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.