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An Optimal Reservoir Operation Model Based on Ecological Requirement and Its Effect on Electricity Generation

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Abstract

Rather than optimizing water regimes for one or a few species, a better approach is to approximate the natural flow regime that maintains the entire panoply of species. RVA method generally describes flow regimes through 32 hydrologic indicators, quantifies the changes of the indicators post-dam, and distinguishes the highly changed indicators. Based on this function, an ecological objective which takes natural flow regimes into consideration is established. The objective synthesize highly changed indicators selected from the 32 hydrologic indicators and minimize their degree of change to approximate the natural status. The function of the hydrologic indicators are quantified through fuzziology according to their effect on river environment when changed. The model is applied to Xiangyang section of the Han River downstream the Danjiangkou reservoir, which maximizes the ecological objective to close to a natural flow regime. By optimization, model results indicate that the maximum synthetical membership of the selected indicators is 0.5, which means that the post-regulation optimum release is 50 % closer to the natural flow regime. It is much better than the traditional power generation regulation model, the membership of which is only 0.2. The result indicates that the ecological model is better in improving the river ecosystem, but with reduced power generation because of excessive abandon water. However, when deeply evaluate the effect of the new model, it shows the possibility of a win-win scenario between maintaining ecosystem health and power generation.

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Acknowledgements

This research was supported financially by the National Natural Science Foundation of China (50779049) and the Priority Academic Program Development of Jiangsu Higher Education Institutions of China.

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Correspondence to Na Yang.

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Yang, N., Mei, Y. & Zhou, C. An Optimal Reservoir Operation Model Based on Ecological Requirement and Its Effect on Electricity Generation. Water Resour Manage 26, 4019–4028 (2012). https://doi.org/10.1007/s11269-012-0126-x

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  • DOI: https://doi.org/10.1007/s11269-012-0126-x

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