified for every drought characteristics. The results showed that the gamma and generalized extreme values distributions had the best fitness on the drought severity and duration, respectively. Furthermore, the goodness-of-fit tests were considered for Clayton, Ali-Mikhail-Haq, Frank, Gamble, Gamble-Hougaard, and Joe using Akaike information criterion and Bayesian information criterion. Frank copula is the best function for constructing the multivariate distribution in the study area. Results showed that the developed plans increased the probabilistic values of soil moisture content in root zone for the cultivated crops in the study area. Groundwater resource index was deceased to negative amounts related to existing conditions in the five recent years. Furthermore, optimal irrigation scheduling increased the soil moisture content by the average of 60% at the peak point of water requirement curve.

水文不确定性被认为是农业用水管理的主要组成部分。应将干旱影响确定为一种气象现象，以研究用于灌溉的地下水开采策略。这项研究的主要目的是显示如何在干旱的双变量分析中使用copula函数，并提高伊朗的Chaharmahal和Bakhtiari省的Khanmirza平原的用水效率。此外，使用粒子群优化算法，将通过概率分析在不同的回报期内估算出的水量分配给种植模式。因此，使用归一化降雨指数提取了干旱特征，包括严重程度和持续时间。然后，将频率分布拟合到上述干旱特征，并为每种干旱特征指定了最合适的边际分布。结果表明，伽玛值和广义极值分布分别对干旱的严重程度和持续时间具有最佳适应性。此外，考虑使用Akaike信息准则和贝叶斯信息准则对Clayton，Ali-Mikhail-Haq，Frank，Gamble，Gamble-Hougaard和Joe进行拟合优度检验。弗兰克·科普拉（Frank copula）是在研究区域内构建多元分布的最佳功能。结果表明，制定的计划提高了研究区耕作作物根区土壤水分含量的概率值。近五年来，地下水资源指数下降到与现有状况相关的负数。此外，