Journal of Water Resources Engineering
Year:2015 | Volume:8 | Issue:24|Papers Count:8

Estimates, forecasts and Runoff management have always been an interest to researchers. Therefore, using any of the methods commonly used in estimating seemingly destructive phenomenon that, unfortunately, due to the complexity of the relationship between rainfall and Runoff and non-linearity of the relationship, the results are not good. Todays, with the advancement of science and the development of new techniques in all aspects of science, understanding and settle in for a good hope to have created such relationship. One of approaches that have attracted attention of researchers in recent decades is using of Neural Networks. In this study, the Neural- Wavelet Network for Estimating Runoff in Khersan catchment area is used. The results obtained from this model with results from a Neural Network of Return Propagation and Neural Network of Fundamental-Radial, as older models, compared and analyzed. Comparison of results was performed by correlation coefficient and Root Mean Square Error. The results show that the accuracy of the Neural- Wavelet Network compared to Neural Network of Return Propagation and Neural Network of Fundamental-Radial is better.

The main purpose of this study is finding a numerical solution of the modified Burger's equation with appropriate initial and boundary conditions, by using finite difference method for dimensionless state, and comparing the results with the other research. Finite difference method results were compared with those of LBM, and relevant figures and tables describe the comparison. This paper also addressed the effect of time, viscosity and the exponent of sedimentation velocity (in Burger equation) on sedimentation velocity. The results showed that an increase in time and viscosity parameters resulted in a decrease in the maximum sedimentation velocity, and the maximum sedimentation velocity occurrence was transported to the end of the range under investigation. In addition, the results indicated that if viscosity parameter was multiplied 100 times, the particles falling velocity at the peak point would decrease by 60%. The analysis of the results is included in the paper.

Nowadays, it is believed that anthropogenic activities, such as changes in land use and deforestation, have resulted in atmospheric concentrations of greenhouse gases. One consequence of this ruinous activity, is an alteration of the energy balance that tends to warm the atmosphere that has resulted in climate change. Precipitation forecast is one of the most important element in water resources management and planning. In this study, precipitation depth of Kerman, Ravar and Rabor Stations have been predicted using the HadCM3 model outputs under the A2 scenario, SDSM downscaling models and artificial neural network, for three periods: 2010-2039, 2040-2069 and 2070-2099. Precipitation data for the 1971-2001 period were selected as the base one. The results obtained by using the two models were evaluated and compared according to the statistical criteria. The artificial neural network model showed superior performance for the Kerman and Ravar stations. Annual precipitation of Kerman, Ravar and Rabor stations by 2099, using the AMM model decreases by 12.86, 11.68, and 11.39 percentage points, respectively. These are for 0.89, 18.48, and 1.55 percentage points, respectively, for the same year.

Soil water retention curve is an indicator of one of the most important soil hydrological properties that have high applications in various soil water issues, and it is widely estimated by the van Genuchten’s model. This research was performed to estimate the soil water characteristic curve by method of van Genuchten’s model. Thus for determination of Inverse solution, Two points model H5 of Rosetta program and two point-Rosetta methods were used. Using these coefficients, the performance and accuracy of these models in retention curve estimation were evaluated by correlation coefficient (R2), magnitude of absolute error (MAE), root mean square error (RMSE), Akaike information coefficient (AIC) and model efficiency (EF). The overall results showed that the statistical parameters of R2, EF, MAE (cm3/cm3), RMSE (cm3/cm3) and AIC for inverse solution were 0.997, 0.981, 0.013, 0.020, and -7344, respectively for Two point-Rosetta model were equal 0.983, 0.898, 0.026, 0.047 and -5754, for Two-point model were equal 0.98, 0.879, 0.027, 0.051 and -5595, respectively and for Rosetta were equal 0.978, 0.859, 0.039, 0.055 and -5440, respectively. According to these results, inverse method, two point-Rosetta and two-point had the highest accuracy in retention curve estimation, respectively. The Rosetta program had the lowest accuracy in this case. Comparison of methods for different textures showed that the inverse solution had the lowest error in all textures. Rosetta program, Two point-Rosetta and Two-point are recommended for coarse, Medium and fine textures, respectively.

