Developing a Water Quantity-Quality Equitable Allocation Model in River-Reservoir Systems: Application of Scenario Optimization Technique

Water quantity-quality equitable allocation model in river-reservoir systems provides the possibility of decision making on the amount of allocated water by considering the hydrological, economical, and environmental effects. Water allocation is done according to three criteria, namely equity, efficiency, and sustainability by considering uncertainties in hydrological parameters. In this method, at first a water quantity-quality allocation model was developed in GAMS optimization model based on the mentioned three criteria. Scenarios were built based on scenario optimization technique by identifying the uncertainties in the model inputs. Water was allocated using the initial model for each sub-scenario. Afterward, water allocation in each scenario was obtained by the tracking model. To assess the applicability of the proposed method, it was applied to Roodbal river basin in south-east of Fars province, south of Iran. After analyzing basic data, upstream inflow and TDS concentration were identified as the uncertain input and the most critical quality parameter respectively. Due to the existing uncertainty, three scenarios of wet, normal and dry year were built. The results of using the proposed model showed that 100 percent of the users’ demands are supplied in wet and normal years and 61 percent in dry years. The volume of reservoir was always more than minimum volume (35 MCM) and TDS concentration was lower than 1000 mg/L (TDS water quality standard). The value of these three criteria were also at their highest possible. Based on the results of the model, the Roodbad River-Reservoir system faces no problem in supplying agricultural water demands in wet and normal years. Therefor, water can be stored in wet and normal years to be used during dry years. In dry years, water is allocated to the users in a way that the mentioned criteria have the maximum values and the reservoir storage doesn’t reach the minimum value except for the last month of the optimization period.