Journal of Korea Water Resources Association (한국수자원학회논문집)

Korea Water Resources Association (한국수자원학회)
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Development of dam inflow simulation technique coupled with rainfall simulation and rainfall-runoff model

강우모의기법과 강우-유출 모형을 연계한 댐 유입량 자료 생성기법 개발

  • Received : 2016.01.29
  • Accepted : 2016.02.23
  • Published : 2016.04.30
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Abstract

Generally, a natural river discharge is highly regulated by the hydraulic structures, and the regulated flow is substantially different from natural inflow characteristics for the use of water resources planning. The natural inflow data are necessarily required for hydrologic analysis and water resources planning. This study aimed to develop an integrated model for more reliable simulation of daily dam inflow. First, a piecewise Kernel-Pareto distribution was used for rainfall simulation model, which can more effectively reproduce the low order moments (e.g. mean and median) as well as the extremes. Second, a Bayesian Markov Chain Monte Carlo scheme was applied for the SAC-SMA rainfall-runoff model that is able to quantitatively assess uncertainties associated with model parameters. It was confirmed that the proposed modeling scheme is capable of reproducing the underlying statistical properties of discharge, and can be further used to provide a set of plausible scenarios for water budget analysis in water resources planning.

일반적으로 하천의 유량은 댐과 같은 수공구조물에 의해 조정된 유량으로 수자원계획을 위해서 필요한 자연유량과는 차이가 크다. 수자원계획을 수립함에 있어 자연 유입량 정보는 댐 운영과 수문분석을 위한 필수적인 정보이다. 본 연구에서는 댐 유역 일유입량 모의기법을 위한 통합 모형을 개발하였다. 첫째, 장기 강우-유출 모형의 입력강우자료로 사용하기 위하여 평균 및 중앙값과 같은 통계적 모멘트를 효과적으로 재현하고 극치 강우량 재현에 유리한 불연속 Kernel-Pareto 확률분포 기반의 강우모의기법을 통하여 강우모의를 수행하였다. 둘째, SAC-SMA 장기 강우-유출 모형의 매개변수를 Bayesian MCMC 기법을 통하여 최적화하여 산정된 매개변수의 사후분포를 활용하여 댐 유입량 시나리오 도출하였다. 댐 유역을 대상으로 개발된 모형을 평가한 결과 자연유량과 통계적으로 유사한 특성을 가지는 시나리오를 생성할 수 있었으며, 물수지 분석 등과 같은 수자원계획을 위한 시나리오로 활용이 가능할 것으로 판단된다.

