JWSS - Journal of Water and Soil Science

Optimizing the water resources operation, especially in the agricultural sector, which has the largest share in the water resources operation, is extremely important. Therefore, in this research, while introducing Whale, Gray Wolf and Crow Search Optimization Algorithms, their performance in the optimum operation of Golestan single-reservoir system Dam was evaluated with the aim of providing water demand for the downstream lands based on reliability, Reversibility, and vulnerability indices. In this optimization problem, the objective function was defined as the minimization of the total deficiency during the operation period. Meanwhile, the constraints of continuity equation, overflow, storage and reservoir release volume were applied to the objective function of the problem. Then, the results were compared with the absolute optimal value based on the nonlinear programming method obtained from GAMS software; finally, a multi-criteria decision-making model was developed to rank the optimization algorithms in terms of performance. The absolute optimal response obtained by the GAMS software based on the nonlinear programming method was 19.41. The results showed that the Gray Wolf algorithm performed better than the other algorithms in optimizing the objective function, so that the average responses in Gray Wolf, Crow Search and Whale algorithms were 92, 84 and 67% of the absolute optimal response, respectively. Furthermore, the Gray Wolf optimization algorithm performs better than the Whale and Crow Search algorithms in all parameters. In addition, the coefficient of variation of the responses obtained by the Gray Wolf algorithm is 2 and 1.43 times smaller than that in the Whale and Crow Search Algorithms, respectively. Finally, the results of the multi-criteria decision-making model showed that the gray wolf algorithm had the first rank, as compared to the other two algorithms studied in solving the problem of the optimal operation of the Golestan dam reservoir. 

This study aimed to optimize the site selection of artificial groundwater recharge zones in the Yazd-Ardakan watershed (covering an area of 116,765 hectares) to address challenges such as water scarcity, severe groundwater depletion, and annual rainfall below 100 mm. The research integrated Geographic Information Systems (GIS) and Multi-Criteria Decision-Making (MCDM) methods. Seventeen influential criteria, including elevation, slope, land use, geology, soil type, climate, distance from faults, and isothermal lines were analyzed using ArcGIS 10.5. Criteria weighting was performed using the Best-Worst Method (BWM), and layer integration was achieved through the Weighted Linear Combination (WLC) approach. Results indicated that slope (weight: 0.203), elevation (correlation >0.75), and land use (correlation ≈0.5) had the highest impact on zone suitability, while climate and isothermal lines were less influential. The final suitability map (900×900 m resolution) revealed that central and southern plain areas with slopes <2%, permeable alluvial formations, flat topography, and optimal distance from faults were prioritized for artificial recharge. Sensitivity analysis identified eight key criteria (correlation >0.5), and by eliminating 53% of non-essential parameters, an efficient framework for sustainable water resource management was established. This study not only contributes to raising groundwater levels, improving soil fertility, and preserving local ecosystems but also offers a practical solution for water crisis management in arid regions. 
 

Managed Aquifer Recharge (MAR) using treated wastewater is a viable strategy for water-resource sustainability in arid regions; however, the simultaneous assessment of its hydrogeological and economic aspects remains challenging. The objective of this study was to hydro-economically evaluate the implementation of an MAR project in the Qotb-Abad Plain by integrating numerical modeling and cost-benefit analysis (CBA). The quantitative and qualitative responses of the aquifer were simulated using the MODFLOW and MT3DMS models to estimate changes in groundwater levels, nitrate concentrations, and total dissolved solids (TDS). Economic benefits, including pumping energy savings, the value of stored water, and improvements in water quality, were evaluated alongside capital and operational costs under lower- and upper-bound scenarios. Subsequently, economic indicators, including net present value (NPV), benefit-cost ratio (BCR), internal rate of return (IRR), and payback period, were calculated. The results indicated that implementing MAR with an annual treated wastewater volume of 1.898 million m³ resulted in an approximately 4.5 m rise in groundwater level, accompanied by reductions in TDS from 1859 to 1752 mg/L and in nitrate concentration from 22.2 to 15.4 mg/L by the end of the five-year simulation period. Under the upper-bound scenario, the NPV, BCR, and payback period were 43.7 billion Rials, 1.36, and 3.23 years, respectively. In contrast, the lower-bound scenario was not economically justified, as its BCR was less than 1. Overall, integrating numerical modeling with cost-benefit analysis provides a comprehensive framework for supporting groundwater-management decision-making.