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Evaluation of extreme rainfall estimation obtained from NSRP model based on the objective function with statistical third moment

통계적 3차 모멘트 기반의 목적함수를 이용한 NSRP 모형의 극치강우 재현능력 평가

  • Cho, Hemie (Department of Civil and Environmental Engineering, Sejong University) ;
  • Kim, Yong-Tak (Department of Civil and Environmental Engineering, Hong Kong University of Science and Technology) ;
  • Yu, Jae-Ung (Department of Civil and Environmental Engineering, Sejong University) ;
  • Kwon, Hyun-Han (Department of Civil & Environmental Engineering, Sejong University)
  • 조혜미 (세종대학교 건설환경공학과) ;
  • 김용탁 (홍콩과기대 건설환경공학과) ;
  • 유재웅 (세종대학교 건설환경공학과) ;
  • 권현한 (세종대학교 건설환경공학과)
  • Received : 2022.02.03
  • Accepted : 2022.06.09
  • Published : 2022.07.31

Abstract

It is recommended to use long-term hydrometeorological data for more than the service life of the hydraulic structures and water resource planning. For the purpose of expanding rainfall data, stochastic simulation models, such as Modified Bartlett-Lewis Rectangular Pulse (BLRP) and Neyman-Scott Rectangular Pulse (NSRP) models, have been widely used. The optimal parameters of the model can be estimated by repeatedly comparing the statistical moments defined through a combination of parameters of the probability distribution in the optimization context. However, parameter estimation using relatively small observed rainfall statistics corresponds to an ill-posed problem, leading to an increase in uncertainty in the parameter estimation process. In addition, as shown in previous studies, extreme values are underestimated because objective functions are typically defined by the first and second statistical moments (i.e., mean and variance). In this regard, this study estimated the parameters of the NSRP model using the objective function with the third moment and compared it with the existing approach based on the first and second moments in terms of estimation of extreme rainfall. It was found that the first and second moments did not show a significant difference depending on whether or not the skewness was considered in the objective function. However, the proposed model showed significantly improved performance in terms of estimation of design rainfalls.

수공구조물 설계 및 수자원 계획에서는 목표연도 이상의 수문기상자료를 활용하는 것이 추천된다. 강우 자료의 확장을 위해 추계학적 강수 모의 모형을 활용하는데, Bartlett-Lewis Rectangular Pulse Modified Model (BLRPM)과 Neyman-Scott Rectangular Pulse Model(NSRPM)이 대표적이다. 이 모형들은 확률분포의 매개변수 조합을 통해 추정되는 통계적 모멘트와 관측값의 통계적 모멘트를 반복 비교하여 최적 매개변수를 추정한다. 그러나 상대적으로 적은 관측값을 이용하여 매개변수를 추정하는 것은 부적절하게 정의된 문제(ill-posed problem)에 해당하며, 최적화 과정에서 매개변수 추정이 어려울 뿐만 아니라, 매개변수의 변동성도 매우 크다. 또한, 일부 연구에서 드러나듯이, 모형 매개변수 추정과정에서 다양한 목적함수를 활용해도 2차 모멘트에 국한되어 있어, 극치 강수량 재현에는 한계가 있다. 따라서 본 연구는 3차 모멘트를 포함한 목적함수를 활용하여 NSRPM 매개변수를 추정하고, 기존 2차 모멘트를 이용한 매개변수 접근방법과 극치강수량 재현 측면에서 비교를 수행하였다. 그 결과, 목적함수의 왜곡도 포함 여부에 따라 1, 2차 모멘트는 큰 차이를 나타내지 않았지만, 극치강우 재현 측면에서는 왜곡도를 포함한 경우가 포함하지 않은 경우보다 개선된 결과를 나타냈다.

Keywords

Acknowledgement

본 결과물은 환경부의 재원으로 한국환경산업기술원의 수생태계 건강성 확보 기술개발사업의 지원을 받아 연구되었습니다(과제번호:2021003030004).

References

  1. Boughton, W., and Droop, O. (2003). "Continuous simulation for design flood estimation - a review." Environmental Modelling & Software, Vol. 18, No. 4, pp. 309-318. https://doi.org/10.1016/S1364-8152(03)00004-5
  2. Cowpertwait, P.S.P., O'Connell, P.E., Metcalfe, A.V., and Mawdsley, J.A. (1996). "Stochastic point process modelling of rainfall. I. Single-site fitting and validation." Journal of Hydrology, Vol. 175, No. 1, pp. 17-46. https://doi.org/10.1016/S0022-1694(96)80004-7
  3. Kim, D., Kwon, H.H., Lee, S.O., and Kim, S. (2016). "Regionalization of the Modified Bartlett-Lewis rectangular pulse stochastic rainfall model across the Korean Peninsula." Journal of Hydro-Environment Research, Vol. 11, pp. 123-137. https://doi.org/10.1016/j.jher.2014.10.004
  4. Kim, D., Shin, J.Y., Lee, S.O., and Kim, T.W. (2013). "The application of the poisson cluster rainfall generation model to the flood analysis." Journal of Korea Water Resources Association, Vol. 46, No. 5, pp. 439-447. https://doi.org/10.3741/JKWRA.2013.46.5.439
  5. Kim, J. (2018). A development of multisite hourly rainfall simulation model using bayesian poisson cluster rainfall generators. Ph. D. Dissertation, Jeonbuk National University. pp. 7-20.
  6. Kim, S.D., Yoo, C.S., Kim, J.H., and Yoon, Y.N. (2000). "A study of temporal characteristics from multi-dimensional precipitation model." Journal of Korea Water Resources Association, Vol. 33, No. 6, pp. 783-791.
  7. Lee, J., and Kim, Y. (2016). "A spatial analysis of Neyman-Scott rectangular pulses model using an approximate likelihood function." Journal of the Korean Data and Information Science Society, Vol. 27, No. 5, pp. 1119-1131. https://doi.org/10.7465/jkdi.2016.27.5.1119
  8. Rodriguez-Iturbe, I., Cox, D.R., and Isham, V. (1987). "Some models for rainfall based on stochastic point processes." Proceedings of the Royal Society of London. A. Mathematical, Physical and Engineering Sciences, Vol. 410, No. 1839, pp. 269-288. https://doi.org/10.1098/rspa.1987.0039
  9. So, B.J., Kwon, H.H., Kim, D., 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. 1442-1450. https://doi.org/10.1016/j.jhydrol.2015.08.037
  10. Verhoest, N., Troch, P.A., and De Troch, F.P. (1997). "On the applicability of Bartlett-Lewis rectangular pulses models in the modeling of design storms at a point." Journal of Hydrology, Vol. 202, No. 1-4. pp. 108-120. https://doi.org/10.1016/s0022-1694(97)00060-7
  11. Yu, J.U., Park, M.H., Kim, J.G., and Kwon, H.H. (2021). "Evaluation of conceptual rainfall-runoff models for different flow regimes and development of ensemble model." Journal of Korea Water Resources Association, Vol. 54, No. 2, pp. 105-119. https://doi.org/10.3741/JKWRA.2021.54.2.105