Multi-model integration in a decision support system as an approach for river basin management / Mehran Homayounfar

Mehran , Homayounfar (2018) Multi-model integration in a decision support system as an approach for river basin management / Mehran Homayounfar. PhD thesis, University of Malaya.

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Abstract

For a long time, traditional methodologies have been used in water resources management without any concerns about the possibility of increasing the conflict on limited water. Recently, a number of dynamic game models, both discrete and continuous, have been developed for reservoir operation. Most of them come up with potential solutions for conflict situations while allocating limited available water among various users. This dissertation, as a step towards consideration of the above issues in reservoir operation and management, explains the problem of competition for limited water resources and proposes the appropriate modeling methods based on the game theory for its exploration. The goal is to gain insight into how water users and reservoir managers choose strategies regarding resources, so that public policies can be designed to motivate individuals, firms and nations to generate greater values with limited resources. In this study, we first developed two monthly based non-discrete optimization models based on the Nash Bargaining Solution for a single reservoir system. In the first model, based on constrained state formulation, the first and second moments (mean and variance) of the state variable (water level in the reservoir) are calculated. The second model is a structured dynamic approach based on continuous state Markov decision models. The corresponding solution based on the collocation method is structured for a reservoir system. In this model, the reward function is defined based on the Nash Bargaining Solution. Since resource states are usually interconnected, the management of a given resource or location depends on the state of the rest of the system. In order to evaluate uncertainty in the rest-of-the-system flow, we modified the latter dynamic structure, based on continuous state Markov decision models, to incorporate uncertainty from the rest-of-the-system flow into the model. Subsequently, we derived the corresponding seasonal solution based on collocation. Also, in the demand sides, we developed an optimization model to determine optimal water allocation and crop pattern during droughts with particular attention to proportionality in crop, water allocation to ensure the feasibility of the optimization solution. Finally, in order to identify the efficiency of the proposed methods to conflict resolution and to evaluate the effect of uncertainty on decision variables, two case studies were carried out, at the Zayandeh-Rud river basin in central Iran and the Langat river basin in peninsular Malaysia. According to the results, the presented models are capable of tackling conflict issues in water allocation in situations of water scarcity. Moreover, bringing the stochasticity, associated with flows in the system, into the equation provided more practical and reliable outcomes in comparison with similar continuous dynamic game models working on an annual basis and has an impact on the value functions and operating polices. In the crop water optimization model, we determine optimal crop water allocation, using various irrigation efficiency values (60–100 % in 10 % interval), water reduction ratios (0.0– 0.4) and cutback parameters (λa=0.3, λn=0.7, λa=0.51, λn=0.49 and λa=λn=0.5). The values of λa=0.51 and λn=0.49 provided the maximum proportionality in terms of water reduction among all the allocation units or users. Although different values of cutback parameters (e.g.: λa=0.3, λn=0.7) maximize net benefits, they are not considered as proportionate decisions in the study areas.

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