This paper critically examines Brazil's transbasin diversion experiences and two select, large-scale transbasin projects outside of Brazil. Rather than simply compare Brazil's experiences with international cases, the authors explore the historical context of the projects, focusing on the water resources and environmental regulatory and institutional frameworks in place during the initial proposals and how they influenced the projects' development and completion rates. Huge investments, the lengthy time span between the date of the initial project proposals and the start of construction, the need to garner public and political support, and more recently, the need to fulfill national and regional environmental laws are major factors underlying heated public debates about the proposed projects. After a broad discussion about the subject, the authors examine a recently approved large-scale Brazilian project, the Sao Francisco river transbasin diversion. This river diversion project is considered crucial to the economic development of Brazil's semi-arid northeastern region. Despite the fact that this government-funded project was debated for several decades among the various stakeholders, it was approved under Brazil's newly formed water resources regulatory and environmental framework, which enabled improvements in various key technical aspects of the project, and greater involvement of various key social players.

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A network flow algorithm has been developed for the optimization of real‐time operation of a multiple reservoir system. Two purposes have been considered in the operation: flood control and hydropower generation. A special network structure was developed which allows the consideration of river routing. A multiobjective formulation is utilized thus allowing generation of a non‐dominated curve. The effect of imperfect forecast on the performance of the real‐time operation model is also evaluated. An application is made to a subsystem of the Brazilian hydroelectric system, located in the Paranapanema river basin. In this case study, the model showed good performance under the largest flood of the historical records.