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1 May 2017 Evaluation of Probabilistic Storage Prediction Model (PSPM) for Optimal Reservoir Operation during a Drought
Minsung Kwon, Kyung Soo Jun, Tae-Woong Kim
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Abstract

Kwon, M.; Jun, K.S., and Kim, T.-W., 2017. Evaluation of probabilistic storage prediction model (PSPM) for optimal reservoir operation during a drought. In: Lee, J.L.; Griffiths, T.; Lotan, A.; Suh, K.-S., and Lee, J. (eds.), The 2nd International Water Safety Symposium. Journal of Coastal Research, Special Issue No. 79, pp. 314–318. Coconut Creek (Florida), ISSN 0749-0208.

The Probabilistic Storage Prediction Model (PSPM) is a model that probabilistically predicts the future reservoir storages considering the uncertainty of natural inflow. This study simulated reservoir operation using the PSPM and evaluated the usefulness of the PSPM compared to the actual reservoir operation during the recent severe drought of the Chungju Dam basin in South Korea. The initial storage was set to observed storage at the end of January 2015, and the reservoir operation for achieving target storage at the end of June was simulated for various achievement probabilities. The differences between the simulated storages and the actual storage at the end of June 2015 was as large as 14–20% of effective storage capacity of the reservoir. The maximum supply reduction for achieving target storage simulated for the achievement probability of 0.8 was less than actual maximum supply reduction. This is possible by storing more water in advance to prepare for more severe drought. PSPM can offer valuable information as a decision-making tool, which will enable reservoir managers to secure water in advance, and thus mitigate severe drought damages.

©Coastal Education and Research Foundation, Inc. 2017
Minsung Kwon, Kyung Soo Jun, and Tae-Woong Kim "Evaluation of Probabilistic Storage Prediction Model (PSPM) for Optimal Reservoir Operation during a Drought," Journal of Coastal Research 79(sp1), 314-318, (1 May 2017). https://doi.org/10.2112/SI79-064.1
Received: 30 September 2016; Accepted: 31 October 2016; Published: 1 May 2017
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