This paper presents approaches and case studies for the introduction of ensemble hydrological predictions to reservoir operation for water supply. Medium-term operational ensemble forecasts of precipitation are employed to improve the real-time reservoir operation for drought management considering longer prospects with respect to future hydrological conditions in the target river basin. Real-time optimization of the water release strategy is conducted using dynamic programming approaches considering ensemble hydrological predictions. A case study on the application of ensemble hydrological predictions to reservoir operation for water use is reported as an example, with a hypothetical target river basin whose hydrological characteristics are derived from an actual reservoir and river basin.

1. [1] B. A. Faber and J. Stedinger, “Reservoir optimization using sampling SDP with ensemble streamflow prediction (ESP) forecasts,” J. of Hydrology, Vol.249, pp. 113-133, 2001.
2. [2] Y. O. Kim, H. I. Eum, E. G. Lee, and I. H. Ko, “Optimizing operational policies of a Korean multireservoir system using sampling stochastic dynamic programming with ensemble streamflow prediction,” J. of Water Resources Planning and Management, Vol.133, No.1, pp. 4-14, 2007.
3. [3] E. T. Alemu, R. N. Palmer, A. Polebitski, and B. Meaker, “Decision support system for optimizing reservoir operation using ensemble streamflow predictions,” J. of Water Resources Planning and Management, Vol.137, No.1, pp. 72-82, 2011.
4. [4] D. Nohara, A. Tsuboi, and T. Hori, “Long-term reservoir operation optimized by DP models with one-month ensemble forecast of precipitation,” IAHS Publications, Vol.331, pp. 284-295, 2009.
5. [5] D. Nohara, T. Hori, and Y. Sato, “Real-time reservoir operation for drought management considering operational ensemble predictions of precipitation in Japan,” P. Gourbesville, J. Cunge, and G. Caignaert eds., Advances in Hydroinformatics SimHydro 2017, Springer Singapore, 2018.
6. [6] M. Tokutsu, D. Nohara, and T. Hori, “A Monte Carlo-based method to analyze effectiveness of reservoir operation for water supply considering imperfect ensemble inflow prediction,” Proc. 7th Int. Conf. on Water Resources and Environment Research, 2016.
7. [7] J. S. Windsor, “Optimization model for the operation of flood control systems,” Water Resources Research, Vol.9, No.5, pp. 1219-1226, 1973.
8. [8] K. D. W. Nandalal and J. J. Bogardi, “Dynamic Programming Based Operation of Reservoirs Applicability and Limits,” Cambridge University Press, 2007.
9. [9] R. Wardlaw and M. Sharif, “Evaluation of Genetic Algorithms for Optimal Reservoir System Operation,” J. of Water Resources Planning and Management, Vol.125, No.1, pp. 25-33, 1999.
10. [10] B. Zahraie and S. M. Hosseini, “Development of reservoir operation policies considering variable agricultural water demands,” Expert System with Applications, Vol.36, pp. 4980-4987, 2009.
11. [11] D. N. Kumar and M. J. Reddy, “Ant colony optimization for multi-purpose reservoir operation,” Water Resources Management, Vol.20, pp. 879-798, 2006.
12. [12] D. P. Loucks, J. R. Stedinger, and D. A. Haith, “Water Resources Systems Planning and Analysis,” Prentice Hall, 1981.
13. [13] J. Kelman, J. R. Stedinger, L. A. Cooper, E. Hsu, and S. Yuan, “Sampling stochastic dynamic programming applied to reservoir operation,” Water Resources Research, Vol.26, No.3, pp. 447-454, 1990.
14. [14] T. Kojiri, “Hydrological river basin assessment model (Hydro-BEAM),” V. P. Singh and D. K. Frevent eds., Watershed Models, CRC Press, pp. 613-626, 2006.
15. [15] Y. Sato, T. Kojiri, Y. Suzuki, and E. Nakakita, “Assessment of climate change impacts on river discharge in Japan using the super-high-resolution MRI-AGCM,” Hydrological Processes, Vol.27, pp. 3264-3279, 2013.
16. [16] S. Ikebuchi, T. Kojiri, and H. Miyakawa, “A study of long-term and real-time reservoir operation by using middle and long-term weather forecast,” Annuals. Disas. Prev. Res. Inst., Kyoto Univ., Vol.33, No.B-2, pp. 167-192, 1990 (in Japanese).