Journal of Korea Water Resources Association (한국수자원학회논문집)

Korea Water Resources Association (한국수자원학회)
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An analysis of effects of seasonal weather forecasting on dam reservoir inflow prediction

장기 기상전망이 댐 저수지 유입량 전망에 미치는 영향 분석

  • Kim, Seon-Ho (Department of Civil & Environmental Engineering, Sejong University) ;
  • Nam, Woo-Sung (National Drought Information Analysis Center, K-water corporation) ;
  • Bae, Deg-Hyo (Department of Civil & Environmental Engineering, Sejong University)
  • 김선호 (세종대학교 건설환경공학과) ;
  • 남우성 (한국수자원공사 국가가뭄정보분석센터) ;
  • 배덕효 (세종대학교 건설환경공학과)
  • Received : 2019.05.21
  • Accepted : 2019.06.08
  • Published : 2019.07.31
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Abstract

The dam reservoir inflow prediction is utilized to ensure for water supply and prevent future droughts. In this study, we predicted the dam reservoir inflow and analyzed how seasonal weather forecasting affected the accuracy of the inflow for even multi-purpose dams. The hindcast and forecast of GloSea5 from KMA were used as input for rainfall-runoff models. TANK, ABCD, K-DRUM and PRMS models which have individual characteristics were applied to simulate inflow prediction. The dam reservoir inflow prediction was assessed for the periods of 1996~2009 and 2015~2016 for the hindcast and forecast respectively. The results of assessment showed that the inflow prediction was underestimated by comparing with the observed inflow. If rainfall-runoff models were calibrated appropriately, the characteristics of the models were not vital for accuracy of the inflow prediction. However the accuracy of seasonal weather forecasting, especially precipitation data is highly connected to the accuracy of the dam inflow prediction. It is recommended to consider underestimation of the inflow prediction when it is used for operations. Futhermore, for accuracy enhancement of the predicted dam inflow, it is more effective to focus on improving a seasonal weather forecasting rather than a rainfall-runoff model.

장기 기상전망 기반 댐 유입량 전망은 가뭄 대비, 용수 공급 관리 등에 활용성이 높다. 본 연구에서는 국내 7개 다목적댐 유역에 대해 유입량 전망을 수행하고 장기 기상전망 정확도가 댐 유입량 전망 정확도에 미치는 영향을 분석하였다. 강우-유출 모델의 입력자료로 활용된 장기 기상전망 자료는 기상청 GloSea5의 과거재현자료(hindcast) 및 미래전망자료(forecast)를 활용하였다. 강우-유출 모델은 다양한 특성을 가지고 있는 TANK, ABCD, K-DRUM, PRMS를 활용하였다. 댐 유입량 전망 정확도는 과거재현기간(1996~2009)과 미래전망기간(2015~2016)에 대하여 평가하였다. 댐 유입량 전망 평가결과 전망값은 관측값에 비해 과소추정하는 경향을 보였으며, 매개변수 검보정이 적절히 수행된 강우-유출 모델은 댐 유입량 전망 정확도에 미치는 영향이 거의 없는 것으로 나타났다. 반면 장기 기상전망 자료, 특히 강수량은 댐 유입량 전망 정확도에 매우 큰 영향을 미치는 것으로 나타났다. 현업에서 댐 유입량 전망 자료 활용시 과소추정하는 경향을 고려하여 활용할 필요가 있다. 향후 댐 유입량 전망 정확도 개선은 강우-유출 모델 보다 장기 기상전망의 강수량 정확도 향상을 위주로 수행할 필요가 있다.

Keywords

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Fig. 1. Study area

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Fig. 2. Schematic diagram of study procedure

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Fig. 3. Inflow prediction results of chungju dam (HCST)

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Fig. 4. Inflow prediction results of chungju dam (FCST)

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Fig. 5. Relative error of dam inflow prediction

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Fig. 6. Comparison between meteorological variable and dam inflow

Table 1. Characteristics of study area

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Table 2. Characteristics of rainfall-runoff models

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Table 3. Statistics of dam inflow prediction

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Table 4. Correlation coefficient of meteorological variables and dam inflow

