Regarding temporal variation of rivers discharge and demands, for suitable use of dam reservoirs stored water, application of operation Rule curves is necessary. Operation Rules are expressed with Rule curves. Application of yield model is one of the methods of operation curve preparation, which correlates reservoir operation with reservoir storage volume in first period. In this study after description Standard Operation Policy and yield model, based on these models two computer programs in FORTRAN language were prepared for reservoir operation. Then based on yield model, Doroodzan dam Rule curve achieved based on yield model and results were compared with SOP model. Results showed that sum square of deficit in yield model was significantly 73.27 percent less than SOP model, which expresses model ability in derivation of dam reservoir operation Rule curve. Yield model in addition to decrease of water deficiency, supplies absolute demands and distributes deficit uniformly in more years.

Dam Rule curves should be derived using operation models and considering reservoir storage as well as input flows, which results in better management of reservoir water. In this paper, Rule curves of Doroudzan dam in Fars province, has been derived, using standard operating policy (SOP), hedging Rules and linear programming. The objective function is water deficit minimization during 2009-2013. Standard operating policies resulted in severe water shortages. To mitigate the severe water shortage in SOP, the hedging policy has been used, which accepts some present delivery deficit to reduce greater shortages in the future. The Genetic Algorithm has been used to find the hedging optimal factors. Finally, the results of SOP and hedging-GA are compared to a linear programming model, based on hedging Rules’ model. Total shortage in hedging-GA was 84. 19 MCM, hedging-LP was 219. 5 MCM and in SOP was 216. 57 MCM. The GA-hedging model by supplying 88. 33% of demands, controlled the amount of shortage as well as the shortage severity. In addition, the average reservoir storage was highest among the three methods. Comparison of the mentioned models using assessment criteria including time reliability, volume reliability and vulnerability showed that hedging-GA model was less vulnerable and more reliable. Therefore, this model performance was better than the other two models.

This study tries to introduce the flexibility to the yield model using fuzzy approach in order to insert the hydrologic uncertainties and improve the system performance. This purpose attained through artificial time series and applying fuzzy membership function which converts the Rule curve into Rule band in the yield model. To evaluate the improved Rule curve, further artificial time series are generated and the results of reservoir’s simulating via new model and classic model are compared with each other for downstream demands. Performance criterions have been hired for model evaluation. The results for Karaj Dam reservoir in 50 models shows that the average reliability of the systems for firm yield are equal in both classic and new fuzzy model (approximately 98%). The second yield founds around 82% and 55% in the classic and fuzzy model, respectively. The reliability averages for the third yield were 48% for classic model and 31% for fuzzy model. Vulnerability for classic and fuzzy models achieved 28% and 22%, respectively for the firm yield. It found 98% and 45% for the second yield in classic and fuzzy models. For the third yield this criteria was equal to 100% for all series in the classic model, but 82% in average for the fuzzy model.

