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Paper Information

Journal:   SCIENTIA IRANICA   FEBRUARY 2008 , Volume 15 , Number 1; Page(s) 34 To 49.
 
Paper: 

DEALING WITH CONFLICT OVER WATER QUALITY AND QUANTITY ALLOCATION: A CASE STUDY

 
 
Author(s):  KARAMOUZ M.*, MORIDI A., FAYAZI H.M.
 
* TECHNICAL DEPARTMENT OF ENGINEERING, UNIVERSITY OF TEHRAN, TEHRAN, I.R. IRAN
 
Abstract: 
Available water resources are often not sufficient or too polluted to satisfy the needs of all water users. Therefore, conflict over water, as a result of limitations on quantity and quality, is a major challenge in water allocation. In this paper, a methodology for conflict resolution over water allocation in river-reservoir systems is presented. The proposed model includes the genetic algorithm (GA)-based optimization and a water quantity/quality simulation model. The objective function of the optimization model is based on the Nash bargaining theory. Nash theory can incorporate the utility functions of the decision makers and the stakeholders, as well as their relative authorities over the water allocation process. The WQRRS (Water Quality for River- Reservoir Systems) model of the U.S. Hydrologic Engineering Center (HEC) and Qual2e model of the U.S. Environmental Protection Agency (EPA) are used for simulating the Karkheh reservoir and river water quality. In these models, the reservoir thermal stratification cycle, the reservoir discharge quality and the water quality downstream of the reservoir are simulated. The model is applied to the Karkheh river-reservoir system in the southern part of Iran. The utility functions are based on the reliability of the allocated water to different sectors, the environmental water demands (quality of the allocated water and in-stream flow), water storage in the reservoir and the quantity and quality of the return flows. The results show that this model can be effectively used in optimal water allocation of river-reservoir systems with conflicting objectives. In this paper, in order to generate the policies of the Karkheh reservoir operation and the river water quality management, the results of the optimization model are used to train the ANN model.
 
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