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References

  1. Chang, H. J., Franczyk, F., Im, E. S., Kwon, W. T., Bae, D. H., and Jung, I. W. (2007). "Vulnerability of Korean water resources to climate change and population growth." Water Science & Technology, Vol. 56, No. 4, pp. 57-62. https://doi.org/10.2166/wst.2007.536
  2. K-water (2019). The study on selecting optimal time and spatial scale of seasonal climate prediction. Final report, K-water Corporation, Daejeon, Korea, pp. 60-81.
  3. Leavesley, G. H., Lichty, R. W., Troutman, B. M., and Saindon, L. G. (1983). Precipitation-runoff modeling system: User's manual. Water Resources Investigation report, U.S. Geological Survey, Reston, Virginia, U.S.A., pp. 83-4238.
  4. Lee, M. H., Im, E. S., and Bae, D. H. (2019). "A comparative assessment of climate change impacts on drought over Korea based on multiple climate projections and multiple drought indices." Climate Dynamics, pp. 1-16, https://doi.org/10.1007/s00382-018-4588-2
  5. Mazrooei, A., Sinha, T., Sankarasubramanian, A., Kumar, S., and Peters-Lidard, C. D. (2015). "Decomposition of sources of errors in seasonal streamflow forecasting over the U.S. Sunbelt." Journal of Geophysical Reserach: Atmospheres, Vol. 120, No. 11, pp. 11809-11825.
  6. Park, J. H., and Heo, Y. T. (2008). "Development of kinematic wave-based distributed model for flood discharge analysis." Journal of Korea Water Resources Association, No. 41, Vol. 5, pp. 455-462. https://doi.org/10.3741/JKWRA.2008.41.5.455
  7. Peng, A., Zhang, X., Peng, Y., Xu, W., and You, F. F. (2018). "The application of ensemble precipitation forecast to reservoir operation." Water Supply, Vol. 19, No. 2, pp. 588-595. https://doi.org/10.2166/ws.2018.105
  8. Shrestha, R. R., Schnorbus, M. A., and Cannon, A. J. (2015). "A dynamical climate model-driven hydrologic prediction system for the fraser river, Canada." Journal of Hydrometeorology, Vol. 16, pp.1273-1292. https://doi.org/10.1175/JHM-D-14-0167.1
  9. Shukla, S., and Lettenmaier, D. P. (2013). "Multi-RCM ensemble downscaling of NCEP CFS winter season forecast: Implications for seasonal hydrologic forecast skill." Journal of Geophysical Reserach: Atmospheres, Vol. 118, No. 19, pp. 10770-10790. https://doi.org/10.1002/jgrd.50628
  10. Sinha, T., Sankarasubramanian, A., and Mazrooei A. (2014). "Decomposition of sources of errors in monthly to seasonal streamflow forecasts in a rainfall-runoff regime." Journal of Hydrometeorology, Vol. 15, pp. 2470-2483. https://doi.org/10.1175/JHM-D-13-0155.1
  11. Son, K. H., and Bae, D. H. (2015). "Drought analysis according to shifting of climate zones to arid claimte zone over Asia Monsson region." Journal of Hydrology, Vol. 529, No. 3, pp. 1021-1029. https://doi.org/10.1016/j.jhydrol.2015.09.010
  12. Song, J. H., Cho, Y. H., Kim, I. S., and Yi, J. H. (2017). "Development of decision support for water resources management using GloSea5 long-term rainfall forecasts and K-DRUM rainfall-runoff model." Journal of Satellite, Information and Communication, Vol. 12, No. 3, pp. 22-34.
  13. Sugawara, M. (1961). "Automatic callibration of TANK model." Hydrological Sciences-Bulletin-des Sciences Hydrologiques, Vol. 24, No. 3, pp. 375-388. https://doi.org/10.1080/02626667909491876
  14. Thomas, H. A. (1981). Improved methods for national water assessment. Report, United States Water Resources Council, Washington, D.C., U.S.A.
  15. Yuan, X., and Wood, E. F. (2012). "Downscaling precipitation or bias-correcting streamflow? Some implications for coupled general circulation model (CGCM)-based ensemble seasonal hydrologic forecast." Water Resources Research, Vol. 48, No. 12.
  16. Yuan, X., Wood, E. F., and Ma, Z. (2015). "A review on climatemodel-based seasonal hydrologic forecasting: physical understanding and system development." Advanced Review, Vol. 2, No. 5, pp. 523-536.