Using a Bayesian Probabilistic Forecasting Model to Analyze the Uncertainty in Real-Time Dynamic Control of the Flood Limiting Water Level for Reservoir Operation
Publication: Journal of Hydrologic Engineering
Volume 20, Issue 2
Abstract
Dynamic control of the flood limiting water level (FLWL) is a valuable and effective way to maximize the benefits from reservoir operation without exceeding the design risk. In order to analyze the impacts of input uncertainty, a Bayesian forecasting system (BFS) is adopted. Applying quantile water inflow values and their uncertainties obtained from the BFS, the reservoir operation results from different schemes can be analyzed in terms of benefits, dam safety, and downstream impacts during the flood season. When the reservoir FLWL dynamic control operation is implemented, there are two fundamental kinds of dynamic control bounds. One is the flood subseasonal FLWL dynamic control bounds, which are based on the segmentation of the flood season and the ranges of the FLWL in every flood subseason (Scheme I); the other one is the flood seasonal FLWL dynamic control bound, which takes the flood season as a whole, thus producing only one boundary [Scheme II]. The Three Gorges Reservoir (TGR) in China was selected as a case study in this paper. The application results show that the thresholds of maximum outflow, which impact the downstream and maximum reservoir levels, are not exceeded during the flood season under the analyzed FLWL control schemes. The benefits in terms of the floodwater utilization rate, hydropower generation, and water level at the end of the flood season from two dynamic controls of the FLWL scheme are better than the current design, which applies a static FLWL. For comparison, also deterministic water inflow was tested. The proposed model in the paper emphasizes the importance of analyzing the uncertainties of the water inflow forecasting system for real-time dynamic control of the FLWL for reservoir operation. For the case study, the selected quantile inflow from the Bayesian forecasting system and the matching operation are beneficial for the decision makers of the Three Gorges Reservoir.
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Acknowledgments
The authors gratefully acknowledge the financial support from the National Natural Science Foundation of China (No. 51190094, 51379148, 51179130).
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Journal of Hydrologic Engineering
Volume 20 • Issue 2 • February 2015
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© 2014 American Society of Civil Engineers.
History
Received: Jan 22, 2013
Accepted: Feb 7, 2014
Published online: Feb 10, 2014
Discussion open until: Dec 21, 2014
Published in print: Feb 1, 2015
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