KSCE Journal of Civil and Environmental Engineering Research (대한토목학회논문집)

Korean Society of Civil Engeneers (대한토목학회)
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Prediction of Andong Reservoir Inflow Using Ensemble Technique

앙상블 기법을 이용한 안동댐 유입량 예측

  • Received : 2013.11.14
  • Accepted : 2014.04.26
  • Published : 2014.06.01
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Abstract

In this study, Andong Reservoir monthly and ten days inflows from July 2011 to September 2011 are predicted using SWAT model and ensemble technique. The weight method using monthly and ten days rainfall forecasts from Korea Meteorological Administration is applied for accurate analysis. If the rainfall prediction announced by Korea Meteorological Administration is close to the actual rainfall, the PDF-Ratio Method shows the best result. If the past high rainfall occurrence is close to the actual rainfall, the modified PDF-Ratio method shows the best result. This method can improve the prediction accuracy even though the Korea Meteorological Administration forecast is not accurate. On the contrary, if Korea Meteorological Administration forecast is different from the actual rainfall and the past rainfall occurrence statistics of lower section, the uniform method shows the best result.

본 연구에서는 앙상블유량예측기법과 SWAT 모형을 이용하여 안동댐의 2011년 7월~9월의 각 댐유입량 예측을 실행하였으며 월별 및 순별 분석을 수행하였다. 또한 정확한 분석을 위해 기상청의 월별 및 순별 강우예보자료를 이용한 가중값 부여방법을 사용하였다. 분석 결과 기상청에서 발표한 강우 예측 구간이 실제 강우 구간과 동일하면 PDF-Ratio 가중값 부여방법이 가장 높은 정확성을 보이며, 과거 강우발생 구간 통계 중 높은 구간이 실제 강우 구간과 동일하다면 수정 PDF-Ratio 가중값 부여방법이 가장 높은 정확성을 보였다. 이는 기상청 예측이 맞지 않은 경우에도 과거 강우발생 구간의 빈도에 따라 정확성을 높일 수 있을 것으로 판단된다. 반대로 기상청의 예측이 실제와 다르면서 과거 강우발생 구간 통계에서도 낮은 구간의 강우가 발생하면 균일 가중값 부여방법의 정확성이 가장 높게 분석되었다.

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References

  1. Ahn, J. M., Lee, S. J., Kim, J. K., Kim, J. C., Maeng, S. J. and Woo, D. H. (2011). "Estimation of ESP probability considering weather outlook." Journal of Korea Society on Water Quality, Vol. 27, pp. 264-272 (in Korean).
  2. Bradley, A. A., Schwartz, S. S. and Hashino, T. (2003). "Distributions-Oriented verification of ensemble streamflow predictions." Journal of Hydrometeorology, Vol. 5, pp. 532-545.
  3. Faber, B. A. (2000). Reservoir optimization using sampling stochastic dyamic programming (SSDP) with ensemble streamflow prediciton (ESP) forecasts, Ph.D. Dissertation, University of Cornell, America.
  4. Franz, K. J., Hartmann, H. C., Sorooshian, S. and Bales, R. (2003). "Verification of national weather service ensemble streamflow predictions for water supply forecasting in the Colorado river basin." Journal of Hydrometeorology, Vol. 4, pp. 1105-1118. https://doi.org/10.1175/1525-7541(2003)004<1105:VONWSE>2.0.CO;2
  5. Jung, D. I. (2002). Forecasting monthly inflow to Chungju Dam using ensemble streamflow prediction, MS. Thesis, University of Seoul, Korea.
  6. Kang, T. H. (2008). Pre- and post-processors of ensemble streamflow prediction system, MS. Thesis, University of Seoul, Korea.
  7. Olsson, J. and Lindstrom, G. (2008). "Evaluation and calibration of operational hydrological ensemble forecasts in Sweden." Journal of Hydrology, Vol. 350, pp. 14-24. https://doi.org/10.1016/j.jhydrol.2007.11.010
  8. Roulin, E. (2006). "Skill and relative economic value of medium-range hydrological ensemble predictions." Hydrol. Earth Syst. Sci. Discuss., 3, pp. 1369-1406. https://doi.org/10.5194/hessd-3-1369-2006
  9. Smith, J. A., Day, G. N. and Kane, M. D. (1992). "Nonparametric framework for long-range streamflow forecasting." Journal of Water Resources Planning and Management, ASCE, Vol. 118, pp. 82-92. https://doi.org/10.1061/(ASCE)0733-9496(1992)118:1(82)
  10. Stedinger, J. R., Vogel, R. M. and Foufoula-Georgiou, E. (1992). Handbook of hydrology, McGraw-Hill, p. 1811.
  11. Stedinger, J. R. and Kim, Y. O. (2010). "Probabilities for ensemble forecasts reflecting climate information." Journal of Hydrology, Vol. 391, pp. 9-23. https://doi.org/10.1016/j.jhydrol.2010.06.038
  12. Wilks, D. S. (1995). Forecast verification : Statistical Methods in the Atmospheric Sciences, Academic Press, USA

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