Introduction: Mainly the flood is caused by the surface runoff resulted from the properties of precipitation and river basin. The reduction of flooding by the effect of vegetation and soil in a small basin is less than a basin with a large area. Hence, to have a flood zoning map, the first step is studying economic flood management and flood control projects. This paper focuses on Baranduz-chay River as a case study, located in the Urmia lake basin. The river reach having 3 km long, was studied between two hydrology stations namely Bibakran at the upstream and Dizaj at the downstream. The annual peak discharge data of Baranduz-chay has surveyed during the years from 1974 to 2013, where the appropriate Manning roughness coefficient, n, by averaging 0. 0325 as an upstream coefficient and 0. 0301 as a downstream coefficient were both implemented in the HEC-RAS software and its result including floodplain zones elevation extraction by the Muskingum-Cunge method, based on the floods with different return periods obtained. After converting these zones to their corresponding risk for each return period time, it has been delineated in Arc-Map software through HEC-geo-RAS extension, floodplain zones were then defined. The maximum inundated area is 97. 34 Hectares and belongs to 1000 years return period which has the most risk as 63. 58% within 3 years of useful periods. The Rule curve is obtained by inundated areas with both different return and useful periods from the risk formula in which the general Area-Period-Risk formula was extracted. Basically, the magnitude of the floods and their repetition over time is subject to rainfall intensity, permeability, and topographic conditions in the area. The occurrence of floods as one of the natural disasters that cause many financial losses in many parts of the world. The first step in economic studies of flood management or flood control is flood zoning. Flood zoning means the extent to which the flood covers the area. Today, via modern science and technology, human beings are trying to optimize designs and to reduce these costs. Therefore, it seems that flood zoning study in the permanent and seasonal rivers path appears to be of great importance by conducting case studies in vulnerable areas. ShahiriParsa et al. (2016: 55-62) used the integration of the HEC-RAS one-dimensional model and the two-dimensional CCHE2D model on the Sungai Maka river in the state of Kelanten, Malaysia. They concluded that in this case, some important factors are: Manning’ s flow resistance coefficient, n, the geometric profile of the river section and the choice of the most suitable flood return period. The mentioned parameters have a major role in providing flood zoning outcome, which has caused the most changes in the geometric shape of the river section. Their results showed that the greatest difference between the models was 6% in the location of the meandering rivers. The results of both models were also consistent in most of the transverse sections, and, due to the difference in the shape of the rivers, the greatest difference was the difference between the two models. Sung et al. (2011: 1-12) used the Maskingum method to process unqualified basins by analyzing the HEC-HMS hydrologic model and the HEC-geo-HMS geo-hydrologic model, the extraction of sub-basins and characteristics of the basin was extracted. The results showed that the percentage of flood events proportional to the maximum discharge errors of a moment of less than 20% and a runoff volume of less than 10% to reaches 100%. Methodology: Baranduz-chay river as the main river and permanent water catchment area of the study area. It originates from the highlands of Iran and Turkey border. The catchment area of this riverside in Saatlu is about 666 square kilometers and in Babarood is 1012 square kilometers. This research was associated with a similar risk due to the risk relationship with different return periods for the restricted areas around the river, based on different return periods. To determine risk areas or certain return periods, peak discharges were fitted with the best statistical distribution and through that, peak discharges were then calculated with different return periods and each of them was determined along the river and its expansion area. Results and discussion: Fig. 3, shows the risk versus area (A, RISK), the risk with a downward trend, which means that the area risk is decreasing with the area covered by the risk area. By fitting a variety of exponential, linear, logarithmic, polynomial and power statistical functions, among those functions as shown in Fig. 3, risks with different useful lives are plotted simultaneously and from among functions, the power function was selected as a suitable fit function in order to obtain the general probabilistic distribution function and its parameters based on different useful life. Fig. (3) Risk diagram versus area (Rule curve) with a different useful life. Conclusions: For the Manning roughness coefficient, n, in the hydraulic model, the Manning’ s n for the upstream and downstream stations were computed. The roughness coefficients, n, were then obtained for the upstream and downstream stations as 0. 0325 and 0. 0301, respectively. In order to obtain the corresponding risks for the areas covered by a flood of 3 km long from the Baranduz-chay between the upstream Bibakaran station and the lower reaches of the Hoerl's model, which is a type of power function. The risk-space-period curve for the specified periods is 2, 3, 5, 10, 25, 35 and 75 years (for more details, see Mohammadi, 2016).

Operators usually follow the Rule curves for operation of a reservoirs system in actual conditions. Rule curve indicates desired release or target storage volume for a reservoir in the specified period of year. In the present study, combination of a simple genetic algorithm with the simulation model (ARSP) was used for multiple reservoir systems to find optimum Rule curves. In this hybrid model, a linear programming was used for water allocation per single period. In addition, the genetic algorithm through a nonlinear programming was utilized for searching the Rule curves. Capability of the developed model was evaluated by defining and analyzing of the three dam water resources systems in Zohre River. Calculations showed that the above combination improved the limitation modeling greatly, increased optimization convergence rate and made considerable flexibility for total process of planning and management in complex systems.

Proper operation of gated spillways, which plays an important role in safety of dams during flood event, remains as a prominent challenge. Although telecommunication networks used in flood warning systems facilitate flood management by dams to some certain level but accurate prediction of both the magnitude and timing of peak flood flows with few days of lead time is not still possible. Therefore, operation of gated spillways may mostly benefit from the observed reservoir water level and observed flood discharges in the upstream gagging stations. In Iran, many of river-reservoir systems face severe floods every year. Most of these reservoirs are operated based on engineering judgments of dam personnel and static policies such as Rule curves, which are not proper formulated for flood events with various return periods. In this study, we proposed a Rule curve for operation of gated spillways using sequential approach and its performances was compared with previously developed operation policies. For this purpose, nine stages as critical control levels were assigned and spillway gate openings associated with each critical level were determined for Mahabad Dam, located in northwest of Iran, as the case study of this research. Each control level was related to given percentage of the design flood volume. When using the proposed Rule curve, as a critical control level is reached, the discharge through the spillway and related opening of its gate are accordingly increased. The gate openings associated with each critical water level were optimized using a real coded Genetic Algorithm (GA). This attitude or policy of nine-stage operation includes an appropriate algorithm for determining critical levels, development of operational models spillway gates, calculation of gate opening in each stage, and applying the model to the case study of Mahabad dam. This study adopts the nine stages approach for which nine control levels and gate openings are determined by GA. As it mentioned before, the nine critical levels should be placed in flood retention reservoir between initial water surface elevation and maximum water surface elevation. So, it will have crucial role to determinate gate opening spillway during flood events, and increase of flood storage in the flood retention reservoir. This model was employed to route a flood with the same return period of design spillway flood through Mahabad reservoir. The performance of the proposed Rule curve was then assessed by determining the amount peak flow discharge reduction. The results of the case study shows superiority of the proposed Rule curve over previously developed operation policies for flood control in Mahabad Dam. One of the main advantages of the proposed methodology is that for using the proposed Rule curve not flood forecast data is needed and therefore, it can be considered as a reliable tool for operation of gated spillways when there is no flood warning system in the upstream basin. The future studies can be focused on testing the proposed algorithm on other reservoirs. In the cases of dams with large flood control volumes, critical control levels can also be considered as decision variables of the GA optimization model.

