The total phosphorus (TP) concentration, as the primary limiting eutrophication factor in the Mahabad Dam reservoir in Iran, was studied, considering the combined impacts of climate change, as well as the scenarios on changes in upstream TP loadings and downstream dam water allocations. Downscaled daily projected climate data were obtained from the Beijing Normal University Earth System Model (BNU-ESM) under moderate (RCP4.5) and extreme (RCP8.5) scenarios. These data were used as inputs of a calibrated Soil and Water Assessment Tool (SWAT) model of the watershed in order to determine the effects of climate change on runoff yields in the watershed from 2020 to 2050. The SWAT model was calibrated/validated using the SUFI-2 algorithm in the SWAT Calibration Uncertainties Program (SWAT-CUP). Moreover, to model TP concentration in the reservoir and to investigate the effects of upstream/downstream scenarios, along with forecasted climate-induced changes in streamflow and evaporation rates, the System Dynamics (SD) model was implemented. The scenarios covered a combination of changes in population, agricultural and livestock farming activities, industrialization, water conservation, and pollution control. Relative to the year 2011 in which the water quality data were available, the SD results showed the highest TP concentrations in the reservoir under scenarios in which the inflow to the reservoir had decreased, while the upstream TP loadings and downstream dam water allocations had increased (+29.9%). On the other hand, the lowest TP concentration was observed under scenarios in which upstream TP loadings and dam water allocations had decreased (−18.5%).

The present study compares the results of the Soil and Water Assessment Tool (SWAT) with a Support Vector Machine (SVM) to predict the monthly streamflow of arid regions located in the southern part of Iran, namely the Roodan watershed. Data collected over a period of 19 years (1990–2008) was used to predict the monthly streamflow. Calibration (training) and validation (testing) were performed within the same period for both the models after the preparation of the required data. A semi auto-calibration was performed for the SWAT model. Also, the best input combination of the SVM model was identified using the Gamma Test (GT). Finally, the reliability of the SWAT and SVM models were evaluated based on performance criteria such as the Nash-Sutcliffe (NS) model efficiency coefficient and the Root Mean Square Error (RMSE). The obtained results from the development of the SWAT model and SVM model indicated satisfying performance in predicting the monthly streamflow in the large arid region. The SWAT obtained NS and RMSE values of 0.83 and 6.1 respectively, and the SVM obtained NS and RMSE values of 0.84 and 6.75 respectively for the validation (testing) period. Results indicate that for high flows of more than 19 (m ³ /s), both models predict flow with over and under estimation in the validation (testing) period. Moreover, the SVM has a closer value for the average flow in comparison to the SWAT model; whereas the SWAT model outperformed for total runoff volume with a lower error in the validation period.

Sustainable development in river basins depends on sound management of land use and water allocation policies. Integrated water resources management (IWRM) is considered a path to bring many elements within the development schemes together toward a unified land-water planning and management process. In this study, an integrated water resources management model is developed to connect three groups of decision makers in pollution control, agricultural planning, and water resources allocation with economic, environmental, and social objectives. A genetic algorithm–based optimization model is developed for providing desirable water quality and quantity while maximizing agricultural production in the upstream region, mitigating the unemployment (social) impacts of land use changes, and providing reliable water supply to the downstream region. The upstream region is divided into subbasins, and a fuzzy-based multiobjective optimization model is used to determine the optimal land uses in each subbasin and...

The interbasin water transfer project is an alternative to balance the nonuniform temporal and spatial distribution of water resources and water demands, especially in arid and semi arid regions. A water transfer project can be executed if it is environmentally and economically justified. In this study, the feasibility of two interbasin water transfer projects from Karoon River in the western part of Iran to the central part of the country is investigated. An optimization model with an economic objective function to maximize the net benefit of the interbasin water transfer projects is developed. The planning horizon of the model is 23 years (the length of historical data); and it is solved using genetic algorithm. In order to consider environmental impacts of water transfer projects, a water quality simulation model has been used. Then, an Artificial Neural Network model is trained based on the simulation results of a river water quality model in order to be coupled with the optimization model. The outputs of the optimization model are the value of economic gain of the sending (Karoon) basin to offset the loss of agricultural income and environmental costs. The optimal polices for water transfer during the planning horizon has been generated using the coupled simulation-optimization model. Then, operating rules are developed using a K Nearest Neighborhood model for the real time water transfer operation. The results show the significant value of using the proposed algorithm and economic evaluation for water transfer projects.