Journal of Irrigation and Drainage Engineering

One of the main challenges for solving complex water-resources optimization is choosing an appropriate solution method. An important feature of optimization problems is the convexity and extent of their solution spaces. The solution space is the set whose elements are all the reservoir releases that meet the optimization problem’s constraints and are thus feasible. The solution space of optimization problems can be convex or nonconvex. This study presents a method for determining the convexity or nonconvexity of the optimization problem solution space. The convexity and the extent of the solution space for a water-supply and a hydropower-production reservoir operation problem are evaluated by the proposed method. It is shown that the solution spaces of the former and latter problems are convex and nonconvex, respectively. The dependence of the solution spaces of the two reservoir operation problems on changes in evaporation, water demand for the water-supply reservoir, power plant capacity (PPC) for the hydropower reservoir, dead storage, reservoir capacity, and reservoir inflow is evaluated. The results demonstrate that the generalized reduced gradient (GRG) method finds an optimal value faster and more accurately than does the genetic algorithm (GA) when solving the water-supply problem, and that the GRG search is trapped in a local optimum when solving the hydropower-production problem.

Determining dimensions of a dam diversion system has always been a challenge for designers because of uncertainties regarding flood characteristics and acceptable risk to the diversion system. This study presents a numerical model to determine optimal features of a diversion system. The optimization model uses a genetic algorithm to determine optimal dimensions of a water diversion system based on the construction costs, failure costs, and acceptable risk to the diversion system by considering hydraulic uncertainties and flood risk analysis. The objective function of the optimization model aims to minimize construction costs and expected value of damages caused by floods with varying return periods. Karun-IV Dam located in southwest Iran is considered as a case study. The results show that the expected annual average cost of the diversion system (capital cost + expected damage) is lower than the consultant’s estimated figure. In this study, a spillway is added to handle the maximum observed flood in order to match the consultant’s reliability with the model’s reliability. The results indicate that even with the added spillway, the optimal system is more effective than the premier plan suggested by the project consultant. Therefore, investigating flood risk and its uncertainty could provide a better framework for the design and cost for the configuration of diversion systems in dams.

In this research, the SALTMED model was used for soil salinity and yield prediction using data from a tomato field experiment. Surface and subsurface drip irrigation systems were used to study the interaction of irrigation water salinity levels and irrigation management practices in a complete randomized, split-plot design with three replications in which irrigation management strategies were the main plots and different levels of water salinity including 0.68, 2, 4, 6, and $8\mathrm{dS}{\mathrm{m}}^{-1}$ were subplots. Irrigation management strategies were irrigation with alternately saline and nonsaline water (${\mathrm{m}}_1$) and irrigation with saline water in the first half of each irrigation event followed by fresh water in the second half (${\mathrm{m}}_2$). The results of the surface drip irrigation and subsurface drip irrigation systems were used for calibration and validation of the model, respectively. The results showed that the differences ($\mathit{P}<0.05$) between the predicted and measured tomato yield and soil salinity in the calibration and validation of the model are not significant. The values of statistical parameters including index of agreement (d) and normalized root-mean-square error (NRMSE) showed that the accuracy of the model was acceptable. Soil salinity in the ${\mathrm{m}}_1$ strategy was predicted with higher accuracy than the ${\mathrm{m}}_2$ strategy for surface and subsurface drip irrigation.