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Development of a Stochastic Precipitation Generation Model for Generating Multi-site Daily Precipitation

다지점 일강수 모의를 위한 추계학적 강수모의모형의 구축

  • Received : 2009.03.02
  • Accepted : 2009.08.03
  • Published : 2009.09.30

Abstract

In this study, a stochastic precipitation generation framework for simultaneous simulation of daily precipitation at multiple sites is presented. The precipitation occurrence at individual sites is generated using hybrid-order Markov chain model which allows higher-order dependence for dry sequences. The precipitation amounts are reproduced using Anscombe residuals and gamma distributions. Multisite spatial correlations in the precipitation occurrence and amount series are represented with spatially correlated random numbers. The proposed model is applied for a network of 17 locations in the middle of Korean peninsular. Evaluation statistics are reported by generating 50 realizations of the precipitation of length equal to the observed record. The analysis of results show that the model reproduces wet day number, wet and dry day spell, and mean and standard deviation of wet day amount fairly well. However, mean values of 50 realizations of generated precipitation series yield around 23% Root Mean Square Errors (RMSE) of the average value of observed maximum numbers of consecutive wet and dry days and 17% RMSE of the average value of observed annual maximum precipitations for return periods of 100 and 200 years. The provided model also reproduces spatial correlations in observed precipitation occurrence and amount series accurately.

본 연구에서는 다지점의 일단위 강수량을 동시에 모의할 수 있는 추계학적 강수모의모형을 제시하였다. 각 지점의 강수발생은 무강수 기간에 대해 고차를 허용하는 혼합차수 마코프 모형을 이용하였으며, 강수량은 Anscombe 잔차와 감마분포를 이용하여 모의하였다. 다지점에 대한 강수발생과 강수량의 공간적 상관관계는, 상관관계를 가진 랜덤자료를 생성하여 재현하였다. 구축된 강수모의모형을 이용하여 우리나라 중부지역에 위치한 17개 관측지점의 강수량을 모의하고 모의정확성을 검토 하였다. 검증에 필요한 통계값들은 50번의 반복실행에 의해 생성된 강수량 시계열로부터 추정하여 제시하였다. 검토결과, 강수모의모형이 관측강수의 강수일수, 강수 지속기간, 무강수 지속기간, 강수일의 평균강수량과 표준편차 등을 비교적 잘 모의 하였다. 최대 강수 지속일과 무강수 지속일의 50번 반복실행의 평균값의 RMSE는 관측자료 평균값의 약 23% 정도, 100년 빈도와 200년빈도의 강수량의 RMSE는 관측자료 평균값의 약 17% 정도에 달하는 것으로 확인되었다. 강우발생과 강우량에 대한 공간적 상관관계는 비교적 정확히 재현하고 있음을 확인하였다.

