The linear form of the Muskingum model has been widely applied to river flood routing. However, a nonlinear relationship between weighted-flow and storage volume exists in most rivers, making the use of the linear Muskingum model inappropriate. On the other hand, the application of the nonlinear Muskingum model suffers from hydrologic parameters estimation. The current study aims at presenting the objective approach of the Nelder-Mead simplex (NMS) algorithm for the purpose of estimating the parameters of the nonlinear Muskingum model. The performance of this algorithm is compared with other reported parameter estimation techniques together with a historical example. Results of the implementation of this procedure indicate that the NMS algorithm is efficient for the estimating parameters of the nonlinear Muskingum models. This algorithm is easy to be programmed, and it is quite efficient for finding an optimal solution very quickly. Although this technique requires an initial guess for the parameter estim...

The Muskingum model continues to be a popular procedure for river flood routing. An important aspect in nonlinear Muskingum models is the calibration of the model parameters. The current study presents the application of commonly available spreadsheet software, Microsoft Excel 2010, for the purpose of estimating the parameters of nonlinear Muskingum routing models. Main advantage of this approach is that it can calibrate the parameters using two different ways without knowing the exact details of optimization techniques. These procedures consist of (1) Generalized Reduced Gradient (GRG) solver and (2) evolutionary solver. The first one needs the initial values assumption for the parameter estimation while the latter requires the determination of the algorithm parameters. The results of the simulation of an example that is a benchmark problem for parameter estimation of the nonlinear Muskingum models indicate that Excel solver is a promising way to reduce problems of the parameter estimation of the nonlinear Muskingum routing models. Furthermore, the results indicate that the efficiency of Excel solver for the parameter estimation of the models can be increased, if both GRG and evolutionary solvers are used together.

Iran's groundwater hydrochemistry has not been well understood. In this study, Iran's groundwater hydrochemistry is evaluated using a rich, ground‐trusted data sampled from 9,468 wells distributed across the country in 2011. Twelve groundwater quality parameters were analyzed in each sample, resulting in 113,616 parameters over the study period. Examination of anions‐cations shows that concentrations of sodium, calcium, chloride, and sulphate are higher than the acceptable threshold for drinking‐use suggested by the World Health Organization in about 40%, 21%, 25%, and 20% of the samples, respectively. The results of the water quality index reveal that most of the groundwater resources in the central, southern and eastern regions of Iran, which supply the majority of the domestic water for populated cities, do not meet the requirements for drinking‐use. Although the groundwater in northern parts fulfills the requirements for irrigation‐use, it is only suitable for irrigation of salinity‐friendly crops in central, eastern and southern regions. Ionic types and hydrochemistry facies indicate the dominance of mix water type in 13 out of 30 of Iran's sub‐basins, followed by sodium‐chloride water type in nine sub‐basins. Local geology and lithology are mainly attributed to the distribution of groundwater facies in Iran. In general, our findings reveal a distinctive relationship between Iran's geological‐geomorphological features and hydrochemical facies/groundwater quality. The findings can be used in the formulation of new strategies and policies for Iran's groundwater quality management in the future.

The labyrinth spillway continues to be a popular type of the control structures. The focus of the present study is that on the optimization of the shape of the labyrinth spillway through the minimization of the construction costs because the use of it is a suitable solution in the rehabilitation process of existing reservoirs. An Adaptive Neural Fuzzy Inference System (ANFIS) model was developed to determine the discharge coefficient of the labyrinth spillway as a function of the angle between alignment of the crest and direction of flow, the relative depth of flow over spillway and its crest height. Differential Evolution (DE) algorithm and Genetic Algorithm (GA) were adopted to minimize the spillway cost as the objective function by considering certain hydraulic conditions as the constraints of the optimization procedure. The results indicated that DE and GA can respectively reduce 19.3% and 16.6% the construction cost of a real benchmark design.