Keywords

References

  1. Anderson, E.A. (1973). "National Weather Service river forecast system : Snow accumulation and ablation model." US Department of Commerce, National Oceanic and Atmospheric Administration, National Weather Service.
  2. Burnash, R.J.C., Ferral, R.L., and McGuire, R.A. (1973). "A generalized streamflow simulation system, conceptual modeling for digital computers." Joint Federal, State River Forecast Center, Sacramento, CA.
  3. Diffenbaugh, N.S., Swain, D.L., and Touma, D. (2015). "Anthropogenic warming has increased drought risk in California." Proceedings of the National Academy of Sciences, Vol. 112, No. 13, pp. 3931-3936. https://doi.org/10.1073/pnas.1422385112
  4. Fallah, B., and Cubasch, U. (2015). "A comparison of model simulations of Asian mega-droughts during the past millenium with proxy reconstructions." Climate of the Past, Vol. 11, No. 2, pp. 253-263. https://doi.org/10.5194/cp-11-253-2015
  5. Gelman, R. (2004). "Cognitive development. Stevens." handbook of experimental psychology.
  6. Hall, P., Sheather, S.J., Jones, M.C., and Marron, J.S. (1991). "On optimal data-based bandwidth selection in kernel density estimation." Biometrika, Vol. 78, No. 2, pp. 263-269. https://doi.org/10.1093/biomet/78.2.263
  7. Hastings, W.K. (1970). "Monte Carlo sampling methods using Markov chains and their applications." Biometrika, Vol. 57, No. 1, pp. 97-109. https://doi.org/10.1093/biomet/57.1.97
  8. Hosking, J.R., and Wallis, J.R. (1987). "Parameter and quantile estimation for the generalized Pareto distribution." Technometrics, Vol. 29, No. 3, pp. 339-349. https://doi.org/10.1080/00401706.1987.10488243
  9. Kim, Y.O., Seo, Y.W., Lee, D.R., and Yoo, C.S. (2005). "Potential Effects of global warming on a water resources system in korea." Water International, Vol. 30, No. 3, pp. 400-405. https://doi.org/10.1080/02508060508691881
  10. Kim, T.J., Kwon, H.H., Lee, D.Y., and Yoon, S.K. (2014). "Development of Stochastic Downscaling Method for Rainfall Data Using GCM" Journal of Korea Water Resources Association, Vol. 47, No. 9, pp. 825-838. https://doi.org/10.3741/JKWRA.2014.47.9.825
  11. Kim. T.J., Jeong, G.I., Kim, K.Y., and Kwon, H.H. (2015). "A Study on Regionalization of Parameters for Sacramento Continuous Rainfall-Runoff Model Using Watershed Characteristics." Journal of Korea Water Resources Association, Vol. 48, No. 10, pp. 793-806. https://doi.org/10.3741/JKWRA.2015.48.10.793
  12. Kwon, H.H., Kim, J.G., and Park, S.H. (2013). "Derivation of Flood Frequency Curve with Uncertatiny of Rainfall and Rainfall-Runoff Model." Journal of Korea Water Resources Association, Vol. 46, No. 3, pp. 59-71. https://doi.org/10.3741/JKWRA.2013.46.1.59
  13. Kwon, H.H., Sivakumar, B., Moon, Y.I., and Kim, B.S. (2011). "Assessment of change in design flood frequency under climate change using a multivariate downscaling model and a precipitation-runoff model." Stochastic Environmental Research and Risk Assessment, Vol. 25, No. 4, pp. 567-581. https://doi.org/10.1007/s00477-010-0422-z
  14. Kwon, H.H., Brown, C., and Lall, U. (2008). "Climate informed flood frequency analysis and prediction in Montana using hierarchical Bayesian modeling." Geophysical Research Letters, Vol. 35, No. 5. DOI: 10.1029/2007GL032220.
  15. Larson, L., Singh, V.P., and Frevert, D. (2002). "National Weather Service River Forecast System (NWSRFS)." Mathematical models of small watershed hydrology and applications pp. 657-703.
  16. Lima, C.H., and Lall, U. (2010). "Spatial scaling in a changing climate: A hierarchical bayesian model for non-stationary multi-site annual maximum and monthly streamflow." Journal of Hydrology, Vol. 383, No, 3, pp. 307-318. https://doi.org/10.1016/j.jhydrol.2009.12.045
  17. Metropolis, N., Rosenbluth, A.W., Rosenbluth, M.N., Teller, A.H., and Teller, E. (1953). "Equation of state calculations by fast computing machines." The journal of chemical physics, Vol. 21, No. 6, pp. 1087-1092. https://doi.org/10.1063/1.1699114
  18. Rosenblatt, M. (1956). "Remark on some nonparametric estimates of a density function." The Annals of Mathematical Statistics. Vol. 27, No. 3, pp. 832-837. https://doi.org/10.1214/aoms/1177728190
  19. Ruppert, D., Sheather, S.J., and Wand, M.P. (1995). "An effective bandwidth selector for local least squares regression." Journal of the American Statistical Association, Vol. 90, No. 432, pp. 1257-1270. https://doi.org/10.1080/01621459.1995.10476630
  20. So, B.J., Kwon. H.H., Kim, D.K., and Lee, S.O. (2015). "Modeling of daily rainfall sequence and extremes based on a semiparametric Pareto tail approach at multiple locations." Journal of Hydrology, Vol. 529, pp. 1442-1540. https://doi.org/10.1016/j.jhydrol.2015.08.037
  21. So, B.J. Development of Multisite Daily Rainfall Simulation Model Using Piecewise Kernel-Pareto Continuous Distribution. Master's Thesis, Chonbuk National University, Jeonju, Jeollabuk, Republic of Korea.
  22. Russo, T.A., Devineni, N., and Lall, U. (2015). "Assessment of Agricultural Water Management in Punjab, India, Using Bayesian Methods." In Sustainability of Integrated Water Resources Management, Springer International Publishing, pp. 147-162.
  23. Thompson, C.S. (1984). "Homogeneity analysis of rainfall series: an application of the use of a realistic rainfall model." Journal of Climatology, Vol. 4, No. 6, pp. 609-619. https://doi.org/10.1002/joc.3370040605
  24. Wang, S.Y., Hipps, L., Gillies, R.R., and Yoon, J.H. (2014). "Probable causes of the abnormal ridge accompanying the 2013-2014 California drought: ENSO precursor and anthropogenic warming footprint." Geophysical Research Letters, Vol. 41, No. 9, pp. 3220-3226. https://doi.org/10.1002/2014GL059748