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카오스를 이용한 일 강우자료의 시간적 분해

Chaotic Disaggregation of Daily Rainfall Time Series

  • 발행 : 2008.09.02

초록

분해기법은 일 단위 강수시계열 자료를 시간단위로 분해하는 데 주로 사용되고 있으며, 시단위 자료는 홍수예측을 위하여 주요하게 사용될 수 있다. 그러나 현재까지 제시된 대부분의 분해기술은 강우데이터가 추계학적 특성을 가지고 있다는 기본 가정을 전제로 하고 있기 때문에 모형을 구성하는데 있어서 강우자료의 물리적 특성을 반영하는 데는 한계를 보이고 있다. 이에 본 연구에서는 강우자료를 각기 다른 해상도로 변환하는데 따른 가중치의 동역학적 거동이 카오스 특성을 보이는지와 카오스적 분해가 가능한지를 비선형의 확정론적 방법(카오스이론)을 이용하여 규명하는 방안을 소개하였다. 우선, 기상청 산하 서울지점을 대상으로 24h-12h, 12h-6h, 6h-3h으로 해상도를 변환하는데 따른 가중치를 계산하여 사용하였다. 가중치 시계열자료의 카오스 특성을 규명하는 데는 상관차원방법을 이용하였으며, 부분근사화 기법을 이용하여 강우를 분해하였다. 서울 지점의 모든 해상도 변환에 따른 가중치는 저차원의 상관 차수를 가지는 카오스 특성을 보임을 확인하였으며, 분해결과 실제 관측치와 유사한 값을 보임을 확인하였다(높은 상관계수와 작은 평균제곱근오차를 보임). 또한 강우의 일반적인 경향성(총량, 강우의 발생 시점)은 보존되나 극값의 경우 대부분 과소 추정됨을 알 수 있었다.

Disaggregation techniques are widely used to transform observed daily rainfall values into hourly ones, which serve as important inputs for flood forecasting purposes. However, an important limitation with most of the existing disaggregation techniques is that they treat the rainfall process as a realization of a stochastic process, thus raising questions on the lack of connection between the structure of the models on one hand and the underlying physics of the rainfall process on the other. The present study introduces a nonlinear deterministic (and specifically chaotic) framework to study the dynamic characteristics of rainfall distributions across different temporal scales (i.e. weights between scales), and thus the possibility of rainfall disaggregation. Rainfall data from the Seoul station (recorded by the Korea Meteorological Administration) are considered for the present investigation, and weights between only successively doubled resolutions (i.e., 24-hr to 12-hr, 12-hr to 6-hr, 6-hr to 3-hr) are analyzed. The correlation dimension method is employed to investigate the presence of chaotic behavior in the time series of weights, and a local approximation technique is employed for rainfall disaggregation. The results indicate the presence of chaotic behavior in the dynamics of weights between the successively doubled scales studied. The modeled (disaggregated) rainfall values are found to be in good agreement with the observed ones in their overall matching (e.g. correlation coefficient and low mean square error). While the general trend (rainfall amount and time of occurrence) is clearly captured, an underestimation of the maximum values are found.