Access to accurate information is one of the basic tools in making key decisions on water resources issues. Accordingly, a lack of information on the exact geographical location and watershed area of dams leads to disorder in location-based integrated system. Thus a comprehensive utilization of the GIS program and satellite imagery has been employed in the delineation of the hydrological boundaries of the major elements and their exact locations. Therefore, the geographical location of dams and their respective watersheds were identified and their digital maps were prepared. The geographic location of dams and its watershed hydrological boundaries determine and in form of the applicable digital maps were prepared. The results show that the net area of watershed of operating, under construction and in the study phase while eliminating interfering watershed is 31.5 percentage of the total area of the country. Also, the result indicate that the reservoirs of 10 operating dams that provide a significant part of the water demands is shared between four provinces. A justifiable utilization policy must be enacted for such dams based on conflict resolution models. An overlay showing watershed boundaries placed on the erosion digital layer reveals that 50% of the watersheds have suffered critical or super critical erosion, with a consequence of reservoir sedimentation.

The confluence of two streams creates a complex flow which accelerates downstream. In a junction area, the separation zone is developed in the main channel. By decreasing the longitudinal velocity in the separation zone the sedimentation and blocking of section are increased. In this research, the separation zone dimensions for the junction 30o, 45o, 60o and 90o, and discharge ratios are simulated by using the FLUENT software. The Volume of Fluid (VOF) scheme and the RSM turbulence model were used for numerical simulation. The results showed that reducing the junction angle leads to decreasing separation zone dimensions at the junction in the main channel. Results also showed that the model simulated the experimental velocity values by an average of 4 percent accuracy. The maximum width of separation zone is about 50% of the main channel width occurred at an angle of 90o of junction channel, and the minimum dimensions of the flow separation zone near zero and occurred at the junction angle of 30o. Furthermore, increasing junction angle widens the separation zone dimensions. Maximum and minimum separation zone dimensions occurred at 90 and 30 degree of junction, respectively. Further changes in the discharge ratio have a great effect on separation zone dimensions and disappear when the discharge ratio is decreased. Decreasing junction angle and increasing the discharge ratio simultaneously, can reduce the separation zone dimensions and therefore, the problems developed the junction.

Determining of the probable maximum flood magnitude at certain frequency is a major undertaking in flood control programs and hydraulic structure design. This is a difficult problem when and where accurate and long term data are not available. Regional analysis is a robust method to estimate flood magnitude in such circumstances, using existing local data and considering effective parameters. In this work, we have developed a model using regional analysis and extended it through the river network in a GIS-based environment. Regional analysis was performed with multiple regressions and spatial extensions for the Karkheh, Karoun and Dez basins. Frequency analysis was performed for annual maximum discharges. Then, by getting from the hydrologic variables obtained in the previous step, their archival cluster analysis performed to identity the similar regions, and the best model for each region was developed by multiple regression. This was achieved by developing an improved MATLAB program, which is capable of mapping the regional analysis equations for the flooding river networks, using computational and stochastical operators for effective parameter determination on flood discharge.

Uncertainty analysis is an essential part of every project dealing with uncertain phenomena, therefore the policy -makers might face unexpected happenings that may delay, or at the extremes, cause complete failure of any projects. Uncertainty analysis is usually preformed in the framework of risk management. The Monte Carlo simulation method is used for integrated and simultaneous analysis of different combinations of uncertainties. This method is a powerful tool to study the various states of the outcomes of a system due to the uncertainties that have significant advantages such as considering all the uncertainties and the ability to show different aspects of the utility function. Existence of dependent random variables is quite common and likely in natural and engineered systems. In order to evaluate the need for correlation actions uncertainty, the design of flood control systems in terms of hydrological, hydraulic and economic uncertainties is discussed. In particular, design of levees that are one of the most common structural flood control, has been studied. In the developed nonlinear optimization model, uncertainty once without correlation and again by considering correlation was achieved. Models have been solved by LINGO-13 software. In the model without correlation, the height and other dimensions of levee is less than what the model with correlation predicts. The results indicated that ignoring the type and level of correlations between each uncertainty, combinations of the outputs of the system will provide results that are impossible in the real world and are somewhat far from reality.