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Reservoir Operation Considering Various Kinds of Real-Time Hydro-Meteorological Information: An Adaptive Approach for Sustainable Water Resources Management with Existing Water Infrastructures

Author(s): Daisuke Nohara; Tomoharu Hori

Linked Author(s): Tomoharu Hori, Daisuke Nohara

Keywords: Reservoir operation; Adaptation; Hydro-meteorological information; Water resources management

Abstract: Changes in hydrological systems associated with climate change may threaten to reliability of water resources management systems. Adaptive management of water resources infrastructures such as reservoirs is therefore considered important to enhance the performance of existing water resources management systems. Thanks to the advancement in monitoring and forecasting techniques of meteorological and hydrological states as well as data distribution techniques, various types of hydro-meteorological information, which can potentially include useful information for basin water resources management from flood management to drought management in real time, have been available in recent years. Reservoir operation can be considered to become more flexible and sophisticated by utilizing these available information sources with consideration of effective combination in light of their characteristics such as temporal and spatial scales or updating frequency. In order to contribute to development of adaptive and robust water resources management techniques which can be also effective under climate change, real-time operation methods of a multi-purpose reservoir considering various types of hydro-meteorological information sources are discussed in this paper. Observed and predicted information sources on meteorological and hydrological states with different temporal and spatial scales, from long-term and global scale to short-term and local scale, are utilized with consideration of effective combination of information sources to be employed. Impact analysis of hydrological prediction on reservoir operation for flood management is also conducted in order to develop a method to effectively utilize the prediction for more robust and integrated reservoir management.


Year: 2013

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