Magnetized water has been studied by many researchers. However, to date, there is no study about the effects of magnetized water on soil chemical characteristics such as cations and anions in trickle irrigation. This study was performed at Gorgan Agricultural and Natural Resources Research Center, Gorgan Province, Iran, to investigate soil chemical characteristics under trickle irrigation using magnetized water. Two main treatments of magnetic and nonmagnetized water, and three subtreatments of irrigation water salts, including well water as control and addition of 1 or $2\mathrm{meq}/\mathrm{L}$ calcium carbonate (${\mathrm{CaCO}}_3$) to irrigation water, were used. The experiment was laid out with a completely randomized block design with three replications. The results showed that mean soil cations (such as calcium, sodium, and magnesium) and anions (such as bicarbonate, chloride, and sulfate) at soil depths of 0–20, 20–40, and 40–60 cm below the emitter for magnetized irrigation water treatment were lower than those with nonmagnetized irrigation water treatment, and the differences were significant ($p<0.05$). With experiments that continued to the end of the season, the effect of magnetized water was increased. Therefore, soil moisture for each irrigation in magnetized irrigation water was higher than nonmagnetized irrigation water and caused high leaching of soil salt so that this effect was significant. The use of magnetized water is recommended for trickle irrigation to reclaim soils with high cations and anions content such as calcium, sodium, and bicarbonate.

One of the most important issues in the field of water resource management is the optimal utilization of dam reservoirs. In the current study, the optimal utilization of the Aydoghmoush Dam Reservoir is examined based on a hybrid of the bat algorithm (BA) and particle swarm optimization algorithm (PSOA) by increasing the convergence rate of the new hybrid algorithm (HA) without being trapped in the local optima. The main goal of the study was to reduce irrigation deficiencies downstream of this reservoir. The results showed that the HA reduced the computational time and increased the convergence rate. The average downstream irrigation demand over a 10-year period (1991–2000) was 25.12 × 10⁶ m³, while the amount of water release based on the HA was 24.48 × 10⁶ m³. Therefore, the HA was able to meet the irrigation demands better than some other evolutionary algorithms. Moreover, lower indices of root mean square error (RMSE) and mean absolute error (MAE) were obtained for the HA. In addition, a multicriteria decision-making model based on the vulnerability, reliability, and reversibility indices and the objective function performed better with the new HA than with the BA, PSOA, genetic algorithm (GA), and shark algorithm (SA) in terms of providing for downstream irrigation demands.