Fuzzy Stress-based Modeling for Probabilistic Irrigation Planning Using Copula-NSPSO

Hydrological uncertainties are the main components of a sustainable framework in agricultural water management. Prediction of drought as a meteorological phenomenon should be considered to define the groundwater exploitation strategies. This study was conducted to develop a multiobjective-bivariate structure for reducing the soil moisture deficit and groundwater withdrawal in the Qazvin Irrigation District, Qazvin province, Iran. Therefore, non-dominated sorting theory, self-organizing particle swarm optimization and bivariate copula functions were incorporated under fuzzy uncertainty analysis. The results showed that the generalized extreme values and log-normal distribution functions had the best fitness on the drought peak and severity with Kolmogorov Smirnov amounts of 0.08 and 0.17, respectively. Furthermore, the goodness-of-fit tests were indicated the Joe joint function (MLE = 11) is the appropriate function for estimating the probabilistic values of drought characteristics. Proposed plans were to increase the water use efficiency for improving the expected yield production by an average of 20%. Furthermore, the standardized groundwater index was decreased from 1.1 to –4.3 for winter crops.