Uncertainty assessment of ensemble streamflow prediction method
Seon-Ho Kima Shin-Uk Kangb Deg-Hyo Baea,*
aDepartment of Civil and Environmental Engineering, Sejong University bNational Drought Information Analysis Center, Korea Water Resources Cooperation
The objective of this study is to analyze uncertainties of ensemble-based streamflow prediction method for model parameters and input
data. ESP (Ensemble Streamflow Prediction) and BAYES-ESP (Bayesian-ESP) based on ABCD rainfall-runoff model were selected as
streamflow prediction method. GLUE (Generalized Likelihood Uncertainty Estimation) was applied for the analysis of parameter
uncertainty. The analysis of input uncertainty was performed according to the duration of meteorological scenarios for ESP. The result
showed that parameter uncertainty was much more significant than input uncertainty for the ensemble-based streamflow prediction. It
also indicated that the duration of observed meteorological data was appropriate to using more than 20 years. And the BAYES-ESP was
effective to reduce uncertainty of ESP method. It is concluded that this analysis is meaningful for elaborating characteristics of ESP
method and error factors of ensemble-based streamflow prediction method.
1. Beven, K., and Binley, A. (1992). “The future of distributed models: model calibration and uncertainty prediction.” Hydrological Process, Vol. 6, No. 3, pp. 279-298.
2. DeChant, C. M., and Moradkhani, H. (2011). “Improving the characterization of initial condition for ensemble streamflow prediction using data assimilation.” Hydrology and Earth System Science, Vol. 15, pp. 3399-3410.
3. Duan, Q., Sorooshian, S., and Gupta, V. K. (1992). “Effective and efficient global optimization for conceptual rainfall-runoff models.” Water Resources Research, Vol. 28, No. 4, pp. 1015- 1031.
4. Eum, H. I., Kim, Y. O., and Ko, I. H. (2006). “Value of ensemble streamflow forecasts for reservoir operation during the draw-down period.” Journal of Korea Water Resources, Vol. 39, No. 3, pp. 187-198.
5. Hay, L. E., McCabe, G. J., Clark, M. P., and Risley, J. C. (2009). “Reducing streamflow forecast uncertainty: application and qualitative assessment of the upper Klamath river basin, Oregon.” Journal of the American Water Resources Association, Vol. 45, No. 3, pp. 580-596.
6. Kang, M. S., Yu, M. S., and Yi, J. E. (2014). “Prediction of Andong reservoir inflow using ensemble technique.” Journal of the Korean Society of Civil Engineers, Vol. 34, No. 3, pp. 795-804.
7. Kim, H. S., Kim, H. S., Jeon, G. I., and Kang, S.W (2016). “Assess-ment of 2014-2015 Drought Events.” Journal of Korea Water Resources, Vol. 49, No. 7, pp. 61-75.
8. Kim, J. C., Kim, J. K., and Lee, S. J. (2011). “Improvement of mid/ long-term ESP scheme using probabilistic weather forecasting.” Journal of Korea Water Resources, Vol. 44, No. 10, pp. 843-851.
9. Kim, J. H., and Bae, D. H. (2006). “Probabilistic medium- and long- term reservoir inflow forecasts (II) use of GDAPS for ensemble reservoir inflow forecasts.” Journal of Korea Water Resources, Vol. 39, No. 3, pp. 275-288.
10. Kim, S. H., So, J. M., Kang, S. U., and Bae, D. H. (2017). “Development and evaluation of dam inflow prediction method based on Bayesian method.” Journal of Korea water Resources, Vol. 50, No. 7, pp. 489-502.
11. Kim, T. M. (2011). Accuracy and uncertainty assessment of real-time system for dam inflow forecasting, Master dissertation, Sejong University, Seoul, Korea, pp. 44-46.
12. Kim, Y. O., Jeong, D. I., and Kim, H. S. (2001). “Improving water supply outlook in Korea with ensemble streamflow prediction.” Water International, Vol. 26, No. 4, pp. 563-568.
13. K-water (2017). Drought information analysis improvement and development direction. Report, K-water, Daejeon, Korea, pp. 163-166.
14. Lee, S. J., Jeong, C. S., Kim, J. C., and Hwang, M. H. (2011). “Long- term streamflow prediction using ESP and RDAPS model.” Journal of Korea Water Resources, Vol. 44, No. 12, pp. 967-974.
15. Najafi, M. R., Moradkhani, H., and Piechota, Y. C. (2012). “Ensemble streamflow prediction: climate signal weighting methods vs. climate forecast system reanalysis.” Journal of Hydrology, Vol. 442-443, No. 6, pp.105-116.
16. Nash, J. E., and Sutcliffe, J. V. (1970). “River flow forecasting through conceptual models partⅠ-A discussion of principles.” Journal of Hydrology, Vol. 10, No. 3, pp. 282-290.
17. Pavia, R. C. D., Collischonn, W., Bonnet, M. P., and de Goncalves, L. G. G. (2012). “On the sources of hydrological prediction uncertainty in the Amazon.” Hydrology and Earth System Science, Vol. 16, pp. 3127-3137.
18. Son, K. H., and Bae, D. H. (2015). “Applicability assessment of hydrological drought outlook using ESP method.” Journal of Korea Water Resources, Vol. 48, No. 7, pp. 581-593.
19. Thomas, H. A. (1981). Improved methods for national water assessment. Report, United States Water Resources Council, Washington, D.C.
20. Wood, A. W., and Lettenmaier, D. P. (2008). “An ensemble approach for attribution of hydrologic prediction uncertainty.” Geophysical Research Letters, Vol. 35, L14401.
21. Yang J., Reichert, P., Abbaspour, K. C., Xia, J., and Yang, H. (2008). “Comparing uncertainty analysis technique for a SWAT application to the Chaohe basin in China.” Journal of Hydrology, Vol. 358, No. 1-2, pp. 1-23.