Keywords

References

  1. 강경석(2000) 다지점 일 강수모형에 의한 일 유출량의 모의발생, 박사학위논문, 인하대학교
  2. 김문성, 안재현, 신현석, 한수희, 김상단(2008) 다지점 일강수 발생모형: 낙동강유역 강수관측망에의 적용, 한국물환경학회논문집, 한국물환경학회, 제24권, 제6호, pp. 725-740.
  3. 문영일, 차영일(2004) 비동질성 마코프모형을 이용한 일강수자료모의발생-이론-, 대한토목학회논문집, 대한토목학회, 제24권, 제5B호, pp. 431-435.
  4. 문영일, 차영일, 서병하(2004) 비동질성 Markov 모형을 이용한 일강수자료 모의발생-이론-, 대한토목학회논문집, 대한토목학회, 제24권, 제5B호, pp. 437-441.
  5. 유철상(2007) 추계학적 기상모의모형에 대한 검토. 한국수자원학회지, 한국수자원학회, 제40권, 제3호, pp.41-51
  6. Bars, R. and Rodriguez-Iturbe, I. (1976) Rainfall generation: a non stationary time varying multi-dimensional model. Water Resources Research, Vol. 12, pp. 450-456. https://doi.org/10.1029/WR012i003p00450
  7. Entekhabi, D., Rodriguez-Iturbe, I., and Eagleson, P.S. (1989) Probabilistic representation of the temporal rainfall by a modified Neymann-Scott rectangular pulses model: Parameter estimation and validation. Water Resources Research, Vol. 25, No. 2, pp. 295-302. https://doi.org/10.1029/WR025i002p00295
  8. Islam, S., Entekhabi, D., and Bras, R.L. (1990) Parameter estimation and sensitivity analysis for the modified Bartlett-Lewis rectangular pulses model of rainfall. Journal of Geophysical Research, Vol. 95, No. D3, pp. 2093-2100. https://doi.org/10.1029/JD095iD03p02093
  9. Katz, R.W. (1977) Precipitation as a chain-dependent process. Journal of Applied Meteorology, Vol. 16, pp. 671-676. https://doi.org/10.1175/1520-0450(1977)016<0671:PAACDP>2.0.CO;2
  10. Mehrotra, R., and Sharma, A. (2007) A semi-parametric model for stochastic generation of multi-site daily rainfall exhibiting lowfrequency variability. Journal of Hydrology, Vol. 335, pp. 180-193. https://doi.org/10.1016/j.jhydrol.2006.11.011
  11. Mehrotra, R., Srikanthan, R., and Sharma, A. (2006) A comparison of three stochastic multi-site precipitation occurrence generators. Journal of Hydrology, Vol. 331, pp. 280-292. https://doi.org/10.1016/j.jhydrol.2006.05.016
  12. Rajagopalan, B., Lall, U., and Tarboton, D. (1996) Nonhomogeneous markov model for daily precipitation. Journal of Hydrologic Engineering, Vol. 1, No. 1, pp. 33-40. https://doi.org/10.1061/(ASCE)1084-0699(1996)1:1(33)
  13. Richardson, C.W. (1981) Stochastic simulation of daily precipitation, temperature, and solar radiation. Water Resource Research, Vol. 17, No. 1, pp. 182-190. https://doi.org/10.1029/WR017i001p00182
  14. Rodriguez-Iturbe, I., Gupta, V.K., and Waymire, E. (1984) Scale consideration in the modeling of temporal rainfall. Water Resources Research, Vol. 20, No. 11, pp. 1611-1619. https://doi.org/10.1029/WR020i011p01611
  15. Semenov, M.A. and Porter, J.R. (1995) Climatic variability and the modelling of crop yields. Agricultural and Forest Meteorology, Vol. 73, pp. 265-283. https://doi.org/10.1016/0168-1923(94)05078-K
  16. Stern, R.D. and Coe, R. (1984) A model fitting analysis of daily rainfall data. Journal of the Royal Society of Statistical Analysis, Vol. A147, pp. 1-34.
  17. Todorovic, P. and Woolhiser, D.A. (1975) A stochastic model of nday precipitation. Journal of Applied Meteorology, Vol. 14, pp. 1-34.
  18. Wilby, R.L. (1994) Stochastic weather type simulation for regional climate change impact assessment. Water Resources Research, Vol. 30, No. 12, pp. 3395-3403. https://doi.org/10.1029/94WR01840
  19. Wilby, R.L., Dawson, C.W., and Barrow E.M. (2002) SDSM- a decision support tool for the assessment of regional climate change impacts. Environmental Modelling & Software, Vol. 17, pp. 147-159.
  20. Wilks, D.S. (1998) Multisite generation of a daily stochastic precipitation generation model. Journal of Hydrology, Vol. 210, pp. 178-191. https://doi.org/10.1016/S0022-1694(98)00186-3
  21. Wilks, D.S. (1999) Interannual variability and extreme-value characteristics of several stochastic daily precipitation models. Agricultural and Forest Meteorology, Vol. 93, pp. 153-169. https://doi.org/10.1016/S0168-1923(98)00125-7
  22. Yang, C., Chandler, R.E., and Isham, V.S. (2005) Spatial-temporal rainfall simulation using generalized linear models. Water Resources Research, Vol. 41, Wll415, doi:10.1029/2004WR003739.