키워드

참고문헌

  1. 김형수, 강두선, 김종우, 김중훈 (1998). "BDS 통계: 수문자료에의 응용." 한국수자원학회 논문집, 한국수자원학회, 제31권, 제6호, pp. 169-777
  2. 김형수, 윤용남 (1999). "자기상관함수의 비선형 유추해석." 한국수자원학회논문집, 한국수자원학회, 제32권, 제6호, pp.731-740
  3. 김형수, 최시중, 김중훈 (1998). "DVS 알고리즘을 이용한 일 유량자료의 예측." 대한토목학회논문집, 대한토목학회, 제18권, 제II-6호, pp. 563-570
  4. 박대규, 조원철 (2003). "카오스 특성을 갖는 일 유출량 자료의 비선형 예측." 대한토목학회 논문집, 대한토목학회, 제23권, 제6B호, pp. 479-487
  5. Bo, Z., Islam, S., and Eltahir, E. A. B. (1994). "Aggregation-disaggregation properties of a stochastic rainfall model." Water Resources Research, Vol. 30, No. 12, pp. 3423-3435 https://doi.org/10.1029/94WR02026
  6. Gaume, E., Mouhous, N., and Andrieu, H. (2007). "Rainfall stochastic disaggregation models: Calibration and validation of a multiplicative cascade model." Advances in Water Resources, Vol. 30, Issue 5, pp. 1301-1319 https://doi.org/10.1016/j.advwatres.2006.11.007
  7. Glasbey, C. A., Cooper, G., and McGechan, M. B. (1995). "Disaggregation of daily rainfall by conditional simulation from a point-process model." Journal of Hydrology, Vol. 165, pp. 1-9 https://doi.org/10.1016/0022-1694(94)02598-6
  8. Gyasi-Agyei, Y. (2005). "Stochastic disaggregation of daily rainfall into one-hour time scale," Journal of Hydrology, Vol. 309, Issues 1-4, pp. 178-190 https://doi.org/10.1016/j.jhydrol.2004.11.018
  9. Gyasi-Agyei, Y., Mahbub, S. M., and Parvez Bin (2007). "A stochastic model for daily rainfall disaggregation into fine time scale for a large region" Journal of Hydrology, Vol. 347, Issues 3-4, pp. 358-370 https://doi.org/10.1016/j.jhydrol.2007.09.047
  10. Kottegoda, N. T., Natale, L., Raiteri, E. (2003). "A parsimonious approach to stochastic multisite modelling and disaggregation of daily rainfall" Journal of Hydrology, Vol. 274, Issues 1-4, pp. 47-61 https://doi.org/10.1016/S0022-1694(02)00356-6
  11. Koutsoyiannis, D., and Onof, C. (2001). "Rainfall disaggregation using adjusting procedures on a Poisson cluster model." Journal of Hydrology, Vol. 246, Issues 1-4, pp. 109-122 https://doi.org/10.1016/S0022-1694(01)00363-8
  12. Packard, N. H., Crutchfield, J. P., Farmer, J. D., and Shaw, R. S. (1980). "Geometry from a timeseries." Physical Review Letters, Vol. 45, Issue 9, pp.712-716 https://doi.org/10.1103/PhysRevLett.45.712
  13. 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. Series A, Mathematical and Physical Sciences, Vol. 410, Issue 1839, pp. 269-288
  14. Rodriguez-Iturbe, I., Cox, D. R., and Isham, V. (1988). "A point process model for rainfall: Further developments." Proceedings of the Royal Society of London. Series A, Mathematical and Physical Sciences, Vol. 417, No. 1853, pp. 283-298 https://doi.org/10.1098/rspa.1988.0061
  15. Segond, M.-L., Onof, C., and Wheater, H. S. (2006). "Spatial–temporal disaggregation of daily rainfall from a generalized linear model." Journal of Hydrology, Vol. 331, Issues 3-4, pp. 674-689 https://doi.org/10.1016/j.jhydrol.2006.06.019
  16. Sivakumar, B., (1999). Identification of chaos and influence of noise on prediction: Singapore rainfall. Ph.D. dissertation, National University of Singapore, Singapore
  17. Sivakumar, B. (2000). "Chaos theory in hydrology: Important issues and interpretations." Journal of Hydrology, Vol. 227, Issues 1–4, pp. 1-20 https://doi.org/10.1016/S0022-1694(99)00186-9
  18. Sivakumar, B., Sorooshian, S., Gupta, H. V., and Gao, X. (2001). "A chaotic approach to rainfall disaggregation." Water Resources Research, Vol. 37, No. 1, pp. 61-72 https://doi.org/10.1029/2000WR900196
  19. Takens, F. (1980). Detecting strange attractors in turbulence, in dynamical, systems and turbulence. Lecture Notes in Mathematics 898, Edited by D. A. Rand and L. S. Young, Springer-Verlag, New York, pp. 366–381

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  1. A Development of Hourly Rainfall Simulation Technique Based on Bayesian MBLRP Model vol.34, pp.3, 2014, https://doi.org/10.12652/Ksce.2014.34.3.0821
  2. A development of multisite hourly rainfall simulation technique based on neyman-scott rectangular pulse model vol.49, pp.11, 2016, https://doi.org/10.3741/JKWRA.2016.49.11.913
  3. A Study on the Monthly Trend of Seoul Hourly Rainfall Using BLRPM vol.14, pp.4, 2014, https://doi.org/10.9798/KOSHAM.2014.14.4.267