Optimum reservoir performance is the most important discussion in water resources management. Constantly increasing demand for sufficient quantity and quality of water along with drought periods emphasizes the problem. In this study, reservoir performance of Maroon dam simulated with ARSP package for demand supply. Environmental concerns, urban and industrial demand full supply and deficit impose on agriculture demand. Optimum Rule curve with scope minimizes deficit on agriculture water calculate based on reservoir performance criteria such as reliability, mean deficit and vulnerability. Optimum Rule curve supplies agriculture demand with minimum reliability of 80% and maximum vulnerability of 20%. The Rule curve with impose hedging Rule can provide full agriculture demand as well.

Background and Objectives: Regarding the recent droughts, water resources management and prioritization of water allocation to different demands are important and essential issues. The amount of water allocation in storage dams at different months is determined based on reservoir storage. Therefore, the objective of this study is to determine the suitable operation policy regarding the sum squares of water deficit.Materials and Methods: In this study the Standard Operation Policy (SOP) and Yield models were developed for planning and water resources management. In these models amount of water release from reservoir is determined by storage volume at beginning of the period. For assessment of models efficiency in development of Rule curve, Shahid Rajaei dam was studied.In mentioned models, river discharge in dam site and middle basin between Shahid Rajaei dam and Tajan diversion dam were used. By means of these models, five operation scenarios consisted of supplying 100 percent of drinking, industrial and environmental demands; with 40 to 80 percent of agricultural demand from the reservoir were simultaneously investigated.Results: In the first, second and third operation scenarios, deficit sum squares in Yield model compared to SOP model was less, equal to 83.72, 63.32 and 34.70 percent; respectively. In the fourth and fifth operation scenarios this parameter in the Yield model was greater than SOP model. Average annual water deficit in the SOP and Yield models was 23.6 and 15.16 MCM; respectively. Average and minimum reliability in Yield model were 3.81 and 52.29 percent more than SOP model; respectively; whereas, maximum vulnerability in Yield model was 80 percent less than SOP model.Conclusion: First, second and third operation scenarios with the aim of supplying 100 percent drinking, industrial and environmental demands and 60, 70 and 80 percent agricultural demand were acceptable with sum squares of water deficit of 3.97, 2.23 and 0.99; respectively. Fourth and fifth operation scenarios with the aim of supplying 100 percent drinking, industrial and environmental demands, and 40 and 50 percent agricultural demand were not acceptable with sum square of water deficit of 8.93 and 6.20; respectively.

A double ambiguity has been charged against Rawls’s difference principle (DP). Is it Maximin, Leximin, or something else? Usually, following A. Sen, scholars identify DP with the so-called Leximin. One argues here that one has to distinguish 1° the Leximin, 2° the Maximin (as Rule of justice formally analogous to the maximin Rule of decision), represented by the figure in L of the perfectly substitutable goods, and 3° the genuine DP. When the augmentation of inequality benefits the worse off, only Pareto-strong improvements are permitted. Leximin would also permit Pareto-weak improvements too (after the first maximum D), where only the richest improves: from (2, 3) to (2, 5), say. This is forbidden by DP. With two classes, unlike Maximin, DP has no curve of indifference and is always decisive, as Leximin is. For undecisive Rules of Justice, which admit indifferent curves, I propose to add a lexically secondary Rule, to break ties. That move is able to clarify the links and the differences between on the one hand Maximin alone, with its typical indifference curves in L, and on the other hand, the DP properly understood and the Leximin, which both have no indifferent curves. With two classes of persons (best off/worse off), DP appears more egalitarian than Leximin, because it's secondary Rule is MinIn (Minimization of Inequality). But the intuition behind the distinction is that it cannot possible “fair” that only the best off improves in a productive social cooperation.

Lake Urmia located in north-west of Iran has faced a serious environmental crisis as a result of some natural and anthropogenic factors. Regulation of Rule curve of dams and their optimum redistribution is among essential actions for ecosystem conservation of the lake. In a simple hydrological method, optimum release from dams is estimated by calculating the runoff of downstream catchment. Accordingly, in this study the Rule curves of the major reservoir dams in the Lake Urmia basin (ShahidKazemi, ShahrChai, Zola, Derik, Venyar, Ajabshir and Alavian) has been calculated under three different operation policies (30%, 50% and 80% of MAF). Then by using measured data, monthly value of QResidual (the runoff of the downstream catchment) has been calculated. In the next step, the term of QAAD (the volume of annual available water in the last station on the river) for each scenario has been determined. The results revealed that by using 80% of MAF (as scenario 3), the value of QAAD would be positive for all the assessed stations. So scenario 3 was selected as effective scenario for restoration of the lake. Finally for scenario 3, monthly value of QCAH (the nearest hydrograph to natural hydrograph of rivers) in the last station for each rivers and the term of QRW (the monthly volume of water for releasing from dam) have been calculated.